diff --git a/PERFORMANCE.md b/PERFORMANCE.md
new file mode 100644
index 0000000..043832d
--- /dev/null
+++ b/PERFORMANCE.md
@@ -0,0 +1,356 @@
+# Performance Results
+
+We have collected SmartSim performance results on Horizon, a Cray XC50 supercomputer.
+
+Horizon Node Hardware Summary:
+
+| Nodes | Cores | Threads | Processor | Memory | GPU |
+| :--- | --- | --- | --- | --- | --- |
+| 34 | 18 | 36 | Xeon E5-2699 v4 @ 2.20GHz BDW | 64 GB DDR4-2400 | --- |
+| 16 | 18 | 36 | Xeon E5-2699 v4 @ 2.20GHz BDW | 64 GB DDR4-2400 | 1 Nvidia Tesla_P100-PCIE-16GB |
+| 100 | 48 | 96 | Xeon 8160 CPU @ 2.10GHz SKL | 192 GB DDR4-2666 | --- |
+| 60 | 56 | 112 | Xeon 8176 CPU @ 2.10GHz SKL | 192 GB DDR4-2666 | --- |
+| 48 | 48 | 96 | Xeon 8260 CPU @ 2.40GHz CSL | 192 GB DDR4-2666 | --- |
+| 53 | 48 | 96 | Xeon 8260 CPU @ 2.40GHz CSL | 384 GB DDR4-2933 | --- |
+| 28 | 64 | 256 | ThunderX2 CN9980 v2.2 @ 2.50GHz | 256 GB DDR4-2666 | --- |
+
+We have provided scaling results represented in the form of violin plots for the following:
+
+1. Inference Standard & Colocated
+2. Throughput Standard & Colocated
+3. Data Aggregation Standard
+
+All scaling tests utilize a redis database excluding the last data aggregation test that uses the file system. Note that the first iteration can take longer (up to several seconds) than the rest of the execution. This
+is due to the DB loading libraries when the first RedisAI call is made. In the following plots, we excluded
+the first iteration time.
+
+## Inference Standard
+
+The following are standard deployment scaling results from the cpp-inference and fortran-inference scaling tests using the resNet-50 model and the imagenet dataset. ResNet-50 model is a convolutional neural network that is 50 layers deep. We train the model using more than a million images from the imageNet database. The imageNet database holds 14 million hand annotated images that are used for visual object recognition software research. For more information on these scaling tests, please see
+the SmartSim paper on [arXiv](https://www.sciencedirect.com/science/article/pii/S1877750322001065).
+
+#### Inference Standard Run Configuration File
+```bash
+[run]
+name = run-2023-08-17-16:10:12
+path = results/inference-standard-scaling/run-2023-08-17-16:10:12
+smartsim_version = 0.5.0
+smartredis_version = 0.3.1
+db = redis-server
+date = 2023-08-17
+language = ['cpp', 'fortran']
+
+[attributes]
+colocated = 0
+client_per_node = [18]
+client_nodes = [25, 50, 75, 100]
+database_hosts = []
+database_nodes = [4, 8, 16]
+database_cpus = [8]
+database_port = 6780
+batch_size = [1000]
+device = GPU
+num_devices = 1
+iterations = 100
+language = ['cpp', 'fortran']
+db_node_feature = {'constraint': 'P100'}
+node_feature = {'constraint': 'SK48'}
+wall_time = 15:00:00
+```
+
+#### Put Tensor (send image to database)
+
+
+#### Run Script (preprocess image)
+
+
+#### Run Model (run resnet50 on the image)
+
+
+#### Unpack Tensor (retrieve the inference result)
+
+
+## Colocated Inference
+
+The following are colocated deployment scaling results from the cpp-inference and fortran-inference scaling tests with ResNet-50 and the imagenet dataset. For more information on these scaling tests, please see
+the SmartSim paper on [arXiv](https://arxiv.org/pdf/2104.09355.pdf).
+
+#### Inference Colocated Run Configuration File
+```bash
+[run]
+name = run-2023-08-17-18:23:38
+path = results/inference-colocated-scaling/run-2023-08-17-18:23:38
+smartsim_version = 0.5.0
+smartredis_version = 0.3.1
+db = redis-server
+date = 2023-08-17
+language = ['cpp', 'fortran']
+
+[attributes]
+colocated = 1
+client_per_node = [18]
+client_nodes = [4, 8, 12, 16]
+database_cpus = [8]
+database_port = 6780
+batch_size = [96]
+device = GPU
+num_devices = 1
+iterations = 100
+language = ['cpp', 'fortran']
+node_feature = {'constraint': 'P100'}
+```
+
+#### Put Tensor (send image to database)
+
+
+#### Run Script (preprocess image)
+
+
+#### Run Model (run resnet50 on the image)
+
+
+#### Unpack Tensor (retrieve the inference result)
+
+
+## Inference Performance Analysis
+
+In this section, we compare the performance results of client operations: `put_tensor`, `unpack_tensor`, `run_model` and `run_script`
+for colocated and standard deployment.
+
+> Inference is the process of running data points into a machine learning model to calculate an output such as a single numerical score.
+
+- `put-tensor` : Colocated deployment offers a consistent median for put_tensor times. Standard deployment shows a slight
+increase in median as client count grows. However, due to machine constraints, colocated is maxed at 288 clients while
+standard maxes at 1800 clients. Due to Horizon offering 16 GPU nodes, there is no significant performance hit comparing the
+graphs above. However, we do know that there is a delay in network communication when using standard deployment.
+
+- `run_script` : Colocated deployment offers a faster run_script function than standard deployment. We can
+infer colocated deployment is able to transfer information faster when processing data than standard deployment.
+This is likely because communication time is cut when using colocated deployment. There are also not as many requests sent using colocated deployment than standard. This is because the database is split across multiple shards when using standard, the clients must communicate between all shards, adding additional network latency.
+
+- `run_model` : Colocated deployment demonstrates a faster run_model client than standard deployment. Like mentioned before,
+there is no additional network latency. By using standard deployment, you increase the number of requests sent during the runtime. This is because the database is split up into multiple shards instead of being centralized on the same node in colocated deployment.
+
+- `unpack-tensor` : There is no significant performance advantage when using colocated deployment vs standard for the client
+unpack_tensor. However, standard shows larger outside points than colocated. This is likely because the number of requests is greater during standard deployment. Those requests, as they wait to be processed, add additional network communication time.
+
+Due to machine constraints, there is not a large performance hit with `put-tensor` or `unpack-tensor` when using standard versus colocated deployment. Our testing constraints limited the scaling study tests to 16 GPU nodes. Therefore, we were not able to fully scale the colocated deployment to the node size of standard. Future expansive testing may indicate a larger performance hit. We do however notice a colocated deployment advantage with clients `run_model` and `run_script`. We can infer that this is due to the fact that the process is able to complete faster during colocated deployment due to the app and database being centralized on the same nodes. During standard deployment, the database is split into multiple shards. The application node has to travel to the database nodes to complete the `run_model` and `run_script` functions, earning the greater completion time. We can therefore conclude that there is a performance benefit using colocated deployment during functions `run_model` and `run_script`.
+
+## Throughput Standard
+
+The following are standard deployment scaling results from the cpp-throughput.
+
+#### Throughput Standard Run Configuration File
+```bash
+[run]
+name = run-2023-07-05-21:26:18
+path = results/throughput-standard-scaling/run-2023-07-05-21:26:18
+smartsim_version = 0.4.2
+smartredis_version = 0.3.1
+db = redis-server
+date = 2023-07-05
+language = ['cpp']
+
+[attributes]
+colocated = 0
+client_per_node = [48]
+client_nodes = [4, 8, 16, 32, 64, 128]
+database_nodes = [4, 8, 16]
+database_cpus = [8]
+iterations = 100
+tensor_bytes = [1024, 8192, 16384, 32769, 65538, 131076, 262152, 524304, 1024000]
+language = ['cpp']
+wall_time = 05:00:00
+```
+
+#### Put Tensor (send image to database) & Unpack Tensor (retrieve the image)
+
+
+## Throughput Colocated
+
+The following are colocated deployment scaling results from the cpp-throughput.
+
+#### Throughput Colocated Run Configuration File
+
+```bash
+[run]
+[run]
+name = run-2023-08-20-21:03:55
+path = results/throughput-colocated-scaling/run-2023-08-20-21:03:55
+smartsim_version = 0.5.0
+smartredis_version = 0.3.1
+db = redis-server
+date = 2023-08-20
+language = ['cpp']
+
+[attributes]
+colocated = 1
+custom_pinning = [False]
+client_per_node = [32]
+client_nodes = [16, 32, 64, 128]
+database_cpus = [8]
+iterations = 100
+tensor_bytes = [1024]
+language = ['cpp']
+```
+
+#### Put Tensor (send image to database) & Unpack Tensor (retrieve the image)
+
+
+## Throughput Performance Analysis
+
+In this section, we will compare client operations: `put-tensor` and `unpack-tensor`,
+for colocated and standard deployment.
+
+> Throughput measures the total time it takes to push and pull data from a database.
+The SmartSim Scaling studies produces a series of generated tensors to add (put_tensor) and retrieve from (unpack_tensor) a Redis Database.
+
+- `put_tensor` : We notice that for both colocated and standard deployment, put_tensor completes
+extremely quickly with both medians performing faster than .001 seconds. The difference here lies
+within the outside points. Looking at the standard violin plots, the high-end distribution values are much
+higher than colocated. We can attribute this to the network latency added when using standard orchestrator deployment.
+Through colocated deployment, no additional communication time is added since the application and database are
+centralized to the same nodes.
+
+- `unpack_tensor` : We notice that for both colocated and standard, unpack_tensor completes faster than put_tensor. However,
+both deployment options perform similarly to each other with the difference being highlighted in the outside points.
+As mentioned before, standard shows larger outside points than colocated. We can once again attribute this to the added
+network latency during standard deployment.
+
+Since we do not see a significant performance difference with colocated vs standard, in the future we plan
+to expand testing to compare Throughput with a Redis Database and KeyDB.
+
+## Data Aggregation Standard
+
+The following are standard deployment scaling results from the cpp-data-aggregation.
+
+#### Data Agg Standard Run Configuration File
+```bash
+[run]
+name = run-2023-08-20-21:55:15
+path = results/aggregation-standard-scaling/run-2023-08-20-21:55:15
+smartsim_version = 0.5.0
+smartredis_version = 0.3.1
+db = redis-server
+date = 2023-08-20
+language = ['cpp']
+
+[attributes]
+colocated = 0
+client_per_node = [32]
+client_nodes = [16, 32, 64, 128]
+db_cpus = 36
+iterations = 100
+tensor_bytes = [1024]
+tensors_per_dataset = [4]
+client_threads = [32]
+cpu_hyperthreads = 2
+language = ['cpp']
+wall_time = 10:00:00
+```
+#### Poll List (check when the next list is ready) & Get List (retrieve the data from the aggregation list)
+
+
+## Data Aggregation Standard Py
+
+The following are standard deployment scaling results from the cpp-py-data-aggregation/db.
+
+#### Data Agg Py Standard Run Configuration File
+```bash
+[run]
+name = run-2023-08-20-22:47:22
+path = results/aggregation-standard-scaling-py/run-2023-08-20-22:47:22
+smartsim_version = 0.5.0
+smartredis_version = 0.3.1
+db = redis-server
+date = 2023-08-20
+language = ['cpp']
+
+[attributes]
+colocated = 0
+client_per_node = [32]
+client_nodes = [16, 32, 64, 128]
+db_cpus = 32
+iterations = 100
+tensor_bytes = [1024]
+tensors_per_dataset = [4]
+client_threads = [32]
+cpu_hyperthreads = 2
+language = ['cpp']
+wall_time = 05:00:00
+```
+#### Poll List (check when the next list is ready) & Get List (retrieve the data from the aggregation list)
+
+
+## Data Aggregation Standard Py File System
+
+The following are standard deployment scaling results from the cpp-py-data-aggregation/fs.
+
+```bash
+[run]
+name = run-2023-07-20-15:56:58
+path = results/aggregation-standard-scaling-py-fs/run-2023-07-20-15:56:58
+smartsim_version = 0.5.0
+smartredis_version = 0.3.1
+db = redis-server
+date = 2023-07-20
+language = ['cpp']
+
+[attributes]
+colocated = 0
+client_per_node = [32]
+client_nodes = [4, 8, 16, 32, 64, 128]
+iterations = 100
+tensor_bytes = [1024]
+tensors_per_dataset = [4]
+client_threads = [32]
+cpu_hyperthreads = 2
+language = ['cpp']
+```
+
+#### Poll List (check when the next list is ready) & Get List (retrieve the data from the aggregation list)
+
+
+## Data Aggregation Performance Analysis
+
+In this section, we will compare client operations: `get-list` and `poll-list`,
+for standard deployment with a Python and C++ client.
+
+> Data Aggregation is the process of summarizing a large pool of data for high level analysis.
+For the data aggregation tests, we produce and store tensors in the database to poll and get.
+
+- `poll_list` : Polling tensors from the database reveals no large performance difference when comparing the use of a C++ client and a Python client. However, there is a large performance contrast when polling from a file system instead of a database. The file system expectedly demonstrates faster polling of tensors. This is expected because no network communication adds additional time to the completion time but instead local on the machine. Knowing the location of the file, the file system is able to poll quickly, however, you lose the ability to manage complex relationships as well as ensure data accuracy, completeness, and correctness.
+
+- `get_list` : Retrieving tensors from the database demonstrates no performance benefit when comparing a C++ client and a Python client. However, comparing the use of a file system over the database discloses a substantial performance hit. Using a file system adds a significant amount of time since there is no efficient way to query process. A database supports parsing, and optimizing the query contributing to faster retrieval of tensors. The orchestrator supports indexing on any attribute. This helps fast retrieval of data and is not supported by the file system.
+
+Although there is no note-able performance hit when comparing a C++ client and Python client, using the file system over the database adds substantial time to a program's completion. By using the SmartRedis Orchestrator, not only are we able to efficiently query data, validate data concurrency, but also data can be shared easily due to a centralized system. We may also manipulate the data, and rely on secure data recover and data backup options offered by the database. Using a database is especially important when running a large scale test that cannot be stored on a file system.
+
+## Advanced Performance Tips
+
+There are a few places users can look to get every last bit of performance.
+
+ 1. TCP settings
+ 2. Database settings
+
+The communication goes over the TCP/IP stack. Because of this, there are
+a few settings that can be tuned
+
+ - ``somaxconn`` - The max number of socket connections. Set this to at least 4096 if you can
+ - ``tcp_max_syn_backlog`` - Raising this value can help with really large tests.
+
+The database (Redis or KeyDB) has a number of different settings that can increase
+performance.
+
+For both Redis and KeyDB:
+ - ``maxclients`` - This should be raised to well above what you think the max number of clients will be for each DB shard
+ - ``threads-per-queue`` - can be set in ``Orchestrator()`` init. Helps with GPU inference performance (set to 4 or greater)
+ - ``inter-op-threads`` - can be set in ``Orchestrator()`` init. helps with CPU inference performance
+ - ``intra-op-threads`` - can be set in ``Orchestrator()`` init. helps with CPU inference performance
+
+For Redis:
+ - ``io-threads`` - we set to 4 by default in SmartSim
+ - ``io-use-threaded-reads`` - We set to yes (doesn't usually help much)
+
+For KeyDB:
+ - ``server-threads`` - Makes a big difference. We use 8 on HPC hardware. Set to 4 by default.
+
diff --git a/README.md b/README.md
index 24e977a..e3149a0 100644
--- a/README.md
+++ b/README.md
@@ -1,39 +1,104 @@
+
+
+
+
+
+
+
+
# ex. cpp-inference for the inference tests
+ cd - # ex. cpp-inference for the cpp inference tests
mkdir build && cd build
- cmake ..
+ cmake .. -DSMARTREDIS=/path/to/SmartRedis
make
```
+The CMake files used to build the various apps are shown below:
+
+1. Inference
+ - `cpp-inference/CMakeLists.txt`
+ - `fortran-inference/CMakeLists.txt`
+2. Throughput
+ - `cpp-throughput/CMakeLists.txt`
+3. Data Aggregation
+ - `cpp-data-aggregation/CMakeLists.txt`
+ - `cpp-py-data-aggregation/db/CMakeLists.txt`
+ - `cpp-py-data-aggregation/fs/CMakeLists.txt`
+
+> There are three different `CMakeLists.txt` files for the Data Aggregation tests.
+A separate build folder will need to be created within each CMake folder if you plan to run
+all three data agg tests. You will need to navigate into the respective CMake file per Data Aggregation scaling test and run the app steps above. This is the same for the C++ and Fortran inference tests.
+
## Running
A single SmartSim driver script can be used to launch both tests. The ``Fire`` CLI
@@ -71,605 +154,102 @@ SYNOPSIS
COMMANDS
COMMAND is one of the following:
- process_scaling_results
- Create a results directory with performance data and plots
-
inference_colocated
Run ResNet50 inference tests with colocated Orchestrator deployment
-
+ Client: C++
+
inference_standard
Run ResNet50 inference tests with standard Orchestrator deployment
+ Client: C++
+
+ throughput_colocated
+ Run throughput scaling tests with colocated Orchestrator deployment
+ Client: C++
+
+ throughput_standard
+ Run throughput scaling tests with standard Orchestrator deployment
+ Client: C++
+
+ aggregation_scaling
+ Run aggregation scaling tests with standard Orchestrator deployment
+ Client: C++
+
+ aggregation_scaling_python
+ Run aggregation scaling tests with standard Orchestrator deployment
+ Client: Python
+
+ aggregation_scaling_python_fs
+ Run aggregation scaling tests with standard File System deployment
+ Client: Python
- throughput_scaling
- Run the throughput scaling tests
-```
-
-Each of the command provides their own help menu as well that shows the
+ process_scaling_results
+ Create a results directory with performance data and performance plots
+ Client: None
+
+ scaling_read_data
+ Read performance results and store to file system
+ Client: None
+
+ scaling_plotter
+ Create just performance plots
+ Client: None
+
+```
+
+Each of the command provides their own help menu that shows the
arguments possible for each.
-### Inference
-
-The inference tests run as an MPI program where a single SmartRedis C++ client
-is initialized on every rank.
-
-Currently supported inference tests
-
- 1) Resnet50 CNN with ImageNet dataset
-
-Each client performs 101 executions of the following commands. The first iteration is a warmup;
-the next 100 are measured for inference throughput.
-
- 1) ``put_tensor`` (send image to database)
- 2) ``run_script`` (preprocess image)
- 3) ``run_model`` (run resnet50 on the image)
- 4) ``unpack_tensor`` (Retrieve the inference result)
-
-The input parameters to the test are used to generate permutations
-of tests with varying configurations.
-
-### The model
-As Neural Network, we use Pytorch's implementation of Resnet50. The script `imagenet/model_saver.py`
-can be used to generate the model for CPU or GPU. By navigating to the `imagenet` folder, the CPU model
-can be created running
-
-```bash
-python model_saver.py
-```
-
-and the GPU model can be created running
-
-```bash
-python model_saver.py --device=GPU
-```
-
-
-If the benchmark driver is executed and
-no model exists, an attempt is made to generate the model on the fly. In both cases,
-the specified device must be available on the node where the script is called (this
-means that it could be required to run the script through the workload manager launcher
-to execute it on a node with a GPU, for example).
-
-### Co-located inference
-
-Co-located Orchestrators are deployed on the same nodes as the
-application. This improves inference performance as no data movement
-"off-node" occurs with co-located deployment. For more information
-on co-located deployment, see [our documentation](https://www.craylabs.org/docs/orchestrator.html)
-
-Below is the help output. The arguments which are lists control
-the possible permutations that will be run.
-
-```text
-NAME
- driver.py inference_colocated - Run ResNet50 inference tests with colocated Orchestrator deployment
-
-SYNOPSIS
- driver.py inference_colocated
-
-DESCRIPTION
- Run ResNet50 inference tests with colocated Orchestrator deployment
-
-FLAGS
- --exp_name=EXP_NAME
- Default: 'inference-scaling'
- name of output dir, defaults to "inference-scaling"
- --launcher=LAUNCHER
- Default: 'auto'
- workload manager i.e. "slurm", "pbs"
- --nodes=NODES
- Default: [12]
- compute nodes to use for synthetic scaling app with a co-located orchestrator database
- --clients_per_node=CLIENTS_PER_NODE
- Default: [18]
- client tasks per compute node for the synthetic scaling app
- --db_cpus=DB_CPUS
- Default: [2]
- number of cpus per compute host for the database
- --db_tpq=DB_TPQ
- Default: [1]
- number of device threads to use for the database
- --db_port=DB_PORT
- Default: 6780
- port to use for the database
- --pin_app_cpus=PIN_APP_CPUS
- Default: [False]
- pin the threads of the application to 0-(n-db_cpus)
- --batch_size=BATCH_SIZE
- Default: [1]
- batch size to set Resnet50 model with
- --device=DEVICE
- Default: 'GPU'
- device used to run the models in the database
- --num_devices=NUM_DEVICES
- Default: 1
- number of devices per compute node to use to run ResNet
- --net_ifname=NET_IFNAME
- Default: 'ipogif0'
- network interface to use i.e. "ib0" for infiniband or "ipogif0" aries networks
- --rebuild_model=FORCE_REBUILD
- Default: False
- force rebuild of PyTorch model even if it is available
-```
-
-So for example, the following command could be run to execute a battery of
-tests in the same allocation
-
-```bash
-python driver.py inference_colocated --clients_per_node=[24,28] \
- --nodes=[16] --db_tpq=[1,2,4] \
- --db_cpus=[1,2,4,8] --net_ifname=ipogif0 \
- --device=GPU
-```
-
-This command can be executed in a terminal with an interactive allocation
-or used in a batch script such as the following for Slurm based systems
-
-```bash
-#!/bin/bash
-
-#SBATCH -N 16
-#SBATCH --exclusive
-#SBATCH -C P100
-#SBATCH -t 10:00:00
-
-module load slurm
-python driver.py inference_colocated --clients_per_node=[24,28] \
- --nodes=[16] --db_tpq=[1,2,4] \
- --db_cpus=[1,2,4,8] --net_ifname=ipogif0
- --device=GPU
-```
-
-Examples of batch scripts to use are provided in the ``batch_scripts`` directory
-
-
-### Standard Inference
-
-Co-locacated deployment is the preferred method for running tightly coupled
-inference workloads with SmartSim, however, if you want to deploy the Orchestrator
-database and the application on different nodes you may want to use standard
-deployment.
-
-For example, if you only have a small number of GPU nodes and want to test a large
-CPU application you may want to use standard deployment. For more information
-on Orchestrator deployment methods, please see
-[our documentation](https://www.craylabs.org/docs/orchestrator.html)
-
-Like the above inference scaling tests, the standard inference tests also provide
-a method of running a battery of tests all at once. Below is the help output.
-The arguments which are lists control the possible permutations that will be run.
-
-```text
-NAME
- driver.py inference_standard - Run ResNet50 inference tests with standard Orchestrator deployment
-
-SYNOPSIS
- driver.py inference_standard
-
-DESCRIPTION
- Run ResNet50 inference tests with standard Orchestrator deployment
-
-FLAGS
- --exp_name=EXP_NAME
- Default: 'inference-scaling'
- name of output dir
- --launcher=LAUNCHER
- Default: 'auto'
- workload manager i.e. "slurm", "pbs"
- --run_db_as_batch=RUN_DB_AS_BATCH
- Default: True
- run database as separate batch submission each iteration
- --batch_args=BATCH_ARGS
- Default: {}
- additional batch args for the database
- --db_hosts=DB_HOSTS
- Default: []
- optionally supply hosts to launch the database on
- --db_nodes=DB_NODES
- Default: [12]
- number of compute hosts to use for the database
- --db_cpus=DB_CPUS
- Default: [2]
- number of cpus per compute host for the database
- --db_tpq=DB_TPQ
- Default: [1]
- number of device threads to use for the database
- --db_port=DB_PORT
- Default: 6780
- port to use for the database
- --batch_size=BATCH_SIZE
- Default: [1000]
- batch size to set Resnet50 model with
- --device=DEVICE
- Default: 'GPU'
- device used to run the models in the database
- --num_devices=NUM_DEVICES
- Default: 1
- number of devices per compute node to use to run ResNet
- --net_ifname=NET_IFNAME
- Default: 'ipogif0'
- network interface to use i.e. "ib0" for infiniband or "ipogif0" aries networks
- --clients_per_node=CLIENTS_PER_NODE
- Default: [48]
- client tasks per compute node for the synthetic scaling app
- --client_nodes=CLIENT_NODES
- Default: [12]
- number of compute nodes to use for the synthetic scaling app
- --rebuild_model=FORCE_REBUILD
- Default: False
- force rebuild of PyTorch model even if it is available
-```
-
-The standard inference tests will spin up a database for each iteration in the
-battery of tests chosen by the user. There are multiple ways to run this.
-
-1. Everything in the same interactive (or batch file) without caring about placement
-```bash
-# alloc must contain at least 120 (max client_nodes) + 16 nodes (max db_nodes)
-python driver.py inference_standard --client_nodes=[20,40,60,80,100,120] \
- --db_nodes=[4,8,16] --db_tpq=[1,2,4] \
- --db_cpus=[1,4,8,16] --run_db_as_batch=False \
- --net_ifname=ipogif0 --device=GPU
-```
-
-This option is recommended as it's easy to launch in interactive allocations and
-as a batch submission, but if you need to specify separate hosts for the database
-you can look into the following two methods.
-
-A batch submission for this first option would look like the following for Slurm
-based systems.
-
-```bash
-#!/bin/bash
-
-#SBATCH -N 136
-#SBATCH --exclusive
-#SBATCH -t 10:00:00
-
-python driver.py inference_standard --client_nodes=[20,40,60,80,100,120] \
- --db_nodes=[4,8,16] --db_tpq=[1,2,4] \
- --db_cpus=[1,4,8,16] --run_db_as_batch=False
- --net_ifname=ipogif0 --device=CPU
-```
-
-2. Same as 1, but specify hosts for the database
-```bash
-# alloc must contain at least 120 (max client_nodes) + 16 nodes (max db_nodes)
-# db nodes must be fixed if hostlist is specified
-python driver.py inference_standard --client_nodes=[20,40,60,80,100,120] \
- --db_nodes=[16] --db_tpq=[1,2,4] \
- --db_cpus=[1,4,8,16] --db_hosts=[nid0001, ...] \
- --net_ifname=ipogif0 --device=CPU
-
-```
-
-3. Launch database as a separate batch submission each time
-```bash
-# must obtain separate allocation for client driver through interactive or batch submission
-# if batch submission, compute nodes must have access to slurm
-python driver.py inference_standard --client_nodes=[20,40,60,80,100,120] \
- --db_nodes=[4,8,16] --db_tpq=[1,2,4] \
- --db_cpus=[1,4,8,16] --batch_args='{"C":"V100", "exclusive": None}' \
- --net_ifname=ipogif0 --device=GPU
-```
-
-All three options will conduct ``n`` scaling tests where ``n`` is the multiple of
-all lists specified as options.
-
-### Throughput
-
-The throughput tests run as an MPI program where a single SmartRedis C++ client
-is initialized on every rank.
-
-Each client performs 10 executions of the following commands
+## Results
- 1) ``put_tensor`` (send image to database)
- 2) ``unpack_tensor`` (Retrieve the inference result)
+The output organization of the performance results is detail below.
+### Results File Structure
-The input parameters to the test are used to generate permutations
-of tests with varying configurations.
+When a scaling test is first initialized, a nested folder named `results/'exp_name'`
+is created. The `exp_name` is captured by the `exp_name` flag value when you run your
+scaling test. For example, running the standard inference test via
+`python driver.py inference_standard` with the default name `exp_name=inference-standard-scaling`,
+places results in the `results/inference-standard-scaling` directory.
-```text
-
-NAME
- driver.py throughput_scaling - Run the throughput scaling tests
-
-SYNOPSIS
- driver.py throughput_scaling
-
-DESCRIPTION
- Run the throughput scaling tests
-
-FLAGS
- --exp_name=EXP_NAME
- Default: 'throughput-scaling'
- name of output dir
- --launcher=LAUNCHER
- Default: 'auto'
- workload manager i.e. "slurm", "pbs"
- --run_db_as_batch=RUN_DB_AS_BATCH
- Default: True
- run database as separate batch submission each iteration
- --batch_args=BATCH_ARGS
- Default: {}
- additional batch args for the database
- --db_hosts=DB_HOSTS
- Default: []
- optionally supply hosts to launch the database on
- --db_nodes=DB_NODES
- Default: [12]
- number of compute hosts to use for the database
- --db_cpus=DB_CPUS
- Default: [2]
- number of cpus per compute host for the database
- --db_port=DB_PORT
- Default: 6780
- port to use for the database
- --net_ifname=NET_IFNAME
- Default: 'ipogif0'
- network interface to use i.e. "ib0" for infiniband or "ipogif0" aries networks
- --clients_per_node=CLIENTS_PER_NODE
- Default: [32]
- client tasks per compute node for the synthetic scaling producer app
- --client_nodes=CLIENT_NODES
- Default: [128, 256, 512]
- number of compute nodes to use for the synthetic scaling producer app
- --tensor_bytes=TENSOR_BYTES
- Default: [1024, 8192, 16384, 32769, 65538, 131076, 262152, 524304, 10...
- list of tensor sizes in bytes
-```
+Each time you run a scaling test it is considered a single run. This is how the
+`results/'exp_name'` is organized. The results will be within a folder named
+`run-YEAR-MONTH-DAY-TIME`. A result's folder with multiple
+runs of inference standard with the default `exp_name` would look like:
-### Data aggregation
-
-The data aggregation scaling test runs two applications. The first application
-is an MPI application that produces datasets that are added to an aggregation list.
-In this producer application, each MPI rank has a single-threaded client. The second
-application is a consumer application. This application consumes the aggregation
-lists that are produced by the first application. The consumer application
-can be configured to use multiple threads for data aggregation. The producer and consumer
-applications are running at the same time, but the producer application waits for the
-consumer application to finish an aggregation list before starting to produce
-the next aggregation list.
-
-By default, the clients in the producer application perform 100 executions of the following command:
-
- 1) ``append_to_list`` (add dataset to the aggregation list)
-
-Note that the client on rank 0 of the producer application performs a ``get_list_length()``
-function invocation prior to an ``MPI_BARRIER`` in order to only produce the next aggregation
-list after the previous aggregation list was consumed by the consumer application.
-
-There is only a single MPI rank for the consumer application, which means there is only
-one SmartRedis client active for the consumer application. The consumer application client
-invokes the following SmartRedis commands:
-
- 1) ``poll_list_length`` (check when the next aggregation list is ready)
- 2) ``get_datasets_from_list`` (retrieve the data from the aggregation list)
-
-
-The input parameters to the test are used to generate permutations
-of tests with varying configurations.
-
-```text
-
-NAME
- driver.py aggregation-scaling - Run the data aggregation scaling tests
-
-SYNOPSIS
- driver.py aggregation-scaling
-
-DESCRIPTION
- Run the data aggregation scaling tests
-
-FLAGS
- --exp_name=EXP_NAME
- Default: 'aggregation-scaling'
- name of output dir
- --launcher=LAUNCHER
- Default: 'auto'
- workload manager i.e. "slurm", "pbs"
- --run_db_as_batch=RUN_DB_AS_BATCH
- Default: True
- run database as separate batch submission each iteration
- --batch_args=BATCH_ARGS
- Default: {}
- additional batch args for the database
- --db_hosts=DB_HOSTS
- Default: []
- optionally supply hosts to launch the database on
- --db_nodes=DB_NODES
- Default: [12]
- number of compute hosts to use for the database
- --db_cpus=DB_CPUS
- Default: 36
- number of cpus per compute host for the database
- --db_port=DB_PORT
- Default: 6780
- port to use for the database
- --net_ifname=NET_IFNAME
- Default: 'ipogif0'
- network interface to use i.e. "ib0" for infiniband or "ipogif0" aries networks
- --clients_per_node=CLIENTS_PER_NODE
- Default: [32]
- client tasks per compute node for the synthetic scaling app
- --client_nodes=CLIENT_NODES
- Default: [128, 256, 512]
- number of compute nodes to use for the synthetic scaling app
- --iterations=ITERATIONS
- Default: 20
- number of append/retrieve loops run by the applications
- --tensor_bytes=TENSOR_BYTES
- Default: [1024, 8192, 16384, 32769, 65538, 131076, 262152, 524304, 10...
- list of tensor sizes in bytes
- --tensors_per_dataset=TENSORS_PER_DATASET
- Default: [1, 2, 4]
- list of number of tensors per dataset
- --client_threads=CLIENT_THREADS
- Default: [1, 2, 4, 8, 16, 32]
- list of the number of client threads used for data aggregation
-```
-
-### Collecting Performance Results
-
-The ``process_scaling_results`` function will collect the timings for each
-of the tests and make a series of plots for each client function called in
-each run as well as make a collective csv of timings for all runs. These
-artifacts will be in a ``results`` folder inside the directory where the
-function was pointed to the scaling data with the ``scaling_dir`` flag
-shown below. The default will work for the inference tests.
-
-Similar to the inference and throughput tests, the result collection has
-it's own options for execution
-
-```text
-NAME
- driver.py process_scaling_results - Create a results directory with performance data and plots
-
-SYNOPSIS
- driver.py process_scaling_results
-
-DESCRIPTION
- With the overwrite flag turned off, this function can be used
- to build up a single csv with the results of runs over a long
- period of time.
-
-FLAGS
- --scaling_dir=SCALING_DIR
- Default: 'inference-scaling'
- directory to create results from
- --overwrite=OVERWRITE
- Default: True
- overwrite any existing results
-```
-
-For example for the inference tests (if you don't change the output dir name)
-you can run
-
-```bash
-python driver.py process_scaling_results
-```
-
-For the throughput tests
```bash
-python driver.py process_scaling_results --scaling_dir=throughput-scaling
-```
-
-
-## Performance Results
-
-The performance of SmartSim is detailed below across various types of systems.
-
-### Inference
-
-The following are scaling results from the cpp-inference scaling tests with ResNet-50
-and the imagenet dataset. For more information on these scaling tests, please see
-the SmartSim paper on arXiv
-
-
-
-
-### Colocated Inference
-
-The following are scaling results for a colocated inference test, run on 12 36-core Intel Broadwell nodes,
-each one equipped with 8 Nvidia V100 GPUs. On each node, 28 client threads were run, and the databases
-were run on 8 CPUs and 8 threads per queue.
-
-Note that the first iteration can take longer (up to several seconds) than the rest of the execution. This
-is due to the DB loading libraries when the first RedisAI call is made. In the following plots, we excluded
-the first iteration time.
-
-
-
-
-### Throughput
-
-The following are results from the throughput tests for Redis. For results obtained using KeyDB, see section below.
-
-All the throughput data listed here is based on the ``loop time`` which is the time to complete a single put and get. Each client
-in the test performs 100 loop iterations and the aggregate throughput for all clients is displayed in the plots below.
-
-Each test has three lines for the three database sizes tested: 16, 32, 64. Each of the plots represents a different number of total clients
-the first is 4096 clients (128 nodes x 32 ranks per node), followed by 8192 (256 nodes x 32 ranks per node) and lastly 16384 clients
-(512 nodes x 32 ranks per node)
-
-
-
-
-
-
-
-
-
-
-### Using KeyDB
-
-KeyDB is a multithreaded version of Redis with some strong performance claims. Luckily, since
-KeyDB is a drop in replacement for Redis, it's fairly easy to test. If you are looking for
-extreme performance, especially in throughput for large data sizes,
-we recommend building SmartSim with KeyDB.
-
-In future releases, switching between Redis and KeyDB will be achieved by setting an environment variable specifying the backend.
-
-The following plots show the results for the same throughput tests of previous section, using KeyDB as a backend.
-
-
-
-
-
-
-
-
-
-
-
-### Result analysis
-
-> :warning: from the above plots, it is clear that there is a performance decrease at 64 and 128 KiB, which is visible in all cases,
-but is most relevant for large DB node counts and for KeyDB. We are currently investigating this behavior, as we are not exactly
-sure of what the root cause could be.
-
-A few interesting points:
-
- 1. Client connection time: KeyDB connects client MUCH faster than base Redis. At this time, we
- are not exactly sure why, but it does. So much so, that if you are looking to use the SmartRedis
- clients in such a way that you will be disconnecting and reconnecting to the database, you
- should use KeyDB instead of Redis with SmartSim.
-
- 2. In general, according to the throughput scaling tests, KeyDB has roughly up to 2x the throughput
- of Redis and reaches a maximum throughput of ~125 Gb/s, whereas we could not get Redis to achieve
- more than ~90 Gb/s.
-
- 3. KeyDB seems to handle higher numbers of clients better than Redis does.
-
- 4. There is an evident bottleneck on throughput around 128 kiB
-
-
-## Advanced Performance Tips
-
-There are a few places users can look to get every last bit of performance.
-
- 1. TCP settings
- 2. Database settings
-
-The communication goes over the TCP/IP stack. Because of this, there are
-a few settings that can be tuned
-
- - ``somaxconn`` - The max number of socket connections. Set this to at least 4096 if you can
- - ``tcp_max_syn_backlog`` - Raising this value can help with really large tests.
-
-The database (Redis or KeyDB) has a number of different settings that can increase
-performance.
-
-For both Redis and KeyDB:
- - ``maxclients`` - This should be raised to well above what you think the max number of clients will be for each DB shard
- - ``threads-per-queue`` - can be set in ``Orchestrator()`` init. Helps with GPU inference performance (set to 4 or greater)
- - ``inter-op-threads`` - can be set in ``Orchestrator()`` init. helps with CPU inference performance
- - ``intra-op-threads`` - can be set in ``Orchestrator()`` init. helps with CPU inference performance
-
-For Redis:
- - ``io-threads`` - we set to 4 by default in SmartSim
- - ``io-use-threaded-reads`` - We set to yes (doesn't usually help much)
-
-For KeyDB:
- - ``server-threads`` - Makes a big difference. We use 8 on HPC hardware. Set to 4 by default.
-
+results/
+├─ inference-standard-scaling/ # Holds all the runs for a scaling test
+│ ├─ run-2023-07-17-13:21:17/ # Holds all information for a specific run
+│ │ ├─ database/ # Holds orchestrator information
+│ │ │ ├─ orchestrator.err # Will output an error within the Orchestrator
+│ │ │ ├─ orchestrator.out # Will output messages pushed during an Orchestrator run
+│ │ │ ├─ smartsim_db.dat
+│ │ ├─ infer-sess-cpp-N4-T18-DBN4-DBCPU8-ITER100-DBTPQ8-80e4/ # Holds all information for a session
+│ │ │ ├─ cat.raw # Holds all timings from infer run
+│ │ │ ├─ data_processing_script.txt # Script used during the infer session
+│ │ │ ├─ infer-sess-cpp-N4-T18-DBN4-DBCPU8-ITER100-DBTPQ8-80e4.err # Stores error messages during inf session
+│ │ │ ├─ infer-sess-cpp-N4-T18-DBN4-DBCPU8-ITER100-DBTPQ8-80e4.out # Stores print messages during inf session
+│ │ │ ├─ rank_0_timing.csv # Holds timings for the given rank, in this case rank 0
+│ │ │ ├─ resnet50.GPU.pt # The model used for the infer session
+│ │ │ ├─ run.cfg # Setting file for the infer session
+│ │ │ ├─ srlog.out # Log file for SmartRedis
+│ │ ├─ infer-sess-fortran-N4-T18-DBN4-DBCPU8-ITER100-DBTPQ8-f8a6/
+│ │ ├─ run.cfg # Setting file for the run
+│ │ ├─ scaling-2023-07-19.log # Log file for information on run
+│ ├─ stats/ # Holds all the statistical results per run
+│ │ ├─ run-2023-07-17-13:21:17/ # The run
+│ │ │ ├─ infer-sess-cpp-N4-T18-DBN4-DBCPU8-ITER100-DBTPQ8-80e4/ # certain sessiom
+│ │ │ │ ├─ infer-sess-cpp-N4-T18-DBN4-DBCPU8-ITER100-DBTPQ8-80e4.csv
+│ │ │ │ ├─ put_tensor.pdf # PDF version of b/w plots
+│ │ │ │ ├─ run_model.pdf # PDF version of b/w plots
+│ │ │ │ ├─ run_script.pdf # PDF version of b/w plots
+│ │ │ │ ├─ unpack_tensor.pdf # PDF version of b/w plots
+│ │ │ ├─ infer-sess-fortran-N4-T18-DBN4-DBCPU8-ITER100-DBTPQ8-f8a6/
+│ │ ├─ dataframe.csv.gz # Dataframe wit
+│ │ ├─ put_tensor.png # Violin plot for put_tensor timings
+│ │ ├─ run_model.png # Violin plot for run_model timings
+│ │ ├─ run_script.png # Violin plot for run_script timings
+│ │ ├─ unpack_tensor.png # Violin plot for unpack_tensor timings
+```
+
+Within each run folder there is a subset of files that will be useful to you.
\ No newline at end of file
diff --git a/batch_scripts/run_aggregation_python_fs_slurm.sh b/batch_scripts/run_aggregation_python_fs_slurm.sh
index a09499c..6bb59ce 100644
--- a/batch_scripts/run_aggregation_python_fs_slurm.sh
+++ b/batch_scripts/run_aggregation_python_fs_slurm.sh
@@ -5,7 +5,6 @@
#SBATCH -t 24:00:00
cd ..
-module load slurm
python driver.py aggregation_scaling_python_fs --exp_name='aggregation-scaling-py-fs-batch' \
--client_nodes=[60] \
--clients_per_node=[48] \
diff --git a/batch_scripts/run_aggregation_python_slurm.sh b/batch_scripts/run_aggregation_python_slurm.sh
index 4d54da7..2f3a1d2 100644
--- a/batch_scripts/run_aggregation_python_slurm.sh
+++ b/batch_scripts/run_aggregation_python_slurm.sh
@@ -3,9 +3,8 @@
#SBATCH -N 93
#SBATCH --exclusive
#SBATCH -t 24:00:00
-
+echo "Note: The flag net_ifname should be replaced with the appropriate value on the target system"
cd ..
-module load slurm
python driver.py aggregation_scaling_python --exp_name='aggregation-scaling-py-batch' \
--client_nodes=[60] \
--clients_per_node=[48] \
diff --git a/batch_scripts/run_aggregation_slurm.sh b/batch_scripts/run_aggregation_slurm.sh
index 489c411..d1b878f 100644
--- a/batch_scripts/run_aggregation_slurm.sh
+++ b/batch_scripts/run_aggregation_slurm.sh
@@ -5,13 +5,16 @@
#SBATCH -t 12:00:00
#SBATCH -C SK48
#SBATCH --oversubscribe
-
+echo "Note: The flag net_ifname should be replaced with the appropriate value on the target system"
cd ..
-module load slurm
-python driver.py aggregation_scaling --client_nodes=[60] \
+python driver.py aggregation_scaling --exp_name='aggregation-scaling-batch' \
+ --client_nodes=[60] \
--clients_per_node=[48] \
- --db_nodes=[16,32] \
+ --db_nodes=[16] \
--db_cpus=32 --net_ifname=ipogif0 \
--run_db_as_batch=False \
- --tensors_per_dataset=[1,4]
+ --tensors_per_dataset=[4] \
+ --tensor_bytes=[1024000] \
+ --iterations=20 \
+ --tensors_per_dataset=[4]
diff --git a/batch_scripts/run_inference_colo_slurm.sh b/batch_scripts/run_inference_colo_slurm.sh
index 6a09b42..39ad46b 100644
--- a/batch_scripts/run_inference_colo_slurm.sh
+++ b/batch_scripts/run_inference_colo_slurm.sh
@@ -1,15 +1,9 @@
#!/bin/bash
-#SBATCH -N 1
+#SBATCH -N 16
+#SBATCH -C "P100*16"
#SBATCH --exclusive
-#SBATCH -p allgriz
-#SBATCH -t 1:00:00
-
-module load cudatoolkit/11.7 cudnn PrgEnv-intel
-source ~/pyenvs/smartsim-dev/bin/activate
-
+#SBATCH -t 10:00:00
+echo "Note: The flag net_ifname should be replaced with the appropriate value on the target system"
cd ..
-python driver.py inference_colocated --clients_per_node=[12,24,36,60,96] \
- --nodes=[1] --db_tpq=[2] \
- --db_cpus=[12] --pin_app_cpus=[True] \
- --net_type="uds" --node_feature='{}' --languages=['fortran','cpp']
+python driver.py inference_colocated --nodes=[4, 8, 12, 16]
diff --git a/batch_scripts/run_inference_standard_slurm.sh b/batch_scripts/run_inference_standard_slurm.sh
index da35ac5..262dec0 100644
--- a/batch_scripts/run_inference_standard_slurm.sh
+++ b/batch_scripts/run_inference_standard_slurm.sh
@@ -1,11 +1,11 @@
#!/bin/bash
-#SBATCH -N 60
+#SBATCH -N 116
+#SBATCH -C "[P100*16&SK48*100]"
#SBATCH --exclusive
#SBATCH -t 10:00:00
-
+echo "Note: The flag net_ifname should be replaced with the appropriate value on the target system"
cd ..
-module load slurm
-python driver.py inference_standard --client_nodes=[20,40,60] \
- --db_nodes=[4,8,16] --db_tpq=[1,2,4] \
- --db_cpus=[8,16]
+python driver.py inference_standard --client_nodes=[25, 50, 75, 100] \
+ --db_nodes=[4, 8, 16] --db_tpq=[1] \
+ --db_cpus=[8]
diff --git a/batch_scripts/run_throughput_pbs.sh b/batch_scripts/run_throughput_pbs.sh
index 43bd963..e0b550f 100644
--- a/batch_scripts/run_throughput_pbs.sh
+++ b/batch_scripts/run_throughput_pbs.sh
@@ -5,7 +5,7 @@
#PBS -o throughput.out
#PBS -N smartsim-throughput
#PBS -V
-
+echo "Note: The flag net_ifname should be replaced with the appropriate value on the target system"
PYTHON=/lus/snx11242/spartee/miniconda/envs/0.4.0/bin/python
cd $PBS_O_WORKDIR/../
$PYTHON driver.py throughput_standard --client_nodes=[128,256,512] \
diff --git a/batch_scripts/run_throughput_slurm.sh b/batch_scripts/run_throughput_slurm.sh
index 448e6ab..e67bbff 100644
--- a/batch_scripts/run_throughput_slurm.sh
+++ b/batch_scripts/run_throughput_slurm.sh
@@ -5,12 +5,11 @@
#SBATCH -t 10:00:00
#SBATCH -C SK48
#SBATCH --oversubscribe
-
+echo "Note: The flag net_ifname should be replaced with the appropriate value on the target system"
cd ..
-module load slurm
python driver.py throughput_standard --client_nodes=[60] \
--clients_per_node=[48] \
--db_nodes=[32] \
- --db_cpus=32 --net_ifname=ipogif0 \
+ --db_cpus=[32] --net_ifname=ipogif0 \
--run_db_as_batch=False
diff --git a/cpp-data-aggregation/aggregation_consumer.cpp b/cpp-data-aggregation/aggregation_consumer.cpp
index fa0fe32..50740a0 100644
--- a/cpp-data-aggregation/aggregation_consumer.cpp
+++ b/cpp-data-aggregation/aggregation_consumer.cpp
@@ -35,6 +35,9 @@ void run_aggregation_consumer(std::ofstream& timing_file,
// Allocate arrays to hold timings
std::vector get_list_times;
+ // Allocate arrays to hold timings
+ std::vector poll_list_times;
+
// Retrieve the number of iterations to run
int iterations = get_iterations();
log_data(context, LLDebug, "Running with iterations: " + std::to_string(iterations));
@@ -59,6 +62,7 @@ void run_aggregation_consumer(std::ofstream& timing_file,
log_data(context, LLInfo, "Consuming list " + std::to_string(i));
}
+ double poll_list_start = MPI_Wtime();
// Have rank 0 check that the aggregation list is full
if(rank == 0) {
bool list_is_ready = client.poll_list_length(list_name,
@@ -73,7 +77,10 @@ void run_aggregation_consumer(std::ofstream& timing_file,
throw std::runtime_error(list_size_error);
}
}
-
+ double poll_list_end = MPI_Wtime();
+ log_data(context, LLDebug, "poll_list completed");
+ delta_t = poll_list_end - poll_list_start;
+ poll_list_times.push_back(delta_t);
// Have all ranks wait until the aggregation list is full
MPI_Barrier(MPI_COMM_WORLD);
@@ -104,6 +111,8 @@ void run_aggregation_consumer(std::ofstream& timing_file,
for (int i = 0; i < iterations; i++) {
timing_file << rank << "," << "get_list" << ","
<< get_list_times[i] << "\n";
+ timing_file << rank << "," << "poll_list" << ","
+ << poll_list_times[i] << "\n";
}
// Write loop time to file
diff --git a/cpp-inference/inference_scaling_imagenet.cpp b/cpp-inference/inference_scaling_imagenet.cpp
index 0aecce7..b4cd2e2 100644
--- a/cpp-inference/inference_scaling_imagenet.cpp
+++ b/cpp-inference/inference_scaling_imagenet.cpp
@@ -98,8 +98,19 @@ void run_mnist(const std::string& model_name,
int num_devices = get_num_devices();
bool use_multigpu = (0 == device.compare("GPU")) && num_devices > 1;
bool should_set = get_set_flag();
+
std::string model_key = "resnet_model";
+ bool poll_model_code = client.poll_model(model_key, 100, 100);
+ if (!poll_model_code) {
+ log_error(context, LLInfo, "SR Error finding model");
+ }
+
std::string script_key = "resnet_script";
+ bool poll_script_code = client.poll_key(script_key, 100, 100);
+ if (!poll_script_code) {
+ log_error(context, LLInfo, "SR Error finding script");
+ }
+
// setting up string to debug set vars
std::string program_vars = "Running rank with vars should_set: ";
program_vars += std::to_string(should_set) + " - num_device: ";
@@ -108,102 +119,6 @@ void run_mnist(const std::string& model_name,
program_vars += std::to_string(is_colocated) + " - cluster: " + std::to_string(cluster);
log_data(context, LLDebug, program_vars);
- if (should_set) {
- log_data(context, LLDebug, "Entered should_set code block");
- int batch_size = get_batch_size();
- int n_clients = get_client_count();
- std::string should_set_vars = "Running rank with batch_size: ";
- should_set_vars += std::to_string(batch_size) + " and n_clients: ";
- should_set_vars += std::to_string(n_clients);
- log_data(context, LLDebug, should_set_vars);
- if (!is_colocated && rank == 0) {
- log_data(context, LLDebug, "Setting script/model for Standard test");
-
- std::cout<<"Setting Resnet Model from scaling app" << std::endl;
- log_data(context, LLInfo, "Setting Resnet Model from scaling app");
-
- std::cout<<"Setting with batch_size: " << std::to_string(batch_size) << std::endl;
- log_data(context, LLInfo, "Setting with batch_size: " + std::to_string(batch_size));
-
- std::cout<<"Setting on device: " << device << std::endl;
- log_data(context, LLInfo, "Setting on device: " + device);
-
- std::cout<<"Setting on " << std::to_string(num_devices) << " devices" < Note that the number of threads per client should be less than or equal
+(most performant) to the number of database shards.
+
+```text
+
+NAME
+ driver.py aggregation_scaling - Run the data aggregation scaling tests with standard Orchestrator deployment
+
+SYNOPSIS
+ driver.py aggregation-scaling
+
+DESCRIPTION
+ Run the data aggregation scaling tests with standard Orchestrator deployment
+
+FLAGS
+ --exp_name=EXP_NAME
+ Default: 'aggregation-standard-scaling'
+ name of output dir
+ --launcher=LAUNCHER
+ Default: 'auto'
+ workload manager i.e. "slurm", "pbs"
+ --run_db_as_batch=RUN_DB_AS_BATCH
+ Default: True
+ run database as separate batch submission each iteration
+ --db_node_feature=DB_NODE_FEATURE
+ Default: {}
+ dict of runsettings for the db
+ --prod_node_feature=PROD_NODE_FEATURE
+ Default: {}
+ dict of runsettings for the producer
+ --cons_node_feature=CONS_NODE_FEATURE
+ Default: {}
+ dict of runsettings for the consumer
+ --db_hosts=DB_HOSTS
+ Default: []
+ optionally supply hosts to launch the database on
+ --db_nodes=DB_NODES
+ Default: [16]
+ number of compute hosts to use for the database
+ --db_cpus=DB_CPUS
+ Default: 36
+ number of cpus per compute host for the database
+ --db_port=DB_PORT
+ Default: 6780
+ port to use for the database
+ --net_ifname=NET_IFNAME
+ Default: 'ipogif0'
+ network interface to use i.e. "ib0" for infiniband or "ipogif0" aries networks
+ --clients_per_node=CLIENTS_PER_NODE
+ Default: [48]
+ client tasks per compute node for the synthetic scaling app
+ --client_nodes=CLIENT_NODES
+ Default: [128, 256, 512]
+ number of compute nodes to use for the synthetic scaling app
+ --iterations=ITERATIONS
+ Default: 20
+ number of append/retrieve loops run by the applications
+ --tensor_bytes=TENSOR_BYTES
+ Default: [1024, 8192, 16384, 32769, 65538, 131076, 262152, 524304, 10...
+ list of tensor sizes in bytes
+ --tensors_per_dataset=TENSORS_PER_DATASET
+ Default: [1, 2, 4]
+ list of number of tensors per dataset
+ --client_threads=CLIENT_THREADS
+ Default: [1, 2, 4, 8, 16, 32]
+ list of the number of client threads used for data aggregation
+ --cpu_hyperthreads==CPU_HYPERTHREADS
+ Default: 2
+ the number of hyperthreads per cpu. This is done
+ to request that the consumer application utilizes
+ all physical cores for each client thread.
+ --languages=LANGUAGES
+ Default: ['cpp']
+ list of languages to use for the tester "cpp" and/or "fortran"
+ --wall_time=WALL_TIME
+ Default: '05:00:00'
+ allotted time for database launcher to run
+ --plot=PLOT
+ Default: 'database_nodes'
+ flag to plot against in process results
+```
+For example, the following command could be run to execute a battery of
+tests in the same allocation. The battery of test will be determined by the number
+of permutations computed based on the list inputs.
+
+> The interface name may be different on your target system. Please update the `net_ifname` flag to the appropriate value.
+
+```bash
+python driver.py aggregation_scaling --client_nodes=[60] \
+ --clients_per_node=[48] \
+ --db_nodes=[16,32] \
+ --db_cpus=32 --net_ifname="ipogif0" \
+ --run_db_as_batch=False \
+ --tensors_per_dataset=[1,4]
+```
+
+This command can be executed in a terminal with an interactive allocation
+or used in a batch script such as the following for Slurm based systems
+
+```bash
+#!/bin/bash
+
+#SBATCH -N 93
+#SBATCH --exclusive
+#SBATCH -t 12:00:00
+#SBATCH -C SK48
+#SBATCH --oversubscribe
+
+cd ..
+module load slurm
+python driver.py aggregation_scaling --client_nodes=[60] \
+ --clients_per_node=[48] \
+ --db_nodes=[16,32] \
+ --db_cpus=32 --net_ifname="ipogif0" \
+ --run_db_as_batch=False \
+ --tensors_per_dataset=[1,4]
+```
+
+Examples of batch scripts to use are provided in the ``batch_scripts`` directory
+
+
+## Data Aggregation Standard - Python Client
+
+The ``aggregation_scaling_python`` test uses a Python client and a SmartRedis Orchestrator.
+
+
+```text
+
+NAME
+ driver.py aggregation_scaling_python - Run the data aggregation scaling tests with standard Orchestrator deployment
+
+SYNOPSIS
+ driver.py aggregation_scaling_python
+
+DESCRIPTION
+ Run the data aggregation scaling tests with standard Orchestrator deployment
+
+FLAGS
+ --exp_name=EXP_NAME
+ Default: 'aggregation-standard-scaling-py'
+ name of output dir
+ --launcher=LAUNCHER
+ Default: 'auto'
+ workload manager i.e. "slurm", "pbs"
+ --run_db_as_batch=RUN_DB_AS_BATCH
+ Default: True
+ run database as separate batch submission
+ each iteration
+ --db_node_feature=DB_NODE_FEATURE
+ Default: {}
+ dict of runsettings for db
+ --prod_node_feature=PROD_NODE_FEATURE
+ Default: {}
+ dict of runsettings for the producer
+ --cons_node_feature=CONS_NODE_FEATURE
+ Default: {}
+ dict of runsettings for the consumer
+ --db_hosts=DB_HOSTS
+ Default: []
+ optionally supply hosts to launch the database on
+ --db_nodes=DB_NODES
+ Default: [16]
+ number of compute hosts to use for the database
+ --db_cpus=DB_CPUS
+ Default: 36
+ number of cpus per compute host for the database
+ --db_port=DB_PORT
+ Default: 6780
+ port to use for the database
+ --net_ifname=NET_IFNAME
+ Default: 'ipogif0'
+ network interface to use i.e. "ib0" for infiniband or
+ "ipogif0" aries networks
+ --clients_per_node=CLIENTS_PER_NODE
+ Default: [48]
+ client tasks per compute node for the aggregation
+ producer app
+ --client_nodes=CLIENT_NODES
+ Default: [128, 256, 512]
+ number of compute nodes to use for the aggregation
+ producer app
+ --iterations=ITERATIONS
+ Default: 20
+ number of append/retrieve loops run by the applications
+ --tensor_bytes=TENSOR_BYTES
+ Default: [1024, 8192, 16384, 32769, 65538, 131076, 262152, 524304, 10...
+ list of tensor sizes in bytes
+ --tensors_per_dataset=TENSORS_PER_DATASET
+ Default: [1, 2, 4]
+ list of number of tensors per dataset
+ --client_threads=CLIENT_THREADS
+ Default: [1, 2, 4, 8, 16, 32]
+ list of the number of client threads used for data
+ aggregation
+ --cpu_hyperthreads=CPU_HYPERTHREADS
+ Default: 2
+ the number of hyperthreads per cpu. This is done
+ to request that the consumer application utilizes
+ all physical cores for each client thread
+ --languages=LANGUAGES
+ Default: ['cpp']
+ list of languages to use for the tester "cpp" and/or "fortran"
+ --wall_time=WALL_TIME
+ Default: '05:00:00'
+ allotted time for database launcher to run
+ --plot=PLOT
+ Default: 'database_nodes'
+ flag to plot against in process results
+```
+
+For example, the following command could be run to execute a battery of
+tests in the same allocation. The number of tests executed will be computed based
+on the number of permutations from the list inputs given.
+
+> The interface name may be different on your target system. Please update the `net_ifname` flag to the appropriate value.
+
+```bash
+python driver.py aggregation_scaling_python --exp_name='aggregation-scaling-py-batch' \
+ --client_nodes=[60] \
+ --clients_per_node=[48] \
+ --db_nodes=[16] \
+ --db_cpus=32 \
+ --net_ifname="ipogif0" \
+ --run_db_as_batch=False \
+ --tensors_per_dataset=[1,4] \
+ --tensor_bytes=[1024,8192,16384,32769,65538,131076,262152,524304,1024000] \
+ --client_threads=[1,2,4,8,16,32] \
+ --cpu_hyperthreads=2 \
+ --iterations=20
+```
+
+This command can be executed in a terminal with an interactive allocation
+or used in a batch script such as the following for Slurm based systems
+
+```bash
+#!/bin/bash
+
+#SBATCH -N 93
+#SBATCH --exclusive
+#SBATCH -t 24:00:00
+
+cd ..
+module load slurm
+python driver.py aggregation_scaling_python --exp_name='aggregation-scaling-py-batch' \
+ --client_nodes=[60] \
+ --clients_per_node=[48] \
+ --db_nodes=[16] \
+ --db_cpus=32 \
+ --net_ifname="ipogif0" \
+ --run_db_as_batch=False \
+ --tensors_per_dataset=[1,4] \
+ --tensor_bytes=[1024,8192,16384,32769,65538,131076,262152,524304,1024000] \
+ --client_threads=[1,2,4,8,16,32] \
+ --cpu_hyperthreads=2 \
+ --iterations=20
+```
+
+Examples of batch scripts to use are provided in the ``batch_scripts`` directory
+
+
+## Data Aggregation Standard - Python Client and File System
+
+The ``aggregation_scaling_python_fs`` test uses a Python client with the file system in replacement of SmartRedis.
+
+```text
+
+NAME
+ aggregation_scaling_python_fs - Run the data aggregation scaling tests with standard File System deployment
+
+SYNOPSIS
+ driver.py aggregation_scaling_python_fs
+
+DESCRIPTION
+ Run the data aggregation scaling tests with standard File System deployment
+
+FLAGS
+ --exp_name=EXP_NAME
+ Default: 'aggregation-standard-scaling-py-fs'
+ name of output dir
+ --launcher=LAUNCHER
+ Default: 'auto'
+ workload manager i.e. "slurm", "pbs"
+ --prod_node_feature=PROD_NODE_FEATURE
+ Default: {}
+ dict of runsettings for the producer
+ --cons_node_feature=CONS_NODE_FEATURE
+ Default: {}
+ dict of runsettings for the consumer
+ --clients_per_node=CLIENTS_PER_NODE
+ Default: [48]
+ client tasks per compute node for the aggregation
+ producer app
+ --client_nodes=CLIENT_NODES
+ Default: [128, 256, 512]
+ number of compute nodes to use for the aggregation
+ producer app
+ --iterations=ITERATIONS
+ Default: 20
+ number of append/retrieve loops run by the applications
+ --tensor_bytes=TENSOR_BYTES
+ Default: [1024, 8192, 16384, 32769, 65538, 131076, 262152, 524304, 10...
+ list of tensor sizes in bytes
+ --tensors_per_dataset=TENSORS_PER_DATASET
+ Default: [1, 2, 4]
+ list of number of tensors per dataset
+ --client_threads=CLIENT_THREADS
+ Default: [1, 2, 4, 8, 16, 32]
+ list of the number of client threads used for data
+ aggregation
+ --cpu_hyperthreads=CPU_HYPERTHREADS
+ Default: 2
+ the number of hyperthreads per cpu. This is done
+ to request that the consumer application utilizes
+ all physical cores for each client thread
+ --languages=LANGUAGES
+ Default: ['cpp']
+ list of languages to use for the tester "cpp" and/or "fortran"
+ --plot=PLOT
+ Default: 'clients_per_node'
+ flag to plot against in process results
+```
+
+For example, the following command could be run to execute a battery of
+tests in the same allocation. As mentioned before, the number of tests executed are
+made up of all permutations of the given list inputs.
+
+```bash
+python driver.py aggregation_scaling_python_fs --exp_name='aggregation-scaling-py-fs-batch' \
+ --client_nodes=[60] \
+ --clients_per_node=[48] \
+ --tensors_per_dataset=[1,4] \
+ --tensor_bytes=[1024,8192,16384,32769,65538,131076,262152,524304,1024000] \
+ --client_threads=[1,2,4,8,16,32] \
+ --cpu_hyperthreads=2 \
+ --iterations=20
+```
+
+This command can be executed in a terminal with an interactive allocation
+or used in a batch script such as the following for Slurm based systems
+
+```bash
+#!/bin/bash
+
+#SBATCH -N 61
+#SBATCH --exclusive
+#SBATCH -t 24:00:00
+
+cd ..
+module load slurm
+python driver.py aggregation_scaling_python_fs --exp_name='aggregation-scaling-py-fs-batch' \
+ --client_nodes=[60] \
+ --clients_per_node=[48] \
+ --tensors_per_dataset=[1,4] \
+ --tensor_bytes=[1024,8192,16384,32769,65538,131076,262152,524304,1024000] \
+ --client_threads=[1,2,4,8,16,32] \
+ --cpu_hyperthreads=2 \
+ --iterations=20
+```
+
+Examples of batch scripts to use are provided in the ``batch_scripts`` directory
\ No newline at end of file
diff --git a/driverdataaggregation/main.py b/driverdataaggregation/main.py
index e6f357f..f3c6bde 100644
--- a/driverdataaggregation/main.py
+++ b/driverdataaggregation/main.py
@@ -31,16 +31,16 @@ def aggregation_scaling(self,
db_cpus=36,
db_port=6780,
net_ifname="ipogif0",
- clients_per_node=[48],
- client_nodes=[60],
- iterations=20,
- tensor_bytes=[1024,8192,16384,32769,65538,
- 131076,262152,524304,1024000],
+ clients_per_node=[32],
+ client_nodes=[128, 256, 512],
+ iterations=100,
+ tensor_bytes=[1024, 8192, 16384, 32768, 65536, 131072,
+ 262144, 524288, 1024000, 2048000, 4096000],
tensors_per_dataset=[4],
- client_threads=[8],
+ client_threads=[32],
cpu_hyperthreads=2,
languages=["cpp"],
- wall_time="05:00:00",
+ wall_time="10:00:00",
plot="database_nodes"):
"""Run the data aggregation scaling tests. Permutations of the test
@@ -167,9 +167,6 @@ def aggregation_scaling(self,
# stop database after this set of permutations have finished
exp.stop(db)
- #Added to clean up db folder bc of issue with exp.stop()
- time.sleep(5)
- check_database_folder(result_path, logger)
self.process_scaling_results(scaling_results_dir=exp_name, plot_type=plot)
@classmethod
@@ -378,12 +375,12 @@ def aggregation_scaling_python(self,
db_port=6780,
net_ifname="ipogif0",
clients_per_node=[32],
- client_nodes=[60],
- iterations=20,
- tensor_bytes=[1024,8192,16384,32769,65538,
- 131076,262152,524304,1024000],
+ client_nodes=[128, 256, 512],
+ iterations=100,
+ tensor_bytes=[1024, 8192, 16384, 32768, 65536, 131072,
+ 262144, 524288, 1024000, 2048000, 4096000],
tensors_per_dataset=[4],
- client_threads=[1,2,4,8,16,32],
+ client_threads=[32],
cpu_hyperthreads=2,
languages=["cpp"],
wall_time="05:00:00",
@@ -510,9 +507,6 @@ def aggregation_scaling_python(self,
# stop database after this set of permutations have finished
exp.stop(db)
- #Added to clean up db folder bc of issue with exp.stop()
- time.sleep(5)
- check_database_folder(result_path, logger)
self.process_scaling_results(scaling_results_dir=exp_name, plot_type=plot)
@classmethod
@@ -560,12 +554,12 @@ def aggregation_scaling_python_fs(self,
prod_node_feature = {},
cons_node_feature = {},
clients_per_node=[32],
- client_nodes=[24, 48],
- iterations=20,
- tensor_bytes=[1024,8192,16384,32769,65538,
- 131076,262152,524304,1024000],
- tensors_per_dataset=[1,4],
- client_threads=[1,2,4,8,16,32],
+ client_nodes=[128, 256, 512],
+ iterations=100,
+ tensor_bytes=[1024, 8192, 16384, 32768, 65536, 131072,
+ 262144, 524288, 1024000, 2048000, 4096000],
+ tensors_per_dataset=[4],
+ client_threads=[32],
cpu_hyperthreads=2,
languages=["cpp"],
plot="clients_per_node"):
@@ -613,7 +607,7 @@ def aggregation_scaling_python_fs(self,
write_run_config(result_path,
colocated=0,
- client_per_node=clients_per_node,
+ clients_per_node=clients_per_node,
client_nodes=client_nodes,
iterations=iterations,
tensor_bytes=tensor_bytes,
diff --git a/driverinference/README.md b/driverinference/README.md
new file mode 100644
index 0000000..f628297
--- /dev/null
+++ b/driverinference/README.md
@@ -0,0 +1,305 @@
+# Inference Scaling Test
+
+SmartSim-Scaling offers two inference versions:
+
+ 1. Inference Standard (C++ client and SmartRedis Orchestrator)
+ 2. Inference Colocated Python (C++ client and SmartRedis Orchestrator)
+
+Continue below for more information on both respective tests.
+
+## Client Description
+
+The inference tests run as an MPI program where a single SmartRedis C++ client
+is initialized on every rank.
+
+Supported inference tests:
+
+ 1) Resnet50 CNN with ImageNet dataset
+
+Each client performs 101 executions of the following commands. The first iteration is a warmup;
+the next 100 are measured for inference throughput.
+
+ 1) ``put_tensor`` (send image to database)
+ 2) ``run_script`` (preprocess image)
+ 3) ``run_model`` (run resnet50 on the image)
+ 4) ``unpack_tensor`` (Retrieve the inference result)
+
+The input parameters to the test are used to generate permutations
+of tests with varying configurations.
+
+## The model
+As Neural Network, we use Pytorch's implementation of Resnet50. The script `imagenet/model_saver.py`
+can be used to generate the model for CPU or GPU. By navigating to the `imagenet` folder, the CPU model
+can be created running
+
+```bash
+python model_saver.py
+```
+
+and the GPU model can be created running
+
+```bash
+python model_saver.py --device=GPU
+```
+
+> Note that if you would like to generate the GPU model, you must run the
+command on a GPU node.
+
+If the benchmark driver is executed and
+no model exists, an attempt is made to generate the model on the fly. In both cases,
+the specified device must be available on the node where the script is called (this
+means that it could be required to run the script through the workload manager launcher
+to execute it on a node with a GPU, for example).
+
+
+## Colocated inference
+
+Colocated Orchestrators are deployed on the same nodes as the
+application. This improves inference performance as no data movement
+"off-node" occurs with colocated deployment. For more information
+on colocated deployment, see [our documentation](https://www.craylabs.org/docs/orchestrator.html)
+
+Below is the help output. The arguments which are lists control
+the possible permutations that will be run.
+
+```text
+NAME
+ driver.py inference_colocated - Run ResNet50 inference tests with colocated Orchestrator deployment
+
+SYNOPSIS
+ driver.py inference_colocated
+
+DESCRIPTION
+ Run ResNet50 inference tests with colocated Orchestrator deployment
+
+FLAGS
+ --exp_name=EXP_NAME
+ Default: 'inference-colocated-scaling'
+ name of output dir, defaults to "inference-scaling"
+ --node_feature=NODE_FEATURE
+ Default: {'constraint': 'P100'}
+ --launcher=LAUNCHER
+ Default: 'auto'
+ workload manager i.e. "slurm", "pbs"
+ --nodes=NODES
+ Default: [4,8,16,32,64,128]
+ compute nodes to use for synthetic scaling app with a colocated orchestrator database
+ --clients_per_node=CLIENTS_PER_NODE
+ Default: [18]
+ client tasks per compute node for the synthetic scaling app
+ --db_cpus=DB_CPUS
+ Default: [8]
+ number of cpus per compute host for the database
+ --db_tpq=DB_TPQ
+ Default: [8]
+ number of device threads to use for the database
+ --db_port=DB_PORT
+ Default: 6780
+ port to use for the database
+ --batch_size=BATCH_SIZE
+ Default: [96]
+ batch size to set Resnet50 model with
+ --device=DEVICE
+ Default: 'GPU'
+ device used to run the models in the database
+ --num_devices=NUM_DEVICES
+ Default: 1
+ number of devices per compute node to use to run ResNet
+ --net_type=NET_TYPE
+ Default: 'uds'
+ type of connection to use ("tcp" or "uds")
+ --net_ifname=NET_IFNAME
+ Default: 'lo'
+ network interface to use i.e. "ib0" for infiniband or "ipogif0" aries networks
+ --iterations=ITERATIONS
+ Default: 100
+ number of put/get loops run by the applications
+ --languages=LANGUAGES
+ Default: ['cpp','fortran']
+ list of languages to use for the tester "cpp" and/or "fortran"
+ --plot=PLOT
+ Default: 'database_cpus'
+ flag to plot against in process results
+```
+
+> The interface name may be different on your target system. Please update the `net_ifname` flag to the appropriate value.
+
+So for example, the following command could be run to execute a battery of
+tests in the same allocation
+
+```bash
+# alloc must contain at least 16 GPU nodes
+python driver.py inference_colocated --clients_per_node=[18] \
+ --nodes=[16] --db_tpq=[1,2,4] \
+ --db_cpus=[1,2,4,8] --net_ifname="ipogif0" \
+ --device="GPU"
+```
+
+This command can be executed in a terminal with an interactive allocation
+or used in a batch script such as the following for Slurm based systems
+
+```bash
+#!/bin/bash
+
+#SBATCH -N 16
+#SBATCH --exclusive
+#SBATCH -C P100
+#SBATCH -t 10:00:00
+
+python driver.py inference_colocated --clients_per_node=[18] \
+ --nodes=[16] --db_tpq=[1,2,4] \
+ --db_cpus=[1,2,4,8] --net_ifname="ipogif0" \
+ --device="GPU"
+```
+
+Examples of batch scripts to use are provided in the ``batch_scripts`` directory
+
+
+## Standard Inference
+
+Colocated deployment is the preferred method for running tightly coupled
+inference workloads with SmartSim, however, if you want to deploy the Orchestrator
+database and the application on different nodes, you want to use standard
+deployment.
+
+For example, if you only have access to a small number of GPU nodes and want to test a large
+CPU application, standard deployment is optimal. For more information
+on Orchestrator deployment methods, please see
+[our documentation](https://www.craylabs.org/docs/orchestrator.html)
+
+Like the above colocated inference tests, the standard inference tests also provide
+a method of running a battery of tests all at once. Below is the help output.
+The arguments which are lists control the possible permutations that will be run.
+
+```text
+NAME
+ driver.py inference_standard - Run ResNet50 inference tests with standard Orchestrator deployment
+
+SYNOPSIS
+ driver.py inference_standard
+
+DESCRIPTION
+ Run ResNet50 inference tests with standard Orchestrator deployment
+
+FLAGS
+ --exp_name=EXP_NAME
+ Default: 'inference-standard-scaling'
+ name of output dir
+ --launcher=LAUNCHER
+ Default: 'auto'
+ workload manager i.e. "slurm", "pbs"
+ --run_db_as_batch=RUN_DB_AS_BATCH
+ Default: False
+ run database as separate batch submission each iteration
+ --db_node_feature=DB_NODE_FEATURE
+ Default: {'constraint': 'P100'}
+ dict of runsettings for the database
+ --node_feature=NODE_FEATURE
+ Default: {}
+ dict of runsettings for the app
+ --db_hosts=DB_HOSTS
+ Default: []
+ optionally supply hosts to launch the database on
+ --db_nodes=DB_NODES
+ Default: [4,8,16]
+ number of compute hosts to use for the database
+ --db_cpus=DB_CPUS
+ Default: [8]
+ number of cpus per compute host for the database
+ --db_tpq=DB_TPQ
+ Default: [8]
+ number of device threads to use for the database
+ --db_port=DB_PORT
+ Default: 6780
+ port to use for the database
+ --batch_size=BATCH_SIZE
+ Default: [1000]
+ batch size to set Resnet50 model with
+ --device=DEVICE
+ Default: 'GPU'
+ device used to run the models in the database
+ --num_devices=NUM_DEVICES
+ Default: 1
+ number of devices per compute node to use to run ResNet
+ --net_ifname=NET_IFNAME
+ Default: 'ipogif0'
+ network interface to use i.e. "ib0" for infiniband or "ipogif0" aries networks
+ --clients_per_node=CLIENTS_PER_NODE
+ Default: [18]
+ client tasks per compute node for the synthetic scaling app
+ --client_nodes=CLIENT_NODES
+ Default: [4,8,16,32,64,128]
+ number of compute nodes to use for the synthetic scaling app
+ --iterations=ITERATIONS
+ Default: 100
+ number of put/get loops run by the applications
+ --wall_time=WALL_TIME
+ Default: "05:00:00"
+ allotted time for database launcher to run
+ --languages=LANGUAGES
+ Default: ['cpp','fortran']
+ list of languages to use for the tester "cpp" and/or "fortran"
+ --plot=PLOT
+ Default: 'database_nodes'
+ flag to plot against in process results
+```
+
+The standard inference tests will spin up a database for each iteration in the
+battery of tests chosen by the user. There are multiple ways to run this.
+
+> The interface name may be different on your target system. Please update the `net_ifname` flag to the appropriate value.
+
+1. Everything in the same interactive (or batch file) without caring about placement
+```bash
+# alloc must contain at least 120 (max client_nodes) + 16 GPU nodes (max db_nodes)
+python driver.py inference_standard --client_nodes=[20,40,60,80,100,120] \
+ --db_nodes=[4,8,16] --db_tpq=[1,2,4] \
+ --db_cpus=[1,4,8,16] --run_db_as_batch=False \
+ --net_ifname="ipogif0" --device="GPU"
+```
+
+This option is recommended as it's easy to launch in interactive allocations and
+as a batch submission, but if you need to specify separate hosts for the database
+you can look into the following two methods.
+
+A batch submission for this first option would look like the following for Slurm
+based systems.
+
+```bash
+#!/bin/bash
+
+#SBATCH -N 136
+#SBATCH -C "[P100*16&SK48*120]"
+#SBATCH --exclusive
+#SBATCH -t 10:00:00
+
+python driver.py inference_standard --client_nodes=[20,40,60,80,100,120] \
+ --db_nodes=[4,8,16] --db_tpq=[1,2,4] \
+ --db_cpus=[1,4,8,16] --run_db_as_batch=False \
+ --net_ifname="ipogif0" --device="GPU"
+```
+
+2. Same as 1, but specify hosts for the database
+```bash
+# alloc must contain at least 120 (max client_nodes) + 16 nodes (max db_nodes)
+# db nodes must be fixed if hostlist is specified
+python driver.py inference_standard --client_nodes=[20,40,60,80,100,120] \
+ --db_nodes=[16] --db_tpq=[1,2,4] \
+ --db_cpus=[1,4,8,16] --db_hosts=[nid0001, ...] \
+ --net_ifname="ipogif0" --device="GPU"
+
+```
+
+3. Launch database as a separate batch submission each time
+```bash
+# must obtain separate allocation for client driver through interactive or batch submission
+# if batch submission, compute nodes must have access to slurm
+python driver.py inference_standard --client_nodes=[20,40,60,80,100,120] \
+ --db_nodes=[4,8,16] --db_tpq=[1,2,4] \
+ --db_cpus=[1,4,8,16] --batch_args='{"C":"V100", "exclusive": None}' \
+ --net_ifname="ipogif0" --device="GPU" \
+ --run_db_as_batch=True
+```
+
+All three options will conduct ``n`` scaling tests where ``n`` is the product of
+all lists specified as options.
\ No newline at end of file
diff --git a/driverinference/main.py b/driverinference/main.py
index f535f12..50be795 100644
--- a/driverinference/main.py
+++ b/driverinference/main.py
@@ -23,23 +23,22 @@ class Inference:
def inference_standard(self,
exp_name="inference-standard-scaling",
launcher="auto",
- run_db_as_batch=True,
+ run_db_as_batch=False,
db_node_feature = {"constraint": "P100"},
node_feature = {},
db_hosts=[],
- db_nodes=[4,8],
+ db_nodes=[4,8,16],
db_cpus=[8],
- db_tpq=[1],
+ db_tpq=[8],
db_port=6780,
batch_size=[1000], #bad default min_batch_time_out
device="GPU",
num_devices=1,
net_ifname="ipogif0",
- clients_per_node=[48],
- client_nodes=[1],
- rebuild_model=False,
- iterations=2,
- languages=["cpp", "fortran"],
+ clients_per_node=[18],
+ client_nodes=[4,8,16,32,64,128],
+ iterations=100,
+ languages=["cpp","fortran"],
wall_time="05:00:00",
plot="database_nodes"):
"""Run ResNet50 inference tests with standard Orchestrator deployment
@@ -76,8 +75,6 @@ def inference_standard(self,
:type clients_per_node: list[int], optional
:param client_nodes: number of compute nodes to use for the synthetic scaling app
:type client_nodes: list[int], optional
- :param rebuild_model: force rebuild of PyTorch model even if it is available
- :type rebuild_model: bool
:param iterations: number of put/get loops run by the applications
:type iterations: int
:param languages: list of languages to use for the tester "cpp" and/or "fortran"
@@ -87,15 +84,14 @@ def inference_standard(self,
:param plot: flag to plot against in process results
:type plot: str
"""
- logger.info("Starting inference standard scaling tests")
- check_node_allocation(client_nodes, db_nodes)
- logger.info("Experiment allocation passed check")
+ logger.info("Starting inference standard scaling test")
exp, result_path = create_experiment_and_dir(exp_name, launcher)
+
logger.debug("Experiment and Results folder created")
write_run_config(result_path,
colocated=0,
- clients_per_node=clients_per_node,
+ client_per_node=clients_per_node,
client_nodes=client_nodes,
database_hosts=db_hosts,
database_nodes=db_nodes,
@@ -116,8 +112,6 @@ def inference_standard(self,
for i, perm in enumerate(perms, start=1):
c_nodes, cpn, dbn, dbc, dbtpq, batch, language = perm
logger.info(f"Running permutation {i} of {len(perms)}")
- print(perm)
-
db = start_database(exp,
db_node_feature,
db_port,
@@ -129,7 +123,7 @@ def inference_standard(self,
db_hosts,
wall_time)
# setup a an instance of the synthetic C++ app and start it
- infer_session, resnet_model = self._create_inference_session(exp,
+ infer_session = self._create_inference_session(exp,
node_feature,
c_nodes,
cpn,
@@ -139,44 +133,39 @@ def inference_standard(self,
batch,
device,
num_devices,
- rebuild_model,
iterations,
language)
logger.debug("Inference session created")
address = db.get_address()[0]
- setup_resnet(resnet_model,
+ attach_resnet(infer_session,
+ f"./imagenet/resnet50.{device}.pt",
device,
num_devices,
- batch,
- address,
- cluster=dbn>1)
+ batch)
logger.debug("Resnet model set")
exp.start(infer_session, block=True, summary=True)
- # confirm scaling test run successfully
stat = exp.get_status(infer_session)
if stat[0] != status.STATUS_COMPLETED:
logger.error(f"One of the scaling tests failed {infer_session.name}")
exp.stop(db)
- check_database_folder(result_path, logger)
self.process_scaling_results(scaling_results_dir=exp_name, plot_type=plot)
-
+
+
def inference_colocated(self,
exp_name="inference-colocated-scaling",
node_feature={"constraint": "P100"},
launcher="auto",
- nodes=[1],
- clients_per_node=[12,24,36,60,96],
- db_cpus=[12],
- db_tpq=[1],
+ nodes=[4,8,16,32,64,128],
+ clients_per_node=[18],
+ db_cpus=[8],
+ db_tpq=[8],
db_port=6780,
- pin_app_cpus=[False],
batch_size=[96],
device="GPU",
num_devices=1,
net_type="uds",
net_ifname="lo",
- rebuild_model=False,
iterations=100,
languages=["cpp","fortran"],
plot="database_cpus"
@@ -199,8 +188,6 @@ def inference_colocated(self,
:type db_tpq: list[int], optional
:param db_port: port to use for the database
:type db_port: int, optional
- :param pin_app_cpus: pin the threads of the application to 0-(n-db_cpus)
- :type pin_app_cpus: list[bool], optional
:param batch_size: batch size to set Resnet50 model with
:type batch_size: list, optional
:param device: device used to run the models in the database
@@ -212,16 +199,13 @@ def inference_colocated(self,
:param net_ifname: network interface to use i.e. "ib0" for infiniband or
"ipogif0" aries networks
:type net_ifname: str, optional
- :param rebuild_model: force rebuild of PyTorch model even if it is available
- :type rebuild_model: bool
:param languages: which language to use for the tester "cpp" or "fortran"
:type languages: str
:param plot: flag to plot against in process results
:type plot: str
"""
logger.info("Starting inference colocated scaling tests")
-
- check_model(device, force_rebuild=rebuild_model)
+ check_model(device)
check_node_allocation(nodes, [0])
logger.info("Experiment allocation passed check")
@@ -230,27 +214,18 @@ def inference_colocated(self,
logger.debug("Experiment and Results folder created")
write_run_config(result_path,
colocated=1,
- node_feature=node_feature,
- experiment_name=exp_name,
- launcher=launcher,
- nodes=nodes,
- clients_per_node=clients_per_node,
+ client_per_node=clients_per_node,
+ client_nodes=nodes,
database_cpus=db_cpus,
- database_threads_per_queue=db_tpq,
database_port=db_port,
- pin_app_cpus=pin_app_cpus,
batch_size=batch_size,
device=device,
num_devices=num_devices,
- net_type=net_type,
- net_ifname=net_ifname,
- rebuild_model=rebuild_model,
iterations=iterations,
language=languages,
- plot=plot
- )
+ node_feature=node_feature)
print_yml_file(Path(result_path) / "run.cfg", logger)
- perms = list(product(nodes, clients_per_node, db_cpus, db_tpq, batch_size, pin_app_cpus, languages))
+ perms = list(product(nodes, clients_per_node, db_cpus, db_tpq, batch_size, languages))
for i, perm in enumerate(perms, start=1):
c_nodes, cpn, dbc, dbtpq, batch, pin_app, language = perm
logger.info(f"Running permutation {i} of {len(perms)}")
@@ -268,34 +243,22 @@ def inference_colocated(self,
batch,
device,
num_devices,
- rebuild_model,
iterations,
language)
logger.debug("Inference session created")
+ attach_resnet(infer_session,
+ f"./imagenet/resnet50.{device}.pt",
+ device,
+ num_devices,
+ batch)
exp.start(infer_session, block=True, summary=True)
- # confirm scaling test run successfully
stat = exp.get_status(infer_session)
if stat[0] != status.STATUS_COMPLETED:
logger.error(f"One of the scaling tests failed {infer_session.name}")
self.process_scaling_results(scaling_results_dir=exp_name, plot_type=plot)
-
- @staticmethod
- def _set_resnet_model(device="GPU", force_rebuild=False):
- resnet_model = f"./imagenet/resnet50.{device}.pt"
- if not Path(resnet_model).exists() or force_rebuild:
- logger.info(f"AI Model {resnet_model} does not exist or rebuild was asked, it will be created")
- try:
- save_model(device)
- except:
- logger.error(f"Could not save {resnet_model} for {device}.")
- sys.exit(1)
-
- logger.info(f"Using model {resnet_model}")
- return resnet_model
-
@classmethod
def _create_inference_session(cls,
exp,
@@ -308,11 +271,9 @@ def _create_inference_session(cls,
batch_size,
device,
num_devices,
- rebuild_model,
iterations,
language
):
- resnet_model = cls._set_resnet_model(device, force_rebuild=rebuild_model) #the resnet file name does not store full length of node name
cluster = 1 if db_nodes > 1 else 0
run_settings = exp.create_run_settings(f"./{language}-inference/build/run_resnet_inference", run_args=node_feature)
run_settings.set_nodes(nodes)
@@ -348,7 +309,7 @@ def _create_inference_session(cls,
model = exp.create_model(name, run_settings)
model.attach_generator_files(to_copy=["./imagenet/cat.raw",
- resnet_model,
+ f"./imagenet/resnet50.{device}.pt",
"./imagenet/data_processing_script.txt"])
exp.generate(model, overwrite=True)
write_run_config(model.path,
@@ -366,7 +327,7 @@ def _create_inference_session(cls,
iterations=iterations,
node_feature=node_feature)
- return model, resnet_model
+ return model
@classmethod
def _create_colocated_inference_session(cls,
@@ -374,7 +335,6 @@ def _create_colocated_inference_session(cls,
node_feature,
nodes,
tasks,
- pin_app_cpus,
net_type,
net_ifname,
db_cpus,
@@ -383,10 +343,8 @@ def _create_colocated_inference_session(cls,
batch_size,
device,
num_devices,
- rebuild_model,
iterations,
language):
- resnet_model = cls._set_resnet_model(device, force_rebuild=rebuild_model)
# feature = db_node_feature.split( )
run_settings = exp.create_run_settings(f"./{language}-inference/build/run_resnet_inference", run_args=node_feature)
run_settings.set_nodes(nodes)
@@ -418,13 +376,12 @@ def _create_colocated_inference_session(cls,
))
model = exp.create_model(name, run_settings)
model.attach_generator_files(to_copy=["./imagenet/cat.raw",
- resnet_model,
+ f"./imagenet/resnet50.{device}.pt",
"./imagenet/data_processing_script.txt"])
db_opts = dict(
db_cpus=db_cpus,
- limit_app_cpus=pin_app_cpus,
- threads_per_queue=db_tpq,
+ threads_per_queue=db_tpq, #limit_app_cpus=False,
# turning this to true can result in performance loss
# on networked file systems(many writes to db log file)
debug=True,
@@ -442,23 +399,17 @@ def _create_colocated_inference_session(cls,
**db_opts
)
exp.generate(model, overwrite=True)
- write_run_config(
- model.path,
- colocated=1,
- nodes=nodes,
- client_total=tasks*nodes,
- clients_per_node=tasks,
- database_cpus=db_cpus,
- database_threads_per_queue=db_tpq,
- database_port=db_port,
- pin_app_cpus=pin_app_cpus,
- batch_size=batch_size,
- device=device,
- num_devices=num_devices,
- net_type=net_type,
- net_ifname=net_ifname,
- rebuild_model=rebuild_model,
- iterations=iterations,
- language=language
- )
+ write_run_config(model.path,
+ colocated=1,
+ client_total=tasks*nodes,
+ client_per_node=tasks,
+ client_nodes=nodes,
+ database_cpus=db_cpus,
+ database_threads_per_queue=db_tpq,
+ batch_size=batch_size,
+ device=device,
+ num_devices=num_devices,
+ language=language,
+ iterations=iterations,
+ node_feature=node_feature)
return model
\ No newline at end of file
diff --git a/driverprocessresults/README.md b/driverprocessresults/README.md
new file mode 100644
index 0000000..c84c59a
--- /dev/null
+++ b/driverprocessresults/README.md
@@ -0,0 +1,156 @@
+# Process Results
+
+SmartSim-Scaling offers support to plot numeric data produced by
+a complete scalability test. Currently, we support one statistical graphing
+method: violin plot. The violin plots give a data summary and histogram of
+each client function associated with the respective scaling test.
+
+The following client functions per scaling tests are listed below:
+
+#### Inference
+ 1) ``put_tensor`` (send image to database)
+ 2) ``run_script`` (preprocess image)
+ 3) ``run_model`` (run resnet50 on the image)
+ 4) ``unpack_tensor`` (retrieve the inference result)
+
+
+#### Throughput
+ 1) ``put_tensor`` (send image to database)
+ 2) ``unpack_tensor`` (retrieve the data)
+
+
+#### Data Aggregation
+ 1) ``append_to_list`` (add dataset to the aggregation list)
+ 2) ``poll_list_length`` (check when the next aggregation list is ready)
+ 3) ``get_datasets_from_list`` (retrieve the data from the aggregation list)
+
+> Note that the process results function is called after a completed scaling test
+> meaning the graphs will automatically be produced.
+
+
+## Collecting Performance Results
+
+The ``process_scaling_results`` function will collect the produced timings
+from ``results/SCALING-TEST-NAME/RUN#`` and make a series of plots for each client function.
+The function will make a collective csv of timings per each run. These
+artifacts will be in a ``results/SCALING-TEST-NAME/stats/RUN`` folder inside
+the directory where the function was pointed to the scaling data
+with the ``scaling_dir`` flag shown below. This function is
+automatically called after a scaling test has completed.
+
+Below you will find the options for process results execution.
+
+```text
+NAME
+ driver.py process_scaling_results - Create a results directory with performance data and plots
+
+SYNOPSIS
+ driver.py process_scaling_results
+
+DESCRIPTION
+ With the overwrite flag turned off, this function can be used
+ to build up a single csv with the results of runs over a long
+ period of time.
+
+FLAGS
+ --scaling_dir=SCALING_DIR
+ Default: 'inference-standard-scaling'
+ directory to create results from
+ --plot_type=PLOT_TYPE
+ Default: 'database_nodes'
+ directory to create results from
+ --overwrite=OVERWRITE
+ Default: True
+ overwrite any existing results
+```
+
+For example for the inference standard tests (if you don't change the output dir name)
+you can run:
+
+```bash
+python driver.py process_scaling_results
+```
+
+If you would like to rather run the `throughput-colocated-scaling`:
+
+```bash
+python driver.py process_scaling_results --scaling_dir="throughput-colocated-scaling"
+```
+
+## Plot Performance Results
+
+The ``scaling_read_data`` function will collect the produced timings
+from ``results/SCALING-TEST-NAME/RUN#`` and create a pandas dataframe
+to use within the ``scaling_plotter`` function. The dataframe is stored
+in a compressed csv.gz file within ``results/SCALING-TEST-NAME/stats/RUN#``.
+This function is useful when debugging to avoid the timely cost
+of reprocessing your data if you need to reproduce the violin plots.
+
+Below you will find the options for scaling read data execution.
+
+```text
+NAME
+ driver.py scaling_read_data - Create a dataframe to store in a compressed file
+
+SYNOPSIS
+ driver.py scaling_read_data
+
+DESCRIPTION
+ This function produces a dataframe and stores it into a compressed file.
+
+FLAGS
+ --run_cfg_path=RUN_CFG_PATH
+ Default: No Default
+ path to a specific run file
+ Example: results/throughput-standard-scaling/run-2023-07-05-21:26:18
+ --scaling_test_name=SCALING_TEST_NAME
+ Default: No Default
+ directory to create dataframe from
+ Example: throughput-standard-scaling
+```
+
+For example for the inference standard tests you can run:
+
+```bash
+python driver.py scaling_read_data --scaling_dir="inference-standard-scaling" --run_cfg_path="results/inference-standard-scaling/run-2023-07-05-21:26:18"
+```
+
+## Read and Store Results
+
+The ``scaling_plotter`` function will plot the performance data. Using the
+dataframe produced by ``scaling_read_data``, the function will create
+graphs per client function associated with the scaling test. The graphs are
+saved to ``results/SCALING-TEST-NAME/stats/RUN#`` as a png file.
+
+Below you will find the options for scaling plotter execution.
+
+```text
+NAME
+ driver.py scaling_plotter - Create performance plots
+
+SYNOPSIS
+ driver.py scaling_plotter
+
+DESCRIPTION
+ This function will plot your results using the stored dataframe.
+
+FLAGS
+ --run_cfg_path=RUN_CFG_PATH
+ Default: No Default
+ path to a specific run file
+ Example: results/throughput-standard-scaling/run-2023-07-05-21:26:18
+ --scaling_test_name=SCALING_TEST_NAME
+ Default: No Default
+ directory to create dataframe from
+ Example: throughput-standard-scaling
+ --var_input=VAR_INPUT
+ Default: No Default
+ permutation to plot on
+ Example: database_nodes
+```
+
+For example for the inference standard tests you can run:
+
+```bash
+python driver.py scaling_plotter --scaling_dir="inference-standard-scaling" --run_cfg_path="results/inference-standard-scaling/run-2023-07-05-21:26:18" --var_input="database_nodes"
+```
\ No newline at end of file
diff --git a/driverprocessresults/main.py b/driverprocessresults/main.py
index aabc602..e4992de 100644
--- a/driverprocessresults/main.py
+++ b/driverprocessresults/main.py
@@ -5,7 +5,8 @@
import matplotlib.pyplot as plt
from tqdm import tqdm
from utils import *
-from driverprocessresults.scaling_plotter import *
+from driverprocessresults.scaling_plotter import PlotResults
+import sys
from pathlib import Path
from statistics import median
@@ -16,9 +17,9 @@
class ProcessResults:
- def process_scaling_results(self,
- scaling_results_dir="inference-colocated-scaling",
- plot_type="",
+ def process_scaling_results(self,
+ scaling_results_dir="aggregation-standard-scaling",
+ plot_type="database_nodes",
overwrite=True):
"""Create a results directory with performance data and plots
With the overwrite flag turned off, this function can be used
@@ -55,18 +56,29 @@ def process_scaling_results(self,
# want to catch all exceptions and skip runs that may
# not have completed or finished b/c some reason i.e. node failure
except Exception as e:
- logger.warning(f"Skipping {session_folder} could not process results")
+ logger.warning(f"Skipping {session_folder}: could not create csv")
logger.error(e)
continue
- # collect all written csv into dataframes to concat
+ #collect all written csv into dataframes to concat
+ for run in tqdm(run_list, desc="Creating dataframe...", ncols=80):
+ try:
+ PlotResults.scaling_read_data(self, run, scaling_results_dir)
+ logger.debug(f"Data read and saved for: {run}")
+ # want to catch all exceptions and skip runs that may
+ # not have completed or finished b/c some reason i.e. node failure
+ except Exception as e:
+ logger.warning(f"Skipping {run}: could not read performance results")
+ logger.error(e)
+ continue
+ #collect all written csv into dataframes to concat
for run in tqdm(run_list, desc="Creating scaling plots...", ncols=80):
try:
- scaling_plotter(run, scaling_results_dir, plot_type)
- logger.debug(f"Plots created for run: {run}")
+ PlotResults.scaling_plotter(run, scaling_results_dir, plot_type)
+ logger.debug(f"Plots created for : {run}")
# want to catch all exceptions and skip runs that may
# not have completed or finished b/c some reason i.e. node failure
except Exception as e:
- logger.warning(f"Skipping {run} in {scaling_results_dir}: could not process results")
+ logger.warning(f"Skipping {run}: could not plot performance results")
logger.error(e)
continue
for session in tqdm(session_folders, desc="Collecting scaling results...", ncols=80):
@@ -106,7 +118,6 @@ def _create_run_csv(cls, session_path, delete_previous=False, verbose=False):
logger.debug(f"Running with all stats dir: {all_stats_dir}")
if delete_previous and session_stats_dir.is_dir():
shutil.rmtree(session_stats_dir)
-
if not session_stats_dir.is_dir():
os.makedirs(session_stats_dir)
function_times = {}
@@ -141,7 +152,7 @@ def _create_run_csv(cls, session_path, delete_previous=False, verbose=False):
cls._make_hist_plot(function_times['run_script'], 'run_script()', 'run_script.pdf', session_stats_dir)
cls._make_hist_plot(function_times['run_model'], 'run_model()', 'run_model.pdf', session_stats_dir)
logger.debug("Run model completed")
- function_types = ["client()", "put_tensor", "unpack_tensor", "get_list", "main()"]
+ function_types = ["put_tensor", "unpack_tensor", "get_list", "main()"]
for function in function_types:
if function in function_times:
logger.debug(f"{function} started")
@@ -150,7 +161,6 @@ def _create_run_csv(cls, session_path, delete_previous=False, verbose=False):
except KeyError as e:
raise KeyError(f'{e} not found in function_times for run {session_name}')
-
data = cls._make_stats(session_path, function_times)
data_df = pd.DataFrame(data, index=[0])
file_name = session_stats_dir / ".".join((session_name, "csv"))
@@ -166,7 +176,6 @@ def _make_hist_plot(data, title, fname, session_stats_dir):
min_ylim, max_ylim = plt.ylim()
plt.axvline(med, color='red', linestyle='dashed', linewidth=1)
plt.text(med, max_ylim*0.9, ' Median: {:.2f}'.format(med))
-
# save the figure in the result dir
file_path = Path(session_stats_dir) / fname
plt.savefig(file_path)
diff --git a/driverprocessresults/scaling_plotter.py b/driverprocessresults/scaling_plotter.py
index 70683a4..a6ef51e 100644
--- a/driverprocessresults/scaling_plotter.py
+++ b/driverprocessresults/scaling_plotter.py
@@ -1,85 +1,180 @@
import os
import matplotlib.pyplot as plt
import matplotlib
+import gzip
+from matplotlib.ticker import AutoMinorLocator
import pandas as pd
import numpy as np
from glob import glob
-import seaborn as sns
from tqdm.auto import tqdm
import matplotlib.patches as mpatches
-
+import time
import sys
+import traceback # give me the traceback
import configparser
import json
from pathlib import Path
+import asyncio
+from joblib import Parallel, delayed
+from multiprocessing import Manager
+from itertools import chain
+import pprint
+import math
from smartsim.log import get_logger, log_to_file
logger = get_logger("Plotter")
+configs = []
+class PlotResults:
+ def _fast_flatten(cls, input_list):
+ """Define a function to flatten large 2D lists quickly.
+ """
+ return list(chain.from_iterable(input_list))
+
+ def _readCSV(cls, timing_file, config, frames):
+ """Read in the Data as Pandas DataFrames
+ """
+ # NOTE: can't use "engine="pyarrow" because not all features are there
+ tmp_df = pd.read_csv(timing_file, header=0, names=["rank", "function", "time"])
+ for key, value in config._sections['attributes'].items():
+ tmp_df[key] = value
+ frames.append(tmp_df)
-def scaling_plotter(run_cfg_path, scaling_test_name, var_input):
- logger.debug("Entered plotter method")
- palette = sns.set_palette("colorblind", color_codes=True)
-
- font = {'family' : 'sans',
- 'weight' : 'normal',
- 'size' : 14}
- matplotlib.rc('font', **font)
-
- configs = []
-
- for run_cfg in Path(run_cfg_path).rglob('run.cfg'):
- config = configparser.ConfigParser()
- config.read(run_cfg)
- configs.append(config)
- df_list = []
- for config in configs:
- timing_files = Path(config['run']['path']).glob('rank*.csv')
- for timing_file in timing_files:
- tmp_df = pd.read_csv(timing_file, header=0, names=["rank", "function", "time"])
- for key, value in config._sections['attributes'].items():
- tmp_df[key] = value
- df_list.append(tmp_df)
- df = pd.concat(df_list, ignore_index=True)
- logger.debug("Dataframe created")
- violin_opts = dict(
- showmeans = True,
- showextrema = True,
- )
- plt.style.use('default')
+ def scaling_read_data(self, run_cfg_path, scaling_test_name):
+ """Read performance results and create a dataframe.
+ To mitigate performance runtime, outside code from
+ https://gist.github.com/TariqAHassan/fc77c00efef4897241f49
+ e61ddbede9e?permalink_comment_id=2987243
+ is implemented.
+ :param run_cfg_path: directory to create plots from
+ :type run_cfg_path: str
+ :param scaling_test_name: name of scaling test your are plotting
+ :type scaling_test_name: str
+ """
+ logger.debug("Entered plotter method")
+ try:
+ # creating a list that can be shared across memory
+ frames = list()
+ # read run.cfg to create columns in list
+ for run_cfg in Path(run_cfg_path).rglob('run.cfg'):
+ config = configparser.ConfigParser()
+ config.read(run_cfg)
+ configs.append(config)
+ for config in tqdm(configs, desc="Processing configs...", ncols=80):
+ timing_files = Path(config['run']['path']).glob('rank*.csv')
+ # NOTE: setting n_jobs to -1 makes it use all available cpus
+ timingFiles = tqdm(timing_files, desc="Processing timing files...", ncols=80)
+ # reading timing files in parallel
+ Parallel(n_jobs=-1, prefer="threads")(delayed(self._readCSV)(timing_file, config, frames) for timing_file in timingFiles)
+ #construct a dictionary using the column names from one of the dataframes
+ COLUMN_NAMES = frames[0].columns
+ # construct a dictionary from the column names
+ df_dict = dict.fromkeys(COLUMN_NAMES, [])
+ logger.debug(f"columns were {COLUMN_NAMES}")
+ #Iterate through the columns
+ for col in COLUMN_NAMES:
+ extracted = (frame[col] for frame in frames if col in frame.columns.tolist())
+ df_dict[col] = self._fast_flatten(extracted)
+ #produce the combined DataFrame
+ df = pd.DataFrame.from_dict(df_dict)[COLUMN_NAMES]
+ logger.debug(f"df: {df}")
+ except Exception as e:
+ exc_info = sys.exc_info()
+ traceback.print_tb(e.__traceback__)
+ traceback.print_exception(*exc_info)
+ # write dataframe to file
+ df.to_csv(Path("results/" + scaling_test_name + "/stats") / os.path.basename(run_cfg_path) / "dataframe.csv.gz", chunksize=100000, encoding='utf-8', index=False, compression='gzip')
- ordered_client_total = sorted(df['client_total'].unique())
+ def scaling_plotter(run_cfg_path, scaling_test_name, var_input):
+ """Create violin plots with performance data.
+ :param run_cfg_path: directory to create plots from
+ :type run_cfg_path: str
+ :param scaling_test_name: name of scaling test your are plotting
+ :type scaling_test_name: str
+ :param var_input: plot on a specific flag
+ :type var_input: str
+ """
+ df = pd.read_csv(Path("results/" + scaling_test_name + "/stats") / os.path.basename(run_cfg_path) / "dataframe.csv.gz")
+ try:
+ font = {'family' : 'sans',
+ 'weight' : 'normal',
+ 'size' : 14}
+ matplotlib.rc('font', **font)
+ logger.debug("Dataframe created")
+ plt.style.use('default') #plt.style.use("dark_background")
+ client_total = [int(x) for x in df['client_total'].unique()]
+ client_per_n = [int(x) for x in df['client_per_node'].unique()]
+ if 'colo' in scaling_test_name:
+ database_nodes = sorted([int(x) for x in df['client_nodes'].unique()])
+ else:
+ database_nodes = sorted([int(x) for x in df['database_nodes'].unique()])
+ database_cpus = [int(x) for x in df['database_cpus'].unique()]
+ client_nodes = [int(x) for x in df['client_nodes'].unique()]
+ grid_spacing = np.min(np.diff(client_nodes))*(client_per_n[0])
+ logger.debug(f"grid_spacing: {grid_spacing}")
+ ordered_client_total = sorted(df['client_total'].unique())
+ start = 48
+ stop = ordered_client_total[len(ordered_client_total) - 1]
+ logger.debug(f"Ordered client total: {ordered_client_total}")
+ step = math.ceil((stop-start) / (len(ordered_client_total)))
+ xticks = list(range(start, stop, step))
+ logger.debug(f"xticks: {xticks}")
+ function_names = df['function'].unique()
+ languages = df['language'].unique()
+ legend_entries = []
+ var_list = sorted(df[var_input].unique())
+ violin_opts = dict(
+ showmeans = True, #will display mean
+ showextrema = True, #will display extrema
+ widths= grid_spacing/(len(database_nodes)*5)
+ )
+ for function_name in tqdm(function_names, desc="Processing function name...", ncols=80):
+ #declare a figure and figsize is width, height in inches
+ fig = plt.figure(figsize=[16,5]) #keep it constant since it is just plotting it - everything else is relative to the data
+ axs = fig.subplots(1,2,sharey=True)
+
+ for lang_idx, language in tqdm(enumerate(languages), desc="Processing languages...", ncols=80):
+ language_df = df.groupby('language').get_group(language)
+ for idx, var in tqdm(enumerate(var_list), desc="Processing vars...", ncols=80):
+ #group by database number
+ var_df = language_df.groupby(var_input).get_group(var)
+ step2 = math.ceil((stop-start) / (len(ordered_client_total)))
+ logger.debug(f"step2: {step2}")
+ #group by function - take client total and time
+ function_df = var_df.groupby('function').get_group(function_name)[ ['client_total','time'] ]
+ #loop through client_total - assign times in data list
+ data = [function_df.groupby('client_total').get_group(client)['time'] for client in ordered_client_total]
+ new_xticks = []
+ # what we're doing here is offsetting xticks by 250 relative to idx
+ # (this prevents the graphs from stacking on top of one another)
+ #
+ for aidx, val in enumerate(xticks):
+ if len(var_list) > 1:
+ new_xticks.append(val + (200*idx) - 200)
+ else:
+ new_xticks.append(val)
+ plot = axs[lang_idx].violinplot(data, positions=new_xticks, **violin_opts)
+ [col.set_alpha(0.3) for col in plot["bodies"]]
+ props_dict = dict(color=plot["cbars"].get_color().flatten())
+ entry = plot["cbars"]
+ legend_entries.append(entry)
+ means = [np.mean(function_df.groupby('client_total').get_group(client)['time']) for client in ordered_client_total]
+ logger.debug(f"MEANS: {means}\n")
+ axs[lang_idx].plot(new_xticks, means, ':', color=props_dict['color'], alpha=0.5)
- function_names = df['function'].unique()
- languages = df['language'].unique()
- legend_entries = []
- var_list = df[var_input].unique()
- logger.debug("Values initialized")
- for function_name in function_names:
- fig = plt.figure(figsize=[12,4])
- logger.debug(f"Looping through function name: {function_name}")
- for lang_idx, language in enumerate(languages):
- logger.debug(f"Looping through language: {language}")
- axs = fig.subplots(1,2,sharey=True)
- language_df = df.groupby('language').get_group(language)
- for idx, var in enumerate(var_list):
- logger.debug("Looping through var: {var}")
- var_df = language_df.groupby(var_input).get_group(var)
- function_df = var_df.groupby('function').get_group(function_name)[ ['client_total','time'] ]
- data = [function_df.groupby('client_total').get_group(client)['time'] for client in ordered_client_total]
- pos = [int(client)-idx*36 for client in ordered_client_total]
- plot = axs[lang_idx].violinplot(data, pos, **violin_opts, widths=24)
- [col.set_alpha(0.3) for col in plot["bodies"]]
- props_dict = dict(color=plot["cbars"].get_color().flatten())
- entry = plot["cbars"]
- legend_entries.append(entry)
- data_labels = [f"{var} DB nodes" for var in var_list]
- axs[lang_idx].legend(legend_entries, data_labels, loc='upper left')
- axs[lang_idx].set_xlabel('Number of Clients')
- axs[lang_idx].set_title(language)
- axs[lang_idx].set_xticks(pos)
- axs[0].set_ylabel(f'{function_name}\nTime (s)')
- png_file = Path("results/" + scaling_test_name + "/stats") / os.path.basename(run_cfg_path) / f"{function_name}.png"
- plt.savefig(png_file)
- logger.debug(f"Plot created and saved for function name: {function_name} and saved to path: {png_file}")
- logger.debug(f"Plotting complete")
\ No newline at end of file
+ data_labels = [f"{var} {var_input}" for var in var_list]
+ axs[lang_idx].legend(legend_entries, data_labels, loc='upper left')
+ axs[lang_idx].set_xlabel('Number of Clients')
+ axs[lang_idx].set_title(language)
+ axs[lang_idx].set_xticks(xticks, labels=ordered_client_total, minor=False)
+ axs[lang_idx].set_ylabel(f'{function_name}\nTime (s)')
+ axs[lang_idx].yaxis.set_major_formatter(matplotlib.ticker.FormatStrFormatter('%2.3f'))
+ axs[lang_idx].yaxis.set_minor_locator(AutoMinorLocator())
+ plt.tight_layout()
+ plt.draw()
+ png_file = Path("results/" + scaling_test_name + "/stats") / os.path.basename(run_cfg_path) / f"{function_name}.png"
+ plt.savefig(png_file)
+ except Exception as e:
+ exc_info = sys.exc_info()
+ traceback.print_tb(e.__traceback__)
+ traceback.print_exception(*exc_info)
\ No newline at end of file
diff --git a/driverthroughput/README.md b/driverthroughput/README.md
new file mode 100644
index 0000000..5a92f62
--- /dev/null
+++ b/driverthroughput/README.md
@@ -0,0 +1,257 @@
+# Throughput Scaling Tests
+
+SmartSim-Scaling offers two throughput test versions:
+
+ 1. Throughput Colocated (C++ Client and SmartRedis Orchestrator)
+ 2. Throughput Standard (C++ Client and SmartRedis Orchestrator)
+
+
+## Client Description
+
+The throughput tests run as an MPI program where a single SmartRedis C++ client
+is initialized on every rank.
+
+Each client performs 10 executions of the following commands
+
+ 1) ``put_tensor`` (send image to database)
+ 2) ``unpack_tensor`` (Retrieve the image)
+
+The input parameters to the test are used to generate permutations
+of tests with varying configurations.
+
+## Colocated throughput
+
+Colocated Orchestrators are deployed on the same nodes as the
+application. This improves throughput performance as no data movement
+"off-node" occurs with colocated deployment. For more information
+on colocated deployment, see [our documentation](https://www.craylabs.org/docs/orchestrator.html)
+
+Below is the help output. The arguments which are lists control
+the possible permutations that will be run.
+
+```text
+NAME
+ driver.py throughput_colocated - Run throughput tests with colocated Orchestrator deployment
+
+SYNOPSIS
+ driver.py throughput_colocated
+
+DESCRIPTION
+ Run throughput tests with colocated Orchestrator deployment
+
+FLAGS
+ --exp_name=EXP_NAME
+ Default: 'throughput-colocated-scaling'
+ name of output dir
+ --launcher=LAUNCHER
+ Default: 'auto'
+ workload manager i.e. "slurm", "pbs"
+ --node_feature=NODE_FEATURE
+ Default: {}
+ dict of runsettings for both app and db
+ --nodes=NODES
+ Default: [4,8,16,32,64,128]
+ compute nodes to use for synthetic scaling app with
+ a colocated orchestrator database
+ --db_cpus=DB_CPUS
+ Default: [8]
+ number of cpus per compute host for the database
+ --db_port_DB_PORT
+ Default: 6780
+ port to use for the database
+ --net_ifname=NET_IFNAME
+ Default: 'lo'
+ network interface to use i.e. "ib0" for infiniband or
+ "ipogif0" aries networks
+ --clients_per_node=CLIENT_PER_NODE
+ Default: [48]
+ client tasks per compute node for the synthetic scaling app
+ --pin_app_cpus=PINE_APP_CPUS
+ Default: [False]
+ pin the threads of the application to 0-(n-db_cpus)
+ --iterations=ITERATIONS
+ Default: 100
+ number of put/get loops run by the applications
+ --tensor_bytes=TENSOR_BYTES
+ Default: [1024, 8192, 16384, 32769, 65538, 131076, 262152, 524304, 10...
+ list of tensor sizes in bytes
+ --languages=LANGUAGES
+ Default: ['cpp']
+ which language to use for the tester "cpp" or "fortran"
+ --plot=PLOT
+ Default: 'database_cpus'
+ flag to plot against in process results
+```
+
+> The interface name may be different on your target system. Please update the `net_ifname` flag to the appropriate value.
+
+For demonstration, the following command could be run to execute a battery of
+tests in the same allocation
+
+```bash
+# alloc must contain at least 60 (max client_nodes)
+python driver.py throughput_colocated --nodes=[20,40,60] --db_tpq=[1,2,4] \
+ --db_cpus=[8,16] --tensor_bytes=[1024] \
+ --clients_per_node=[48]
+```
+
+This command can be executed in a terminal with an interactive allocation
+or used in a batch script such as the following for Slurm based systems
+
+```bash
+#!/bin/bash
+
+#SBATCH -N 60
+#SBATCH --exclusive
+#SBATCH -t 10:00:00
+
+module load slurm
+python driver.py throughput_colocated --nodes=[20,40,60] --db_tpq=[1,2,4] \
+ --db_cpus=[8,16] --tensor_bytes=[1024] \
+ --clients_per_node=[48]
+```
+
+Examples of batch scripts to use are provided in the ``batch_scripts`` directory
+
+## Standard throughput
+
+Colocated deployment is the preferred method for running tightly coupled
+throughput workloads with SmartSim, however, if you want to deploy the Orchestrator
+database and the application on different nodes, you want to use standard
+deployment.
+
+Like the above colocated throughput tests, the standard throughput tests also provide
+a method of running a battery of tests all at once. Below is the help output.
+The arguments which are lists control the possible permutations that will be run.
+
+```text
+
+NAME
+ driver.py throughput_standard - Run throughput tests with standard Orchestrator deployment
+
+SYNOPSIS
+ driver.py throughput_standard
+
+DESCRIPTION
+ Run throughput tests with standard Orchestrator deployment
+
+FLAGS
+ --exp_name=EXP_NAME
+ Default: 'throughput-standard-scaling'
+ name of output dir
+ --launcher=LAUNCHER
+ Default: 'auto'
+ workload manager i.e. "slurm", "pbs"
+ --run_db_as_batch=RUN_DB_AS_BATCH
+ Default: True
+ run database as separate batch submission each iteration
+ --node_feature=NODE_FEATURE
+ Default: {}
+ dict of runsettings for both app
+ --db_node_feature=DB_NODE_FEATURE
+ Default: {}
+ dict of runsettings for the db
+ --db_hosts=DB_HOSTS
+ Default: []
+ optionally supply hosts to launch the database on
+ --db_nodes=DB_NODES
+ Default: [4,8,16]
+ number of compute hosts to use for the database
+ --db_cpus=DB_CPUS
+ Default: [8]
+ number of cpus per compute host for the database
+ --db_port=DB_PORT
+ Default: 6780
+ port to use for the database
+ --net_ifname=NET_IFNAME
+ Default: 'ipogif0'
+ network interface to use i.e. "ib0" for infiniband or "ipogif0" aries networks
+ --clients_per_node=CLIENTS_PER_NODE
+ Default: [48]
+ client tasks per compute node for the synthetic scaling producer app
+ --client_nodes=CLIENT_NODES
+ Default: [4,8,16,32,64,128]
+ number of compute nodes to use for the synthetic scaling producer app
+ --iterations=ITERATIONS
+ Default: 100
+ number of put/get loops run by the applications
+ --tensor_bytes=TENSOR_BYTES
+ Default: [1024, 8192, 16384, 32769, 65538, 131076, 262152, 524304, 10...
+ list of tensor sizes in bytes
+ --languages=LANGUAGES
+ Default: ['cpp']
+ which language to use for the tester "cpp" or "fortran"
+ --wall_time=WALL_TIME
+ Default: '05:00:00'
+ allotted time for database launcher to run
+ --plot=PLOT
+ Default: 'database_nodes'
+ flag to plot against in process results
+```
+
+The standard throughput tests will spin up a database for each iteration in the
+battery of tests chosen by the user. There are multiple ways to run this.
+
+> The interface name may be different on your target system. Please update the `net_ifname` flag to the appropriate value.
+
+1. Everything in the same interactive (or batch file) without caring about placement
+```bash
+# alloc must contain at least 60 (max client_nodes) + 32 nodes (max db_nodes)
+python driver.py throughput_standard --client_nodes=[60] \
+ --clients_per_node=[48] \
+ --db_nodes=[32] \
+ --db_cpus=[32] --net_ifname="ipogif0" \
+ --run_db_as_batch=False
+```
+
+This option is recommended as it's easy to launch in interactive allocations and
+as a batch submission, but if you need to specify separate hosts for the database
+you can look into the following two methods.
+
+A batch submission for this first option would look like the following for Slurm
+based systems.
+
+```bash
+#!/bin/bash
+
+#SBATCH -N 92
+#SBATCH --exclusive
+#SBATCH -t 10:00:00
+#SBATCH -C SK48
+#SBATCH --oversubscribe
+
+cd ..
+python driver.py throughput_standard --client_nodes=[60] \
+ --clients_per_node=[48] \
+ --db_nodes=[32] \
+ --db_cpus=[32] --net_ifname="ipogif0" \
+ --run_db_as_batch=False
+```
+
+2. Same as 1, but specify hosts for the database
+```bash
+# alloc must contain at least 60 (max client_nodes) + 32 nodes (max db_nodes)
+# db nodes must be fixed if hostlist is specified
+python driver.py throughput_standard --client_nodes=[60] \
+ --clients_per_node=[48] \
+ --db_nodes=[32] \
+ --db_cpus=[32] --net_ifname="ipogif0" \
+ --run_db_as_batch=False \
+ --db_hosts=["nid0001", ...]
+
+```
+
+3. Launch database as a separate batch submission each time
+```bash
+# must obtain separate allocation for client driver through interactive or batch submission
+# if batch submission, compute nodes must have access to slurm
+python driver.py throughput_standard --client_nodes=[60] \
+ --clients_per_node=[48] \
+ --db_nodes=[32] \
+ --db_cpus=[32] --net_ifname="ipogif0" \
+ --run_db_as_batch=True \
+ --db_node_feature='{"C":"V100", "exclusive": None}' \
+```
+
+All three options will conduct ``n`` scaling tests where ``n`` is the product of
+all lists specified as options.
\ No newline at end of file
diff --git a/driverthroughput/main.py b/driverthroughput/main.py
index 5985ec1..39eada3 100644
--- a/driverthroughput/main.py
+++ b/driverthroughput/main.py
@@ -22,7 +22,7 @@ class Throughput:
def throughput_standard(self,
exp_name="throughput-standard-scaling",
launcher="auto",
- run_db_as_batch=True,
+ run_db_as_batch=False,
node_feature={},
db_node_feature={},
db_hosts=[],
@@ -30,15 +30,14 @@ def throughput_standard(self,
db_cpus=[2],
db_port=6780,
net_ifname="ipogif0",
- clients_per_node=[32],
- client_nodes=[10],
- iterations=3,
- tensor_bytes=[1024,8192,16384,32769,65538,
- 131076,262152,524304,1024000],
+ clients_per_node=[48],
+ client_nodes=[4,8,16,32,64,128],
+ iterations=100,
+ tensor_bytes=[1024, 8192, 16384, 32768, 65536, 131072,
+ 262144, 524288, 1024000, 2048000, 4096000],
languages=["cpp"],
wall_time="05:00:00",
- plot="database_nodes",
- smartsim_logging=False):
+ plot="database_nodes"):
"""Run the throughput scaling tests with standard Orchestrator deployment
@@ -95,49 +94,50 @@ def throughput_standard(self,
wall_time=wall_time)
print_yml_file(Path(result_path) / "run.cfg", logger)
first_perms = list(product(db_nodes, db_cpus))
- for i, first_perm in enumerate(first_perms, start=1):
- dbn, dbc = first_perm
- # start the database only once per value in db_nodes so all permutations
- # are executed with the same database size without bringin down the database
- db = start_database(exp,
- db_node_feature,
- db_port,
- dbn,
- dbc,
- None, # not setting threads per queue in throughput tests
- net_ifname,
- run_db_as_batch,
- db_hosts,
- wall_time)
- logger.debug("database created and returned")
-
- second_perms = list(product(client_nodes, clients_per_node, tensor_bytes, db_cpus, languages))
- for j, second_perm in enumerate(second_perms, start=1):
- c_nodes, cpn, _bytes, db_cpu, language = second_perm
- logger.info(f"Running permutation {i} of {len(second_perms)} for database node index {j} of {len(first_perms)}")
- # setup a an instance of the C++ driver and start it
- throughput_session = self._create_throughput_session(exp,
- node_feature,
- c_nodes,
- cpn,
- dbn,
- db_cpu,
- iterations,
- _bytes,
- language)
- logger.debug("Throughput session created")
- exp.start(throughput_session, summary=True)
- logger.debug("experiment started")
- # confirm scaling test run successfully
- stat = exp.get_status(throughput_session)
- if stat[0] != status.STATUS_COMPLETED: # might need to add error check to inference tests
- logger.error(f"ERROR: One of the scaling tests failed {throughput_session.name}")
+ try:
+ for i, first_perm in enumerate(first_perms, start=1):
+ dbn, dbc = first_perm
+ # start the database only once per value in db_nodes so all permutations
+ # are executed with the same database size without bringin down the database
+ db = start_database(exp,
+ db_node_feature,
+ db_port,
+ dbn,
+ dbc,
+ None, # not setting threads per queue in throughput tests
+ net_ifname,
+ run_db_as_batch,
+ db_hosts,
+ wall_time)
+ logger.debug("database created and returned")
+
+ second_perms = list(product(client_nodes, clients_per_node, tensor_bytes, db_cpus, languages))
+ for j, second_perm in enumerate(second_perms, start=1):
+ c_nodes, cpn, _bytes, db_cpu, language = second_perm
+ logger.info(f"Running permutation {i} of {len(second_perms)} for database node index {j} of {len(first_perms)}")
+ # setup a an instance of the C++ driver and start it
+ throughput_session = self._create_throughput_session(exp,
+ node_feature,
+ c_nodes,
+ cpn,
+ dbn,
+ db_cpu,
+ iterations,
+ _bytes,
+ language)
+ logger.debug("Throughput session created")
+ exp.start(throughput_session, summary=True)
+ logger.debug("experiment started")
+ # confirm scaling test run successfully
+ stat = exp.get_status(throughput_session)
+ if stat[0] != status.STATUS_COMPLETED: # might need to add error check to inference tests
+ logger.error(f"ERROR: One of the scaling tests failed {throughput_session.name}")
- # stop database after this set of permutations have finished
- exp.stop(db)
- #Added to clean up db folder bc of issue with exp.stop()
- time.sleep(5)
- check_database_folder(result_path, logger)
+ # stop database after this set of permutations have finished
+ exp.stop(db)
+ except Exception as e:
+ #logger.warning(f"Skipping {run} in {scaling_results_dir}: could not process results")
+ logger.error(e)
self.process_scaling_results(scaling_results_dir=exp_name, plot_type=plot)
@classmethod
@@ -214,15 +214,15 @@ def throughput_colocated(self,
exp_name="throughput-colocated-scaling",
launcher="auto",
node_feature={},
- nodes=[10],
- db_cpus=[5],
+ nodes=[16,32,64,128],
+ db_cpus=[8],
db_port=6780,
net_ifname="lo",
clients_per_node=[48],
- pin_app_cpus=[False],
- iterations=3,
- tensor_bytes=[1024,8192,16384,32769,65538,
- 131076,262152,524304,1024000],
+ pin_db_cpus=[False],
+ iterations=100,
+ tensor_bytes=[1024, 8192, 16384, 32768, 65536, 131072,
+ 262144, 524288, 1024000, 2048000, 4096000],
languages=["cpp"],
plot="database_cpus"):
@@ -247,8 +247,8 @@ def throughput_colocated(self,
:type net_ifname: str, optional
:param clients_per_node: client tasks per compute node for the synthetic scaling app
:type clients_per_node: list[int], optional
- :param pin_app_cpus: pin the threads of the application to 0-(n-db_cpus)
- :type pin_app_cpus: list[bool], optional
+ :param pin_db_cpus: pin the threads of the database to 0-(n-db_cpus)
+ :type pin_db_cpus: list[bool], optional
:param iterations: number of put/get loops run by the applications
:type iterations: int
:param tensor_bytes: list of tensor sizes in bytes
@@ -259,13 +259,13 @@ def throughput_colocated(self,
:type plot: str
"""
logger.info("Starting throughput colocated scaling tests")
- check_node_allocation(nodes, [0])
+ #check_node_allocation(nodes, [0])
logger.info("Experiment allocation passed check")
exp, result_path = create_experiment_and_dir(exp_name, launcher)
write_run_config(result_path,
colocated=1,
- pin_app_cpus=str(pin_app_cpus),
+ custom_pinning=str(pin_db_cpus),
client_per_node=clients_per_node,
client_nodes=nodes,
database_cpus=db_cpus,
@@ -274,9 +274,9 @@ def throughput_colocated(self,
language=languages)
print_yml_file(Path(result_path) / "run.cfg", logger)
- perms = list(product(nodes, clients_per_node, db_cpus, tensor_bytes, pin_app_cpus, languages))
+ perms = list(product(nodes, clients_per_node, db_cpus, tensor_bytes, pin_db_cpus, languages))
for i, perm in enumerate(perms, start=1):
- c_nodes, cpn, dbc, _bytes, pin_app, language = perm
+ c_nodes, cpn, dbc, _bytes, pin_db, language = perm
logger.info(f"Running permutation {i} of {len(perms)}")
# setup a an instance of the C++ driver and start it
@@ -288,7 +288,7 @@ def throughput_colocated(self,
db_port,
iterations,
_bytes,
- pin_app,
+ pin_db,
net_ifname,
language)
logger.debug("Throughput session created")
@@ -310,7 +310,7 @@ def _create_colocated_throughput_session(cls,
db_port,
iterations,
_bytes,
- pin_app_cpus,
+ pin_db_cpus,
net_ifname,
language):
"""Run the throughput scaling tests with colocated Orchestrator deployment
@@ -331,8 +331,8 @@ def _create_colocated_throughput_session(cls,
:type iterations: int
:param _bytes: size in bytes of tensors to use for throughput scaling
:type _bytes: int
- :param pin_app_cpus: pin the threads of the application to 0-(n-db_cpus)
- :type pin_app_cpus: bool, optional
+ :param pin_db_cpus: pin the threads of the database to 0-(n-db_cpus)
+ :type pin_db_cpus: bool, optional
:param net_ifname: network interface to use i.e. "ib0" for infiniband or
"ipogif0" aries networks
:type net_ifname: str, optional
@@ -356,7 +356,7 @@ def _create_colocated_throughput_session(cls,
"N"+str(nodes),
"T"+str(tasks),
"DBCPU"+str(db_cpus),
- "PIN"+str(pin_app_cpus),
+ "PIN"+str(pin_db_cpus),
"ITER"+str(iterations),
"TB"+str(_bytes),
get_uuid()
@@ -367,7 +367,7 @@ def _create_colocated_throughput_session(cls,
model.colocate_db(port=db_port,
db_cpus=db_cpus,
ifname=net_ifname,
- limit_app_cpus=pin_app_cpus,
+ custom_pinning=pin_db_cpus,
debug=True,
loglevel="notice")
@@ -375,7 +375,7 @@ def _create_colocated_throughput_session(cls,
write_run_config(model.path,
colocated=1,
- pin_app_cpus=int(pin_app_cpus),
+ custom_pinning=int(pin_db_cpus),
client_total=tasks*nodes,
client_per_node=tasks,
client_nodes=nodes,
diff --git a/figures/aggregation-plots.ipynb b/figures/aggregation-plots.ipynb
deleted file mode 100644
index 8a85296..0000000
--- a/figures/aggregation-plots.ipynb
+++ /dev/null
@@ -1,592 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Set up env"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [],
- "source": [
- "import matplotlib.pyplot as plt\n",
- "import pandas as pd\n",
- "import seaborn as sns\n",
- "\n",
- "TENSOR_SIZE = 1_024_000\n",
- "TENSORS_PER_DATASET = 4\n",
- "DARK_MODE = False\n",
- "ADD_GRAPH_TITLES = True"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Load Results"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [],
- "source": [
- "# From binary files over the file system\n",
- "results_fs = pd.read_csv(\"../4_1mb_tensors/aggregation-scaling-py-fs-batch-thread-pool/results/aggregation-scaling-py-fs-batch-thread-pool-2022-10-13.csv\")\n",
- "\n",
- "# From 48 threads per 60 clients when using a 32 thread per 16 node Redis Orchestrator\n",
- "results_redis_16 = pd.read_csv(\"../4_1mb_tensors/aggregation-scaling-py-redis-16-batch/results/aggregation-scaling-py-redis-16-batch-2022-10-11.csv\")\n",
- "# From 48 threads per 60 clients when using a 32 thread per 32 node Redis Orchestrator\n",
- "results_redis_32 = pd.read_csv(\"../4_1mb_tensors/aggregation-scaling-py-redis-32-batch/results/aggregation-scaling-py-redis-32-batch-2022-10-11.csv\")\n",
- "\n",
- "# From 48 threads per 60 clients when using a 32 thread per 16 node KeyDB Orchestrator\n",
- "results_keydb_16 = pd.read_csv(\"../4_1mb_tensors/aggregation-scaling-py-key-16-batch/results/aggregation-scaling-py-key-16-batch-2022-10-11.csv\")\n",
- "# From 48 threads per 60 clients when using a 32 thread per 32 node KeyDB Orchestrator\n",
- "results_keydb_32 = pd.read_csv(\"../4_1mb_tensors/aggregation-scaling-py-key-32-batch/results/aggregation-scaling-py-key-32-batch-2022-10-11.csv\")\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Filter Results"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [],
- "source": [
- "results_fs = results_fs[(results_fs[\"tensor_bytes\"] == TENSOR_SIZE) & (results_fs[\"t_per_dataset\"] == TENSORS_PER_DATASET)]\n",
- "results_redis_16 = results_redis_16[(results_redis_16[\"tensor_bytes\"] == TENSOR_SIZE) & (results_redis_16[\"t_per_dataset\"] == TENSORS_PER_DATASET)]\n",
- "results_redis_32 = results_redis_32[(results_redis_32[\"tensor_bytes\"] == TENSOR_SIZE) & (results_redis_32[\"t_per_dataset\"] == TENSORS_PER_DATASET)]\n",
- "results_keydb_16 = results_keydb_16[(results_keydb_16[\"tensor_bytes\"] == TENSOR_SIZE) & (results_keydb_16[\"t_per_dataset\"] == TENSORS_PER_DATASET)]\n",
- "results_keydb_32 = results_keydb_32[(results_keydb_32[\"tensor_bytes\"] == TENSOR_SIZE) & (results_keydb_32[\"t_per_dataset\"] == TENSORS_PER_DATASET)]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Cursory Look at Results"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "\n",
- "\n",
- "
\n",
- " \n",
- " \n",
- " client_threads \n",
- " get_list_mean_fs \n",
- " loop_time_fs \n",
- " get_list_mean_redis_16 \n",
- " loop_time_redis_16 \n",
- " get_list_mean_redis_32 \n",
- " loop_time_redis_32 \n",
- " get_list_mean_keydb_16 \n",
- " loop_time_keydb_16 \n",
- " get_list_mean_keydb_32 \n",
- " loop_time_keydb_32 \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " 4 \n",
- " 1 \n",
- " 43.274310 \n",
- " 949.089412 \n",
- " 45.510888 \n",
- " 933.688209 \n",
- " 74.056899 \n",
- " 1507.877728 \n",
- " 41.282547 \n",
- " 864.429354 \n",
- " 74.056899 \n",
- " 1507.877728 \n",
- " \n",
- " \n",
- " 3 \n",
- " 2 \n",
- " 34.226965 \n",
- " 769.431668 \n",
- " 24.086804 \n",
- " 514.710097 \n",
- " 38.628376 \n",
- " 796.897871 \n",
- " 22.579015 \n",
- " 472.622118 \n",
- " 38.628376 \n",
- " 796.897871 \n",
- " \n",
- " \n",
- " 5 \n",
- " 4 \n",
- " 30.320078 \n",
- " 691.688106 \n",
- " 13.324377 \n",
- " 290.945971 \n",
- " 20.531650 \n",
- " 435.867276 \n",
- " 12.741331 \n",
- " 284.953725 \n",
- " 20.531650 \n",
- " 435.867276 \n",
- " \n",
- " \n",
- " 1 \n",
- " 8 \n",
- " 18.898627 \n",
- " 460.983685 \n",
- " 8.331711 \n",
- " 189.236700 \n",
- " 11.820698 \n",
- " 257.939992 \n",
- " 8.070015 \n",
- " 183.984052 \n",
- " 11.820698 \n",
- " 257.939992 \n",
- " \n",
- " \n",
- " 2 \n",
- " 16 \n",
- " 9.430240 \n",
- " 261.508696 \n",
- " 6.033627 \n",
- " 150.117243 \n",
- " 7.642517 \n",
- " 180.448871 \n",
- " 5.659973 \n",
- " 134.592571 \n",
- " 7.642517 \n",
- " 180.448871 \n",
- " \n",
- " \n",
- " 0 \n",
- " 32 \n",
- " 5.967385 \n",
- " 207.074023 \n",
- " 6.219164 \n",
- " 149.494738 \n",
- " 6.136555 \n",
- " 146.747323 \n",
- " 5.889435 \n",
- " 139.870367 \n",
- " 6.136555 \n",
- " 146.747323 \n",
- " \n",
- " \n",
- "
\n",
- "
"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "(\n",
- " sns.relplot(\n",
- " data=concatenated,\n",
- " kind=\"line\",\n",
- " x=\"client_threads\",\n",
- " y=\"get_list_mean\",\n",
- " hue=\"backend\",\n",
- " style=\"backend\",\n",
- " )\n",
- " .set(\n",
- " title=\"Runtime of get_list vs Number of Consumer Client Threads\" if ADD_GRAPH_TITLES else None,\n",
- " xlabel=\"Number of Consumer Client Threads\",\n",
- " ylabel=\"get_list Runtime (seconds)\",\n",
- " )\n",
- " .legend\n",
- " .set_title(\"Aggregation Backend\")\n",
- ")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Get List Loop Runtime"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "(\n",
- " sns.relplot(\n",
- " data=concatenated,\n",
- " kind=\"line\",\n",
- " x=\"client_threads\",\n",
- " y=\"loop_time\",\n",
- " hue=\"backend\",\n",
- " style=\"backend\",\n",
- " )\n",
- " .set(\n",
- " title=\"Loop Runtime vs Number of Consumer Client Threads\" if ADD_GRAPH_TITLES else None,\n",
- " xlabel=\"Number of Consumer Client Threads\",\n",
- " ylabel=\"Loop Runtime (seconds)\",\n",
- " )\n",
- " .legend\n",
- " .set_title(\"Aggregation Backend\")\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "throughput_df = concatenated.copy()\n",
- "throughput_df[\"throughput\"] = (throughput_df[\"client_nodes\"]\n",
- " * throughput_df[\"client_per_node\"]\n",
- " * throughput_df[\"tensor_bytes\"]\n",
- " * throughput_df[\"t_per_dataset\"]\n",
- " * throughput_df[\"iterations\"]\n",
- " / throughput_df[\"loop_time\"]\n",
- " / 1e9)\n",
- "(\n",
- " sns.relplot(\n",
- " data=throughput_df,\n",
- " kind=\"line\",\n",
- " x=\"client_threads\",\n",
- " y=\"throughput\",\n",
- " hue=\"backend\",\n",
- " style=\"backend\",\n",
- " )\n",
- " .set(\n",
- " title=\"Data Throughput with Various Backends\" if ADD_GRAPH_TITLES else None,\n",
- " xlabel=\"Number of Consumer Client Threads\",\n",
- " ylabel=\"Data Throughput (GB/s)\",\n",
- " )\n",
- " .legend\n",
- " .set_title(\"Aggregation Backend\")\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "refline_color = \"red\"\n",
- "db16_plot = (\n",
- " sns.relplot(\n",
- " data=throughput_df[throughput_df[\"backend\"].isin([\n",
- " \"File System\",\n",
- " \"16 Redis Nodes\",\n",
- " \"16 KeyDB Nodes\"\n",
- " ])],\n",
- " kind=\"line\",\n",
- " x=\"client_threads\",\n",
- " y=\"throughput\",\n",
- " hue=\"backend\",\n",
- " style=\"backend\",\n",
- " )\n",
- " .set(\n",
- " title=\"Data Throughput of a 16 Node Orchestrator\" if ADD_GRAPH_TITLES else None,\n",
- " xlabel=\"Number of Consumer Client Threads\",\n",
- " ylabel=\"Data Throughput (GB/s)\",\n",
- " )\n",
- ")\n",
- "expected_max = 16\n",
- "ax ,= db16_plot.axes[0]\n",
- "ax.axvline(expected_max, ls=\"-.\", c=refline_color)\n",
- "plt.text(\n",
- " expected_max + 2,\n",
- " 0.45,\n",
- " \"Thread Count with Expected\\nMax Throughput\",\n",
- " transform=ax.get_xaxis_transform(),\n",
- " rotation=\"vertical\",\n",
- " horizontalalignment=\"center\",\n",
- " verticalalignment=\"center\",\n",
- " c=refline_color,\n",
- ")\n",
- "db16_plot.legend.set_title(\"Aggregation Backend\")\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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aAKZ7nyT+Ebe2tqZbt26G4xYWFrz22mvcuHGDwMDADN+vZMmS9OrViwULFhAcHJzi/N27d4mMjEzzfaXX67l+/TqgfV/LlCmTotyT1164cAHQ+tW6ubkZbdu2bePOnTvpxpwvX76nTqWUeP7JhPDJz82lS5cAMjRHXtGiRVMkf/nz5zd6b1+4cIFTp06leF2J7+3E1/baa6/RoEEDBgwYQJEiRXj99df55ZdfMpX0eXl5pTro4dSpU3Tp0gUXFxecnZ1xc3MzJKgZeR8m/hwtLIz/hKX1eyS130VC5BXSR+8Fu3HjBqGhoUZ/wLt3787+/fsZO3Ysvr6+ODk5odfradOmTYZ+qV66dIkWLVpQvnx55s6dS7FixbCxsWHz5s18/vnnz/zfeFqS1zYkp5Qyevw8I2yf/GOVKHln7WeV0fgzIivjTO/+zyIhIYFWrVrx4MEDxo8fT/ny5XF0dCQoKIi+ffua7H1SoEAB7OzscHV1TfG9Lly4MKAl1U8m3On54IMPWL58OZ9++imdO3c2SZzpSfxeLF++HHd39xTnk9cep6ZChQr4+/sTExODra1tqmVOnDiBtbV1isTzeT43GXlv6/V6qlSpwty5c1MtW6xYMUMce/bsYdeuXWzatIktW7awevVqmjdvzrZt29J8ruRSey0hISE0adIEZ2dnpk2bRqlSpbCzs+PYsWOMHz/e5L+v0opDiLxCEr0XLHHeusQmzYcPH7Jjxw6mTp3KRx99ZCiXWKOQXFp/9H///XdiYmLYuHGj0R/PpzUvmYO3tzfnzp1LcTyxucbb2xtIqmV7cnLoJ/9TTyx/8eJFmjVrZjgeHx/P1atXn2m+wkQ3b95MMb3F+fPngaTRpxmNEzKXtHl7e6PX67lw4YKhlgLg9u3bhISEGF53Zvz333+cP3+epUuX0rt3b8Px7du3Z/pe6bGwsMDX15fDhw8TGxtrVKOTOHo2s81opUqV4s0332T+/PmGgRWJ3NzccHBwSPN9ZWFhYUhevL29U/1sPXlt4kjZwoUL07Jly0zFCtChQwcOHDjAr7/+mupURVevXmXv3r20bNnyqUlIYiwnT55MUcP7LEqVKsXx48dp0aLFU9+TFhYWtGjRghYtWjB37lxmzJjBBx98wK5du2jZsuUz/SOye/du7t+/z9q1a2ncuLHh+JUrV1KUTev+3t7enDhxAr1eb1Sr9+TvESGENN2+UDt37uTjjz+mRIkShj5Bif8VP1mblNps8IkJx5NJRWr3CA0NZfHixaYK3WTatWvHoUOHOHDggOFYREQECxYswMfHh4oVKwJJf9z27NljKJeQkMCCBQuM7lezZk0KFizIwoULiY+PNxxfsWJFiqbYzIqPjzdMCQMQGxvL/PnzcXNzo0aNGpmKE7SfX0abRxMn2n3yfZBYC9O+ffuMv5DHUnufKKX44osvMn2vp3nttddISEgwmu4iOjqaFStWULFixWeaAHnSpEnExcWlmC7E0tKSl156iQ0bNhg1qd++fZuVK1fSsGFDnJ2dAe37+u+//3Lo0CFDubt377JixQqje7Zu3RpnZ2dmzJhBXFxcilienE7oSYMHD6Zw4cKMHTs2Rd/P6Oho+vXrh1LK6J+7tLz00kvky5ePmTNnEh0dbXTuWWqhu3fvTlBQEAsXLkxxLioqyjDS98GDBynOJ06KnDgNS1q/k9KT2vswNjaWb7/9NkXZtD4z7dq149atW0Yj/ePj4/nqq69wcnIydEcQQkiNXpb5888/OXv2LPHx8dy+fZudO3eyfft2vL292bhxo6FTtbOzM40bN2bWrFnExcXh5eXFtm3bUv3vNjG5+OCDD3j99dextramY8eOvPTSS9jY2NCxY0cGDx5MeHg4CxcupHDhwqn2aTKnCRMmsGrVKtq2bcuIESMoUKAAS5cu5cqVK/z222+G/84rVapE3bp1mThxIg8ePKBAgQL8/PPPRskcaJ3Np0yZwvDhw2nevDndu3fn6tWrLFmyhFKlSj1X06enpyeffvopV69epWzZsqxevZqAgAAWLFiAtbV1puIE7ee3evVqRo8eTa1atXBycqJjx46pPne1atXo06cPCxYsMDR1HTp0iKVLl9K5c2ej2suMKl++PKVKlWLMmDEEBQXh7OzMb7/9lqmEePny5Vy7do3IyEhAS3ATB1f06tXLUJMyePBgfvjhB4YNG8b58+cpXry44drff/8907FDUq3ek3OlAUyfPt0w59vbb7+NlZUV8+fPJyYmxigxHDduHMuXL6dNmzaMHDnSML1KYg1RImdnZ7777jt69epF9erVef3113FzcyMwMJBNmzbRoEEDvv766zRjLViwIGvWrKF9+/ZUr149xcoYFy9e5Isvvkh3KpzksXz++ecMGDCAWrVq0bNnT/Lnz8/x48eJjIxM9fuRnl69evHLL78wZMgQdu3aRYMGDUhISODs2bP88ssvhrkvp02bxp49e2jfvj3e3t7cuXOHb7/9lqJFixoGU5UqVQpXV1e+//578uXLh6OjI3Xq1Em3T1z9+vXJnz8/ffr0YcSIEeh0OpYvX55q0prWZ2bQoEHMnz+fvn37cvToUXx8fFizZg379u1j3rx5Tx0II0SeYp7BvrlX4hQJiZuNjY1yd3dXrVq1Ul988YVh2H9yN27cUF26dFGurq7KxcVFvfrqq+rmzZupTl/x8ccfKy8vL2VhYWE0HcTGjRtV1apVlZ2dnfLx8VGffvqp+vHHH9OcjiUtGZle5cmpY1KbSiT59BhPunTpkurWrZtydXVVdnZ2qnbt2uqPP/5ItVzLli2Vra2tKlKkiHr//ffV9u3bUzyXUkp9+eWXytvbW9na2qratWurffv2qRo1aqg2bdqkiDP5tBxKJU3BkHyKiCZNmqhKlSqpI0eOqHr16ik7Ozvl7e2tvv7662eOMzw8XPXs2VO5uroq4KlTrcTFxampU6eqEiVKKGtra1WsWDE1ceJEo+knlMrc9CqnT59WLVu2VE5OTqpQoUJq4MCB6vjx42lOkfGkxCkzUtue/Jncvn1b9enTRxUoUEDZ2tqqOnXqpJhuJy1pvX8uXLigLC0tU/05Hjt2TLVu3Vo5OTkpBwcH1axZM7V///4U9zhx4oRq0qSJsrOzU15eXurjjz9WixYtSnOanNatWysXFxdlZ2enSpUqpfr27auOHDmSoddx5coVNXDgQFW8eHFlbW2tChUqpDp16pRiahOlnj4108aNG1X9+vWVvb29cnZ2VrVr11arVq0ynE98zz6pT58+Kd5rsbGx6tNPP1WVKlVStra2Kn/+/KpGjRpq6tSpKjQ0VCml1I4dO9TLL7+sPD09lY2NjfL09FQ9evRQ58+fN7rXhg0bVMWKFZWVlZXR+yiteJRSat++fapu3brK3t5eeXp6qnHjxqmtW7dm6jNz+/Zt1a9fP1WoUCFlY2OjqlSpkuI9nPjZnj17dqpxCJEX6JR6hrp/IbI5vV6Pm5sbXbt2TbWJ6mmaNm3KvXv3OHnyZBZEJ4QQQrwY0kdP5HjR0dEpmn2WLVvGgwcPUl0CTQghhMgrpI+eyPH+/fdfRo0axauvvkrBggU5duwYixYtonLlyrz66qvmDk8IIYQwG0n0RI7n4+NDsWLF+PLLLw0DInr37s3//ve/VCdrFUIIIfIK6aMnhBBCCJFLSR89IYQQQohcShI9IYQQQohcKs8lekopwsLCnmlGeSGEEEKInCTPJXqPHj3CxcWFR48emTsUIbKHiAjQ6bTt8fJXQgghcoc8l+gJIYQQQuQVkugJIYQQQuRSkugJIYQQQuRSkugJIYQQQuRSkugJIYQQQuRSkugJIYQQQuRSstatEHmdjQ18/XXSvhBCiFwjz611GxYWhouLC6GhoTg7O5s7HCGEEEKILGPWpts9e/bQsWNHPD090el0rF+//qnXxMTE8MEHH+Dt7Y2trS0+Pj78+OOPWR+sEEIIIUQOY9am24iICKpVq0b//v3p2rVrhq7p3r07t2/fZtGiRZQuXZrg4GD0en0WRypELpaQAHv3avuNGoGlpXnjEUIIYTJmTfTatm1L27ZtM1x+y5Yt/P3331y+fJkCBQoA4OPjk0XRCZFHREdDs2bafng4ODqaNx4hhBAmk6NG3W7cuJGaNWsya9YsvLy8KFu2LGPGjCEqKirNa2JiYggLCzPahBDJ6HRQsaK26XTmjkYIIYQJ5ahRt5cvX+aff/7Bzs6OdevWce/ePd5++23u37/P4sWLU71m5syZTJ069QVHKkQO4uAAp06ZOwohhBBZINuMutXpdKxbt47OnTunWeall15i79693Lp1CxcXFwDWrl1Lt27diIiIwN7ePsU1MTExxMTEGB6HhYVRrFgxGXUrhBBCiFwvR9XoeXh44OXlZUjyACpUqIBSihs3blCmTJkU19ja2mJra/siwxRCCCGEyBZyVB+9Bg0acPPmTcLDww3Hzp8/j4WFBUWLFjVjZELkYJGRUKmStkVGmjsaIYQQJmTWRC88PJyAgAACAgIAuHLlCgEBAQQGBgIwceJEevfubSjfs2dPChYsSL9+/Th9+jR79uxh7Nix9O/fP9VmWyFEBigFp09rW/boySGEEMJEzNp0e+TIEZolTusAjB49GoA+ffqwZMkSgoODDUkfgJOTE9u3b2f48OHUrFmTggUL0r17d6ZPn/7CYxdCiBxPKVB60CdoX1VC0mNrB7B6vCRedCjEhBufV0p7rE8AK1soWEormxAHNwOSziXeN/k1RWuBgzZFFjeOwsMrSeeejMe5KJR9SSsb+QD8f0p2T33KuOsOBafCWvkjP8KdM6m8RqU9Ll4XavZ7od9yIV60bDMY40WRJdCEeEJEBDg5aftZPY9eYmKR+IfX0gYsHjcsxIRDfHT6iYezh1Y2NhLuX3h8LpU/9koPxWqD9eOa/uuHIOLuE4lHsusKltbKA4QEwrktqd8zMUloMFJLbgAOfAOPbhm/ruTXlnkJyrfXyl7bD4d/eKJcsn0LK+j5c9L3a+XrEPUgle/H4+sajYaq3bWyRxbD3jmPyyaP+3HM+TzgnUNJ956aXyuTlm4/QuVXtP1tH8L+L9Mu6+ELg//W9iPuw+yS6b4N6L0RSjbR9te/DQEr0i5buiW8+Zu2f/ccfFM7/XsP3Q9FKmn7K7rDha1pl636OnSdn/79hMjhctRgDCFEFgg+nrS/rDPY6IyThNYzwaeBdv7v2XDiZ+MEInkS4lUDeq7WyoYGwdc1jROPJxOLt/6CYrW0/Y3D4dTatOOs0BFe+0nbv3cOFjRN/3WN8IcCjxOOHdPg6t60y9bol5To3T0Pf45N/951hiQlev4/wZ3TaZd1dEtK9EIC4eRvaZe1tDF+HHQUIu6kXT7yQdJ+bASEXk+7rI1T2udSk7wOwNJG23QWoLPUvlpYJD22Sxogh4Ul5PdJOmdhmWz/8TXJYylUFnwaGZfTWSQ99qiWVNbORUvOnnx+Q3lLsM+fVL5SF3CvknTOcN3j/cIVMvc9ESIHkkRPiLzs0S1Y8WrS4+sHtUQvuaiHSfuR9+D+xbTvl7ysTgdxTxnckTzx01kY7z+ZJFjZJZ23tNVqqJ5MIJL/0bdI9uutcAWtttAoKdAlPXYrn1Q2XxGo+HLKBCJ5kpD83r49Ifx26kmKzlJrHkzk6aclzobzT97/iV/JL38DCbFP3DPZdQVKJZWt8ip410/5/Kl9PwBGn035vTBKypKVb/GhtmWEvSuMPP7UYgYN39W2jMjnnrkaON8eGS8rRC4lTbdC5HXbZkDrD7T9Q6vAydE4SfD0TerzdP/S46QmldoRC0utebVACa1sQjyEBaWReDxOVqwdwfJxQqFPAHRJTblCCCGemyR6QuRF8bFJHe1fZB89IYQQL5T86yxEXhN4EL6uAcEnzB2JEEKILCaJnhB5SeC/8FNXbVDA3s/MHY0QQogsJomeEHnFtQPw0ysQGw4lGkPn780dkRBCiCwmiZ4QecG1/UlJXsmm0GM12DiYOyohhBBZTKZXESK3u7pPm0IlLuJxkvdz0kTCQgghcjVJ9ITIzW6fSpbkNYMeq1ImedbWMHly0r4QQohcQ6ZXESI3S4iH396CmEfw+gqpyRNCiDxGEj0hciOltNUOQFtkXp8A1nbpXyOEECLXkcEYQuQ2l/+GH1pAxD3tsaV1+kmeXg+nTmmbPp1F7oUQQuQ4kugJkZtc3g0rX4Ogo7Ang/PkRUVB5craFhWVpeEJIYR4sWQwhhC5xaVdsOp1iI+GMq2h1dSMX1uoUNbFJYQQwmwk0RMiN7i0E1b10JK8sm2g+zKwss3YtY6OcPdu1sYnhBDCLKTpVoic7uKOZEle28wleUIIIXI1qdETIie7uk9L8hJioFw7eHWJJHlCCCEMJNETIicrXAHcyoJL8cdJnk3m7xEVBW3bavt//gn2MteeEELkFjKPnhA5XdRDsHZ8tiQPICICnJy0/fBwrc+eEEKIXEH66AmR05zfBmv6Q3ys9tg+/7MneUIIIXI1aboVIic5vxVWvwkJseDpB/WHmzsiIYQQ2ZjU6AmRU5zbkpTkVegEdYaYOyIhhBDZnNToCZETnPsTVvcCfRxUfBleWaQtbSaEEEKkQ2r0hMjuzm5OluR1liRPCCFEhkmNnhDZ2bkt8EtvLcmr1AW6/gCW8rEVQgiRMfIXQ4jszLU42LlAySbQZYEkeUIIITJF/moIkZ0VqQgDd4BzUUnyhBBCZJr00RMiuzm9EXZ/mvQ4v48keUIIIZ6J/PUQIjs5vQF+7QcqQVverGInc0ckhBAiB5MaPSGyi1Prk5K8Kt2hfHtzRySEECKHkxo9IbKDU+tgzVtaklf1dej8LVhYvpjntrKCt99O2hdCCJFr6JRSytxBvEhhYWG4uLgQGhqKs7OzucMRAk6uhd8GaEletR7w8jcvLskTQgiRq0nTrRDmdPK3ZEleT0nyhBBCmJS00whhTlZ2oNNBtTeh05fmSfKUgnv3tP1ChbR4hBBC5ArSdCuEuQUdAw9fsDBTBXtEBDg5afvh4eDoaJ44hBBCmJw03Qrxop34Fc78nvTYq7r5kjwhhBC5mjTdCvEiHV8N64eAzgIG/AWefuaOSKvBy1sV+0IIkWdINYIQL8rxn2HdYFB68H0D3KuZOyIhhBC5nCR6QrwIAatg3RBAQY2+0GGeNNcKIYTIcvKXRoisFrAS1g8FFNTsD+0/z15JXnQ0vPqqtkVHmzsaIYQQJiSjboXISv4rYMMwtCTvLWj3WfZK8kBG3QohRC4mgzGEyEphQYCCWgO0JE/mqBNCCPECSaInRFZqMk6bI69MK0nyhBBCvHDZrA1JiFwgYBXcPpX0uOxLkuQJIYQwC0n0hDClo0u0efKWdoSwm+aORgghRB4niZ4QpnJkMfw+Utuv0h3yeZg3HiGEEHmeWRO9PXv20LFjRzw9PdHpdKxfvz7D1+7btw8rKyt8fX2zLD4hMuzIj/DHu9p+3behzUxprhVCCGF2Zk30IiIiqFatGt98802mrgsJCaF37960aNEiiyITIhMOL4I/Rmn7dYdB6xmS5AkhhMgWzDrqtm3btrRt2zbT1w0ZMoSePXtiaWmZqVpAIUzu8A+w6T1tv9478NJ0SfKEEEJkGzmuj97ixYu5fPkykydPzlD5mJgYwsLCjDYhTEIpuPy3tl9/uCR5Qgghsp0cNY/ehQsXmDBhAnv37sXKKmOhz5w5k6lTp2ZxZCJP0unglUVw8jeo9rokeUIIIbKdHFOjl5CQQM+ePZk6dSply5bN8HUTJ04kNDTUsF2/fj0LoxR5wolfIPKBtm9lA749JMkTQgiRLeWYGr1Hjx5x5MgR/P39eeeddwDQ6/UopbCysmLbtm00b948xXW2trbY2tq+6HBFbvXvd7BlAhSpAm9tBZtcsC6spSV065a0L4QQItfIMYmes7Mz//33n9Gxb7/9lp07d7JmzRpKlChhpshEnnHgW9g6Udsv0wqsHcwbj6nY2cGvv5o7CiGEEFnArIleeHg4Fy9eNDy+cuUKAQEBFChQgOLFizNx4kSCgoJYtmwZFhYWVK5c2ej6woULY2dnl+K4ECZ34BvY+r623+g9aP6hNNcKIYTI9sya6B05coRmzZoZHo8ePRqAPn36sGTJEoKDgwkMDDRXeEJo9n8N2z7Q9huNgeaTJMkTQgiRI+iUUsrcQbxIYWFhuLi4EBoairOzs7nDEdndvi9h+4fafuNx0Oz93JfkRUSAk5O2Hx4Ojrmg36EQQgggB/XRE+KFi4uG4z9r+03GQ9OJuS/JE0IIkatJoidEWqztoM9GOLUOag80dzRZx8EB7txJ2hdCCJFr5Jh59IR4Yc78AQlx2r5jodyd5IFWS+nmpm1SYymEELmKJHpCJLd3Lqx+A34bAHq9uaMRQgghnoskekIk2vMZ7Hi8XF6RymCRRz4eMTEwbJi2xcSYOxohhBAmJKNuhQDYMxt2Ttf2m0+CxmPNG8+LJKNuhRAi15LBGEL8PQt2faLtN/8QGo8xbzxCCCGEiUiiJ/K23f+D3TO1/RaTodFo88YjhBBCmJAkeiLvCr8DB+dr+y2nQMNRZg1HCCGEMDVJ9ETe5VRYmyfv6j6oO8Tc0QghhBAmJ4meyFuUgmv7waeB9ti9irYJIYQQuVAemT9CCLQkb9cMWNJOm0pFCCGEyOWkRk/kDUpp06fsfZzgWdmZNx4hhBDiBZBET+R+SsHOj2HvHO1x6xlQb5h5YxJCCCFeAEn0RO6mFOyYBv/M1R63ngn13jZvTEIIIcQLIomeyL2Ugr+mwL552uM2n8roWiGEEHmKJHoi97pzGvZ/pe23nQV1Bps3HiGEEOIFk0RP5F5FKkG3HyHiLtQeaO5osi8LC2jSJGlfCCFErqFTSilzB/EihYWF4eLiQmhoKM7OzuYOR5iaUnD3LBSuYO5IhBBCCLOTf99F7qEUbJsE3zeCc3+aOxohhBDC7KTpVuQOSsHWD+Dfb7THj4LNG48QQgiRDUiNnsj5lIItE5OSvA7zoGZ/s4aUo0REgJubtkVEmDsaIYQQJiQ1eiJnUwq2TICD32uPO8yDmv3MGlKOdO+euSMQQgiRBSTREzmXUvDneDg0X3vc8Quo0desIeVI9vZw8mTSvhBCiFxDEj2Rc53fmizJ+xJq9DFvPDmVhQVUqmTuKIQQQmQBSfREzlW2NTQeC67FoXpvc0cjhBBCZDsyj57IWfR6CL8Fzp7mjiT3iI2FGTO0/fffBxsb88YjhBDCZCTREzmHXg+b34Mzf0DfP8CtnLkjyh0iIsDJSdsPDwdHR/PGI4QQwmRkehWRM+j1sGk0HHm8pNmt/8wdkRBCCJHtSR89kf3p9bBpFBxdAuigy/dQpZu5oxJCCCGyPUn0RPam18Mf78KxpaCzgM7fQ7XXzB2VEEIIkSNIoieyL70e/hgJx5ZpSV6X+VC1u7mjEkIIIXIMSfRE9nVkUbIkbwFUfdXcEQkhhBA5iiR6Ivuq3hsu74aKnSXJE0IIIZ6BJHoie9EnQGwE2DmDlS289hPodOaOSgghhMiRZHoVkX3oE2DDO7C0A0Q91I5JkieEEEI8M0n0RPagT4ANw+D4Srh1Em4cMXdEQgghRI4niZ7IHrZNguOrQGcJ3RZBmVbmjkgIIYTI8STRE+YXFQKHF2n7XRdApS5mDUcIIYTILWQwhjC/U2shIQYKV4LKr5g7mrxHp4OKFZP2hRBC5BqS6AnzC1ipffXtKYmGOTg4wKlT5o5CCCFEFpCmW2Fed8/DjcNa3zxZ9UIIIYQwKanRE+aVzx06fA4Pr4FTYXNHI4QQQuQqkugJ87Jzhpr9zR1F3hYZCbVqafuHD2tNuUIIIXIFSfSEyOuUgtOnk/aFEELkGpLoCfPZMAwS4qDhaChc3tzR5F12drBrV9K+EEKIXEMSPWEeUSFw4ldtWpW6Q80dTd5maQlNm5o7CiGEEFlARt0K8zDMnVcRPHzNHY0QQgiRK5k10duzZw8dO3bE09MTnU7H+vXr0y2/du1aWrVqhZubG87OztSrV4+tW7e+mGCFacncedlHXBx88422xcWZOxohhBAmZNZELyIigmrVqvHNN99kqPyePXto1aoVmzdv5ujRozRr1oyOHTvi7++fxZEKk0o+d14VmTvP7GJj4Z13tC021tzRCCGEMCGz9tFr27Ytbdu2zXD5efPmGT2eMWMGGzZs4Pfff8fPz8/E0Yksc/xxbV6ZVpCviHljEUIIIXKxHD0YQ6/X8+jRIwoUKJBmmZiYGGJiYgyPw8LCXkRoIi36BDi+Wtv37WneWIQQQohcLkcPxvjss88IDw+ne/e0m/9mzpyJi4uLYStWrNgLjFCkEHQMHt0E+/xQto25oxFCCCFytRyb6K1cuZKpU6fyyy+/ULhw2ktnTZw4kdDQUMN2/fr1FxilSKFYLRh+DDp/D1a25o5GCCGEyNVyZNPtzz//zIABA/j1119p2bJlumVtbW2xtZWEIlspWErbhBBCCJGlMpXohYSEsG7dOvbu3cu1a9eIjIzEzc0NPz8/WrduTf369bMqToNVq1bRv39/fv75Z9q3b5/lzydMKCwYnAqDhaW5IxFCCCHyhAw13d68eZMBAwbg4eHB9OnTiYqKwtfXlxYtWlC0aFF27dpFq1atqFixIqtXr87wk4eHhxMQEEBAQAAAV65cISAggMDAQEBrdu3du7eh/MqVK+nduzdz5syhTp063Lp1i1u3bhEaGpqJlyzM5pfe8HlluLbf3JEIIYQQeUKGavT8/Pzo06cPR48epWLFiqmWiYqKYv369cybN4/r168zZsyYp973yJEjNGvWzPB49OjRAPTp04clS5YQHBxsSPoAFixYQHx8PMOGDWPYsGGG44nlRTZ27wLcOKTNnVegpLmjEUIIIfIEnVJKPa3Q/fv3KViwYIZvmtnyL1JYWBguLi6Ehobi7Oxs7nDyjr+mwj9zoUxreOMXc0cjkouIACcnbT88HBwdzRuPEEIIk8lQ021mk7bsmuQJM9EnwPGftX2ZO08IIYR4YTI9vcrSpUvZtGmT4fG4ceNwdXWlfv36XLt2zaTBiVzi8m5t7jw7VyiX8ZVQhBBCCPF8Mp3ozZgxA3t7ewAOHDjAN998w6xZsyhUqBCjRo0yeYAiFzi+Svta5VWZOy+7KlRI24QQQuQqmZ5H7/r165QuXRqA9evX88orrzBo0CAaNGhA06ZNTR2fyOmiQ+HM79q+NNtmT46OcPeuuaMQQgiRBTJdo+fk5MT9+/cB2LZtG61atQLAzs6OqKgo00Yncr4HV7S589wqgKefuaMRQggh8pRM1+i1atWKAQMG4Ofnx/nz52nXrh0Ap06dwsfHx9TxiZzO0xdGHIfwW6DTmTsaIYQQIk/JdI3eN998Q7169bh79y6//fabYYTt0aNH6dGjh8kDFLmAhQU4e5o7CpGWqCho2lTbpFZeCCFylQzNowfw448/0qlTJwrl8A7bMo/eC3TxL3AuCoXLmzsSkR6ZR08IIXKtDNfo/fTTTxQtWpT69evz6aefcvbs2ayMS+R0+gTYOAK+raMlfCL7srWFX37RNlsZFS2EELlJhmv0AB4+fMimTZvYuHEjW7ZsoUiRInTq1ImXX36Zhg0bYmGR6ZbgF05q9F6QSztheRdt7rz3zoG1nbkjEkIIg5shUawPCOLYtYcs7F0TnfQhFrlUpgZj5M+fnzfffJM333yT2NhYdu7cycaNG3njjTeIioqiXbt2dOrUibZt2+IozT95W8BK7WuVbpLkCSGyhUfRcfx58hbrjgXx75X7JFZzBFwPwa94fvMGJ0QWyVSNXnqOHDnCxo0b2bBhA926dePDDz80xW1NTmr0XoDoUPisHMRHwcCd4FXD3BGJ9MTHw7p12n6XLmCV6cH4QmRb8Ql69l64x1r/ILadukVMvN5wrk6JAnSt7kW7Kh7ks7M2Y5RCZB2TJXrJxcXFYW2dPT80kui9AEeXwu8jwK08vP2vTKuS3clgDJHLKKU4GRTGWv8b/H78JvfCYw3nSrk50rV6UV729aRofgczRinEi5Gpf90vXLjAiRMnqF69OiVKlGDTpk18+umnREVF0blzZ95//310Ol22TfLEC5LYbOvbU5I8IcQLExQSxXr/INb5B3HxTrjheEFHGzpW86RrdS+qeLlIfzyRp2Q40Vu3bh3du3fHwsICnU7HggULGDx4ME2bNsXZ2ZkpU6ZgZWXF+PHjszJekd3dvwTX/wWdBVR9zdzRCCFyubDoOP78L5i1x4I4eOWB4bitlQWtKhaha3UvGpVxw9oy+w8WFCIrZLjptmbNmrRu3Zrp06ezZMkShg0bxowZM3j33XcBWLBgAZ9//jlnzpzJynifmzTdZrEHl2HvXIiNgFcXmzsakRHSdCtymLgEPXvO32WtfxB/nb5t1O+ubskCdPUrSpsq7jhLvzshMp7o5cuXj4CAAEqVKoVer8fGxoaAgAAqV64MwNWrV6lYsSKRkZFZGvDzkkRPiCdIoidyAKUUJ26Ess4/iN+P3+R+RFK/u9KFneji50VnPy+8XO3NGKUQ2U+Gm24jIiLIly8fABYWFtjb2+PgkNSR1d7enpiYGNNHKIQQIs+68TCS9f5BrPUP4vLdCMPxQk42dKrmRdfqXlTydJZ+d0KkIcOJnk6nM/ogPflYCHb/D+zzQ9Xu2lchhHgGoVGP+935B3EoWb87O2sLXqroTpfqXjQqXQgr6XcnxFNlONFTSlG2bFlDchceHo6fn59hNYwsmKVF5CTRofDPPG3uvKI1Ze48IUSmxCXo+fvcXdb5B7H9zG1iH/e70+mgXsmCdPHzok1ld5nvTohMynCit3ixdKwX6Ti1XkvyCpUDz+rmjkYIkQMopTh+I5R1x27w+4lgHiTrd1e2iBNd/LT57jyl350QzyzDiV6fPn2yMg6R08nceUKIDLr+INIw393le8n73dnysq8nXfyk350QpvLMax09evTIqLnWwsICp8SReyJvkbnzhBBPERoZx6b/glnnf4PDVx8ajttZW9C6kjtd/LxoKP3uhDC5DCd6AQEBvP/++2zevBkAT09Po6lUdDodBw4coFatWqaPUmRvx1dpX0u1AGcP88YihMg2YuP17D53h3X+Qew4c4fYhKR+d/VLFaSLX1HaVHbHyVbWVxYiq2T40/XVV1/RsGFDo2PLly/Hy8sLpRQ//vgjX375JcuXLzd5kCIb0+sh4HGi59vTvLEIIcxOKYX/9RDWHQvijxM3eRgZZzhXrkg+ulT34mVfTzxcpN+dEC9ChhO9/fv388477xgdq1u3LiVLlgS0efS6d+9u2uhE9nd1D4TdADsXKNfO3NGIZ+HoCDJqXjynwPuRrPMPYn1AEFeS9btzy2fLy9U86VLdi4oe0u9OiBctw4netWvXcHNzMzyeNm0ahQoVMjz28PDg9u3bpo1OZH/F6sAriyDqIVjbmTsaIcQLFBoZxx//3WTdsSCOXEvqd2dvbUmbylq/uwalC2FpIcmdEOaS4UTPzs6Oa9euUbRoUQBGjRpldP769etGK2WIPMLaHqp0M3cUQogXJDZez65zd1h3LIidZ5P63VnooEHpQnTx86J1JXccpd+dENlChj+Jfn5+rF+/ngYNGqR6fu3atfj5+ZksMJED6BPAwtLcUYjnFR0NvXpp+8uXg53UzApjSimOBYawzv8Gf5wIJiRZv7vy7vnoWt2Ll329KOIs7x0hspsMJ3pvv/02r7/+Oj4+PgwdOtSwIkZCQgLffvstX331FStXrsyyQEU2tOxlsHWGVlOhUBlzRyOeVUICrFmj7S9ZYtZQRPZy7X6E1u/OP4ir95NmWSicz5bOfl508fOigoezGSMUQjyNTmVi7bLx48cze/Zs8uXLZxiEcfnyZcLDwxk9ejSzZ8/OskBNJSwsDBcXF0JDQ3F2ll9Qz+z+JfiqujZ33qjTMq1KThYXBwsWaPuDBoG1LDGVl4VExvLHiWDW+QdxNFm/OwcbS9pU0taZrV9K+t0JkVNkKtED+Pfff1m1ahUXLlwAoEyZMvTo0YO6detmSYCmJomeieycDntmQ+mW8OZv5o5GCPEcYuIT2HX2DmuPBbHr3B3iErQ/C4n97rpW1/rdOdhIvzshcppMf2rr1q2bY5I6kUVk7jwhcjylFEevPWStfxCbTgQTGpXU766ChzNd/bT57gpLvzshcrQMJXqBgYEUL148wzcNCgrCy8vrmYMS2Vzi3Hm2LlCuvbmjEc8rIQH27tX2GzUCSxlgk5tdvRfB2sf97gIfJPW7K+JsS2dfL7pU96K8u7R2CJFbZCjRq1WrFp07d2bAgAFpLnEWGhrKL7/8whdffMGgQYMYMWKESQMV2UjA40E3VV6RufNyg+hoaNZM2w8P1yZQFrnKw4hY/jhxk7X+QfgHhhiOO9pY0qayB138vKhXqqD0uxMiF8pQonf69Gk++eQTWrVqhZ2dHTVq1MDT0xM7OzsePnzI6dOnOXXqFNWrV2fWrFm0aycrJORa0WFweqO27/uGeWMRQqQpJj6BnWfusNY/iN1P9LtrVMaNrtW9aFWxiPS7EyKXy9RgjKioKDZt2sQ///zDtWvXiIqKolChQvj5+dG6dWsqV66clbGahAzGeE4XtsPK7lCwNAw7pK1OLnK2iAhwctL2pUYvR1NKceTaQ9YeC2LTiZuERccbzlXydKaLnxedfD0pnE9q4oXIKzI96jank0TPBMKCIfQGFEu9GV/kMJLo5XhX7kWw7tgN1gUEcf1BlOG4h4sdL/t60bW6F2WL5DNjhEIIc5E6e5F5zh4yb54QZvYgsd/dsSACrocYjjvaWNK2igdd/byoU1L63QmR10miJzLu7jnI7wNWtuaORIg8KTougZ2P57vbfe4O8XqtQcbSQkejMto6sy9VdMfeRkZOCyE0kuiJjNHr4adXIDYceq0HT19zRyREnqDXKw5ffcA6/yA2/RfMo2T97ip7OdPFryidqnnilk/+ARNCpCSJnsiYq3sh9Lo2d55beXNHI0Sud+luOOuOBbE+IIgbD5P63Xm62PGynxdd/bwoI/3uhBBPkelEb8+ePdSvXx8rK+NL4+Pj2b9/P40bNzZZcCIbSZw7r3JXmTtPiCxyPzyG34/fZJ1/EMdvhBqOO9la0bayts5s3RIFsZB+d0KIDMp0otesWTOCg4MpXLiw0fHQ0FCaNWtGQkKCyYIT2UR0GJzeoO3L3HlCmFR0XAJ/nbnNumNB/H3+rlG/uyZl3eji50XLCkWk350Q4plkOtFTSqFLZe60+/fv4yjTMuROpzdAfBQULANFa5o7GiFyPL1ecejqA9YdC2Lzf8E8iknqd1e1qAtd/LzoWM2TQk7S704I8XwynOh17doVAJ1OR9++fbG1TfoFlJCQwIkTJ6hfv77pIxTml9hs69tTJkgW4jlcvPOItceC2BBwk6CQpH53Xq72dPbzpIufF6ULS787IYTpZDjRc3FxAbQavXz58mFvb284Z2NjQ926dRk4cKDpIxTmdf8SBO4HnQVUe93c0QiR49wLj2FjgNbv7r+gpH53+WytaFfFgy7VvajtU0D63QkhskSGE73FixcD4OPjw5gxY0zSTLtnzx5mz57N0aNHCQ4OZt26dXTu3Dnda3bv3s3o0aM5deoUxYoVY9KkSfTt2/e5YxFpiLgLbhXA2VPbRO7j4AB37iTti+cWHZfAttO3WXfsBnsu3CPhcb87q8R+d9W1fnd21tLvTgiRtTLdR2/y5Mkme/KIiAiqVatG//79DU3D6bly5Qrt27dnyJAhrFixgh07djBgwAA8PDxo3bq1yeISyRSvC28fgJhH5o5EZBWdDtzczB1FjqfXK/69cp91x4L48+QtwpP1u6uWrN9dQel3J4R4gTK91m2JEiVSHYyR6PLly88WiE731Bq98ePHs2nTJk6ePGk49vrrrxMSEsKWLVsy9Dyy1q0QwpQu3H7EWv8gNvgHcTM02nDcy9WeLn5edPbzonRhJzNGKITIyzJdo/fuu+8aPY6Li8Pf358tW7YwduxYU8WVqgMHDtCyZUujY61bt04RU3IxMTHExMQYHoeFhWVVeLlPwEooVBa8asggjNwsJgZGj9b2584FW6lxepq7j2LYePwm6/xvcDIo6XdKPjsr2lfxoIufF7Wk350QIhvIdKI3cuTIVI9/8803HDly5LkDSs+tW7coUqSI0bEiRYoQFhZGVFSU0QCRRDNnzmTq1KlZGleuFPMINr0HcZEwcBd4VTd3RCKrxMfDt99q+7NmSaKXhqjYBLadvsU6/yD2PtHvrmk5N7r4FaVFhcLS704Ika2YbAm0tm3bMnHiRMOgjexi4sSJjE6srUCr0StWrJgZI8ohTm/QkryCpcHTz9zRiKxkbQ2JfW+trc0bSzaj1yv+vXyftf5BbHmi351vMVe6VveifRUP6XcnhMi2TJborVmzhgIFCpjqdqlyd3fn9u3bRsdu376Ns7NzqrV5ALa2tkZz/okMkrnz8g4bG5gyxdxRZCvnbyfOdxdEcLJ+d8UK2NPFV+t3V9JN+t0JIbK/TCd6fn5+RoMxlFLcunWLu3fv8m1i808WqVevHps3bzY6tn37durVq5elz5vnPLgM1/YBOqgqc+eJvOHOo2g2Btxk7bEgTgcn9btztrOifVVPulb3oqZ3/nQHowkhRHaT6UTvyVGxFhYWuLm50bRpU8qXL5+pe4WHh3Px4kXD4ytXrhAQEECBAgUoXrw4EydOJCgoiGXLlgEwZMgQvv76a8aNG0f//v3ZuXMnv/zyC5s2bcrsyxDpOf6z9rVUM3DxMm8sIuvp9XDmjLZfoQJYWJg3nhcoMjaebadus9Y/iH8u3OVxtzusLXU0LVeYrn5eNCsv/e6EEDlXpqdXMaXdu3fTrFmzFMf79OnDkiVL6Nu3L1evXmX37t1G14waNYrTp09TtGhRPvzww0xNmCzTqzyFXg9fVIPQQHhlEVTpZu6IRFaLiACnx82Q4eGQy9esTtArDly6z1r/G2w9eYuI2ATDOb/irnT186JDVU/yO9qYMUohhDCNZ0r0EhISWLduHWce1wJUrFiRl19+GSsrk3X5yzKS6D3FlT2wtCPYOsOY82Cdet9HkYvkkUTv7K0w1j1eZ/ZWWFK/u+IFHOjs50UXPy9KFMqdr10IkXdlOjM7deoUHTt25Pbt25QrVw6ATz/9FDc3N37//XcqV65s8iDFC+RSFOoMBStbSfJEjncnLJoNATdZ6x/EmWT97lzsrWlf1YOufl7UkH53QohcLNM1evXq1cPNzY2lS5eSP39+AB4+fEjfvn25e/cu+/fvz5JATUVq9IR4Qi6r0YuMjWfrqVusPRbEvov3jPrdNS9fmC6P+93ZWkm/OyFehClTprB+/XoCAgLMHcpzu3r1KiVKlMDf3x9fX98X9ryJXd0ePnyIq6trpq7NdI1eQEAAR44cMSR5APnz5+eTTz6hVq1amb2dEEI8twS9Yv+le6w7FsSWU7eITNbvroZ3frr4edGhqgeuDtLvTpjPgQMHaNiwIW3atMm1gwhTW850zJgxDB8+PMuf28fHh2vXrgHaQNEiRYrQtm1bPvvsM6OcJa/JdKJXtmxZbt++TaVKlYyO37lzh9KlS5ssMGEG64dBgRJQsz84ZO2ciEKYwpngMNb5a/Pd3Q5LWurQu6ADXR73u/MumLNrKEXusWjRIoYPH86iRYu4efMmnp6eWf6csbGx2NiY9x8cJycnnJxezLyT06ZNY+DAgSQkJHD+/HkGDRrEiBEjWL58+Qt5/uwo0/MozJw5kxEjRrBmzRpu3LjBjRs3WLNmDe+++y6ffvopYWFhhk3kIA+uQMBPsHO6tiKGENnU7bBoFuy5RJt5e2j7xV4W7LnM7bAYXB2sebNucX4bWp/dY5rybsuykuSJbCM8PJzVq1czdOhQ2rdvz5IlS1KU2bhxI2XKlMHOzo5mzZqxdOlSdDodISEhhjILFy6kWLFiODg40KVLF+bOnWvUlDdlyhR8fX354YcfKFGiBHZ2dgCEhIQwYMAA3NzccHZ2pnnz5hw/ftzo+adPn07hwoXJly8fAwYMYMKECUbNk4cPH6ZVq1YUKlQIFxcXmjRpwrFjxwznfXx8AOjSpQs6nc7wODGmRHq9nmnTplG0aFFsbW3x9fVly5YthvNXr15Fp9Oxdu1amjVrhoODA9WqVePAgQNP/T7ny5cPd3d3vLy8aNasGX369DGK8f79+/To0QMvLy8cHByoUqUKq1atMrqHXq9n1qxZlC5dGltbW4oXL84nn3yS6vMlJCTQv39/ypcvT2BgIAAbNmygevXq2NnZUbJkSaZOnUp8fNKqOjqdjh9++IEuXbrg4OBAmTJl2Lhxo9F9N2/eTNmyZbG3t6dZs2ZcvXr1qa89TSqTdDqdYbOwsFAWFhapPrawsMjsrV+I0NBQBajQ0FBzh5K97Jyh1GRnpZa+bO5IxIsWHq4UaFt4uLmjSVV4dJxac+S6emPhv8pnwh/Ke7y2lXl/sxq87IjacjJYxcQlmDtMIdK0aNEiVbNmTaWUUr///rsqVaqU0uv1hvOXL19W1tbWasyYMers2bNq1apVysvLSwHq4cOHSiml/vnnH2VhYaFmz56tzp07p7755htVoEAB5eLiYrjP5MmTlaOjo2rTpo06duyYOn78uFJKqZYtW6qOHTuqw4cPq/Pnz6v33ntPFSxYUN2/f18ppdRPP/2k7Ozs1I8//qjOnTunpk6dqpydnVW1atUM996xY4davny5OnPmjDp9+rR66623VJEiRVRYWJhSSqk7d+4oQC1evFgFBwerO3fuGGJKfp+5c+cqZ2dntWrVKnX27Fk1btw4ZW1trc6fP6+UUurKlSsKUOXLl1d//PGHOnfunOrWrZvy9vZWcXFxaX6Pvb291eeff254fOPGDVW7dm3Vr18/o2OzZ89W/v7+6tKlS+rLL79UlpaW6uDBg4Yy48aNU/nz51dLlixRFy9eVHv37lULFy40is3f319FR0erLl26KD8/P8Nr3bNnj3J2dlZLlixRly5dUtu2bVM+Pj5qypQphvsDqmjRomrlypXqwoULasSIEcrJycnwswgMDFS2trZq9OjR6uzZs+qnn35SRYoUMXovZEamE73du3dneMuOJNFLRUKCUp9X1hK947+YOxrxomXTRC8+Qa92n7ujRq46pspP+tOQ3HmP/0O98u0+9dO/V1VIRKy5wxQiQ+rXr6/mzZunlFIqLi5OFSpUSO3atctwfvz48apy5cpG13zwwQdGf9xfe+011b59e6Myb7zxRopEz9ra2pB4KKXU3r17lbOzs4qOjja6tlSpUmr+/PlKKaXq1Kmjhg0bZnS+QYMGRgnakxISElS+fPnU77//bjgGqHXr1hmVezLR8/T0VJ988olRmVq1aqm3335bKZWUTP3www+G86dOnVKAOnPmTJrxeHt7KxsbG+Xo6Kjs7OwUoOrUqfPU5Kh9+/bqvffeU0opFRYWpmxtbQ2J3ZMSY9u7d69q0aKFatiwoQoJCTGcb9GihZoxY4bRNcuXL1ceHh6Gx4CaNGmS4XF4eLgC1J9//qmUUmrixImqYsWKRvcYP378Myd6me6j16RJk2evPhTZ07V9EBKozZ1Xvr25oxF5mFKK08GP57s7fpO7j5L63fkUdKCLX1G6+HlRvKCDGaMUInPOnTvHoUOHWLduHQBWVla89tprLFq0iKZNmxrKPDmgsXbt2inu06VLlxRl/vjjD6Nj3t7euLm5GR4fP36c8PBwChYsaFQuKiqKS5cuGe799ttvp7j3zp07DY9v377NpEmT2L17N3fu3CEhIYHIyEhDk2VGhIWFcfPmTRo0aGB0vEGDBimakqtWrWrY9/DwALTxAOmtwjV27Fj69u2LUorr16/z/vvv0759e/bs2YOlpSUJCQnMmDGDX375haCgIGJjY4mJicHBQfudcubMGWJiYmjRokW6r6NHjx4ULVqUnTt3Ym+fNBXZ8ePH2bdvn1FTb0JCAtHR0URGRhqeJ/lrc3R0xNnZmTt37hhiqFOnjtHzPc9Sr880w3FISAiHDh3izp076PV6o3O9e/d+5mCEmRx/3D+hUhewkT+g4sW7FRrN+oAg1h0L4tztR4bj+R2s6VjNk85+XvgVc5X57kSOtGjRIuLj440GXyilsLW15euvv8bFxcWkz+f4xBRJ4eHheHh4GK0ylSgzU3X06dOH+/fv88UXX+Dt7Y2trS316tUjNjb2OSNOnbW1tWE/8bP/ZM7xpEKFChkGhpYpU4Z58+ZRr149du3aRcuWLZk9ezZffPEF8+bNo0qVKjg6OvLuu+8aXkPypC097dq146effuLAgQM0b97ccDw8PJypU6fStWvXFNck9pd88rUlvr6nvbZnlelE7/fff+eNN94gPDwcZ2dno1+8Op1OEr2cJiYcTq3X9n3fMGsoIm8Jj4lny8lbrPO/wf5L90mc0dPG0oKWFQvTxa8oTcq6YWOVd9beFblPfHw8y5YtY86cObz00ktG5zp37syqVasYMmQI5cqVY/PmzUbnDx8+bPS4XLlyKY49+Tg11atX59atW1hZWRkGSDwp8d7J/4Y/ee99+/bx7bff0q5dOwCuX7/OvXv3jMpYW1uTkJBAWpydnfH09GTfvn1GLYT79u1LUYNpCpaW2nyZUVFRhud5+eWXefPNNwEtcTx//jwVK1YEtOTQ3t6eHTt2MGDAgDTvO3ToUCpXrkynTp3YtGmT4bVUr16dc+fOPdcsJBUqVEgxOOPff/995vtlOtF777336N+/PzNmzDBUQYoc7OwfEBcBBUpBMdN/yIRILj5Bzz8X77HOP4itp24RHZf0H2xtnwJ0qe5FuyoeuNhbp3MXIXKOP/74g4cPH/LWW2+lqLl75ZVXWLRoEUOGDGHw4MHMnTuX8ePH89ZbbxEQEGAYmZtYoTJ8+HAaN27M3Llz6dixIzt37uTPP/98ak13y5YtqVevHp07d2bWrFmULVuWmzdvsmnTJrp06ULNmjUZPnw4AwcOpGbNmtSvX5/Vq1dz4sQJSpYsabhPmTJlWL58OTVr1iQsLIyxY8emqAHz8fFhx44dNGjQAFtb21Tnrxs7diyTJ0+mVKlS+Pr6snjxYgICAlixYsWzfIuNPHr0iFu3bhmabseNG4ebmxv169c3vIY1a9awf/9+8ufPz9y5c7l9+7Yh0bOzs2P8+PGMGzcOGxsbGjRowN27dzl16hRvvfWW0XMNHz6chIQEOnTowJ9//knDhg356KOP6NChA8WLF6dbt25YWFhw/PhxTp48yfTp0zP0GoYMGcKcOXMYO3YsAwYM4OjRo6mO0s6wzHbqc3BwUJcuXcp0Z8DsQgZjPCE+Vqmzm5U6td7ckQhzSUhQ6uRJbUsw/chVvV6v/rsRoqb9fkrV+Hi70aCKZrN3qS//Oq8C70eY/HmFyA46dOig2rVrl+q5gwcPKsAwMnbDhg2qdOnSytbWVjVt2lR99913ClBRUVGGaxYsWKC8vLyUvb296ty5s5o+fbpyd3c3nH9y4EOisLAwNXz4cOXp6amsra1VsWLF1BtvvKECAwMNZaZNm6YKFSqknJycVP/+/dWIESNU3bp1DeePHTumatasqezs7FSZMmXUr7/+mmKk68aNG1Xp0qWVlZWV8vb2TjWmhIQENWXKFOXl5aWsra1VtWrVDAMRlDIe2Zro4cOHCjAawPIkb29vBRg2Nzc31a5dO6P73L9/X7388svKyclJFS5cWE2aNEn17t1bvfzyy0bxTZ8+XXl7eytra2tVvHhxwwCL1GKbM2eOypcvn9q3b59SSqktW7ao+vXrK3t7e+Xs7Kxq166tFixYYChPKgNWXFxc1OLFiw2Pf//9d8N7oVGjRurHH3985sEYmV4CrWvXrrz++ut079792bNLM5Il0IR4MYJDo1jvf5N1/jc4fzvccLyAow0dq3rQpXpRqhV1kX53QqThk08+4fvvv+f69etplhk4cCBnz55l7969Jn/+Vq1a4e7unqcnG84NMtR0m7ytuH379owdO5bTp09TpUqVFB0KO3XqZNoIRdaJjZTBF8KkHkXHPe53F8SBy8n63VlZ0KpCEbr4edGknBvWltLvTognffvtt9SqVYuCBQuyb98+Zs+ezTvvvGNU5rPPPqNVq1Y4Ojry559/snTpUr799tvnfu7IyEi+//57WrdujaWlJatWreKvv/5i+/btz31vYV4ZqtGzsMjYL2WdTpduJ8zsQGr0HtPr4avq4FoMOn0F+X3MHZEwl9hYmDFD23//fcjkcknxCXr2XrjHWv8gtp9+ot9diQJ09fOirfS7E+KpRo0axerVq3nw4AHFixenV69eTJw4ESurpDqZ7t27s3v3bh49ekTJkiUZPnw4Q4YMee7njoqKomPHjvj7+xMdHU25cuWYNGlSqqNHRc6S6abbnE4Svceu/gNL2oNNPhhzXmr28rKICEhchzI8HByfvmyYUoqTQWGs9b/B78dvci88aXqFkm6OdPXz4mVfL4oVkPeVEEKY0zPNoydygYCV2tfKMndenmdlBYkTpVql/yshKCSK9f5BrPMP4uKdpH53BR1t6FjNky5+XlSVfndCCJFtZDrR+/LLL1M9rtPpsLOzo3Tp0jRu3Ngwd43IhmTuPJGcrS18802apx9Fx/Hnf7dY63+Dg1ceGPrd2VpZ0Kqi1u+ucVnpdyeEENlRphO9zz//nLt37xIZGWmYH+fhw4c4ODjg5OTEnTt3KFmyJLt27aJYsWImD1iYwJmNj+fOKwnF6jy9vMhz4hL07L1wl7XHgth++jYx8Un97uqWLEBXv6K0qeKOs530uxNCiOws04nejBkzWLBgAT/88AOlSpUC4OLFiwwePJhBgwbRoEEDXn/9dUaNGsWaNWtMHrAwgcRmW9+eIE1sQim4dw+lFP/FWLPW/ya/H7/J/YikfnelCzvRxc+Lzn5eeLlmbIkgIYQQ5pfpwRilSpXit99+w9fX1+i4v78/r7zyCpcvX2b//v288sorBAcHmzJWk8jzgzEeXoUvqgE6ePc/bdStyNMiH4biUMAVgAqj1hBlo63HWMhJ63fX1a8olb2cpd+dEELkQJmu0QsODiY+Pj7F8fj4eG7dugWAp6cnjx49SlFGZAM3/cHSBrzrS5IniE/QM2KVPz88fmxrZUHLap509fOiYZlC0u9OCCFyuEz/Fm/WrBmDBw/G39/fcMzf35+hQ4fSvHlzAP777z9KlChhuiiF6VTqAu+dg3afmTsSkQ1M33SGfRfvGx7vHd+Mr3r40ax8YUnyhBA0bdqUd9991/DYx8eHefPmmS0ekXmZ/k2+aNEiChQoQI0aNbC1tcXW1paaNWtSoEABFi1aBICTkxNz5swxebDCRBwKQKEy5o5CmNnyf6+xZP9Vo2P5ZHCFEHlO37590el0KbaLFy+ydu1aPv74Y5M+399//03z5s0pUKAADg4OlClThj59+hAbG/v0izNAp9Oxfv16k9wrN8h00627uzvbt2/n7NmznD9/HoBy5cpRrlw5Q5lmzZqZLkJhOtcOgHsVsHUydyTCzP65cI8pG08B8G7LMvC5mQMSQphVmzZtWLx4sdExNzc3k0+Vdvr0adq0acPw4cP58ssvsbe358KFC/z222/ZfmWtnOqZ22bKly9Pp06d6NSpk1GSJ7KpmHD46RX4rCzcv2TuaIQZXbobztsrjpKgV3T182JQ45LmDkkIYWa2tra4u7sbbZaWlimabp8UEhLCgAEDcHNzw9nZmebNm3P8+PE0y2/btg13d3dmzZpF5cqVKVWqFG3atGHhwoXY29sTERGBs7Nzilk71q9fj6OjI48ePSI2NpZ33nkHDw8P7Ozs8Pb2ZubMmYDWtAzQpUsXdDqd4THAhg0bqF69OnZ2dpQsWZKpU6cajTnQ6XTMnz+fDh064ODgQIUKFThw4AAXL16kadOmODo6Ur9+fS5dyll/QzNdo9e/f/90z//444/PHIzIQmd+1+bOy19Cmz9P5EkhkbEMWHqEsOh4anjnZ+YrVdDFRJs7LCFyJaUUUXHmqaWyt7Z8ISPlX331Vezt7fnzzz9xcXFh/vz5tGjRgvPnz1OgQIEU5d3d3QkODmbPnj00btw4xXlHR0def/11Fi9eTLdu3QzHEx/ny5ePzz77jI0bN/LLL79QvHhxrl+/zvXr1wE4fPgwhQsXZvHixbRp08ZQI7l371569+7Nl19+SaNGjbh06RKDBg0CYPLkyYbn+fjjj5k7dy5z585l/Pjx9OzZk5IlSzJx4kSKFy9O//79eeedd/jzzz9N+n3MSplO9B4+fGj0OC4ujpMnTxISEmIYjCGyoYAV2lffN2TuvDwqLkHP2yuOceVeBF6u9szvVQNbK0uIMXdkQuROUXEJVPxoq1me+/S01jjYZPxP/B9//IGTU1K3nrZt2/Lrr7+me80///zDoUOHuHPnDra2tgB89tlnrF+/njVr1hgSqeReffVVtm7dSpMmTXB3d6du3bq0aNGC3r17G6Y8GzBgAPXr1yc4OBgPDw/u3LnD5s2b+euvvwAIDAykTJkyNGzYEJ1Oh7e3t+H+bm5uALi6uuLu7m44PnXqVCZMmECfPn0AKFmyJB9//DHjxo0zSvT69etH9+7dARg/fjz16tXjww8/pHXr1gCMHDmSfv36ZfC7mj1kOtFbt25dimN6vZ6hQ4caJlAW2czDa3B1L6CDaq+bOxphBkopJm88xf5L93G0seSHPjUp5GRr7rCEENlEs2bN+O677wyPHR0dn3rN8ePHCQ8Pp2DBgkbHo6Ki0mzetLS0ZPHixUyfPp2dO3dy8OBBZsyYwaeffsqhQ4fw8PCgdu3aVKpUiaVLlzJhwgR++uknvL29DTWAffv2pVWrVpQrV442bdrQoUMHXnrppafGum/fPj755BPDsYSEBKKjo4mMjMTBQVvzvWrVqobzRYoUAaBKlSpGx6KjowkLC8sxc/FmOtFLjYWFBaNHj6Zp06aMGzfOFLcUpnT8Z+1ricYyd14etXT/VVYeDESngy9e96OCR874BSVETmZvbcnpaa3N9tyZ4ejoSOnSpTN1TXh4OB4eHuzevTvFOVdX13Sv9fLyolevXvTq1YuPP/6YsmXL8v333zN16lRAq9X75ptvmDBhAosXL6Zfv36Gpujq1atz5coV/vzzT/766y+6d+9Oy5Yt012NKzw8nKlTp9K1a9cU5+zs7Az71tZJMw8kPl9qx/T6pGUhszuTJHoAly5dSnUiZWFmer1xs63Ic3afu8O0P04DMLFteVpWLGLmiITIG3Q6XaaaT3Oa6tWrc+vWLaysrIwGPWRW/vz58fDwICIiwnDszTffZNy4cXz55ZecPn3a0OSayNnZmddee43XXnuNbt260aZNGx48eECBAgWwtrZOMYK3evXqnDt3LtPJbG6Q6Xfg6NGjjR4rpQgODmbTpk0pfhAiGwg8ACHXwCYfVOhg7mjEC3bxziOGr/RHr+DVGkUZ2EgG4gghTKNly5bUq1ePzp07M2vWLMqWLcvNmzfZtGkTXbp0oWbNmimumT9/PgEBAXTp0oVSpUoRHR3NsmXLOHXqFF999ZWhXP78+enatStjx47lpZdeomjRooZzc+fOxcPDAz8/PywsLPj1119xd3c31CL6+PiwY8cOGjRogK2tLfnz5+ejjz6iQ4cOFC9enG7dumFhYcHx48c5efIk06dPz/LvlTllenoVf39/o+3EiRMAzJkzR2bLzo6UHorWgkqdwebpfS5E7vEgIpb+S47wKCae2j4F+KRLFVmvVghhMjqdjs2bN9O4cWP69etH2bJlef3117l27Zqhf9uTateuTXh4OEOGDKFSpUo0adKEf//9l/Xr19OkSROjsm+99RaxsbEpZvvIly8fs2bNombNmtSqVYurV6+yefNmLCy0lGbOnDls376dYsWK4efnB0Dr1q35448/2LZtG7Vq1aJu3bp8/vnnRgM5ciudUkqZO4gXKSwsDBcXF0JDQ3NMR0qTSIgDS1n1IK+Ijdfz5qKDHLrygGIF7NkwrCEFHG1SL5yQAHv3avuNGoGJJ0gVQohnsXz5ckaNGsXNmzexsUnj95d4qmfuPHD37l3OnTsHaCtjJA5pFtmUJHl5hlKKSev/49CVB+SzteLHPrXSTvJAS+yaNn1h8QkhRHoiIyMJDg7mf//7H4MHD5Yk7zlluuk2IiKC/v374+HhQePGjWncuDGenp689dZbREZGZkWM4lntnQOXd2sDMkSeseifK/xy5AYWOviypx9liuQz7ROULAn376c8HhKinRNCiOcwa9Ysypcvj7u7OxMnTjR3ODlepptuBw8ezF9//cXXX39NgwYNAG3SxBEjRtCqVSujeXiyozzTdBsSCPMez/3z7n/gWty88YgXYseZ2wxYdgSl4KMOFenfsMTTL4qLgwULtP1Bg8D6KbW/FhZw6xYULmx8/PZtKF4cYjIwA/OFC7BrF9y5k/IfkY8+evr1QgghMiTTiV6hQoVYs2YNTZ9o6tm1axfdu3fn7t27pozP5PJMovf3LNj1iTZ3Xp/fzR2NeAHO3XpE12/3ERGbQI/axZnRpXLGBl9EREDijPjh4ZDWRKkbN2pfO3eGpUvBxSXpXEIC7NgB27fD4y4daVq4EIYOhUKFwN3deKUWnQ6OHXt6zEIIITIk0330IiMjUx1NU7hwYWm6zS6Ukrnz8ph74TH0X3KYiNgE6pYswLSXK2V8hK2lJSSuKZneQIzOnbWvOh08OZWStTX4+MCcOU9/vunT4ZNPYPz4jMUnhBDimWW6Rq9FixYULFiQZcuWGWaTjoqKok+fPjx48MCwFl12lSdq9K7th8VtwcYJxpyXaVVyuZj4BHouPMjRaw/xKejA+mENcHXIws7LJUrA4cNajdyzcHaGgADpzyeEEC9Apmv05s2bR5s2bShatCjVqlUDtDXk7Ozs2LrVPIs3iyck1ubJ3Hm5nlKKiWv/4+i1h+Szs2JR31pZm+QBXLnyfNe/+ips2wZDhpgmHiGEEGnKdKJXpUoVLly4wIoVKzh79iwAPXr04I033sDe3t7kAYpMio2AU+u1fWm2zfW+//sya48FYWmh49s3qlPKzSnrn3TatPTPP20wRenS8OGH8O+/UKVKysEfI0Y8X3xCCCEMMtV0GxcXR/ny5fnjjz+oUKFCVsaVZXJ90+3x1bBuEOT3gREBxh3dRa6y9dQthvx0FKXg45cr0auez7PdKKODMRI9nmneIC5Oq+WzsoJSpZ4+mKJEOiOBdTq4fPnpMQshhMiQTNXoWVtbEx0dnVWxCFMoXhcaj4V87pLk5WKnboYyanUASkHvet7PnuQ9C3//lMfCwqBvX+jS5enXP2/TrxAiT7t69SolSpTA398fX19fdu/eTbNmzXj48KFhvdvsRKfTsW7dOjonDmh7wTI9YfKwYcP49NNPiY+Pz4p4xPPK7w3NJ0GtAeaORGSRO4+iGbj0CJGxCTQsXYiPOlQ0d0jaAIupU7Um2cxQStuEEGazZ88eOnbsiKenJzqdjvXr16da7syZM3Tq1AkXFxccHR2pVasWgYGBad53ypQp6HQ6dDodlpaWFCtWjEGDBvHgwQOTxl+/fn2Cg4NxST7lUyY0bdoUnU7Hzz//bHR83rx5+Pj4mCBC88p0onf48GHWrl1L8eLFad26NV27djXahBBZJzougUHLjnIzNJqSbo5880Z1rCwz/THOGqGh2pYRy5Zp/fPs7bWtalVYvjxr4xNCpCoiIoJq1arxzTffpFnm0qVLNGzYkPLly7N7925OnDjBhx9+aJh9Iy2VKlUiODiYwMBAFi9ezJYtWxg6dKhJ47exscHd3T3jU0qlws7OjkmTJhEXF2fCyLKHTP+FcHV15ZVXXqF169Z4enri4uJitAkzUQoWt4e/pkDUQ3NHI7KAUopxa04QcD0EF3trfuxTCxd7M6xh/OWXxtsXX8CECfDaa9C27dOvnztXmzC5XTv45Rdta9NGG4X7+edZH78Qwkjbtm2ZPn06XdLpevHBBx/Qrl07Zs2ahZ+fH6VKlaJTp04UfnKFnCdYWVnh7u6Ol5cXLVu25NVXX2X79u1GZX744QcqVKiAnZ0d5cuX59tvvzU6f+jQIfz8/LCzs6NmzZr4P9F9ZPfu3eh0OkJCQgC4du0aHTt2JH/+/Dg6OlKpUiU2b96cbpw9evQgJCSEhQsXplvuu+++o1SpUtjY2FCuXDmWP/EP6oULF2jcuDF2dnZUrFgxxWsFuH79Ot27d8fV1ZUCBQrw8ssvc/XqVaPXU7t2bRwdHXF1daVBgwZcu3Yt3bjSk+lRt4sXL37mJ0vLN998w+zZs7l16xbVqlXjq6++onbt2mmWnzdvHt999x2BgYEUKlSIbt26MXPmzKf+Z5GrBf4L1/6B4ACtj57Idb7eeZGNx29iZaHjuzer41PITFPnPJmMWViAm5s2iXJG1qX86iv47jvo3TvpWKdOUKkSTJkCo0aZNFwhzC42Iv3zlrZg+fjPcXws6NOpVdJZgPXjGS6UgrhUFiow8bRaer2eTZs2MW7cOFq3bo2/vz8lSpRg4sSJmep3dvXqVbZu3YqNTdIUUCtWrOCjjz7i66+/xs/PD39/fwYOHIijoyN9+vQhPDycDh060KpVK3766SeuXLnCyJEj032eYcOGERsby549e3B0dOT06dM4OaU/I4GzszMffPAB06ZNo0+fPjimMiht3bp1jBw5knnz5tGyZUv++OMP+vXrR9GiRWnWrBl6vZ6uXbtSpEgRDh48SGhoKO+++67RPeLi4mjdujX16tVj7969WFlZMX36dNq0acOJEyewsLCgc+fODBw4kFWrVhEbG8uhQ4eeq7YSZWY///yzsrGxUT/++KM6deqUGjhwoHJ1dVW3b99OtfyKFSuUra2tWrFihbpy5YraunWr8vDwUKNGjcrQ84WGhipAhYaGmvJlmN+2D5Wa7KzUb4PMHYnIAptO3FTe4/9Q3uP/UCsPXjPtzcPDE3vKaftZzdZWqQsXUh4/f147J0RuM9k5/e3k2qSyWz9Iv+z8Jkllw++mXuY5AGrdunVGx4KDgxWgHBwc1Ny5c5W/v7+aOXOm0ul0avfu3Wm/7MmTlYWFhXJ0dFR2dnYKUICaO3euoUypUqXUypUrja77+OOPVb169ZRSSs2fP18VLFhQRUVFGc5/9913ClD+/v5KKaV27dqlAPXw4UOllFJVqlRRU6ZMyfBrbtKkiRo5cqSKjo5W3t7eatq0aUoppT7//HPl7e1tKFe/fn01cOBAo2tfffVV1a5dO6WUUlu3blVWVlYqKCjIcP7PP/80+p4uX75clStXTun1ekOZmJgYZW9vr7Zu3aru37+vgHS/r5mV6abb27dv06tXLzw9PbGyssLS0tJoy6y5c+cycOBA+vXrR8WKFfn+++9xcHDgxx9/TLX8/v37adCgAT179sTHx4eXXnqJHj16cOjQoUw/d64SeFD7WqKxeeMQJvffjVBG/xIAQP8GJehRu7h5A0ru+nVty4zSpbXm2ietXg1lypgmLiGEyej1egBefvllRo0aha+vLxMmTKBDhw58//336V5brlw5AgICOHz4MOPHj6d169YMHz4c0PoGXrp0ibfeegsnJyfDNn36dC5dugRoA0CqVq1q1GJXr169dJ9zxIgRTJ8+nQYNGjB58mROnDiRoddpa2vLtGnT+Oyzz7h3716K82fOnKFBgwZGxxo0aMCZM2cM54sVK4anp2easR4/fpyLFy+SL18+w+stUKAA0dHRXLp0iQIFCtC3b19at25Nx44d+eKLLwgODs5Q/GnJdNNt3759CQwM5MMPP8TDw+O5qhNjY2M5evQoE5M191hYWNCyZUsOHDiQ6jX169fnp59+4tChQ9SuXZvLly+zefNmevXqlWr5mJgYYmJiDI/DwsKeOd5sKy4abj6eu6x4XfPGIkzqdlg0A5YdJjpOT9Nybrzfrry5Q4L4eG2E7ZdfavPugTYP3/DhMHlyygmQnzR1qtafb88eSPyluW8f7NiRegIoRE73/s30z1vaJu03/wiaptMFQpesfsah4NPvbQKFChXCysqKihWNR/hXqFCBf/75J91rbWxsKF26NAD/+9//aN++PVOnTuXjjz8m/PHvj4ULF1KnTh2j656l4ijRgAEDaN26NZs2bWLbtm3MnDmTOXPmGBLM9Lz55pt89tlnTJ8+PUtG3IaHh1OjRg1WrFiR4pybmxugdZEbMWIEW7ZsYfXq1UyaNInt27dTt+6z/X3PdKL3zz//sHfvXnx9fZ/pCZO7d+8eCQkJFClSxOh4kSJFDKtuPKlnz57cu3ePhg0bopQiPj6eIUOG8P7776dafubMmUydOvW5Y83WggMgIRYcCkEBWT80t4iKTWDgsiPcDouhTGEnvuzhlz1G2A4fDmvXwqxZkPjf6oEDWv+6+/e1/nfpeeUVOHhQ6+uXOI1DhQpw6FDKyZiFyA0y02fOygbI4DKGOt0LWebSxsaGWrVqce7cOaPj58+fx9vbO1P3mjRpEs2bN2fo0KF4enri6enJ5cuXeeON1FdyqlChAsuXLyc6OtpQq/fvv/8+9XmKFSvGkCFDGDJkCBMnTmThwoUZSvQsLCyYOXMmXbt2TTE6uEKFCuzbt48+ffoYju3bt8+QAFeoUIHr168THByMh4dHqrFWr16d1atXU7hw4XQXbfDz88PPz4+JEydSr149Vq5c+cyJXqb76FWoUEEdO3bMJO3GQUFBClD79+83Oj527FhVu3btVK/ZtWuXKlKkiFq4cKE6ceKEWrt2rSpWrJihTf1J0dHRKjQ01LBdv3499/XR2/u51i9jVU9zRyJMJCFBr97+6ajyHv+H8pu2TV27F5F1T5bZPnrOzkpt3pzy+KZN2jkhRI7y6NEj5e/vr/z9/Q196Pz9/dW1a0n9gdeuXausra3VggUL1IULF9RXX32lLC0t1d69e9O87+TJk1W1atVSHK9du7YaNmyYUkqphQsXKnt7e/XFF1+oc+fOqRMnTqgff/xRzZkzxxBboUKF1JtvvqlOnTqlNm3apEqXLp1uH72RI0eqLVu2qMuXL6ujR4+qOnXqqO7du6cZZ2IfveQaNWqk7OzsjProrVu3TllbW6tvv/1WnT9/Xs2ZM0dZWlqqXbt2KaWUSkhIUBUrVlStWrVSAQEBas+ePapGjRpGffQiIiJUmTJlVNOmTdWePXvU5cuX1a5du9Tw4cPV9evX1eXLl9WECRPU/v371dWrV9XWrVtVwYIF1bfffptm/E+T6URv69at6qWXXlJXrlx55idNFBMToywtLVN0/Ozdu7fq1KlTqtc0bNhQjRkzxujY8uXLlb29vUpISHjqc+bKwRgrX9cSvX++MHckwkTmbDunvMf/oUq/v0kdvHw/a58ss4mem5tSp0+nPH76tFKFCqV+TfLPW2ho+psQ4oVKTJSe3Pr06WNUbtGiRap06dLKzs5OVatWTa1fvz7d+6aV6K1atUrZ2tqqwMBApZQ2yNLX11fZ2Nio/Pnzq8aNG6u1a5MGqBw4cEBVq1ZN2djYKF9fX/Xbb7+lm+i98847qlSpUsrW1la5ubmpXr16qXv37qUZZ2qJ3v79+xVglOgppdS3336rSpYsqaytrVXZsmXVsmXLjM6fO3dONWzYUNnY2KiyZcuqLVu2pBjgEhwcrHr37q0KFSqkbG1tVcmSJdXAgQNVaGiounXrlurcubPy8PBQNjY2ytvbW3300UcZym/SkqG1bvPnz2/UFy8iIoL4+HgcHBywfqI/TmZnvK5Tpw61a9fmq6++ArROn8WLF+edd95hwoQJKcrXqFGDli1b8umnnxqOrVq1irfeeotHjx49tV0/V651e/lvuPI3VO4GRbLBKgniuWwICGLkzwEAzO5WlVdrFsvaJ8zsWrfTpsHZs7B4Mdg+7lsUEwNvvaUNppg8OeU1lpYQHAyFC2vTsaTWt1cp7XhCwvO9HiGEEAYZ6qM3b968LAtg9OjR9OnTh5o1a1K7dm3mzZtHREQE/fr1A6B37954eXkxc+ZMADp27MjcuXPx8/OjTp06XLx4kQ8//JCOHTs+V+fNHK1kE20TOZ5/4EPGrtFGiA1uXDLrkzzQkrXEQRC2tumXBW2t2x07oGhRqFZNO3b8OMTGQosWkHyFnLVrta87d0KBAtr+rl2mi10IIUS6MpTo9enTh2XLlvHaa69hm5E/BJnw2muvcffuXT766CNu3bqFr68vW7ZsMQzQCAwMxMIiqQP6pEmT0Ol0TJo0iaCgINzc3OjYsSOffPKJSeMS4kW7GRLFoOVHiY3X07JCYca1eUEjbK2s4NVXM17e1VUbUJFcsackpE2S/SNSooRW/slaPaUyP1WLEEKIdGWo6Ra0oc7BwcFPXe4ku8t1Tbf7v4LoMKjaHQrJHGQ5VWRsPN2+O8Dp4DDKu+djzdD6ONlmelB8zpC8GTe5+/e1Y9J0K4QQJpPhvyQZzAfFi3ZkMTy4BEVrSqKXQ+n1ilGrAzgdHEYhJxt+6FPzxSZ58fGwbp2236WLVsOXlRL74j0pPBzy8jKGQgiRBTL1G/251loTphd+V0vyAIrWMm8s4pl9tu0cW0/dxsbSgvm9alA0v8OLDSAmBrp31/bDw5+e6N2+DWPGaP307tzRErfk0qqRGz1a+6rTwYcfgoOD8TUHD4IJ5ucUQgiRJFOJXosWLbB6yh+BY8eOPVdAIhOuP172zK0COBQwbyzimaw9doNvd2vJ+qfdqlDD2ww/RwuLpD50FhmYkLlvXwgM1JI1D4/Ua+dS4++vfVUK/vsPki1sjo2NNrBjzJhMhS6EECJ9mUr0WrdujVPiNAzC/AIfLxNXvE765US2dPTaAyb89h8Aw5qVootfUfMEYm8Pu3dnvPw//8DevZmvfUscbduvH3zxBeSGPrJCCJHNZSrRGzt2bI4fjJGrJNboFZP1bXOaGw8jGbTsKLEJelpXKsJ7rcqZO6SMK1YsZXNtZixebLpYhBBCpCvDiZ70z8tm4qLgZoC2LzV6OUp4TDwDlh7hfkQsFT2c+fw1XywsctDna948mDAB5s+HZ130+8gRbe6+wEBt/r3kEufeE0II8dxk1G1OddMf9HHgVATylzB3NCKDEvSKkav8OXvrEW75bPmhT00cbMw8jUpERFLCdvVq6itj5M9v3BcvIgJKldIGVDyxOg5PWx3n55+hd29o3Rq2bYOXXoLz57VBHl26PM8rEUII8YQM/4W5cuUKbm5uWRmLyAw7V6j5Ftg4ZrwzvDC7WVvOsuPsHWytLFjYuyaervbmDklz71765025Os6MGfD55zBsGOTLp/XXK1ECBg/WBncIIYQwmQxPmJxb5LoJk0WO8cuR64x7vLzZVz386FjN08wRPZbZtW6fl6MjnDql1SIWLKgNBKlSBc6cgebNtcmUhRBCmEQunXpfiOzl4OX7fLBOG2E7skWZ7JPkPYuwsNSP63TaWrnJp01JTf788OiRtu/lBSdPaoleSAhERpo0VCGEyOsk0cuJQgLhzO/g3QA8fc0djXiKwPuRDPnpKHEJivZVPRjZIoevYOLqmn53gaJFtbn2Jk9OfV6+xo1h+3YtuXv1VRg5Enbu1I61aJFVUQshRJ4kiV5OdGknbH0ffBpB3z/MHY1IR1h0HG8tPczDyDiqFnXhs27VctYI29QsWQIffKAlc7Vra8cOHYKlS2HSJLh7Fz77TKvde//9lNd//TVER2v7H3ygDebYvx9eeUW7XgghhMk8c6IXGRlJYGAgsU9MjVC1atXnDko8ReC/2tfiMn9edhafoGf4Sn8u3AmniLMtC3vXxN7G0txhPb+lS2HOnKRl0wA6dtRq6ObP15ZGK14cPvkk9USvQLLVPywstKlahBBCZIlMJ3p3796lX79+/Pnnn6meT0hrnUthOomJnkyUnK3N2HyWv8/fxc7agh9616KIs525QzKN/fvh++9THvfzgwOPV2tp2FCbIy81z9vHTwghRIZlYGFLY++++y4hISEcPHgQe3t7tmzZwtKlSylTpgwbN27MihhFco9uw8MrgA6K1TJ3NCINKw8G8uO+KwDM7e5LlaIuZo7IhIoVg0WLUh5ftEg7B3D/vjboIjWurtq5JzdXV205Nm9vrX+fXp9Vr0AIIfKMTNfo7dy5kw0bNlCzZk0sLCzw9vamVatWODs7M3PmTNq3b58VcYpE1x/X5hWpBHa5KHnIRfZfvMdHG04CMOalsrSrksvmhvvsM20QxZ9/Qq3H/2wcOQJnz8KaNdrjw4fhtddSv/55+/gJIYTIsEwnehEREYb1bvPnz8/du3cpW7YsVapU4dixYyYPUDwhMHF9W1n2LDu6ci+CoSuOEa9XvOzrybBmpc0dkul16qQldfPnaytaALRtC+vXJ62wMXRo2tc/bx8/IYQQGZbpRK9cuXKcO3cOHx8fqlWrxvz58/Hx8eH777/HQ2a1z3qBj/tAyUCMbCc0Mo63lhwmNCoO32KufPpK1dy7RnSJEvC//z3btc/bx08IIUSGZTrRGzlyJMGPZ66fPHkybdq0YcWKFdjY2LBkyRJTxyeSUwoKlYXw21Kjl83EJegZtvIYl+9F4Olix4LeNbCzzgUjbFOzZ0/65xs3Tv98Yh+/JxPFjPbxE0IIkWHPvQRaZGQkZ8+epXjx4hQqVMhUcWWZXLMEmlKyxm028tGGkyw7cA0HG0vWDKlPRc8c9N6Ki4MFC7T9QYO0ee3Sk9okyMnfi08beb9xo9bHr3z51Pv4degA330HFy7A3LkZfx1CCCFSyHSiN23aNMaMGYODg4PR8aioKGbPns1HH31k0gBNLdckeiLbWHbgKh9tOIVOB9+/WYPWldzNHVLWCg01fhwXB/7+8OGHWr+6jKxuceWKcR+/cuVg8OCkPn5CCCFMItOJnqWlJcHBwYYBGYnu379P4cKFs/08ejk60bu0E1y9oUBJqc3LJvZeuEvfxYdJ0CvGtynP0KalzB2S+fz9N4weDUePmjsSIYQQj2V6Hj2lVKodzI8fP06B5DPeC9PS6+GXvvBVdbh1wtzRCODinXDeXnGMBL3ilepFGdKkpLlDejYJCbB7t7Y9zz9qRYrAuXMZK7t3L7z5JtSvD0FB2rHly+Gff579+YUQQqSQ4cEY+fPnR6fTodPpKFu2rFGyl5CQQHh4OEOGDMmSIAVw9wzEhIK1IxSuZO5o8ryHEbG8tfQwj6LjqemdnxldK+fcEbbR0dCsmbYfHg6OjumXP/HEPxpKQXCwNrjC1/fpz/fbb9CrF7zxBhw7BjEx2vHQUJgxAzZvzvRLEEIIkboMJ3rz5s1DKUX//v2ZOnUqLi5Jk/Xa2Njg4+NDvXr1siRIQdKyZ0VrguUzL1EsTCA2Xs/QFUe5dj+Sovntmd+rBrZWOXiErU4HFSsm7T+Nr69W7sleH3Xrwo8/Pv366dO16VV694aff0463qCBdk4IIYTJZDhj6NOnDwAlSpSgfv36WD9tZJ4wrcREr7gk0+aklGLyxpP8e/kBjjaWLOpTi4JOtuYO6/k4OMCpUxkvf+WK8WMLC3BzA7sMruV77lzqU7C4uEBISMbjEEII8VSZrhpq0qSJYT86OprY2Fij8zlugENOkbj0WXGZP8+cFu+7yqpD19Hp4KuefpRzz2fukF48b+/nu97dHS5eTDnC9p9/oGQO7ecohBDZVKYHY0RGRvLOO+9QuHBhHB0dyZ8/v9EmskDYTQgJBJ0FFK1l7mjyrF3n7jB902kAPmhXgebli5g5IjP6+29t2bLSpbWtUydtgEVGDBwII0fCwYNaE/DNm7BiBYwZk/7SaUIIITIt04ne2LFj2blzJ9999x22trb88MMPTJ06FU9PT5YtW5YVMYrEZtsilcE2D9YgZQPnbz9i+Ep/9Apeq1mMtxqWMHdIphMZCZUqaVtk5NPL//QTtGypNfmOGKFt9vba/HkrVz79+gkToGdPrXx4uNaMO2CANo/e8OHP/3qEEEIYZHoeveLFi7Ns2TKaNm2Ks7Mzx44do3Tp0ixfvpxVq1axOZuPmMuR8+gdWgh/TQXfHtButrmjyXPuh8fQ+dt9XH8QRZ0SBVj+Vh1srDL9P1L2FREBTk7afkZG3VaooK2gMWqU8fG5c2HhQjhzJu1rExJg3z6oWlVLFC9e1J6zYsWkGIQQQphMphM9JycnTp8+TfHixSlatChr166ldu3aXLlyhSpVqhAeHp5VsZpEjkz0APQJEBsBdjko5lwgJj6BN384yOGrD/Eu6MD6txuQ39HG3GGZVmYTPVtbbfBG6dLGxy9ehMqVtela0mNnpyWDJXJRragQQmRTma6WKFmyJFcej7orX748v/zyCwC///47rq6uJg1OJGNhKUneC6aUYtK6kxy++pB8tlYs6lMz9yV5z6JYMdixI+Xxv/7Szj1N5cpw+bLp4xJCCJFCpkfd9uvXj+PHj9OkSRMmTJhAx44d+frrr4mLi2OuLEBuehH3AB04FjR3JHnOwr2X+fXoDSx08PUb1SldWPpHAvDee1q/vIAAbWUL0JpjlyyBL754+vXTp2sDLz7+GGrUSFmDmJNq2oUQIpvLdNPtk65du8bRo0cpXbo0VatWNVVcWSbHNd3u/hR2z4C6b0ObmeaOJs/46/RtBi4/glIwpWNF+jbIxc2MmW26BVi3DubMSeqPV6ECjB0LL7/89GstkjUkJJ+gWSntcTZfL1sIIXKS515iwdvbG+/nnVdLpC1x/rwCMr/Yi3ImOIyRP/ujFLxRpzh96vuYO6TsIz5eW6asf/9nX5d21y7TxiSEECJNmUr09Ho9S5YsYe3atVy9ehWdTkeJEiXo1q0bvXr1yrlrfWZX+gS4fljbLyYTJb8Idx/FMGDpESJiE2hQuiBTOlWS93VyVlYwa5a2fNmzSjbpuhBCiKyV4URPKUWnTp3YvHkz1apVo0qVKiilOHPmDH379mXt2rWsX78+C0PNg26fgthHYJMPilQydzS5XnRcAoOXHyEoJIqShRz5tmcNrC1z0TQqptKihTZh8pMrW2TUiROpH9fptBG5xYtrI3uFEEI8twwnekuWLGHPnj3s2LGDZs2aGZ3buXMnnTt3ZtmyZfR+nv/0hbHrB7WvxWppo25FllFKMXHtfxwLDMHZzoof+tTExUHWc05V27bapMf//Zf6YIpOndK/3tfXuG/ek6yt4bXXYP78jK+fK4QQIlUZHozx0ksv0bx5cyZMmJDq+RkzZvD333+zdetWkwZoajlqMMaat+DkGmj6PjQdb+5ocrVvdl1k9tZzWFroWNa/Ng1KFzJ3SC9OZgdjWKRTy5mRwRQbNsD48drgjdq1tWOHDmmDOyZP1voBTpigJXuffZbx1yGEECKFDNfonThxglmzZqV5vm3btnz55ZcmCUo8llijV7yueePI5bacDGb21nMATO1UKW8lec9Cr3++6z/5RJuGpXXrpGNVqkDRovDhh1rS5+ioTeMiiZ4QQjyXDHdAevDgAUWKpL2Ie5EiRXj48KFJghJA+F14FAw6Syha09zR5Fong0IZtfo4AH3r+/BmXRlBnuX++w9SG6nv7a2dA615Nzj4hYYlhBC5UYZr9BISErCySru4paUl8fHxJglKAE5uMOE63D0DNhmY10xk2p2waAYuO0JUXAKNy7oxqX0Fc4dkHtbWWpNp4n5G7NihbXfupKzh+/HH9K8tXx7+9z9YsABsHq80EhenHStfXnscFATp/GMphBAiYzI16rZv377YpjEaLiYmxmRBicdsHMCrhrmjyJWi4xIYuPwowaHRlC7sxNc9/bDKqyNsbWxgypSMl586FaZNg5o1wcMj/YEVqfnmG23ARtGikDjJ+n//aX37/vhDe3z5Mrz9dubuK4QQIoUMD8bo169fhm64ePHi5wooq+WYwRjxsWAl66pmBaUUw1f588eJYFwdrNkwrAHeBaXWNMM8PLS59Hr1evZ7PHoEK1bA+fPa43LloGdPyCfLzAkhhCk99xJoOU2OSPSiw+CzMuBeBXpvkKZbE/virwt8/td5rCx0/DSgDnVL5vF1hPV646XM0htVC1CwoDZgolSprI9NCCHEc3nuJdBEFrhxGOKjIfy2JHkm9seJm3z+l1aL9EmXypLkAURFQeXK2n5GplcZMABWrtRGyD6LjRtTP544YXLp0lAiF68tLIQQL5AketmRYVqVeuaNI5c5fj2E937RRtgOaFiC12oVN3NE2Uihp0wpM3p00r5erw2k+OsvrY/dkwM45s5N/16dO2tJ3ZONCYnHdDpo2BDWr4f8+TP6CoQQQqQij/Y+z+YC/9W+yvq2JnMrVBthGxOvp1k5Nya2y6MjbFPj6Ah372pbWrV5/v5J2/Hj2vQnFhZw8qTxuYCApz/f9u1Qq5b2NTRU27Zvhzp1tMEYe/bA/fswZowpX6UQQuRJ2aJG75tvvmH27NncunWLatWq8dVXX1E7ccb8VISEhPDBBx+wdu1aHjx4gLe3N/PmzaNdu3YvMOoskhAPN45o+zJRsklExsYzYNlh7jyKoVyRfHzZww9Li0yOFM3rdu0y3b1GjtRqBOvXTzrWooXWbDtoEJw6BfPmQf/+pntOIYTIo8ye6K1evZrRo0fz/fffU6dOHebNm0fr1q05d+4chQsXTlE+NjaWVq1aUbhwYdasWYOXlxfXrl3D1dX1xQefFW7/B3ERYOsCblLr9Lz0esV7vxznZFAYBR1t+KFPTfLZyRq2z8TSUpvEOJXPZaZcugSpDYRydtamVQEoUwbu3Xu+5xFCCGH+ptu5c+cycOBA+vXrR8WKFfn+++9xcHDgxzQmXf3xxx958OAB69evp0GDBvj4+NCkSROqVav2giPPIoGP++cVq/300Y/iqT7/6zx/nryFjaUF3/eqQbECDuYOKfuJioKmTbUtKirtcqYaoF+jhrbO7d27Scfu3oVx47QmXYALF6BYMdM8nxBC5GFmzSRiY2M5evQoLVu2NByzsLCgZcuWHDhwINVrNm7cSL169Rg2bBhFihShcuXKzJgxg4Q0FlKPiYkhLCzMaMvWrj/un1dc+uc9rw0BQXy18yIAM7pWoZZPATNHlE3p9fD339r2vOvYZsSiRXDlijZhcunS2la0KFy9Cj/8oJUJD4dJk7I+FiGEyOXM2nR77949EhISUqyhW6RIEc6ePZvqNZcvX2bnzp288cYbbN68mYsXL/L2228TFxfH5MRlnJKZOXMmU6dOzZL4s0Tb2VD5FShc0dyR5GjHAh8yds0JAIY0KUW3GkXNHFEu8cMP4OSUfpkRI9I/X64cnD4N27YZT5jcqlVSLXbnzs8dqhBCCDNPmHzz5k28vLzYv38/9eolTSUybtw4/v77bw4ePJjimrJlyxIdHc2VK1ewtLQEtObf2bNnE5zKIugxMTFGy7OFhYVRrFix7D1hsnguQSFRvPz1Pu6Fx9CqYhHmv1kDCxl8kbaIiKTkLb159CwstJq3x5+7VOl0Sf3sMiskBH76Cd5559muF0IIkYJZa/QKFSqEpaUlt2/fNjp++/Zt3N3dU73Gw8MDa2trQ5IHUKFCBW7dukVsbCw2NsbLhtna2qa5Pq/IfSJi4hmw9Aj3wmOo4OHMvNd8JckzpSNHnn8wxpN27NCac9etAwcHSfSEEMKEzNpHz8bGhho1arBjxw7DMb1ez44dO4xq+JJr0KABFy9eRJ+sL9H58+fx8PBIkeTlONsnw0/d4JIJp7LIQ/R6xburAzgTHEYhJ1t+6FMTR1uzDyzPPXQmTJivX4dp07QVMF56STu2bh3cumW65xBCCGH+UbejR49m4cKFLF26lDNnzjB06FAiIiLo168fAL1792bixImG8kOHDuXBgweMHDmS8+fPs2nTJmbMmMGwYcPM9RJM58J2uLgdYrL5gJFsatbWc2w/fRsbKwsW9K6Bl6u9uUPKXZ63l0dcHPz6K7RurfXJCwiA2bO1JuFJk6BNm5SrbAghhHguZq/ueO2117h79y4fffQRt27dwtfXly1bthgGaAQGBmKRbJqRYsWKsXXrVkaNGkXVqlXx8vJi5MiRjB8/3lwvwTSiQuDOaW2/mEyUnFlrjt7g+78vATC7W1WqF5els0xu8uSnD8RIj5cXlC8Pb74JP/+ctLxZjx6miU8IIUQKZh2MYQ5hYWG4uLhkv8EYF/6CFa9A/hIwMsDc0eQoh68+oOfCf4lLUAxvXpr3Xipn7pBylowOxnheBQpAlSpaovfaa0mTJltba8uqVZSR5kIIYWpmb7oVjwU+njdQlj3LlOsPIhm8/ChxCYq2ld0Z1bKsuUMSabl5U1vibNUqcHeHV17R+uWZsu+fEEIII5LoZRfXE1fEkImSM+pRdBwDlh7hQUQslb2cmdO9moywzc7s7OCNN2DnTvjvP6hQQZtzLz4ePvkEtm+HNCY+F0II8Wwk0csOEuLgxhFtv3jqo42FsQS9YuTPAZy7/YjC+Wz5oXctHGzM3uVUZFSpUjB9Oly7Bps2QUwMdOgAT0yeLoQQ4vlIopcd3DoB8VFg5wqFpOkxI2ZuPsPOs3ewtbLghz41cXexM3dIeUcaq9YAsHVr5u5lYQFt28KaNXDjBrz//vPFJoQQwohUgWQHrj7Q6WuIDU9aAkqk6edDgfzwzxUA5nSvRtWiruYNKKezsoK3307af5rq1bVpUZJPaRQTA++9py2RFh39bHG4ucHo0c92rRBCiFTJqFuRoxy4dJ9eiw4Sr1eMalmWkS3LmDukvOeXX2DoUKhTBxYvhuBg6NkT9HpYvhxq1TJ3hEIIIR6T6iORY1y7H8HQFUeJ1ys6VvNkRIvS5g4pb+reXZsOJS4OKlWCevWgSRM4dkySPCGEyGYk0TO3kED4bSAcW2buSLK1sOg4+i85TEhkHNWKujC7W1V0Mi2HaSgFd+9qW2Yq+GNjtVGyCQng4aGNqhVCCJGtSKJnbtf2w3+/wLHl5o4k24pP0PPOSn8u3Y3A3dmOhb1rYmdtae6wco/ISChcWNsiI59e/ueftYmPXVzg/Hlt1OyCBdCoEVy+nPXxCiGEyDAZjGFuhomSZf68tEzfdIY95+9ib23JD31qUthZao7M6q234LPPtH56AK1aafPiDR4Mvr4Q9pS1mpXSRtnu2gV37mh9+5JbuzZLwhZCiLxIEj1zC0ycKFlWxEjNT/9eY8n+qwB8/povlb1czBtQbuTomLkm22PHoNwTy8zlz68N0liegZrpd9+F+fOhWTNt3jxpghdCiCwjiZ45RT2Eu2e0fVkRI4V/Ltxj8sZTAIxtXY42ld3NHJEAUiZ5yfXq9fTrly/Xau3atTNdTEIIIVIliZ45XT+kfS1YGpzczBtLNnP5bjhvrzhKgl7Rxc+Lt5uWMndIIrkbN2DjRggM1AZlJDd3bvrXurhAyZJZF5sQQggDSfTMKfBf7as02xoJjYzjraVHCIuOp3pxV2Z2rSIjbLNSdHRSTdzy5U8fPbtjB3TqpCVrZ89C5cpw9arW/Fu9+tOfb8oUmDoVfvwR7O2fN3ohhBDpkFG35nT9cf88GYhhEJeg5+2VR7lyLwIvV3vm95IRtlkuIUEbHLFmjbb/NBMnwpgx2gAMOzv47Te4fl2bS+/VV59+fffu8PChNsq3ShUtOUy+CSGEMBmp0TOnip3BPj94NzB3JNmCUoopG0+x7+J9HG20EbZu+WzNHZZ40pkzsGqVtm9lBVFR4OQE06bByy8njcZNS58+cPQovPmmDMYQQogsJomeOdUZpG0CgGUHrrHiYCA6HXzxuh8VPGSJumzJ0TGpX56HB1y6pK2QAXDv3tOv37QJtm6Fhg2zLkYhhBCAJHoim/j7/F2m/q6NsJ3YtjwtKxYxc0QiTXXrwj//QIUK2sjZ997TmnHXrtXOPU2xYiDrTAshxAshffTM5egS+G+NNsVKHnf02gOG/nQUvYJXaxRlYCMZkZmtzZ0LdR73K506FVq0gNWrwccHFi16+vVz5sC4cdoADiGEEFlKp1RmZkrN+cLCwnBxcSE0NBRnc9UqKAWzS0PkPei/LU8PxjgZFMr/27vzuKjq/fHjr2EHQRZRFmVx31g0UeR6zVIStUzTck1xa7mhZpiZ3Zu4XbfUa5blNyvN0rJFzRa3n7ncTFFR3BIyXNAE3EFQWYbz++NcBkdQBphhhHk/H4/zmHM+c5b3OWeKt59zPp/P4I/2cTO3gM5NPfk4Ogx7G2l8UaVyctR37ACys9VHs6bk7q4OtVZQAE5OYGur//21a6Y9vhBCWBB5dGsO106rSZ61Pfi2MXc0ZpOcfpNhn8RzM7eADg09+GiYJHkWYfFic0cghBAWQxI9cyjqP8+3LdhYZqvSM1dyGPpxPNdv5RPq58anI9rjaCdJ3kPN0E6OT59+8PfR0ZWPRQghhEEk0TOH1L3qp79ldpR84fothi7fx5XsXFr61GbVyA4428tP8aF39iwEBMCQIWofeOWRlVXcACMr68HrSkMNIYQwGvnrag66jpItL9HLyLrDkOXxXMy8Q+O6tfh8dAdcnWzL3lCY39q16mgWixZBz54wapTa6tbKgDZd7u6QlqYmiG5upfedpyhquSGdNgshhDCIJHpVLecqXPlDnfezrEYYV7NzGfpxPKnXbuHv4cTqMR3xdLbMR9fV0nPPqdNff8HKlfDaa/DSS+rwaaNHQ9Om99/2l1/Aw0Od37GjSsIVQgghrW6rPoCkn+GrweDZDMYeqPrjm0nmrXwGLd/HybQsfFwd+PqlCPw8nMwdloDKtbrdtUsdu3b3brWzZHd3k4QohBCiYqRGr6rZ2EHA38GrtbkjqTLZuQVEr9jPybQsPJ3tWT0mXJK8h4m1NTz7bPG8Ie7cUcfG/fRTiI9Xa/qcyrinV66oSWVAQHHZiROwYIFa3rev+v6fEEIIo5EaPWFSt/O0RK/Yz/4z13BzsmXtixE093Yxd1iiouLj1U6Rv/5abYU7ahQMHWpYTd7gweDrq3aYDHDpErRooZY1bgybNqn7HjbMtOcghBAWRGr0hMnkFmh56YsE9p+5hou9DZ+PCpckrzpr3VpNzoYMUR/ZhoaWb/t9+9R3+4qsWqW+t5eYCDY2as3e0qWS6AkhhBFJjV5Vun4Orp6CBu3BwbVqj13F8rWFxKw+xNbfM3C0tebz0R0IC/Qwd1iiMqys1Pf3bGxKbzVb5H4jWzg6QlJS8aPbXr0gKAjmz1eX//gDIiLg6lXjxi2EEBZMavSq0on18P/ioMVTMGi1uaMxGW2hwsSvj7D19wzsbKz4ODpMkryHmaGNMVasqNxxateGGzeKE739+9XWukU0GsjNrdwxhBBC6JFErypZQP95hYUKb607xsYjF7Gx0rDs+Ufo1MTT3GEJY6jsiBYdO8KSJbB8OaxbBzdvQteuxd//8Qf4+VXuGEIIIfRIoldVFKV46DO/mpnoKYrCjB9/Z+3B81hp4N1BbenawsvcYYmyODmp794VzZvKzJnQrRt88QUUFMBbb+k34vjqK+jSxXTHF0IICySJXlW5cgpuXwMbB/Ap50vs1YCiKMzfkszK384C8M6zoTwZ4mPeoIRhNBqoW9f0xwkJgZMnYc8e8PaG8Hs6DB80SG3wIYQQwmgMGLtIGMVfCeqnb1u1L70a5v1f/uTDnSkAzOobRP92DcwckXgoeXpCZia0aVPyuyeeUDteFkIIYTSS6FWVtET106eNOaMwiY//e5qF29Rh3f71ZEue7xhQxhbioZKbCzEx6lQVjSFGjlSTvXvdvKl+J4QQwmgk0asqFxPVT9825ozC6NbEpzLrp5MAxD7RjDGdG5k5IlFuBQXwwQfqVFBg+uMpSunds1y4AK41u9shIYSoavKOXlVQFCjMB41VjarRW3/4Av/ccAyAl7s0ZlzXJmaOSJhMbKzh6y5aVHp527ZqgqfRqI0ybO76349WC2fOQI8elYtTCCGEHkn0qoJGAy/8Ank5YONo7miMYtOxNCZ+fQRFgRF/C2Ryj+ZoHtSJrqjeDh/WXz50SK39a95cXf7jD3Wc3Hbt7r+Pvn3Vz8REiIoq7rsPwM4OAgOhf38jBi2EEEISvapkd5+OaKuZHUmXGP/VYQoVGBDWgKlPtZIkr6bbsaN4ftEicHGBzz4r7h7l+nX1/brOne+/j7g4teYuMBC6dwcfaZUthBCmJkOgVYWbGeDkAda2VXM8E/rtzyuMWHmAvIJCeof6snhgG6ytJMmr1gwdGaNI/fqwdWvJrlCOH1cTuIsXH7y9g4PazUrDhhWPWQghhEGkMUZV+GYEzK4PyZvMHUmlJJy7xphVB8krKOSJVl4sGhAqSZ4lysqCy5dLll++rLacLUtQEJw+bfy4hBBClCCJnqkVFkL6UdDmglv17Xbk2IVMRnx6gFt5Wjo39eT9IW2xtZafj0V65hn1Me26dWpL2QsX4Lvv1HFr+/Ure/tZs+D11+HHHyEtTU0c756EEEIYjbyjZ2rXUiAvW22E4dnM3NFUSHL6TYZ9Gs/N3AI6NPTgo2Fh2NtYmzssYS7LlqmJ2pAhkJ+vltnYqIneO++UvX2vXurn00/rd7NS1O2KVmv8mIUQwkJJomdqRf3neQeDdfW73Geu5DD043hu3Mon1M+NT0e0x9FOkjyL5uSk9rn3zjuQoo6GQuPGZb/bV+Tuhh1CCCFMqvplHtWNbkSM6je+7YXrtxi6fB9XsnNp6VObVSM74GwvPxnxP2lp6vToo+DoeP+OkO/VpYvpYxNCCAFIomd61XREjIysOwxZHs/FzDs0rluLz0d3wNWp+rcaFkZw9SoMGKDWzGk0cOoUNGqkPrp1d4eFCw3bz61bkJoKeXn65SEhxo9ZCCEs1EPxNv3SpUsJDAzEwcGB8PBw9u/fb9B2X331FRqNhr5FHbE+bIoaYkC1GhHjanYuQz+OJ/XaLfw9nFg9piOezvbmDks8LF57DWxt1STNyam4fOBA2Ly57O0vX4annlL74mvdWh0x4+5JCCGE0Zg90Vu7di2xsbHExcVx6NAhQkNDiYqK4tKlSw/c7uzZs7z++ut0flAHreZ2/QzkZoGNA9RtYe5oDJJ5K5/nP9nPn5ey8XF1YPWYcLxdHcwdljAlKyv1cWqXLup8WbZuhXnzoEED/fKmTeHcubK3nzABbtyA+Hj1ke/mzWrny02bwsaNFTkDIYQQ92H2RG/RokW88MILjBw5klatWrFs2TKcnJz49NNP77uNVqtl6NChTJ8+nUaNGlVhtOXk0QheOwFDv6kWDTGycwuIXrGfk2lZeDrbs3pMOH4eTmVvKKo3R0fYuVOdHA0Yoi8nR78mr8i1a2BvQM3vL7+oo2uEhamJZUAAPP88zJ8Pc+aUN3ohhBAPYNZELy8vj4SEBCIjI3VlVlZWREZGsnfv3vtuN2PGDOrVq8fo0aPLPEZubi5ZWVl6U5XRaMC1ATR8tOqOWUG387SMWnmAxPM3cHOyZfWYcBrVdS57Q2F5OneGVauKlzUa9TWF+fPh8cfL3j4nB+rVU+fd3Ys7Xw4OVsfQFUIIYTRmrWa6cuUKWq0WLy8vvXIvLy+SkpJK3ebXX3/lk08+ITEx0aBjzJkzh+nTp1c21Bott0DLS18ksP/MNVzsbfh8VDjNvV3MHZZ4WM2fD926wcGDakOKN96AEyfUGr09e8revnlzSE5Wx7wNDYX/+z91ftkyGf9WCCGMzOyPbsvj5s2bDBs2jOXLl+Pp6WnQNlOmTCEzM1M3nT9/3sRR/o+iwPsdYPUAyC5luKiHRL62kHFrDrP7j8s42lqzYmR7ghu4mjssUZVycqBuXXXKySl7/aAg+OMP+PvfoU8fdZt+/eDwYbU/vbK8+qraLQtAXBxs2gT+/rBkCcyeXblzEUIIocesNXqenp5YW1uTkZGhV56RkYG3t3eJ9VNSUjh79iy9e/fWlRUWFgJgY2NDcnIyje/5Q2Nvb4+9Ie8NGdv1M3AlGa6fBUe3qj++AbSFChO/PsLW3zOws7Hi4+gwwgI9zB2WMIcrVwxfNzUV/Pzgn/8s/Tt//wdv//zzxfPt2qkNOJKS1O0M/AecEEIIw5i1Rs/Ozo527dqxfft2XVlhYSHbt28nIiKixPotWrTg2LFjJCYm6qann36axx9/nMTERPz8/Koy/Acr6j/PqzVYP3z9zxUWKry17hgbj1zE1lrDsucfoVMT+SNrkRwd4fhxdTKkMUbDhsXv1d3t6lX1u/JycoJHHpEkTwghTMDsTUFjY2OJjo4mLCyMDh06sHjxYnJychg5ciQAw4cPp379+syZMwcHBweCgoL0tndzcwMoUW52D/GIGIqiMOPH31l78DxWGnh3UFu6tvAqe0NRM1lZqf3ZGep+I2BkZ4ODAV3xKAp8+63a4fKlS2pDjrutW2d4LEIIIR7I7InewIEDuXz5MlOnTiU9PZ02bdqwefNmXQON1NRUrAzp2+th85COiKEoCvO3JLPyt7NoNLDguVB6BcsL8MIAsbHqp0YDb7+t38WKVqv2i9emTdn7mTBBbYDx+OPg5WXYsGlCCCEqRKMoimLuIKpSVlYWrq6uZGZmUrt2bdMcRFFgXiDcuQEv7nqokr33tp9i4bY/APj3M0EMDQ8wc0TC7PLyihtBvPUW2NmVvl5R1ym7dkFEhP56dnZqy9nXX1c7Pn4QDw/44gvo1avSoQshhHgws9fo1Ug3zqlJnrUd1Gtl7mh0Pv7vaV2S968nW0qSJ1T5+VDUBdGkSfdP9HbsUD9HjoR334WK/kPJ1VUdG1cIIYTJVcNnotVA0WPbeq3A5j5/NKvYmvhUZv10EoDYJ5oxprP8oRUVtHgxFBSULL92DQzpkHzaNDWxvH3b2JEJIYS4h9TomUL9dvDUYrB9OIYPW3/4Av/ccAyAl7s0ZlzXJmaOSFRrgwZB797wyiv65V9/rY5V+/PPD95+wAD48kt1dIzAQLC9p1W6jI4hhBBGI4meKbj5QdhIc0cBwKZjaUz8+giKAiP+FsjkHs3RyMvvojLi49Wxau/12GOl9613r+hoSEhQ+9OTxhhCCGFSkujVYDuSLjH+q8MUKjAgrAFTn2olSZ6ovNzc0h/d5ucb9jj2p59gyxZ1ZA0hhBAmJe/oGVvmX7AhBhJWmjWM3/68wktfJJCvVXg61Jc5/UKwspIkTxhBhw7w0Ucly5ctU0e6KIufX8UbcgghhCgXqdEztr8SIPELSD8K7UaYJYSEc9cYs+ogeQWFPNHKi4UDQrGWJE8Yy6xZEBkJR45At25q2fbtcOAAbN1a9vYLF8Ibb6iJYWCgSUMVQghLJ4mesRWNiGGmvvOOXchkxKcHuJWnpXNTT94f0hZba6m4FUbUqRPs3Qvz56sNMBwdISQEPvmk7D70QH0379YtaNxY7XT53sYY166ZJm4hhLBAkugZW1HXKmYY+iw5/SbDPo3nZm4BHRp68NGwMOxtrKs8DmEB2rSBNWsqtu3ixcaMRAghxANIomdMigJpR9R5n7ZVdth8bSFbTqQzbePv3LiVT6ifG5+OaI+jnSR5wkRSUmDFCjh9Wk3c6tWDTZvA37/scXOjo6skRCGEEJLoGVfWX3DrCljZgFc5BomvoEs37/Bl/HnW7D9HRlYuAC19arNqZAec7eXWChPZtQt69lQf4e7erb6zV6+e+s7eJ5/At9+Wvl1BgTomrr19cVlGhvquXk4OPP20tMQVQggjk2zAmIoe29ZtCbYOJjmEoigcSr3Bqr1n+flYGvladahiT2d7hnTwY3TnRrg62paxFyEq4c031eQuNhZcXIrLu3aF99+//3YvvKAOr/Z//6cu37wJ7dvDnTvg4wP/+Q98/72MgSuEEEYkiZ4xFTXEMMH7eXfytWxMvMiqfWc5/lfxMFPtAtwZHhFAzyAf7Gyk0YWoAI0GWrUqni/LsWOlv59Xrx5cuXL/7fbs0U8EV61Sa/hOnVLHv508Gd55RxI9IYQwIkn0jKluC2j+JDR81Gi7PH/tFl/En2PtgfPcuJUPgJ2NFX1CfYn+WyBB9V2NdixhoZyc4MQJw9d3c4O0NGjYUL/88GGoX//+2/31l36r3O3boX9/NckD9d29FSsMj0MIIUSZJNEzpuBn1amSCgsV9qRc4bPfzrE9KQNFfTpLfTdHhkUEMDDMD/dadpU+jhAVMmiQWvv2zTdqDWBhoVpb9/rrMHz4/bdzcNAfOWPfPrUG7+7vs7NNF7cQQlggSfQeIjfv5PNdwgVW7TvH6cs5uvLOTT0ZHhFI1xb1pONjYX6zZ0NMjDrChVarPvbVamHIEPjXv+6/XZs28PnnMGcO/Pe/akOMrl2Lv09JAV9fk4cvhBCWRKMoRfVFliErKwtXV1cyMzOpbcxhmC4lweWTUD8M3PzKtempjJus2nuOdYcukJOnBcDZ3oZn2zXg+Y4BNKnnbLw4hbjXrVtqowhQR7dwcjJsu9RUOH5crYVr27bszpKLWuv6+KiPfgcPVlvpFnnlFbX17WefVew8hBBClCA1esZyYh3smgdthkLfD8pcvUBbyP87eYlVe8/yW8pVXXmTes5ERwTwzCMNpIsUUTUUBX7/vXjeUP7+aq0eGNaIo0sXSEhQh0nz9obnntP/vk0bdRxdIYQQRiOZhLEYOCLG1excvjpwntX7znEx8w4AVhp4opUX0RGBRDSug8aQP5pCGIuDA+zYUTxviE8+UbtDOXVKXW7aFCZMgDFjHrxdy5bqVJoXXzTs2EIIIQwmiZ6x6LpWaVPq13fytUz/4QTfJfxFnrYQAI9adgxq78fQjgHUd3OsmjiFuJe1NTz2mOHrT50KixbBuHEQEaGW7d0Lr72mPs6dMcMkYQohhCg/eUfPGG6mw8LmoLGCKX+BXcl3nKZ+f5xVe88BENLAleiIQJ4M8cHBVoYpE9VM3bqwZIn6jt3dvvxSTf4e1JeeEEKIKiU1esZQ9NjWs3mpSd6OpEu6JG/Z8+3oEeRdhcEJUYb8fPjoI3X+xRfBtoyRVfLzISysZHm7duowZ0IIIR4aMpSCMTxgRIyr2blM+vYoACM7BUqSJx4+eXkwdqw65eWVvf6wYfDhhyXLP/oIhg598LZarTo+7o0bFQpVCCFE+UiNnjEU1ej5ttErVhSFN9cd40p2Ls28nJnco0WVhyaESXzyidp6tmNHdTk+Xn0/b/hwdQzcIosW6W9nbQ3du8PJk+oIG0IIIUxKEj1jsLIGG8cSDTHWHjjPtt8zsLO2YvHAtvI+nqgZjh+HRx5R51NS1E9PT3U6frx4vfu1Hg8KgtOnSw6hJoQQwugk0TOGQatBW6D3h+3MlRym/6D2TfZ6VDNa+Rqxc2YhzKmoK5aKmjVLHS5t5kz1vb5atfS/N2ZH5kIIYeEk0TMW6+JLma8tZMLaRG7na4loVIcxf29kxsCEMLLLl9WWt6U5dgyCgx+8fa9e6ufTT+vX+imKuqzVGidOIYQQkuhVWuYFcHAFexdd0fu//MmR8zdwcbBh4YBQrGR8WlGTBAer7+g9+aR++YIF8PbbcPv2g7evbI2gEEIIg0miV1k/vwHJP8PT78Ejw0g4d533d/wJwL+fCcZXOkIWNU1sLPTvDyNHqo0trl1TG2EcOwZr1pS9fZcupo9RCCEEIIle5aUlAgp4NCI7t4DYrxPRFir0bePL06G+5o5OCON74w144gm1m5WQEDXRCw+Ho0fVMWwNceOGWit48qS63Lo1jBoFrq4mC1sIISyR9KNXGdmXIesvQAM+Icz84XfOXb1FfTdHpvcJMnd0QphOkyZq69mzZyErCwYONDzJO3gQGjdWx8q9dk2dFi1Syw4dMmnYQghhaSTRq4y0I+pnnSZsPpXD2oPn0Whg4YBQXB3LGF1AiOpqzx61Ju/UKbUW78MP1aHPBg6E69fL3v6119SGGGfPwrp16nTmDDz1FEyYYOrohRDCokiiVxlphwG4UzeYKevU0S9eerQxHRvVMWdUQphW165qUrdvH7RsCWPGwOHDaofJZbW4BbVGb/JksLnrzREbG/WR8MGDpotbCCEskLyjVxn/GxFjXbon12/l09q3NrFPNDNvTEKY2tatJRtUNG6s1vT9+99lb1+7tpoUtrhnpJjz58HFpfRthBBCVIgkepWRptbibbxUD3sbK94d1AY7G6kkFdWQp6fh696v1ayVldq9SlkGDoTRo9XuWP72N7Vszx6YNAkGDzY8DiGEEGWSrKSibl+HzFQAThQG8s8nW9KkntRGiGqoVi21E+TLl0uOUnG3Xr0gM7N4ee5ctfVskatXoVWrso+3YAH066d2yRIYqE4jRsCzz8K8eRU7ByGEEKXSKIqimDuIqpSVlYWrqyuZmZnUrsRQS3kFhQx9bwvaS0m4NO3EypHt0dxvbE8hagJra0hLg3r11OXatSExERr9b+SXjAzw9S19ZIujR9VWulZ3/dvy1q3isXIbNwYnJ5OGL4QQlkge3VbQ5hPpHMgoxKNWEJufC5EkT9R89/6bsDz/RmzbtjhJbNQIDhyAOnUMa7whhBCiwiTRq6CizpCdbK2p5+Jg5miEqITbt6FnT3V+0yZwNMFoLm5uahcq9eqp3aoUFhr/GEIIIUqQRK8SZOQLUSMUFsKuXcXz96PRqNO9ZYbo319txOHjo24TFqY+Ci7N6dOG7VMIIUSZJNETwtLZ28PXXxfP34+iqI0mita5cwdefrm4AUdu7v23/egjtQHGn3/C+PHwwgvSlYoQQlQBaYwhhDDMyJGGrbdiRdn7WbJEEj0hhKgCkugJIYQQQtRQ8uhWCEtXUADr16vzzzyjPzSZEEKIak3+jy6EpcvNhQED1PnsbEn0hBCiBpGRMYQQQgghaihJ9IQQQgghaqiHItFbunQpgYGBODg4EB4ezv79+++77vLly+ncuTPu7u64u7sTGRn5wPWFEEIIISyV2RO9tWvXEhsbS1xcHIcOHSI0NJSoqCguXbpU6vo7d+5k8ODB7Nixg7179+Ln50f37t3566+/qjhyIYQQQoiHm9m7VwkPD6d9+/a8//77ABQWFuLn58e4ceN48803y9xeq9Xi7u7O+++/z/Dhw8tcX7pXEeIeOTng7KzOZ2cXd4AshBCi2jNrjV5eXh4JCQlERkbqyqysrIiMjGTv3r0G7ePWrVvk5+fj4eFhqjCFEEIIIaols/ajcOXKFbRaLV5eXnrlXl5eJCUlGbSPyZMn4+vrq5cs3i03N5fcu4ZmysrKqnjAQgghhBDViNnf0auMuXPn8tVXX7F+/XocHBxKXWfOnDm4urrqJj8/vyqOUgghhBDCPMya6Hl6emJtbU1GRoZeeUZGBt7e3g/cdsGCBcydO5etW7cSEhJy3/WmTJlCZmambjp//rxRYhdCCCGEeNiZNdGzs7OjXbt2bN++XVdWWFjI9u3biYiIuO928+fPZ+bMmWzevJmwsLAHHsPe3p7atWvrTUIIIYQQlsDsYx3FxsYSHR1NWFgYHTp0YPHixeTk5DBy5EgAhg8fTv369ZkzZw4A8+bNY+rUqaxZs4bAwEDS09MBcHZ2xrmo5aAQQgghhDB/ojdw4EAuX77M1KlTSU9Pp02bNmzevFnXQCM1NRUrq+KKxw8//JC8vDyeffZZvf3ExcUxbdq0Mo9X1JuMNMoQ4n9ycorns7JAqzVfLEJUcy4uLmg0GnOHIYSO2fvRq2oXLlyQBhlCCCFMQvpoFQ8bi0v0CgsLuXjxYqn/6srKysLPz4/z589bxH+olnS+lnSuYFnna0nnCpZ1vtXxXKVGTzxszP7otqpZWVnRoEGDB65jaY02LOl8LelcwbLO15LOFSzrfC3pXIUwtmrdj54QQgghhLg/SfSEEEIIIWooSfTuYm9vT1xcHPb29uYOpUpY0vla0rmCZZ2vJZ0rWNb5WtK5CmEqFtcYQwghhBDCUkiNnhBCCCFEDSWJnhBCCCFEDSWJnhBCCCFEDSWJnhBCCCFEDSWJ3v8sXbqUwMBAHBwcCA8PZ//+/eYOySSmTZuGRqPRm1q0aGHusIxm9+7d9O7dG19fXzQaDRs2bND7XlEUpk6dio+PD46OjkRGRnLq1CnzBFtJZZ3riBEjStzrHj16mCfYSpozZw7t27fHxcWFevXq0bdvX5KTk/XWuXPnDjExMdSpUwdnZ2f69+9PRkaGmSKuHEPO97HHHitxf19++WUzRVw5H374ISEhIbqOkSMiIti0aZPu+5p0b4WoapLoAWvXriU2Npa4uDgOHTpEaGgoUVFRXLp0ydyhmUTr1q1JS0vTTb/++qu5QzKanJwcQkNDWbp0aanfz58/nyVLlrBs2TLi4+OpVasWUVFR3Llzp4ojrbyyzhWgR48eevf6yy+/rMIIjWfXrl3ExMSwb98+tm3bRn5+Pt27dycnJ0e3zmuvvcYPP/zAN998w65du7h48SL9+vUzY9QVZ8j5Arzwwgt693f+/PlmirhyGjRowNy5c0lISODgwYN07dqVPn36cOLECaBm3VshqpwilA4dOigxMTG6Za1Wq/j6+ipz5swxY1SmERcXp4SGhpo7jCoBKOvXr9ctFxYWKt7e3so777yjK7tx44Zib2+vfPnll2aI0HjuPVdFUZTo6GilT58+ZonH1C5duqQAyq5duxRFUe+jra2t8s033+jWOXnypAIoe/fuNVeYRnPv+SqKonTp0kV59dVXzReUibm7uysff/xxjb+3Qpiaxdfo5eXlkZCQQGRkpK7MysqKyMhI9u7da8bITOfUqVP4+vrSqFEjhg4dSmpqqrlDqhJnzpwhPT1d7167uroSHh5eY+/1zp07qVevHs2bN+cf//gHV69eNXdIRpGZmQmAh4cHAAkJCeTn5+vd2xYtWuDv718j7u2951tk9erVeHp6EhQUxJQpU7h165Y5wjMqrVbLV199RU5ODhERETX+3gphajbmDsDcrly5glarxcvLS6/cy8uLpKQkM0VlOuHh4axcuZLmzZuTlpbG9OnT6dy5M8ePH8fFxcXc4ZlUeno6QKn3uui7mqRHjx7069ePhg0bkpKSwltvvUXPnj3Zu3cv1tbW5g6vwgoLC5kwYQKdOnUiKCgIUO+tnZ0dbm5ueuvWhHtb2vkCDBkyhICAAHx9fTl69CiTJ08mOTmZdevWmTHaijt27BgRERHcuXMHZ2dn1q9fT6tWrUhMTKyx91aIqmDxiZ6l6dmzp24+JCSE8PBwAgIC+Prrrxk9erQZIxPGNmjQIN18cHAwISEhNG7cmJ07d9KtWzczRlY5MTExHD9+vEa9W/og9zvfF198UTcfHByMj48P3bp1IyUlhcaNG1d1mJXWvHlzEhMTyczM5NtvvyU6Oppdu3aZOywhqj2Lf3Tr6emJtbV1iRZcGRkZeHt7mymqquPm5kazZs34888/zR2KyRXdT0u9140aNcLT07Na3+uxY8fy448/smPHDho0aKAr9/b2Ji8vjxs3buitX93v7f3OtzTh4eEA1fb+2tnZ0aRJE9q1a8ecOXMIDQ3l3XffrbH3VoiqYvGJnp2dHe3atWP79u26ssLCQrZv305ERIQZI6sa2dnZpKSk4OPjY+5QTK5hw4Z4e3vr3eusrCzi4+Mt4l5fuHCBq1evVst7rSgKY8eOZf369fzyyy80bNhQ7/t27dpha2urd2+Tk5NJTU2tlve2rPMtTWJiIkC1vL+lKSwsJDc3t8bdWyGqmjy6BWJjY4mOjiYsLIwOHTqwePFicnJyGDlypLlDM7rXX3+d3r17ExAQwMWLF4mLi8Pa2prBgwebOzSjyM7O1qvROHPmDImJiXh4eODv78+ECROYNWsWTZs2pWHDhrz99tv4+vrSt29f8wVdQQ86Vw8PD6ZPn07//v3x9vYmJSWFN954gyZNmhAVFWXGqCsmJiaGNWvW8P333+Pi4qJ7N8vV1RVHR0dcXV0ZPXo0sbGxeHh4ULt2bcaNG0dERAQdO3Y0c/TlV9b5pqSksGbNGnr16kWdOnU4evQor732Go8++ighISFmjr78pkyZQs+ePfH39+fmzZusWbOGnTt3smXLlhp3b4WocuZu9vuweO+99xR/f3/Fzs5O6dChg7Jv3z5zh2QSAwcOVHx8fBQ7Ozulfv36ysCBA5U///zT3GEZzY4dOxSgxBQdHa0oitrFyttvv614eXkp9vb2Srdu3ZTk5GTzBl1BDzrXW7duKd27d1fq1q2r2NraKgEBAcoLL7ygpKenmzvsCintPAFlxYoVunVu376tvPLKK4q7u7vi5OSkPPPMM0paWpr5gq6Ess43NTVVefTRRxUPDw/F3t5eadKkiTJp0iQlMzPTvIFX0KhRo5SAgADFzs5OqVu3rtKtWzdl69atuu9r0r0VoqppFEVRqjKxFEIIIYQQVcPi39ETQgghhKipJNETQgghhKihJNETQgghhKihJNETQgghhKihJNETQgghhKihJNETQgghhKihJNETQgghhKihJNET1cbZs2fRaDS6oZ4eBklJSXTs2BEHBwfatGlj7nAshkajYcOGDcDD+bu424gRIx6qkVd27tyJRqMpMXasEKJmkkRPGGzEiBFoNBrmzp2rV75hwwY0Go2ZojKvuLg4atWqRXJyst5YnPdKT09n3LhxNGrUCHt7e/z8/Ojdu/cDt7FU5b1Wfn5+pKWlERQUZNQ47k4mS7Ny5Uo0Gs0Dp7Nnzxo1JiGEKC8Z61aUi4ODA/PmzeOll17C3d3d3OEYRV5eHnZ2dhXaNiUlhSeffJKAgID7rnP27Fk6deqEm5sb77zzDsHBweTn57NlyxZiYmJISkqqaOjV1v2ueUWulbW1Nd7e3lURtp6BAwfSo0cP3XK/fv0ICgpixowZurK6detWaN+V+U0KIcTdpEZPlEtkZCTe3t7MmTPnvutMmzatxGPMxYsXExgYqFsuepw1e/ZsvLy8cHNzY8aMGRQUFDBp0iQ8PDxo0KABK1asKLH/pKQk/va3v+Hg4EBQUBC7du3S+/748eP07NkTZ2dnvLy8GDZsGFeuXNF9/9hjjzF27FgmTJiAp6cnUVFRpZ5HYWEhM2bMoEGDBtjb29OmTRs2b96s+16j0ZCQkMCMGTPQaDRMmzat1P288soraDQa9u/fT//+/WnWrBmtW7cmNjaWffv26dZLTU2lT58+ODs7U7t2bQYMGEBGRkaJ6/r5558TGBiIq6srgwYN4ubNm7p1vv32W4KDg3F0dKROnTpERkaSk5OjO+8JEyboxda3b19GjBihWw4MDGTWrFkMHz4cZ2dnAgIC2LhxI5cvX9bFFhISwsGDB/X28+uvv9K5c2ccHR3x8/Nj/PjxuuMW7XfmzJkMHz6c2rVr8+KLL1bqWt2ttEe3hvwGxo8fzxtvvIGHhwfe3t5696/ot/rMM8+g0Wj0frtFHB0d8fb21k12dnY4OTnplVlbW+vWX7BgAT4+PtSpU4eYmBjy8/PLvD5lXdfPP/+csLAwXFxc8Pb2ZsiQIVy6dEkvzp9//plmzZrh6OjI448/XqKW8dy5c/Tu3Rt3d3dq1apF69at+fnnn0u91kKI6kcSPVEu1tbWzJ49m/fee48LFy5Ual+//PILFy9eZPfu3SxatIi4uDieeuop3N3diY+P5+WXX+all14qcZxJkyYxceJEDh8+TEREBL179+bq1asA3Lhxg65du9K2bVsOHjzI5s2bycjIYMCAAXr7+Oyzz7Czs2PPnj0sW7as1PjeffddFi5cyIIFCzh69ChRUVE8/fTTnDp1CoC0tDRat27NxIkTSUtL4/XXXy+xj2vXrrF582ZiYmKoVatWie/d3NwANans06cP165dY9euXWzbto3Tp08zcOBAvfVTUlLYsGEDP/74Iz/++CO7du3SPUpPS0tj8ODBjBo1ipMnT7Jz50769etHeYez/s9//kOnTp04fPgwTz75JMOGDWP48OE8//zzHDp0iMaNGzN8+HDdflNSUujRowf9+/fn6NGjrF27ll9//ZWxY8fq7XfBggWEhoZy+PBh3n777Qpfq7KU5zdQq1Yt4uPjmT9/PjNmzGDbtm0AHDhwAIAVK1aQlpamW66oHTt2kJKSwo4dO/jss89YuXIlK1eu1Fvn3utjyHXNz89n5syZHDlyhA0bNnD27Fm9xP38+fP069eP3r17k5iYyJgxY3jzzTf1jhsTE0Nubi67d+/m2LFjzJs3D2dn50qdrxDiIaIIYaDo6GilT58+iqIoSseOHZVRo0YpiqIo69evV+7+KcXFxSmhoaF62/7nP/9RAgIC9PYVEBCgaLVaXVnz5s2Vzp0765YLCgqUWrVqKV9++aWiKIpy5swZBVDmzp2rWyc/P19p0KCBMm/ePEVRFGXmzJlK9+7d9Y59/vx5BVCSk5MVRVGULl26KG3bti3zfH19fZV///vfemXt27dXXnnlFd1yaGioEhcXd999xMfHK4Cybt26Bx5r69atirW1tZKamqorO3HihAIo+/fvVxRFva5OTk5KVlaWbp1JkyYp4eHhiqIoSkJCggIoZ8+eLfUYXbp0UV599VW9sj59+ijR0dG65YCAAOX555/XLaelpSmA8vbbb+vK9u7dqwBKWlqaoiiKMnr0aOXFF1/U2+9///tfxcrKSrl9+7Zuv3379n3gNTD0WimKogDK+vXrFUUp/l0cPnxYURTDfwN///vf9dZp3769Mnny5FKPYYjSrq+iFP/WCwoKdGXPPfecMnDgQN1yadfHkOt6rwMHDiiAcvPmTUVRFGXKlClKq1at9NaZPHmyAijXr19XFEVRgoODlWnTphl8nkKI6kVq9ESFzJs3j88++4yTJ09WeB+tW7fGyqr4J+jl5UVwcLBu2dramjp16pR4FBUREaGbt7GxISwsTBfHkSNH2LFjB87OzrqpRYsWgFrzVKRdu3YPjC0rK4uLFy/SqVMnvfJOnTqV65wVA2vTTp48iZ+fH35+frqyVq1a4ebmpne8wMBAXFxcdMs+Pj666xMaGkq3bt0IDg7mueeeY/ny5Vy/ft3gWIuEhITo5r28vAD07ktRWdFxjxw5wsqVK/WueVRUFIWFhZw5c0a3XVhY2AOPa+i1Kouhv4G7zxP0r6WxtW7dWu8xbmnHuvf6GHJdExIS6N27N/7+/ri4uNClSxdAfQ0A1N9VeHi43n7v/u8HYPz48cyaNYtOnToRFxfH0aNHjXPSQoiHgjTGEBXy6KOPEhUVxZQpU/QeFQFYWVmV+KN99/tIRWxtbfWWNRpNqWWFhYUGx5WdnU3v3r2ZN29eie98fHx086U9GjSFpk2botFojNbg4kHXx9ramm3btvHbb7+xdetW3nvvPf75z38SHx9Pw4YNK3RfilpTl1ZWdNzs7Gxeeuklxo8fX2Jf/v7+uvmyrrmxrpWhv4HK/tbKw5Bj3Xt9yrquOTk5REVFERUVxerVq6lbty6pqalERUWRl5dncGxjxowhKiqKn376ia1btzJnzhwWLlzIuHHjynGGQoiHldToiQqbO3cuP/zwA3v37tUrr1u3Lunp6XpJhTH7OLv7pfyCggISEhJo2bIlAI888ggnTpwgMDCQJk2a6E3lSe5q166Nr68ve/bs0Svfs2cPrVq1Mng/Hh4eREVFsXTpUr2X6IsU9WXWsmVLzp8/z/nz53Xf/f7779y4caNcx9NoNHTq1Inp06dz+PBh7OzsWL9+PaDel7S0NN26Wq2W48ePG7zv+3nkkUf4/fffS1zvJk2alKvlqKHXypB4jPEbsLW1RavVGry+sZV1XZOSkrh69Spz586lc+fOtGjRokQtYcuWLdm/f79eWWmNWvz8/Hj55ZdZt24dEydOZPny5SY9NyFE1ZFET1RYcHAwQ4cOZcmSJXrljz32GJcvX2b+/PmkpKSwdOlSNm3aZLTjLl26lPXr15OUlERMTAzXr19n1KhRgPpi+bVr1xg8eDAHDhwgJSWFLVu2MHLkyHL/0Z40aRLz5s1j7dq1JCcn8+abb5KYmMirr75a7ni1Wi0dOnTgu+++49SpU5w8eZIlS5boHqNFRkbqruehQ4fYv38/w4cPp0uXLmU+8iwSHx/P7NmzOXjwIKmpqaxbt47Lly/rkuCuXbvy008/8dNPP5GUlMQ//vEPo3SaO3nyZH777TfGjh1LYmIip06d4vvvvy/RGMMQhlyrshjrNxAYGMj27dtJT0+v0CPwyirruvr7+2NnZ8d7773H6dOn2bhxIzNnztTbx8svv8ypU6eYNGkSycnJrFmzpkQjkAkTJrBlyxbOnDnDoUOH2LFjh+43I4So/iTRE5UyY8aMEo+gWrZsyQcffMDSpUsJDQ1l//79pbZIrai5c+cyd+5cQkND+fXXX9m4cSOenp4Aulo4rVZL9+7dCQ4OZsKECbi5uem9D2iI8ePHExsby8SJEwkODmbz5s1s3LiRpk2blms/jRo14tChQzz++ONMnDiRoKAgnnjiCbZv386HH34IqDVx33//Pe7u7jz66KNERkbSqFEj1q5da/Bxateuze7du+nVqxfNmjXjX//6FwsXLqRnz54AjBo1iujoaF0C2ahRIx5//PFynUtpQkJC2LVrF3/88QedO3embdu2TJ06FV9f33Lvy5BrVRZj/QYWLlzItm3b8PPzo23btuU+l8oq67rWrVuXlStX8s0339CqVSvmzp3LggUL9Pbh7+/Pd999x4YNGwgNDWXZsmXMnj1bbx2tVktMTAwtW7akR48eNGvWjA8++KDKzlMIYVoaxVhvQAshhBBCiIeK1OgJIYQQQtRQkugJIYQQQtRQkugJIYQQQtRQkugJIYQQQtRQkugJIYQQQtRQkugJIYQQQtRQkugJIYQQQtRQkugJIYQQQtRQkugJIYQQQtRQkugJIYQQQtRQkugJIYQQQtRQkugJIYQQQtRQ/x+pp9aNVp0VUwAAAABJRU5ErkJggg==",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "refline_color = \"red\"\n",
- "db16_plot = (\n",
- " sns.relplot(\n",
- " data=throughput_df[throughput_df[\"backend\"].isin([\n",
- " \"File System\",\n",
- " \"16 Redis Nodes\",\n",
- " ])],\n",
- " kind=\"line\",\n",
- " x=\"client_threads\",\n",
- " y=\"throughput\",\n",
- " hue=\"backend\",\n",
- " style=\"backend\",\n",
- " )\n",
- " .set(\n",
- " title=\"Data Throughput of a 16 Node Orchestrator\" if ADD_GRAPH_TITLES else None,\n",
- " xlabel=\"Number of Consumer Client Threads\",\n",
- " ylabel=\"Data Throughput (GB/s)\",\n",
- " )\n",
- ")\n",
- "expected_max = 16\n",
- "ax ,= db16_plot.axes[0]\n",
- "ax.axvline(expected_max, ls=\"-.\", c=refline_color)\n",
- "plt.text(\n",
- " expected_max + 2,\n",
- " 0.25,\n",
- " \"Expected Max Throughput\\nfor SmartSim Aggregation\",\n",
- " transform=ax.get_xaxis_transform(),\n",
- " rotation=\"vertical\",\n",
- " horizontalalignment=\"center\",\n",
- " verticalalignment=\"center\",\n",
- " c=refline_color,\n",
- ")\n",
- "db16_plot.legend.set_title(\"Aggregation Backend\")"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3.9.13",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.9.13"
- },
- "orig_nbformat": 4,
- "vscode": {
- "interpreter": {
- "hash": "97a07020978c785faaeeebe96c4a4bf17e346a0eaa570fc6c22c4e27557768dd"
- }
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/figures/all_in_one_violin_dark.pdf b/figures/all_in_one_violin_dark.pdf
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diff --git a/figures/loop_time-128-redis_dark.png b/figures/loop_time-128-redis_dark.png
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-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [],
- "source": [
- "import os\n",
- "import matplotlib.pyplot as plt\n",
- "import matplotlib\n",
- "import pandas as pd\n",
- "import numpy as np\n",
- "from pathlib import Path\n",
- "import configparser\n",
- "\n",
- "font = {'family' : 'sans',\n",
- " 'weight' : 'normal',\n",
- " 'size' : 14}\n",
- "matplotlib.rc('font', **font)\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [],
- "source": [
- "class hashableDict(dict):\n",
- " def __hash__(self):\n",
- " return hash(tuple(sorted(self.items())))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [],
- "source": [
- "results_path = '../results'\n",
- "scaling_test = 'inference-colocated-scaling'\n",
- "run_path = 'run-2023-05-23-16:49:05'\n",
- "full_path = Path(results_path, scaling_test, run_path)\n",
- "\n",
- "configs = []\n",
- "\n",
- "functions = ['put_tensor', 'run_script', 'run_model', 'unpack_tensor']\n",
- "\n",
- "for run_cfg in full_path.rglob('run.cfg'):\n",
- " config = configparser.ConfigParser()\n",
- " config.read(run_cfg)\n",
- " configs.append(config)\n",
- "\n",
- "df_list = []\n",
- "\n",
- "for config in configs:\n",
- " timing_files = Path(config['run']['path']).glob('rank*.csv')\n",
- " df_config_list = []\n",
- " for timing_file in timing_files:\n",
- " tmp_df = pd.read_csv(timing_file, header=0, names=[\"rank\", \"function\", \"time\"])\n",
- " for key, value in config._sections['attributes'].items():\n",
- " tmp_df[key] = value\n",
- " df_list.append(tmp_df)\n",
- "\n",
- "df = pd.concat(df_list, ignore_index=True)\n",
- "\n",
- " \n",
- " "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
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- "outputs": [
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QBgKIiIiIiIiICggDAUREREREREQFhIEAIiIiIiIiogLCQAARERERERFRAWEggIiIiIiIiKiAMBBAREREREREVED0uW4AEREREREVtmA0nusmJMxq5BCK1I+/xURERERElFPT73o1101IWMPqK3LdBKK0cWkAERERERERUQFhRgAREREREeXU3nsXy3atYDSOeT/ZCAB46ob5OGdciWzXJsoXDAQQEREREVFOZWrdvc2k55p+omFwaQAREREREeUlvY7DHaLh8C+DiIiIiIjyklnP4Q7RcPiXQUREREREecmk1+W6CUSKxEAAERERERHlJZNBk+smECkSAwFERERERJSXzAZmBBANh4EAIiIiIiLKSwYWCyQaFv8yiIioIImilOsmEBEREeUEAwFERFSQBImBACIiIipMDAQQEVFBEhkIICIiogLFQAARERUkxgGIiIioUDEQQEREBYkZAURERFSoGAggIqKCxFqBREREVKjyIhCwZs0ajB8/HmazGQsWLMDmzZtPe/zzzz+PqVOnwmw2Y+bMmXjllVdGPPYb3/gGNBoNHn74YZlbTUREucSMACIiIipUqg8EPPfcc1i5ciXuvvtubN++HbNmzcLixYvR3t4+7PHvvPMOli1bhhtuuAE7duzAkiVLsGTJEuzZs2fIsS+++CLeffdd1NTUZPplEBFRlklirltARERElBuqDwQ89NBD+NrXvobrrrsO06dPx9q1a2G1WvH4448Pe/yvfvUrXHbZZbj11lsxbdo03HfffTjnnHPwyCOPDDquubkZ3/72t/GnP/0JBoMhGy+FiIiyiEsDiIiIqFCpOhAQjUaxbds2LFq0qP8xrVaLRYsWYdOmTcOes2nTpkHHA8DixYsHHS+KIr7yla/g1ltvxVlnnTVqOyKRCLxe76AvIiJSNoGRAEoS+3siIsoXqg4EdHZ2QhAEVFZWDnq8srISLpdr2HNcLteox99///3Q6/W4+eabE2rHqlWr4HQ6+7/q6+uTfCVERJRtrBFAyWJ/T0RE+UKf6wYozbZt2/CrX/0K27dvh0ajSeic22+/HStXruz/t9fr5c0BEZECDcwCiDMjgJLE/p76BKPxXDchIVYjb/WJaHiq/nQoLy+HTqdDW1vboMfb2tpQVVU17DlVVVWnPf5f//oX2tvbMXbs2P7nBUHA9773PTz88MNoaGgYck2TyQSTyZTmqyEiokyLiycrBMYFVguk5LC/pz7T73o1101ISMPqK3LdBCJSKFUvDTAajZg7dy42btzY/5goiti4cSMWLlw47DkLFy4cdDwAbNiwof/4r3zlK9i1axd27tzZ/1VTU4Nbb70Vr76qjg99IiIaXkw4mQUQY0YAERFRXovGGfQfiaozAgBg5cqVuOaaazBv3jzMnz8fDz/8MAKBAK677joAwPLly1FbW4tVq1YBAG655RZcdNFFePDBB3HFFVfg2WefxdatW/HYY48BAMrKylBWVjboexgMBlRVVeHMM8/M7osjIiJZCQMCAcwIIKJU7b13sWzXCkbjmPeT3kmqrXdcwnR+IhmF40Kum6BYqv+kufrqq9HR0YG77roLLpcLs2fPxvr16/sLAjY2NkKrPZn4cP755+Ppp5/GHXfcgR/+8IeYPHky1q1bhxkzZuTqJRARUZYMnBngLAERpSpTg3WrUc9AAJGMwlH29SPJi0+aFStWYMWKFcM+9+abbw55bOnSpVi6dGnC1x+uLgAREalPZMDgP8aMACIiorzmV0lhz1xQdY0AIiKiZEQHFAuMMCOAiIgorwXCDASMhIEAIiIqGJHYybWCUa4bJCIiyjsDtwr2hmI5bImyMRBAREQFY2AWgCgCEQYDiIiI8oo3dDILIBoXEYgwK2A4DAQQEVHBCEcHD/zDMS4PICIiyifdwejgfweiIxxZ2BgIICKigiBJ0pAMgFCUGQFERET5pMsfGfTvjlP+Tb0YCCAiooIQjokQT0kACMUYCCAiIsoXwWh8SIFAdyDKnYKGwUAAEREVhMAwWwhx3SAREVH+6PANnf2XJKCTWQFDMBBAREQFIRgZOvvPQAAREVH+aPWEh33cNcLjhYyBACIiKgi+yNAthALROMQB2wwRERGROvkjcfjDwwf4uwNR7hR0CgYCiIioIPiGuTkQxeGXDBAREZG6nG7WX5KAdi+XBwzEQAAREeU9QZRGXAYwXICAiIiI1KXde/r0/7ZRni80DAQQEVHe84VjkEZYAeAND10yQEREROrhCcUQHGVL4J5gDGHuFtSPgQAiIsp73tDIs/6eIAMBREREajbcbgHpHFcIGAggIqK85wmNPNj3R+IQWDCQiIhItboD0YSO60rwuELAQAAREeW90wUCJKl36QARERGpTzQuwnuafn4gdyDK3YI+xEAAERHltUhcGHVN4OkCBURERKRcyQTzBVHibkEfYiCAiIjy2unqAyRzDBERESmPf4RdgUYSiLBgIMBAABER5blEZgq4NICIiEidkh3YJxs4yFcMBBARUV7zhUfv8INRAXFBzEJriIiISE6xJPvvZI/PVwwEEBFRXgskGPlnqiAREZH6xMXkBvZxgcUCAQYCiIgojwmihNAohQL7sHgQERGR+iQ7wS9IDAQADAQQEVEeC8UEJNrfBxkIICIiUh2tJrPH5ysGAoiIKG8lM7gPRrk0gIiISG00SQ7sNWAkAGAggIiI8lgoicE9AwFERETqo00yEqDlCBgAAwFERJTHwrHEFw6GE6wlQERERMph0CU3pE32+HzFd4GIiPJWooUCgd4qwtxSiIiISF10SS7617NIAAAGAoiIKI8lO8vPrAAiIiJ1SXZgn2zgIF8xEEBERHkrmYwAILmlBERERJR7WgYCUsJAABER5aWYIEIQktsrmBkBRERE6qJLtlhgstsM5CkGAoiIKC8lmw2Q6jlEREREasNAABER5aVwCtsBMiOAiIhIXZLL/Uv++HzFQAAREeWlVNb7h1IIHhAREVHuiFJyQ3tRZCgAYCCAiIjyVDieQkZAnMUCiYiI1CSaZN/NrYJ7MRBARER5KZU0/1hc5EwBERGRiiQ7sI8yEACAgQAiIspTyc4Q9IkwK4CIiEg1kl3Wx62CezEQQEREeSnViH9M5A0CERGRGkiShGCSgYBAJJ6h1qgLAwFERJSX4kJqKf4xZgQQERGpQigmQEhySV8oKiDO5QEMBBARUX4Skqwi3IclAoiIiNShJxhL7bxQauflEwYCiIgoL0kpBgJSPY+IiIiyqzsQzep5+YSBACIiogEYBiAiIlI+SZLQleKAvsvPQAADAURElJc00KR2XmqnERERURZ1BaIp1/UJROLwhQt7eQADAURElJdSHdCnGkAgIiKi7GntCad1fkua56sdAwFERJSXtClGAnRaBgKIiIiULBIX0OFPbyDf6gklveNAPmEggIiI8lKqA3od1wYQEREpWlN3CGKaOwDGBQktPSF5GqRCDAQQEVFeSjUjQMuekYiISLFigogmd1CWax3vCkIs0KwA3u4QEVFeSjXDP9UAAhEREWVeY3cQgiDP4D0cE9DiKcysAAYCiIgoL6W6NICBACIiImUKxwQ0dsmTDdDnaEcAcSHNdQYqlBeBgDVr1mD8+PEwm81YsGABNm/efNrjn3/+eUydOhVmsxkzZ87EK6+8Muj5e+65B1OnToXNZkNJSQkWLVqE9957L5MvgYiIZJbyrgGMAxARESnSsc6A7AX+onERjd3yBhfUQPWBgOeeew4rV67E3Xffje3bt2PWrFlYvHgx2tvbhz3+nXfewbJly3DDDTdgx44dWLJkCZYsWYI9e/b0HzNlyhQ88sgj2L17N/79739j/PjxuPTSS9HR0ZGtl0VERGnjiJ6IiChf+CPxjBX3O94VRDgmZOTaSqX6QMBDDz2Er33ta7juuuswffp0rF27FlarFY8//viwx//qV7/CZZddhltvvRXTpk3Dfffdh3POOQePPPJI/zFf/OIXsWjRIkycOBFnnXUWHnroIXi9XuzatStbL4uIiNLEjAAiIqL8cbDNBylDdf0EUcLhdn9mLq5Qqg4ERKNRbNu2DYsWLep/TKvVYtGiRdi0adOw52zatGnQ8QCwePHiEY+PRqN47LHH4HQ6MWvWrGGPiUQi8Hq9g76IiCi3pBTvFjJ1k0Hqx/6eiCg3OnwRdPujGf0eLk8YnlAso99DSVQdCOjs7IQgCKisrBz0eGVlJVwu17DnuFyuhI5/+eWXUVRUBLPZjF/+8pfYsGEDysvLh73mqlWr4HQ6+7/q6+vTeFVERCSHVAf0DATQSNjfExFlnyhKONTmy8r3ytb3UYKUAgHXX389fL6hb1IgEMD111+fdqOU4GMf+xh27tyJd955B5dddhk+//nPj1h34Pbbb4fH4+n/ampqynJriYjoVKkWExIYCaARsL8nIsq+5p4QgtHsrN/vCcbQ7g1n5XvlWkqBgCeffBKh0NBCDaFQCH/84x/TblSiysvLodPp0NbWNujxtrY2VFVVDXtOVVVVQsfbbDZMmjQJ5513Hn7/+99Dr9fj97///bDXNJlMcDgcg76IiCi3Ug4EyLQ3MeUf9vdERNkVE0Qc7Qxk9XseavdDlHlnAiVKKhDg9Xrh8XggSRJ8Pt+gNXJutxuvvPIKKioqMtXWIYxGI+bOnYuNGzf2PyaKIjZu3IiFCxcOe87ChQsHHQ8AGzZsGPH4gdeNRCLpN5qIiLIilmInHhcLby9hIqJ8VQgDunzW2B1ELJ7dfjkUFdCcod0JlESfzMHFxcXQaDTQaDSYMmXKkOc1Gg1+/OMfy9a4RKxcuRLXXHMN5s2bh/nz5+Phhx9GIBDAddddBwBYvnw5amtrsWrVKgDALbfcgosuuggPPvggrrjiCjz77LPYunUrHnvsMQC9yxt++tOf4sorr0R1dTU6OzuxZs0aNDc3Y+nSpVl9bURElBpRlFK+cYgKDAQQEeULLvdSr0hcQGNXMCff+1hnADXFFui0+buVUFKBgDfeeAOSJOHjH/84/vKXv6C0tLT/OaPRiHHjxqGmpkb2Rp7O1VdfjY6ODtx1111wuVyYPXs21q9f318QsLGxEVrtycSH888/H08//TTuuOMO/PCHP8TkyZOxbt06zJgxAwCg0+mwf/9+PPnkk+js7ERZWRnOPfdc/Otf/8JZZ52V1ddGRESpSWcwH44xEEBElC9iDO6q1vGuYMrL/NIVjYto6g5ifLktJ98/G5IKBFx00UUAgGPHjqG+vn7QADtZsVgMLpcLwWAQY8aMGRRUSNaKFSuwYsWKYZ978803hzy2dOnSEWf3zWYzXnjhhZTbQkREuReOpV5UKJ1ziYhIWWJxZgSoUTgm4IQ7N9kAfRq6AqgrsUCvU/VGeyNKKhDQZ9y4cXC73fj973+Pffv2AQCmT5+O66677rQDep/Ph6eeegrPPvssNm/ejGg0CkmSoNFoUFdXh0svvRRf//rXce6556b2aoiIiAD4I/GUzw2kcS4RESlLJMvry0keDV0B5LpkT1yQ0NgdxMQxRbltSIakFN54++23MX78ePz617+G2+2G2+3Gr3/9a0yYMAFvv/32sOc89NBDGD9+PJ544gksWrQI69atw86dO3Hw4EFs2rQJd999N+LxOC699FJcdtllOHToUFovjIiIClc6gYB0ziUiImUJZWnbOZJPKCqg2a2MYn3Hu4OI5mkwKaWMgJtuuglXX301Hn30Ueh0OgCAIAj41re+hZtuugm7d+8ecs6WLVvw9ttvj7jOfv78+bj++uuxdu1aPPHEE/jXv/6FyZMnp9I8IiIqcL5w6oP5uCAhGI3DakypiyQiIgWJCAwEqM2RDj+UUuNRECQc7wpgcqU9102RXUp3OYcPH8af//zn/iAA0Ftkb+XKlfjjH/847DnPPPNMQtc2mUz4xje+kUqziIiIIIgSfOFYWtfwhGIMBBARqZQ0YBQZYQFYVfGFY3B5wrluxiBN7iDqS60wG3SjH6wiKS0NOOecc/prAwy0b98+zJo1K+nreb1erFu3bthrEhERJcMbiqW9rtAdSC+QQEREuTNw5xgWgFWXQ+3+XDdhCFEEDiuwXelKabrj5ptvxi233ILDhw/jvPPOAwC8++67WLNmDVavXo1du3b1H3v22WcPOf/zn/88PvrRj2LFihUIhUKYN28eGhoaIEkSnn32WXzuc59L8eUQEVGh6w5G076GW4ZrEBFRbgxc0x0TFJJjTqPq8EXQ7Vdm/+vyhFFfaoXTYsh1U2STUiBg2bJlAIDvf//7wz6n0Wj6dwMQhlmX8/bbb+NHP/oRAODFF1+EJEno6enBk08+iZ/85CcMBBARUcq6ZLiJCEUF1gkgIlKpgVlhQq5Lz1NCRFHCoTZfrptxWgfbfJg3rgQajSbXTZFFSnc4x44dS+ubejye/m0G169fj8997nOwWq244oorcOutt6Z1bSIiKlzRuAhvSJ60/i5/FNZSBgKIiNRGEE9mAYhKqTpHp9XQFUBQ4Ts8eIIxtHjCqC225LopskjpDmfcuHFpfdP6+nps2rQJpaWlWL9+PZ599lkAgNvthtlsTuvaRERUuLoD8qUUdvojqC+1ynY9IiLKjnyZsS0UwWgcDV2BXDcjIYfafCgvMsKkV3/hwIQDAS+99BIuv/xyGAwGvPTSS6c99sorrzzt89/5znfwpS99CUVFRRg3bhwuvvhiAL1LBmbOnJlok4iIiAbp9Edku1ZPMAZRlKDV8oaSiEhNBn5qMyigfPtafWkX+c2WuCDhUJsfM2qduW5K2hIOBCxZsgQulwsVFRVYsmTJiMeNVBdgoG9961tYsGABGhsb8YlPfAJabe/mBRMnTsRPfvKTRJtERAUiGE19T/hs4nry3JIkSdaMAEGU4A5GUVZkku2aRESUeZoB+6JpGQhQtBPuINwy9t3Z4PKEUeEwocKu7kz2hO9axQFhGlGGkM3cuXMxd+7cQY9dccUVaV+XiPLP9LtezXUTEtKwmp9hueSLxAdVipZDV4CBACIitTHoTg7+9czqUqxwTFDkdoGJ2N/qQ4nVCINOO/rBCpV0y2OxGC655BIcOnQoqfNWr16NUCiU0LHvvfce/v73vyfbNCIiKmCZ2HJIjh0IiIgou3QDBv96FQ/U8pkkSfigxQtBpds7RuMiDriUvcvBaJLOYzUYDNi1a1fS32jv3r0YO3Ysli5dik9/+tOYN28exowZAwCIx+PYu3cv/v3vf+Opp55CS0sL/vjHPyb9PYgoP+29d7Fs12r3hnHxL94CALxy80cwvpzF4PJFVwZSCwOROMIxAWaD+osCEREVCuOAwf/A7ABSjhPukOqWBJzK5QljjN2ESoc6lwiktKD1y1/+Mn7/+99j9erVCZ/zxz/+Ee+//z4eeeQRfPGLX4TX64VOp4PJZEIwGAQAzJkzB1/96ldx7bXXcvcAIuon59r7IvPJaxWZ9FzXnycEUYInlJkbiu5AFDV5slUQEVEhGFggUM2p2/kqEInjULu6Z9P77Hf54LQYVDlhkNIdcDwex+OPP45//vOfmDt3Lmw226DnH3rooWHPmzVrFn7729/if/7nf7Br1y4cP34coVAI5eXlmD17NsrLy1NpDhFRwjhLkJ/cwWjGKg4zEEBEpF4MBCiLKPYuCVDLLgGjicVF7Gv1Ys7Yklw3JWkpBQL27NmDc845BwBw8ODBQc8lskWHVqvF7NmzMXv27FS+PRFRygauFTQZeHOQLzK5lr8rEIUkSdyCiohIhXQM+ivKsa4AvKFYrpshqy5/FE3dQdSXqmu5aUqBgDfeeEPudhARZZ1Jz0BAvujyRzJ27VhchDcch9NiyNj3ICKizDBo2NcrRU8wiobOQK6bkRGH2/0otRlhM6lnyWlKfxkejwfd3d1DHu/u7obX6027UURE2cAZ3vwQiMQRjAoZ/R4dvswFGoiIKHOYEaAMcUHEBy1eSOrcJGBUgihhT7MHoqieF5hSIOALX/gCnn322SGP/9///R++8IUvpN0oIiKiRLVnYZDe7gtn/HsQEZH8dAz6K8J+lw+hDAftc80XjuNopz/XzUhYSoGA9957Dx/72MeGPH7xxRfjvffeS7tRREREiWrzZn6QHowI8IXza00jEVEh0HJlQM61ecNweQojoN7QGVTNtogp/WlEIhHE4/Ehj8diMYRCoYSvc/jwYbz66qv950j5mitCREQZ4QnF4A8P7Y8yobVAbmKIiPKJBswIyKVwTMC+1sJaOv5BixcxQfnbIqQUCJg/fz4ee+yxIY+vXbsWc+fOHfX8rq4uLFq0CFOmTMEnP/lJtLa2AgBuuOEGfO9730ulSUREVIBaehIPPsvxvdS09o+IiAAt4wA5I0m9WwXGhcLqO8MxAQdcvlw3Y1QplTX8yU9+gkWLFuH999/HJZdcAgDYuHEjtmzZgtdee23U87/73e9Cr9ejsbER06ZN63/86quvxsqVK/Hggw+m0iwiIiog0biY1VTDuCCh1RtGbbEla9+TiIjSw8LAuXPCHVJNmrzcXJ4wKuwmVDjMuW7KiFLKCLjggguwadMm1NfX4//+7//wt7/9DZMmTcKuXbvwkY98ZNTzX3vtNdx///2oq6sb9PjkyZNx/PjxVJpEREQFpskdhJDlGfrjnQEuYyMiUhGGAXIjFBVwuF09hfMyYb/Lh2hcuUsEUt7ocPbs2fjTn/6U0rmBQABWq3XI493d3TCZTKk2iYiICkRMENHUHcz69w1GBbR5I6hyKjfCT0REJzEhIDf2tnqzHqxXmmhcxME2H2bUOnPdlGGllBGwfft27N69u//ff/3rX7FkyRL88Ic/RDQ6evrHRz7yEfzxj3/s/7dGo4EoinjggQeG3Y2AiIhooGOdgZytOTzc7i/4mxsiIqKRuDzhgl0ScCqXJ4xuhb4XKQUCbrzxRhw8eBAAcPToUVx99dWwWq14/vnn8f3vf3/U8x944AE89thjuPzyyxGNRvH9738fM2bMwNtvv437778/lSYREVGBCETiOckG6BOOCTjeFcjZ9yciIlIqQZQKfknAqQ62+RS5rDClQMDBgwcxe/ZsAMDzzz+Piy66CE8//TT+8Ic/4C9/+cuo58+YMQMHDx7EhRdeiKuuugqBQACf/exnsWPHDpxxxhmpNImIiArEfpcPue5Pj3cFEYxmZ9tCIiIitWjsDiIcE3LdDEXxh+NoUeAWxCnVCJAkCaLYW/jgn//8Jz71qU8BAOrr69HZ2ZnQNZxOJ370ox+l8u2JiKhAnXAHFZFuKIgS9rZ4MXdcCStSExERobdvbMxhxp6SHe8MoMZpVtQ9Q0qBgHnz5vVvIfjWW2/h0UcfBQAcO3YMlZWVCV0jHA5j165daG9v7w8q9LnyyitTaRYREeWxUFTAIQWlG/YEY2jqDmFs2dDit0RERIWm1RNCTMFV8nMpGBXQ4Y+gwq6cYsMpBQIefvhhfOlLX8K6devwox/9CJMmTQIA/PnPf8b5558/6vnr16/H8uXLh80e0Gg0EASmkxBR5oks+KYaoihhT4sHQo4KBI7kcIcPJTYD7GZDrptCRESUU03doVw3QdFOuEPqDwScffbZg3YN6PPzn/8cOp2u/9/PPPMMrrzySthstkHHffvb38bSpUtx1113JZxBQKQGalozbDWmvHto3ogKjFqrxZEOPzzBWK6bMYQoArtPeDB/Qin0upTK7hAREaleMBpHIKKe++BccAeiiAkiDAq5X5B1JGA2D45w3HjjjViwYAEmTpw46PG2tjasXLmSQQDKO9PvejXXTUhYw+orct2EnBhYtTXOjABV6PBFcLxLuWsOg1EB+13K3SeYiIgo07r8ua/fo3SSBHQHoqh0KCMrIKPhiJG2Sfh//+//4c0338zktyYiGtbAjyUlbuVCgwWjcXzQ4sl1M0bl8oTRqOBgBRFRoWJXnx3dCijkqwZKep9ykhv8yCOPYOnSpfjXv/6FmTNnwmAYvLby5ptvzkWziNK2997Fsl4vGI1j3k82AgC23nEJ0/lloNWerNaq0yqncisNJYgS3m/yIK6wugAjOdTug92sR4nNmOumEBHRh9TRg6hfQEXLY3NJScuIczKqeOaZZ/Daa6/BbDbjzTffHLSNgkajYSCAVCuTA3WrUc9AgMz0WmWs0aLh7Wv1qmq9oSQBu5t76wWYDbrRTyAioowTmRKQcaIoIRRlsfdEBCLKeZ9yMqr40Y9+hB//+Me47bbboOWNOBHliFHPzx+lauwKwuUJ57oZSYvGRexu9mDu2JJB2SdERJQb3CEo86KCyCUYCYrGRUiSNGgiPFdychccjUZx9dVXMwhARERDuANRHGr35boZKfMEYzjQpt72ExHlkxgDARnHrIvkKOVXMqMj8XHjxg1Z/w8A11xzDZ577rlMfmsiIlKhSFzA7maP6mcWmt0hVWY0EBHlm1icWwVnmqCUka1KxEVl/E6mtTQgGo2ivb0d4ikvZuzYsQCAPXv2DHueIAh44IEH8Oqrr+Lss88eEix46KGH0mkWERGpkCRJ2NPsRTRPbtr2tXphN+thM7G2BxFRrkQE5azJJlKSlO5ODh06hOuvvx7vvPPOoMf71jsIo/zB7d69G3PmzAEwNFighPUSRESUfcc6A3AraFuddAmihF0nPFgwoZT1AoiIsmhgXYAgi9hlHMdvydFAGe9XSoGAa6+9Fnq9Hi+//DKqq6uT/uG/8cYbqXxbIiLKU55gDMc6A7luhuwCkTgOd/gxpdKe66YQERUM34AdZwJh9ew+o1bKGNaqh1LiJikFAnbu3Ilt27Zh6tSpcreHiIgKTFwQ8UGL+usCjKSxK4gymxFlRaZcN4WIqCB4grH+/4/GRfgjcRRxmVbGKGVgqxZahbxhKf1FTJ8+HZ2dnUmd89nPfhZ/+MMf4HA48NnPfva0x77wwgupNIuIiFTocIc/71M397Z6sXBiGfQ67pZDRJRpXacsM+v2RxkIyCClDGzVQinvVkp/Effffz++//3v42c/+xlmzpw5pNifw+EYco7T6exfQuB0OlP5tkRElGfcgShOdIdy3YyMi8REHGr3Y1r10P6RiIjkE4jE0XNKIOBETxD1pRauZSdFUMqvYUqBgEWLFgEALrnkkkGPn65Y4BNPPIF7770X//Vf/4UnnngilW9LRER5RBAl7Gv15roZWdPsDqHSYUapzZjrphAR5a0md3DIY8GIgK5AFOVcokXUL6UcxTfeeANvvPEGXn/99UFffY+N5Mc//jH8fn/KjR3JmjVrMH78eJjNZixYsACbN28+7fHPP/88pk6dCrPZjJkzZ+KVV17pfy4Wi+EHP/gBZs6cCZvNhpqaGixfvhwtLS2yt5uIqJAdLYAlAafa1+rlfstERBkSiMTR0jN8ltnhdv+g3QSICl1KGQEXXXRRSt9MykAlqOeeew4rV67E2rVrsWDBAjz88MNYvHgxDhw4gIqKiiHHv/POO1i2bBlWrVqFT33qU3j66aexZMkSbN++HTNmzEAwGMT27dtx5513YtasWXC73bjllltw5ZVXYuvWrbK3n+h0MvE3Q6QE3nAMjd1DZ23yXSgq4GiHH5O5iwARkawkScLeVi9Ecfjn/eE4GroCmDimKLsNIzqFJCljeUBKgYC33377tM9/9KMfHfE5udfmPPTQQ/ja176G6667DgCwdu1a/P3vf8fjjz+O2267bcjxv/rVr3DZZZfh1ltvBQDcd9992LBhAx555BGsXbsWTqcTGzZsGHTOI488gvnz56OxsRFjx46Vtf1EpxOJj9CbkSw4M5Aboihhb4s3b3cJGE1jdxAVDjOcFsPoBxMRUUIauoKDdgsY/pgAymwmOK38/JUTM92SI0gStAooGZhSIODiiy8e8tjAAf5wNQL6TJkyZdRgQHd3d0LtiEaj2LZtG26//fb+x7RaLRYtWoRNmzYNe86mTZuwcuXKQY8tXrwY69atG/H7eDweaDQaFBcXD/t8JBJBJBLp/7fXWzhrXimzwlEGAuQ2cPAfigkoMvNmINuOdwfhL+B9nSWpd4nA/PGl0GpzfyNAiWN/T6RMrZ4QjrSPvvxYFIGdJ3owb1wJbNxFQDZioUb2U6SU9yulvwC32z3o37FYDDt27MCdd96Jn/70p6c998c//rFsuwZ0dnZCEARUVlYOeryyshL79+8f9hyXyzXs8S6Xa9jjw+EwfvCDH2DZsmXD7oYAAKtWrcKPf/zjFF4B0ekFY4W1fjobQgPe02BEAJihnVWBSBzHOuWvFaM2/nAcx7uDmFBuy3VTKAns74mUp9Mfwd6WxINysbiIHY09mDe+BGaDLoMtKxzMCEjOSMtXsi2lQMBwA/lPfOITMBqNWLlyJbZt2zbiuV/4wheGXbuvRLFYDJ///OchSRIeffTREY+7/fbbB2UZeL1e1NfXZ6OJlOcicQYC5OYPn3xPvZHCnZXOBUnq3SVAKR1grh3r9KPSYYLVyFkptWB/T6Qs7d4w9rR4kl5qFo4J2HbcjTlji/kZLAMGApITF0UAuQ9CyfqbX1lZiQMHDoz4vNz1AcrLy6HT6dDW1jbo8ba2NlRVVQ17TlVVVULH9wUBjh8/jtdff33EbAAAMJlMMJm4HQnJLyZwxCQ3b/jk+kFv6PRrCUleLZ4wekZZv1lIRLF3icA5Y0u4t7VKsL8nUo6m7iAOuHwpnx+KCtjS4Mbs+mLWbEkTAwHJUcr7ldL2gbt27Rr09f7772P9+vX4xje+gdmzZ494ntwV0I1GI+bOnYuNGzf2PyaKIjZu3IiFCxcOe87ChQsHHQ8AGzZsGHR8XxDg0KFD+Oc//4mysjJZ202UKM6cyq/LP2B9byiqmA/jfBeNizjUlvoNW75yB2Jo9YRz3QwiItWQJAkH23xpBQH6xOIith93o93Hz+F0xHgvlZS4Qt6vlDICZs+eDY1GM2Rgf9555+Hxxx8f8TwxA6OalStX4pprrsG8efMwf/58PPzwwwgEAv27CCxfvhy1tbVYtWoVAOCWW27BRRddhAcffBBXXHEFnn32WWzduhWPPfYYgN4gwP/7f/8P27dvx8svvwxBEPrrB5SWlsJoNMr+GohGwklCeXnDMYQG7FsvikCHL4IqpzmHrSoMRzr8iAvK6PiU5nC7HxV2E/S6lGLzREQFIxoXsbvZA3cgKts1BVHCriYPxpfHccYYGzO0UiCwf0+KUu6Hkg4ExGIxXHzxxVi7dm1/epxWq8WYMWNgNmf/Zvrqq69GR0cH7rrrLrhcLsyePRvr16/vLwjY2NgIrfbkzdX555+Pp59+GnfccQd++MMfYvLkyVi3bh1mzJgBAGhubsZLL70EAEOyG954441hd0wgyhQdK4rL6kR3aOhj7iADARnmC8fQ7B763lOvaFzEsc4AJleyciUR0Ui84Rh2NXkQzlAh5YbOAHzhGGbUOmFgYDYpMaawJkUpS3+TDgQYDAbs3r0bWq0W48aNy0SbkrZixQqsWLFi2OfefPPNIY8tXboUS5cuHfb48ePHy76EgShVBi07IrkEInG0eoYORnuCMXT4Ihhj57rfTDnSEch1ExSvyR3E2DIrTPrcFw8iIlKaE+4gDrb5Mr5ksssfxeZj3ZhR62TdgCQoZYZbLVS9NODLX/4yfve732H16tVyt4eIBjAZGAiQgyhK+KDFO2JV4f0uL5yWMhj1fL/l5gvH0OmLjH5ggRPF3sJXkyqYFUBE1CcuiNjv8sGVxVoqoaiAbce7MbnCjvpSa9a+r5rJMcMdkTHTY+CuW5nYgcuU5raTgkIyKFIKBMTjcTz++OP45z//iblz58JmG7wP8kMPPSRL44gKHVPT5HGo3X/aHQIiMREftHgwq64YWi7HkNXxrmCum6AaTe4QxpfZWCuAiAiAPxLHrhM9CEayv5WyKAIHXD70BGOYVm3n5/Io5JjhvumZHTK0ZKiVz++S/Zq/Wz4vrfOjcRVnBOzZswfnnHMOAODgwYODnmOBDSL56HX8e0rXsc4AmrpHH4x2+aPY2+rFWTUOfo7JRBAldDAbIGGCIKHTH2XNCiIqeK2eEPa3+nK+s0+bNwxfOIaZdU7YzVwqMJK4Qta8q0VczRkBb7zxhtztIKJh6Dk7nZbD7T40dCY+I+3yhCFJwFk1DmYGyKDDF8n5TZzauLxhBgKIqGAJooQDLh9aepRTYDYYFbCloRtnVjlQW2zJdXMUKSpDIGDNsjkytKRXJC70ZwI8tPRsxdXfiSmkpkJKgQAiyg4tZ6ZTIogS9rV6U1pT2OYNIyoImFlbzJoBaer0MxsgWd2B3uAJdwwhokITjMax64QH/nA8100ZQhSBfS1e9ASjmFrl4Gf0KeQI+qe77n7E6+p1Gbt2qpSSQcG7XCLKK+GYgK0N3WkVFnIHYth8rBve8Mh1BWh07qB8+zwXClHEaetZEBHlo3ZfGJuPdSsyCDBQa08YWxq6EYwqu53Zxl0DkqOUXQMYCCBSMAnK+KBQi05/BO8d64ZPhhuJvoDCCTeL3aUiFBUQiSkj4q02DKAQUaGQJAmH2/3Y1eRRzWDSH45j87Fu1sD5kChKXAaYJAYCiGhU/GBNjChKONzuw87GHsTi8g0+RRHY3+rDnmaPYtK41MLHbIqUyRHIIiJSupggYkdTDxo6A7luStLigoT3m3pwpMMPaaS9iQuEWOCvPxWiQu7vWSOASMEUUlRU0cIxAXuaPegJZm7g6fKE4Q3FMKPOCQerBifEy8FsyvwRvndElN984Rh2nfAgFM3+1oByOtYRgC8cx1k1joLd8lkhY1pVUUrwpDB/Y4lUghkBp9fpj+Ddo10ZDQL0CUa5VCAZAQ5mUxaKCsxAIaK81e4LY2uDW/VBgD6dvkhB1w1QyqBWTZTyljEQQKRgglI+KRSmd01h71KAbK4p5FKBxDEQkJ5AJD9ukImIBjrWGcCuJk/eTXQEIwI2H+tGd6Dwarxwh6vkKeUt49IAIlKVSFzAnmYv3DnsbF2eMLzhGGbVFcNm4sfoqQRRQlDGmZ5ITMZrxYVh/18ucm1R5I/G4bRyGQoR5QdRlLA3xW191SIuSNjR6Ma0agdqii25bk7WKGVQqyZKCZ7wDpZIwXQK+aBQCk8whl3NPYqoRh+MCNjc0I2zqh2ocJhz3RxFCcicHnnTMztkvV6flc/vkv2av1s+T5brMKOCiPJFXBCxq9mDbn/+z5ZLErC3xYtIXMSEcluum5MVvFdNnlLeMi4NIFIwnU4hnxQK0NQdxLbGbkUEAfoIgoRdJzw43O4r+KrBA3EQmz4WDCSifBCNi9h23F0QQYCBjrT7sd/lLYh7A61Ww/vVJBkVUliSGQFECmbSK+ODIpcEUcI+hacTNnQG4QnFMKPWCZNentRwNfPLvGPAmmVzZLtWJC70ZwI8tPRsxf68GEwhIrWLxAVsP95TsJ9nJ7pDEEVgWrUdGqVMAWeIQauFILC2TaL0DAQQ0WiUEjHMFX8kjt0nPKq4iXAHYnjvaDdm1DpRajPmujk55ZP55yXXuvsh19XrMnbtdEViIqJxEUYGA4lIhQo9CNCnpScECRKmVzvyOhhg0GkQzvwGTnlDKX27MlpBRMPSavO30xhNc08IW451q+omIhoXsf24G4fb/RDzrCJyMnwyZwQUKh/vqohIhWKCyCDAAK09Yex3+XLdjIxSalBdqZQy0aeMVhARfSgaF/F+Uw/2tXhVu71QQ2cA2xrdBbmncDgmIBZXTh0HNWOdACJSG0GU8H4TgwCnanaHcKTDn+tmZIxSBrZqoZSMAC4NIFVQy4DKauSfVDrafWHsb/UhmgcDSU+wd6nApIoi1JVY8jolcCBmA8iH7yURqYkkSdjT7EFPkNlMwznWEYBJr0VdiTXXTZGdUV8Y9zhyUUoNMI5aSBWm3/VqrpuQkIbVV+S6CaoUjYs42OZTdEHAVAiihAMuH9p9YUyrdhREoIiz2PLxcmkAEanIkY4AOnyRXDdD0Q64fLAZ9SjJs1pCBmYEJEUp75cyWkFEBaulJ4R3jnTmXRBgIHcghnePduFYZyDvawfIvWNAIQtFBdUujyGiwtLuDaOhM5DrZiieJAG7mz0Ix/Krwr5SquCrhV4h2y3m//QU5YW99y6W7VrBaBzzfrIRALD1jksKYpZWiQKROPa7fHAHCmNvYVHs3VfY5QljWrUdxdb8mg3o44twFlsuktSbYeG0GHLdFCKiEYVjAj5o9ea6GaoRjYvY0+zB3HElebNsMD9eRfYo5f3iCIhUIVODdatRz0BAlomihGNdARzvCkBUfymApAUicWxtcKOm2ILJlUWKSQ+TgyhKCEXza5Yj1wIMBBCRgkmShA9avBAEZi8loycYQ2N3EOPKbLluChUwjoCIKGu6A1Hsb/UiyMEiWnpC6PBHcGalHVVOc66bI4tQTIDEe0FZ8W+FiJTshDtUMJl9cjvS4Ud5kQk2k/qHYyI7/6QICnm/8mcqiogUKyaI+KDFg+3H3RzYDBD7MD1we6M7L9YL8mcrv3z4vSCi/BQTRBxlXYCUiSJwuD0/thRkX5WccFQZKbEMBBBRRnX5I3j3aBdae/K3GGC6uv1RbDrahZaeUK6bkpaooIyOLZ9E4ry5IiJlOt4VQCwPtvvNpQ5fBD1B9WdUcCIgOcGYMgorMxBApGCSQlKHUiFJEo50+LGjsQeRGG8URiMIEva2eLGn2aPaSvFxBgJkF+O6WyJSIEGU0ORWd/BaKY53BXPdhLR5QiwUnAxPUBnvFwMBRAoWUWmkXRQlvH/Cg2MdTBlMlssTxtaGbkRV+LOPqzSAoWRqDQoRUX5r84ZZIFAmnf6IqlPrPaEYJ3yS5AvHFfEzZyCASMHUGAgQRQk7T/Sg0xfJdVNUyxeOY9txt+qCAUrZDief5MnOUkSUZ1xeLveTiyQB7V713jN18H4vJUr4mTMQQKRgsbj6ou1HO/3o9qt/vVuuBSJx7FPZvsw6LUetctMyEkBECiNJElPBZeYNq/P9FERJ9fWNcuVETzDnS4AZCCBSMCWkDSUjGI3nxVo3pejwRdDpz33EOFF6HbsUuRl0DAQQ5btg9GThsJgKaq2EYgKXBcjMq9LASktPSHXZi0oRjAjoyPE9Hu/aiBQspLKK4W3eCPeRl5nLo570S4tBl+sm5B0z31OivNc64HPe1aP84G+Y68FlF1bZ/R7QuxS0sZuTP+lo6Mzt+8dAAJGC+QZEiEMq2JpFDW1UGzVlhZgN7FLkxkAAUX6LCyJaBlTfb3LnPl14NHGRgQC5iaL6dopyecO870uTNxTLaeanPmffmYhOKxoXB6WKdfmjKCsy5bBFo+MacflpVfSeWgw6aLW9NzQkjyITu2kqHANT5JXOapTnb7OhK4D4gDT7SExAU3cIY8usslw/E1Q2XlUNUQLUshpMFCUc6+TOUHI42hFAeY7u73mHQaRQrZ7QoM62xRPClCp77hqUAKfFgKZcNyLPOMyGXDchYRqNBjajHr6wem7mlc7GQAAVkOl3vZrrJiSsYfUVaV/DE4oNW1fnSIcf5XajbMEGuTHoLz+NRl3va7svwmwAmXhDMXQHoii1GbP+vZnHSaRAcUEccnMQCMfR7lP2evGyIqOqOjI1qHQoOwvkVEVmZd64qpFWC1i5NIAoLwmihA9aPMPOrguihL0tXsWmihu0HD7ITW33Ttw+Ul65qgfFO7YCVogpeGpxpCMwbBXWgy4/Sq1GxVZnN+i0qCm2oInFY2RRYjPCrqKMAICp7HKyGvWqWhpClK699y6W7VrBaBzzfrIRALD1jksUdx+x3+VFMDLyjGpPMIYjHX5MqlBeJqBRr8x7EDVT03saE0R0B5Rf1FJN2n1hTBXtWe/zlfWpSFlVaCl4atHpj4w4kA7HBOx3+TCj1pnlViVufLkVLZ4QtxaSwaSKolw3IWkMBMiH7yUVmkwN1q1GvaICAS09IbT2jD4D2NAZRLHVmLP1wyMxqWjQqhYmvXqyv/zhOGsBySwuSAjHhax/TvEvmUhB/JE49jR7TnuMyxNGg4ILtJj0Okwao74BrNLUlVrgtKgrGwDgmnY5MRBAlH984Rj2u7wJH7+n2aO43WO0Wg0MDAbISk3BFQ0T1fIG7zIKWCGl4KlBMBrHjkb3oOrBIznc7odBr0VtsSULLUteXYkF7b4I3IForpuiSlajeoMpJr0WOq0GgsiMkHRZjeqZISKi0cUEEbtPeJKaTY0LEnad8GDeuBJFLRUy6bWIDbOEkVLDrWIpFzhaK2CFkoKnBr5wDDubehCJJd6p7mvxQhAkRW4xpNFoMKPWgc3HupN6TdRbIG5mnVOxdSBGo9FoYDHq4OfOAWmzMBBAlDckScLuZg+CKVRa94Zi2NvqVdSyQLOBn/NyUlNGgN1sgIGBIFlZjTpYchAM4miNKMc6fBHsafGktKb+YJsPoZiAyRVFipopAHqXCJxdV4ztx92cHU7CWTVO1RUIPJWFN4iyYECVKH/sd/nQ7U89S87lCcNq1GGiQrLFMjFwjci4BCISF4b9f7mYZB60qSkjQKfVoLbYouhlqmpTX2qFJgdrLniXQZQjkiThaGcAxzrS+yBt6g7CF45hRq1TcR2J02LAWTUO7Dpx+roH1OuMiiJUOsy5bkbaOJOdPsOHSyyISN0kScKhdj+a3aG0r3W0IwCtRoPx5TYZWpaeTAQCbnpmh+zXBICVz++S/Zq/Wz5P1uuZDOrJCACA+lILmtxBFoaWgVHfu+NWLjAQQJQDoaiAD1o86AnGZLleTzCGd492YXq1AxUKG0hWOMyYVCHgcLs/5WuoZZYgnRmCKqcZExRwcyeHXKS35Ru+h0TqJ4oS9rZ6Zd0j/HC7H1FBxOSKopzMIPaRe0a80BlVthzQpNdhRo0Tu070QGIsIGVaLTCrrjhngX8GAoiySJIknHCHcLjdL3u6fF9BoUpHBFOqihS1Fc34chs8oRg6fKntO6uWWYJUZwiKzHpMr3bI2pZcYkZA+lgokEjdwjEBH7R4M1I0t7EriFBUwLRqR872n8/EwHXNsjmyXSsSF/r7+IeWnq2oe6JTaTTqqhHQZ4zdhMkVdhxs8+W6Kap1Vo0TTmvuloMyEECUJd5wDPtbffCG5MkCGEmbN4yuQASTKopQW2zJ6YzBQNOqHegJdbG4zCk0GmBGrVNxNR7SwUFs+hhMIVKv5p4QDrb5Mpo23eGLoCfUhWlV9pxkAmYilT1TWQYmvU7RGQwGnVYx92rJGltmRSQu4HhXMNdNUZ0plfacLwdlIIAow6JxEUc6/GjpCWUtfSouSNjf6kNLTxhTKotQbDVm5xufhlGvRV2JJaWaCPk8SzDGbsq7/eItBh23EEyTPc9+J4gKQSASx4G29IoCJiMWF/szASdXFmW1TpBBq74ZbKXS69QZBOgzudIOk17HzIAEabXA9Gonqpy5X8rLOw2iDBFECSfcQRztDOSsmIo3FMPWBjcqHWacUWHLeRXy8iJTSoGAfJ4lKC8y5fT7Z4JGo4HdrJetBkYhcljUvXMEUSEJxwQc7Qig1ZO9gP9Abd4wOvxh1JVYMb7MlpXlAixmKh+DyuoDDGdsmRVmgxZ7WjwQmfg5Ip1Og1l1xSi15X6CDgDU/5sHYM2aNRg/fjzMZjMWLFiAzZs3n/b4559/HlOnToXZbMbMmTPxyiuvDHr+hRdewKWXXoqysjJoNBrs3Lkzg62nfCNJElp6QnjnSCcOtfkVUVG1zRvGpiNdOODyZWQbnUQZVB71zgS1zwSMxMmBbMpMBq3idgAhoqGicRGH2nx450hnVrP+hiOKvbUD/nOkE0c7/IgLmR2NMRAgn3x5KyscZswdW6q6HRCyxWrU4dzxpYoJAgB5EAh47rnnsHLlStx9993Yvn07Zs2ahcWLF6O9vX3Y49955x0sW7YMN9xwA3bs2IElS5ZgyZIl2LNnT/8xgUAAF154Ie6///5svQzKE+2+MN492o29LV5EYsoKiUpS71aD7xzuwpEs3CQMxxvi3vKnytf3ZIw9/zIdsiUfs0SI8kkkLuBQmw//OdyJ411BRc2ACoKEox0B/Ptwb0AglqG+XmSpeNnk01vptBowf0IpSmycDBhojN2EcyeUKm4pqLJak4KHHnoIX/va13DdddcBANauXYu///3vePzxx3HbbbcNOf5Xv/oVLrvsMtx6660AgPvuuw8bNmzAI488grVr1wIAvvKVrwAAGhoaEmpDJBJBJHKyGrrX603nJZEKeYIxHGr3qSIVWhAlHOsI4IQ7hInlNtSVZK+gYKsn/X2U8027N4wzxthUWyhoJE6LAUa9FlEWh0xaBYMoisX+vrCFYwIau4NodocUXwMl/mFA4Hh3EPUlVowttcq6ZCCfBq+5pvBfpaSZ9DrMqS/B4Q4/GllEEGdUFGF8mVWR93mqzgiIRqPYtm0bFi1a1P+YVqvFokWLsGnTpmHP2bRp06DjAWDx4sUjHp+IVatWwel09n/V19enfC3KPH/k5AxsKJpemnw4JmBPswdbGrpVEQQYKBYXccDlw6ajXejOwPZGp+oJRtGVpQJKahKMCmiRcY9ppdBoNKgpzn0hHLWxGHWKShukwdjfF6ZIXMDBD5cANHYFFR8EGEgQJDR0BvCfI5043C5fhkAulxnmm3wMmGu1GkyptGNGrbNgl5HodRrMHluMCeXKnexRdSCgs7MTgiCgsrJy0OOVlZVwuVzDnuNyuZI6PhG33347PB5P/1dTU1PK16LM6/KdHIymOjCVJAlN3UFsOtoFl8oHccGIgO3H3djT7MlYxy5JEg64WE12JEdkvDlTkvpSK1hYOjljS5U5a0C92N+rUyCS2hKs/hoAh7vQqLAlAMnqDwgclqeGQCQPB6+5EokLkPI0xaLKaca5E0oLblthu1mPBRPKFL/UT/VLA5TAZDLBZFL2D5pO6gycTOvs8EcwBfakzg/HBHzQ4oU7C7Po2eTyhNEViGJatR0Vdnlncls8YfjC+bkWXg7RuIiGzgAmVyb3u6h0Jr0OtcVWNHUzNTARJoMWtcWWXDeDToP9vXqIA2btO3xRjEmiXxNFCce7g2joyt2uP5nSt2SgsTuIieVFqCuxQJvCjG26GZV0kiQB4ZgIS54OlotMepw7oRR7W7zo8EVGP0HlqovNmFrlUEUmhKrnasrLy6HT6dDW1jbo8ba2NlRVVQ17TlVVVVLHU34JRQV4B6Twe4JRhGOJd2a9xQC78i4I0CcWF7GryYP9Lu+gm6h0iKKEox1+Wa6Vz5rcwaR+F9Vi4hgbKwgn6MxKe0o35EQ0VPuAAUfvtn6J9WntvjA2He3CkXZl7PqTKXFBwsE2H9471p3S8sBAlMF9OflTzFpRC4NOi7PrnBhfbst1UzJGowGmVNpxVo16lkOo+u7MaDRi7ty52LhxY/9joihi48aNWLhw4bDnLFy4cNDxALBhw4YRj6f80nJKsTpJQkKp/ZLU22HuavIgnsc3Bn1OdIewpaFbloh/VyCquB0UlEgUE/tdVBuDToszq/Ir0yETKh1mVDhYU4FILo0DMpHCUWHUmchQVMD2Rjd2NXkKarY7EIlj+3E3dp3oSWp5YCBSOO9RNgQLILCi0WgwqaIIZ9U68m7ZoE6nwdl1xRhbZs11U5Ki+qUBK1euxDXXXIN58+Zh/vz5ePjhhxEIBPp3EVi+fDlqa2uxatUqAMAtt9yCiy66CA8++CCuuOIKPPvss9i6dSsee+yx/mt2d3ejsbERLS0tAIADBw4A6M0mYOaAesUFESfcQ6vWN7mDqC+1jhi9i8ZF7G725G0WwEh84Tg2N3RjZq0zreJlXYH8TwOTS1cgmpfR8gq7GdXFEbT25F+gQw4mgxZTqopy3QyivNEdiCJwynK0xu7giMG2dl8Ye1u8BRHoH0m7NwJPKIYZNU6UJNDn52OBu1zKxzpBI6l2WmA16LGjyZ0Xf3MmgxZzxpYobmvARKg+HnP11VfjF7/4Be666y7Mnj0bO3fuxPr16/sLAjY2NqK1tbX/+PPPPx9PP/00HnvsMcyaNQt//vOfsW7dOsyYMaP/mJdeeglz5szBFVdcAQD4whe+gDlz5vRvL0jqdLjDj9gwHVckJo6Yuh6MxrG1obvgggB9YnEROxrdaO5Jfds/ZgMkLpKHSwP6TKtywGnlvsKn0mk1mFVfDJM+P9eGEuXCCffQuiQ9wRh84cG7+4hiYWX7jSYSE7G90Y2jHf5Rl1IU0sA1Gwqt+KLTasC88aWqXzpoNepw7vhSVQYBgDzICACAFStWYMWKFcM+9+abbw55bOnSpVi6dOmI17v22mtx7bXXytQ6UoJWTwgnukcezB7vCsJpMQyaLfCGY9jR2DNs8KCQSBKwr8WLaFzEhBRmq9WyTkoJ8vm90mo1OLvOiS3H3HlZCyFV02sccJgZICGSSyQ+8jKA5p4Qplb1/r1JkoS9rd68XJKVDkkCjnYEEBclTDlNAVu9VoPCnCLJDINO3QPiVBSZ9Jg7rgQ7GntUuRynyKzHnLHqDuQX3m8dFZxOfwR7W7yjHrenxdNfMMcTimHbcXfBBwEGOtLux5EUiv6pNUqaC0Xm/H6vTHod5owthlHPrgcAzqyyo5J1AYhkdcIdwkiT2a2ecP9M9oE2H4MAp9HYFTxtoV8b+3ZZFer7aTX2BgPMBnUNpovMve1WcxAAYCCA8pw7EMWuEz0j3hQMJIrA+009aO0JYWdTT15XC07VsY5A0lvBcaCTOLm3bVQim0mPc8aVwFDgwYAplXbUl6qrqBCR0oVjwqAigacSBAlHOvxo6g6eNkuQeh3tCKDdO3ywpJhLvWTltBTu+2k26HDOuGLV3BdYjDrMri/OiywO9b8CohEEo3G8f6IHYhKT+pG4gL++3zKkyBCddLDNhy5/4gUALUYdgwEJKDLrUV6UelFGNSky6XHOWPV0+nKbVFGkusrCREoXjYsJBfGPtPmx6UhXllqlfvtcvmELA9YWWwr2M1xuFQ5TwWdPWo16zK4vhk6n7CWSBr0Wc8YWqy6DYST8C6a8JIoSdp1IvvjPCXcI/nAcDV2BhPccLjSSBOxp8Sa1zvuMClvebRUjt8kVRdBolN0BysluNmDeuBLVFwpK1plV9rzcGYIol7r8EWw+1g3/KEF8SZLQ6A6isTuIpu4g4snMFBSoWFzEvtahyyv1Oi3GM6Api4ljuGsM0JsVcVaNI9fNGJFGA8yqc8JqzJ+gTWHdgVHBONrpH/WG4FTuYBQ9wd6KwsGogDYf1w6OZKQbg5FYjXqcwY5uRNXFZpQVmXLdjKyzmfSYN64UVmN+RNZPR6MBzqp1cDkAkYzcgSh2NLqxo7EnoeB0uy+CQKT3uK5AFPtdPrT5wgwIjKLDF0GrZ+hSivoSK5cIpOmMiqKCzwYYqMJuVmywfEqlHcXW/MrcZCCA8k6nP4KGzuTWscdFEc3uwZ1cmzcCX4RLBEbS5Y8mVWhpbKkVpQWS+p4Mq1GHM09TmTnfWYw6nDOuJK8LJWq1wMw6J6qdllw3hUj1QlEBDZ0BvHOkE9uOu9HlT6x2fSgqoO2U9e5xQUJrTxh7W7043hWAJxRjNuAI9rt8Q4ItWq0GM+ucBZfZJZcxdhOzKoZxxhib4u4Xq5zmvAzk8y+X8kqnP4JdJ3qSPq/dG0FcHNz5SxLQ0BkYsu8wnXS43Q9RTOymSaPR4KwaR96sq5KD7sObKH0eFJxJh9mgw9xxJSix5d/Mkl6nwTljSwqiECRRJkiShJ5gFIfb/Xj3aBf+c7gTh9v9CEYSX54mShKOdwcwUnclioA7GMOxzgD2tPQGBboDUUQFZgr0EQQJe5o9QwIlJr0OZ9cW5/X2t5lgM+lxVo2joJYEJkqj0WB6tQN6hdQLsBh1mFqVnxM2+TsFQwVFFCUc7QygoTOQ9LkxQUTnCMXvhA+vW+kwodJuHvUDOyLz/uiRuDDs/8vBJMOAPBwT0NwTSjhKatLrcHa9E9sa3BASDCDks7NqHLBzD3kAvXsoz6kvwe5mz4h7gKuNyaDF7Ppi/owpp4JR9WS2WY16SJIEfyQOdyCG7mAU7mA07V18XN4IwrHEBvWCKMEdjMH94VJBs0EHu1mPIpMONpMe+gIueNMTjKGhK4gJp6RuO60GzKh1JrxLU6EzGXoLzhX6JMDpmA06TKt2YPcJT66b8mFQIj9/VgwEkOp1+CI41OZDMJraQLnLHx1xlgDozQxweSLoCcZRW2w+7U39Tc/sSKkNiVj5/C5Zr/e75fNkuU5TdxB1JZaEo9oOswFn1fZ+uBfyDcPkyiJUcDeFQbRaDc6uc2Jfqw8tPere2sv64ZIHZsBQrk2/69VcNyFhf73pAriD0aQL/Y6mKxBJeQAfjgkIxwR0+Hr/bTX2BgQMOg2KTDroNOkPEJQe9B+ooSuA+hLLkIHRGLsJU6sd2NeSeP2gQqTXaTBnLPuGRFQ6zOhwRpJahiq3sWVWlNiUtUxBTgwEkGp5wzEcbvejO8H1gcMRJQldgcRmH8MxAUc6AnBY9Kh2mmEx8M8H6C2s2BWIojyJYncVdjOmVksFe8MwrsyKcWXKLIaTaxqNBtNrHDDqtSll+CiBw2LA7PpiGLm1FlFSMpUNJGfQORgVEIwKeOSNw/JddAClBv37CIIElzeMupKhmYC1xRYEInE0diVXp6lQaDTAzFoniwMmYUqlHV2BKGLDbGGZaVajLu8LXfM3kWQXz/CaunBMwOF2vywRQm84jliSMw/eUBzekB+lNgOqnBYYB0TF1yybk3abBorEhf6bgoeWng2TXpkR5KbuYFKBAKD3hkGSJOxv9WWoVco0tsyKyQVcHDBRkyqKYNJrccClrt+PEpsRs1j3gRRk772L076GJEk41O5Hc3doSL9UW2LFWTUOaEdZIx4XRLT7omj3hdETiBZ0Rpja+U6zK9PkiiL4I/G0Jmny1eQKe0HuEJQOo16LMyvt2NOc/SUCU6sdeV/7goEAkkVswOD/eHcQM2vlT6MRRAkNXQEc7wpArp1+OtOYfegOxOAJxVHhMKGiyASNRiN7Ct5AJr0uo9dPR5c/imA0nvTeqnUlVui1Wuxt9cj2M1WyMyqKhqytpJHVl1qh12mwt8WrikHDGLsJM2udow6IiLIp3T2vJUnCfpcPnb7okD7IpNfBF47jSEdgxN/9aFzE8a4ATvSE+tf6GxUa1E7GjR+deNrnrUYdKhwmOEyj1whRS9C/j+00v1MajQYza51492gXIgnWZSgEFQ4TxnKHgJRUOc1wecNp3bMnq6bYgtI8XhLQh4EAStups7pNXUHUOC2yRj27/BHsd/kQSrEOwEiCMQGGNGbuBLF366FufxR1pVbYCzjdq6k7hDNTqKpa5TTDpNdid7MH0RykfmWDTqvBtGoHqpysCZCsaqcFOq0Ge5qVHSyqcppZAZry0n6Xb8j2uqfq8EWwu9kzJBjg8oSx3+WVfc2/Eox27xATJDS7w/BZBNSXWmBIsEaBkoP+QG96+2g7vBh0WpxZZceuptwXelMCg16b0v0RnTS1yo5NMhQOTYRRr8XkyvxeEtCHuYuUtkPt/kFROkkCdjV7ZNl2LyaI+KDFgx2NPbIHAeQUiYs40u5HkzsIQcmjlQxq8YQS3krwVCU2I+ZPKEWxNf+qq1uNOswbX8IgQBoq7GbMqiuGUot115VaGASgvHS43T9qEKBPhy+Cfa7eui+9WQRe7Gn2KCIIkMuZPW8ohoNtPgRUtHvD6YwvtyW0E0qF3cx+70NnVtoVn+WhdGaDDpOytF5/apU9rUlCNSmMV0kZ09QdHLYojCBI2NnUg3Aa2+m1+8J492gXWntyVy00WV3+KA60+eEJpR8EURtBkNCTxus2G3Q4Z2wJJoyxIV/GUzXFFsyfUMrt42RQVmTC7PoSxa3XG1tmxdQqBgEo/3T4IkkX7GztCaOhy4+dTT040a2cnT+qnWaYDbm75Y3FJRztCMATUve6eafVgAlJFLqdVFGk2AButjgsBgZEZFJXYsn4hNEYu6mgdnQq8D9PSkcoKuBQ+8iFvCIxEYfa/ElfNxCJY0ejG7uaPKpcXxaNizjWGcCxzoDs2wApXbqp/VqtBmeMKcK8caWwGtUbPTfotTi7zonpNfm792wulNqMmDO2GDqdMgbd48qsmMLCj5SHJEnCwbbkC3WGYwL+sduFdq+yAvg6jQYTym0w5PCzQxAlHOsMwuUNQULusySSZTXqPszMSvw9NBt0w+4uUEgmVRRGink2aDS9yywzFVzS6zQFt4SDd6iUMpc3POqa3TZveFAhwdMJROLY0+zBu0e70JUH1WY9oRj2u3xo6g4WTEDAapJn8O60GrBgYhnqS9V3A1HhMOG8iaUFFVHOpmKrEXPqi3OeGTCOuz9QHnMHY0kvx3MHozjU7kcgIsATUl4avEmvw8TyIuhz/Nnh8kRwtDOAmIqWEZoMWswZW5LSlqjjy2w5/7zOlRKbsSAKzmWTzaTHhPLMBFemVNphVnB9jkxgIIBSlujg9nSBAFGU0O4LY3ujG5uOdMHlCauiOniiJAnoCkSxr9WHo529SwakfHqBAzitBlmLJeq0vZHZueNKYFFBdoBep8GMWifOrivmWsAMK7YaMTuHwQBuAUn5zn+a7eFOJYgSGruDON4VhPBhnZh0lgVmksWow7gya86Xn/lCcRx0+WSppZRpOq0Gs+qLU+6HjXqtKoP6cjhjDHcJyoRxpVbYzfIW5y4tMqKm2CLrNdWAgQBKmQaJ9aSnHidJEnqCURxw+fCvw53Y1eTJ2X6zRVms8u8NxXGsM4C9rV4094QQiMRVmR44HL2uN10rE+ukS2xGLJhQitoS5X5AlxUZcd7EMq4DzKISmxGz6rNfQLC2xMLlAEQfCsd7lwh2Bwb34Uru2exmA+oUcMMfEyQc6QigVeFLBWbUOuFIs87N2FKrYpZ0ZUtpkRHFVmYDZIJWq8H0GodsAT2dVoNpVQ55LqYyhbvXGaUt0fXgkbgAs0GLnmAM7b4I2n1hxaz9H19mRasnDG8Ssx/pigkSOnwRdPgiMOg1KLYYUWwxwGrSJRxcURKDXos5Y4szGlTR67SYVu3AGLsJe1u8itlmUKfVYHJlUcGvgcyVUpsRM2qd2H3Ck5VMoiqnGVMLbP0gFaZEb7CPdgSGDQArPRO8rMiESFxEexb3JR9JmycCvwKXUgDA5MoijLGnvxW0Ua/FGeVFKdWdUCONBgwYZ5jdbMC4MlvSBU2HM6miSBWZp5nAQAClJBwT0Ok/fQcqSRL8kTj+dagDDosRMYUM3gbSflhAqM0XgcuT/eJGsfiAoIBOA4fFgBKrETaVBAWKrQbMqHVmbU1VeZEJ500sw75WLzpyfAPnsBgwo9YBq5Efo7lUYTfjrBpgT3Nm96seYzdheoayXoiUpieYWMp6XJSGLcAXiChzYDtQtdOMUEyAL4sTASNJZ8edTKlymjEuiR0CRlNXYunPhsx3Y0utWc04LVQTy21o94YRTGN7cafVgDoFZ5xmGn9LKWnhmICdTT39awFPFRNFuL0xdPkjiH24f3BNsRkV9tTTpiMyrjccWNug7/9LLAYYdBo0u0OIpBGwSGff0ZggocsfRZc/CoNeg/IiE4pkKr4nN51Wg4ljbBhbas36wMio12JWfTFOuIM42OYbtWBlJowvt2FiuS2p6smUOVVOMyJxIaVdShLh/DDgxZ835TtRlHC0M4C2NKv++yMCmrqDqC2xQJtEH5Hpvv5UVU4TApF4Sv1+Pu8zXmw1YFq1vKnSfenc245356TfzharSYcJ5awNkA1abe+y1G3H3Smdr9EAU6vsBR3gZyCAEiaIElp6QjjS4UdcGDkP92Cbf0jH39IThjccQ7XTAlsKM6g3PbMj6XMSsfL5XbJeb8XHJslynVhcQmtPGKICCwuWFRlxZpU95zPhdSVWOC0G7D7hSSsanAyDXosZNQ6UFaWfKknyGldmQzgmoqk7KOt1+7bMKtSq11QYonERLk8YTe5g0rsFjKQrEIU3HEO53YQSqxHGBAbOaunrAfn6e6Wxm/WYlaFirE6LAVMq7djfmp9LBHRaDc6uK+a2wVlUYjOiutiM1p7kg5fjyqywp1n/Qu0YCKBRhaICmntCaOkJJbQ2WxAlaIdJFfSHBRwK+1Fk1qHMZkKxxVDQUbhEjJR1kQtmgw5TqorSyuyQm91swPwJpfigJfNLBRwWA86uy94yCErelMoihGOCbL8Lep0Gs8cWp7RlFpHSBSJxdPmj6PBH0BOMplRnY7St+GJCb1C7tSeMIpMODosBdrMeZoM6lr9li9OijMGIzaTHnLElGc12qCuxwhOKpTRwU7rpNQ4uCciBSRVFaPdFIJxmkvJUJoMW42Vc+qJW/G2lYfXdTLu8YXgSXCvYp9Juhi8S618WcCp/WIA/HMQJrQZOix4lViOKTPrTBgXWLJuTVBtOJxIX+mcHHlp69mm3evOGY2j1hNNaLpAqg16DsqLcD7q12t59gMcpdC9gvU6Ls+ucONoZwLGO9IvGDKfKaca0aociXz+dpNFocFaNA5sbuhGMpDerqdEAM2udOc98IZJLOCbAHYyiOxCFOxCTZYu/yZVF6PRHE6op4I8I8H/4d2nQaVBk1qPIZECRSdffD+eqrweAqCDiWGcgq/29QadBTbEFVgUEmB0WA2bXZyfwOb3aAVFE2ktQlGRajQOVjtzfsxUik16HieW2pJYHTq6wM3MDDATQADGht4KuyxOGO5D6dn6VDhPq9RZ4w3F4QzF4w8MHBQRRQncghu5ADHqdBk6LAaVWI2zDRFNNGeokTXrdaa89xqBDWZEJHb4I2rxhZHqC3qjXwm7Ww/nhrEk0x7srjLGbMKXSrvhqqhqNBmeMKYLZoMP+Vq+sFeTHcc94VdHrtJhVV4zNDd1JzQ6calJFEZeAkKqJooTuYG/dma5AJO3g2HAM2t5ZNb8tjhM9QYQT7LNiggR3IAZ3oDeAYNRrYTfp4bDoYTcbkqorkIjR+nqg9z5jWrUDRzsCCMlYq2A4Gg0wpsiESocJOq1W1toIqSixGTAriyntfUFbUZJyXvhXDmdW2VGrgC0pC1l9iRUn3KGEljY5LAZu9/whBgIIwWgcx7uCcHnCsqWiazS9A/u+dLdQVIAvEoM/HIc/Eh8yoI4PKJRnNuhQbjeizGpUxNIBrUaDSocZJVYjWnpCslb31Wk1sJn0sJv0sFv0MI8yY5EtFqMOZ1bZUa6ygVBtsQU6jQYftMizndzEMTZMHFOU/oUoq2wmPaZXO7D7RGo7CZTbTbJWyybKJk8whhM9waRTZdNRZNZjSqU95aB5NC6iKx5FVyAKrRZwmg0otRmzvn7XoNPijAobjnUGEMhA4ATorTtSX2qBxaCMW/Byuwkza51Zz3jTajWYWevEPpdXtcsEtFpgapUDNQwC5JxW27sL2N4W76jHThzD/r2PMj6FKCcEUcKRDj+auoMZ34PbYtTBYtShwg6IkoRAJA5fuPfr1Mh7OCbgRHcIHd4I6kosiinkYdRrMb7cBm8ohuae1HcXsBh1cJh7Zz1sRp0igh19tNreomvjFboMIBFVTjNESUqoMzid8eVWBgFUrNJhRocz+W1BDXotplUzA4TUJxCJY7/Ll1ZGXzr6guYOswENXamn2Isi4A7G4A7GYDXqUFtsGTZTMFP02t497xu6AvDKvLVgpcOEKodZMf1+ldOM6dWOnO2IotVqcFZN7xKsI+2Z2fUlU/Q6DWbVFaPEZsx1U+hD1U4zGjoDpy0g7bQaVDfJlUkMBBSovi0A/TnYP1er0cBuNvQP8CNxAZ5QDJ5gDIHowO1+RBzpCKDS+WHHqZDCQg6LAUVmPdp9EbQnOPNRZNLBaTHCaTUkVDk5F5SyG4AcaootCMcEHE2xZkCV04xJFRwMqt2ZVXa4g1FEklhiM63aPupaYiKlafWEsL/Vp4gCsxajDpMri3C8KwhfmvcYwaiAwx1+VDvT24I4WVqtBuPLbWjuCaHLn35gRavt3Vu+2KKcQeP4chvOGGNTRFBiQrkNNqMOH7R6s5bFkg6rSYfZ9cV5cb+UTzQaDcaWWU+7K8W4MmsWW6R8/A0uQO3eMPa5fIjloADecEx6HSrsOlTYzQjHhP71jH33M22eCEJREXUlFsUMorUaDaocZpRYDWjpCcMzzHIBnVaDUpsRZUVGxaT8D0eJuwHIYeKYIgQiQtLFiOzm3rRyUj+DTovJFXbsaU5siUBZkTHv/g4ov0XjIg61+xSXWq3XajGh3IaWnhA60xxIS1LvFsSBiIDaLN4HaDUa1JdYYTXocKInlHLmpEGvwfhSW1azGk5Hp9VgugIL21U4zLCZ9Njd7MnJJFWiWDxY2aocZhxu9yMyzCpes0GHMcwGGEQZn0qUcaIooSsQRWN3oL84jxKZDTrUllhQ6TChzRdBpz8CSQK8oRgOROIotRlRXmRUzIydSa/DhFOWC2i1vUWAxthN0GuVEbgYjk6rwbgyq2J3A5DDtGo7fJFYwkWy9LrePYBzlSZJ8qtymnHCHRy1qrlW25tBQKQGvdv6BtHkDil2BlWr0aCuxAqrUY8T7mDaxXY9oRh8kdiH9wGmrAXYy4pMMBl0aOoOJr3cocisx7gyKwwKuRcoMutxVo1DMUsuT2Uz6XHu+FIcbPOh2R3KdXMG0Wk1OLPKznoACqfXaVFTbMEh19CsgLoSiyIyYJSEgYA8FokL6AnG0OWPot0XRlyhNwvD0eu0qC22oNRqxPHuAMIxEYLYW122wxeB1aiD02pAkVEPqwLW2fctFwhGBZgNWkUHAIC+1PfeKvv5TK/TYmatE1sauiEmcP82vcah+B0SKHmTK+zY0tB92mNqii1M8yRF69vWt90XTiqgL3dF+khcGPb/R2Iz6jC+3Ipmdxj+yOlnekfbv14UgU5fFJ2+KKxGHYqtvUWJMz05UGTS48xKO9p8YbT7IqNmB+i0GlQ7zSgrMipiWaNOq8HEMTbUl1gVH+jWaTWYVu1Amc2omOxVh8WAs2ocisnqoNOrdJiHDQRwp4Ch+BudJyRJQiB6cq29JxRDYJQOVw0sRh0mVRQNqeAbjAr9xUB0Wg2sJh2sBh1spt7AQC4G4mVFJny8zgkJwPGuIJq6g4pYrzlQaZERkyuKFDsbkAl2swGTxthxsG3kNWNA70CQaeH5yWk1oMRmGHHwpNEA40pZRZiURZIkeMNxdPoj6PRFUl5vf9MzO2Ru2Ukrn98l6/VWfGxSwsf23Qe09IRhMmjhNBvgsBhgM+kyMvjWajWodlpQYjWiyR0ccVeBEpsB1U7lLGUsKzJiapX6gtwVDjMcFgP2tXplqdOQCo2mt5bChDKb4gModJLTYoD5lN93p9WQ95NfqWAgQKUicaF/wO8Nx+ANx5NOD5RzliDZGYJkTSi34VCbf9i0PEGU4AvF4QvFAfTuR2vSa2H9cKcCWxayBqwmHWbVOfv34J1UUYT6Ugsau4I44Q7lPCBQWmTEGeVFcFoLJwAwUH2pBS5vGN4Rtn40GbSYXMkdAvLZuDIb3IGeYZ+rdJhVd5NM+csTiqHVE0KHL5JUoctCFomJaI9F0O6LQKfVoNhigNWUmb9ps0GHyRX2/p9T+MOfkdmgQ12JBUUKmTV2WAyYVFGEUhVXtTcbdJgztgQn3EEcavNn9V7KatThrBpnwd43qV25fXAtgAo7awMMRxmfVjQqUZTQHYyiwxdBdyCK0Gm2xkhUpmYJ5J4hAIDfLZ+H+hIrDncktr1MJC4iEhfh/nBdsFYLWA06OCwGmAzyR+nPqj4ZBOhj0uswudKOcWU2NHYHccIdzPryjDF2E8aX2+C0FHZHptFocGblyOnhZ4wpGjUlldStzGaE2aAbNgBayzWfpACiKGFXswedvois112zbI6s14vEhf5+/qGlZ6eclh+KCWjqDiEs8+SB8GFNJJc3s0EUp8UAh1kPd7B3trrEasz5MkXgZCZlhd2kiPbIoa7EilKbEXtbvKPWe5FDfakVkyqK8rZ+UiEoOWWHDm7zODwGAlTAE4ph9wkPwjKv81ObIrMeZTYjulLYI1kUAX9EgD8iICbIe3Mwtsx62oixUa/FpIoijCuz4oQ7hMbuYMbXvFU5zRhfblPMzIQSOK0GlNtNaO4ODnrcatShmuvG8p5Go0GV0wRPcPDnh9nQu86YKNckAHGZ+ycAMGUwHdak16V8fdOHwXk5dhbIFbNRh8UTqlFk1qOpO4iGrkDO6jEZ9b07NdQWW/Iyjd1q1GPuuBI0dgdxpMOfUN2fZJkNOkyvcag6i4J6Oa0n73/1Og3svB8eFt8VFdBqer/kJucsgVwzBKOpLbYgFDtZHyDXnFYDJo1JLKXcoOvtpMeWWnHCHURDl/wBgSqnuXc/Xn7gDauuxDIkEFBXYs2bWRM6vUqHGQdO2V+4ypk/s2akbjqtBvPGl8IdiMLlDaPDF0FUAYXSMqlvZwGLQYfmnlDaOwsAve+jw6KHJcPrgZ1WA86uc/bf74wvt6G2xJL1GkF6nQbjy2yoL7Xm/Qy2RqPBuDIbSm1G7G72JLwjUCIqHWZMrbYzOzBPDPw52i0G9vMj4GhBBexmA86bWIbOQARtngg6AxFZtgvK1CxBOjMEo9FqNZhYbustHphkMMBk0KLYaoBVprXAJTZDSlvN9W7bZ0NNsXw3DGVFRkwqsCKAqSizGaHXDf55VTi4bqxQ2M0GGE9ZGlRm48+flKXEZkSJzYipVRL8kTi6A1F0BaLwBGM5rzeTKWVFJliMOhzvSn6LPq22N7OryKSH3dRbG0ADjey7JQw0tsyKSWOKhvT/Bp22v0bQ8a7eJYGZmLkGeu8l6kutvdsTFtjg1W42YMGEMhxw+dDSk942gzqtBlOq7FwilseYHTsyvjMqodVqUGE3o8JuhihK8IRi6ApE0OWPplxJWK30Oi3OqChCq6d3xmQkOq2m98bAoofDbOiv4CvHzcG4MivOGOYmIBl9Nwy1xRbsc3nRnUJqpMmgxZlVdla7T5BGo0Gx9WTKn8WoYxXZAjMw5VOn1RR8/QxSLo1GA7vZALvZgHFlNoiiBHewNyjQ4YvIUitISazGk1v0dfgip80OMOg1cJgNcJgNKDLpoMvSTkF2sx5nVtkH9SPDMel1mFJpx9hSK450+OHyhEfdcjBRWi1QW2zF+HJrxrdNVDKdVoPpNQ6U2Hp3Fkgl4GIx6jCrvpgDxTxn49bAI+I7o0JaraZ/xmBSBRCNi+gORHu3GPJHcrY+LZu0Gg1qiy1wfri+sG+pgMWog8PcO/C3ZmALIafVgCmVdlkHDxajDueMLUFTdxCH2n0Jd2Zj7CZMr3EU3ExAumzmkx97zKAoPMUDCgg5rIa8XEtL+Umr1aCsyISyIhOmVNrhCcZwoieINm84Y7PO2da3RV95kQnt3gi6AoMDAsVWA8qKTCjK0BaBI7Gb9ZhQbsOYJAvwmQ29lefHl9lwqN2fdiHIKqcZZ4wp4i4nA1Q7LbAYdNjZ1JPU/a/TasCsumIY9byHyndyZQLnIwYC8oBRr0WV04wqp7l/xqDVE0a7L39uDkZSZNJjVp0TJoMOgUgcsQwFQexmPSaOKcKYDG4/Ul9qhcNswM4TPaPWDpgwxoaJ5TaueUqBdUAGwKn7zJIyBaPyZT0NXBpi0GllvTbQO6tJlA1OqwFOqxMTym1Zq6aeLQadFrUlFlQ6TXAHYvBH4qh0mmA1ZO/vS6sFxhSZUVdiSbviuM2kx+z6YnT5IzjY5kcgktznjsNiwJmVdm5lN4JiqxHzJ5RiZ2NPQjWkKhwmnFXjzPuaCmojZ3888FqiJLGvH0F+vArqN3DGIBq3o7E7u0VrsqnIrMfEMTaMKToZoe/yR9DQFYQ7hZ0FhlNWZOwvTJMNTqsB54wtxvbGkYMBkyqKML7clpX25CPzgFRKM2cCVGH6Xa9m5LpL126S/ZoNq6+Q/ZpEp2M16nHO2BJsb3TnVTAAAPRaLcaVW3Hu+FIEInE094TQ7pOnTtJI7GY9qp0WVDnNss8WlxWZcJ7NiBPuEA63+0e9N9PrNDizyo4qh7kgAv/pDtbOrLJj23E3YnERkQHbUg78/5IiIyaW2wY9lop8GQgqSab6+gvuf0P2a+ZLX8/f4jzWt21dbbEFe1s9cAfy4wZBqwUmlvdux3dqx9gXBPGFYzje1Zsymey6PI2mt3psrrbfs5sNOLvWie2N7iHP1ZVaGARIk9Fw8nemkNdXElH+0Go1mFHrxOZj3Xm104BOq8HZdcUw6LQothpRbDViqiihwxeByxtGlz8iy9p7s0GHKqcJVU5Lwv1+OoPWsiIjTAYH9pzwIBQVhh20Fpn1mFHrhMWoQyiN2kZqGrBmaiDYt6uVnPJlIEiFTT2fDpSyk2vQQzjSMXoEWskcFgOmVdtHXdttNxswo9aJiWNsONoRgMsTTuj61cVmTCzP/fq7EpsRE8cUYW+zp/8xq0mPKRX2HLYqPwwc/A8MCpBy7b13ca6bQKR4ZoMOc8YWY/cJj2K22E2HUa/FzFrnkIG5TqvpXw4ZiQtwecJo7gklvZWcVgtU2M2oKbagxJr89mJqGbRywEpqwb4++xgIKBAajQZjy6yocprR2B2EyxNGOINb68hJo+m9wakrsaC8yAiNRpNUJH7iGBvKiow44PIhEI6PGHmf+mGAQYL8a4lSMabIiIH3JfUlVoTTTGUbSE2zBHL+PAb+/CVR3mur6T1VE76vRImxmw1YMLEMze4QTriDqgsIRGICjAYtaoutqC0xJ1THY4zdhDF2E3oCMTS6g+j6sCDfSKnhRr0WtSVW1BSfTP1PZ8ad5MOBYGFjX599fMcLTN9ygTPG2ODyhNEVjKInEIUvHEursOBIHW467Syx9aYBlhUZMOfef6Z9zeFkIl0sUxb98i1Zr6emWYJMzbwsXP26rNdT03tKRPlJp+0N/I8ts8IbjqHTF0F3IApvmv18pmg0vQGMUpsRix6St5/rk4m+noNW+XEgSJRd/IsrUBqNRvZBUB81Da6JiIjylcNsgP7DVHpBlOALxeEJxeCLxOAJxRCNJR8ZSDfwb9RrYbcYYDfr4TQb4LDooVfhNrgctBKR2uXFp9iaNWvw85//HC6XC7NmzcJvfvMbzJ8/f8Tjn3/+edx5551oaGjA5MmTcf/99+OTn/xk//OSJOHuu+/Gb3/7W/T09OCCCy7Ao48+ismTJ2fj5dAwGHkvbPz5ExGlJlMZVYD8gX9+1hMRZY/qAwHPPfccVq5cibVr12LBggV4+OGHsXjxYhw4cAAVFRVDjn/nnXewbNkyrFq1Cp/61Kfw9NNPY8mSJdi+fTtmzJgBAHjggQfw61//Gk8++SQmTJiAO++8E4sXL8bevXthNpuz/RIzRk0dLiPvhY0/fyKi/MfPeiKi7NFIkhwbr+TOggULcO655+KRRx4BAIiiiPr6enz729/GbbfdNuT4q6++GoFAAC+//HL/Y+eddx5mz56NtWvXQpIk1NTU4Hvf+x7+67/+CwDg8XhQWVmJP/zhD/jCF74w5JqRSASRSKT/316vF/X19fB4PHA4HHK/ZCIioqR5vV44nU72TWlQY3+vhOK3iWIggIgoPcn09epblDVANBrFtm3bsGjRov7HtFotFi1ahE2bNg17zqZNmwYdDwCLFy/uP/7YsWNwuVyDjnE6nViwYMGI11y1ahWcTmf/V319fbovjYiIiBRGjf291ahXzRcREWWPqgMBnZ2dEAQBlZWVgx6vrKyEy+Ua9hyXy3Xa4/v+m8w1b7/9dng8nv6vpqamlF4PERERKRf7eyIiyhcMv8rAZDLBZDLluhlERESUQezviYgoX6g6I6C8vBw6nQ5tbW2DHm9ra0NVVdWw51RVVZ32+L7/JnNNIiIiIiIiIrVQdSDAaDRi7ty52LhxY/9joihi48aNWLhw4bDnLFy4cNDxALBhw4b+4ydMmICqqqpBx3i9Xrz33nsjXpOIiIiIiIhILVS/NGDlypW45pprMG/ePMyfPx8PP/wwAoEArrvuOgDA8uXLUVtbi1WrVgEAbrnlFlx00UV48MEHccUVV+DZZ5/F1q1b8dhjjwEANBoNvvOd7+AnP/kJJk+e3L99YE1NDZYsWZKrl0lEREREREQkC9UHAq6++mp0dHTgrrvugsvlwuzZs7F+/fr+Yn+NjY3Qak8mPpx//vl4+umncccdd+CHP/whJk+ejHXr1mHGjBn9x3z/+99HIBDA17/+dfT09ODCCy/E+vXrYTabs/76iIiIiIiIiOSkkSRJynUj8g33aiYiIqVh3yQ/vqdERKQkyfRLqq4RQERERERERETJYSCAiIiIiIiIqIAwEEBERERERERUQFRfLFCJ+soueL3eHLeEiIioV1+fxNJA8mF/T0RESpJMX89AQAb4fD4AQH19fY5bQkRENJjP54PT6cx1M/IC+3siIlKiRPp67hqQAaIooqWlBXa7HRqNJtfNyRqv14v6+no0NTUpvnoy21rY+J4WtkL9+UuSBJ/Ph5qamkHb6lLq2N8r+29ILe0E1NVWteB7WtgK9eefTF/PjIAM0Gq1qKury3UzcsbhcKjmD45tLWx8TwtbIf78mQkgL/b36vgbUks7AXW1VS34nha2Qvz5J9rXc0qAiIiIiIiIqIAwEEBERERERERUQBgIINmYTCbcfffdMJlMuW7KqNjWwsb3tLDx50+UHrX8DamlnYC62qoWfE8LG3/+o2OxQCIiIiIiIqICwowAIiIiIiIiogLCQAARERERERFRAWEggIiIiIiIiKiAMBBAREREREREVEAYCKCkvf322/j0pz+NmpoaaDQarFu3rv+5WCyGH/zgB5g5cyZsNhtqamqwfPlytLS0ZL2dq1atwrnnngu73Y6KigosWbIEBw4cGPZYSZJw+eWXD3k92fLoo4/i7LPPhsPhgMPhwMKFC/GPf/xj0DGbNm3Cxz/+cdhsNjgcDnz0ox9FKBTKelvVpLm5GV/+8pdRVlYGi8WCmTNnYuvWrf3PS5KEu+66C9XV1bBYLFi0aBEOHTqUwxaTnHw+H77zne9g3LhxsFgsOP/887Fly5ZBx+zbtw9XXnklnE4nbDYbzj33XDQ2NuaoxUTKoZa+HlBPf8++PnPY3xcu9vWpYyCAkhYIBDBr1iysWbNmyHPBYBDbt2/HnXfeie3bt+OFF17AgQMHcOWVV2a9nW+99RZuuukmvPvuu9iwYQNisRguvfRSBAKBIcc+/PDD0Gg0WW9jn7q6OqxevRrbtm3D1q1b8fGPfxxXXXUVPvjgAwC9NwaXXXYZLr30UmzevBlbtmzBihUroNXyT3gkbrcbF1xwAQwGA/7xj39g7969ePDBB1FSUtJ/zAMPPIBf//rXWLt2Ld577z3YbDYsXrwY4XA4hy0nuXz1q1/Fhg0b8L//+7/YvXs3Lr30UixatAjNzc0AgCNHjuDCCy/E1KlT8eabb2LXrl248847YTabc9xyotxTS18PqKe/Z1+fGezvCxv7+jRIRGkAIL344ounPWbz5s0SAOn48ePZadQI2tvbJQDSW2+9NejxHTt2SLW1tVJra2tCrydbSkpKpN/97neSJEnSggULpDvuuCPHLVKXH/zgB9KFF1444vOiKEpVVVXSz3/+8/7Henp6JJPJJD3zzDPZaCJlUDAYlHQ6nfTyyy8Pevycc86RfvSjH0mSJElXX3219OUvfzkXzSNSFTX19ZKkrv6efX362N8XLvb16WGIkTLO4/FAo9GguLg45+0AgNLS0v7HgsEgvvjFL2LNmjWoqqrKVdMGEQQBzz77LAKBABYuXIj29na89957qKiowPnnn4/KykpcdNFF+Pe//53rpiraSy+9hHnz5mHp0qWoqKjAnDlz8Nvf/rb/+WPHjsHlcmHRokX9jzmdTixYsACbNm3KRZNJRvF4HIIgDIn4WywW/Pvf/4Yoivj73/+OKVOmYPHixaioqMCCBQtysjSIKB8opa/vawug7P6efb182N8XLvb16WEggDIqHA7jBz/4AZYtWwaHw5GzdoiiiO985zu44IILMGPGjP7Hv/vd7+L888/HVVddlbO29dm9ezeKiopgMpnwjW98Ay+++CKmT5+Oo0ePAgDuuecefO1rX8P69etxzjnn4JJLLuH6ttM4evQoHn30UUyePBmvvvoqvvnNb+Lmm2/Gk08+CQBwuVwAgMrKykHnVVZW9j9H6mW327Fw4ULcd999aGlpgSAIeOqpp7Bp0ya0traivb0dfr8fq1evxmWXXYbXXnsNn/nMZ/DZz34Wb731Vq6bT6QqSunrAeX39+zr5cf+vnCxr0+PPtcNoPwVi8Xw+c9/HpIk4dFHH81pW2666Sbs2bNnUGT9pZdewuuvv44dO3bksGUnnXnmmdi5cyc8Hg/+/Oc/45prrsFbb70FURQBADfeeCOuu+46AMCcOXOwceNGPP7441i1alUum61Yoihi3rx5+NnPfgag9z3bs2cP1q5di2uuuSbHraNs+N///V9cf/31qK2thU6nwznnnINly5Zh27Zt/X9XV111Fb773e8CAGbPno133nkHa9euxUUXXZTLphOphpL6ekD5/T37evmxvy9s7OtTx4wAyoi+G4Pjx49jw4YNOZ0hWLFiBV5++WW88cYbqKur63/89ddfx5EjR1BcXAy9Xg+9vjcu9rnPfQ4XX3xx1ttpNBoxadIkzJ07F6tWrcKsWbPwq1/9CtXV1QCA6dOnDzp+2rRprHh6GtXV1ad9z/pSQ9va2gYd09bWpoi0UUrfGWecgbfeegt+vx9NTU3YvHkzYrEYJk6ciPLycuj1ev5dEaVBSX09oI7+nn29/NjfFzb29aljIIBk13djcOjQIfzzn/9EWVlZTtohSRJWrFiBF198Ea+//jomTJgw6PnbbrsNu3btws6dO/u/AOCXv/wlnnjiiRy0eDBRFBGJRDB+/HjU1NQM2Qrp4MGDGDduXI5ap3wXXHDBad+zCRMmoKqqChs3bux/3uv14r333sPChQuz2lbKLJvNhurqarjdbrz66qu46qqrYDQace655/LviihFSunrAXX39+zr08f+ngD29SnJba1CUiOfzyft2LFD2rFjhwRAeuihh6QdO3ZIx48fl6LRqHTllVdKdXV10s6dO6XW1tb+r0gkktV2fvOb35ScTqf05ptvDmpHMBgc8RzkqIrwbbfdJr311lvSsWPHpF27dkm33XabpNFopNdee02SJEn65S9/KTkcDun555+XDh06JN1xxx2S2WyWDh8+nPW2qsXmzZslvV4v/fSnP5UOHTok/elPf5KsVqv01FNP9R+zevVqqbi4WPrrX/8q7dq1S7rqqqukCRMmSKFQKIctJ7msX79e+sc//iEdPXpUeu2116RZs2ZJCxYskKLRqCRJkvTCCy9IBoNBeuyxx6RDhw5Jv/nNbySdTif961//ynHLiXJPLX29JKmnv2dfnxns7wsb+/rUMRBASXvjjTckAEO+rrnmGunYsWPDPgdAeuONN7LazpHa8cQTT5z2nFwEAq6//npp3LhxktFolMaMGSNdcskl/TcGfVatWiXV1dVJVqtVWrhwIT/AEvC3v/1NmjFjhmQymaSpU6dKjz322KDnRVGU7rzzTqmyslIymUzSJZdcIh04cCBHrSW5Pffcc9LEiRMlo9EoVVVVSTfddJPU09Mz6Jjf//730qRJkySz2SzNmjVLWrduXY5aS6QsaunrJUk9/T37+sxhf1+42NenTiNJkpS5fAMiIiIiIiIiUhLWCCAiIiIiIiIqIAwEEBERERERERUQBgKIiIiIiIiICggDAUREREREREQFhIEAIiIiIiIiogLCQAARERERERFRAWEggIiIiIiIiKiAMBBAREREREREVEAYCCCihDU0NECj0WDnzp25bkq//fv347zzzoPZbMbs2bPTupZGo8G6desAKPO1EhERZZoS+z/29UTyYyCASEWuvfZaaDQarF69etDj69atg0ajyVGrcuvuu++GzWbDgQMHsHHjxhGPc7lc+Pa3v42JEyfCZDKhvr4en/70p0c8p76+Hq2trZgxY4as7R14A0JERHQq9vVDsa8nkh8DAUQqYzabcf/998Ptdue6KbKJRqMpn3vkyBFceOGFGDduHMrKyoY9pqGhAXPnzsXrr7+On//859i9ezfWr1+Pj33sY7jpppuGPUen06Gqqgp6vT7lthEREaWCff1g7OuJ5MdAAJHKLFq0CFVVVVi1atWIx9xzzz1DUucefvhhjB8/vv/f1157LZYsWYKf/exnqKysRHFxMe69917E43HceuutKC0tRV1dHZ544okh19+/fz/OP/98mM1mzJgxA2+99dag5/fs2YPLL78cRUVFqKysxFe+8hV0dnb2P3/xxRdjxYoV+M53voPy8nIsXrx42NchiiLuvfde1NXVwWQyYfbs2Vi/fn3/8xqNBtu2bcO9994LjUaDe+65Z9jrfOtb34JGo8HmzZvxuc99DlOmTMFZZ52FlStX4t133x32nOHSBRN5XTfffDO+//3vo7S0FFVVVYPa1Pf+f+Yzn4FGo+n/9/vvv4+PfexjsNvtcDgcmDt3LrZu3Tpsu4iIKP+xr2dfT5RpDAQQqYxOp8PPfvYz/OY3v8GJEyfSutbrr7+OlpYWvP3223jooYdw991341Of+hRKSkrw3nvv4Rvf+AZuvPHGId/n1ltvxfe+9z3s2LEDCxcuxKc//Wl0dXUBAHp6evDxj38cc+bMwdatW7F+/Xq0tbXh85///KBrPPnkkzAajfjPf/6DtWvXDtu+X/3qV3jwwQfxi1/8Art27cLixYtx5ZVX4tChQwCA1tZWnHXWWfje976H1tZW/Nd//deQa3R3d2P9+vW46aabYLPZhjxfXFyc0HuVzOuy2Wx477338MADD+Dee+/Fhg0bAABbtmwBADzxxBNobW3t//eXvvQl1NXVYcuWLdi2bRtuu+02GAyGhNpFRET5h309+3qijJOISDWuueYa6aqrrpIkSZLOO+886frrr5ckSZJefPFFaeCf89133y3NmjVr0Lm//OUvpXHjxg261rhx4yRBEPofO/PMM6WPfOQj/f+Ox+OSzWaTnnnmGUmSJOnYsWMSAGn16tX9x8RiMamurk66//77JUmSpPvuu0+69NJLB33vpqYmCYB04MABSZIk6aKLLpLmzJkz6uutqamRfvrTnw567Nxzz5W+9a1v9f971qxZ0t133z3iNd577z0JgPTCCy+M+v0ASC+++KIkSSdf644dO5J6XRdeeOGQ9v7gBz8Y9nv0sdvt0h/+8IdR20dERPmPfT37eqJsYEYAkUrdf//9ePLJJ7Fv376Ur3HWWWdBqz35MVBZWYmZM2f2/1un06GsrAzt7e2Dzlu4cGH//+v1esybN6+/He+//z7eeOMNFBUV9X9NnToVQO8avz5z5849bdu8Xi9aWlpwwQUXDHr8ggsuSOo1S5KU8LGnk+jrOvvsswedV11dPeT9O9XKlSvx1a9+FYsWLcLq1asHXY+IiAoX+/rEsK8nSh4DAUQq9dGPfhSLFy/G7bffPuQ5rVY7pFOMxWJDjjs1JU2j0Qz7mCiKCbfL7/fj05/+NHbu3Dno69ChQ/joRz/af9xwqXuZMHnyZGg0Guzfvz+t6yT6ulJ5/+655x588MEHuOKKK/D6669j+vTpePHFF9NqLxERqR/7+sSwrydKHgMBRCq2evVq/O1vf8OmTZsGPT5mzBi4XK5BNwhy7pE7sOhOPB7Htm3bMG3aNADAOeecgw8++ADjx4/HpEmTBn0lc0PgcDhQU1OD//znP4Me/89//oPp06cnfJ3S0lIsXrwYa9asQSAQGPJ8T09PQteR63UZDAYIgjDk8SlTpuC73/0uXnvtNXz2s58dtnATEREVHvb1o2NfT5Q8BgKIVGzmzJn40pe+hF//+teDHr/44ovR0dGBBx54AEeOHMGaNWvwj3/8Q7bvu2bNGrz44ovYv38/brrpJrjdblx//fUAgJtuugnd3d1YtmwZtmzZgiNHjuDVV1/FddddN2yneDq33nor7r//fjz33HM4cOAAbrvtNuzcuRO33HJL0u0VBAHz58/HX/7yFxw6dAj79u3Dr3/960Gpj6cj1+saP348Nm7cCJfLBbfbjVAohBUrVuDNN9/E8ePH8Z///Adbtmzpv9kiIqLCxr4+8fayrydKHAMBRCp37733DklHmzZtGv77v/8ba9aswaxZs7B58+Zhq+ymavXq1Vi9ejVmzZqFf//733jppZdQXl4OAP2RfUEQcOmll2LmzJn4zne+g+Li4kFrFBNx8803Y+XKlfje976HmTNnYv369XjppZcwefLkpK4zceJEbN++HR/72Mfwve99DzNmzMAnPvEJbNy4EY8++mhC15DrdT344IPYsGED6uvrMWfOHOh0OnR1dWH58uWYMmUKPv/5z+Pyyy/Hj3/846ReIxER5S/29aNjX0+UHI0kV3UNIiIiIiIiIlI8ZgQQERERERERFRAGAoiIiIiIiIgKCAMBRERERERERAWEgQAiIiIiIiKiAsJAABEREREREVEBYSCAiIiIiIiIqIAwEEBERERERERUQBgIICIiIiIiIiogDAQQERERERERFRAGAoiIiIiIiIgKCAMBRERERERERAXk/wPkIrPzp3k0yAAAAABJRU5ErkJggg==",
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- "output_type": "display_data"
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- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
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- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "violin_opts = dict( \n",
- " showmeans = True,\n",
- " showextrema = True, \n",
- ")\n",
- "\n",
- "plt.style.use('default')\n",
- "\n",
- "ordered_client_total = sorted(df['client_total'].unique())\n",
- "\n",
- "function_names = ['put_tensor', 'run_script', 'run_model', 'unpack_tensor']\n",
- "languages = ['fortran', 'cpp']\n",
- "\n",
- "for function_name in function_names:\n",
- " fig = plt.figure(figsize=[12,4])\n",
- " axs = fig.subplots(1,2,sharey=True)\n",
- " for i, language in enumerate(languages):\n",
- " language_df = df.groupby('language').get_group(language)\n",
- " function_df = language_df.groupby('function').get_group(function_name)[ ['client_total','time'] ]\n",
- "\n",
- " data = [function_df.groupby('client_total').get_group(client)['time'] for client in ordered_client_total]\n",
- " pos = [int(client) for client in ordered_client_total]\n",
- " axs[i].violinplot(data, pos, **violin_opts, widths=24)\n",
- " axs[i].set_xlabel('Number of Clients')\n",
- " axs[i].set_title(language)\n",
- " axs[i].set_xticks(pos)\n",
- " axs[0].set_ylabel(f'{function_name}\\nTime (s)')\n",
- "# plt.box(put_tensor_df['client_total'], put_tensor_df['time'])\n",
- "\n"
- ]
- }
- ],
- "metadata": {
- "interpreter": {
- "hash": "42ef06aa430c622e3ddccbf02d7ec3dc00d83ca4e5f62eadf9159f81b4640997"
- },
- "kernelspec": {
- "display_name": "smartsim-dev",
- "language": "python",
- "name": "smartsim-dev"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.9.12"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/figures/plot_inference.ipynb b/figures/plot_inference.ipynb
deleted file mode 100644
index 532237f..0000000
--- a/figures/plot_inference.ipynb
+++ /dev/null
@@ -1,838 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [],
- "source": [
- "import os\n",
- "import matplotlib.pyplot as plt\n",
- "import matplotlib\n",
- "import pandas as pd\n",
- "import numpy as np\n",
- "from glob import glob\n",
- "import seaborn as sns\n",
- "from tqdm.auto import tqdm\n",
- "\n",
- "\n",
- "palette = sns.set_palette(\"colorblind\", color_codes=True)\n",
- "\n",
- "font = {'family' : 'sans',\n",
- " 'weight' : 'normal',\n",
- " 'size' : 14}\n",
- "matplotlib.rc('font', **font)\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "d183700b63c34298a3f511c743179ee6",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "../scaling-results/inference-scaling/: 0%| | 0/3 [00:00"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "image/png": 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\n",
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- "output_type": "display_data"
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\n",
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\n",
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- "output_type": "display_data"
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- {
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\n",
- "text/plain": [
- ""
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- "metadata": {},
- "output_type": "display_data"
- },
- {
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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "save = True\n",
- "all_in_one = False\n",
- "labels = [\"put_tensor\", \"unpack_tensor\", \"run_model\", \"run_script\"]\n",
- "palette = sns.set_palette(\"colorblind\", color_codes=True)\n",
- "\n",
- "for style in tqdm([\"light\", \"dark\"], desc=\"Plotting\"):\n",
- " if style == \"light\":\n",
- " plt.style.use(\"default\")\n",
- " else:\n",
- " plt.style.use(\"dark_background\")\n",
- "\n",
- " grid_spacing = np.min(np.diff(nnodes))*threads\n",
- " legend_entries = []\n",
- " ranks = [node*threads for node in nnodes]\n",
- "\n",
- " widths = grid_spacing/5\n",
- " spacing = grid_spacing/3.5\n",
- " color_short = \"brgmy\"\n",
- "\n",
- " aggregate_suffix = \"_agg\" if aggregate else \"\"\n",
- " plot_type = \"violin\"\n",
- "\n",
- " # Set subplot_index to None to plot to separate files, to 1 to have all plots in one\n",
- " subplot_index = 1 if all_in_one else None\n",
- " if subplot_index:\n",
- " plt.figure(figsize=(8*2,5*2+3))\n",
- " for label in tqdm(labels, desc=f\"{style} style\"):\n",
- " if subplot_index:\n",
- " ax = plt.subplot(2,2,subplot_index)\n",
- " else:\n",
- " fig, ax = plt.subplots(figsize=(8,5))\n",
- "\n",
- " for i, DB_node in enumerate(tqdm(DB_nodes, desc=label, leave=False)):\n",
- " dfs = df_dbs[DB_node]\n",
- " positions = ranks+spacing*(i-(len(DB_nodes)-1)/2)\n",
- " \n",
- " data_list = [dfs[node][label] for node in nnodes]\n",
- " \n",
- " if plot_type==\"violin\":\n",
- " plot = ax.violinplot(data_list, positions=positions,\n",
- " widths=grid_spacing/2.5, showextrema=True)\n",
- " [col.set_alpha(0.3) for col in plot[\"bodies\"]]\n",
- " props_dict = dict(color=plot[\"cbars\"].get_color().flatten())\n",
- " entry = plot[\"cbars\"]\n",
- " legend_entries.append(entry)\n",
- " else:\n",
- " props_dict = dict(color=color_short[i])\n",
- " plot = ax.boxplot(data_list, showfliers=True, positions=positions, whis=1e9, \n",
- " boxprops=props_dict, whiskerprops=props_dict, medianprops=props_dict, capprops=props_dict, widths=widths)\n",
- " legend_entries.append(plot[\"whiskers\"][0])\n",
- " means = [np.mean(dfs[node][label]) for node in nnodes]\n",
- " ax.plot(positions, means, ':', color=props_dict['color'], alpha=0.5)\n",
- "\n",
- " \n",
- " data_labels = [f\"{db_node} DB nodes\" for db_node in DB_nodes]\n",
- " ax.legend(legend_entries, data_labels, loc='upper left')\n",
- " \n",
- " ax.set_xticks(ranks, minor=False)\n",
- " ax.set_xticklabels([rank for rank in ranks], fontdict={'fontsize': 12})\n",
- "\n",
- " plt.title(label)\n",
- " plt.xlabel(\"MPI Ranks\")\n",
- " plt.ylabel(\"Time [s]\")\n",
- " ax.yaxis.set_major_formatter(matplotlib.ticker.FormatStrFormatter('%2.2f'))\n",
- "\n",
- " plt.tight_layout()\n",
- " plt.draw()\n",
- "\n",
- " \n",
- " if not subplot_index:\n",
- " if save:\n",
- " plt.savefig(f\"{label}_{plot_type}{aggregate_suffix}_{style}.pdf\")\n",
- " plt.savefig(f\"{label}_{plot_type}{aggregate_suffix}_{style}.png\")\n",
- " else:\n",
- " subplot_index += 1\n",
- "\n",
- " if subplot_index and save:\n",
- " plt.savefig(f'all_in_one_{plot_type}{aggregate_suffix}_{style}.pdf')\n",
- " plt.savefig(f'all_in_one_{plot_type}{aggregate_suffix}_{style}.png')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "interpreter": {
- "hash": "42ef06aa430c622e3ddccbf02d7ec3dc00d83ca4e5f62eadf9159f81b4640997"
- },
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.8.12"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/figures/put_tensor_inf_colo.png b/figures/put_tensor_inf_colo.png
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diff --git a/figures/run_script_inf_colo.png b/figures/run_script_inf_colo.png
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diff --git a/figures/std1_data_agg.png b/figures/std1_data_agg.png
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diff --git a/figures/std1_py_data_agg.png b/figures/std1_py_data_agg.png
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diff --git a/figures/test.png b/figures/test.png
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diff --git a/figures/throughput-plotter.py b/figures/throughput-plotter.py
deleted file mode 100644
index 9600f4c..0000000
--- a/figures/throughput-plotter.py
+++ /dev/null
@@ -1,176 +0,0 @@
-import os
-import matplotlib.pyplot as plt
-import matplotlib
-import pandas as pd
-import numpy as np
-from glob import glob
-import seaborn as sns
-from itertools import product
-from tqdm.auto import tqdm
-
-
-palette = sns.set_palette("colorblind", color_codes=True)
-
-backends = ["Redis","KeyDB"]
-nnodes_all = [128,256,512]
-
-
-for backend, nnodes in tqdm(product(backends, nnodes_all), total=len(backends)*len(nnodes_all), desc="Product loop"):
-
- # Adapt to your setup
- base_path = f"../throughput-scaling-{backend.lower()}"
-
- DB_nodes = [16,32,64]
- sizes = [1024, 1024000, 131072, 16384, 2048000, 262144, 32768, 4096000, 524288, 65536, 8192]
- threads = 36
- loop_iters = 100
- sizes.sort()
-
- df_dbs = dict()
-
- for DB_node in tqdm(DB_nodes, leave=False, desc=f"{backend}-{nnodes}"):
-
- dfs = dict()
-
- for size in tqdm(sizes, leave=False, desc=f"{DB_node} DB nodes"):
- path_root = os.path.join(base_path, f'throughput-sess-N{nnodes}-T{threads}-DBN{DB_node}-ITER{loop_iters}-TB{size}-*')
- try:
- globbed = glob(path_root)
- path = globbed[0]
-
- files = os.listdir(path)
-
- function_times = {'loop_time': []}
-
- for file in tqdm(files, leave=False, desc=f"Size {size}"):
- if '.csv' in file and 'rank_' in file:
- fp = os.path.join(path, file)
- with open(fp) as f:
- for i, line in enumerate(f):
- vals = line.split(',')
- if vals[1] in function_times.keys():
- speed = size*loop_iters/float(vals[2])/1e9
- function_times[vals[1]].append(speed)
-
- speed = function_times['loop_time']
-
- speed = function_times['loop_time']
- data_df = pd.DataFrame(function_times)
- dfs[size] = data_df
-
- except:
- print("WARNING, MISSING PATH:", path_root)
-
-
- df_dbs[DB_node] = dfs
-
- # Set to false if this code is run inside a notebook
- save = True
-
- for dark in tqdm([True, False], leave=False, desc="Plot style loop"):
- if dark:
- plt.style.use("dark_background")
- plot_color="dark"
- else:
- plt.style.use("default")
- plot_color="light"
-
-
- labels = ["loop_time"]
-
- legend_entries = []
-
- ranks = np.asarray(sizes)
- whiskers = 1e9
- color_short = "rgbmy"
- plot_type = "agg"
-
- rank_pos = np.log(ranks/ranks[0])+1
-
- distance = np.min(np.diff(rank_pos))
- widths = distance/(len(DB_nodes))
- spacing = distance/(len(DB_nodes)+0.5)
-
- quantiles = [[0.25, 0.75] for _ in ranks]
-
- for label in tqdm(labels, desc=f"Dark plot: {dark}", leave=False):
-
- fig, ax = plt.subplots(figsize=(8,5))
-
- if plot_type != "agg":
- ax2 = ax.twinx()
-
- for i, DB_node in enumerate(tqdm(DB_nodes, leave=False, desc="DB node plot loop")):
- dfs = df_dbs[DB_node]
- data_list = [dfs[size][label] for size in sizes]
- props_dict = {"color": sns.color_palette()[i]}
-
- positions = rank_pos if plot_type == "agg" else rank_pos+spacing*(i-(len(DB_nodes)-1)/2)
- means = [np.sum(dfs[size][label]) for size in sizes]
- ax.plot(positions, means, '.-', color=props_dict['color'], alpha=0.75)
- if plot_type != "agg":
- if plot_type=="violin":
- plot = ax2.violinplot(data_list, positions=positions,
- widths=widths, showextrema=True)
- [col.set_alpha(0.3) for col in plot["bodies"]]
- entry = plot["cbars"]
- legend_entries.append(entry)
- elif plot_type=="boxplot":
- plot = ax2.boxplot(data_list, showfliers=True, positions=positions, whis=whiskers, labels=['']*len(ranks),
- boxprops=props_dict, whiskerprops=props_dict, medianprops=props_dict, capprops=props_dict, widths=widths/2)
- legend_entries.append(plot["whiskers"][0])
- else:
- raise ValueError("Only boxplot, violin, and agg are valid plot types")
-
-
- ax.set_ylim([0, 200])
- if plot_type != "agg":
- ax2.set_ylim([0, 200/(threads*nnodes)])
- ax.yaxis.set_major_formatter(matplotlib.ticker.FormatStrFormatter('%2.0f'))
-
- ax.set_xlim([rank_pos[0]-distance/2, rank_pos[-1]+distance/2])
- ax.set_xticks(rank_pos, minor=False)
-
- if plot_type != means:
- x_minor_ticks = []
- for i, pos in enumerate(rank_pos[:-1]):
- if i and pos-rank_pos[i-1] > distance*1.5:
- x_minor_ticks.append(pos-distance/2)
- x_minor_ticks.append(pos+distance/2)
-
- ax.set_xticks(x_minor_ticks, minor=True)
-
- labels = ["1", "8", "16", "32", "64", "128", "256", "512", "1000", "2000", "4000"]
- ax.set_xticklabels(labels, fontdict={'fontsize': 10})
-
- if plot_type != "agg":
- ax.grid(True, which="minor", axis="x", ls=":", markevery=rank_pos[:-1]+distance/2)
-
- if plot_type == means:
- ax2.legend(legend_entries, [f'{db_node} DB nodes' for db_node in DB_nodes],
- loc='upper left')
- else:
- ax.legend([f'{db_node} DB nodes' for db_node in DB_nodes],
- loc='upper left')
-
- plt.title(f"{nnodes} client nodes, {threads} clients per node - {backend} backend")
- plt.xlabel("Message size [kiB]")
- if plot_type != "agg":
- ax2.set_ylabel("Single client throughput distribution [GB/s]")
- ax.set_ylabel("Throughput [GB/s]")
- plt.tick_params(
- axis='x', # changes apply to the x-axis
- which='minor', # both major and minor ticks are affected
- bottom=False, # ticks along the bottom edge are off
- top=False, # ticks along the top edge are off
- labelbottom=True)
-
-
- plt.tight_layout()
- plt.draw()
-
- if save:
- plt.savefig(f"{label}-{nnodes}-{backend.lower()}_{plot_color}.png")
-
-
-
diff --git a/figures/unpack_tensor_inf_colo.png b/figures/unpack_tensor_inf_colo.png
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index 0000000..9d610da
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diff --git a/figures/unpack_tensor_inf_std.png b/figures/unpack_tensor_inf_std.png
new file mode 100644
index 0000000..41c9d3a
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diff --git a/figures/unpack_tensor_violin_dark.pdf b/figures/unpack_tensor_violin_dark.pdf
deleted file mode 100644
index 9949a01..0000000
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diff --git a/figures/unpack_tensor_violin_dark.png b/figures/unpack_tensor_violin_dark.png
deleted file mode 100644
index 2e1063a..0000000
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diff --git a/figures/unpack_tensor_violin_light.pdf b/figures/unpack_tensor_violin_light.pdf
deleted file mode 100644
index d926c59..0000000
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diff --git a/figures/unpack_tensor_violin_light.png b/figures/unpack_tensor_violin_light.png
deleted file mode 100644
index 0b7ae4e..0000000
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diff --git a/fortran-inference/CMakeLists.txt b/fortran-inference/CMakeLists.txt
index 7e9fd40..7dc44bc 100644
--- a/fortran-inference/CMakeLists.txt
+++ b/fortran-inference/CMakeLists.txt
@@ -26,7 +26,8 @@ add_executable(run_resnet_inference
utils.F90
)
target_link_libraries(run_resnet_inference
- MPI::MPI_CXX
+ #link the fortran inference against the following libraries
+ MPI::MPI_CXX #might need to turn this to the fortran lib MPI::MPI_Fortran
${SR_LIB}
${SR_LIB_FORTRAN}
)
diff --git a/fortran-inference/inference_scaling_imagenet.F90 b/fortran-inference/inference_scaling_imagenet.F90
index dcc8724..ff6974f 100644
--- a/fortran-inference/inference_scaling_imagenet.F90
+++ b/fortran-inference/inference_scaling_imagenet.F90
@@ -4,13 +4,21 @@ program main
use smartredis_client, only : client_type
use smartredis_errors, only : print_last_error
use mpi
+use, intrinsic :: iso_fortran_env, only: error_unit
implicit none
! Configuration parameters
integer :: batch_size, num_devices, client_count
character(len=255) :: device_type
-logical :: should_set, use_cluster
+logical :: use_cluster
+logical :: poll_model_bool
+logical :: poll_script_bool
+integer :: poll_model_code
+integer :: poll_script_code
+
+! File imports
+include "enum_fortran.inc"
! MPI-related variables
integer :: rank, ierror
@@ -32,7 +40,6 @@ program main
batch_size = get_env_var("SS_BATCH_SIZE", 1)
device_type = get_env_var("SS_DEVICE", "GPU")
num_devices = get_env_var("SS_NUM_DEVICES", 1)
-should_set = get_env_var("SS_SET_MODEL", .false.)
use_cluster = get_env_var("SS_CLUSTER", .false.)
client_count = get_env_var("SS_CLIENT_COUNT", 18)
@@ -48,10 +55,16 @@ program main
)
call init_client(client, rank, use_cluster, timing_unit)
-if (should_set .and. rank == 0) call set_model(client, device_type, num_devices, batch_size)
model_key = "resnet_model"
+poll_model_code = client%poll_model(model_key, 200, 100, poll_model_bool)
+if (poll_model_code /= SRNoError) stop 'Something went wrong during poll_model execution'
+if (.not. poll_model_bool) stop 'Model was not found'
+
script_key = "resnet_script"
+poll_script_code = client%poll_model(script_key, 200, 100, poll_script_bool)
+if (poll_script_code /= SRNoError) stop 'Something went wrong during poll_model execution'
+if (.not. poll_script_bool) stop 'Script was not found'
call MPI_Barrier(MPI_COMM_WORLD, ierror)
call run_mnist(rank, num_devices, device_type, model_key, script_key, timing_unit)
@@ -82,52 +95,6 @@ subroutine init_client( client, rank, use_cluster, timing_unit )
end subroutine init_client
-subroutine set_model(client, device_type, num_devices, batch_size)
- type(client_type), intent(in) :: client
- character(len=*), intent(in) :: device_type
- integer, intent(in) :: num_devices
- integer, intent(in) :: batch_size
-
- include "enum_fortran.inc"
-
- integer :: i
- integer :: return_code
- character(len=255) :: model_filename, script_filename
- character(len=255) :: model_key, script_key
-
- write(model_filename,'(A,A,A)') "./resnet50.", TRIM(device_type), '.pt'
- script_filename = "./data_processing_script.txt"
-
- if (num_devices > 1 .and. device_type == 'GPU') then
- model_key = 'resnet_model'
- script_key = 'resnet_script'
- return_code = client%set_model_from_file_multigpu( &
- model_key, model_filename, "TORCH", 0, num_devices, batch_size)
- if (return_code /= SRNoError) then
- call print_last_error()
- endif
- return_code = client%set_script_from_file_multigpu(script_key, script_filename, 0, num_devices)
- if (return_code /= SRNoError) then
- call print_last_error()
- endif
- else
- do i=1,num_devices
- model_key = 'resnet_model'
- script_key = 'resnet_script'
- return_code = client%set_model_from_file(model_key, model_filename, "TORCH", device_type, batch_size)
- if (return_code /= SRNoError) then
- call print_last_error()
- stop 'Error in set model'
- endif
- return_code = client%set_script_from_file(script_key, device_type, script_filename)
- if (return_code /= SRNoError) then
- call print_last_error()
- stop 'Error in set script'
- endif
- enddo
- endif
-end subroutine set_model
-
subroutine run_mnist(rank, num_devices, device_type, model_key, script_key, timing_unit)
integer, intent(in ) :: rank
integer, intent(in ) :: num_devices
@@ -172,17 +139,25 @@ subroutine run_mnist(rank, num_devices, device_type, model_key, script_key, timi
if (use_multigpu) then
return_code = client%run_script_multigpu( &
script_key, "pre_process_3ch", [in_key], [script_out_key], rank, 0, num_devices)
+ write(error_unit, *) 'is multi 1'
else
- return_code = client%run_script(script_key, "pre_process_3ch", [in_key], [script_out_key])
+ return_code = client%run_script(trim(script_key), "pre_process_3ch", [in_key], [script_out_key])
+ write(error_unit, *) 'is not multi 1', script_key
+ endif
+ if (return_code /= SRNoError) then
+ call print_last_error()
+ stop "Error in run_script (warmup)"
endif
- if (return_code/=SRNoError) stop "Error in run_script"
if (use_multigpu) then
return_code = client%run_model_multigpu(model_key, [script_out_key], [out_key], rank, 0, num_devices)
else
return_code = client%run_model(model_key, [script_out_key], [out_key])
endif
- if (return_code/=SRNoError) stop "Error in run_model"
+ if (return_code /= SRNoError) then
+ call print_last_error()
+ stop "Error in run_model"
+ endif
return_code = client%unpack_tensor(out_key, result, [1,1000])
if (return_code/=SRNoError) stop "Error in put tensor"
diff --git a/plot_colocated_inference.ipynb b/plot_colocated_inference.ipynb
new file mode 100644
index 0000000..fb1e9a9
--- /dev/null
+++ b/plot_colocated_inference.ipynb
@@ -0,0 +1,1300 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "import matplotlib.pyplot as plt\n",
+ "import matplotlib\n",
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "from glob import glob\n",
+ "import seaborn as sns\n",
+ "from pathlib import Path\n",
+ "import configparser\n",
+ "\n",
+ "\n",
+ "palette = sns.set_palette(\"colorblind\", color_codes=True)\n",
+ "\n",
+ "font = {'family' : 'sans',\n",
+ " 'weight' : 'normal',\n",
+ " 'size' : 14}\n",
+ "matplotlib.rc('font', **font)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class hashableDict(dict):\n",
+ " def __hash__(self):\n",
+ " return hash(tuple(sorted(self.items())))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "results_path = 'results'\n",
+ "scaling_test = 'inference-colocated-scaling'\n",
+ "run_path = 'run-2023-06-13-16:08:40'\n",
+ "full_path = Path(results_path, scaling_test, run_path)\n",
+ "\n",
+ "configs = []\n",
+ "\n",
+ "for run_cfg in Path(full_path).rglob('run.cfg'):\n",
+ " config = configparser.ConfigParser()\n",
+ " config.read(run_cfg)\n",
+ " configs.append(config)\n",
+ "df_list = []\n",
+ "for config in configs:\n",
+ " timing_files = Path(config['run']['path']).glob('rank*.csv')\n",
+ " for timing_file in timing_files:\n",
+ " tmp_df = pd.read_csv(timing_file, header=0, names=[\"rank\", \"function\", \"time\"])\n",
+ " for key, value in config._sections['attributes'].items():\n",
+ " tmp_df[key] = value\n",
+ " df_list.append(tmp_df)\n",
+ "\n",
+ "df = pd.concat(df_list, ignore_index=True)\n",
+ "\n",
+ " \n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " rank \n",
+ " function \n",
+ " time \n",
+ " colocated \n",
+ " pin_app_cpus \n",
+ " client_total \n",
+ " client_per_node \n",
+ " client_nodes \n",
+ " database_nodes \n",
+ " database_cpus \n",
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+ " \n",
+ " \n",
+ " 35 \n",
+ " 1 \n",
+ " main() \n",
+ " 18.620300 \n",
+ " 1 \n",
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+ "
\n",
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "violin_opts = dict( \n",
+ " showmeans = True,\n",
+ " showextrema = True, \n",
+ ")\n",
+ "\n",
+ "plt.style.use('default')\n",
+ "\n",
+ "ordered_client_total = sorted(df['client_total'].unique())\n",
+ "\n",
+ "function_names = df['function'].drop_duplicates().tolist()\n",
+ "languages = ['cpp']\n",
+ "\n",
+ "for function_name in function_names:\n",
+ " fig = plt.figure(figsize=[12,4])\n",
+ " axs = fig.subplots(1,2,sharey=True)\n",
+ " for i, language in enumerate(languages):\n",
+ " language_df = df.groupby('language').get_group(language)\n",
+ " function_df = language_df.groupby('function').get_group(function_name)[ ['client_total','time'] ]\n",
+ " data = [function_df.groupby('client_total').get_group(client)['time'] for client in ordered_client_total]\n",
+ " pos = [int(client) for client in ordered_client_total]\n",
+ " axs[i].violinplot(data, pos, **violin_opts, widths=24)\n",
+ " axs[i].set_xlabel('Number of Clients')\n",
+ " axs[i].set_title(language)\n",
+ " axs[i].set_xticks(pos)\n",
+ " axs[0].set_ylabel(f'{function_name}\\nTime (s)')\n",
+ "# plt.box(put_tensor_df['client_total'], put_tensor_df['time'])\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 125,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "['12', '24', '36', '60', '96']"
+ ]
+ },
+ "execution_count": 125,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "put_tensor_df"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "put_tensor_df"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "## Compare Fortran and C++\n",
+ "\n",
+ "for key, value in run_data:\n",
+ " value['client_total'] = \n",
+ "\n",
+ "\n",
+ "fortran_dfs = {hashed_config:run_data[hashed_config] for hashed_config in hashed_configs if hashed_config['language']=='fortran'}\n",
+ "cpp_dfs = {hashed_config:run_data[hashed_config] for hashed_config in hashed_configs if hashed_config['language']=='cpp'}\n",
+ "\n",
+ "fields = [\"put_tensor\", \"run_model\", \"unpack_tensor\"]\n",
+ "\n",
+ "\n",
+ "\n",
+ "# for field in fields:\n",
+ "\n",
+ " \n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "hashed_config"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "config = configs[0]\n",
+ "\n",
+ "\n",
+ "df_list = []\n",
+ "for timing_file in timing_files:\n",
+ " df_list.append(pd.read_csv(timing_file, header=0, names=[\"rank\", \"function\", \"time\"]))\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df = pd.concat(df_list, ignore_index=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df.groupby('function').get_group()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "n_clients = [12,24,48,96]\n",
+ "n_nodes = [1]\n",
+ "DB_cpus = 12\n",
+ "db_tpq = 2\n",
+ "\n",
+ "aggregate = False\n",
+ "\n",
+ "df_dbs = dict()\n",
+ "infer_path = '../results/inference-colocated-scaling'\n",
+ "run_paths = glob(os.path.join(infer_path,'run-2023*'))\n",
+ "\n",
+ "functions = ['put_tensor', 'run_script', 'run_model', 'unpack_tensor']\n",
+ "\n",
+ "dfs = { path:dict() for path in run_paths }\n",
+ "\n",
+ "for run_path in run_paths:\n",
+ " base_path = run_path \n",
+ " for n_node in n_nodes:\n",
+ "\n",
+ " for n_client in n_clients: \n",
+ " path_roots = os.path.join(base_path, f'infer-sess-colo-N{n_node}-T{n_client}-DBN1-DBCPU{DB_cpus}-DBTPQ{db_tpq}-*')\n",
+ " for path_root in path_roots:\n",
+ " path = glob(path_root)[0]\n",
+ " files = os.listdir(path)\n",
+ " \n",
+ " function_times = {}\n",
+ "\n",
+ " for file in files:\n",
+ " if '.csv' in file and 'rank_' in file:\n",
+ " fp = os.path.join(path, file)\n",
+ " function_rank_times = {}\n",
+ " with open(fp) as f:\n",
+ " for i, line in enumerate(f):\n",
+ " vals = line.split(',')\n",
+ " if vals[1] not in functions:\n",
+ " continue\n",
+ " if not aggregate:\n",
+ " if vals[1] in function_times.keys():\n",
+ " function_times[vals[1]].append(float(vals[2]))\n",
+ " else:\n",
+ " function_times[vals[1]] = [float(vals[2])]\n",
+ " else:\n",
+ " if vals[1] in function_rank_times.keys():\n",
+ " function_rank_times[vals[1]] += float(vals[2])\n",
+ " else:\n",
+ " function_rank_times[vals[1]] = float(vals[2])\n",
+ " \n",
+ " for k,v in function_rank_times.items():\n",
+ " if k in function_times:\n",
+ " function_times[k].append(v)\n",
+ " else:\n",
+ " function_times[k] = [v]\n",
+ " \n",
+ " data_df = pd.DataFrame(function_times)\n",
+ " dfs[run_path][n_client] = data_df\n",
+ "\n",
+ " labels = [\"put_tensor\", \"unpack_tensor\", \"run_model\", \"run_script\"]\n",
+ " for n_client in n_clients:\n",
+ " dfs[run_path][n_client]['total'] = np.sum([dfs[run_path][n_client][label] for label in labels],axis=0) \n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "for run_path in run_paths:\n",
+ " print(run_path)\n",
+ " print(dfs[run_path][96].describe())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "for run_path in run_paths:\n",
+ " print(run_path)\n",
+ " print(dfs[run_path][96].describe())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "save = False\n",
+ "all_in_one = False\n",
+ "labels = [\"put_tensor\", \"unpack_tensor\", \"run_model\", \"run_script\", \"total\"]\n",
+ "palette = sns.set_palette(\"colorblind\", color_codes=True)\n",
+ "\n",
+ "\n",
+ "dfs_plot = dfs[run_paths[1]]\n",
+ "\n",
+ "for style in tqdm([\"light\", \"dark\"], desc=\"Plotting\"):\n",
+ " if style == \"light\":\n",
+ " plt.style.use(\"default\")\n",
+ " else:\n",
+ " plt.style.use(\"dark_background\")\n",
+ "\n",
+ " legend_entries = []\n",
+ "\n",
+ " color_short = \"brgmy\"\n",
+ "\n",
+ " aggregate_suffix = \"_agg\" if aggregate else \"\"\n",
+ " plot_type = \"violin\"\n",
+ "\n",
+ " # Set subplot_index to None to plot to separate files, to 1 to have all plots in one\n",
+ " subplot_index = 1 if all_in_one else None\n",
+ " if subplot_index:\n",
+ " plt.figure(figsize=(8*2,5*2+3))\n",
+ " for label in tqdm(labels, desc=f\"{style} style\"):\n",
+ " if subplot_index:\n",
+ " ax = plt.subplot(2,2,subplot_index)\n",
+ " else:\n",
+ " fig, ax = plt.subplots(figsize=(8,5))\n",
+ "\n",
+ " data_list = [dfs_plot[n_client][label] for n_client in n_clients]\n",
+ " \n",
+ " if plot_type==\"violin\":\n",
+ " plot = ax.violinplot(data_list, positions=n_clients, showextrema=True, showmeans=True, showmedians=True ,widths=12)\n",
+ " [col.set_alpha(0.3) for col in plot[\"bodies\"]]\n",
+ " props_dict = dict(color=plot[\"cbars\"].get_color().flatten())\n",
+ " entry = plot[\"cbars\"]\n",
+ " legend_entries.append(entry)\n",
+ " means = [np.mean(dfs_plot[n_client][label]) for n_client in n_clients]\n",
+ " ax.plot(n_clients, means, ':', color=props_dict['color'], alpha=0.5)\n",
+ "\n",
+ " \n",
+ " ax.set_xticks(n_clients, minor=False)\n",
+ " ax.set_xticklabels([rank for rank in n_clients], fontdict={'fontsize': 12})\n",
+ "\n",
+ " plt.title(label)\n",
+ " plt.xlabel(\"MPI Ranks\")\n",
+ " plt.ylabel(\"Time [s]\")\n",
+ " # plt.ylim([0,0.06])\n",
+ " # ax.yaxis.set_major_formatter(matplotlib.ticker.FormatStrFormatter('%2.2f'))\n",
+ "\n",
+ " plt.tight_layout()\n",
+ " plt.draw()\n",
+ "\n",
+ " \n",
+ " if not subplot_index:\n",
+ " if save:\n",
+ " plt.savefig(f\"{label}_{plot_type}{aggregate_suffix}_{style}.pdf\")\n",
+ " plt.savefig(f\"{label}_{plot_type}{aggregate_suffix}_{style}.png\")\n",
+ " else:\n",
+ " subplot_index += 1\n",
+ "\n",
+ " if subplot_index and save:\n",
+ " plt.savefig(f'all_in_one_{plot_type}{aggregate_suffix}_{style}.pdf')\n",
+ " plt.savefig(f'all_in_one_{plot_type}{aggregate_suffix}_{style}.png')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ax.violinplot?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "dfs[96].describe()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\n",
+ "dfs[96].describe?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ax.violinplot?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "plz3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.9.16"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/requirements.txt b/requirements.txt
index 01248cb..d72b354 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,4 +1,5 @@
pandas>=1.4.0
matplotlib>=3.5.1
fire>=0.4.0
-mpi4py
\ No newline at end of file
+mpi4py>=3.1.4 #wrapper around gcc
+joblib>=1.2.0
\ No newline at end of file
diff --git a/utils.py b/utils.py
index b839918..2ed0dd1 100644
--- a/utils.py
+++ b/utils.py
@@ -47,7 +47,7 @@ def get_time():
current_time = now.strftime("%H:%M:%S")
return current_time
-def check_model(device, force_rebuild=False):
+def check_model(device):
"""Regenerate model on specified device if True.
This function will rebuild the model on the specified node type.
@@ -57,7 +57,7 @@ def check_model(device, force_rebuild=False):
:param force_rebuild: force rebuild of PyTorch model even if it is available
:type force_rebuild: bool
"""
- if device.startswith("GPU") and (force_rebuild or not Path("./imagenet/resnet50.GPU.pt").exists()):
+ if device.startswith("GPU") and (not Path("./imagenet/resnet50.GPU.pt").exists()):
from torch.cuda import is_available
if not is_available():
message = "resnet50.GPU.pt model missing in ./imagenet directory. \n"
@@ -133,13 +133,16 @@ def start_database(exp, db_node_feature, port, nodes, cpus, tpq, net_ifname, run
db.set_walltime(wall_time)
for k, v in db_node_feature.items():
db.set_batch_arg(k, v)
+ if not run_as_batch:
+ for k, v in db_node_feature.items():
+ db.set_run_arg(k, v)
db.set_cpus(cpus)
exp.generate(db, overwrite=True)
exp.start(db)
logger.info("Orchestrator Database created and running")
return db
-def setup_resnet(model, device, num_devices, batch_size, address, cluster=True):
+def attach_resnet(model, res_model_path, device, num_devices, batch_size):
"""Set and configure the PyTorch resnet50 model for inference
:param model: path to serialized resnet model
@@ -155,31 +158,19 @@ def setup_resnet(model, device, num_devices, batch_size, address, cluster=True):
:param cluster: true if using a cluster orchestrator
:type cluster: bool
"""
- client = Client(address=address, cluster=cluster)
device = device.upper()
- if (device == "GPU") and (num_devices > 1):
- client.set_model_from_file_multigpu("resnet_model",
- model,
- "TORCH",
- 0, num_devices,
- batch_size)
- client.set_script_from_file_multigpu("resnet_script",
- "./imagenet/data_processing_script.txt",
- 0, num_devices)
- logger.info(f"Resnet Model and Script in Orchestrator on {num_devices} GPUs")
- else:
- # Redis does not accept CPU:. We are either
- # setting (possibly multiple copies of) the model and script on CPU, or one
- # copy of them (resnet_model_0, resnet_script_0) on ONE GPU.
- client.set_model_from_file(f"resnet_model",
- model,
- "TORCH",
- device,
- batch_size)
- client.set_script_from_file(f"resnet_script",
- "./imagenet/data_processing_script.txt",
- device)
- logger.info(f"Resnet Model and Script in Orchestrator on device {device}")
+ model.add_ml_model(name="resnet_model",
+ devices_per_node=num_devices,
+ backend="TORCH",
+ model_path=res_model_path,
+ batch_size=batch_size,
+ device=device)
+ model.add_script("resnet_script",
+ devices_per_node=num_devices,
+ script_path="./imagenet/data_processing_script.txt",
+ device="GPU")
+
+ logger.info(f"Resnet Model and Script in Orchestrator on device {device}")
def write_run_config(path, **kwargs):
"""Write config attributes to run file.
@@ -263,22 +254,4 @@ def print_yml_file(path, logger):
for key, value in config._sections['run'].items():
logger.info(f"Running {key} with value: {value}")
for key, value in config._sections['attributes'].items():
- logger.info(f"Running {key} with value: {value}")
-
-def check_database_folder(result_path, logger):
- """Cleans the database folder within results.
-
- :param result_path: path to results folder
- :type result_path: str
- :param logger: name of logger
- :type logger: str
- """
- time.sleep(5)
- for _ in range(5):
- rdb_folders = os.listdir(Path(result_path) / "database")
- for fold in rdb_folders:
- if '.rdb' in fold:
- logger.debug(f"Database folder removed: {fold}")
- os.remove(Path(result_path) / "database" / fold)
- break
- time.sleep(1)
\ No newline at end of file
+ logger.info(f"Running {key} with value: {value}")
\ No newline at end of file
