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1 change: 1 addition & 0 deletions build_and_train_models/README.md
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Original file line number Diff line number Diff line change
@@ -43,3 +43,4 @@ The example notebooks within this folder showcase the capabilities of Amazon Sag
- [Tabular classification with Amazon SageMaker LightGBM and CatBoost algorithm](sm-lightgbm_catboost_tabular_classification.ipynb)
- [An Introduction to Linear Learner with MNIST](sm-linear_learner_mnist.ipynb)
- [Tabular classification with Amazon SageMaker TabTransformer algorithm](sm-tabtransformer_tabular_classification.ipynb)
- [Finetuning Text generation model with Tensorboard](sm-jumpstart_tensorflow_finetune_flan/sm-jumpstart_tensorflow_finetune_flan.ipynb)
575 changes: 575 additions & 0 deletions ..._models/sm-jumpstart_tensorflow_finetune_flan/sm-jumpstart_tensorflow_finetune_flan.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"id": "48b9f283-12e1-4c30-924d-d6bac1f14d6a",
"metadata": {},
"source": [
"# Fine-tuning a HuggingFace FLAN-T5 Model on Amazon SageMaker with TensorBoard Integration\n",
"\n",
"---\n",
"\n",
"This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook.\n",
"\n",
"![This us-west-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-west-2/ build_and_train_models|sm-jumpstart_tensorflow_finetune_flan|sm-jumpstart_tensorflow_finetune_flan.ipynb)\n",
"\n",
"---\n",
"\n",
"**Author**: Hubert Gabryel\n",
"\n",
"**Date**: 2023-10-05\n",
"\n",
"## Table of Contents\n",
"\n",
"1.\t[Introduction](#1-introduction)\n",
"\n",
"\t1.1 [Background](#11-background)\n",
"\n",
"\t1.2 [Objective](#12-objective)\n",
"\n",
"2.\t[Setup](#2-setup)\n",
"\n",
"\t2.1 [Import Libraries](#21-import-libraries)\n",
"\n",
"\t2.2 [Initialize SageMaker Session and Role](#22-initialize-sagemaker-session-and-role)\n",
"\n",
"\t2.3 [Model Configuration](#23-model-configuration)\n",
"\n",
"3.\t[Data Preparation](#3-data-preparation)\n",
"\n",
"\t3.1 [Download and Prepare the Dataset](#31-download-and-prepare-the-dataset)\n",
"\n",
"\t3.2 [Load and Preprocess the Data](#32-load-and-preprocess-the-data)\n",
"\n",
"\t3.3 [Prepare the Data for Training](#33-prepare-the-data-for-training)\n",
"\n",
"\t3.4 [Upload Data to S3](#34-upload-data-to-s3)\n",
"\n",
"4.\t[Training Script Modification](#4-training-script-modification)\n",
"\n",
"\t4.1 [Download the Training Script](#41-download-the-training-script)\n",
"\n",
"\t4.2 [Modify the Training Script for TensorBoard Integration](#42-modify-the-training-script-for-tensorboard-integration)\n",
"\n",
"5.\t[Model Training with TensorBoard Integration](#5-model-training-with-tensorboard-integration)\n",
"\n",
"\t5.1 [Set Up TensorBoard Output Configuration](#51-set-up-tensorboard-output-configuration)\n",
"\n",
"\t5.2 [Define Hyperparameters](#52-define-hyperparameters)\n",
"\n",
"\t5.3 [Create and Fit the Estimator](#53-create-and-fit-the-estimator)\n",
"\n",
"6.\t[TensorBoard Visualization](#6-tensorboard-visualization)\n",
"\n",
"\t6.1 [Start TensorBoard from the SageMaker Console](#61-start-tensorboard-from-the-sagemaker-console)\n",
"\t\n",
"7.\t[Conclusion](#7-conclusion)\n",
"\n",
"8.\t[References](#8-references)\n",
"\n",
"\n",
"## 1. Introduction\n",
"\n",
"### 1.1 Background\n",
"\n",
"In this notebook, we demonstrate how to fine-tune a HuggingFace FLAN-T5 model using Amazon SageMaker’s JumpStart models with TensorBoard integration. This integration allows us to monitor and visualize the training process in real-time, providing valuable insights into model performance.\n",
"\n",
"### 1.2 Objective\n",
"\n",
"Our goal is to fine-tune the FLAN-T5 small model on a subset of the Tiny Shakespeare dataset and visualize the training metrics using TensorBoard. We will:\n",
"\n",
"- Set up the SageMaker environment and import necessary libraries.\n",
"- Prepare the dataset for training.\n",
"- Modify the training script to include TensorBoard logging.\n",
"- Train the model with TensorBoard integration.\n",
"- Visualize the training metrics using TensorBoard.\n",
"\n",
"## 2. Setup\n",
"\n",
"### 2.1 Import Libraries"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c3644c9c-adb0-4eb9-9d95-89120ab22dde",
"metadata": {},
"outputs": [],
"source": [
"# Install or upgrade the SageMaker Python SDK\n",
"!pip install -U sagemaker --quiet"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0d6bcbc8-6abf-4b62-80b3-3e58ece51b66",
"metadata": {},
"outputs": [],
"source": [
"# Import necessary libraries\n",
"import os\n",
"import random\n",
"import numpy as np\n",
"import pandas as pd\n",
"from sklearn.model_selection import train_test_split\n",
"\n",
"import boto3\n",
"import sagemaker\n",
"from sagemaker import get_execution_role, script_uris\n",
"from sagemaker.s3 import S3Uploader, S3Downloader\n",
"from sagemaker.jumpstart.estimator import JumpStartEstimator\n",
"from sagemaker.debugger import TensorBoardOutputConfig\n",
"\n",
"# Set random seeds for reproducibility\n",
"RANDOM_SEED = 42\n",
"random.seed(RANDOM_SEED)\n",
"np.random.seed(RANDOM_SEED)"
]
},
{
"cell_type": "markdown",
"id": "2b572c67-a33c-4b2d-b1dc-aa71192a9682",
"metadata": {},
"source": [
"### 2.2 Initialize SageMaker Session and Role"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e462af99-d22a-4fb8-93e0-d03edb4351eb",
"metadata": {},
"outputs": [],
"source": [
"sagemaker_session = sagemaker.Session()\n",
"role = get_execution_role()\n",
"\n",
"# Verify S3 access\n",
"try:\n",
" s3_client = sagemaker_session.boto_session.client(\"s3\")\n",
" s3_client.head_bucket(Bucket=sagemaker_session.default_bucket())\n",
" print(\"S3 access confirmed.\")\n",
"except Exception as e:\n",
" print(f\"Unable to access S3 bucket: {e}\")"
]
},
{
"cell_type": "markdown",
"id": "a46f6523-ddb1-489f-9757-dc9543f475de",
"metadata": {},
"source": [
"### 2.3 Model Configuration"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "ea3f3cda-803f-4bea-a678-f4284efbbb4b",
"metadata": {},
"outputs": [],
"source": [
"# Model configuration\n",
"MODEL_ID = \"huggingface-text2text-flan-t5-small\" # Small model to keep training cost low\n",
"MODEL_VERSION = \"2.1.2\" # Latest model version at the time of writing"
]
},
{
"cell_type": "markdown",
"id": "ab6ad668-ac5a-4b88-99c6-17b8c962b69a",
"metadata": {},
"source": [
"## 3. Data Preparation\n",
"\n",
"### 3.1 Download and Prepare the Dataset\n",
"\n",
"We will use the Tiny Shakespeare dataset for this example."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9a444e79-dd2c-4f3f-9a64-33c6d6f713a6",
"metadata": {},
"outputs": [],
"source": [
"# Download the Tiny Shakespeare dataset\n",
"!wget https://raw.githubusercontent.com/karpathy/char-rnn/master/data/tinyshakespeare/input.txt --no-check-certificate"
]
},
{
"cell_type": "markdown",
"id": "40d302be-4997-42cd-b83f-5fe732ac0fa8",
"metadata": {},
"source": [
"### 3.2 Load and Preprocess the Data"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b3c61092-8b0c-4104-8943-bfe8098160d3",
"metadata": {},
"outputs": [],
"source": [
"# Read the data\n",
"with open(\"input.txt\", \"r\") as f:\n",
" data = f.read()\n",
"\n",
"# Limit the data to the first MAX_DATA_LENGTH characters\n",
"MAX_DATA_LENGTH = 10000\n",
"data = data[:MAX_DATA_LENGTH]\n",
"\n",
"# Split the data into training and validation sets\n",
"TEST_SIZE = 0.2\n",
"train_text, val_text = train_test_split(data, test_size=TEST_SIZE, random_state=RANDOM_SEED)\n",
"\n",
"print(f\"Training data length: {len(train_text)}\")\n",
"print(f\"Validation data length: {len(val_text)}\")"
]
},
{
"cell_type": "markdown",
"id": "2db6e736-4088-4518-8515-efd31bbef8ef",
"metadata": {},
"source": [
"### 3.3 Prepare the Data for Training\n",
"\n",
"We need to format the data into prompts and completions."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "21329da7-f9ed-4869-b8ab-88abdb6f5255",
"metadata": {},
"outputs": [],
"source": [
"def prepare_data(text, sequence_length=256, prompt_length=128):\n",
" data = []\n",
" max_index = len(text) - sequence_length + 1\n",
" for i in range(0, max_index, prompt_length):\n",
" prompt = text[i : i + prompt_length]\n",
" completion = text[i + prompt_length : i + sequence_length]\n",
" if len(completion) == (sequence_length - prompt_length):\n",
" data.append({\"prompt\": prompt, \"completion\": completion})\n",
" return data\n",
"\n",
"\n",
"# Prepare the training and validation data\n",
"train_data = prepare_data(train_text)\n",
"val_data = prepare_data(val_text)\n",
"\n",
"print(f\"Number of training samples: {len(train_data)}\")\n",
"print(f\"Number of validation samples: {len(val_data)}\")"
]
},
{
"cell_type": "markdown",
"id": "517c62b7-cc20-4c2a-b338-880d488a70db",
"metadata": {},
"source": [
"### 3.4 Upload Data to S3"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a204e6f9-8ae8-4199-bec4-c7c8b443bbfe",
"metadata": {},
"outputs": [],
"source": [
"# Define S3 bucket and prefix\n",
"bucket = sagemaker_session.default_bucket()\n",
"data_prefix = \"jumpstart-example-data\"\n",
"\n",
"# Save the data to local files\n",
"pd.DataFrame(train_data).to_json(\"train.jsonl\", orient=\"records\", lines=True)\n",
"pd.DataFrame(val_data).to_json(\"val.jsonl\", orient=\"records\", lines=True)\n",
"\n",
"# Upload training data\n",
"train_s3_uri = sagemaker_session.upload_data(\n",
" path=\"train.jsonl\", bucket=bucket, key_prefix=f\"{data_prefix}/train.jsonl\"\n",
")\n",
"\n",
"# Upload validation data\n",
"val_s3_uri = sagemaker_session.upload_data(\n",
" path=\"val.jsonl\", bucket=bucket, key_prefix=f\"{data_prefix}/val.jsonl\"\n",
")\n",
"\n",
"print(f\"Training data uploaded to: {train_s3_uri}\")\n",
"print(f\"Validation data uploaded to: {val_s3_uri}\")"
]
},
{
"cell_type": "markdown",
"id": "2e75bf64-0948-4a74-bd67-b1441d00917d",
"metadata": {},
"source": [
"## 4. Training Script Modification\n",
"\n",
"### 4.1 Download the Training Script\n",
"\n",
"We need to obtain the default training script provided by the JumpStart model and modify it to integrate TensorBoard."
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "a5191d10-8dac-4a3a-9ee1-597a3208bcb2",
"metadata": {},
"outputs": [],
"source": [
"from sagemaker.s3 import S3Downloader\n",
"\n",
"# Retrieve the training script URI\n",
"train_script_uri = script_uris.retrieve(\n",
" model_id=MODEL_ID, model_version=MODEL_VERSION, script_scope=\"training\"\n",
")\n",
"\n",
"# Download the training script\n",
"S3Downloader.download(train_script_uri, \".\")\n",
"\n",
"# Unpack the training script\n",
"import tarfile\n",
"\n",
"with tarfile.open(\"./sourcedir.tar.gz\") as tar:\n",
" tar.extractall(\"./training_script\")"
]
},
{
"cell_type": "markdown",
"id": "87befce4-deb1-426f-acbf-803099438ac2",
"metadata": {},
"source": [
"### 4.2 Modify the Training Script for TensorBoard Integration\n",
"\n",
"We need to modify the train.py script to include TensorBoard logging.\n",
"\n",
"- Import the TensorBoardCallback:\n",
" In training_script/train.py, add:\n",
"\n",
"```python\n",
"from transformers.integrations import TensorBoardCallback\n",
"```\n",
"\n",
"- Modify the Seq2SeqTrainingArguments to include TensorBoard parameters:\n",
"\n",
" ```python\n",
" training_args = Seq2SeqTrainingArguments(\n",
" # ... other arguments ...\n",
" logging_dir=\"/opt/ml/output/tensorboard\",\n",
" report_to=['tensorboard'],\n",
" # ... other arguments ...\n",
")\n",
"```\n",
"\n",
"- Add the TensorBoardCallback to the trainer:\n",
"\n",
"```python\n",
"if callbacks is None: # Added line\n",
" callbacks = [] # Added line\n",
"callbacks.append(TensorBoardCallback()) # Added line\n",
"\n",
"# Create Trainer instance\n",
" trainer = Seq2SeqTrainer(\n",
" model=model,\n",
" args=training_args,\n",
" train_dataset=dataset[constants.TRAIN],\n",
" eval_dataset=dataset[constants.VALIDATION],\n",
" data_collator=data_collator,\n",
" callbacks=callbacks,\n",
" )\n",
"```\n",
"\n",
"Note: Ensure that the \"/opt/ml/output/tensorboard\" in the training script matches the container_local_output_path in the TensorBoardOutputConfig.\n",
"\n",
"## 5. Model Training with TensorBoard Integration\n",
"\n",
"### 5.1 Set Up TensorBoard Output Configuration"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8fdbc2e0-9b81-420d-bbe2-01dba31aa103",
"metadata": {},
"outputs": [],
"source": [
"tensorboard_output_config = TensorBoardOutputConfig(\n",
" s3_output_path=f\"s3://{bucket}/tensorboard-output\",\n",
" container_local_output_path=\"/opt/ml/output/tensorboard\", # Should match LOG_DIR in your script\n",
")\n",
"\n",
"print(f\"TensorBoard logs will be saved to: s3://{bucket}/tensorboard-output\")"
]
},
{
"cell_type": "markdown",
"id": "e7e316f7-6a1e-4f68-932b-9a495d3a56e5",
"metadata": {},
"source": [
"### 5.2 Define Hyperparameters"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "d4c9fe44-cb9c-407b-a80c-5577105b18f6",
"metadata": {},
"outputs": [],
"source": [
"hyperparameters = {\n",
" \"epochs\": \"5\",\n",
" \"batch_size\": \"4\",\n",
" \"learning_rate\": \"5e-5\",\n",
" \"logging_strategy\": \"steps\",\n",
" \"logging_steps\": \"5\",\n",
" \"evaluation_strategy\": \"steps\",\n",
" \"save_strategy\": \"steps\",\n",
" \"eval_steps\": \"25\",\n",
" \"save_steps\": \"25\",\n",
" \"gradient_accumulation_steps\": \"1\",\n",
" \"fp16\": \"true\",\n",
" \"bf16\": \"false\",\n",
"}"
]
},
{
"cell_type": "markdown",
"id": "137786e8-56f4-4acc-b4cb-3bf2e2f219fc",
"metadata": {},
"source": [
"### 5.3 Create and Fit the Estimator"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ebc3d8f5-a505-4754-a578-7de4c7221f28",
"metadata": {},
"outputs": [],
"source": [
"estimator = JumpStartEstimator(\n",
" model_id=MODEL_ID,\n",
" model_version=MODEL_VERSION,\n",
" instance_type=\"ml.g5.xlarge\",\n",
" hyperparameters=hyperparameters,\n",
" entry_point=\"transfer_learning.py\", # Name of main script\n",
" source_dir=\"training_script\", # Directory containing your scripts\n",
" tensorboard_output_config=tensorboard_output_config,\n",
")\n",
"\n",
"# Start the training job\n",
"estimator.fit({\"train\": train_s3_uri, \"validation\": val_s3_uri})"
]
},
{
"cell_type": "markdown",
"id": "ac2186f6-8767-4773-ad21-c7e3458ae818",
"metadata": {},
"source": [
"## 6. TensorBoard Visualization\n",
"\n",
"### 6.1 Start TensorBoard from the SageMaker Console\n",
"\n",
"\t1.\tNavigate to the SageMaker Console:\n",
" \t- Go to the Amazon SageMaker Console.\n",
"\t2.\tAccess TensorBoard:\n",
" \t- In the left-hand navigation pane, click on Applications and IDEs.\n",
" \t- Select TensorBoard.\n",
"\t3.\tOpen TensorBoard:\n",
" \t- Click on Open TensorBoard to launch the TensorBoard landing page.\n",
"\t4.\tAdd Your Training Job:\n",
" \t- On the TensorBoard page, click on Add job.\n",
" \t- Select your most recent completed training job from the list.\n",
"\t5.\tView Training Metrics:\n",
" \t- After the data loads, navigate to the Scalars tab.\n",
" \t- Here, you can see charts and graphs of your training metrics.\n"
]
},
{
"cell_type": "markdown",
"id": "731283bc-212b-4810-9e3c-d6f84368b960",
"metadata": {},
"source": [
"## 7. Conclusion\n",
"\n",
"In this notebook, we demonstrated how to fine-tune a HuggingFace FLAN-T5 model using Amazon SageMaker with TensorBoard integration. We prepared a subset of the Tiny Shakespeare dataset, modified the training script to include TensorBoard logging, and visualized the training metrics.\n",
"\n",
"Next Steps:\n",
"\n",
"- Experiment with Hyperparameters: Adjust learning rates, batch sizes, and other hyperparameters to improve model performance.\n",
"- Use a Larger Dataset: Try using a larger dataset for better results.\n",
"- Deploy the Model: After training, deploy the model using SageMaker’s deployment capabilities for inference.\n",
"\n",
"## 8. References\n",
"\n",
"- [Amazon SageMaker Documentation](https://docs.aws.amazon.com/sagemaker/latest/dg/debugger-htb-prepare-training-job.html)\n",
"- [TensorBoard Documentation](https://www.tensorflow.org/tensorboard/get_started)\n",
"- [Tiny Shakespeare Dataset](https://github.com/karpathy/char-rnn/tree/master/data/tinyshakespeare)\n",
"- [HuggingFace Transformers Documentation](https://huggingface.co/docs/transformers/main_classes/trainer#transformers.Seq2SeqTrainingArguments)\n",
"- [SageMaker JumpStart Models](https://sagemaker.readthedocs.io/en/stable/doc_utils/pretrainedmodels.html)\n",
"\n",
"\n",
"\n",
" ## Notebook CI Test Results\n",
"\n",
"This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
"\n",
"\n",
"![This us-east-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-east-1/ build_and_train_models|sm-jumpstart_tensorflow_finetune_flan|sm-jumpstart_tensorflow_finetune_flan.ipynb)\n",
"\n",
"![This us-east-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-east-2/ build_and_train_models|sm-jumpstart_tensorflow_finetune_flan|sm-jumpstart_tensorflow_finetune_flan.ipynb)\n",
"\n",
"![This us-west-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-west-1/ build_and_train_models|sm-jumpstart_tensorflow_finetune_flan|sm-jumpstart_tensorflow_finetune_flan.ipynb)\n",
"\n",
"![This ca-central-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ca-central-1/ build_and_train_models|sm-jumpstart_tensorflow_finetune_flan|sm-jumpstart_tensorflow_finetune_flan.ipynb)\n",
"\n",
"![This sa-east-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/sa-east-1/ build_and_train_models|sm-jumpstart_tensorflow_finetune_flan|sm-jumpstart_tensorflow_finetune_flan.ipynb)\n",
"\n",
"![This eu-west-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/eu-west-1/ build_and_train_models|sm-jumpstart_tensorflow_finetune_flan|sm-jumpstart_tensorflow_finetune_flan.ipynb)\n",
"\n",
"![This eu-west-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/eu-west-2/ build_and_train_models|sm-jumpstart_tensorflow_finetune_flan|sm-jumpstart_tensorflow_finetune_flan.ipynb)\n",
"\n",
"![This eu-west-3 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/eu-west-3/ build_and_train_models|sm-jumpstart_tensorflow_finetune_flan|sm-jumpstart_tensorflow_finetune_flan.ipynb)\n",
"\n",
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"\n",
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