Push version to 0.14.0 (manually)

Author: tomfaulhaber

CHANGELOG.md

@@ -0,0 +1,34 @@
+# Changelog
+
+## v0.14.0 (2020-09-14)
+
+### Features
+
+* Run notebooks written in R or other languages. See the newly added [R example][example] for information.
+* When using the CLI or Library, you can supply extra arguments to the SageMaker Processing Job used to execute the notebook. For example to allow your notebook to run for up to a full day, invoke your notebook with `run-notebook run foo.ipynb --extra '{"StoppingCondition":{"MaxRuntimeInSeconds":86400}}'`. Using this mechanism, you can add extra inputs and outputs, connect to a VPC, add environment variables, expand disk space, add the run to a specific experiment, etc. See the [documentation for SageMaker Processing Jobs][processing-jobs] for more.
+  
+[example]: https://github.com/aws-samples/sagemaker-run-notebook/tree/master/examples/R
+[processing-jobs]: https://docs.aws.amazon.com/cli/latest/reference/sagemaker/create-processing-job.html
+
+### Running Notebooks
+
+* Add an option to pass extra parameters to SageMaker Processing jobs from the CLI/Library (not yet supported in the JupyterLab extension).
+* Add the ability to use the CLI to run the notebook using local Docker containers (`run-notebook local`).
+
+### Container building (`run-notebook create-container`)
+
+* Support base containers from ECR repos in other AWS accounts (to enable using [AWS Deep Learning Containers][dlc]).
+* Add the `-k` option to allow alternate Jupyter kernels
+* Add the `--script` option for running arbitrary shell scripts as part of building the container.
+* Tail the build logs when running the build so you can see any errors that happen inline.
+* Automatically install Python if the base image doesn't already have it.
+
+[dlc]: https://docs.aws.amazon.com/deep-learning-containers/latest/devguide/deep-learning-containers-images.html
+
+### JupyterLab Extension
+
+* Fixes for running in Safari
+
+## v0.13.0 (2020-06-15)
+
+Initial release.

QuickStart.md

@@ -164,12 +164,12 @@ When the notebook has run, you can find the jobs with `aws sagemaker list-proces
 
 To follow this recipe, you'll need to have AWS credentials set up that give you full permission on CloudFormation. You'll add more permissions with the installed policy later in the recipe.
 
-You'll need installation file that you can download from the [latest release][release]: sagemaker_run_notebook-0.13.0.tar.gz.
+You'll need installation file that you can download from the [latest release][release]: sagemaker_run_notebook-0.14.0.tar.gz.
 
 #### 1. Install the library
 
 ```sh
-$ pip install sagemaker_run_notebook-0.13.0.tar.gz
+$ pip install sagemaker_run_notebook-0.14.0.tar.gz
 ```
 
 This installs the sagemaker run notebook library and CLI tool. It also installs the JupyterLab plug-in but does not activate it. See below in "Using the JupyterLab Extension" for more information.
@@ -250,7 +250,7 @@ Once you have the infrastructure and containers set up, the best way to activate
 
 #### In a SageMaker Notebook instance
 
-1. Upload the installation package, sagemaker_run_notebook-0.13.0.tar.gz, from the [latest release][release] to a location of your choosing in S3.
+1. Upload the installation package, sagemaker_run_notebook-0.14.0.tar.gz, from the [latest release][release] to a location of your choosing in S3.
 2. On the AWS SageMaker console, go to Lifecycle Configuration. Create a new lifecycle configuration and add the `start.sh` script (available on [GitHub][start.sh]) to the start action. Edit the S3 location to where you uploaded the installation tar file.
 3. Start or restart your notebook instance after setting the lifecycle configuration to point at your newly created lifecycle configuration.
 
@@ -260,7 +260,7 @@ Once you have the infrastructure and containers set up, the best way to activate
 
 When you open SageMaker Studio, you can add the extension with the following steps:
 
-1. Upload the the installation package, sagemaker_run_notebook-0.13.0.tar.gz, from the [latest release][release] to a location of your choosing in S3.
+1. Upload the the installation package, sagemaker_run_notebook-0.14.0.tar.gz, from the [latest release][release] to a location of your choosing in S3.
 2. Save the  `install-run-notebook.sh` script (available on [GitHub][install-run-notebook.sh]) to your home directory in Studio. The easiest way to do this is to open a text file and paste the contents in. Then edit the S3 location to where you uploaded the installation tar file and save it as `install-run-notebook.sh`.
 3. Open a terminal tab (`File`->`New`->`Terminal`) and run the script as `bash install-run-notebook.sh`.
 4. When it's complete, refresh your Studio browser tab and you'll see the sidebar scheduler tab.

build-dist-files.sh

@@ -10,7 +10,7 @@
 #
 # They all live in the subdirectory "manual_dist/"
 
-VERSION=0.13.0
+VERSION=0.14.0
 
 make artifacts
 

labextension/package.json

@@ -1,6 +1,6 @@
 {
   "name": "sagemaker_run_notebook",
-  "version": "0.13.0",
+  "version": "0.14.0",
   "description": "Execute notebooks on a schedule in SageMaker",
   "keywords": [
     "jupyter",

sagemaker_run_notebook/server_extension/_version.py

@@ -11,7 +11,7 @@
 # ANY KIND, either express or implied. See the License for the specific
 # language governing permissions and limitations under the License.
 
-version_info = (0, 13, 0)
+version_info = (0, 14, 0)
 flag = ""
 
 __version__ = ".".join(map(str, version_info)) + flag

scripts/lifecycle-config/ReadMe.md

@@ -4,7 +4,7 @@ This directory has a lifecycle configuration script that lets you set up the Jup
 
 To use this lifecycle configuration, perform the following steps:
 
-1. Use the [AWS CLI][1] to copy the SageMaker Run Notebook tar file to a location in S3, for example `aws s3 cp sagemaker_run_notebook-0.13.0.tar.gz s3://my-bucket/some-path/`
+1. Use the [AWS CLI][1] to copy the SageMaker Run Notebook tar file to a location in S3, for example `aws s3 cp sagemaker_run_notebook-0.14.0.tar.gz s3://my-bucket/some-path/`
 2. Edit `start.sh` to point at the s3 location and correct plugin version from the previous step.
 3. On the SageMaker AWS console, select "Lifecycle configurations" in the left column and create a new lifecycle configuration called `install-run-notebook-extension`. Copy the contents of `start.sh` into the "Start Notebook" script. You can leave the "Create Notebook" script blank.
 4. When creating a new notebook instance, open the "Additional configuration" option in the "Notebook instance settings" and select the `install-run-notebook-extension`. You can also add this configuration to an existing notebook instance by stopping the instance, opening the instance on the console (by clicking on the instance name in the list of instances), and adding the new configuration there. Then restart your notebook instance.

scripts/lifecycle-config/start.sh

@@ -27,7 +27,7 @@ sudo -u ec2-user -i <<'EOF'
 
 # PARAMETERS
 S3_LOCATION=s3://REPLACE-WITH-MY-BUCKET-PREFIX/
-VERSION=0.13.0
+VERSION=0.14.0
 TARBALL=sagemaker_run_notebook-${VERSION}.tar.gz
 
 

scripts/studio/install-run-notebook.sh

@@ -1,5 +1,5 @@
 bucket=<bucket>
-version=0.13.0
+version=0.14.0
 pkg=sagemaker_run_notebook-${version}.tar.gz
 aws s3 cp s3://${bucket}/${pkg} /tmp
 pip install /tmp/${pkg}