tensorflow-jupyter-aws-docker

Docker container with Python3, Tensorflow and Jupyter for training models on AWS Stores all notebooks to S3 bucket*

Steps:

1. Create a S3 bucket on AWS

2. Clone the repo

3. Build docker image

   docker build -t tensorflow-jupyter-aws-docker .
   

4. Credentials

   Ensure AWS credentials are available in your shell, if not set them manually:

   export AWS_ACCESS_KEY_ID=###
   export AWS_SECRET_ACCESS_KEY=###
   export AWS_SESSION_TOKEN=###
   

5. Run image

   docker run -it --rm \
       -p=8888:8888 \
       --name=tensorflow-jupyter-aws-docker \
       -e "AWS_ACCESS_KEY_ID=$AWS_ACCESS_KEY_ID" \
       -e "AWS_SECRET_ACCESS_KEY=$AWS_SECRET_ACCESS_KEY" \
       -e "AWS_SESSION_TOKEN=$AWS_SESSION_TOKEN" \
       -e "AWS_REGION=$AWS_REGION" \
       -e "AWS_S3_BUCKET=your-s3-bucket-name" \
       tensorflow-jupyter-aws-docker develop
   

   NOTE: AWS_SESSION_TOKEN might be optional

6. Open the Jupyter notebook

   From the terminal, copy the notebook url with token in browser

   [I 07:19:30.621 NotebookApp] Writing notebook server cookie secret to /root/.local/share/jupyter/runtime/notebook_cookie_secret
   jupyter_http_over_ws extension initialized. Listening on /http_over_websocket
   [I 07:19:31.551 NotebookApp] Serving contents
   [I 07:19:31.551 NotebookApp] The Jupyter Notebook is running at:
   [I 07:19:31.552 NotebookApp] http://(b4e1cc10327f or 127.0.0.1):8888/?token=a3e4a939f857d0a9ba40daa73dc7a20cbd6219947843f3ca
   [I 07:19:31.552 NotebookApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation).
   [C 07:19:31.558 NotebookApp]
   
       To access the notebook, open this file in a browser:
           file:///root/.local/share/jupyter/runtime/nbserver-12-open.html
       Or copy and paste one of these URLs:
           http://(b4e1cc10327f or 127.0.0.1):8888/?token=a3e4a939f857d0a9ba40daa73dc7a20cbd6219947843f3ca