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ML-Dev-Environment

Example of a development environment setup when using NVIDIA Docker toolkit, and Tensorflow-gpu for ML development.

Building the docker image.

Run below command to build the docker containers with required libraries

docker-compose build

Launch Jupyter notebook for your development environment.

docker-compose up

You would see something like this in console log, use the link to open notebook

tf-docker_1  | [I 16:27:47.506 NotebookApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation).
tf-docker_1  | [C 16:27:47.511 NotebookApp] 
tf-docker_1  |     
tf-docker_1  |     To access the notebook, open this file in a browser:
tf-docker_1  |         file:///root/.local/share/jupyter/runtime/nbserver-1-open.html
tf-docker_1  |     Or copy and paste one of these URLs:
tf-docker_1  |         http://cf64133c6103:8888/?token=ea6a26036b2ee55b9cd562f675f90214e0ceec4076a250b4
tf-docker_1  |      or http://127.0.0.1:8888/?token=ea6a26036b2ee55b9cd562f675f90214e0ceec4076a250b4

Adding libraries.

To add libraries to your environment add them to requirements.txt file inside tf-docker folder.