docker-swift-tensorflow's Introduction

docker-swift-tensorflow

Dockerized Swift for TensorFlow with Jupyter and GPU Support.

Usage

CLI

nvidia-docker run -ti --rm \
  --privileged \
  --userns=host \
  \
  -v "$(pwd)":/notebooks \
  --entrypoint /bin/bash \
  zixia/swift

--privileged and --userns=host might be required for some docker deamon configuration. (see this issue)

Jupyter

nvidia-docker run -ti --rm \
  -p 8888:8888 \
  --cap-add SYS_PTRACE \
  -v "$(pwd)":/notebooks \
  zixia/swift

The functions of these parameters are:

-p 8888:8888 exposes the port on which Jupyter is running to the host.
--cap-add SYS_PTRACE adjusts the privileges with which this container is run, which is required for the Swift REPL.
-v <host path>:/notebooks bind mounts a host directory as a volume where notebooks created in the container will be stored. If this command is omitted, any notebooks created using the container will not be persisted when the container is stopped.

Run a Swift File

To run a local file main.swift form the current path:

nvidia-docker run -ti --rm \
  --privileged \
  --userns=host \
  \
  -v "$(pwd)":/notebooks \
  zixia/swift \
  swift ./main.swift

Develop

Testing

After building the docker image according to the instructions above,

docker run --rm \
  --cap-add SYS_PTRACE \
  zixia/swift \
  python3 /swift-jupyter/test/all_test_docker.py

History

v0.1 (22 Aug 2019)

Init version based on Swift-Jupyter, working under Ubuntu 19.04 without any problem.

Resources

Author

Huan (李卓桓) [email protected]

Copyright & License

Code & Docs © 2019-now Huan LI (李卓桓) [email protected]
Code released under the Apache-2.0 License
Docs released under Creative Commons

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docker-swift-tensorflow's Issues

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