Python 3.7+ builds of the tensorflow Docker image.
137
This directory houses TensorFlow's Dockerfiles and the infrastructure used to create and deploy them to TensorFlow's Docker Hub.
DO NOT EDIT THE DOCKERFILES/ DIRECTORY MANUALLY! The files within are
maintained by assembler.py, which builds Dockerfiles from the files in
partials/ and the rules in spec.yml. See
the Contributing section for more information.
The Dockerfiles in the dockerfiles directory must have their build context set
to the directory with this README.md to copy in helper files. For example:
$ docker build -f ./dockerfiles/cpu.Dockerfile -t tf .
Each Dockerfile has its own set of available --build-args which are documented
in the Dockerfile itself.
After building the image with the tag tf (for example), use docker run to
run the images.
Note for new Docker users: the -v and -u flags share directories and
permissions between the Docker container and your machine. Without -v, your
work will be wiped once the container quits, and without -u, files created by
the container will have the wrong file permissions on your host machine. Check
out the
Docker run documentation for
more info.
# Volume mount (-v) is optional but highly recommended, especially for Jupyter.
# User permissions (-u) are required if you use (-v).
# CPU-based images
$ docker run -u $(id -u):$(id -g) -v $(pwd):/my-devel -it tf
# GPU-based images,
# 1) On Docker versions earlier than 19.03 (set up nvidia-docker2 first)
$ docker run --runtime=nvidia -u $(id -u):$(id -g) -v $(pwd):/my-devel -it tf
# 2) On Docker versions including and after 19.03 (with nvidia-container-toolkit)
$ docker run --gpus all -u $(id -u):$(id -g) -v $(pwd):/my-devel -it tf
# Images with Jupyter run on port 8888 and need a volume for your notebooks
# You can change $(PWD) to the full path to a directory if your notebooks
# live outside the current directory.
$ docker run --user $(id -u):$(id -g) -p 8888:8888 -v $(PWD):/tf/notebooks -it tf
These images do not come with the TensorFlow source code -- but the development
images have git included, so you can git clone it yourself.
To make changes to TensorFlow's Dockerfiles, you'll update spec.yml and the
*.partial.Dockerfile files in the partials directory, then run
assembler.py to re-generate the full Dockerfiles before creating a pull
request.
You can use the Dockerfile in this directory to build an editing environment
that has all of the Python dependencies you'll need:
# Build the tools-helper image so you can run the assembler
$ docker build -t tf-tools -f tools.Dockerfile .
# Set --user to set correct permissions on generated files
$ docker run --user $(id -u):$(id -g) -it -v $(pwd):/tf tf-tools bash
# Next you can make a handy alias depending on what you're doing. When building
# Docker images, you need to run as root with docker.sock mounted so that the
# container can run Docker commands. When assembling Dockerfiles, though, you'll
# want to run as your user so that new files have the right permissions.
# If you're BUILDING OR DEPLOYING DOCKER IMAGES, run as root with docker.sock:
$ alias asm_images="docker run --rm -v $(pwd):/tf -v /var/run/docker.sock:/var/run/docker.sock tf-tools python3 assembler.py "
# If you're REBUILDING OR ADDING DOCKERFILES, remove docker.sock and add -u:
$ alias asm_dockerfiles="docker run --rm -u $(id -u):$(id -g) -v $(pwd):/tf tf-tools python3 assembler.py "
# Check assembler flags
$ asm_dockerfiles --help
# Assemble all of the Dockerfiles
$ asm_dockerfiles --release dockerfiles --construct_dockerfiles
# Build all of the "nightly" images on your local machine:
$ asm_images --release nightly --build_images
# Save the list of built images to a file:
$ asm_images --release nightly --build_images > tf-built.txt
# Build version release for version 99.0, except "gpu" tags:
$ asm_images --release versioned --arg _TAG_PREFIX=99.0 --build_images --exclude_tags_matching '.*gpu.*'
# Test your changes to the devel images:
$ asm_images --release nightly --build_images --run_tests_path=$(realpath tests) --only_tags_matching="^devel-gpu-py3$"
Content type
Image
Digest
Size
1.5 GB
Last updated
over 6 years ago
docker pull softwrdev/tensorflow:latest-gpu-py37