Tensorflow Container Manager
Packaging Tensorflow for Linux distributions is notoriously difficult, if not impossible. Every release of Tensorflow is accommodated by a myriad of possible build configurations, which requires building many variants of Tensorflow for each Tensorflow release. To make matters worse, each new version of Tensorflow will depend on a wide number of shared dependencies which may not be supported on older versions of a Linux distribution that is still actively supported by the distribution maintainers.
To solve this problem, the Tensorflow project provides official Docker container builds, which allows Tensorflow to operate in an isolated environment that is contained from the rest of the system. This virtual environment can operate independent of the base system, allowing you to use any version of Tensorflow on any version of a Linux distribution that supports the Docker runtime.
However, configuring and managing Docker containers for Tensorflow using the
docker command line is currently tedious, and managing multiple versions for different projects is even moreso. To solve this problem for our users, we have developed
tensorman as a convenient tool to manage the installation and execution of Tensorflow Docker containers. It condenses the command-line soup into a set of simple commands that are easy to memorize.
Comparison to Docker Command
Take the following Docker invocation as an example:
docker run -u $UID:$UID -v $PWD:/project -w /project \ --runtime=nvidia --it --rm tensorflow/tensorflow:latest-gpu \ python ./script.py
This designates for the latest version of Tensorflow with GPU support to be used, mounting the working directory to
/project, launching the container with the current user account, and and executing
script.py with the Python binary in the container. With
tensorman, we can achieve the same with:
tensorman run --gpu python -- ./script.py
Which defaults to the latest version, and whose version and tag variants can be set as defaults per-run, per-project, or user-wide.
By default, docker will automatically install a container when running a container that it is not already installed. However, if you would like to install a container beforehand, you may do so using the
tensorman pull 1.14.0 tensorman pull latest
Running commands in containers
run subcommand allows you to execute a command from within the container. This could be the
bash shell, for an interactive session inside the container, or the program / compiler which you wish to run.
# Default container version with Bash prompt tensorman run bash # Default container version with Python script tensorman run python -- script.py # Default container version with GPU support tensorman run --gpu bash # With GPU, Python3, and Juypyter support tensorman run --gpu --python3 --jupyter bash
Setting the container version
Taking inspiration from rustup, there are methods to set the container version per-run, per-project, and per-user. The per-run version always takes priority over a per-project definition, which takes priority over the per-user configuration.
If a version is specified following a
tensorman will prefer this version.
tensorman +1.14.0 run --python3 --gpu bash
Custom images may be specified with a
tensorman =custom-image run --gpu bash
There are two files that can be used for configuring Tensorman locally:
Tensorman.toml. These files will be automatically detected if they can be found in a parent directory.
This file overrides the tensorflow image, defined either in
Tensorman.toml, or the user-wide configuration file.
1.14.0 gpu python3
Or specifying a custom image:
This file supports additional configuration parameters, with a user-wide configuration located at
~/.config/tensorman/config.toml, and a project-wide location at
Tensorman.toml. One of the reasons in which you may want to use this file is to declare some additional Docker flags, with the
Using a default tensorflow image:
docker_flags = [ '-p', '8080:8080' ] tag = '2.0.0' variants = ['gpu', 'python3']
Defining a custom image:
docker_flags = [ '-p', '8080:8080' ] image = 'custom-image' variants = ['gpu']
you can set a default version user-wide using the
default subcommand. This version of Tensorflow will be launched whenever you use the
tensorman run command.
tensorman default 1.14.0 tensorman default latest gpu python3 tensorman default nightly
latestas the default per-user version tag.
Showing the active container version
If you would like to know which container will be used when launched from the current working directory, you can use the
Removing container images
Having many containers installed simultaneously on the same system can quickly use a lot of disk storage. If you find yourself in need of culling the containers installed on your system, you may do so with the
tensorman remove 1.14.0 tensorman remove latest tensorman remove 481cb7ea88260404 tensorman remove custom-image
Listing installed container images
To aid in discovering what containers are installed on the system, the
list subcommand is available.
Creating a custom image
In most projects, you will need to pull in more dependencies than the base Tensorflow image has. To do this, you will need to create the image by running a tensorflow container as root, installing and setting up the environment how you need it, and then saving those changes as a new custom image.
To do so, you will need to build the container in one terminal, and save it from another.
Build new image
First launch a terminal where you will begin configuring the docker image:
tensorman run --gpu --python3 --root --name CONTAINER_NAME bash
Once you've made the changes needed, open another terminal and save it as a new image:
tensorman save CONTAINER_NAME IMAGE_NAME
Running the custom image
You should then be able to specify that container with tensorman, like so:
tensorman =IMAGE_NAME run --gpu bash
--jupyterflags do nothing for custom containers, but
--gpuis required to enable runtime support for the GPU.
Removing the custom image
Images saved through tensorman are manageable through tensorman. Listing and removing works the same:
tensorman remove IMAGE_NAME
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