Tensorman is a tool for managing TensorFlow toolchains in Pop!_OS. It can be installed with this command:
sudo apt install tensorman
For NVIDIA CUDA support, the following package must be installed:
sudo apt install nvidia-container-runtime
The user account working with Tensorman must be added to the
docker group if that hasn’t been done already:
sudo usermod -aG docker $USER
If Docker was just installed, then a reboot will be needed before Tensorman can be used.
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, even if that distribution is actively supported by the distribution maintainers.
To solve this problem, the Tensorflow project provides official Docker container builds, which allow Tensorflow to operate in a contained environment that is isolated from the rest of the system. This virtual environment can operate independently 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 more-so. 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.
Take the following Docker invocation as an example:
docker run -u $UID:$UID -v $PWD:/project -w /project \ --runtime=nvidia --init --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.
The following commands can be used for installing either the latest version of a container or a certain version:
tensorman pull latest tensorman pull 1.14.0
Commands are executed within the container using the
# 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
Given the following example, which will print a “Hello World” message, the TensorFlow version, and the output of a calculation made using the GPU:
#!/usr/bin/python3 import tensorflow as tf hello = tf.constant('Hello, TensorFlow!') tf.print(hello) tf.print('Using TensorFlow version: ' + tf.__version__) with tf.device('/gpu:0'): a = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[2, 3], name='a') b = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[3, 2], name='b') c = tf.matmul(a, b) tf.print(c)
If the Python file is named
hello-world.py, it can be run with TensorFlow using this command:
tensorman run --gpu python ./hello-world.py
If a certain version is specified with the
+ argument, Tensorman will use that version instead.
tensorman +1.14.0 run --python3 --gpu bash
Custom images may be specified with an
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 in either
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 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']
The default version user-wide can be changed using the
default subcommand. This version of TensorFlow will be launched whenever the
tensorman run command is used:
tensorman default 1.14.0 tensorman default latest gpu python3 tensorman default nightly tensorman default =custom-image gpu
* By default, Tensorman will use the latest as the default per-user version tag.
If the active containers from the current working directory need to be listed, the
show command can be used:
Having many containers installed at the same time can use a lot of disk space. If some need to be removed, the
remove command can be used:
tensorman remove 1.14.0 tensorman remove latest tensorman remove 481cb7ea88260404 tensorman remove =custom-image
To find installed containers, the
list command can be used:
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.
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
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.
Images saved through Tensorman are manageable through Tensorman. Listing and removing work the same way:
tensorman remove IMAGE_NAME
To see the source code and suggest features, visit the project on GitHub.