This is the official PXL TensorFlow Machine Learning / Deep Learning docker development environment. The PyTorch version can be found here.

!!! Note: Do not clone this repository into a path containing a space !!!*

• Ubuntu: 22.04
• Python: 3.11
• CUDA: 11.5

• Tensorflow / Keras: 2.14

• scikit-learn: 1.3.1
• statsmodels: 0.14.0
• prophet: 1.1.5
• ...

• matplotlib: 3.8.0
• plotly: 5.17.0
• seaborn: 0.13.0
• ggplot: 0.11.5
• PDPbox: 0.3.0
• tensorboard: 2.14.0
• Weights & Biases: 0.15.12
• graphviz: 0.20.1
• ...

• flask: 3.0.0
• fastapi: 0.103.2
• onnx: 1.14.1

• treeinterpreter
• tf-explain
• shap

• jupyter_ai: 2.6.0

(more info about installation and usage here: https://github.com/jupyterlab/jupyter-ai)

Note: This is just a snapshot of the libraries included. There are many more utilities and libraries embedded within the container.

• A UNIX-like operating system, preferably Linux. (Ubuntu 20.04+ is recommended.) or Windows with Docker Desktop installed.
• 10.3GB free space
• An operational docker daemon.
• Standard Bash knowledge.

You can run the container without GPU support, but your performance (with Deep Learning frameworks) will be low.

Nvidia

If you have an Nvidia graphics card capable of running hardware accelerated graphics, follow the instructions in the guide here to install all the necessary drivers, CUDA and the Nvidia docker toolkit.

You can test GPU support by executing the following steps (after having built the container, see How to build the container):

003_start_pxl_ml_container.sh

nvidia-smi

start_jupyter

Open the TestGPU.ipynb notebook and exectute the different steps.

A bash script is provided to build the container, it can be executed by entering the following command:

./001_build_images.sh

To start the container execute the script below:

003_start_pxl_ml_container.sh

This script will check the available GPU and start the container accordingly.

To use multiple bash shells in the container, It's advised to either work with tmux or execute the script with prefix 005 from the host:

./004_attach_bash_to_ml_container.sh

To start jupyter notebooks, you can use the command

start_jupyter

inside the container.

TBD