1. AI-lab: The Ideal Tool for Data Scientists to Develop and Export Machine Learning Models

1. AI-lab: The Ideal Tool for Data Scientists to Develop and Export Machine Learning Models

1.1. Description

This project is reserved for creating a new development environment using docker for developing AI models in data science, in particular, computer vision.

I hand-crafted AI-lab (on top of NVIDIA Container) and take advantage of docker capability to have a reproducible and portable development environment. AI-lab allows you developing your artificial intelligence based application in Python using the most used artificial intelligence frameworks.

AI-lab is meant to be used to building, training, validating, testing your deep learning models, for instance is a a good tool to do transfer learning.

It includes

Ubuntu 18.04
NVIDIA CUDA 10.1
NVIDIA cuDNN 7.6.0
OpenCV 4.1.0
Python 3.6
Most used AI framework:
- TensorRT
- TensorFlow
- PyTorch
- ONNX
- Keras
- ONNX-TensorRT
- Jupyter-lab
- VS Code integration with remote development
- numpy
- matplotlib
- scikit-learn
- scipy
- pandas
- and more

1.2. Pre-requisites

An operating system with AMD64 architecture (dpkg --print-architecture), e,g, 64-bit Linux OS (check with lsb_release -a)
NVIDIA drivers and CUDA toolkit: (check with nvidia-smi)
Docker-ce: Install or reinstall docker-ce, please follow the original docker-ce installation guide, including the post-installation steps for Linux

1.3. USAGE

To install AI-lab you must have docker-ce installed on your machine to be able to use the pre-configured development environment. To do that, simply follow the steps to install docker for Ubuntu, or select the suitable version depending on your OS.

Pull AI-lab from Docker Hub

docker pull aminehy/ai-lab

Run the AI-lab and start your development

xhost +
docker run -it --rm
--runtime=nvidia
-v $(pwd):/workspace \
-w /workspace \
-v /tmp/.X11-unix:/tmp/.X11-unix \
-e DISPLAY=$DISPLAY \
-p 8888:8888 -p 6006:6006 aminehy/ai-lab:latest

Install UFF converter : convert-to-uff

Once you run AI-lab, move to /opt/tensorrt/python folder and install convert-to-uff using the following commands:

cd /opt/tensorrt/python
dpkg -i *-tf_*.deb
UFF_PATH="$(python -c 'import uff; print(uff.__path__[0])')"
chmod +x ${UFF_PATH}/bin/convert_to_uff.py
ln -sf ${UFF_PATH}/bin/convert_to_uff.py /usr/local/bin/convert-to-uff

1.4. Launch an IDE and Start Developing

1.4.1. Jupyter notebook

If AI-lab runs correctly on your machine then Jupyter notebook should run automatically. If this is not the case, launch it from the terminal with this command

jupyter notebook --allow-root --port=8888 --ip=0.0.0.0 --no-browser

1.4.2. VS Code

VS Code offers the possibility to develop from inside docker container (thus, inside AI-lab), through the extension Remote Development. Read more in details here.

Two configuration folders .devcontainer and .vscode (inside vscode_remote_dev) are necessary to be able to use VS Code inside AI-lab. These two folders are hidden and must live in the directory of your application so that VS Code automatically detect AI-lab configuration. Therefore, you need to copy them inside your application folder.

To get these folders, first clone this repository and move to it

git clone https://github.com/amineHY/AI-lab.git
cd /AI-lab

Copy the two folders to your application folder, for instance /path_to_folder_application

sudo cp -R AI-lab/vscode_remote_dev/.* /path_to_folder_application

Finally, move to your application folder and launch VS Code

cd /path_to_folder_application
code .

1.5. Display the Memory Usage of the GPU

Depending on you developing, you might want to watch the memory consumption of your GPU. You can do that thanks to gpustat.

watch -n0.5 -c gpustat --c -cupP

Do you have any suggestions ?

Please create an issue or contact me and let me know on LinkedIn.

callezenwaka/AI-lab