ML Development Environment
A fully fledged development environment for OSX, Windows, Linux
Step - 1: Install docker
You need docker! Check out https://docs.docker.com/get-docker/ on information on how to install docker for your system.
Step - 2: NVIDIA docker runtime (not needed if you don't want to use GPUs)
If you have NVIDIA drivers installed, you need the NVIDIA runtime to use GPUs in the development environment. Run the following commands if you are on Ubuntu to set up the NVIDIA runtimes.
# Add the package repositories
distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add -
curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list
sudo apt-get update && sudo apt-get install -y nvidia-container-toolkit
sudo systemctl restart docker
For more information about the NVIDIA docker runtime, take a look here: https://github.com/NVIDIA/nvidia-docker
Step - 3: Build the container
make build
Step - 4: Start the coding environment
WORKSPACE=[PATH_TO_YOUR_CODEBASE] CPORT=[PORT] make code
Where PATH_TO_YOUR_CODEBASE is the path to your code base where all the scripts/notebooks are located and PORT is the port you want to run the IDE on
e.g. WORKSPACE=/home/abhishek/workspace/bert-sentiment CPORT=10012 make code
Step - 5: Open the URL in broswer
http://127.0.0.1:10012/
And have fun coding!