Collection of Jupyter notebooks demonstrating best-practices for using PyTorch on GPU accelerated hardware.
- Demonstrate end-to-end GPU accelerated ML workflow using PyTorch to train various DNN achitectures.
- Demonstrate distributed, GPU accelerated training capable of scaling to clusters of GPUs.
Create the environment...
$ conda env create --prefix ./env --file environment.yml
...then activate the environment...
$ conda activate ./env
...then launch the JupyterLab server.
$ jupyter lab