Distributing training jobs with Ray

You can use Ray, a distributed computing framework, to parallelize Python code across many CPUs or GPUs.

In your notebook environment, open the 8_distributed_training.ipynb file and follow the instructions directly in the notebook. The instructions guide you through setting authentication, creating Ray clusters, and working with jobs.

Optionally, if you want to view the Python code for this step, you can find it in the ray-scripts/train_tf_cpu.py file.

Jupyter Notebook

For more information about TensorFlow training on Ray, see the Ray TensorFlow guide.