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Welcome to the Ray Summit 2022 training tutorials on Ray, the system for scaling your Python and AI/ML applications from a laptop to a cluster.
| Class | Ray Components and Library | Description |
|---|---|---|
| 1 | Ray Core | Introduction to Ray for Distributed Applications |
| 2 | Ray Serve | Machine Learning Model Deployment and Serving with Ray Serve |
| 3 | Ray RLlib | Introduction to Reinforcement Learning and RLlib |
IMPORTANT NOTE: Modules and materials in these tutorials have been tested with
Ray release x.y and supported Python 3.7 and 3.8.
There is nothing you need to setup, as the Anyscale hosted environment will provide everything: all notebooks for each class, data files, and all relevant python packages will be installed on the cluster.
However, consider cloning or downloading a release of the tutorial notebooks and supporting software from the Ray Summit training repo, so you have a local copy of everything.
This is optional if you want to install training material on your laptop at home, after training is over.
If you need to install Anaconda, follow the instructions here.
If you already have Anaconda installed, consider running conda upgrade --all.
conda create -n ray-summit-training python=3.8conda activate ray-summit-traininggit clone git@github.com:anyscale/ray-summit-2022-training.gitcd to <cloned_dir>python3 -m pip install -r requirements.txtpython3 -m ipykernel installjupyter lab
If you are using Apple M1 laptop 🍎 follow the following instructions:
conda create -n ray-summit-training python=3.8conda activate ray-summit-trainingconda install grpciogit clone git@github.com:anyscale/ray-summit-2022-training.gitcd to <cloned_dir>python3 -m pip install -r requirements.txtpython3 -m ipykernel installconda install jupyterlabjupyter lab
git clone git@github.com:anyscale/ray-summit-2022-training.gitcd to <cloned_dir>python3 -m pip install -r requirements.txtpython3 -m ipykernel installjupyter lab
Let's have 😜 fun @ Ray Summit 2022!
Thank you 🙏