Introduction to Ray: Developer

Introduction to Ray: Developer#

Course Welcome and Overview#

This course provides a comprehensive introduction to Ray with hands-on examples and practical use cases, designed to help developers quickly master distributed computing and scale their Python applications from development to production.

📋 Notebook Compute Requirements Legend#

Throughout this course, you’ll see these symbols in each notebook to indicate the recommended compute environment:

Symbol

Meaning

Description

💻

Launch Locally

Whether the notebook can run efficiently on your laptop/local machine

🚀

Launch on Cloud

Benefits from GPU acceleration, cloud recommended

Note: For 🚀 notebooks, we recommend using Anyscale with a Ray cluster that has GPUs.

Why Ray?#

Today’s ML workloads are increasingly compute-intensive. As convenient as they are, single-node development environments such as your laptop cannot scale to meet these demands.

Ray is a unified way to scale Python and AI applications from a laptop to a cluster.

With Ray, you can seamlessly scale the same code from a laptop to a cluster. Ray is designed to be general-purpose, meaning that it can performantly run any kind of workload. If your application is written in Python, you can scale it with Ray, no other infrastructure required.

Setting Up a Local Ray server using Jupyter Notebook#

Prerequisites#

This guide helps you set up a local Jupyter Notebook environment using Conda and install Ray for parallel and distributed computing.

✅ 1. Install Conda#

If you don’t already have Conda, you can install Miniforge or Miniconda on macOS with Apple Silicon (ARM-based M1/M2/M3 chips):

🧩 Miniforge Installation (It depends on your OS. In this case, we use ARM Macs)#

Open your terminal and run:

curl -LO https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-MacOSX-arm64.sh
bash Miniforge3-MacOSX-arm64.sh

Follow the prompts to accept the license, choose the install location, and initialize Miniforge.

Then restart your terminal or run:

source ~/.zprofile  # or ~/.bash_profile if you're using bash

Verify installation:

conda --version

Alternatively, download from:

🛠️ 2. Create a New Conda Environment#

conda create -n ray-jupyter python=3.11 -y

Ray supports Python 3.7 to 3.11 (Python 3.12 is not supported as of Ray 2.20).

▶️ 3. Activate the Environment#

conda activate ray-jupyter

📦 4. Install UV and Dependencies#

First, install UV:

curl -LsSf https://astral.sh/uv/install.sh | sh

Install the dependencies using the lock file:

uv pip install -r requirements.lock

This will install all dependencies with their exact versions as specified in the lock file, ensuring reproducible builds across different environments.

Managing Dependencies#

When you need to add new dependencies:

  1. Add the new package to requirements.txt

  2. Generate a new lock file:

    uv pip compile requirements.txt -o requirements.lock

  3. Install using the updated lock file:

    uv pip install -r requirements.lock

This workflow ensures that all dependencies are properly tracked and versions are locked for reproducibility.

🚀 6. Launch Jupyter Notebook#

jupyter notebook

This will open a browser window for you to create and manage notebooks.

✅ 7. Verify Ray Installation with a Simple Example#

After launching Jupyter Notebook and selecting your ray-jupyter kernel, create a new notebook and run the following code to verify Ray is working:

%%python
import ray
ray.init()

@ray.remote
def hello_world():
    return "Ray is working!"

result = ray.get(hello_world.remote())
print(result)

You should see the output:

2025-06-10 10:41:31,774	INFO worker.py:1879 -- Started a local Ray instance. View the dashboard at http://127.0.0.1:8265 

Ray is working!

If you see this message, Ray is set up correctly! You can now view the local Ray Dashboard at: http://127.0.0.1:8265

🧹 8. Shut Down and Clean Up#

To stop the Jupyter Notebook server:

  • Press Ctrl+C in the terminal

To deactivate the environment:

conda deactivate

To remove the environment completely:

conda remove --name ray-jupyter --all

You’re now ready to start developing Ray applications in a local Jupyter Notebook environment!

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