Parallel Python at Scale with Ray and Anyscale

🚀 Ray: Distributed Python for AI Workloads
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How do you run Python across hundreds of machines without changing your code? With Ray. 🎯

📌 What is Ray?
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Ray is a distributed execution engine for AI workloads, born in Berkeley’s RISE Lab (the same lineage that produced Apache Spark).

💻 Write code that looks like a normal Python script
⚡ Ray fans the work out automatically across many processes and machines
🤖 Used by OpenAI to power ChatGPT’s post-training (RLHF)
📊 Use cases: RL, multimodal processing, inference, large-scale backtesting

⚡ Fun Origin Story
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Ray was created because researchers tried doing reinforcement learning with Spark and found it was the wrong shape for RL’s dynamic, fine-grained workloads. So they built Ray from scratch.

💡 Explanation in a nutshell
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Python has a limitation: it runs in a single process at a time (the GIL). When your models are too large for one machine, you need distributed computing. Ray lets you scale your Python code to clusters of hundreds of GPUs by simply adding a @ray.remote decorator to your functions — no logic rewrite required.