Serverless Reinforcement Learning | PyTorch, Images, Volumes, Scaling

💖 Support BrainOmega ☕ Buy Me a Coffee: https://buymeacoffee.com/brainomega 💳 Stripe: https://buy.stripe.com/aFa00i6XF7jSbfS9T218c00 💰 PayPal: https://paypal.me/farhadrh 🎥 In this video, we bring everything together with Hands-on 1: Serverless Reinforcement Learning on Modal (CartPole-v1 with PyTorch). This is the capstone exercise of the course, where we move beyond isolated features and build a real, end-to-end serverless ML training job. Using CartPole-v1 and a clean DQN implementation, you’ll see how Modal can run full reinforcement learning workflows—not just toy functions—while remaining scalable, reproducible, and persistent. This hands-on project is deliberately comprehensive and ties together Lessons 1 through 5. We define a custom image with PyTorch and Gymnasium, reserve CPU and memory for predictable training performance, and optionally extend to GPU-backed workloads. We persist model checkpoints and training metrics in a Modal Volume so results survive across runs, containers, and days. You’ll also see how to evaluate trained policies in parallel using Modal’s scaling primitives, and how input concurrency lets you efficiently reuse containers for fast rollouts. By the end of this hands-on, you’ll have a concrete mental model for running stateful, long-running ML training jobs in a serverless environment. You’ll understand how training, evaluation, persistence, and parallelism fit together—and how the same patterns apply to real-world systems like RL agents, simulators, hyperparameter sweeps, and large-scale evaluation pipelines. This is the bridge from “learning Modal” to building production-grade AI systems. 💻 Code on GitHub: https://github.com/frezazadeh/serverless-llm-agentic-ai/blob/main/hands_on1.ipynb ⸻ 📚 What You’ll Learn • How to run a full reinforcement learning training loop on Modal • How to combine custom images, volumes, and resource reservations • How to persist checkpoints and training logs across runs • How to safely use