Raylings ⚡

An interactive, client-side WebAssembly learning platform and comprehensive reference manual for Python Ray.

---

⚡ The Modern Way to Master Distributed Ray

Raylings combines a zero-install, 100% client-side WebAssembly interactive playground with 18 comprehensive architectural reference guides spanning distributed tasks, actors, plasma memory, Ray Data, PyTorch Train, Serve, vLLM, and KubeRay.

• Zero-Install Web IDE --- Run Monaco Editor, Pyodide WebAssembly (Python 3.12), and real-time Ray cluster simulation 100% in your browser. No Python environment setup, no cluster configuration, and no cloud GPU costs required.

  Launch Playground →

• 18-Chapter Reference Manual --- Deep architectural documentation, annotated Python API anatomy, production best practices, and diagnostic troubleshooting workflows for modern distributed systems.

  Explore Reference Guides →

---

📚 Comprehensive 18-Chapter Reference Guides

Explore in-depth architectural guides and launch linked practice exercises directly into the playground:

• Ray Core & Distributed Memory

  ---

  • 01. Remote Tasks & Futures • @ray.remote, ObjectRefs, async execution, batching
  • 02. Stateful Actors & Concurrency • Remote classes, state tracking, concurrency groups
  • 03. Plasma Object Store • Shared memory, ray.put(), zero-copy Apache Arrow
  • 04. Resource Scheduling • Fractional CPUs/GPUs, custom tags, placement groups
  • 05. Fault Tolerance & Lineage • Task retries, actor restarts, lineage recovery
  • 06. Cluster Architecture & GCS • Head node, raylets, Global Control Store
  • 07. Design Patterns & Anti-Patterns • Tree aggregation, actor pipelines

• Distributed Data & Scalable ML

  ---

  • 08. Streaming Ray Data • Block partitions, map_batches, streaming pipelines
  • 09. Distributed ML from Scratch • Parameter Servers, Ring All-Reduce, worker shards
  • 10. PyTorch Distributed (Ray Train) • TorchTrainer, DDP, fault-tolerant checkpoints
  • 11. Hyperparameter Search (Ray Tune) • Search spaces, ASHA early stopping, trial tuners
  • 12. Production Serving (Ray Serve) • @serve.deployment, HTTP ingress, dynamic batching

• Observability & Cloud Orchestration

  ---

  • 13. Observability, Tracing & Profiling • Ray Dashboard, OpenTelemetry spans, metrics
  • 14. Kubernetes AI with KubeRay • RayCluster, RayJob, RayService, GPU pod groups

• Generative AI, LLMs & High Performance

  ---

  • 15. High-Throughput vLLM Serving • PagedAttention KV cache, continuous batching
  • 16. Multi-Node LLM Training (FSDP) • ZeRO-3 parameter sharding, mixed precision
  • 17. Multimodal Embeddings & Vector RAG • Distributed chunking, vector indexing
  • 18. Quantitative Finance Risk Engines • Monte Carlo Black-Scholes, VaR simulation

---

💡 How the Playground Works

The Raylings web playground runs entirely on client-side WebAssembly technology:

flowchart LR
    Monaco["Monaco Editor<br/>(User Code)"] -->|"1. postMessage"| Worker["Web Worker<br/>(Pyodide Wasm)"]
    Worker -->|"2. Simulated Ray Cluster"| Engine["Wasm Ray Engine<br/>(Tasks, Actors, Plasma)"]
    Engine -->|"3. Verification"| Tester["Harness Validator & Hints"]
    Tester -->|"4. Stream Output (<5ms)"| Term["xterm.js Terminal Output"]

    style Monaco fill:#1e293b,stroke:#38bdf8,stroke-width:2px,color:#f8fafc
    style Worker fill:#0f172a,stroke:#818cf8,stroke-width:2px,color:#f8fafc
    style Engine fill:#1e1e38,stroke:#34d399,stroke-width:2px,color:#f8fafc
    style Tester fill:#1e293b,stroke:#f59e0b,stroke-width:1px,color:#f8fafc
    style Term fill:#1e1e38,stroke:#c084fc,stroke-width:2px,color:#f8fafc

Diagram Walkthrough & Core Concepts: - 100% Client-Side WebAssembly: Python code executes in a dedicated browser Web Worker powered by Pyodide, requiring zero external server backends or API keys. - In-Browser Ray Simulation: The raylings.wasm_compat runtime accurately emulates @ray.remote tasks, stateful actor mailboxes, and Plasma shared memory locally in your browser. - Sub-5ms Terminal Feedback: Code execution, assertion evaluations, and contextual diagnostic hints stream directly to the xterm.js terminal with instant interactive feedback.

1. Instant Execution: Python code compiles and evaluates inside a background Web Worker running Pyodide v0.26 WebAssembly.
2. Local Cluster Simulation: The pure-Python Ray compatibility engine simulates @ray.remote tasks, stateful actors, Plasma shared memory, and cluster stats directly in your browser.
3. Zero Backend Required: All state is stored locally via localStorage. Work completely offline on airplanes, trains, or behind corporate air-gaps.