⚡ Ray Flashcards
Ray flashcards for quick study. This distributed framework helps Python teams scale ML and AI workloads from laptop to cluster with a consistent API. Use the cards below to revise core ideas—what it is, why it’s great for machine learning, key libraries, scaling model training/serving, and ecosystem integrations. Extra tips and learning resources are included at the end.
🚀 What is Ray?
A distributed framework for building and running Python applications at scale, especially ML and AI workloads.
🧠 What makes it ideal for ML workloads?
It simplifies distributed training, hyperparameter tuning, and model serving using unified APIs.
📦 What are Core, Tune, Serve, and Train?
Key libraries include: Core (distributed execution), Tune (HP search), Train (distributed training), Serve (deployment).
🌍 How does it scale Python code?
By abstracting distributed computing with decorators and actors that schedule work across machines.
🔄 What is an Actor?
A stateful process that executes methods remotely and keeps state across method calls.
🔧 What is Tune?
A scalable library for distributed hyperparameter search using schedulers and optimization algorithms.
📡 What is Serve?
A flexible model serving library for deploying ML models as microservices with autoscaling.
📊 What is Dataset?
A unified API for loading, transforming, and consuming large-scale tabular data pipelines.
🧱 Framework agnostic?
Yes—supports TensorFlow, PyTorch, XGBoost, LightGBM, and other libraries for distributed ML tasks.
💡 What’s in the ecosystem?
Integrations with MLflow, Kubernetes, Airflow, Dask, and Hugging Face enable end-to-end workflows.
Quick tips for success
- Think in tasks & actors: Use tasks for stateless parallelism; use actors when you need stateful coordination.
- Right-size clusters: Match worker resources (CPU/GPU/memory) to model size and experiment concurrency.
- Checkpoints & retries: Enable frequent checkpoints in tuning/training jobs to resume efficiently.
- Autoscale thoughtfully: Set min/max replicas and target utilization to balance latency vs. cost in serving.
- Observability: Use dashboards/metrics to monitor scheduling delays, task backlogs, and memory pressure.
Learn more & related reading
- Official docs: Ray Documentation
- On our site: Uplatz Blog – Data & AI Guides