Ray
Distributed compute framework for Python: scale data loading, training, hyperparameter search, and online serving (Ray Serve).
Why it is included
Common open backbone for distributed Python ML and increasingly for LLM batch + serving patterns.
Best for
Teams that outgrow single-node Python but want to stay in one language ecosystem.
Strengths
- Unified distributed runtime
- Ray Serve
- Rich libraries (Train, Tune)
Limitations
- Cluster ops and debugging have a learning curve
Good alternatives
Spark · Dask · Plain Kubernetes jobs
Related tools
AI & Machine Learning
PyTorch
Deep learning framework with strong research-to-production paths.
AI & Machine Learning
Kubeflow
Kubernetes-native toolkit for ML: notebooks, training jobs, pipelines, tuning, and serving components you compose on-cluster.
AI & Machine Learning
vLLM
High-throughput LLM serving with PagedAttention, continuous batching, and OpenAI-compatible APIs for GPU clusters.
AI & Machine Learning
GPT-NeoX
EleutherAI framework and 20B-class models for training large autoregressive LMs with 3D parallelism—Apache-2.0 training stack.
AI & Machine Learning
DeepSpeed
Microsoft library for extreme-scale model training: ZeRO optimizer states, pipeline parallelism, and inference kernels.
AI & Machine Learning
Accelerate
Hugging Face library to run PyTorch training on CPU, single GPU, multi-GPU, or TPU with minimal code changes.