flexflow/flexflow-train

Health v2 · maintenance only

Adoption not part of the score

1898 stars · 255 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

FlexFlow Train is a deep learning framework that accelerates distributed DNN training by automatically searching for efficient parallelization strategies across samples, operators, attributes, and parameters. It provides a drop-in replacement for PyTorch and TensorFlow Keras and uses a simulator-based search algorithm to outperform manually designed parallelization strategies.

Use cases

• speed up distributed training of large neural networks on multi-GPU clusters
• automatically find the best parallelization strategy instead of hand-tuning data or model parallelism
• import and optimize existing PyTorch models for parallel training
• train models with Keras-style APIs on distributed hardware
• benchmark parallelization strategies with an execution simulator

When to choose

• you train large DNNs on multi-GPU or multi-node clusters and manual parallelization is slow or suboptimal
• you want automatic SOAP-dimension parallelization search for PyTorch or Keras models
• you need a research-grade framework for exploring parallelization strategies

When to avoid

• you only train small models on a single GPU
• you need LLM inference or serving rather than training (use flexflow-serve)
• you require a mature production ecosystem like PyTorch DDP or DeepSpeed with broad community support

Facets

framework · maturity active

machine-learning deep-learning llm-training gpu-computing deep-learning machine-learning gpu-computing microservices python cpp distributed-training parallelization model-parallelism data-parallelism autotuning cuda pytorch keras linux gpu docker

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markdown · JSON · MCP: product_card(name="flexflow/flexflow-train")