open-gigaai/giga-train

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1072 stars · 94 forks observed · 2026-08-28

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

GigaTrain is an efficient and scalable Python training framework for large AI models, supporting distributed multi-GPU/multi-node execution via DeepSpeed ZeRO, FSDP, and DDP. It provides YAML/JSON configuration, mixed precision (FP16/BF16/FP8), gradient checkpointing, EMA, and built-in logging and checkpointing.

Use cases

• fine-tune large AI models on multiple GPUs
• train models across multiple nodes with DeepSpeed ZeRO
• run reproducible training experiments from YAML configs
• fine-tune the Wan video model
• resume long training runs from checkpoints
• train with FP8 or BF16 mixed precision to save memory

When to choose

• you need scalable multi-GPU/multi-node training with ZeRO or FSDP
• you want a lightweight framework that handles backprop, logging, checkpointing, and EMA for you
• you prefer config-driven, reproducible training pipelines
• you are fine-tuning large generative models like Wan

When to avoid

• you only need inference or model serving rather than training
• you need a framework with first-class support for a specific niche model architecture not covered by its registry
• you prefer writing fully custom training loops from scratch

Facets

framework · maturity active

llm-training machine-learning deep-learning gpu-computing monitoring configuration-management machine-learning deep-learning large-language-models artificial-intelligence gpu-computing developer-tools python cross-platform distributed-training deepspeed fsdp mixed-precision fine-tuning training-framework multi-gpu checkpointing linux gpu docker

Member repositories

For agents

markdown · JSON · MCP: product_card(name="open-gigaai/giga-train")