open-gigaai/giga-train

What it solves

GigaTrain addresses the complexity and repetitive nature of training large AI models. It removes the need for developers to manually handle tedious tasks like backpropagation, logging, checkpointing, and multi-node execution, allowing them to focus on implementing core algorithms.

How it works

It functions as a modular training framework that uses a registry-driven design with pluggable components (optimizers, schedulers, samplers, and transforms). It supports a wide range of distributed training strategies, including DeepSpeed ZeRO, FSDP/FSDP2, and DDP, and employs memory-saving techniques like mixed precision (FP16/BF16/FP8) and gradient checkpointing.

Who it’s for

Researchers and developers who need an efficient, scalable way to experiment with and train large-scale AI models across multiple GPUs or nodes.

Highlights

• Unified distributed training: Supports multi-GPU and multi-node execution via DeepSpeed and FSDP.
• Flexible configurations: Uses PY/YAML/JSON configs for reproducible experiments.
• Memory efficiency: Implements mixed precision and gradient accumulation to optimize performance.
• Built-in monitoring: Integrated logging and robust checkpointing for reliable long-term training runs.