#4141st of 61 in Training and fine-tuning
swanlab
Experiment tracking and dashboards for ML training runs
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- Stars
- 4.3k
- License
- Apache-2.0
- Last commit
- Oct 2026
- Last release
- Oct 2026
- Language
- Python
Missed the latest GitHub check; these facts may be older.
Overview
SwanLab is a Python SDK and dashboard for tracking ML training runs, similar in role to Weights & Biases. You call swanlab.init and swanlab.log to record metrics, hyperparameters, media and hardware stats, then compare experiments in a web UI. It integrates with PyTorch, Transformers, LLaMA Factory, veRL, Ultralytics and others, and offers a self-hosted community edition via Docker or Kubernetes.
Who it is for: ML researchers and training teams who want W&B-style experiment tracking
Strengths
- Two-call integration: swanlab.init and swanlab.log, installed with pip install swanlab
- Hardware monitoring covers Nvidia, AMD, Ascend, Cambricon, Kunlunxin, Hygon, MetaX, Moore Threads and Iluvatar
- Integrations for many training frameworks, including veRL, trl, LLaMA Factory, Ultralytics and TensorBoard
- Logs scalars, images, audio, video, text, 3D point clouds and molecules, plus custom Echarts charts
Weaknesses
- Main README and docs are Chinese-first; English README is a separate file
- Default workflow needs a swanlab.cn account and API key for the cloud
- Self-hosted community edition is described as offline dashboard viewing; feature limits vs cloud not detailed
- Self-host setup lives in external docs; no compose file detected in the repo
What it needs
- Needs Docker or Kubernetes (self-hosting)
- Models: PyTorch, HuggingFace Transformers, PyTorch Lightning, LLaMA Factory, veRL
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| 1 | peftPython library for LoRA and other parameter-efficient fine-tuning of Transformers models | 89 out of 100 |
| 2 | unslothDesktop and web app to run and fine-tune local models | 88 out of 100 |
| 3 | trlPost-training library with SFT, DPO, GRPO and KTO trainers | 87 out of 100 |
| #4 | deepspeedPyTorch library for distributed training of very large models with ZeRO | 86 out of 100 |
| #5 | megatron-lmDistributed training library for large transformer models on NVIDIA GPUs | 85 out of 100 |