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An Professional, Modern-Designed AI Training Analysis Platform
For Model Training Teams, Integrated with 50+ Leading AI Training Frameworks, Easily Combined with Your Experimental Code

🔥SwanLab Online · 📃 Documentation · Report Issues · Feedback · Changelog · SKILL

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Table of Contents

• 🌟 Recent Updates
• 👋🏻 What is SwanLab
• 📃 Online Demo
• 🏁 Quick Start
• 💻 Self-Hosting
• 🔥 Tutorials
• 🚗 Framework Integration
• 🔌 Plugins
• 🎮 Hardware Monitoring
• 🆚 Comparison with Familiar Tools
• 👥 Community
• 📃 License

🌟 Recent Updates

• 2026.06.16: 📊 Added HTML charts, supporting pages embedded after LLM Vibe Coding; the Kubernetes deployment Prometheus log monitoring solution is now available;

• 2026.06.05: ⚡️ The refactored SDK (v0.8.0) is now available, significantly improving metric logging performance for large-scale training; SwanLab CLI and Skill are now available for the AutoResearch paradigm;

• 2026.05.18: 📒 Logs now support global filtering, so you can quickly find the log snippets you need across the entire workspace; added chart copy feature; chart search now includes search history; chart details now show relative time;

• 2026.04.28: 🪨 Project Pinning is now live — place the projects your team cares about most in the most prominent spot; improved the metric panel display when hovering over line charts;

• 2026.03.25: 📊 Experiment Pinning is now live — pin your best experiments to the most accessible position with one click; Baseline comparison is now available, supporting comparison of experiments against a baseline with percentage difference display, accelerating the search for optimal parameters;

https://github.com/user-attachments/assets/964380e0-feb2-480d-b1ca-eba1be239ebb

• 2026.03.19: 📊 Added experiment duplication feature, supporting the creation of experiment copies to different projects and teams; parallel mode is now available, supporting multiple processes recording metrics to the same experiment at the same time; experiment ID can now be customized;

• 2026.02.06: 🔥swanlab.Api is now available, providing a more powerful, object-oriented open API interface, documentation; ECharts.Table supports CSV download; now supports one-click placement of charts at the top of sections;

👋🏻 What is SwanLab

SwanLab is an open-source, lightweight AI model training tracking and visualization tool, providing a platform for tracking, recording, comparing, and collaborating on experiments.

SwanLab is designed for AI researchers, offering a friendly Python API and a beautiful UI interface, and providing features such as training visualization, automatic logging, hyperparameter recording, experiment comparison, and multi-user collaboration. With SwanLab, researchers can identify training issues through intuitive visual charts, compare multiple experiments to find research inspiration, and break down team communication barriers through online web sharing and multi-user collaborative training within organizations, improving organizational training efficiency.

https://github.com/user-attachments/assets/7965fec4-c8b0-4956-803d-dbf177b44f54

Here is a list of its core features:

1. 📊 Experiment Metrics and Hyperparameter Tracking: Minimal code integration into your machine learning pipeline to track and record key training metrics.

• ☁️ Supports cloud usage (similar to Weights & Biases), allowing you to check training progress anytime, anywhere. How to view experiments on mobile.

• 🌸 Visualizing the Training Process: By visualizing experiment tracking data through the UI interface, trainers can intuitively observe the results at each step of the experiment, analyze metric trends, and determine which changes led to improved model performance—ultimately enhancing the overall efficiency of model iteration.

• Supports hyperparameter recording and table display.

• Supported metadata types: Scalar metrics, images, audio, text, 3D point clouds, biological chemical molecules, Echarts custom chart...

• Resume Training Record: Supports recording new metrics data to the same experiment after training is completed/interrupted.

• Supported chart types: Line charts, media charts (images, audio, text, 3D point clouds, biological chemical molecules), Bar charts, Scatter charts, Box plots, Heat maps, Pie charts, Radar charts, Custom charts...

• Text Chart: A text chart for large language model training, supporting Markdown rendering.

• Automatic background logging: Logging, hardware environment, Git repository, Python environment, Python library list, project runtime directory.

2. ⚡️ Comprehensive Framework Integration: PyTorch, 🤗HuggingFace Transformers, PyTorch Lightning, 🦙LLaMA Factory, MMDetection, Ultralytics, PaddleDetection, LightGBM, XGBoost, Keras, Tensorboard, Weights&Biases, Swift, XTuner, Stable Baseline3, Hydra, and more, totaling 30+ frameworks.

3. 💻 Hardware Monitoring: Supports real-time recording and monitoring of system-level hardware metrics for CPU, NPU (Ascend), GPU (Nvidia), AMD (ROCm), MLU (Cambricon), XLU (Kunlunxin), DCU (Hygon), MetaX GPU (Moxing), Moore Threads GPU (Moore Threads), Iluvatar GPU (Iluvatar), and memory.

4. 📦 Experiment Management: Through a centralized dashboard designed for training scenarios, quickly overview and manage multiple projects and experiments.

5. 🆚 Result Comparison: Compare hyperparameters and results of different experiments through online tables and comparison charts to uncover iteration insights.

6. 👥 Online Collaboration: Collaborate with your team on training, supporting real-time synchronization of experiments under a single project. You can view team training records online and provide feedback and suggestions based on results.

7. ✉️ Share Results: Copy and send persistent URLs to share each experiment, easily send to partners, or embed in online notes.

8. 💻 Self-Hosting Support: Supports offline usage, and the self-hosted community edition also allows viewing dashboards and managing experiments.

9. 🔌 Plugin Extensions: Supports extending SwanLab's usage scenarios through plugins, such as Lark Notifications, Slack Notifications, CSV Logger, etc.

📃 Online Demo

Check out SwanLab's online demos:

ResNet50 Cat-Dog Classification	Yolov8-COCO128 Object Detection
Track a simple ResNet50 model training on a cat-dog dataset for image classification.	Use Yolov8 on the COCO128 dataset for object detection, tracking training hyperparameters and metrics.

Qwen2 Instruction Fine-Tuning	LSTM Google Stock Prediction
Track Qwen2 large language model instruction fine-tuning for simple instruction following.	Use a simple LSTM model on Google stock price dataset to predict future stock prices.

ResNeXt101 Audio Classification	Qwen2-VL COCO Dataset Fine-Tuning
Progressive experimental process from ResNet to ResNeXt on audio classification tasks.	Fine-tune Qwen2-VL multimodal large model on COCO2014 dataset using Lora.

EasyR1 multimodal LLM RL Training	Qwen2.5-0.5B GRPO Training
Use EasyR1 framework for multimodal LLM RL training	Fine-tune Qwen2.5-0.5B model on GSM8k dataset using GRPO.

More Examples

🏁 Quick Start

1. Installation

pip install swanlab

# Method 1
git clone https://github.com/SwanHubX/SwanLab.git
pip install -e .

# Method 2
pip install git+https://github.com/SwanHubX/SwanLab.git

pip install 'swanlab[dashboard]'

2. Login and Get API Key

1. Register for free at SwanLab.

2. Log in, and copy your API Key from User Settings > API Key.

3. Open the terminal and enter:

swanlab login

When prompted, enter your API Key, press Enter, and complete the login.

3. Integrate SwanLab with Your Code

import swanlab

# Initialize a new SwanLab experiment
swanlab.init(
    project="my-first-ml",
    config={'learning-rate': 0.003},
)

# Log metrics
for i in range(10):
    swanlab.log({"loss": i, "acc": i})

Done! Head over to SwanLab to view your first SwanLab experiment.

💻 Self-Hosted

The self-hosted community edition supports offline viewing of the SwanLab dashboard.

Detailed deployment documentation:

• 🔗 Kubernetes Deployment Documentation
• 🔗 Docker Deployment Documentation
• 🔗 Migration from Docker to Kubernetes Documentation

🔥 Tutorials

Excellent Open-Source Tutorial Projects Using SwanLab:

• happy-llm: A tutorial on the principles and practice of large language models from scratch.
• self-llm: "A Cookbook for Open-Source Large Models" - A tutorial tailored for Chinese users on quickly fine-tuning (full-parameter/LoRA) and deploying domestic and international open-source Large Language Models (LLMs) / Multi-modal Large Models (MLLMs) in a Linux environment.
• Minimind: 🌏 Train a 26M-parameter GPT from scratch in just 2h!
• unlock-deepseek: Interpretation, extension, and reproduction of the DeepSeek series of works.
• Qwen3-SmVL: The visual head of SmolVLM2 was concatenated and fine-tuned with the Qwen3-0.6B model.
• OPPO/Agent_Foundation_Models: Chain-of-Agents: A chain-of-agents model through multi-agent distillation and agent RL.
• Tree-GRPO: [ICLR 2026] Tree Search for LLM Agent Reinforcement Learning
• llm-agent-rl-lab: Reproducing and studying RL algorithms for LLM agents, including PPO, GRPO, GSPO, DAPO, OPD and beyond.

Excellent Research Papers Using SwanLab:

• Accelerating AlphaZero Training for Connect6 via Behavioral Cloning Initialization and Prior Action Masking in Resource-Constrained Environments
• SeePhys Pro: Diagnosing Modality Transfer and Blind-Training Effects in Multimodal RLVR for Physics Reasoning
• MemFactory: Unified Inference & Training Framework for Agent Memory
• ILL: A Lightweight Large Language Model for Legal and Courtroom Assistance
• Retrieval, Reward, and Training Protocols: What Matters in Training Search Agents?
• MolAct: An Agentic RL Framework for Molecular Editing and Property Optimization
• CQLLM: A Framework for Generating CodeQL Security Vulnerability Detection Code Based on Large Language Model
• Animation Needs Attention: A Holistic Approach to Slides Animation Comprehension with Visual-Language Models
• Efficient Model Fine-Tuning with LoRA for Biomedical Named Entity Recognition
• SpectrumWorld: Artificial Intelligence Foundation for Spectroscopy
• CodeBoost: Boosting Code LLMs by Squeezing Knowledge from Code Snippets with RL
• A Joint Classification Method for Traditional Chinese Medicine Diseases and Syndromes Based on BertChinese-RCNNATTN
• A Gradient-Norm-Aware Optimizer for Symmetry-Preserving and Stable Deep Learning
• TrafficBM: A Dual-Modality Pre-Training Framework for Network Traffic Classification
• Using Human Cumulative Prospect Theory to Understand Large Language Models Decision-Making
• BiPACE: Bisimulation-Guided Policy Optimization with Action Counterfactual Estimation for LLM Agents

Tutorial Articles:

• MNIST Handwritten Digit Recognition
• FashionMNIST Apparel Classification
• CIFAR-10 Image Classification
• ResNet Cat vs. Dog Classification
• YOLO Object Detection
• U-Net Medical Image Segmentation
• Audio Classification
• DQN Reinforcement Learning - CartPole
• LSTM Google Stock Price Prediction
• BERT Text Classification
• Stable Diffusion Text-to-Image Fine-Tuning
• LLM Pre-training
• GLM4 Instruction Fine-Tuning
• Qwen Downstream Task Training
• NER (Named Entity Recognition)
• Qwen3 Medical Model Fine-Tuning
• Qwen2-VL Multimodal Large Model Fine-Tuning in Practice
• GRPO Large Model Reinforcement Learning
• Training the Qwen3-SmVL-0.6B Multimodal Model
• Getting Started with Embodied AI using LeRobot
• GLM-4.5-Air-LoRA and SwanLab Visualization
• How to do RAG? SwanLab Document Assistant Solution Released

🚗 Framework Integration

Use your favorite frameworks with SwanLab!
Below is a list of frameworks we have integrated. Feel free to submit an Issue to request integration for your desired framework.

Basic Frameworks

• PyTorch
• MindSpore
• Keras

LLM Training Frameworks

• HuggingFace Transformers
• LLaMA Factory
• MS-Swift
• Unsloth
• MLX-LM
• Torchtune
• PaddleNLP
• Sentence Transformers
• XTuner
• OpenMind

LLM Reinforcement Learning Frameworks

• veRL
• HuggingFace trl
• NVIDIA-NeMo RL
• EasyR1
• AReaL
• ROLL

Robot Frameworks

• RLinf

Text-to-Image/Video Training Frameworks

• DiffSynth Studio

Deep Learning Frameworks

• PyTorch Lightning
• MMEngine
• FastAI

Computer Vision Frameworks

• Ultralytics
• MMDetection
• MMSegmentation
• PaddleDetection
• PaddleYOLO

Machine Learning Frameworks

• LightGBM
• XGBoost
• CatBoost

Evaluation Frameworks

• EvalScope

Traditional Reinforcement Learning Frameworks

• Stable Baseline3

Other Frameworks:

• Tensorboard
• Weights&Biases
• MLFlow
• HuggingFace Accelerate
• Ray
• Hydra
• Omegaconf
• SpecForge

More Integrations

🔌 Plugins

Enhance your experiment management experience by extending SwanLab's functionality through plugins!

• Customize Your Plugin
• Email Notification
• Lark Notification
• DingTalk Notification
• WXWork Notification
• Discord Notification
• Slack Notification
• Bark Notification
• CSV Logger

🎮 Hardware Monitoring

SwanLab records the hardware information and resource usage during AI training. Below is a table of supported hardware:

If you wish to document other hardware, feel free to submit an Issue or PR!

🆚 Comparison with Familiar Tools

Tensorboard vs SwanLab

• ☁️ Online Support: SwanLab allows convenient cloud-based synchronization and storage of training experiments, enabling remote viewing of training progress, managing historical projects, sharing experiment links, sending real-time notifications, and multi-device experiment viewing. Tensorboard, on the other hand, is an offline experiment tracking tool.

• 👥 Multi-User Collaboration: SwanLab facilitates multi-user, cross-team machine learning collaboration by easily managing team training projects, sharing experiment links, and enabling cross-space discussions. Tensorboard is primarily designed for individual use, making multi-user collaboration and experiment sharing difficult.

• 💻 Persistent, Centralized Dashboard: Regardless of where you train your models—on a local computer, a lab cluster, or a public cloud GPU instance—your results are recorded in the same centralized dashboard. TensorBoard requires time-consuming copying and management of TFEvent files from different machines.

• 💪 More Powerful Tables: SwanLab tables allow you to view, search, and filter results from different experiments, making it easy to review thousands of model versions and identify the best-performing models for different tasks. TensorBoard is not suitable for large-scale projects.

Weights and Biases vs SwanLab

• Weights and Biases is a closed-source MLOps platform that requires an internet connection.

• SwanLab not only supports online usage but also offers an open-source, free, self-hosted version.

👥 Community

Repositories

• self-hosted: The repository for private deployment scripts.
• SwanLab-Docs: The official documentation repository.
• SwanLab-Dashboard: The offline dashboard repository, which contains the web code for the lightweight offline dashboard opened by swanlab watch.

Community and Support

• GitHub Issues: Errors and issues encountered while using SwanLab.
• Email Support: Feedback and questions about using SwanLab.
• WeChat Group: Discuss SwanLab usage and share the latest AI technologies.

SwanLab README Badge

If you enjoy using SwanLab in your work, please add the SwanLab badge to your README:

、

[![](https://raw.githubusercontent.com/SwanHubX/assets/main/badge2.svg)](your experiment url)
[![](https://raw.githubusercontent.com/SwanHubX/assets/main/badge1.svg)](your experiment url)

More design materials: assets

Citing SwanLab in Papers

If you find SwanLab helpful in your research journey, please consider citing it in the following format:

@software{Zeyilin_SwanLab_2023,
  author = {Zeyi Lin, Shaohong Chen, Kang Li, Qiushan Jiang, Zirui Cai,  Kaifang Ji and {The SwanLab team}},
  doi = {10.5281/zenodo.11100550},
  license = {Apache-2.0},
  title = {{SwanLab}},
  url = {https://github.com/swanhubx/swanlab},
  year = {2023}
}

Contributing to SwanLab

Considering contributing to SwanLab? First, take a moment to read the Contribution Guide.

We also greatly appreciate support through social media, events, and conference sharing. Thank you!

Contributors

📃 License

This repository is licensed under the Apache 2.0 License.

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