--- url: /en/guide_cloud/general/what-is-swanlab.md --- # Welcome to SwanLab [Official Website](https://swanlab.cn) · [Framework Integration](../integration/integration-huggingface-transformers.md) · [GitHub](https://github.com/swanhubx/swanlab) · [Quick Start](./quick-start.md) · [Sync WandB](../integration/integration-wandb.md#_1-sync-tracking) · [Benchmark Community](https://swanlab.cn/benchmarks) ::: warning 🎉 Self-Hosted Kubernetes Version Officially Released!\ The self-hosted Kubernetes version supports local use with features comparable to the public cloud edition. For deployment instructions, see [this document](../../self_host/kubernetes/deploy.md).\ ::: ![alt text](https://swanlab-docs-1301372061.cos.ap-beijing.myqcloud.com/assets/assets/product-swanlab-1.png) SwanLab is an **open-source, lightweight** AI model training tracking and visualization tool that provides a platform for **tracking, recording, comparing, and collaborating on experiments**. Designed for AI researchers, SwanLab offers a user-friendly Python API and an elegant UI, featuring **training visualization, automatic logging, hyperparameter tracking, experiment comparison, and multi-user collaboration**. With SwanLab, researchers can identify training issues through intuitive visual charts, compare multiple experiments for inspiration, and **share via online webpages** or **collaborative training within organizations**, breaking down communication barriers and improving team efficiency. Using SwanLab, researchers can document every training experience, seamlessly communicate and collaborate with peers, while machine learning engineers can develop production-ready models faster. ## 📹 Online Demos | [ResNet50 Cat-Dog Classification][demo-cats-dogs] | [Yolov8-COCO128 Object Detection][demo-yolo] | | :-----------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------: | | [![][demo-cats-dogs-image]][demo-cats-dogs] | [![][demo-yolo-image]][demo-yolo] | | 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][demo-qwen2-sft] | [LSTM Google Stock Prediction][demo-google-stock] | | :----------------------------------------------------------------------: | :-------------------------------------------------------------------------: | | [![][demo-qwen2-sft-image]][demo-qwen2-sft] | [![][demo-google-stock-image]][demo-google-stock] | | Track Qwen2 LLM instruction fine-tuning for basic instruction following. | Train a simple LSTM model on Google stock data for future price prediction. | | [ResNeXt101 Audio Classification][demo-audio-classification] | [Qwen2-VL COCO Dataset Fine-Tuning][demo-qwen2-vl] | | :----------------------------------------------------------------------: | :-------------------------------------------------------------------: | | [![][demo-audio-classification-image]][demo-audio-classification] | [![][demo-qwen2-vl-image]][demo-qwen2-vl] | | Progressive experiments from ResNet to ResNeXt for audio classification. | Lora fine-tuning for Qwen2-VL multimodal LLM on the COCO2014 dataset. | | [EasyR1 Multimodal LLM RL Training][demo-easyr1-rl] | [Qwen2.5-0.5B GRPO Training][demo-qwen2-grpo] | | :----------------------------------------------------: | :--------------------------------------------------: | | [![][demo-easyr1-rl-image]][demo-easyr1-rl] | [![][demo-qwen2-grpo-image]][demo-qwen2-grpo] | | Multimodal LLM RL training using the EasyR1 framework. | GRPO training for Qwen2.5-0.5B on the GSM8k dataset. | Video Demo: ## What Can SwanLab Do? **1. 📊 Experiment Metrics & Hyperparameter Tracking**: Easily integrate into your ML pipeline to track key training metrics. * ☁️ Supports **cloud** usage (like Weights & Biases), allowing remote training progress monitoring. [How to view experiments on mobile](./app.md). * 🌸 **Visualize Training**: The UI provides intuitive visualizations of experiment data, helping trainers analyze metric trends and identify improvements. * 📝 **Hyperparameter logging**, **metric summaries**, **tabular analysis**. * **Supported Metadata Types**: Scalar metrics, images, audio, text, video, 3D point clouds, biochemical molecules, Echarts custom charts... ![swanlab-table](https://swanlab-docs-1301372061.cos.ap-beijing.myqcloud.com/assets/assets/molecule.gif) * **Supported Chart Types**: Line charts, media (images/audio/text), 3D point clouds, biochemical molecules, bar charts, scatter plots, box plots, heatmaps, pie charts, radar charts... ![swanlab-echarts](https://swanlab-docs-1301372061.cos.ap-beijing.myqcloud.com/assets/en/guide_cloud/general/what_is_swanlab/echarts.png) * **LLM Content Visualization Component**: A text content visualization chart designed for LLM training scenarios, supporting Markdown rendering. ![swanlab-llm-content](https://swanlab-docs-1301372061.cos.ap-beijing.myqcloud.com/assets/assets/text-chart.gif) * **Automatic Background Logging**: Logging, hardware environment, Git repo, Python environment, Python library list, project directory. * **Resume Training Logging**: Supports adding new metric data to the same experiment after training completes/interrupts. **2. ⚡️ Comprehensive Framework Integration**: Supports **40+** frameworks including PyTorch, 🤗HuggingFace Transformers, PyTorch Lightning, 🦙LLaMA Factory, MMDetection, Ultralytics, PaddleDetection, LightGBM, XGBoost, Keras, TensorBoard, Weights & Biases, Swift, XTuner, Stable Baselines3, and Hydra. ![](https://swanlab-docs-1301372061.cos.ap-beijing.myqcloud.com/assets/assets/integrations.png) **3. 💻 Hardware Monitoring**: Real-time tracking for CPU, GPU (**Nvidia**, **MetaX**, **MooreThread**), NPU (**Ascend**), MLU (**Cambricon**), XPU (**KunlunX**), and memory metrics. **4. 📦 Experiment Management**: A centralized dashboard designed for training scenarios, providing an overview and quick management of multiple projects/experiments. **5. 🆚 Result Comparison**: Compare hyperparameters and results across experiments via online tables and charts to uncover insights. ![](https://swanlab-docs-1301372061.cos.ap-beijing.myqcloud.com/assets/en/guide_cloud/general/what_is_swanlab/chart3.png) **6. 👥 Online Collaboration**: Collaborate with teams by syncing experiments in real time, viewing training logs, and sharing feedback. **7. ✉️ Share Results**: Copy and share persistent URLs for experiments, easily sending them to peers or embedding in notes. **8. 💻 Self-Hosting Support**: Use SwanLab offline—the community edition supports dashboards and experiment management. [Self-Hosting Guide](#-self-hosting). **9. 🔌 Plugin Extensions**: Extend functionality with plugins like [Lark Notifications](../../plugin/notification-lark.md), [Slack Notifications](../../plugin/notification-slack.md), [CSV Logger](../../plugin/writer-csv.md). ## Why Choose SwanLab? Unlike software engineering, AI is an **experimental discipline**—generating ideas, rapid testing, and validation are key. Documenting experiments is crucial for researchers and teams to **build knowledge and accelerate progress**. Traditional methods involve manually copying logs or TFEvent files, **but crude logs hinder inspiration, and offline logs make collaboration difficult**. SwanLab offers a cloud-based AI experiment tracking solution with **training visualization, experiment tracking, hyperparameter logging, and multi-user collaboration**. Researchers can **easily find insights through intuitive charts and share experiments via online links**, breaking communication barriers. > Traditional AI research focuses on results, while we focus on the **process**.\ > Community feedback highlights SwanLab as **simple, efficient, and rapidly evolving**.\ > —Zeyi, SwanLab Co-Founder Most importantly, SwanLab is **open-source**, built by ML engineers and the community. The self-hosted version ensures data security and privacy\*\*. We hope this guide helps you understand SwanLab—we believe it can assist you. ## Getting Started * [Quick Start](./quick-start.md): A 5-minute tutorial for experiment tracking! * [API Docs](../../api/api-index.md): Complete API documentation. * [Online Support](../community/online-support.md): Join the community, report issues, and contact us. * [Self-Hosting](../../self_host/docker/deploy.md): Guide for self-hosted (private deployment) usage. * [Examples](../../examples/mnist.md): Case studies of SwanLab with various deep learning tasks. ## Comparison with Familiar Tools ### TensorBoard vs. SwanLab * **☁️ Online Usage**: SwanLab syncs experiments to the cloud for remote monitoring, sharing, and collaboration. TensorBoard is offline-only. * **👥 Collaboration**: SwanLab simplifies team training management, while TensorBoard is designed for individual use. * **💻 Centralized Dashboard**: SwanLab aggregates results from any machine; TensorBoard requires manual TFEvent file management. * **💪 Powerful Tables**: SwanLab tables support searching/filtering thousands of model versions. TensorBoard struggles with large projects. ### W\&B vs. SwanLab * Weights & Biases is a closed-source, online-only MLOps platform. * SwanLab supports **open-source, free, self-hosted** usage alongside cloud features. ## Framework Integrations Use SwanLab with your favorite frameworks! Below is our integration list. Submit an [Issue](https://github.com/swanhubx/swanlab/issues) for framework requests. **Core Frameworks** * [PyTorch](../integration/integration-pytorch.md) * [MindSpore](../integration/integration-ascend.md) * [Keras](../integration/integration-keras.md) **Specialized/Fine-Tuning Frameworks** * [PyTorch Lightning](../integration/integration-pytorch-lightning.md) * [HuggingFace Transformers](../integration/integration-huggingface-transformers.md) * [LLaMA Factory](../integration/integration-llama-factory.md) * [ModelScope Swift](../integration/integration-swift.md) * [DiffSynth-Studio](../integration/integration-diffsynth-studio.md) * [Sentence Transformers](../integration/integration-sentence-transformers.md) * [OpenMind](https://modelers.cn/docs/zh/openmind-library/1.0.0/basic_tutorial/finetune/finetune_pt.html#%E8%AE%AD%E7%BB%83%E7%9B%91%E6%8E%A7) * [Torchtune](../integration/integration-pytorch-torchtune.md) * [XTuner](../integration/integration-xtuner.md) * [MMEngine](../integration/integration-mmengine.md) * [FastAI](../integration/integration-fastai.md) * [LightGBM](../integration/integration-lightgbm.md) * [XGBoost](../integration/integration-xgboost.md) * [CatBoost](../integration/integration-catboost.md) * [MLX-LM](../integration/integration-mlx-lm.md) **Computer Vision** * [Ultralytics](../integration/integration-ultralytics.md) * [MMDetection](../integration/integration-mmdetection.md) * [MMSegmentation](../integration/integration-mmsegmentation.md) * [PaddleDetection](../integration/integration-paddledetection.md) * [PaddleYOLO](../integration/integration-paddleyolo.md) **Reinforcement Learning** * [Stable Baselines3](../integration/integration-sb3.md) * [veRL](../integration/integration-verl.md) * [HuggingFace TRL](../integration/integration-huggingface-trl.md) * [EasyR1](../integration/integration-easyr1.md) * [AReaL](../integration/integration-areal.md) * [ROLL](../integration/integration-roll.md) * [NVIDIA-NeMo RL](../integration/integration-nvidia-nemo-rl.md) * [MindSpeed-RL](../integration/integration-mindspeed-rl.md) **Other Frameworks**: * [TensorBoard](../integration/integration-tensorboard.md) * [Weights & Biases](../integration/integration-wandb.md) * [MLflow](../integration/integration-mlflow.md) * [HuggingFace Accelerate](../integration/integration-huggingface-accelerate.md) * [Hydra](../integration/integration-hydra.md) * [OmegaConf](../integration/integration-omegaconf.md) [More Integrations](../integration/integration-pytorch-lightning.md) ## Online Support * **[GitHub Issues](https://github.com/SwanHubX/SwanLab/issues)**: Report bugs or issues. * **Email Support**: * * * **WeChat & Lark Groups**: See [Online Support](../community/online-support.md). * **WeChat Official Account**: [release-shield]: https://img.shields.io/github/v/release/swanhubx/swanlab?color=369eff&labelColor=black&logo=github&style=flat-square [release-link]: https://github.com/swanhubx/swanlab/releases [license-shield]: https://img.shields.io/badge/license-apache%202.0-white?labelColor=black&style=flat-square [license-shield-link]: https://github.com/SwanHubX/SwanLab/blob/main/LICENSE [last-commit-shield]: https://img.shields.io/github/last-commit/swanhubx/swanlab?color=c4f042&labelColor=black&style=flat-square [last-commit-shield-link]: https://github.com/swanhubx/swanlab/commits/main [pypi-version-shield]: https://img.shields.io/pypi/v/swanlab?color=orange&labelColor=black&style=flat-square [pypi-version-shield-link]: https://pypi.org/project/swanlab/ [pypi-downloads-shield]: https://static.pepy.tech/badge/swanlab?labelColor=black&style=flat-square [pypi-downloads-shield-link]: https://pepy.tech/project/swanlab [swanlab-cloud-shield]: https://img.shields.io/badge/Product-SwanLab%20Cloud-636a3f?labelColor=black&style=flat-square [swanlab-cloud-shield-link]: https://swanlab.cn/ [wechat-shield]: https://img.shields.io/badge/WeChat-%E5%BE%AE%E4%BF%A1-4cb55e?labelColor=black&style=flat-square [wechat-shield-link]: /en/guide_cloud/community/online-support [colab-shield]: https://colab.research.google.com/assets/colab-badge.svg [colab-shield-link]: https://colab.research.google.com/drive/1RWsrY_1bS8ECzaHvYtLb_1eBkkdzekR3?usp=sharing [github-stars-shield]: https://img.shields.io/github/stars/swanhubx/swanlab?labelColor&style=flat-square&color=ffcb47 [github-stars-link]: https://github.com/swanhubx/swanlab [github-issues-shield]: https://img.shields.io/github/issues/swanhubx/swanlab?labelColor=black&style=flat-square&color=ff80eb [github-issues-shield-link]: https://github.com/swanhubx/swanlab/issues [github-contributors-shield]: https://img.shields.io/github/contributors/swanhubx/swanlab?color=c4f042&labelColor=black&style=flat-square [github-contributors-link]: https://github.com/swanhubx/swanlab/graphs/contributors [demo-cats-dogs]: https://swanlab.cn/@ZeyiLin/Cats_Dogs_Classification/runs/jzo93k112f15pmx14vtxf/chart [demo-cats-dogs-image]: https://swanlab-docs-1301372061.cos.ap-beijing.myqcloud.com/assets/assets/example-catsdogs.png [demo-yolo]: https://swanlab.cn/@ZeyiLin/ultratest/runs/yux7vclmsmmsar9ear7u5/chart [demo-yolo-image]: https://swanlab-docs-1301372061.cos.ap-beijing.myqcloud.com/assets/assets/example-yolo.png [demo-qwen2-sft]: https://swanlab.cn/@ZeyiLin/Qwen2-fintune/runs/cfg5f8dzkp6vouxzaxlx6/chart [demo-qwen2-sft-image]: https://swanlab-docs-1301372061.cos.ap-beijing.myqcloud.com/assets/assets/example-qwen2.png [demo-google-stock]: https://swanlab.cn/@ZeyiLin/Google-Stock-Prediction/charts [demo-google-stock-image]: https://swanlab-docs-1301372061.cos.ap-beijing.myqcloud.com/assets/assets/example-lstm.png [demo-audio-classification]: https://swanlab.cn/@ZeyiLin/PyTorch_Audio_Classification/charts [demo-audio-classification-image]: https://swanlab-docs-1301372061.cos.ap-beijing.myqcloud.com/assets/assets/example-audio-classification.png [demo-qwen2-vl]: https://swanlab.cn/@ZeyiLin/Qwen2-VL-finetune/runs/pkgest5xhdn3ukpdy6kv5/chart [demo-qwen2-vl-image]: https://swanlab-docs-1301372061.cos.ap-beijing.myqcloud.com/assets/assets/example-qwen2-vl.jpg [demo-easyr1-rl]: https://swanlab.cn/@Kedreamix/easy_r1/runs/wzezd8q36bb6dlza6wtpc/chart [demo-easyr1-rl-image]: https://swanlab-docs-1301372061.cos.ap-beijing.myqcloud.com/assets/assets/example-easyr1-rl.png [demo-qwen2-grpo]: https://swanlab.cn/@kmno4/Qwen-R1/runs/t0zr3ak5r7188mjbjgdsc/chart [demo-qwen2-grpo-image]: https://swanlab-docs-1301372061.cos.ap-beijing.myqcloud.com/assets/assets/example-qwen2-grpo.png [tracking-swanlab-shield-link]: https://swanlab.cn [tracking-swanlab-shield]: https://raw.githubusercontent.com/SwanHubX/assets/main/badge2.svg [visualize-swanlab-shield-link]: https://swanlab.cn [visualize-swanlab-shield]: https://raw.githubusercontent.com/SwanHubX/assets/main/badge1.svg [dockerhub-shield]: https://img.shields.io/docker/v/swanlab/swanlab-next?color=369eff&label=docker&labelColor=black&logoColor=white&style=flat-square [dockerhub-link]: https://hub.docker.com/r/swanlab/swanlab-next/tags