SwanLab
SwanLab is an open-source AI training tracking and visualization platform written in Python, supporting both cloud and self-hosted deployments. It integrates with 50+ ML frameworks (PyTorch, Transformers, Keras, etc.) and provides experiment tracking, metric logging, and real-time visualization dashboards.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Repository | SwanHubX/SwanLab |
| Owner | SwanHubX |
| Primary language | Python |
| License | Apache-2.0 — OSI-approved |
| Stars | 4k |
| Forks | 209 |
| Open issues | 61 |
| Latest release | v0.8.4 (2026-06-29) |
| Last updated | 2026-07-08 |
| Source | https://github.com/SwanHubX/SwanLab |
What SwanLab is
Apache-2.0 licensed Python project offering experiment tracking via SDK (swanlab.init, swanlab.log), metric visualization (line charts, tables, 3D objects, custom ECharts), hardware monitoring (NVIDIA/AMD/custom GPUs), and distributed training support (parallel mode, resume). Deployable as cloud SaaS, Docker container, or Kubernetes cluster with WebUI dashboards.
Get the SwanLab source
Clone the repository and explore it locally.
git clone https://github.com/SwanHubX/SwanLab.gitcd SwanLab# follow the project's README for install & configurationNeed it deployed, integrated, or customized instead? DEV.co ships production installs.
Best use cases
Implementation considerations
- SDK is non-invasive (import swanlab; swanlab.init(); swanlab.log()) but requires code changes; test on a small training run before scaling to production.
- Self-hosted Kubernetes deployment requires Prometheus + Grafana setup for monitoring; Docker is simpler for single-machine setups. Review docs.swanlab.cn/self_host for exact requirements.
- Metric logging performance was refactored in v0.8.0; verify that your framework's batching/step frequency aligns with SwanLab's design to avoid bottlenecks on large-scale runs.
- Hardware monitoring supports NVIDIA, AMD ROCm, and vendor-specific GPUs (Iluvatar, Kunlun, Cambricon, etc.); confirm your hardware is listed before deployment.
- Webhook and notification plugins (Slack, Discord, email, Feishu) require external account setup; plan API key rotation and firewall rules for outbound HTTPS.
When to avoid it — and what to weigh
- Real-time hyperparameter optimization (AutoML) workflows — If your primary need is automated hyperparameter search or Bayesian optimization, SwanLab is a tracking/visualization tool, not a full HPO system. Consider pairing with Optuna or Ray Tune instead.
- Strict vendor lock-in avoidance — While self-hostable, the cloud version (swanlab.cn) may create operational silos; migrating data between cloud and self-hosted versions or to other platforms is not clearly documented.
- Minimal Python/ML team overhead — Setup requires familiarity with API keys, CLI, Python dependencies, and potentially Docker/Kubernetes. Teams wanting zero-config integrations should evaluate lighter alternatives.
- Compliance with strict OSS policies requiring GPL/AGPL — Apache-2.0 is permissive and does not mandate upstream contribution. If your policy requires copyleft licensing, SwanLab does not meet that requirement.
License & commercial use
Apache License 2.0 (Apache-2.0). This is a permissive OSI-approved license allowing commercial use, modification, and distribution with minimal restrictions (require license notice and state changes). No copyleft obligation.
Apache-2.0 permits commercial deployment and modification. You may use SwanLab in proprietary products (cloud SaaS, on-prem enterprise tools) without contributing changes back. However, confirm with legal counsel that you retain liability clauses and include the license in distributions. No license fee is mentioned in the data; cloud SaaS pricing is not detailed here.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Strong |
| License clarity | Clear |
| Deployment complexity | Moderate |
| DEV.co fit | Strong |
| Assessment confidence | High |
Data is stored on SwanLab cloud servers (swanlab.cn) or your self-hosted instance. HTTPS is implicit but TLS/encryption details are not disclosed in the data provided. API keys are auto-masked in run command logs (v0.25+) to prevent accidental exposure in logs/screenshots. Self-hosted deployments inherit the security posture of your Docker/Kubernetes infrastructure. No third-party security audit or penetration test results are mentioned. Multi-API-key management (v0.7.29+) supports key rotation. Review data handling policies and access controls before logging sensitive training data.
Alternatives to consider
Weights & Biases (W&B)
Mature, widely adopted cloud-first platform with similar integrations, more polished UI, and stronger enterprise support; higher cost and less self-hosting flexibility.
MLflow
Lightweight, open-source (Apache-2.0), self-hosted-friendly experiment tracker with broader model registry features; less polished visualization and smaller integration ecosystem.
TensorBoard
Lightweight, built-in PyTorch/TensorFlow support, requires minimal setup; lacks collaboration, project management, and cross-framework integrations that SwanLab provides.
Build on SwanLab with DEV.co software developers
SwanLab cuts instrumentation time and unifies experiment visualization across teams. Start with the free cloud version (swanlab.cn) or deploy self-hosted in Docker/Kubernetes. Evaluate against W&B and MLflow for your use case.
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SwanLab FAQ
Can I use SwanLab on-premises without paying?
Does SwanLab work with distributed training (multi-GPU, multi-node)?
What happens if my training crashes—can I resume and sync logs?
Are my experiment logs private if I self-host?
Software development & web development with DEV.co
DEV.co is a software development agency delivering custom software development services to companies building on open source. Our software developers and web developers design, integrate, and ship production systems — spanning web development, APIs, AI, data, and cloud. If SwanLab is part of your open-source observability roadmap, our team can implement, customize, migrate, and maintain it.
Ready to streamline your training workflows?
SwanLab cuts instrumentation time and unifies experiment visualization across teams. Start with the free cloud version (swanlab.cn) or deploy self-hosted in Docker/Kubernetes. Evaluate against W&B and MLflow for your use case.