Integrations overview

Explore W&B integrations with ML frameworks, cloud platforms, and workflow orchestration tools

W&B integrates with popular machine learning frameworks, cloud platforms, and workflow orchestration tools to help you track experiments, log metrics, and manage models seamlessly.

Getting started

If you’re looking to integrate W&B with a library not listed here, see Add W&B to any library for best practices and implementation guidance.

ML frameworks and libraries

ML frameworks

Integrate W&B with popular machine learning frameworks:

• Catalyst
• DeepChem
• DSPy
• Keras
• LightGBM
• MMEngine
• MMF
• PaddleDetection
• PaddleOCR
• PyTorch Lightning
• PyTorch Ignite
• Skorch
• TensorFlow
• XGBoost

ML libraries

Extend your workflow with these ML libraries:

• Deepchecks
• Hugging Face
• Diffusers
• AutoTrain
• Fast.ai
• Fast.ai v1
• Composer
• OpenAI Gym
• Prodigy
• PyTorch Geometric
• TorchTune
• Scikit-learn
• Simple Transformers
• spaCy
• Stable Baselines 3
• Ultralytics

Cloud platforms

Deploy and train models on cloud platforms with W&B:

• Amazon SageMaker
• Databricks
• Azure OpenAI Fine-tuning
• OpenAI Fine-tuning
• Cohere Fine-tuning
• OpenAI API
• NVIDIA NIM

Workflow orchestration

Integrate W&B with workflow orchestration tools:

• Kubeflow Pipelines (KFP)
• Metaflow
• Dagster
• Hydra

Other integrations

Additional tools and integrations:

• Docker
• TensorBoard
• W&B for Julia
• YOLOX
• YOLOv5