# wandb/wandb

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10,844 stars · 820 forks · Python · mit

## Links

- GitHub: https://github.com/wandb/wandb
- Homepage: https://wandb.ai
- awesome-repositories: https://awesome-repositories.com/repository/wandb-wandb.md

## Topics

`ai` `collaboration` `data-science` `data-versioning` `deep-learning` `experiment-track` `hyperparameter-optimization` `hyperparameter-search` `hyperparameter-tuning` `jax` `keras` `machine-learning` `ml-platform` `mlops` `model-versioning` `pytorch` `reinforcement-learning` `reproducibility` `tensorflow`

## Description

Wandb is a centralized platform for machine learning experiment tracking, model registry management, and workflow orchestration. It provides a comprehensive suite of tools for logging, visualizing, and versioning training metrics, model artifacts, and hyperparameter sweeps to ensure reproducibility across development cycles. The platform also functions as an observability tool for large language model applications, enabling the tracing of execution steps, token usage, and reasoning processes.

The project distinguishes itself through its event-driven automation capabilities, which allow users to trigger workflows, manage training job lifecycles, and execute serverless fine-tuning tasks based on experiment results or metric thresholds. It supports complex model development by providing standardized interfaces for connecting to foundation models, deploying lightweight model adapters, and enforcing output constraints. Additionally, the platform offers deep observability into model behavior, including the ability to capture intermediate reasoning, validate long-context processing, and assess model safety.

Beyond core tracking, the platform includes extensive support for monitoring system resources and hardware accelerator performance, alongside rich media logging for audio, video, and molecular structures. It facilitates team collaboration through interactive reporting and provides robust data management features, such as versioned artifact lineage, automated retention policies, and secure storage.

The system is designed for integration into existing development environments through a command-line utility and a programmatic software development kit that handles authentication, local service management, and asynchronous data synchronization.

## Tags

### Artificial Intelligence & ML

- [Machine Learning Experiment Trackers](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning-experiment-trackers.md) — Provides a centralized dashboard for logging, visualizing, and versioning machine learning training metrics and artifacts. ([source](https://docs.wandb.ai/models/ref-link-public-api.md))
- [Model Lineage Trackers](https://awesome-repositories.com/f/artificial-intelligence-ml/data-lineage/model-lineage-trackers.md) — Tracks dependencies between datasets, model weights, and training runs using immutable snapshots for reproducibility.
- [Experiment Tracking](https://awesome-repositories.com/f/artificial-intelligence-ml/experiment-tracking.md) — Logs, versions, and visualizes machine learning training metrics and model artifacts to ensure reproducibility. ([source](https://docs.wandb.ai/models/ref/cli/wandb-beta/wandb-beta-leet.md))
- [LLM Observability](https://awesome-repositories.com/f/artificial-intelligence-ml/llm-observability.md) — Captures and monitors nested execution steps, token usage, and costs for complex language model applications. ([source](https://docs.wandb.ai/examples.md))
- [Training Progress Monitoring](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/machine-learning-training/utilities/training-progress-monitoring.md) — Inspects metrics, text, and media across training steps using interactive controls to visualize model behavior and performance over time. ([source](https://docs.wandb.ai/models/app/features/panels/query-panels.md))
- [Hyperparameter Optimization](https://awesome-repositories.com/f/artificial-intelligence-ml/model-optimization/training-efficiency/hyperparameter-optimization.md) — Orchestrates systematic training experiments by defining search strategies to identify optimal model configurations. ([source](https://docs.wandb.ai/models/integrations/hydra.md))
- [Machine Learning Frameworks](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning-frameworks.md) — Connects with common machine learning libraries to automatically capture training data and version model artifacts. ([source](https://cdn.jsdelivr.net/gh/wandb/wandb@main/README.md))
- [Hyperparameter Sweep Orchestrators](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/machine-learning-training/training-configuration-management/training-hyperparameter-configurations/hyperparameter-sweep-orchestrators.md) — Orchestrates automated search processes to identify optimal model configurations by coordinating multiple training agents. ([source](https://docs.wandb.ai/models/ref/python/functions.md))
- [Reasoning Capture Utilities](https://awesome-repositories.com/f/artificial-intelligence-ml/reasoning-models/reasoning-capture-utilities.md) — Captures and displays intermediate reasoning steps generated by models during inference. ([source](https://docs.wandb.ai/inference/response-settings/reasoning.md))
- [Checkpoint Resumption](https://awesome-repositories.com/f/artificial-intelligence-ml/training-checkpointing/checkpoint-resumption.md) — Restores model state from saved artifacts to continue training runs from the exact point of interruption. ([source](https://docs.wandb.ai/models/integrations/huggingface_transformers.md))
- [AI Agent Integrations](https://awesome-repositories.com/f/artificial-intelligence-ml/ai-agent-integrations.md) — Configures automated development assistants to utilize remote open-weight models for code generation tasks. ([source](https://docs.wandb.ai/inference/tutorials/integration-cline.md))
- [Structured Tool Invocations](https://awesome-repositories.com/f/artificial-intelligence-ml/generative-ai-resources/decoding-generation-controls/tool-calling/structured-tool-invocations.md) — Provides structured translation of model-generated tool calls into executable formats for agentic workflows. ([source](https://docs.wandb.ai/inference/response-settings/tool-calling.md))
- [Model Benchmarking Suites](https://awesome-repositories.com/f/artificial-intelligence-ml/model-benchmarking-suites.md) — Runs automated evaluation suites against API models to compare performance and validate quality. ([source](https://docs.wandb.ai/models/launch/evaluate-hosted-model.md))
- [Schema Enforcement Layers](https://awesome-repositories.com/f/artificial-intelligence-ml/model-response-parsers/schema-enforcement-layers.md) — Forces model outputs to adhere to specific JSON schemas to ensure data consistency. ([source](https://docs.wandb.ai/inference/response-settings/structured-output.md))
- [Model Versioning Systems](https://awesome-repositories.com/f/artificial-intelligence-ml/model-versioning-systems.md) — Manages versioned datasets and model weights as immutable snapshots to maintain lineage and reproducibility.
- [Safety and Alignment Frameworks](https://awesome-repositories.com/f/artificial-intelligence-ml/safety-and-alignment-frameworks.md) — Evaluates model alignment, bias, and safety to ensure responsible behavior. ([source](https://docs.wandb.ai/models/launch/evaluations.md))
- [Custom Model Adapters](https://awesome-repositories.com/f/artificial-intelligence-ml/custom-model-adapters.md) — Hosts and dynamically loads lightweight model adapters as versioned artifacts to specialize base models. ([source](https://docs.wandb.ai/inference/lora.md))
- [Foundation Models](https://awesome-repositories.com/f/artificial-intelligence-ml/foundation-models.md) — Connects to open-source foundation models using standard interfaces to build applications without managing infrastructure. ([source](https://docs.wandb.ai/index.md))
- [Long Context Retrieval Testing](https://awesome-repositories.com/f/artificial-intelligence-ml/long-context-training-optimizations/long-context-retrieval-testing.md) — Tests model recall and pattern recognition performance across extended input sequences. ([source](https://docs.wandb.ai/models/launch/evaluations.md))
- [Machine Learning Model APIs](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-deployment-and-serving/inference-servers-and-runtimes/machine-learning-model-apis.md) — Provides standardized programming interfaces to query hosted machine learning models for predictions and completions. ([source](https://docs.wandb.ai/inference/api-reference.md))
- [Hardware-Accelerated](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-optimization-and-inference/hardware-and-acceleration/tensor-computing-libraries/tensor-libraries/hardware-accelerated.md) — Tracks performance metrics from specialized hardware accelerators like GPUs and TPUs. ([source](https://docs.wandb.ai/models/ref/python/experiments/system-metrics.md))
- [Model Registries](https://awesome-repositories.com/f/artificial-intelligence-ml/model-registries.md) — Links trained model artifacts to a centralized collection for team sharing and production management. ([source](https://docs.wandb.ai/models/models_quickstart.md))
- [Multimodal Models](https://awesome-repositories.com/f/artificial-intelligence-ml/multimodal-models.md) — Measures model proficiency in interpreting and reasoning over combined visual and textual data. ([source](https://docs.wandb.ai/models/launch/evaluations.md))
- [Dimensionality Reduction](https://awesome-repositories.com/f/artificial-intelligence-ml/dimensionality-reduction.md) — Projects high-dimensional vector data onto 2D planes to reveal clusters and relationships. ([source](https://docs.wandb.ai/models/app/features/panels/query-panels/embedding-projector.md))
- [Lineage Visualizers](https://awesome-repositories.com/f/artificial-intelligence-ml/end-to-end-training-pipelines/lineage-visualizers.md) — Maps relationships between data and model versions as directed graphs to track asset provenance. ([source](https://docs.wandb.ai/models/artifacts/explore-and-traverse-an-artifact-graph.md))
- [Agent Episode Recorders](https://awesome-repositories.com/f/artificial-intelligence-ml/generative-ai-resources/diffusion-visual-models/generative-ai-tasks/video-to-video-synthesis/agent-episode-recorders.md) — Automatically saves and uploads video recordings of agent episodes from simulation environments. ([source](https://docs.wandb.ai/models/integrations/openai-gym.md))
- [Pretrained Sequence Model Loaders](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-training-and-tuning/data-and-checkpointing/model-loading/pretrained-sequence-model-loaders.md) — Retrieves text and multimodal model outputs through a standardized interface for inference tasks. ([source](https://docs.wandb.ai/inference/models.md))
- [Model Checkpointing](https://awesome-repositories.com/f/artificial-intelligence-ml/model-checkpointing.md) — Uploads saved model states and optimizer parameters to remote cloud storage for reproducibility. ([source](https://docs.wandb.ai/models/integrations/ignite.md))
- [Artifact Logging](https://awesome-repositories.com/f/artificial-intelligence-ml/artifact-logging.md) — Associates custom labels with specific artifact versions to track milestones like production-ready models. ([source](https://docs.wandb.ai/models/artifacts/create-a-custom-alias.md))
- [Audio Processing](https://awesome-repositories.com/f/artificial-intelligence-ml/audio-processing.md) — Logs and visualizes audio files with associated metadata during machine learning experiments. ([source](https://docs.wandb.ai/models/ref/python/data-types/audio.md))
- [Model Discovery Tools](https://awesome-repositories.com/f/artificial-intelligence-ml/model-discovery-tools.md) — Queries inference services to retrieve lists of accessible model identifiers for dynamic selection. ([source](https://docs.wandb.ai/inference/api-reference.md))

### Data & Databases

- [Data Access and Querying](https://awesome-repositories.com/f/data-databases/data-access-querying.md) — Queries and visualizes experiment runs, artifacts, and tables directly within workspaces. ([source](https://docs.wandb.ai/models/app/features/panels/query-panels.md))
- [Hyperparameter](https://awesome-repositories.com/f/data-databases/data-pipelines/data-quality-monitors/impact-analyzers/hyperparameter.md) — Calculates the statistical influence of hyperparameters on model performance using correlation analysis. ([source](https://docs.wandb.ai/models/app/features/panels/parameter-importance.md))
- [Dataset Versioning Platforms](https://awesome-repositories.com/f/data-databases/data-versioning/dataset-versioning-platforms.md) — Groups files and data objects into versioned collections to track assets throughout the machine learning lifecycle. ([source](https://docs.wandb.ai/models/ref/python/experiments/artifact.md))
- [Data Export](https://awesome-repositories.com/f/data-databases/data-export.md) — Exports logged metrics, run history, and hyperparameter search results into standard data formats for analysis. ([source](https://docs.wandb.ai/models/ref-link-public-api.md))
- [Data Transformation](https://awesome-repositories.com/f/data-databases/data-transformation.md) — Filters, maps, and joins datasets using expressions to refine information for analysis. ([source](https://docs.wandb.ai/models/app/features/panels/query-panels.md))

### DevOps & Infrastructure

- [Automated Workflow Orchestration](https://awesome-repositories.com/f/devops-infrastructure/automated-workflow-orchestration.md) — Orchestrates event-driven actions, training job lifecycles, and automated deployment pipelines.
- [Artifact Uploaders](https://awesome-repositories.com/f/devops-infrastructure/software-packaging/artifact-uploaders.md) — Uploads, downloads, and restores versioned datasets and model weights to ensure experiment reproducibility. ([source](https://docs.wandb.ai/models/ref-link-cli.md))
- [Automated Artifact Lifecycle](https://awesome-repositories.com/f/devops-infrastructure/automated-lifecycle-management/automated-artifact-lifecycle.md) — Executes automated workflows whenever a new version of a specific artifact collection is registered. ([source](https://docs.wandb.ai/models/ref/python/automations/oncreateartifact.md))
- [Webhook Triggers](https://awesome-repositories.com/f/devops-infrastructure/automation-orchestration/task-execution-frameworks/event-based-triggers/webhook-triggers.md) — Sends HTTP requests to external services automatically when specific events occur to enable CI/CD integration. ([source](https://docs.wandb.ai/models/automations/create-automations/webhook.md))
- [Serverless Execution Models](https://awesome-repositories.com/f/devops-infrastructure/cloud-infrastructure/cloud-computing-serverless/serverless-execution-environments/serverless-execution-models.md) — Executes fine-tuning and post-training tasks on managed, auto-scaling GPU infrastructure. ([source](https://docs.wandb.ai/get-started.md))
- [State-Change Triggers](https://awesome-repositories.com/f/devops-infrastructure/automation-orchestration/task-execution-frameworks/automation-frameworks/triggers-events/state-change-triggers.md) — Initiates automated workflows when machine learning experiments transition to specific statuses like completion or failure. ([source](https://docs.wandb.ai/models/ref/python/automations/onrunstate.md))
- [Model Inference Deployment](https://awesome-repositories.com/f/devops-infrastructure/deployment-management/model-inference-deployment.md) — Exposes stored model artifacts to interactive playgrounds and programmatic interfaces for real-time testing. ([source](https://docs.wandb.ai/inference/tutorials/creating-lora.md))
- [Local-First Synchronization](https://awesome-repositories.com/f/devops-infrastructure/local-first-synchronization.md) — Synchronizes locally stored experiment files to remote servers to ensure data persistence and accessibility. ([source](https://docs.wandb.ai/models/ref/cli/wandb-beta/wandb-beta-sync.md))
- [Private Cloud Deployments](https://awesome-repositories.com/f/devops-infrastructure/deployment-management-strategies/execution-platforms-and-targets/deployment-environments/private-cloud-deployments.md) — Hosts the experiment management platform in managed cloud or private infrastructure for data isolation. ([source](https://cdn.jsdelivr.net/gh/wandb/wandb@main/README.md))
- [Job Scheduling](https://awesome-repositories.com/f/devops-infrastructure/job-scheduling.md) — Organizes and tracks reusable machine learning job definitions independently of specific training runs. ([source](https://docs.wandb.ai/models/ref/cli/wandb-job.md))
- [Sidecar Proxies](https://awesome-repositories.com/f/devops-infrastructure/sidecar-proxies.md) — Runs background processes alongside training scripts to handle asynchronous data logging and network communication.
- [Metric Condition Evaluators](https://awesome-repositories.com/f/devops-infrastructure/trigger-condition-filters/metric-condition-evaluators.md) — Evaluates performance metric streams against defined thresholds to trigger automated actions. ([source](https://docs.wandb.ai/models/ref/python/automations/metricthresholdfilter.md))

### Development Tools & Productivity

- [Machine Learning Pipelines](https://awesome-repositories.com/f/development-tools-productivity/task-pipeline-managers/machine-learning-pipelines.md) — Triggers automated actions and notifications based on training events to streamline model lifecycle tasks.
- [Containerized Execution](https://awesome-repositories.com/f/development-tools-productivity/command-execution/containerized-execution.md) — Executes training scripts within isolated containers with automated credential and hardware configuration. ([source](https://docs.wandb.ai/models/ref/cli/wandb-docker.md))

### Software Engineering & Architecture

- [Event-Driven Hook Systems](https://awesome-repositories.com/f/software-engineering-architecture/event-driven-hook-systems.md) — Triggers external actions or workflows by monitoring state changes and artifact lifecycle events.
- [Reproducible Build Environments](https://awesome-repositories.com/f/software-engineering-architecture/reproducible-build-environments.md) — Packages code and environment definitions into standardized artifacts for consistent training job execution. ([source](https://docs.wandb.ai/models/ref/cli/wandb-job/wandb-job-create.md))
- [Webhook Event Notifications](https://awesome-repositories.com/f/software-engineering-architecture/integration-extensibility/programmatic-interfaces/webhook-event-notifications.md) — Triggers external alerts or actions when specific project events occur, such as training run failures. ([source](https://docs.wandb.ai/models/automations/project-automation-tutorial.md))
- [Workflow Automation](https://awesome-repositories.com/f/software-engineering-architecture/workflow-automation.md) — Provides a centralized interface for managing and monitoring automated tasks triggered by system events. ([source](https://docs.wandb.ai/models/automations/create-automations/webhook.md))

### System Administration & Monitoring

- [System Usage Monitoring](https://awesome-repositories.com/f/system-administration-monitoring/monitoring-and-observability/observability-platforms/metric-performance-monitors/system-usage-monitoring.md) — Tracks and records hardware performance metrics like CPU, GPU, and memory utilization during the execution of machine learning processes. ([source](https://docs.wandb.ai/models/ref/cli/wandb-beta/wandb-beta-leet/wandb-beta-leet-symon.md))
- [Metric Data Ingestion](https://awesome-repositories.com/f/system-administration-monitoring/logging-and-telemetry/metric-data-ingestion.md) — Ingests metrics from Prometheus or OpenMetrics-compatible endpoints for infrastructure monitoring. ([source](https://docs.wandb.ai/models/ref/python/experiments/system-metrics.md))

### Part of an Awesome List

- [Machine Learning Operations](https://awesome-repositories.com/f/awesome-lists/ai/machine-learning-operations.md) — Tool for experiment tracking, dataset versioning, and model management.
- [Experiment and Data Management](https://awesome-repositories.com/f/awesome-lists/data/experiment-and-data-management.md) — Comprehensive experiment tracking and collaboration platform.
- [Experiment Tracking](https://awesome-repositories.com/f/awesome-lists/data/experiment-tracking.md) — Provides lightweight experiment tracking and visualization for ML projects.

### Business & Productivity Software

- [Team Collaboration Platforms](https://awesome-repositories.com/f/business-productivity-software/team-collaboration-events/collaboration-communication-tools/collaboration-software/team-collaboration-platforms.md) — Enables teams to create and share interactive documents for communicating project findings and status. ([source](https://docs.wandb.ai/examples.md))

### Networking & Communication

- [Response Streaming](https://awesome-repositories.com/f/networking-communication/api-integration-frameworks/http-client-libraries/http-client-utilities/response-streaming.md) — Streams model responses incrementally to the client to improve perceived latency. ([source](https://docs.wandb.ai/inference/response-settings/streaming.md))
- [Remote Procedure Call Interfaces](https://awesome-repositories.com/f/networking-communication/remote-procedure-call-interfaces.md) — Exposes a standardized API for querying experiment history and managing model registries across distributed infrastructure.

### Security & Cryptography

- [Session Authentication](https://awesome-repositories.com/f/security-cryptography/session-authentication.md) — Establishes secure connections to tracking services by validating credentials through environment variables or interactive prompts. ([source](https://docs.wandb.ai/models/ref/cli/wandb-login.md))
- [Cached Artifact Encryption](https://awesome-repositories.com/f/security-cryptography/privacy-data-protection/data-encryption/end-to-end-encryption/cached-artifact-encryption.md) — Encrypts model files and data at rest and in transit while restricting access to authorized members. ([source](https://docs.wandb.ai/models/artifacts/data-privacy-and-compliance.md))

### User Interface & Experience

- [Embedded Data Visualizations](https://awesome-repositories.com/f/user-interface-experience/data-visualization-tools/data-visualization/interactive-presentation-tools/embedded-data-visualizations.md) — Integrates interactive charts and custom HTML directly into experiment tracking interfaces. ([source](https://docs.wandb.ai/models/ref/python/data-types.md))
- [Rich Media Loggers](https://awesome-repositories.com/f/user-interface-experience/rich-media-loggers.md) — Records and displays visual and structured data including images, tables, and specialized formats alongside training metrics. ([source](https://docs.wandb.ai/models/integrations/lightning.md))

### Graphics & Multimedia

- [Declarative Visualization Grammars](https://awesome-repositories.com/f/graphics-multimedia/visualization-mapping/declarative-visualization-grammars.md) — Renders interactive dashboards by mapping logged data fields to flexible, user-defined specifications.

### Programming Languages & Runtimes

- [Run Lifecycle Controls](https://awesome-repositories.com/f/programming-languages-runtimes/runtime-execution-environments/runtime-environments/runtime-management-utilities/run-lifecycle-controls.md) — Initializes, manages, and finalizes experiment runs to ensure complete data capture and synchronization. ([source](https://docs.wandb.ai/models/ref/python/experiments/run.md))