# Weights & Biases Documentation: English Products W&B Models ## W&B Models - [W&B Models](https://docs.wandb.ai/models.md): Use W&B Models for experiment tracking, dataset versioning, model management, and collaborative ML development. - [W&B Quickstart](https://docs.wandb.ai/models/quickstart.md): Install W&B and start tracking, visualizing, and managing machine learning experiments in minutes. - [Get Started with W&B Models](https://docs.wandb.ai/models/models_quickstart.md): Get started with W&B Models by tracking experiments, logging metrics, and visualizing results in a few lines of code. - [Support: Models](https://docs.wandb.ai/models/support-models.md) ### Guides #### Experiments - [Experiments overview](https://docs.wandb.ai/models/track.md): Track machine learning experiments with W&B to log metrics, hyperparameters, system metrics, and model artifacts. - [Create an experiment](https://docs.wandb.ai/models/track/create-an-experiment.md): Create a W&B Experiment using the Python SDK to track run initialization, hyperparameters, and metric logging. - [Configure experiments](https://docs.wandb.ai/models/track/config.md): Use a dictionary-like object to save your experiment configuration - [Projects](https://docs.wandb.ai/models/track/project-page.md): Compare versions of your model, explore results in a scratch workspace, and export findings to a report to save notes and visualizations - [View experiments results](https://docs.wandb.ai/models/track/workspaces.md): A playground for exploring run data with interactive visualizations - [Track Jupyter notebooks](https://docs.wandb.ai/models/track/jupyter.md): Use W&B with Jupyter to get interactive visualizations without leaving your notebook. - [Logging at scale and performance](https://docs.wandb.ai/models/track/limits.md): This page describes how logging patterns impact performance in W&B and provides guidance for scaling experiment tracking in large projects. - [Reproduce experiments](https://docs.wandb.ai/models/track/reproduce_experiments.md): Reproduce a teammate's W&B experiment by downloading the associated code, dependencies, and configuration from a run. - [Export and update data](https://docs.wandb.ai/models/track/public-api-guide.md): Export or update data that you have saved to W&B - [Environment variables](https://docs.wandb.ai/models/track/environment-variables.md): Configure W&B SDK behavior using environment variables for authentication, project settings, logging modes, and more. ##### What are runs? - [Overview](https://docs.wandb.ai/models/runs.md): Learn about the basic building block of W&B, Runs. - [Find and customize a run's ID or name](https://docs.wandb.ai/models/runs/run-identifiers.md): Learn how to find a run's unique identifier and run name, how to create a custom run ID, and how to customize a run's name. - [Initialize runs](https://docs.wandb.ai/models/runs/initialize-run.md): Initialize W&B runs with wandb.init() to start tracking experiments, including handling concurrent runs in one process. - [Run states](https://docs.wandb.ai/models/runs/run-states.md): Learn about the different states a W&B run can have. - [View a specific run in a project](https://docs.wandb.ai/models/runs/view-logged-runs.md): Learn how to view a specific logged run and its properties using the W&B App or the LEET terminal UI. - [View runs in a project](https://docs.wandb.ai/models/runs/customize-run-display.md): Details about customizing how runs are displayed in your project's runs table - [Fork a run](https://docs.wandb.ai/models/runs/forking.md): Explore different parameters or models from a specific point in an experiment without impacting the original run. - [Resume a run](https://docs.wandb.ai/models/runs/resuming.md): Resume paused, stopped, or crashed W&B runs using the resume parameter options in wandb.init(). - [Rewind a run](https://docs.wandb.ai/models/runs/rewind.md): Rewind a run to correct or modify its history without losing original data. - [Pin and compare runs](https://docs.wandb.ai/models/runs/compare-runs.md): Learn how to use pinned and baseline runs to keep track of important runs and efficiently evaluate model experiments. - [Organize runs](https://docs.wandb.ai/models/runs/grouping.md): Organize your runs into groups and other properties. - [Filter runs](https://docs.wandb.ai/models/runs/filter-runs.md): Learn how to filter runs in the Runs table using the expression editor. - [Search runs](https://docs.wandb.ai/models/runs/search-runs.md): Learn how to search for specific runs by name or ID in your project's Runs table or Workspace. - [Stop runs](https://docs.wandb.ai/models/runs/stop-runs.md): Stop runs programmatically using the W&B Python SDK or manually from the W&B App. - [Delete runs](https://docs.wandb.ai/models/runs/delete-runs.md): Delete runs from a W&B project using the W&B App or the Public API, and learn how deleted run data is removed from storage. - [Add labels to runs with tags](https://docs.wandb.ai/models/runs/tags.md): Add, update, and remove tags on W&B runs using the Python SDK, Public API, or the W&B App UI for organization. - [Move a run to a different project or team](https://docs.wandb.ai/models/runs/manage-runs.md): Move runs between projects or teams using the W&B App. - [Customize run colors](https://docs.wandb.ai/models/runs/run-colors.md): Customize, randomize, and reset the colors assigned to individual runs in your W&B project workspace. - [Semantic run plot legends](https://docs.wandb.ai/models/runs/color-code-runs.md): Color-code W&B runs based on metrics or config parameters to create visually meaningful chart legends. - [Send an alert](https://docs.wandb.ai/models/runs/alert.md): Send alerts, triggered from your Python code, to your Slack or email - [Visualize CoreWeave infrastructure alerts](https://docs.wandb.ai/models/runs/infrastructure-alerts.md): View CoreWeave infrastructure alerts such as GPU failures and thermal violations on your W&B experiment run plots. ##### Log objects and media - [Overview](https://docs.wandb.ai/models/track/log.md): Keep track of metrics, videos, custom plots, and more - [Create and track plots from experiments](https://docs.wandb.ai/models/track/log/plots.md): Create and track plots from machine learning experiments. - [Customize log axes](https://docs.wandb.ai/models/track/log/customize-logging-axes.md): Use define_metric() to set a custom x-axis for logged metrics instead of the default W&B step counter. - [Log distributed training experiments](https://docs.wandb.ai/models/track/log/distributed-training.md): Use W&B to log distributed training experiments with multiple GPUs. - [Log media and objects](https://docs.wandb.ai/models/track/log/media.md): Log rich media, from 3D point clouds and molecules to HTML and histograms - [Log models](https://docs.wandb.ai/models/track/log/log-models.md): Log model artifacts to a W&B run and retrieve them later using the log_model and use_model SDK methods. - [Log summary metrics](https://docs.wandb.ai/models/track/log/log-summary.md): Track and customize single summary metrics like best accuracy or minimum loss on a W&B run using run.summary. - [Log tables](https://docs.wandb.ai/models/track/log/log-tables.md): Create, populate, and log W&B Tables to visualize and query structured data from your experiment runs. - [Track CSV files with experiments](https://docs.wandb.ai/models/track/log/working-with-csv.md): Import CSV files into W&B as Tables and Artifacts for visualization, comparison, and analysis in dashboards. #### Sweeps - [Sweeps overview](https://docs.wandb.ai/models/sweeps.md): Hyperparameter search and model optimization with W&B Sweeps - [Tutorial: Define, initialize, and run a sweep](https://docs.wandb.ai/models/sweeps/walkthrough.md): Define, initialize, and run a sweep to search a hyperparameter space and find the configuration that produces the best model. - [Add W&B (wandb) to your code](https://docs.wandb.ai/models/sweeps/add-w-and-b-to-your-code.md): Add W&B to your Python code script or Jupyter Notebook. - [Initialize a sweep](https://docs.wandb.ai/models/sweeps/initialize-sweeps.md): Initialize a sweep using the Python SDK or CLI to start hyperparameter searches with your sweep configuration. - [Start a sweep agent](https://docs.wandb.ai/models/sweeps/start-sweep-agents.md): Start or stop a sweep agent on one or more machines. - [Parallelize agents](https://docs.wandb.ai/models/sweeps/parallelize-agents.md): Parallelize sweep agents on multi-core or multi-GPU machines. - [Visualize sweep results](https://docs.wandb.ai/models/sweeps/visualize-sweep-results.md): Visualize the results of your W&B Sweeps with the W&B App UI. - [Manage sweeps](https://docs.wandb.ai/models/sweeps/pause-resume-and-cancel-sweeps.md): Pause, resume, and cancel a sweep with the CLI. - [Signal handling and sweep runs](https://docs.wandb.ai/models/sweeps/signal-handling-sweep-runs.md): Learn how W&B Sweeps handle UNIX signals, exit codes, and preemption in sweep runs. - [Learn more about sweeps](https://docs.wandb.ai/models/sweeps/useful-resources.md): Find links to academic papers, example reports, tutorials, and the Sweeps GitHub repository for further learning. - [Manage algorithms locally](https://docs.wandb.ai/models/sweeps/local-controller.md): Search and stop algorithms locally instead of using the W&B cloud-hosted service. - [Sweeps troubleshooting](https://docs.wandb.ai/models/sweeps/troubleshoot-sweeps.md): Troubleshoot common sweep issues including CommError, CUDA out of memory, and wandb agent failures. - [Tutorial: Create sweep job from project](https://docs.wandb.ai/models/sweeps/existing-project.md): Tutorial on how to create sweep jobs from a pre-existing W&B project. ##### Define a sweep configuration - [Overview](https://docs.wandb.ai/models/sweeps/define-sweep-configuration.md): Learn how to create configuration files for sweeps. - [Sweep configuration options](https://docs.wandb.ai/models/sweeps/sweep-config-keys.md): Reference for all sweep configuration keys including method, metric, parameters, early termination, and command. #### Eval Tables - [Overview](https://docs.wandb.ai/models/evaltables.md): Learn how to create, compare, and visualize Eval Tables in W&B. - [Create an Eval Table](https://docs.wandb.ai/models/evaltables/create-an-evaluation-table.md): Learn how to create an Eval Table in W&B. - [View Eval Tables](https://docs.wandb.ai/models/evaltables/visualize-evaluation-tables.md) - [Compare runs with Eval Tables](https://docs.wandb.ai/models/evaltables/compare-runs.md) #### Tables - [Tables overview](https://docs.wandb.ai/models/tables.md): Iterate on datasets and understand model predictions - [Tutorial: Log tables, visualize and query data](https://docs.wandb.ai/models/tables/tables-walkthrough.md): Explore how to use W&B Tables with this 5 minute Quickstart. - [Log tables](https://docs.wandb.ai/models/tables/log_tables.md): Create and log W&B Tables with different logging modes including immutable, mutable, and incremental using the Python SDK. - [Visualize and analyze tables](https://docs.wandb.ai/models/tables/visualize-tables.md): Compare, filter, group, sort, and visualize W&B Tables data in merged or side-by-side views for analysis. - [Example tables](https://docs.wandb.ai/models/tables/tables-gallery.md): Explore example W&B Tables projects for image classification, audio, text analysis, and other use cases. - [Export table data](https://docs.wandb.ai/models/tables/tables-download.md): Export W&B Table data to pandas DataFrames and CSV files for offline analysis and data processing. #### Artifacts - [Artifacts overview](https://docs.wandb.ai/models/artifacts.md): Overview of W&B Artifacts, how they work, and how to get started using them. - [Create an artifact](https://docs.wandb.ai/models/artifacts/construct-an-artifact.md): Create and log a W&B Artifact. Learn how to add one or more files or a URI reference to an Artifact. - [Download and use artifacts](https://docs.wandb.ai/models/artifacts/download-and-use-an-artifact.md): Download and use Artifacts from multiple projects. - [Update an artifact](https://docs.wandb.ai/models/artifacts/update-an-artifact.md): Update an existing artifact while a run is active or using only the Public API. - [Create an artifact alias](https://docs.wandb.ai/models/artifacts/create-a-custom-alias.md): Create and manage custom aliases to reference specific W&B artifact versions by meaningful names like best or production. - [Create an artifact version](https://docs.wandb.ai/models/artifacts/create-a-new-artifact-version.md): Create a new artifact version from a single run or from a distributed process. - [Track external files](https://docs.wandb.ai/models/artifacts/track-external-files.md): Track files saved in an external bucket, HTTP file server, or an NFS share. - [Explore artifact lineage graphs](https://docs.wandb.ai/models/artifacts/explore-and-traverse-an-artifact-graph.md): View and traverse artifact lineage graphs to track the inputs and outputs of W&B runs as a directed acyclic graph. - [Artifact data privacy and compliance](https://docs.wandb.ai/models/artifacts/data-privacy-and-compliance.md): Learn where W&B files are stored by default. Explore how to save, store sensitive information. - [Tutorial: Create, track, and use a dataset artifact](https://docs.wandb.ai/models/artifacts/artifacts-walkthrough.md): Create, track, and use a dataset artifact with W&B. ##### Manage data - [Delete an artifact](https://docs.wandb.ai/models/artifacts/delete-artifacts.md): Delete artifacts interactively with the App UI or programmatically with the W&B Python SDK. - [Manage artifact data retention](https://docs.wandb.ai/models/artifacts/ttl.md): Set time-to-live (TTL) policies on W&B artifacts to schedule automatic deletion and manage storage consumption. - [Manage artifact storage and memory allocation](https://docs.wandb.ai/models/artifacts/storage.md): Manage storage, memory allocation of W&B Artifacts. #### Registry - [Registry overview](https://docs.wandb.ai/models/registry.md): Use W&B Registry to manage and share artifact versions across your organization - [Create a registry](https://docs.wandb.ai/models/registry/create_registry.md): Create a W&B Registry with configurable visibility and accepted artifact types using the App UI or Python SDK. - [Configure registry access](https://docs.wandb.ai/models/registry/configure_registry.md): Configure W&B Registry access by managing users and teams, assigning roles, and setting role-based permissions. - [Create a collection](https://docs.wandb.ai/models/registry/create_collection.md): Create a collection of linked artifact versions within a W&B Registry and configure accepted artifact types. - [Link an artifact version to a collection](https://docs.wandb.ai/models/registry/link_version.md): Link an artifact version to a collection in W&B Registry to share it across your organization. - [Link a Weave prompt to a collection](https://docs.wandb.ai/models/registry/link_prompt.md) - [Reference an artifact version with aliases](https://docs.wandb.ai/models/registry/aliases.md): Use default, custom, and protected aliases to reference specific artifact versions in W&B Registry. - [Download an artifact from a registry](https://docs.wandb.ai/models/registry/download_use_artifact.md): Download an artifact linked to a W&B Registry collection by constructing its path and using the Python SDK. - [Find registry items](https://docs.wandb.ai/models/registry/search_registry.md): Learn how to search for registries, collections, and artifact versions interactively with the UI or programmatically with queries. - [Organize versions with tags](https://docs.wandb.ai/models/registry/organize-with-tags.md): Use tags to organize collections or artifact versions within collections. You can add, remove, edit tags with the Python SDK or W&B App UI. - [Annotate collections](https://docs.wandb.ai/models/registry/registry_cards.md): Add descriptions and documentation to W&B Registry collections to help users understand their purpose and contents. - [Lineage graphs and audit history](https://docs.wandb.ai/models/registry/lineage.md): Use lineage graphs to visualize a linked artifact's history and audit a collection's history. - [Delete registry](https://docs.wandb.ai/models/registry/delete_registry.md): Delete a W&B Registry as a Team admin or Registry admin using the Python SDK or the W&B App UI. #### Reports - [Reports overview](https://docs.wandb.ai/models/reports.md): Project management and collaboration tools for machine learning projects - [Create a report](https://docs.wandb.ai/models/reports/create-a-report.md): Create a W&B Report with the W&B App or programmatically. - [Edit a report](https://docs.wandb.ai/models/reports/edit-a-report.md): Edit a report interactively with the App UI or programmatically with the W&B SDK. - [Collaborate on reports](https://docs.wandb.ai/models/reports/collaborate-on-reports.md): Collaborate and share W&B Reports with peers, coworkers, and your team. - [Clone and export reports](https://docs.wandb.ai/models/reports/clone-and-export-reports.md): Export W&B Reports as PDF or LaTeX files, and clone reports using the App UI or the Report and Workspace API. - [Embed a report](https://docs.wandb.ai/models/reports/embed-reports.md): Embed W&B reports directly into Notion or with an HTML `iframe` element. - [Compare runs across projects](https://docs.wandb.ai/models/reports/cross-project-reports.md): Compare runs from two different projects with cross-project reports. - [Example reports](https://docs.wandb.ai/models/reports/reports-gallery.md): Explore example W&B Reports showcasing use cases like experiment summaries, team collaboration, and sharing findings. #### Automations - [Automations overview](https://docs.wandb.ai/models/automations.md): Use W&B Automations for triggering workflows based on events in W&B - [View an automation's history](https://docs.wandb.ai/models/automations/view-automation-history.md): View the execution history of your W&B Automations to check status, triggering events, and action results. - [Automation events and scopes](https://docs.wandb.ai/models/automations/automation-events.md): Learn about events and scopes that trigger W&B Automations, including artifact changes, run status, and metric conditions. - [Manage automations with the API](https://docs.wandb.ai/models/automations/api.md): Programmatic automation management with the Python API. Create and update may be affected on some client versions. Prefer the W&B App until the SDK fix ships. ##### Create an automation - [Overview](https://docs.wandb.ai/models/automations/create-automations.md): Create and manage W&B automations to streamline your ML workflows - [Create a Slack automation](https://docs.wandb.ai/models/automations/create-automations/slack.md): Set up a Slack integration and create a W&B Automation that sends notifications to a Slack channel on specific events. - [Create a webhook automation](https://docs.wandb.ai/models/automations/create-automations/webhook.md): Create a webhook automation in W&B to send HTTP requests to external services when specific events occur. - [Use automations with ARIA](https://docs.wandb.ai/models/automations/create-automations/aria.md): Create a W&B automation that sends a templated prompt to ARIA, starting a new conversation when a specific event occurs. ##### Tutorials - [Automation tutorial overview](https://docs.wandb.ai/models/automations/tutorial.md): Learn to build a project run-failure alert or a registry alias automation. - [Tutorial: Project run-failure alert automation](https://docs.wandb.ai/models/automations/project-automation-tutorial.md): Build a run-failure alert that sends a Slack notification when a run in your project fails. - [Tutorial: Registry artifact alias automation](https://docs.wandb.ai/models/automations/registry-automation-tutorial.md): Build an automation that runs a webhook when a Registry artifact gets a specific alias like "production". #### LLM Evaluation Jobs - [LLM Evaluation Jobs](https://docs.wandb.ai/models/launch.md): Evaluate model checkpoints or hosted API models within W&B and analyze the results using automatically generated leaderboards. - [Evaluate a model checkpoint](https://docs.wandb.ai/models/launch/evaluate-model-checkpoint.md): Evaluate a VLLM-compatible model checkpoint using infrastructure managed by CoreWeave - [Evaluate a hosted API model](https://docs.wandb.ai/models/launch/evaluate-hosted-model.md): Evaluate a hosted API model using infrastructure managed by CoreWeave - [Evaluation benchmark catalog](https://docs.wandb.ai/models/launch/evaluations.md): Browse the evaluation benchmarks available through LLM Evaluation Jobs #### W&B App UI - [Manage workspace, section, and panel settings](https://docs.wandb.ai/models/app/features/cascade-settings.md): Manage workspace, section, and panel settings in the W&B App, including layout and line plot configuration options. - [LEET terminal UI](https://docs.wandb.ai/models/app/leet-tui.md): Explore and compare local W&B runs from the terminal with the LEET (Lightweight Experiment Exploration Tool) TUI. - [Console logs](https://docs.wandb.ai/models/app/console-logs.md): View and debug console log messages including info, warnings, and errors from your W&B experiment runs. - [Keyboard shortcuts](https://docs.wandb.ai/models/app/keyboard-shortcuts.md): Learn about the keyboard shortcuts available in W&B. ##### Panels - [Panels](https://docs.wandb.ai/models/app/features/panels.md): Use and customize workspace panels to visualize your logged data - [Bar plots](https://docs.wandb.ai/models/app/features/panels/bar-plot.md): Visualize metrics, customize axes, and compare categorical data as bars. - [Parallel coordinates](https://docs.wandb.ai/models/app/features/panels/parallel-coordinates.md): Compare results across machine learning experiments - [Scatter plots](https://docs.wandb.ai/models/app/features/panels/scatter-plot.md): Create and customize scatter plots in W&B to compare runs and visualize relationships between experiment metrics. - [Media panels](https://docs.wandb.ai/models/app/features/panels/media.md): Add and configure media panels for images, video, audio, 3D objects, and point clouds in a W&B workspace. - [Save and diff code](https://docs.wandb.ai/models/app/features/panels/code.md): Enable code saving, compare code across W&B runs with the code comparer, and capture Jupyter session history. - [Parameter importance](https://docs.wandb.ai/models/app/features/panels/parameter-importance.md): Visualize the relationships between your model's hyperparameters and output metrics - [Compare run metrics](https://docs.wandb.ai/models/app/features/panels/run-comparer.md): Use the Run Comparer panel to view and compare configuration and metric differences across W&B experiment runs. ###### Line plots - [Line plots overview](https://docs.wandb.ai/models/app/features/panels/line-plot.md): Visualize metrics, customize axes, and compare multiple lines on a plot - [Line plot reference](https://docs.wandb.ai/models/app/features/panels/line-plot/reference.md): Reference for line plot panel settings including x-axis, y-axis, smoothing, aggregation, and grouping options. - [Point aggregation](https://docs.wandb.ai/models/app/features/panels/line-plot/sampling.md): Understand the two point aggregation modes for W&B line plots: full fidelity bucketed sampling and random sampling. - [Smooth line plots](https://docs.wandb.ai/models/app/features/panels/line-plot/smoothing.md): In line plots, use smoothing to see trends in noisy data. ###### Query panels - [Query panels overview](https://docs.wandb.ai/models/app/features/panels/query-panels.md): Some features on this page are in beta, hidden behind a feature flag. Add `weave-plot` to your bio on your profile page to unlock all related features. - [Embed objects](https://docs.wandb.ai/models/app/features/panels/query-panels/embedding-projector.md): W&B's Embedding Projector lets you plot multi-dimensional embeddings on a 2D plane using common dimension reduction algorithms like PCA, UMAP, and t-SNE. ##### Custom charts - [Custom charts overview](https://docs.wandb.ai/models/app/features/custom-charts.md): Create custom charts in W&B projects with Vega visualizations - [Tutorial: Use custom charts](https://docs.wandb.ai/models/app/features/custom-charts/walkthrough.md): Tutorial of using the custom charts feature in the W&B UI ### Integrations - [Integrations overview](https://docs.wandb.ai/models/integrations.md): Explore W&B integrations with ML frameworks, cloud platforms, and workflow orchestration tools - [Add W&B to a Python library](https://docs.wandb.ai/models/integrations/add-wandb-to-any-library.md): Best practices for integrating W&B into your Python library for experiment tracking, system monitoring, and model management. #### ML Frameworks and Libraries ##### ML Frameworks - [Keras](https://docs.wandb.ai/models/integrations/keras.md): Use W&B Keras callbacks to track experiments, checkpoint models, and visualize predictions during training. - [PyTorch](https://docs.wandb.ai/models/integrations/pytorch.md): Integrate W&B with PyTorch for experiment tracking, dataset versioning, and logging of metrics, gradients, and models. - [PyTorch Lightning](https://docs.wandb.ai/models/integrations/lightning.md): Use W&B with PyTorch Lightning through the built-in WandbLogger for experiment tracking and model checkpointing. - [PyTorch Ignite](https://docs.wandb.ai/models/integrations/ignite.md): Integrate W&B with PyTorch Ignite to automatically log training metrics, model parameters, and experiment configs. - [TensorFlow](https://docs.wandb.ai/models/integrations/tensorflow.md): Integrate W&B with TensorFlow for logging custom metrics, using estimator hooks, and TensorBoard log synchronization. - [XGBoost](https://docs.wandb.ai/models/integrations/xgboost.md): Integrate W&B with XGBoost to log gradient boosting metrics, feature importance, and model performance automatically. ##### ML Libraries - [Hugging Face](https://docs.wandb.ai/models/integrations/huggingface.md): Visualize and track Hugging Face model performance with W&B, logging hyperparameters, metrics, and GPU utilization. - [Hugging Face Accelerate](https://docs.wandb.ai/models/integrations/accelerate.md): Training and inference at scale made simple, efficient, and adaptable - [Hugging Face Diffusers](https://docs.wandb.ai/models/integrations/diffusers.md): Use W&B autolog with Hugging Face Diffusers to track prompts, generated media, configs, and pipeline architecture. - [Hugging Face Simple Transformers](https://docs.wandb.ai/models/integrations/simpletransformers.md): How to integrate W&B with the Transformers library by Hugging Face. - [Hugging Face Transformers](https://docs.wandb.ai/models/integrations/huggingface_transformers.md): Use W&B with Hugging Face Transformers Trainer for experiment tracking, model checkpointing, and dataset versioning. - [OpenAI Gym](https://docs.wandb.ai/models/integrations/openai-gym.md): Integrate W&B with OpenAI Gym to track reinforcement learning experiments and record episode performance videos. - [PyTorch Geometric](https://docs.wandb.ai/models/integrations/pytorch-geometric.md): Integrate W&B with PyTorch Geometric for graph visualization and experiment tracking in geometric deep learning. - [PyTorch torchtune](https://docs.wandb.ai/models/integrations/torchtune.md): Use W&B logging in PyTorch torchtune for tracking LLM fine-tuning experiments with the WandBLogger metric logger. #### Cloud Platforms - [Azure OpenAI fine-tuning](https://docs.wandb.ai/models/integrations/azure-openai-fine-tuning.md): Fine-tune Azure OpenAI models with W&B experiment tracking to log metrics, hyperparameters, and training progress. - [OpenAI fine-tuning](https://docs.wandb.ai/models/integrations/openai-fine-tuning.md): Fine-tune OpenAI models with W&B to log training metrics, monitor jobs, and compare model performance over time. - [OpenAI API](https://docs.wandb.ai/models/integrations/openai-api.md): Use W&B with the OpenAI API to log and monitor chat completions, fine-tuning jobs, and token usage metrics. #### Other - [Hydra](https://docs.wandb.ai/models/integrations/hydra.md): Integrate W&B with Hydra to manage complex configurations for ML experiments and log hyperparameters automatically. - [YOLOX](https://docs.wandb.ai/models/integrations/yolox.md): Integrate W&B with YOLOX to track object detection model training, log metrics, and visualize detection results. - [YOLOv5](https://docs.wandb.ai/models/integrations/yolov5.md): Use the built-in W&B integration in YOLOv5 for experiment tracking, model versioning, and prediction visualization. ### Reference - [Python SDK](https://docs.wandb.ai/models/ref-link-python.md) - [CLI](https://docs.wandb.ai/models/ref-link-cli.md) - [Query Expression Language](https://docs.wandb.ai/models/ref-link-query-panel.md) - [Reports and Workspaces API](https://docs.wandb.ai/models/ref-link-workspaces.md)