Reference

• 1: Release Notes
• 1.1: 0.69.x
• 1.2: 0.68.x
• 1.3: 0.67.x
• 1.4: 0.66.x
• 1.5: 0.65.x
• 1.6: 0.63.x
• 1.7: Archived Releases
• 1.7.1:
• 1.7.2: 0.61.0
• 1.7.3: 0.60.0
• 1.7.4: 0.58.1
• 1.7.5: 0.57.2
• 1.7.6: 0.56.0
• 1.7.7: 0.54.0
• 1.7.8: 0.52.2
• 1.7.9: 0.51.0
• 1.7.10: 0.50.2
• 1.7.11: 0.49.0
• 1.7.12: 0.48.0
• 1.7.13: 0.47.3
• 1.7.14: 0.47.2
• 1.7.15: 0.46.0
• 1.7.16: 0.45.0
• 1.7.17: 0.44.1
• 1.7.18: 0.43.0
• 1.7.19: 0.42.0
• 1.7.20: 0.41.0
• 1.7.21: 0.40.0
• 1.7.22: 0.39.0
• 1.7.23: 0.38.0
• 1.7.24: 0.36.0
• 1.7.25: 0.35.0
• 1.7.26: 0.34.0
• 1.7.27: 0.33.0
• 1.8: Release policies and processes
• 2: Command Line Interface
• 2.1: wandb agent
• 2.2: wandb artifact
• 2.2.1: wandb artifact cache
• 2.2.1.1: wandb artifact cache cleanup
• 2.2.2: wandb artifact get
• 2.2.3: wandb artifact ls
• 2.2.4: wandb artifact put
• 2.3: wandb beta
• 2.3.1: wandb beta sync
• 2.4: wandb controller
• 2.5: wandb disabled
• 2.6: wandb docker
• 2.7: wandb docker-run
• 2.8: wandb enabled
• 2.9: wandb init
• 2.10: wandb job
• 2.10.1: wandb job create
• 2.10.2: wandb job describe
• 2.10.3: wandb job list
• 2.11: wandb launch
• 2.12: wandb launch-agent
• 2.13: wandb launch-sweep
• 2.14: wandb login
• 2.15: wandb offline
• 2.16: wandb online
• 2.17: wandb pull
• 2.18: wandb restore
• 2.19: wandb scheduler
• 2.20: wandb server
• 2.20.1: wandb server start
• 2.20.2: wandb server stop
• 2.21: wandb status
• 2.22: wandb sweep
• 2.23: wandb sync
• 2.24: wandb verify
• 3: JavaScript Library
• 4: Python Library
• 4.1: API Walkthrough
• 4.2: agent
• 4.3: Artifact
• 4.4: automations
• 4.4.1: Automation
• 4.4.2: DoNothing
• 4.4.3: MetricChangeFilter
• 4.4.4: MetricThresholdFilter
• 4.4.5: NewAutomation
• 4.4.6: OnAddArtifactAlias
• 4.4.7: OnCreateArtifact
• 4.4.8: OnLinkArtifact
• 4.4.9: OnRunMetric
• 4.4.10: SendNotification
• 4.4.11: SendWebhook
• 4.5: controller
• 4.6: Data Types
• 4.6.1: Audio
• 4.6.2: BoundingBoxes2D
• 4.6.3: Graph
• 4.6.4: Histogram
• 4.6.5: Html
• 4.6.6: Image
• 4.6.7: ImageMask
• 4.6.8: Molecule
• 4.6.9: Object3D
• 4.6.10: Plotly
• 4.6.11: Table
• 4.6.12: Video
• 4.6.13: WBTraceTree
• 4.7: finish
• 4.8: Import & Export API
• 4.8.1: Api
• 4.8.2: File
• 4.8.3: Files
• 4.8.4: Job
• 4.8.5: Project
• 4.8.6: Projects
• 4.8.7: QueuedRun
• 4.8.8: Registry
• 4.8.9: Run
• 4.8.10: RunQueue
• 4.8.11: Runs
• 4.8.12: Sweep
• 4.9: init
• 4.10: Integrations
• 4.10.1: keras
• 4.10.1.1: WandbCallback
• 4.10.1.2: WandbEvalCallback
• 4.10.1.3: WandbMetricsLogger
• 4.10.1.4: WandbModelCheckpoint
• 4.11: launch-library
• 4.11.1: launch
• 4.11.2: launch_add
• 4.11.3: LaunchAgent
• 4.12: log
• 4.13: login
• 4.14: Run
• 4.15: save
• 4.16: sweep
• 4.17: wandb_workspaces
• 4.17.1: Reports
• 4.17.2: Workspaces
• 4.18: watch
• 5: Python Reference
• 5.1: Custom Charts
• 5.1.1: bar()
• 5.1.2: confusion_matrix()
• 5.1.3: histogram()
• 5.1.4: line_series()
• 5.1.5: line()
• 5.1.6: plot
• 5.1.7: plot_table()
• 5.1.8: pr_curve()
• 5.1.9: roc_curve()
• 5.1.10: scatter()
• 5.1.11: visualize()
• 5.2: Analytics and Query API
• 5.2.1: api
• 5.2.2: artifacts
• 5.2.3: automations
• 5.2.4: files
• 5.2.5: history
• 5.2.6: integrations
• 5.2.7: jobs
• 5.2.8: projects
• 5.2.9: query_generator
• 5.2.10: reports
• 5.2.11: runs
• 5.2.12: sweeps
• 5.2.13: teams
• 5.2.14: users
• 5.3: Automations
• 5.3.1: ActionType
• 5.3.2: ArtifactEvent
• 5.3.3: Automation
• 5.3.4: DoNothing
• 5.3.5: EventType
• 5.3.6: MetricChangeFilter
• 5.3.7: MetricThresholdFilter
• 5.3.8: NewAutomation
• 5.3.9: OnAddArtifactAlias
• 5.3.10: OnCreateArtifact
• 5.3.11: OnLinkArtifact
• 5.3.12: OnRunMetric
• 5.3.13: ProjectScope
• 5.3.14: RunEvent
• 5.3.15: ScopeType
• 5.3.16: SendNotification
• 5.3.17: SendWebhook
• 5.3.18: SlackIntegration
• 5.3.19: WebhookIntegration
• 5.4: SDK v(0.19.11)
• 5.4.1: Actions
• 5.4.1.1: Classes
• 5.4.1.1.1: Artifact
• 5.4.1.1.2: ArtifactTTL
• 5.4.1.1.3: Error
• 5.4.1.1.4: Run
• 5.4.1.1.5: Settings
• 5.4.1.2: Functions
• 5.4.1.2.1: agent()
• 5.4.1.2.2: controller()
• 5.4.1.2.3: finish()
• 5.4.1.2.4: init()
• 5.4.1.2.5: login()
• 5.4.1.2.6: restore()
• 5.4.1.2.7: setup()
• 5.4.1.2.8: sweep()
• 5.4.1.2.9: teardown()
• 5.4.1.3: Legacy Functions
• 5.4.1.3.1: define_metric()
• 5.4.1.3.2: link_model()
• 5.4.1.3.3: log_artifact()
• 5.4.1.3.4: log_model()
• 5.4.1.3.5: log()
• 5.4.1.3.6: save()
• 5.4.1.3.7: unwatch()
• 5.4.1.3.8: use_artifact()
• 5.4.1.3.9: use_model()
• 5.4.1.3.10: watch()
• 5.4.2: Data Types
• 5.4.2.1: Audio
• 5.4.2.2: box3d()
• 5.4.2.3: Html
• 5.4.2.4: Image
• 5.4.2.5: Molecule
• 5.4.2.6: Object3D
• 5.4.2.7: Plotly
• 5.4.2.8: Table
• 5.4.2.9: Video
• 5.4.3: Launch Library Reference
• 5.4.3.1: create_and_run_agent()
• 5.4.3.2: launch_add()
• 5.4.3.3: launch()
• 5.4.3.4: LaunchAgent
• 5.4.3.5: load_wandb_config()
• 5.4.3.6: manage_config_file()
• 5.4.3.7: manage_wandb_config()
• 6: Query Expression Language
• 6.1: artifact
• 6.2: artifactType
• 6.3: artifactVersion
• 6.4: audio-file
• 6.5: bokeh-file
• 6.6: boolean
• 6.7: entity
• 6.8: file
• 6.9: float
• 6.10: html-file
• 6.11: image-file
• 6.12: int
• 6.13: joined-table
• 6.14: molecule-file
• 6.15: number
• 6.16: object3D-file
• 6.17: partitioned-table
• 6.18: project
• 6.19: pytorch-model-file
• 6.20: run
• 6.21: string
• 6.22: table
• 6.23: user
• 6.24: video-file

1 - Release Notes

This section includes release notes for supported W&B Server releases. For releases that are no longer supported, refer to Archived releases.

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1.1 - 0.69.x

W&B 0.69 focuses on making the workspace more intuitive, collaborative, and efficient. Clearer visualizations and faster artifact downloads streamline how you interact with your data, so you can gain and share insights more quickly. Updates to Weave improve team workflows and evaluation tracking. A range of quality-of-life fixes tidy up the overall user experience.

This release also marks the end of life for v0.54 and older, which are now officially unsupported.

Support and end of life

• W&B Server v0.54 and below have reached end of life as of May 27, 2025.
• W&B Server v0.56 is scheduled to reach end of life in July 2025

A W&B Server release is supported for 12 months from its initial release date. As a reminder, customers using Self-managed are responsible to upgrade to a supported release in time to maintain support.

Refer to Release policies and processes. For assistance or questions, contact support.

Upgrading

To upgrade to W&B v0.69.x, you must use v0.31.4+ of the operator-wandb Helm chart. Otherwise, after the upgrade, the weave-cache-clear container can fail to start. Ensure that your deployment uses these values:

chart:
  url: https://charts.wandb.ai
  name: operator-wandb
  version: 0.31.4

If you have questions or are experiencing issues with an upgrade, contact support.

Features

• You can now set a custom display name for a run directly in the workspace. Customized run names show up in all plots and tables but only in your workspace, with no impact on your teammates’ views. This provides a clearer and cleaner view in your workspace, with no more labels like *...v6-final-restart...* in every legend and plot.
• When filtering or grouping runs, colors can sometimes overlap and become indistinct. The run selector’s new Randomize Colors option reassigns random colors from the default palette to your current run selection or groups, helping to make the colors more distinguishable.
• In line plots, you can now use Cmd+Click on a line to open a single-run view in a new tab.
• Video media panels now provide more playback controls to play, pause, seek, view full screen, and adjust playback speed.
• Settings for all types of media panels have been reorganized and improved.
• You can now customize the point and background colors for point cloud panels.
• Team-level and organization-level service accounts can now interact with Registry.
• Improved Exponentially-weighted Moving Average (EMA) smoothing provides more reliable smoothed lines when operating on complete, unbinned data. In most cases, smoothing is handled at the back end for improved performance. This feature was in private preview in v0.68.x.

Private preview

Private preview features are available by invitation only. To request enrollment in a private preview, contact support or your AISE.

• You can now color all of your runs based on a secondary metric, such as loss or custom efficiency metrics. This creates a clear gradient color scale across your runs in all plots, so you can spot patterns faster. Watch a video demo.
• Personal workspace templates allow you to save core line plot settings and automatically reapply them in new views. These settings include x-axis key, smoothing algorithm, smoothing factor, max number of lines, whether to use the run selector’s grouping, and which aggregation to apply.

Weave

• Saved views simplify team collaboration and allow you to persist filter and column settings.
• PDFs and generic files are now supported.
• The new EvaluationLogger API provides flexible imperative-style evaluation logging.
• You can now import human annotations into Weave datasets
• Playground now supports saved configurations and prompts.
• Decorators are now supported in TypeScript.
• Added support for tracing generator functions.
• The new dataset.add_rows helper improves the efficiency of appending to an existing dataset.
• To help you understand your usage, trace and object sizes are now shown through the UI.

Performance

• With wandb SDK v0.19.11, artifacts now download 3-5x faster on average. For example, an artifact that previously downloaded at around 100 MB/sec may now download at 450 MB/sec or faster. Actual download speeds vary based on factors such as your network and storage infrastructure.
• Improved caching on Project and User Settings pages.

Fixes

• Improved the startup process for the weave-cache-clear container to ensure compatibility with Python virtual environments.
• Added options for denser display of console logs.
• Workspace loading screens are now more informative.
• When adding a panel from a workspace to a report, the current project’s reports are now shown first in the destination report list.
• Fixed many cases where y-axes would over-round to a degree that caused duplicate values to display.
• Fixed confusing behavior when entering invalid smoothing parameters.
• Removed the Partial Media warning from media panels. This does not change the behavior of the media panels.
• When adding a run filter based on tags, the filter is now selected by default, as when filtering by other fields.
• Removed the green bell icon that could appear on active runs in the run selector.
• Removed the System page for individual runs.
• The project description field now respects new lines.
• Fixed URLs for legacy model registry collections.
• Fixed a bug where the Netron viewer did not expand to fill all available space on the page.
• When you click Delete on a project, the project name now displays in the confirmation modal.

1.2 - 0.68.x

W&B Server v0.68 includes enhancements to various types of panels and visualizations, security improvements for Registry, Weave, and service accounts, performance improvements when forking and rewinding runs, and more.

The latest patch is v0.68.2.

Refer to Patches.

Features

• Release notes for W&B Server are now published in the W&B documentation in addition to on GitHub. Subscribe using RSS.
• Registry admins can define and assign protected aliases to represent key stages of your development pipeline. A protected alias can be assigned only by a registry admin. W&B blocks other users from adding or removing protected aliases from versions in a registry using the API or UI.
• You can now filter console logs based on a run’s x_label value. During distributed training, this optional parameter tracks the node that logged the run.
• You can now move runs between Groups, one by one or in bulk. Also, you can now create new Groups after the initial logging time.
• Line plots now support synchronized zooming mode, where zooming to a given range on one plot automatically zooms into the same range on all other line plots with a common x-axis. Turn this on in the workspace display settings for line plots.
• Line plots now support formatting custom metrics as timestamps. This is useful when synchronizing or uploading runs from a different system.
• You can now slide through media panels using non-_step fields such as epoch or train/global_step (or anything else).
• In Tables and plots in Query Panels that use runs or runs.history expressions, a step slider allows you to step through the progress on your metrics, text, or media through the course of your runs. The slider supports stepping through non-_step metrics.
• You can now customize bar chart labels using a font size control.

Private preview

Private preview features are available by invitation only. To request enrollment in a private preview, contact support or your AISE.

• Personal workspace templates allow you to save your workspace setup so it is automatically applied to your new projects. Initially, you can configure certain line plot settings such as the default X axis metric, smoothing algorithm, and smoothing factor.
• Improved Exponentially-weighted Moving Average (EMA) smoothing provides more reliable smoothed lines when operating on complete, unbinned data. In most cases, smoothing is handled at the back end for improved performance.

Weave

• Chat with fine-tuned models from within your W&B instance. Playground is now supported in Dedicated Cloud. Playground is a chat interface for comparing different LLMs on historical traces. Admins can add API keys to different model providers or hook up custom hosted LLM providers so your team can interact with them from within Weave.
• Open Telemetry Support. Now you can log traces via OpenTelemetry (OTel). Learn more.
• Weave tracing has new framework integrations: CrewAI, OpenAI’s Agent SDK, DSPy 2.x and Google’s genai Python SDK.
• Playground supports new OpenAI models: GPT‑4.1, GPT‑4.1 mini, and GPT‑4.1 nano.
• Build labeled datasets directly from traces, with your annotations automatically converted into dataset columns. Learn more.

Security

• Registry admins can now designate a service account in a registry as either a Registry Admin or a Member. Previously, the service account’s role was always Registry Admin. Learn more.

Performance

• Improved the performance of many workspace interactions, particularly in large workspaces. For example, expanding sections and using the run selector are significantly more responsive.

• Improved Fork and Rewind Performance.

  Forking a run creates a new run that uses the same configuration as an existing run. Changes to the forked run do not the parent run, and vice versa. A pointer is maintained between the forked run and the parent. Rewinding a run lets you log new data from that point in time without losing the existing data.

  In projects with many nested forks, forking new runs is now much more efficient due to improvements in caching.

Fixes

• Fixed a bug that could prevent an organization service account from being added to new teams.
• Fixed a bug that could cause hover marks to be missing for grouped lines.
• Fixed a bug that could include invalid project names in the Import dropdown of a Report panel.
• Fixed a display bug in the alignment of filters in the run selector.
• Fixed a page crash when adding a timestamp Within Last filter
• Fixed a bug that could prevent the X-axis from being set to Wall Time in global line plot settings.
• Fixed a bug that could prevent image captions from appearing when they are logged to a Table.
• Fixed a bug that could prevent sparse metrics from showing up in panels.
• In Run Overview pages, the Description field is now named Notes.

Patches

0.68.1

May 2, 2025

• Fixed a bug introduced in v0.68.0 that could prevent media from loading in media panels.

0.68.2

May 7, 2025

• Fixed a bug introduced in v0.68.0 that could cause background jobs to crash or run inconsistently. After upgrading to v0.68.2, affected background jobs will recover automatically. If you experience issues with background jobs after upgrading, contact Support.
• Fixed a long-standing UI bug where typing an invalid regular expression into the W&B App search field could crash the app. Now if you type an invalid regular expression, it is treated as a simple search string, and you can update the search field and try again.
• Fixed a bug where the SMTP port is set to 25 instead of the port specified in GORILLA_EMAIL_SINK.
• Fixed a bug where inviting a user to a team could fail with the misleading error You have no available seats.

1.3 - 0.67.x

Features

• In Reports, you can now give a run a custom display name per panel grid. This allows you to replace the run’s (often long and opaque) training-time name with one that is more meaningful to your audience. The report updates the name in all panel grids, helping you to explain your hard-won experimental insights to your colleagues in a concise and readable way. The original run name remain intact in the project, so doing this won’t disrupt your collaborators.
• When you expand a panel in the workspace, it now opens in full screen mode with more space. In this view, line plots now render with more granular detail, using up 10,000 bins. The run selector appear next to the panel, letting you easily toggle, group, or filter runs in context.
• From any panel, you can now copy a unique URL that links directly to that panel’s full screen view. This makes it even easier to share a link to dig into interesting or pathological patterns in your plots.
• Run Comparer is a powerful tool you can use to compare the configurations and key metrics of important runs alongside their loss curves. Run Comparer has been updated:
  • Faster to add a Run Comparer panel, as an expanded option in Add Panels.
  • By default, a Run Comparer panel takes up more space, so you can see the values right away.
  • Improved readability and legibility of a Run Comparer panel. You can use new controls to quickly change row and column sizes so you can read long or nested values.
  • You can copy any value in the panel to your clipboard with a single click.
  • You can search keys with regular expressions to quickly find exactly the subset of metrics you want to compare across. Your search history is saved to help you iterate efficiently between views.
  • Run Comparer is now more reliable at scale, and handles larger workspaces more efficiently, reducing the likelihood of poor performance or a crashed panel.

• Segmentation mask controls have been updated:
  • You can now toggle each mask type on or off in bulk, or toggle all masks or all images on or off.
  • You can now change each class’s assigned color, helping to avoid confusion if multiple classes use the same color.
• When you open a media panel in full screen mode, you can now use the left or right arrows on your keyboard to step through the images, without first clicking on the step slider.
• Media panels now color run names, matching the run selector. This makes it easier to associate a run’s media values with related metrics and plots.
• In the run selector, you can now filter by whether a run has certain media key or not.
• You can now move runs between groups in the W&B App UI, and you can create new groups after the run is logged.
• Automations can now be edited in the UI
• An automation can now notify a Slack channel for artifact events. When creating an automation, select “Slack notification” for the Action type.
• Registry now supports global search by default, allowing you to search across all registries by registry name, collection name, alias, or tag.
• In Tables and Query panels that use the runs expression, you can use the new Runs History step slider and drop-down controls to view a table of metrics at each step of a run.
• Playground in Weave supports new models: OpenAI’s gpt-4.5-preview and Deepseek’s deepseek-chat and deepseek-reasoner.
• Weave tracing has two new agent framework integrations: CrewAI and OpenAI’s Agent SDK.
• In the Weave UI, you can now build Datasets from traces. Learn more: https://weave-docs.wandb.ai/guides/core-types/datasets#create-edit-and-delete-a-dataset-in-the-ui
• The Weave Python SDK now provides a way to filter the inputs and outputs of your Weave data to ensure sensitive data does not leave your network perimeter. You can configure to redact sensitive data. Learn more: https://weave-docs.wandb.ai/guides/tracking/redact-pii/
• To streamline your experience, the System tab in the individual run workspace view will be removed in an upcoming release. View full information about system metrics in the System section of the workspace. For questions, contact support@wandb.com.

Security

• golang crypto has been upgraded to v0.36.0.
• golang oauth2 has been upgraded to v0.28.0.
• In Weave, pyarrow is now pinned to v17.0.0.

Performance

• Frontend updates significantly reduce workspace reload times by storing essential data in the browser cache across visits. The update optimizes loading of saved views, metric names, the run selector, run counts, W&B’s configuration details, and the recomputation of workspace views.
• Registry overview pages now load significantly faster.
• Improved the performance of selecting metrics for the X, Y, or Z values in a scatter plot in a workspace with thousands of runs or hundreds of metrics.
• Performance improvements to Weave evaluation logging.

Fixes

• Fixed a bug in Reports where following a link to a section in the report would not open to that section.
• Improved the behavior of how Gaussian smoothing handles index reflection, matching SciPy’s default “reflect” mode.
• A Report comment link sent via email now opens directly to the comment.
• Fixed a bug that could crash a workspace if a sweep takes longer than 2 billion compute seconds by changing the variable type for sweep compute seconds to int64 rather than int32.
• Fixed display bugs that could occur when a report included multiple run sets.
• Fixed a bug where panels Quick Added to an alphabetically sorted section were sorted incorrectly.
• Fixed a bug that generated malformed user invitation links.

1.4 - 0.66.x

Features

• In tables and query panels, columns you derive from other columns now persist, so you can use them for filtering or in query panel plots.

Security

• Limited the maximum depth for a GraphQL document to 20.
• Upgraded pyarrow to v17.0.0.

1.5 - 0.65.x

Features

• From a registry’s Settings, you can now update the owner to a different user with the Admin role. Select Owner from the user’s Role menu.
• You can now move a run to a different group in the same project. Hover over a run in the run list, click the three-vertical-dots menu, and choose Move to another group.
• You can now configure whether the Log Scale setting for line plots is enabled by default at the level of the workspace or section.
  • To configure the behavior for a workspace, click the action ... menu for the workspace, click Line plots, then toggle Log scale for the X or Y axis.
  • To configure the behavior for a section, click the gear icon for the section, then toggle Log scale for the X or Y axis.

1.6 - 0.63.x

Features

Weave is now generally available (GA) in Dedicated Cloud on AWS. Reach out to your W&B team if your teams are looking to build Generative AI apps with confidence and putting those in production.

The release includes the following additional updates:

• W&B Models now seamlessly integrates with Azure public cloud. You could now create a Dedicated Cloud instance in an Azure region directly from your Azure subscription and manage it as an Azure ISV resource. This integration is in private preview.
• Enable automations at the Registry level to monitor changes and events across all collections in the registry and trigger actions accordingly. This eliminates the need to configure separate webhooks and automations for individual collections.

• Ability to assign x_label, e.g. node-0, in run settings object to distinguish logs and metrics by label, e.g. node, in distributed runs. Enables grouping system metrics and console logs by label for visualization in the workspace.
• Coming soon with a patch release this week, you will be able to use organization-level service accounts to automate your W&B workloads across all teams in your instance. You would still be able to use existing team-level service accounts if you would like more control over the access scope of a service account.
  • Allow org-level service accounts to interact with Registry. Such service accounts can be invited to a registry using the invite modal and are displayed in the members table along with respective organization roles.

Fixes

• Fixed an issue where users creating custom roles including the Create Artifact permission were not able to log artifacts to a project.
• Fixed the issue with metadata logging for files in instances that have subpath support configured for BYOB.
• Block webhook deletion if used by organization registry automations.

1.7 - Archived Releases

Archived releases have reached end of life and are no longer supported. A major release and its patches are supported for six months from the initial release date. Release notes for archived releases are provided for historical purposes. For supported releases, refer to Releases.

1.7.1 -

This release is no longer supported. A major release and its patches are supported for six months from the initial release date.

Customers using Self-managed are responsible to upgrade to a supported release in time to maintain support. For assistance or questions, contact support.

1.7.2 - 0.61.0

Features

This is a mini-feature and patch release, delivered at a different schedule than the monthly W&B server major releases

• Organization admins can now configure Models seats and access control for both Models & Weave in a seamless manner from their organization dashboard. This change allows for a efficient user management when Weave is enabled for a Dedicated Cloud or Self-managed instance.
  • Weave pricing is consumption-based rather than based on number of seats used. Seat management only applies to the Models product.
• You can now configure access roles at the project level for team and restricted scoped projects. It allows assigning different access roles to a user within different projects in the same team, and thus adding another strong control to conform to enterprise governance needs.

Fixes

• Fixed an issue where underlying database schema changes as part of release upgrades could timeout during platform startup time.
• Added more performance improvements to the underlying parquet store service, to further improve the chart loading times for users. Parquet store service is only available on Dedicated Cloud, and Self-managed instances based on W&B kubernetes operator.
• Addressed the high CPU utilization issue for the underlying parquet store service, to make the efficient chart loading more reliable for users. Parquet store service is only available on Dedicated Cloud, and Self-managed instances based on W&B kubernetes operator.

1.7.3 - 0.60.0

Features

• Final updates for 1.1.1 Compliance of Level AA 2.2 for Web Content Accessibility Guidelines (WCAG) standards.
• W&B can now disable auto-version-upgrade for customer-managed instances using the W&B kubernetes operator. You can request this to your W&B team.
  • Note that W&B requires all instances to upgrade periodically to comply with the 6-month end-of-life period for each version. W&B does not support versions older than 6 months.

Fixes

• Fixed a bug to allow instance admins on Dedicated Cloud and Customer-managed instances to access workspaces in personal entities.
• SCIM Groups and Users GET endpoints now filter out service accounts from the responses. Only non service account users are now returned by those endpoints.
• Fixed a user management bug by removing the ability of team admins to simultaneously delete a user from the overall instance while deleting them from a team. Instance or Org admins are responsible to delete a user from the overall instance / organization.

Performance improvements

• Reduced the latency when adding a panel by up to 90% in workspaces with many metrics.
• Improved the reliability and performance of parquet exports to blob storage when runs are resumed often.
  • Runs export to blob storage in parquet format is available on Dedicated Cloud and on Customer-managed instances that are enabled using the W&B kubernetes operator.

1.7.4 - 0.58.1

Features

• W&B now supports sub-path for Secure storage connector i.e. Bring your own bucket capability. You can now provide a sub-path when configuring a bucket at the instance or team level. This is only available for new bucket configurations and not for existing configured buckets.
• W&B-managed storage on newer Dedicated Cloud instances in GCP & Azure will by default be encrypted with W&B managed cloud-native keys. This is already available on AWS instances. Each instance storage is encrypted with a key unique to the instance. Until now, all instances on GCP & Azure relied on default cloud provider-managed encryption keys.
• Makes the fields in the run config and summary copyable on click.
• If you’re using W&B kubernetes operator for a customer-managed instance, you can now optionally use a custom CA for the controller manager.
• We’ve modified the W&B kubernetes operator to run in a non-root context by default, aligning with OpenShift’s Security Context Constraints (SCCs). This change ensures smoother deployment of customer-managed instances on OpenShift by adhering to its security policies.

Fixes

• Fixed an issue where exporting panels from a workspace to a report now correctly respects the panel search regex.
• Fixed an issue where setting GORILLA_DISABLE_PERSONAL_ENTITY to true was not disabling users from creating projects and writing to existing projects in their personal entities.

Performance improvements

• We have significantly improved performance and stability for experiments with 100k+ logged points. If you’ve a customer-managed instance, this is available if the deployment is managed using the W&B kubernetes operator.
• Fixed issue where saving changes in large workspaces would be very slow or fail.
• Improved latency of opening workspace sections in large workspaces.

1.7.5 - 0.57.2

Features

You can now use JWTs (JSON Web Tokens) to access your W&B instance from the wandb SDK or CLI, using the identity federation capability. The feature is in preview. Refer to Identity federation and reach out to your W&B team for any questions.

The 0.57.2 release also includes these capabilities:

• New Add to reports drawer improvements for exporting Workspace panels into Reports.
• Artifacts metadata filtering in the artifact project browser.
• Pass in artifact metadata in webhook payload via ${artifact_metadata.KEY}.
• Added GPU memory usage panels to the RunSystemMetrics component, enhancing GPU metrics visualization for runs in the app frontend.
• Mobile users now enjoy a much smoother, more intuitive Workspace experience.
• If you’re using W&B Dedicated Cloud on GCP or Azure, you can now enable private connectivity for your instance, thus ensuring that all traffic from your AI workloads and optionally browser clients only transit the cloud provider private network. Refer to Private connectivity and reach out to your W&B team for any questions.
• Team-level service accounts are now shown separately in a new tab in the team settings view. The service accounts are not listed in the Members tab anymore. Also, the API key is now hidden and can only be copied by team admins.
• Dedicated Cloud is now available in GCP’s Seoul region.

Fixes

• Gaussian smoothing was extremely aggressive on many plots.
• Fixed issue where pressing the Ignore Outliers in Chart Scaling button currently has no effect in the UI workspace.
• Disallow inviting deactivated users to an organization.
• Fixed an issue where users added to an instance using SCIM API could not onbioard successfully.

Performance improvements

• Significantly improved performance when editing a panel’s settings and applying the changes.
• Improved the responsiveness of run visibility toggling in large workspaces.
• Improved chart hovering and brushing performance on plots in large workspaces.
• Reduced workspace memory usage and loading times in workspaces with many keys.

1.7.6 - 0.56.0

Features

The new Full Fidelity line plot in W&B Experiments enhances the visibility of training metrics by aggregating all data along the x-axis, displaying the minimum, maximum, and average values within each bucket, allowing users to easily spot outliers and zoom into high-fidelity details without downsampling loss. Learn more in our documentation.

The 0.56.0 release also includes these capabilities:

• You can now use cross-cloud storage buckets for team-level BYOB (secure storage connector) in Dedicated Cloud and Self-managed instances. For example, in a W&B instance on AWS, you can now configure Azure Blob Storage or Google Cloud Storage for team-level BYOB, and so on for each cross-cloud combination.
• In the same vein, you can now use S3-compatible storage buckets like MinIO for team-level BYOB (secure storage connector) in Dedicated Cloud and Self-managed instances. For example, in a W&B instance on GCP, you can configure a MinIO bucket hosted in cloud or on-prem for team-level BYOB.
• Admins can now automate full deletion of users in their Dedicated Cloud or Self-managed instances using the SCIM API’s DELETE User endpoint. The user deactivation operation has been reimplemented using the PATCH User endpoint, along with the introduction of user reactivation operation.
• If you use the SCIM API, you will also see a couple of minor improvements:
  • The API now has a more pertinent error message in case of authentication failures.
  • Relevant endpoints now return the full name of a user in the SCIM User object if it’s available.

Fixes

• The fix resolves an issue where deleting a search term from a runset in a report could delete the panel or cause the report to crash by ensuring proper handling of selected text during copy/paste operations.
• The fix addresses a problem with indenting bulleted items in reports, which was caused by an upgrade of slate and an additional check in the normalization process for elements.
• The fix resolves an issue where text could not be selected from a panel when the report was in edit mode.
• The fix addresses an issue where copy-pasting an entire panel grid in a Report using command-c was broken.
• The fix resolves an issue where report sharing with a magic link was broken when a team had the Hide this team from all non-members setting enabled.
• The fix introduces proper handling for restricted projects by allowing only explicitly invited users to access them, and implementing permissions based on project members and team roles.
• The fix allows instance admins to write to their own named workspaces, read other personal and shared workspaces, and write to shared views in private and public projects.
• The fix resolves an issue where the report would crash when trying to edit filters due to an out-of-bounds filter index caused by skipping non-individual filters while keeping the index count incremental.
• The fix addresses an issue where unselecting a runset caused media panels to crash in a report by ensuring only runs in enabled runsets are returned.
• The fix resolves an issue where the parameter importance panel crashes on initial load due to a violation of hooks error caused by a change in the order of hooks.
• The fix prevents chart data from being reloaded when scrolling down and then back up in small workspaces, enhancing performance and eliminating the feeling of slowness.

1.7.7 - 0.54.0

Features

• You can now configure Secure storage connector (BYOB) at team-level in Dedicated Cloud or Self-managed instances on Microsoft Azure.
  • Refer to this Terraform Module and reach out to your W&B team for any questions.
• Organization admins can now enforce privacy settings across all W&B teams by setting those at the organization level, from within the Settings tab in the Organization Dashboard.
  • W&B recommends to notify team admins and other users before making such enforcement changes.
• Enable direct lineage option for artifact lineage DAG
• It’s now possible to restrict Organization or Instance Admins from self-joining or adding themselves to a W&B team, thus ensuring that only Data & AI personas have access to the projects within the teams.
  • W&B advises to exercise caution and understand all implications before enabling this setting. Reach out to your W&B team for any questions.
• Dedicated Cloud on AWS is now also available in the Seoul (S. Korea) region.

Fixes

• Fix issue where Reports where failing to load on Mobile.
• Fix link to git diff file in run overview.
• Fixed the intermittently occurring issue related to loading of Organization Dashboard for certain users.

1.7.8 - 0.52.2

Features

• You can now enforce username and full name for users in your organization, by using OIDC claims from your SSO provider. Reach out to your W&B team or support if interested.
• You can now disable use of personal projects in your organization to ensure that all projects are created within W&B teams and governed using admin-enforced guidelines. Reach out to your W&B team or support if interested.
• Option to expand all versions in a cluster of runs or artifacts in the Artifacts Lineage DAG view.
• UI improvements to Artifacts Lineage DAG - the type will now be visible for each entry in a cluster.

Fixes

• Added pagination to image panels in media banks, displaying up to 32 images per page with enhanced grid aesthetics and improved pagination controls, while introducing a workaround for potential offset inconsistencies.
• Resolved an issue where tooltips on system charts were not displaying by enforcing the isHovered parameter, which is essential for the crosshair UI visibility.
• Unset the max-width property for images within media panels, addressing unintended style constraints previously applied to all images.
• Fixed broken config overrides in launch drawer.
• Fixed Launch drawer’s behavior when cloning from a run.

1.7.9 - 0.51.0

Features

You can now save multiple views of any workspace by clicking “Save as a new view” in the overflow menu of the workspace bar.

Learn more about how Saved views can enhance your team’s collaboration and project organization.

The release also includes these capabilities:

• You can now set a project’s visibility scope to Restricted if you want to collaborate on AI workflows related to sensitive or confidential data.
  • When you create a restricted project within a team, you can add specific members from the team. Unlike other project visibility scopes, all members of a team do not get implicit access to a restricted project.
• Enhanced Run Overview page performance: now 91% faster on load, with search functionality improved by 99.9%. Also enjoy RegEx search for Config and Summary data.
• New UX for Artifacts Lineage DAG introduces clustering for 5+ nodes at the same level, preview window to examine a node’s details, and a significant speedup in the graph’s loading time.
• The template variable values used for a run executed by launch, for example GPU type and quantity, are now shown on the queue’s list of runs. This makes it easier to see which runs are requesting which resources.
• Cloning a run with Launch now pre-selects the overrides, queue, and template variable values used by the cloned run.
• Instance admins will now see a Teams tab in the organization dashboard. It can be used to join a specific team when needed, whether it’s to monitor the team activity as per organizational guidelines or to help the team when team admins are not available.
• SCIM User API now returns the groups attribute as part of the GET endpoint, which includes the id of the groups / teams a user is part of.
• All Dedicated Cloud instances on GCP are now managed using the new W&B Kubernetes Operator. With that, the new Parquet Store service is also available.
  • Parquet store allows performant & cost efficient storage of run history data in parquet format in the blob storage. Dedicated Cloud instances on AWS & Azure are already managed using the operator and include the parquet store.
• Dedicated Cloud instances on AWS have been updated to use the latest version of the relational data storage, and the compute infrastructure has been upgraded to a newer generation with better performance.

Advanced Notice: We urge all customers who use Webhooks with Automations to add a valid A-record for their endpoints as we are going to disallow using IP address based Webhook URLs from the next release onwards. This is being done to protect against SSRF vulnerability and other related threat vectors.

Fixes

• Fixed issue where expressions tab was not rendering for line plots.
• Use display name for sweeps when grouped by sweeps in charts and runs table.
• Auto navigation to runs page when selecting job version.

1.7.10 - 0.50.2

Feature

• Add panel bank setting to auto-expand search results
• Better visibility for run queue item issues
• Dedicated Cloud customers on AWS can now use Privatelink to securely connect to their deployments.
  • The feature is in private preview and will be part of an advanced pricing tier at GA. Reach out to your W&B team if interested.
• You can now automate user role assignment for organization or team scopes using the SCIM role assignment API
• All Dedicated Cloud instances on AWS & Azure are now managed using the new W&B Kubernetes Operator. With that, the new Parquet Store service is also available. The service allows for performant & cost efficient storage of run history data in parquet format in the blob storage. That in turn leads to faster loading of relevant history data in charts & plots that are used to evaluate the runs.
• W&B Kubernetes Operator and along with that the Parquet Store service are now available for use in customer-managed instances. We encourage customers that already use Kubernetes to host W&B, to reach out to their W&B team on how they can use the operator. And we highly recommend others to migrate to Kubernetes in order to receive the latest performance improvements and new services in future via operator. We’re happy to assist with planning such a migration.

Fixes

• Properly pass template variables through sweep scheduler
• Scheduler polluting sweep yaml generator
• Display user roles correctly on team members page when search or sort is applied
• Org admins can again delete personal projects in their Dedicated Cloud or Self-managed server instance
• Add validation for SCIM GET groups API for pending users

1.7.11 - 0.49.0

Feature

• Set a default TTL (time-to-live or scheduled deletion) policy for a team in the team settings page.
  • Restrict setting or editing of a TTL policy to either of admin only or admin plus members.
• Test and debug a webhook during webhook creation or after in the team settings UI.
  • W&B will send a dummy payload and display the receiving server’s response.
• View Automation properties in the View Details slider.
  • This includes a summary of the triggering event and action, action configs, creation date, and a copy-able curl command to test webhook automations.
• Replace agent heartbeat with last successful run time in launch overview.
• Service accounts can now use the Report API to create reports.
• Use the new role management API to automate managing the custom roles.
• Enable Kubernetes Operator for Dedicated Cloud deployments on AWS.
• Configure a non-conflicting IP address range for managed cache used in Dedicated Cloud deployments on GCP.

Fixes

• Update the add runset button clickable area in reports
• Show proper truncate grouping message
• Prevent flashing of publish button in reports
• Horizonal Rule get collapsed in report section
• Add section button hidden in certain views
• Allow things like semantic versioning in the plot as string
• Remove requirements for quotes when using template variables in queue config definitions
• Improve Launch queue sorting order
• Don’t auto-open panel sections when searching large workspaces
• Change label text for grouped runs
• Open/close all sections while searching

1.7.12 - 0.48.0

Feature

• All required frontend changes for launch prioritization
  • Refer to this blog on how you can run more important jobs than others using Launch.
• Refer to below changes for access control and user attribution behavior of team service accounts:
  • When a team is configured in the training environment, a service account from that team can be used to log runs in either of private or public projects within that team, and additionally attribute those runs to a user only if WANDB_USERNAME or WANDB_USER_EMAIL variable is configured in the environment and the user is part of that team.
  • When a team is not configured in the training environment and a service account is used, the runs would be logged to the named project within the service account’s team, and those would be attributed to a user only if WANDB_USERNAME or WANDB_USER_EMAIL variable is configured in the environment and the user is part of that team.
  • A team service account can not log runs in a private project in another team, but it can log runs to public projects in other teams.

Fixes

• Reduce column widths for oversized runs selectors
• Fix a couple of bugs related to Custom Roles preview feature

1.7.13 - 0.47.3

Fixes

We’re releasing a couple of important fixes for the Custom Roles preview capability that was launched as part of v0.47.2. If you’re interested in using that feature to create fine-grained roles and better align with least privilege principle, please use this latest server release and reach out to your Weights & Biases team for an updated enterprise license.

1.7.14 - 0.47.2

Feature

Use custom roles with specific permissions to customize access control within a team

• Available in preview to enterprise customers. Please reach out to your Weights & Biases account team or support for any questions.

Also:

• Minor runs search improvements
• Auto-resize runs search for long texts
• View webhook details, including URL, secrets, date created directly from the automations table for webhook automations

Fixes

• Grouping of runs when group value is a string that looks like a number
• Janky report panel dragging behavior
• Update bar chart spec to match the one on public cloud
• Clean up panel padding and plot margins
• Restores workspace settings beta

1.7.15 - 0.46.0

Features

• Deployments on AWS can now use W&B Secrets with Webhooks and Automations
  • Secrets are stored securely in AWS Secret Manager - please use the terraform-aws-wandb module to provision one and
• Update webhooks table to display more information
• Better truncation of long strings to improve the usability of strings in the UI
• Reduce delay for scroll to report section
• Add white background to weave1 panels
• Allow deep link for weave1 panels in reports
• Allow weave1 panel resizing in reports
• Homepage banner will now show CLI login instructions
• User invites will now succeed if invite email can’t be sent for some reason
• Add list of associated queues to agent overview page

Fixes

• Copy function on panel overlay was dropping values
• CSS cleanup for import modal when creating report
• Fixes regression to hide legend when toggled off
• Report comment highlighting
• Remove all caching for view’s LoadMetadataList()
• Let run search stretch
• Associate launch agents with user id from X-WANDB-USERNAME header

1.7.16 - 0.45.0

Features

• Enable artifact garbage collection using environment variable GORILLA_ARTIFACT_GC_ENABLED=true and cloud object versioning or soft deletion.
• The terraform module terrraform-azurerm-wandb now supports Azure Key Vault as a secrets store.
  • Deployments on Azure can now use W&B Secrets with Webhooks and Automations. Secrets are stored securely in Azure Key Vault.

Fixes

• Remove invalid early exit preventing history deletion
• When moving/copying runs, don’t drop key-set info
• Update mutations to no longer use defunct storage plan or artifacts billing plan at all
• Respect skip flag in useRemoteServer

1.7.17 - 0.44.1

Features

Add OpenAI proxy UI to SaaS and Server

Also:

• New version v1.19.0 of our AWS Terraform module terraform-google-wandb is available
• Add support for AWS Secret Manager for Customer Secret Store, which can be enabled after the terraform module terrraform-aws-wandb is updated and released
• Add support for Azure Key Vault for Customer Secret Store, which can be enabled after the terraform module terrraform-azurerm-wandb is updated and released

Fixes

• Quality-of-life improvements in the model registry ui
• int values no longer ignored when determining if a run achieved a sweep’s optimization goal
• Cache runs data to improve workspace loading perf
• Improve TTL rendering in collection table
• Allow service accounts to be made workflow (registry) admins
• Add tooltip for truncated run tags in workspaces
• Fix report page scrolling
• Copy y data values for chart tooltip
• Query secrets for webhooks in local
• Fixing broken domain zoom in panel config
• Hide Customer Secret Store UI if GORILLA_CUSTOMER_SECRET_STORE_SOURCE env var not set

Chores

• Bump langchain to latest
• Adding WB Prompts to quickstart
• Update AWS MIs to use terraform-kubernetes-wandb v1.12.0
• Show correct Teams Plan tracked hours teams settings page and hide on usage page

1.7.18 - 0.43.0

Release 0.43.0 contains a number of minor bug fixes and performance improvements, including fixing the bottom of runs tables when there’s a scrollbar. Check out the other fixes below:

Fixes

• Dramatically improve workspace loading perf
• Fixing broken docs link in disabled add panel menu
• Render childPanel without editor in report
• Copying text from a panel grid when editing
• Run overview crashing with ’length’ key
• Padding for bottom of runs table when there’s a scrollbar
• Eliminate unnecessary history key cache read
• Error handling for Teams Checkout modal
• Memory leak, excess filestream sending, and orphaned processes in Weave Python autotracer

1.7.19 - 0.42.0

Features

W&B Artifacts now supports time-to-live (TTL) policies

Users can now gain more control over deleting and retention of Artifacts logged with W&B, with the ability to set retention and time-to-live (TTL) policies! Determine when you want specific Artifacts to be deleted, update policies on existing Artifacts, and set TTL policies on upstream or downstream Artifacts.

Here are the other new features include in this release:

• Use Launch drawer when creating Sweeps
• Delete run queue items
• Min/max aggregations nested dropdown
• Allow users to connect multiple S3-compatible buckets
• Add disk i/o system metrics
• Use the legacy way to set permissions
• Enable CustomerSecretStore
• Add Kubernetes as a backend for CustomerSecretStore

Fixes

• Disable storage and artifact invoices for ongoing storage calculations refractors
• Panel deletion bug
• Remove link-version event type from project automation slider
• Remove upper case styling for artifact type names
• Keep uncolored tags from changing color on render
• Stale defaults stuck in Launch drawer on reopen
• Trigger alias automations while creating artifact
• Edge case failure in infinite loading tag filters

1.7.20 - 0.41.0

Features

New Launch landing page

We’ve updated the Launch home page, so users looking to get started with Launch will have a much easier way to get setup quickly. Easily access detailed documentation, or simply follow the three Quickstart steps to create a Launch queue, agent, and start launching jobs immediately.

Here are the other new features included in this release:

• Add new reverse proxy to track OpenAI requests and responses
• Show agent version on agent overview page
• New model registry workflow removed from feature flag for all users

Fixes

• Empty projects causing infinite load on storage explorer
• Runs marked failed when run queue items are failed
• Use correct bucket for storing OpenAI proxy artifacts
• SEO tags not properly rendered by host
• Trigger export in background, on context deadline as well
• Transition runs in pending state to running when run is initialized
• Query so Launch queues show most recent completed and failed jobs

1.7.21 - 0.40.0

Features

Webhooks

Enable a seamless model CI/CD workflow using Webhook Automations to trigger specific actions within the CI/CD pipeline when certain events occur. Use webhooks to facilitate a clean hand-off point between ML engineering and devops. To see this in practice for Model Evaluation and Model Deployment, check out the linked demo videos. Learn more in our docs.

New user activity dashboard on for all customers

Fixes

• Removed limit on number of registered models an organization could have.
• Added search history to workspaces to make it easier to find commonly used plots.
• Changed reports “like” icon from hearts to stars.
• Users can now change the selected run in a workspace view with a group of runs.
• Fixed issue causing duplicate panel grids.
• Users can now pass in per-job resource config overrides for Sweeps on Launch
• Added redirect from /admin/users to new organization dashboard.
• Fixed issues with LDAP dropping connections.
• Improvements to run permadeletion.

1.7.22 - 0.39.0

Features

Revamped Organization Dashboard

We’ve made it easier to see who’s making the most W&B with our overhauled Organization Dashboard, accessible to W&B admins. You can now see details on who’s created runs and reports, who’s actively using W&B, who’s invites are pending–and you can export all this in CSV to share across your organization. Learn more in the docs.

For Dedicated Cloud customers, this feature has been turned on. For Customer-Managed W&B customers, contact W&B support and we’ll be happy to work with you to enable it.

Fixes

• Restrict service API keys to team admins
• Launch agent configuration is now shown on the Agents page
• Added navigation panel while viewing a single Launch job.
• Automations can now show configuration parameters for the associated job.
• Fixed issue with grouped runs not live updating
• Removed extra / in magic and normal link url
• Check base for incremental artifacts
• Inviting a user into multiple teams will no longer take up too many seats in the org

1.7.23 - 0.38.0

Features

Metric visualization enhancements

We’re continuing to enhance our core metric visualization experience. You can now define which metrics from regular expressions to render in your plots, up to 100 metrics at once. And to more accurately represent data at high scale, we’ve add a new time-weighted exponential moving average smoothing algorithm for plots (check out all of our supported algorithms).

Feedback surveys

W&B has always built our product based on customer feedback. Now, we’re happy to introduce a new way for you to shape the future of W&B: in-app feedback surveys in your Dedicated Cloud or Customer-Managed W&B install. Starting July 17th, W&B users will start periodically seeing simple 1 - 10 Net Promoter Score surveys in the application. All identifying information is anonymized. We appreciate all your feedback and look forward to making W&B even better, together.

Fixes

• Major improvement to artifact download speed: over a 6x speedup on our 1-million-file artifact benchmark. Please upgrade to SDK version 0.15.5+.
• (Launch) Optuna is now available as a sweeps scheduler with Sweeps on Launch, allowing more efficient exploration of hyperparameters.
• Run data permadeletion is now available (default off). This can be enabled with the GORILLA_DATA_RETENTION_PERIOD environment variable, specified in hours. Please take care before updating this variable and/or chat with W&B Support, since the deletion is permanent. Artifacts will not be deleted by this setting.
• Updated report sharing emails to include a preview.
• Relaxed HTML sanitation rules for reports in projects; this had been causing rare problems with report rendering.
• Expanded the maximum number of metrics that can be matched by a regex in chart configuration; previously this had been always 10, the maximum is now 100.
• Fixed issue with media panel step slider becoming unsynced with the media shown.
• Added time-weighted exponential moving average as an option for smoothing in plots.
• The “Search panels” textbox in workspaces now preserves the user’s last search.
• Applying a username filter when runs are grouped will no longer error.
• (Launch) The loading of the Launch tab should now be much faster, typically under two seconds.
• (Launch) There’s now an option to edit queue configs using YAML instead of JSON. It’s also now more clear how to edit queue configs.
• (Launch) Runs will now show error messages in the UI when they crash or fail.
• (Launch) If you don’t specify a project when creating a job, we’ll now use the value for WANDB_PROJECT from your wandb.init.
• (Launch) Updated support for custom accelerator images—these will run in noninteractive mode when building, which had been blocking some images.
• (Launch) Fixed issue where the run author for sweeps was the agent service account, rather than the real author
• (Launch) Clicking outside the Launch drawer will no longer close the drawer automatically.
• (Launch) Fixed issue where training jobs that had been enqueued by a sweep but not run yet were not correctly removed from the queue if you later stopped the sweep.
• (Launch) The Launch navigation link is now hidden for users who aren’t part of the team.
• (Launch) Fixed formatting and display issues on Agent logs.
• Fixed scrolling, resizing, and cloning issues in Automations panel.
• Fixed pagination on artifact action history.
• Added support for pre-signed URLs using a VPC endpoint URL if the AWS_S3_ENDPOINT_URL env var is set and passed in from the SDK side.
• Fixed enterprise dashboard link when organization name contains “&”
• Updated tag colors to be consistent

1.7.24 - 0.36.0

Features

Clone Runs with Launch

If you want to repeat a run but tweak a couple hyperparameters–say bump the batch size to take advantage of a larger machine–it’s now easy to clone a run using W&B Launch. Go to the run overview, click Clone, and you’ll be able to select new infrastructure to execute the job on, with new hyperparameters. Learn more in the Launch documentation.

Fixes

• Added report creation and update action to audit logs.
• Artifacts read through the SDK will now be captured in the audit logs.
• In report creation, added button to select all plots to add to the new report
• New view-only users signing up via a report link will now be fast tracked to the report, rather than going through the normal signup process.
• Team admins can now add protected aliases.
• Improved media panel handling of intermediate steps.
• Removed inactive ‘New Model’ button from Model Registry homepage for anonymous users
• Ability to copy data from plot legends has been rolled out to all users.
• Fixed incorrect progress indicator in Model Registry onboarding checklist.
• Fixed issue where the Automations page could crash when job name had slashes.
• Fixed issue where a user could update the wrong user profiles.
• Added option to permanently delete runs and their associated metrics after a duration specified in an environment variable.

1.7.25 - 0.35.0

Security

Fixed issue where API keys were logged for recently logged in users. Check for FetchAuthUserByAPIKey in the logs which you can find in gorilla.log from a debug bundle and rotate any keys that are found.

Features

Launch Agent Logs Now in the GUI

W&B Launch allows you to push machine learning jobs to a wide range of specialized compute environments. With this update, you can now use W&B to monitor and debug jobs running in these remote environments, without needing to log into your AWS or GCP console.

Fixes

• Logs tab is no longer trimmed to 1000 rows.
• Fixed scenario where artifact files pagination could get into an infinite loop
• Fixed bug where success toast messages were not appearing
• The Runs table will now correctly show the git commit value

1.7.26 - 0.34.0

Features

New Model Registry UI

We’re making it easier for users to manage a long list of models, and navigate seamlessly between entities in the model registry. With this new UI, users can:

• Look at all your registered models
• Filter to registered models within a specific team
• With the new list view, users can expand each panel to see the individual versions inside of it, including each version’s aliases, and metadata or run metrics. Clicking on a version from this quick view can take you to it’s version-view
• Look at an overview directly by clicking “View Details”
• See a preview of how many version, consumers, and automations are present for each registered model
• Create Automations directly
• See some metadata columns and details in preview
• Change Model Access Controls

Fixes

• Improved search functionality for better universal search ranking results.
• Added functionality to add/delete multiple tags at once in the model registry
• Enhanced the FileMarkdown feature to correctly scroll long content.
• Made the default team selection dropdown scrollable.
• Removed the UI access restriction for Tier 1/2/3 plans based on tracked hour usage.
• Added tooltips to for LLM trace viewer spans
• LLM trace timeline/detail now splits horizontally in fullscreen
• Added entity / team badges to Model Registry entries.
• Improved the navigation bar experience for logged out users
• Disabled storage/artifact banners to avoid issue where UI blocks for orgs with excess artifacts.
• Fixed issues where user avatars were not being displayed correctly.
• Fixed issue using Launch with Azure Git URLs
• Launch configuration boxes now work in airgapped environments
• In Launch queue creation, show teams as disabled (rather than hidden) for non-admins.
• Fixed issue with embedding projector rendering
• Fixes issue that prevented users from being able to reset their password in some cases involving mixed-case usernames.
• Files with special characters now show up in the media panel in Azure
• Added the ability to override the inline display format for timestamps.
• Reports with custom charts now load when not logged in.
• Wide GIFs no longer overflow fullscreen view
• Increase default automations limit from 20 to 200.
• Fixed bug allowing the appearance of deleting the version alias of a registered model (in fact, this could not be deleted on the backend).

1.7.27 - 0.33.0

Features

Prompts: Zoom and pan

Explore complex chains of LLM prompts more easily with new zoom and pan controls in our prompts tracer.

Model registry admin role

Control your model promotion process with a new role for model registry admins. These users can manage the list of protected aliases (for example, “challenger” or “prod”), as well as apply or remove protected aliases for model versions.

Viewer role

You can now share your W&B findings with a broader audience with the introduction of a Viewer role for W&B Server. Users with this role can view anything their team(s) make, but not create, edit, or delete anything. These seats are measured separately from traditional W&B Server seats, so reach out your W&B account team to request an updated license.

Improved sharing: optional magic link, and easier signup for viewers

Team admins can now disable magic link sharing for a team and its members. Disable public sharing on the team setting allows you increase team privacy controls. Meanwhile, it’s now easier for users who receive a report link to access the report in W&B after signing up.

Improved report composition

Reports help share your findings W&B further throughout an organization, including with people outside the ML team. We’ve made several investments to ensure it’s as simple and frictionless as possible to create and share them—including an improved report drafting experience with enhanced draft publication, editing, management, and sharing UX to improve how teams collaborate with Reports.

Updated navigation

As W&B has expanded the parts of the ML workflow we cover, we’ve heard your feedback that it can be hard to move around the application. So we’ve updated the navigation sidebar to include clearer labels on the product area, and added backlinks to certain detail screens. We’ve also renamed “Triggers” to “Automations” to better reflect the power of the feature.

Fixes

• When hovering over a plot in workspaces or a report, you can now use Cmd+C or Ctrl+C to copy run names and plot values shown in the hover control.
• Changes to default workspaces are now no longer auto-saved.
• Metrics in the Overview → Summary section now are formatted with commas.
• Added an install-level option to allow non-admin users to create teams (default off; contact W&B support to enable it).
• Weave plots now support log scales.
• The Launch panel can now be expanded horizontally to give more space for viewing parameters.
• The Launch panel now indicates whether a queue is active
• The Launch panel now allows you to choose a project for the run to be logged in.
• Launch queues can now only be created by team admins.
• Improved Markdown support in Launch panel.
• Improved error message on empty Launch queue configurations.
• Filters on the Sweeps parallel coordinates plot will now apply to all selected runsets.
• Sweeps now no longer require a metric.
• Added support for tracking reference artifact files saved outside W&B in Azure Blob Storage.
• Fixed bug in Markdown editing in Reports
• Fullscreen Weave panels can now share config changes with the original panel.
• Improved display of empty tables
• Fixed bug in which the first several characters of logs were cut off

1.8 - Release policies and processes

This page gives details about W&B Server releases and W&B’s release policies. This page relates to W&B Dedicated Cloud and Self-Managed deployments. To learn more about an individual W&B Server release, refer to W&B release notes.

W&B fully manages W&B Multi-tenant Cloud and the details in this page do not apply.

Release support and end of life policy

W&B supports a major W&B Server release for 6 months from its initial release date.

• Dedicated Cloud instances are automatically updated to maintain support.

• Customers with Self-managed instances are responsible for upgrading in time to maintain support. Avoid staying on an unsupported version.

  W&B strongly recommends customers with Self-managed instances to update their deployments with the latest release at minimum once per quarter to maintain support and receive the latest features, performance improvements, and fixes.

Release types and frequencies

• Major releases are produced monthly, and may include new features, enhancements, performance improvements, medium and low severity bug fixes, and deprecations. An example of a major release is 0.68.0.
• Patch releases within a major version are produced as needed, and include critical and high severity bug fixes. An example of a patch release is 0.67.1.

Release rollout

1. After testing and validation are complete, a release is first rolled out to all Dedicated Cloud instances to keep them fully updated.
2. After additional observation, the release is published, and Self-managed deployments can upgrade to it on their own schedule, and are responsible for upgrading in time to comply with the Release support and End of Life (EOL) policy. Learn more about upgrading W&B Server.

Downtime during upgrades

• When a Dedicated Cloud instance is upgraded, downtime is generally not expected, but may occur in certain situations:
  • If a new feature or enhancement requires changes to the underlying infrastructure, such as compute, storage or network.
  • To roll out a critical infrastructure change such as a security fix.
  • If the instance’s current version has reached its End of Life (EOL) and is upgraded by W&B to maintain support.
• For Self-managed deployments, the customer is responsible for implementing a rolling update process that meets their service level objectives (SLOs), such as by running W&B Server on Kubernetes.

Feature availability

After installing or upgrading, certain features may not be immediately available.

Enterprise features

An Enterprise license includes support for important security capabilities and other enterprise-friendly functionality. Some advanced features require an Enterprise license.

• Dedicated Cloud includes an Enterprise license and no action is required.
• On Self-managed deployments, features that require an Enterprise license are not available until it is set. To learn more or obtain an Enterprise license, refer to Obtain your W&B Server license.

Private preview and opt-in features

Most features are available immediately after installing or upgrading W&B Server. The W&B team must enable certain features before you can use them in your instance.

• Private preview: W&B invites design partners and early adopters to test these features and provide feedback. Private preview features are not recommended for production environments.

  The W&B team must enable a private preview feature for your instance before you can use it. Public documentation is not available; instructions are provided directly. Interfaces and APIs may change, and the feature may not be fully implemented.

• Public preview: Contact W&B to opt in to a public preview to try it out before it is generally available.

  The W&B team must enable a public preview feature before you can use it in your instance. Documentation may not be complete, interfaces and APIs may change, and the feature may not be fully implemented.

To learn more about an individual W&B Server release, including any limitations, refer to W&B Release notes.

2 - Command Line Interface

Usage

wandb [OPTIONS] COMMAND [ARGS]...

Options

Commands

2.1 - wandb agent

Usage

wandb agent [OPTIONS] SWEEP_ID

Summary

Run the W&B agent

Options

2.2 - wandb artifact

Usage

wandb artifact [OPTIONS] COMMAND [ARGS]...

Summary

Commands for interacting with artifacts

Options

Commands

2.2.1 - wandb artifact cache

Usage

wandb artifact cache [OPTIONS] COMMAND [ARGS]...

Summary

Commands for interacting with the artifact cache

Options

Commands

2.2.1.1 - wandb artifact cache cleanup

Usage

wandb artifact cache cleanup [OPTIONS] TARGET_SIZE

Summary

Clean up less frequently used files from the artifacts cache

Options

2.2.2 - wandb artifact get

Usage

wandb artifact get [OPTIONS] PATH

Summary

Download an artifact from wandb

Options

2.2.3 - wandb artifact ls

Usage

wandb artifact ls [OPTIONS] PATH

Summary

List all artifacts in a wandb project

Options

2.2.4 - wandb artifact put

Usage

wandb artifact put [OPTIONS] PATH

Summary

Upload an artifact to wandb

Options

2.3 - wandb beta

Usage

wandb beta [OPTIONS] COMMAND [ARGS]...

Summary

Beta versions of wandb CLI commands. Requires wandb-core.

Options

Commands

2.3.1 - wandb beta sync

Usage

wandb beta sync [OPTIONS] WANDB_DIR

Summary

Upload a training run to W&B

Options

2.4 - wandb controller

Usage

wandb controller [OPTIONS] SWEEP_ID

Summary

Run the W&B local sweep controller

Options

2.5 - wandb disabled

Usage

wandb disabled [OPTIONS]

Summary

Disable W&B.

Options

2.6 - wandb docker

Usage

wandb docker [OPTIONS] [DOCKER_RUN_ARGS]... [DOCKER_IMAGE]

Summary

Run your code in a docker container.

W&B docker lets you run your code in a docker image ensuring wandb is configured. It adds the WANDB_DOCKER and WANDB_API_KEY environment variables to your container and mounts the current directory in /app by default. You can pass additional args which will be added to docker run before the image name is declared, we’ll choose a default image for you if one isn’t passed:

images-public/tensorflow-1.12.0-notebook-cpu:v0.4.0 --jupyter wandb docker
wandb/deepo:keras-gpu --no-tty --cmd "python train.py --epochs=5" ```

By default, we override the entrypoint to check for the existence of wandb
and install it if not present.  If you pass the --jupyter flag we will
ensure jupyter is installed and start jupyter lab on port 8888.  If we
detect nvidia-docker on your system we will use the nvidia runtime.  If you
just want wandb to set environment variable to an existing docker run
command, see the wandb docker-run command.


**Options**

| **Option** | **Description** |
| :--- | :--- |
| `--nvidia / --no-nvidia` | Use the nvidia runtime, defaults to nvidia if   nvidia-docker is present |
| `--digest` | Output the image digest and exit |
| `--jupyter / --no-jupyter` | Run jupyter lab in the container |
| `--dir` | Which directory to mount the code in the container |
| `--no-dir` | Don't mount the current directory |
| `--shell` | The shell to start the container with |
| `--port` | The host port to bind jupyter on |
| `--cmd` | The command to run in the container |
| `--no-tty` | Run the command without a tty |

2.7 - wandb docker-run

Usage

wandb docker-run [OPTIONS] [DOCKER_RUN_ARGS]...

Summary

Wrap docker run and adds WANDB_API_KEY and WANDB_DOCKER environment variables.

This will also set the runtime to nvidia if the nvidia-docker executable is present on the system and –runtime wasn’t set.

See docker run --help for more details.

Options

2.8 - wandb enabled

Usage

wandb enabled [OPTIONS]

Summary

Enable W&B.

Options

2.9 - wandb init

Usage

wandb init [OPTIONS]

Summary

Configure a directory with Weights & Biases

Options

2.10 - wandb job

Usage

wandb job [OPTIONS] COMMAND [ARGS]...

Summary

Commands for managing and viewing W&B jobs

Options

Commands

2.10.1 - wandb job create

Usage

wandb job create [OPTIONS] {git|code|image} PATH

Summary

Create a job from a source, without a wandb run.

Jobs can be of three types, git, code, or image.

git: A git source, with an entrypoint either in the path or provided explicitly pointing to the main python executable. code: A code path, containing a requirements.txt file. image: A docker image.

Options

2.10.2 - wandb job describe

Usage

wandb job describe [OPTIONS] JOB

Summary

Describe a launch job. Provide the launch job in the form of: entity/project/job-name:alias-or-version

Options

2.10.3 - wandb job list

Usage

wandb job list [OPTIONS]

Summary

List jobs in a project

Options

2.11 - wandb launch

Usage

wandb launch [OPTIONS]

Summary

Launch or queue a W&B Job. See https://wandb.me/launch

Options

2.12 - wandb launch-agent

Usage

wandb launch-agent [OPTIONS]

Summary

Run a W&B launch agent.

Options

2.13 - wandb launch-sweep

Usage

wandb launch-sweep [OPTIONS] [CONFIG]

Summary

Run a W&B launch sweep (Experimental).

Options

2.14 - wandb login

Usage

wandb login [OPTIONS] [KEY]...

Summary

Login to Weights & Biases

Options

2.15 - wandb offline

Usage

wandb offline [OPTIONS]

Summary

Disable W&B sync

Options

2.16 - wandb online

Usage

wandb online [OPTIONS]

Summary

Enable W&B sync

Options

2.17 - wandb pull

Usage

wandb pull [OPTIONS] RUN

Summary

Pull files from Weights & Biases

Options

2.18 - wandb restore

Usage

wandb restore [OPTIONS] RUN

Summary

Restore code, config and docker state for a run

Options

2.19 - wandb scheduler

Usage

wandb scheduler [OPTIONS] SWEEP_ID

Summary

Run a W&B launch sweep scheduler (Experimental)

Options

2.20 - wandb server

Usage

wandb server [OPTIONS] COMMAND [ARGS]...

Summary

Commands for operating a local W&B server

Options

Commands

2.20.1 - wandb server start

Usage

wandb server start [OPTIONS]

Summary

Start a local W&B server

Options

2.20.2 - wandb server stop

Usage

wandb server stop [OPTIONS]

Summary

Stop a local W&B server

Options

2.21 - wandb status

Usage

wandb status [OPTIONS]

Summary

Show configuration settings

Options

2.22 - wandb sweep

Usage

wandb sweep [OPTIONS] CONFIG_YAML_OR_SWEEP_ID

Summary

Initialize a hyperparameter sweep. Search for hyperparameters that optimizes a cost function of a machine learning model by testing various combinations.

Options

2.23 - wandb sync

Usage

wandb sync [OPTIONS] [PATH]...

Summary

Upload an offline training directory to W&B

Options

2.24 - wandb verify

Usage

wandb verify [OPTIONS]

Summary

Verify your local instance

Options

3 - JavaScript Library

Similar to our Python library, we offer a client to track experiments in JavaScript/TypeScript.

• Log metrics from your Node server and display them in interactive plots on W&B
• Debug LLM applications with interactive traces
• Debug LangChain.js usage

This library is compatible with Node and modern JS run times.

You can find the source code for the JavaScript client in the Github repository.

Installation

npm install @wandb/sdk
# or ...
yarn add @wandb/sdk

Usage

TypeScript/ESM:

import wandb from '@wandb/sdk'

async function track() {
    await wandb.init({config: {test: 1}});
    wandb.log({acc: 0.9, loss: 0.1});
    wandb.log({acc: 0.91, loss: 0.09});
    await wandb.finish();
}

await track()

Node/CommonJS:

const wandb = require('@wandb/sdk').default;

We’re currently missing a lot of the functionality found in our Python SDK, but basic logging functionality is available. We’ll be adding additional features like Tables soon.

Authentication and Settings

In node environments we look for process.env.WANDB_API_KEY and prompt for it’s input if we have a TTY. In non-node environments we look for sessionStorage.getItem("WANDB_API_KEY"). Additional settings can be found here.

Integrations

Our Python integrations are widely used by our community, and we hope to build out more JavaScript integrations to help LLM app builders leverage whatever tool they want.

If you have any requests for additional integrations, we’d love you to open an issue with details about the request.

LangChain.js

This library integrates with the popular library for building LLM applications, LangChain.js version >= 0.0.75.

import {WandbTracer} from '@wandb/sdk/integrations/langchain';

const wbTracer = await WandbTracer.init({project: 'langchain-test'});
// run your langchain workloads...
chain.call({input: "My prompt"}, wbTracer)
await WandbTracer.finish();

See this test for a more detailed example.

4 - Python Library

Use wandb to track machine learning work.

Train and fine-tune models, manage models from experimentation to production.

For guides and examples, see https://docs.wandb.ai.

For scripts and interactive notebooks, see https://github.com/wandb/examples.

For reference documentation, see https://docs.wandb.com/ref/python.

Classes

class Artifact: Flexible and lightweight building block for dataset and model versioning.

class Run: A unit of computation logged by wandb. Typically, this is an ML experiment.

Functions

agent(...): Start one or more sweep agents.

controller(...): Public sweep controller constructor.

finish(...): Finish a run and upload any remaining data.

init(...): Start a new run to track and log to W&B.

log(...): Upload run data.

login(...): Set up W&B login credentials.

save(...): Sync one or more files to W&B.

sweep(...): Initialize a hyperparameter sweep.

watch(...): Hooks into the given PyTorch model(s) to monitor gradients and the model’s computational graph.

4.1 - API Walkthrough

Learn when and how to use different W&B APIs to track, share, and manage model artifacts in your machine learning workflows. This page covers logging experiments, generating reports, and accessing logged data using the appropriate W&B API for each task.

W&B offers the following APIs:

• W&B Python SDK (wandb.sdk): Log and monitor experiments during training.
• W&B Public API (wandb.apis.public): Query and analyze logged experiment data.
• W&B Reports API (wandb.wandb-workspaces): Create reports to summarize findings.

Sign up and create an API key

To authenticate your machine with W&B, you must first generate an API key at wandb.ai/authorize. Copy the API key and store it securely.

Install and import packages

Install the W&B library and some other packages you will need for this walkthrough.

pip install wandb

Import W&B Python SDK:

import wandb

Specify the entity of your team in the following code block:

TEAM_ENTITY = "<Team_Entity>" # Replace with your team entity
PROJECT = "my-awesome-project"

Train model

The following code simulates a basic machine learning workflow: training a model, logging metrics, and saving the model as an artifact.

Use the W&B Python SDK (wandb.sdk) to interact with W&B during training. Log the loss using wandb.log, then save the trained model as an artifact using wandb.Artifact before finally adding the model file using Artifact.add_file.

import random # For simulating data

def model(training_data: int) -> int:
    """Model simulation for demonstration purposes."""
    return training_data * 2 + random.randint(-1, 1)  

# Simulate weights and noise
weights = random.random() # Initialize random weights
noise = random.random() / 5  # Small random noise to simulate noise

# Hyperparameters and configuration
config = {
    "epochs": 10,  # Number of epochs to train
    "learning_rate": 0.01,  # Learning rate for the optimizer
}

# Use context manager to initialize and close W&B runs
with wandb.init(project=PROJECT, entity=TEAM_ENTITY, config=config) as run:    
    # Simulate training loop
    for epoch in range(config["epochs"]):
        xb = weights + noise  # Simulated input training data
        yb = weights + noise * 2  # Simulated target output (double the input noise)
        
        y_pred = model(xb)  # Model prediction
        loss = (yb - y_pred) ** 2  # Mean Squared Error loss

        print(f"epoch={epoch}, loss={y_pred}")
        # Log epoch and loss to W&B
        run.log({
            "epoch": epoch,
            "loss": loss,
        })

    # Unique name for the model artifact,
    model_artifact_name = f"model-demo"  

    # Local path to save the simulated model file
    PATH = "model.txt" 

    # Save model locally
    with open(PATH, "w") as f:
        f.write(str(weights)) # Saving model weights to a file

    # Create an artifact object
    # Add locally saved model to artifact object
    artifact = wandb.Artifact(name=model_artifact_name, type="model", description="My trained model")
    artifact.add_file(local_path=PATH)
    artifact.save()

The key takeaways from the previous code block are:

• Use wandb.log to log metrics during training.
• Use wandb.Artifact to save models (datasets, and so forth) as an artifact to your W&B project.

Now that you have trained a model and saved it as an artifact, you can publish it to a registry in W&B. Use wandb.use_artifact to retrieve the artifact from your project and prepare it for publication in the Model registry. wandb.use_artifact serves two key purposes:

• Retrieves the artifact object from your project.
• Marks the artifact as an input to the run, ensuring reproducibility and traceability. See Create and view lineage map for details.

Publish the model to the Model registry

To share the model with others in your organization, publish it to a collection using wandb.link_artifact. The following code links the artifact to the core Model registry, making it accessible to your team.

# Artifact name specifies the specific artifact version within our team's project
artifact_name = f'{TEAM_ENTITY}/{PROJECT}/{model_artifact_name}:v0'
print("Artifact name: ", artifact_name)

REGISTRY_NAME = "Model" # Name of the registry in W&B
COLLECTION_NAME = "DemoModels"  # Name of the collection in the registry

# Create a target path for our artifact in the registry
target_path = f"wandb-registry-{REGISTRY_NAME}/{COLLECTION_NAME}"
print("Target path: ", target_path)

run = wandb.init(entity=TEAM_ENTITY, project=PROJECT)
model_artifact = run.use_artifact(artifact_or_name=artifact_name, type="model")
run.link_artifact(artifact=model_artifact, target_path=target_path)
run.finish()

After running link_artifact(), the model artifact will be in the DemoModels collection in your registry. From there, you can view details such as the version history, lineage map, and other metadata.

For additional information on how to link artifacts to a registry, see Link artifacts to a registry.

Retrieve model artifact from registry for inference

To use a model for inference, use use_artifact() to retrieve the published artifact from the registry. This returns an artifact object that you can then use download() to download the artifact to a local file.

REGISTRY_NAME = "Model"  # Name of the registry in W&B
COLLECTION_NAME = "DemoModels"  # Name of the collection in the registry
VERSION = 0 # Version of the artifact to retrieve

model_artifact_name = f"wandb-registry-{REGISTRY_NAME}/{COLLECTION_NAME}:v{VERSION}"
print(f"Model artifact name: {model_artifact_name}")

run = wandb.init(entity=TEAM_ENTITY, project=PROJECT)
registry_model = run.use_artifact(artifact_or_name=model_artifact_name)
local_model_path = registry_model.download()

For more information on how to retrieve artifacts from a registry, see Download an artifact from a registry.

Depending on your machine learning framework, you may need to recreate the model architecture before loading the weights. This is left as an exercise for the reader, as it depends on the specific framework and model you are using.

Share your finds with a report

Create and share a report to summarize your work. To create a report programmatically, use the W&B Reports API.

First, install the W&B Reports API:

pip install wandb wandb-workspaces -qqq

The following code block creates a report with multiple blocks, including markdown, panel grids, and more. You can customize the report by adding more blocks or changing the content of existing blocks.

The output of the code block prints a link to the URL report created. You can open this link in your browser to view the report.

import wandb_workspaces.reports.v2 as wr

experiment_summary = """This is a summary of the experiment conducted to train a simple model using W&B."""
dataset_info = """The dataset used for training consists of synthetic data generated by a simple model."""
model_info = """The model is a simple linear regression model that predicts output based on input data with some noise."""

report = wr.Report(
    project=PROJECT,
    entity=TEAM_ENTITY,
    title="My Awesome Model Training Report",
    description=experiment_summary,
    blocks= [
        wr.TableOfContents(),
        wr.H2("Experiment Summary"),
        wr.MarkdownBlock(text=experiment_summary),
        wr.H2("Dataset Information"),
        wr.MarkdownBlock(text=dataset_info),
        wr.H2("Model Information"),
        wr.MarkdownBlock(text = model_info),
        wr.PanelGrid(
            panels=[
                wr.LinePlot(title="Train Loss", x="Step", y=["loss"], title_x="Step", title_y="Loss")
                ],
            ),  
    ]

)

# Save the report to W&B
report.save()

For more information on how to create a report programmatically or how to create a report interactively with the W&B App, see Create a report in the W&B Docs Developer guide.

Query the registry

Use the W&B Public APIs to query, analyze, and manage historical data from W&B. This can be useful for tracking the lineage of artifacts, comparing different versions, and analyzing the performance of models over time.

The following code block demonstrates how to query the Model registry for all artifacts in a specific collection. It retrieves the collection and iterates through its versions, printing out the name and version of each artifact.

import wandb

# Initialize wandb API
api = wandb.Api()

# Find all artifact versions that contains the string `model` and 
# has either the tag `text-classification` or an `latest` alias
registry_filters = {
    "name": {"$regex": "model"}
}

# Use logical $or operator to filter artifact versions
version_filters = {
    "$or": [
        {"tag": "text-classification"},
        {"alias": "latest"}
    ]
}

# Returns an iterable of all artifact versions that match the filters
artifacts = api.registries(filter=registry_filters).collections().versions(filter=version_filters)

# Print out the name, collection, aliases, tags, and created_at date of each artifact found
for art in artifacts:
    print(f"artifact name: {art.name}")
    print(f"collection artifact belongs to: { art.collection.name}")
    print(f"artifact aliases: {art.aliases}")
    print(f"tags attached to artifact: {art.tags}")
    print(f"artifact created at: {art.created_at}\n")

For more information on querying the registry, see the Query registry items with MongoDB-style queries.

4.2 - agent

Start one or more sweep agents.

agent(
    sweep_id: str,
    function: Optional[Callable] = None,
    entity: Optional[str] = None,
    project: Optional[str] = None,
    count: Optional[int] = None
) -> None

The sweep agent uses the sweep_id to know which sweep it is a part of, what function to execute, and (optionally) how many agents to run.

4.3 - Artifact

Flexible and lightweight building block for dataset and model versioning.

Artifact(
    name: str,
    type: str,
    description: (str | None) = None,
    metadata: (dict[str, Any] | None) = None,
    incremental: bool = (False),
    use_as: (str | None) = None
) -> None

Construct an empty W&B Artifact. Populate an artifacts contents with methods that begin with add. Once the artifact has all the desired files, you can call wandb.log_artifact() to log it.

Methods

add

View source

add(
    obj: WBValue,
    name: StrPath,
    overwrite: bool = (False)
) -> ArtifactManifestEntry

Add wandb.WBValue obj to the artifact.

add_dir

View source

add_dir(
    local_path: str,
    name: (str | None) = None,
    skip_cache: (bool | None) = (False),
    policy: (Literal['mutable', 'immutable'] | None) = "mutable"
) -> None

Add a local directory to the artifact.

add_file

View source

add_file(
    local_path: str,
    name: (str | None) = None,
    is_tmp: (bool | None) = (False),
    skip_cache: (bool | None) = (False),
    policy: (Literal['mutable', 'immutable'] | None) = "mutable",
    overwrite: bool = (False)
) -> ArtifactManifestEntry

Add a local file to the artifact.

add_reference

View source

add_reference(
    uri: (ArtifactManifestEntry | str),
    name: (StrPath | None) = None,
    checksum: bool = (True),
    max_objects: (int | None) = None
) -> Sequence[ArtifactManifestEntry]

Add a reference denoted by a URI to the artifact.

Unlike files or directories that you add to an artifact, references are not uploaded to W&B. For more information, see Track external files.

By default, the following schemes are supported:

• http(s): The size and digest of the file will be inferred by the Content-Length and the ETag response headers returned by the server.
• s3: The checksum and size are pulled from the object metadata. If bucket versioning is enabled, then the version ID is also tracked.
• gs: The checksum and size are pulled from the object metadata. If bucket versioning is enabled, then the version ID is also tracked.
• https, domain matching *.blob.core.windows.net (Azure): The checksum and size are be pulled from the blob metadata. If storage account versioning is enabled, then the version ID is also tracked.
• file: The checksum and size are pulled from the file system. This scheme is useful if you have an NFS share or other externally mounted volume containing files you wish to track but not necessarily upload.

For any other scheme, the digest is just a hash of the URI and the size is left blank.

checkout

View source

checkout(
    root: (str | None) = None
) -> str

Replace the specified root directory with the contents of the artifact.

WARNING: This will delete all files in root that are not included in the artifact.

delete

View source

delete(
    delete_aliases: bool = (False)
) -> None

Delete an artifact and its files.

If called on a linked artifact (i.e. a member of a portfolio collection): only the link is deleted, and the source artifact is unaffected.

Use artifact.unlink() instead of artifact.delete() to remove a link between a source artifact and a linked artifact.

download

View source

download(
    root: (StrPath | None) = None,
    allow_missing_references: bool = (False),
    skip_cache: (bool | None) = None,
    path_prefix: (StrPath | None) = None,
    multipart: (bool | None) = None
) -> FilePathStr

Download the contents of the artifact to the specified root directory.

Existing files located within root are not modified. Explicitly delete root before you call download if you want the contents of root to exactly match the artifact.

file

View source

file(
    root: (str | None) = None
) -> StrPath

Download a single file artifact to the directory you specify with root.

files

View source

files(
    names: (list[str] | None) = None,
    per_page: int = 50
) -> ArtifactFiles

Iterate over all files stored in this artifact.

finalize

View source

finalize() -> None

Finalize the artifact version.

You cannot modify an artifact version once it is finalized because the artifact is logged as a specific artifact version. Create a new artifact version to log more data to an artifact. An artifact is automatically finalized when you log the artifact with log_artifact.

get

View source

get(
    name: str
) -> (WBValue | None)

Get the WBValue object located at the artifact relative name.

get_added_local_path_name

View source

get_added_local_path_name(
    local_path: str
) -> (str | None)

Get the artifact relative name of a file added by a local filesystem path.

get_entry

View source

get_entry(
    name: StrPath
) -> ArtifactManifestEntry

Get the entry with the given name.

get_path

View source

get_path(
    name: StrPath
) -> ArtifactManifestEntry

Deprecated. Use get_entry(name).

is_draft

View source

is_draft() -> bool

Check if artifact is not saved.

Returns: Boolean. False if artifact is saved. True if artifact is not saved.

json_encode

View source

json_encode() -> dict[str, Any]

Returns the artifact encoded to the JSON format.

link

View source

link(
    target_path: str,
    aliases: (list[str] | None) = None
) -> (Artifact | None)

Link this artifact to a portfolio (a promoted collection of artifacts).

logged_by

View source

logged_by() -> (Run | None)

Get the W&B run that originally logged the artifact.

new_draft

View source

new_draft() -> Artifact

Create a new draft artifact with the same content as this committed artifact.

Modifying an existing artifact creates a new artifact version known as an “incremental artifact”. The artifact returned can be extended or modified and logged as a new version.

new_file

View source

@contextlib.contextmanager
new_file(
    name: str,
    mode: str = "x",
    encoding: (str | None) = None
) -> Iterator[IO]

Open a new temporary file and add it to the artifact.

remove

View source

remove(
    item: (StrPath | ArtifactManifestEntry)
) -> None

Remove an item from the artifact.

save

View source

save(
    project: (str | None) = None,
    settings: (wandb.Settings | None) = None
) -> None

Persist any changes made to the artifact.

If currently in a run, that run will log this artifact. If not currently in a run, a run of type “auto” is created to track this artifact.

unlink

View source

unlink() -> None

Unlink this artifact if it is currently a member of a portfolio (a promoted collection of artifacts).

used_by

View source

used_by() -> list[Run]

Get a list of the runs that have used this artifact and its linked artifacts.

verify

View source

verify(
    root: (str | None) = None
) -> None

Verify that the contents of an artifact match the manifest.

All files in the directory are checksummed and the checksums are then cross-referenced against the artifact’s manifest. References are not verified.

wait

View source

wait(
    timeout: (int | None) = None
) -> Artifact

If needed, wait for this artifact to finish logging.

__getitem__

View source

__getitem__(
    name: str
) -> (WBValue | None)

Get the WBValue object located at the artifact relative name.

__setitem__

View source

__setitem__(
    name: str,
    item: WBValue
) -> ArtifactManifestEntry

Add item to the artifact at path name.

4.4 - automations

Classes

class Automation: A local instance of a saved W&B automation.

class DoNothing: Defines an automation action that intentionally does nothing.

class MetricChangeFilter: Defines a filter that compares a change in a run metric against a user-defined threshold.

class MetricThresholdFilter: Defines a filter that compares a run metric against a user-defined threshold value.

class NewAutomation: A new automation to be created.

class OnAddArtifactAlias: A new alias is assigned to an artifact.

class OnCreateArtifact: A new artifact is created.

class OnLinkArtifact: A new artifact is linked to a collection.

class OnRunMetric: A run metric satisfies a user-defined condition.

class SendNotification: Defines an automation action that sends a (Slack) notification.

class SendWebhook: Defines an automation action that sends a webhook request.

4.4.1 - Automation

A local instance of a saved W&B automation.

4.4.2 - DoNothing

Defines an automation action that intentionally does nothing.

4.4.3 - MetricChangeFilter

Defines a filter that compares a change in a run metric against a user-defined threshold.

The change is calculated over “tumbling” windows, i.e. the difference between the current window and the non-overlapping prior window.

4.4.4 - MetricThresholdFilter

Defines a filter that compares a run metric against a user-defined threshold value.

4.4.5 - NewAutomation

A new automation to be created.

4.4.6 - OnAddArtifactAlias

A new alias is assigned to an artifact.

Methods

then

View source

then(
    action: InputAction
) -> NewAutomation

Define a new Automation in which this event triggers the given action.

4.4.7 - OnCreateArtifact

A new artifact is created.

Methods

then

View source

then(
    action: InputAction
) -> NewAutomation

Define a new Automation in which this event triggers the given action.

4.4.8 - OnLinkArtifact

A new artifact is linked to a collection.

Methods

then

View source

then(
    action: InputAction
) -> NewAutomation

Define a new Automation in which this event triggers the given action.

4.4.9 - OnRunMetric

A run metric satisfies a user-defined condition.

Methods

then

View source

then(
    action: InputAction
) -> NewAutomation

Define a new Automation in which this event triggers the given action.

4.4.10 - SendNotification

Defines an automation action that sends a (Slack) notification.

Methods

from_integration

View source

@classmethod
from_integration(
    integration: SlackIntegration,
    *,
    title: str = "",
    text: str = "",
    level: AlertSeverity = AlertSeverity.INFO
) -> Self

Define a notification action that sends to the given (Slack) integration.

4.4.11 - SendWebhook

Defines an automation action that sends a webhook request.

Methods

from_integration

View source

@classmethod
from_integration(
    integration: WebhookIntegration,
    *,
    payload: Optional[SerializedToJson[dict[str, Any]]] = None
) -> Self

Define a webhook action that sends to the given (webhook) integration.

4.5 - controller

Public sweep controller constructor.

controller(
    sweep_id_or_config: Optional[Union[str, Dict]] = None,
    entity: Optional[str] = None,
    project: Optional[str] = None
) -> "_WandbController"

Usage:

import wandb

tuner = wandb.controller(...)
print(tuner.sweep_config)
print(tuner.sweep_id)
tuner.configure_search(...)
tuner.configure_stopping(...)

4.6 - Data Types

This module defines data types for logging rich, interactive visualizations to W&B.

Data types include common media types, like images, audio, and videos, flexible containers for information, like tables and HTML, and more.

For more on logging media, see our guide

For more on logging structured data for interactive dataset and model analysis, see our guide to W&B Tables.

All of these special data types are subclasses of WBValue. All the data types serialize to JSON, since that is what wandb uses to save the objects locally and upload them to the W&B server.

Classes

class Audio: Wandb class for audio clips.

class BoundingBoxes2D: Format images with 2D bounding box overlays for logging to W&B.

class Graph: Wandb class for graphs.

class Histogram: wandb class for histograms.

class Html: A class for logging HTML content to W&B.

class Image: Format images for logging to W&B.

class ImageMask: Format image masks or overlays for logging to W&B.

class Molecule: Wandb class for 3D Molecular data.

class Object3D: Wandb class for 3D point clouds.

class Plotly: Wandb class for plotly plots.

class Table: The Table class used to display and analyze tabular data.

class Video: A class for logging videos to W&B.

class WBTraceTree: Media object for trace tree data.

4.6.1 - Audio

Wandb class for audio clips.

Audio(
    data_or_path, sample_rate=None, caption=None
)

Methods

durations

View source

@classmethod
durations(
    audio_list
)

resolve_ref

View source

resolve_ref()

sample_rates

View source

@classmethod
sample_rates(
    audio_list
)

4.6.2 - BoundingBoxes2D

Format images with 2D bounding box overlays for logging to W&B.

BoundingBoxes2D(
    val: dict,
    key: str
) -> None

Examples:

Log bounding boxes for a single image

import numpy as np
import wandb

run = wandb.init()
image = np.random.randint(low=0, high=256, size=(200, 300, 3))

class_labels = {0: "person", 1: "car", 2: "road", 3: "building"}

img = wandb.Image(
    image,
    boxes={
        "predictions": {
            "box_data": [
                {
                    # one box expressed in the default relative/fractional domain
                    "position": {
                        "minX": 0.1,
                        "maxX": 0.2,
                        "minY": 0.3,
                        "maxY": 0.4,
                    },
                    "class_id": 1,
                    "box_caption": class_labels[1],
                    "scores": {"acc": 0.2, "loss": 1.2},
                },
                {
                    # another box expressed in the pixel domain
                    "position": {
                        "middle": [150, 20],
                        "width": 68,
                        "height": 112,
                    },
                    "domain": "pixel",
                    "class_id": 3,
                    "box_caption": "a building",
                    "scores": {"acc": 0.5, "loss": 0.7},
                },
                # Log as many boxes an as needed
            ],
            "class_labels": class_labels,
        }
    },
)

run.log({"driving_scene": img})

Log a bounding box overlay to a Table

import numpy as np
import wandb

run = wandb.init()
image = np.random.randint(low=0, high=256, size=(200, 300, 3))

class_labels = {0: "person", 1: "car", 2: "road", 3: "building"}

class_set = wandb.Classes(
    [
        {"name": "person", "id": 0},
        {"name": "car", "id": 1},
        {"name": "road", "id": 2},
        {"name": "building", "id": 3},
    ]
)

img = wandb.Image(
    image,
    boxes={
        "predictions": {
            "box_data": [
                {
                    # one box expressed in the default relative/fractional domain
                    "position": {
                        "minX": 0.1,
                        "maxX": 0.2,
                        "minY": 0.3,
                        "maxY": 0.4,
                    },
                    "class_id": 1,
                    "box_caption": class_labels[1],
                    "scores": {"acc": 0.2, "loss": 1.2},
                },
                {
                    # another box expressed in the pixel domain
                    "position": {
                        "middle": [150, 20],
                        "width": 68,
                        "height": 112,
                    },
                    "domain": "pixel",
                    "class_id": 3,
                    "box_caption": "a building",
                    "scores": {"acc": 0.5, "loss": 0.7},
                },
                # Log as many boxes an as needed
            ],
            "class_labels": class_labels,
        }
    },
    classes=class_set,
)

table = wandb.Table(columns=["image"])
table.add_data(img)
run.log({"driving_scene": table})

Methods

type_name

View source

@classmethod
type_name() -> str

validate

View source

validate(
    val: dict
) -> bool

4.6.3 - Graph

Wandb class for graphs.

Graph(
    format="keras"
)

This class is typically used for saving and displaying neural net models. It represents the graph as an array of nodes and edges. The nodes can have labels that can be visualized by wandb.

Examples:

Import a keras model:

Graph.from_keras(keras_model)

Methods

add_edge

View source

add_edge(
    from_node, to_node
)

add_node

View source

add_node(
    node=None, **node_kwargs
)

from_keras

View source

@classmethod
from_keras(
    model
)

pprint

View source

pprint()

__getitem__

View source

__getitem__(
    nid
)

4.6.4 - Histogram

wandb class for histograms.

Histogram(
    sequence: Optional[Sequence] = None,
    np_histogram: Optional['NumpyHistogram'] = None,
    num_bins: int = 64
) -> None

This object works just like numpy’s histogram function https://docs.scipy.org/doc/numpy/reference/generated/numpy.histogram.html

Examples:

Generate histogram from a sequence

wandb.Histogram([1, 2, 3])

Efficiently initialize from np.histogram.

hist = np.histogram(data)
wandb.Histogram(np_histogram=hist)

4.6.5 - Html

A class for logging HTML content to W&B.

Html(
    data: Union[str, 'TextIO'],
    inject: bool = (True),
    data_is_not_path: bool = (False)
) -> None

Methods

inject_head

View source

inject_head() -> None

4.6.6 - Image

Format images for logging to W&B.

Image(
    data_or_path: "ImageDataOrPathType",
    mode: Optional[str] = None,
    caption: Optional[str] = None,
    grouping: Optional[int] = None,
    classes: Optional[Union['Classes', Sequence[dict]]] = None,
    boxes: Optional[Union[Dict[str, 'BoundingBoxes2D'], Dict[str, dict]]] = None,
    masks: Optional[Union[Dict[str, 'ImageMask'], Dict[str, dict]]] = None,
    file_type: Optional[str] = None
) -> None

Note : When logging a torch.Tensor as a wandb.Image, images are normalized. If you do not want to normalize your images, please convert your tensors to a PIL Image.

Examples:

Create a wandb.Image from a numpy array

import numpy as np
import wandb

with wandb.init() as run:
    examples = []
    for i in range(3):
        pixels = np.random.randint(low=0, high=256, size=(100, 100, 3))
        image = wandb.Image(pixels, caption=f"random field {i}")
        examples.append(image)
    run.log({"examples": examples})

Create a wandb.Image from a PILImage

import numpy as np
from PIL import Image as PILImage
import wandb

with wandb.init() as run:
    examples = []
    for i in range(3):
        pixels = np.random.randint(
            low=0, high=256, size=(100, 100, 3), dtype=np.uint8
        )
        pil_image = PILImage.fromarray(pixels, mode="RGB")
        image = wandb.Image(pil_image, caption=f"random field {i}")
        examples.append(image)
    run.log({"examples": examples})

log .jpg rather than .png (default)

import numpy as np
import wandb

with wandb.init() as run:
    examples = []
    for i in range(3):
        pixels = np.random.randint(low=0, high=256, size=(100, 100, 3))
        image = wandb.Image(pixels, caption=f"random field {i}", file_type="jpg")
        examples.append(image)
    run.log({"examples": examples})

Methods

all_boxes

View source

@classmethod
all_boxes(
    images: Sequence['Image'],
    run: "LocalRun",
    run_key: str,
    step: Union[int, str]
) -> Union[List[Optional[dict]], bool]

all_captions

View source

@classmethod
all_captions(
    images: Sequence['Media']
) -> Union[bool, Sequence[Optional[str]]]

all_masks

View source

@classmethod
all_masks(
    images: Sequence['Image'],
    run: "LocalRun",
    run_key: str,
    step: Union[int, str]
) -> Union[List[Optional[dict]], bool]

guess_mode

View source

guess_mode(
    data: Union['np.ndarray', 'torch.Tensor'],
    file_type: Optional[str] = None
) -> str

Guess what type of image the np.array is representing.

to_uint8

View source

@classmethod
to_uint8(
    data: "np.ndarray"
) -> "np.ndarray"

Convert image data to uint8.

Convert floating point image on the range [0,1] and integer images on the range [0,255] to uint8, clipping if necessary.

4.6.7 - ImageMask

Format image masks or overlays for logging to W&B.

ImageMask(
    val: dict,
    key: str
) -> None

Examples:

Logging a single masked image

import numpy as np
import wandb

run = wandb.init()
image = np.random.randint(low=0, high=256, size=(100, 100, 3), dtype=np.uint8)
predicted_mask = np.empty((100, 100), dtype=np.uint8)
ground_truth_mask = np.empty((100, 100), dtype=np.uint8)

predicted_mask[:50, :50] = 0
predicted_mask[50:, :50] = 1
predicted_mask[:50, 50:] = 2
predicted_mask[50:, 50:] = 3

ground_truth_mask[:25, :25] = 0
ground_truth_mask[25:, :25] = 1
ground_truth_mask[:25, 25:] = 2
ground_truth_mask[25:, 25:] = 3

class_labels = {0: "person", 1: "tree", 2: "car", 3: "road"}

masked_image = wandb.Image(
    image,
    masks={
        "predictions": {
            "mask_data": predicted_mask,
            "class_labels": class_labels,
        },
        "ground_truth": {
            "mask_data": ground_truth_mask,
            "class_labels": class_labels,
        },
    },
)
run.log({"img_with_masks": masked_image})

Log a masked image inside a Table

import numpy as np
import wandb

run = wandb.init()
image = np.random.randint(low=0, high=256, size=(100, 100, 3), dtype=np.uint8)
predicted_mask = np.empty((100, 100), dtype=np.uint8)
ground_truth_mask = np.empty((100, 100), dtype=np.uint8)

predicted_mask[:50, :50] = 0
predicted_mask[50:, :50] = 1
predicted_mask[:50, 50:] = 2
predicted_mask[50:, 50:] = 3

ground_truth_mask[:25, :25] = 0
ground_truth_mask[25:, :25] = 1
ground_truth_mask[:25, 25:] = 2
ground_truth_mask[25:, 25:] = 3

class_labels = {0: "person", 1: "tree", 2: "car", 3: "road"}

class_set = wandb.Classes(
    [
        {"name": "person", "id": 0},
        {"name": "tree", "id": 1},
        {"name": "car", "id": 2},
        {"name": "road", "id": 3},
    ]
)

masked_image = wandb.Image(
    image,
    masks={
        "predictions": {
            "mask_data": predicted_mask,
            "class_labels": class_labels,
        },
        "ground_truth": {
            "mask_data": ground_truth_mask,
            "class_labels": class_labels,
        },
    },
    classes=class_set,
)

table = wandb.Table(columns=["image"])
table.add_data(masked_image)
run.log({"random_field": table})