Weights & Biases
Data & MLThe AI developer platform. Use Weights & Biases to train and fine-tune models, and manage models from experimentation to production.
Release History
View all versions →v0.29.0Breaking6 fixes9 featuresThis release removes several deprecated features and properties, introduces new API functionalities for console logs and automations, and enhances the LEET interface with improved chart and layout controls. It also includes important compatibility updates for macOS users.
v0.28.2Breaking20 fixes6 featuresThis release introduces gzip compression for filestream requests and enhances the `wandb login` command with default credential verification. It also includes numerous bug fixes and improvements to API operations and TensorBoard integration.
v0.28.1Breaking10 fixes4 featuresThis release introduces new API features for managing sweeps and organizations, improves error handling for run loading, and removes legacy fallback implementations for artifact operations on older server versions. It also drops support for protobuf v4 and older server versions.
v0.28.0Breaking10 fixes6 featuresThis release introduces significant enhancements to the W&B LEET terminal UI, including high-resolution image rendering and synced scrubbing. It also improves API performance by deferring sweep data loading and fixes several critical bugs related to file downloads and artifact logging.
v0.27.21 fixThis release primarily fixes an issue where Run.scan_history() returned incorrect run history data.
v0.27.1Breaking1 fix2 featuresThis release introduces new event support in the automations API and performance improvements for `Run.scan_history()`. It also removes several legacy and unsupported APIs, including older LLM integrations and callbacks.
v0.27.0Breaking5 featuresThis release drops support for Python 3.9 and Pydantic v1, requiring Pydantic v2.6 or newer. New features include enhanced logging control and sandbox CLI access.
v0.26.12 fixes2 featuresThis release introduces the ability to customize run stopping behavior via `stop_fn` and adds a `start` argument for paginated API calls. Several bugs related to run initialization conflicts and offline API operations have also been resolved.
v0.26.0Breaking5 fixes16 featuresThis release introduces significant enhancements to the W&B LEET TUI, adds advanced system monitoring metrics (TPU and NVML GPM), and improves performance by switching JSON serialization to `orjson`. Compatibility with older server versions has been dropped.
v0.25.12 fixes7 featuresThis release introduces several enhancements to the W&B LEET TUI, including new panes for console logs and system metrics, and adds utility for managing artifact collections. Several bugs related to deadlocks and API iteration errors have also been resolved.
v0.25.0Breaking3 fixes3 featuresThis release drops support for Python 3.8 and introduces new features for the W&B LEET TUI, including multi-run workspace and config editing, alongside several critical bug fixes.
v0.24.21 fix1 featureThis release introduces support for Federated Auth (JWT) in wandb.Api() and fixes an issue with refreshing expired artifact download URLs.
v0.24.14 fixes1 featureThis release fixes several regressions introduced in version 0.24.0, particularly around data synchronization and login host configuration, and adds a new method for downloading run history exports.
v0.24.0Breaking6 fixes2 featuresThis release removes the deprecated `wandb.beta.workflows` module, introduces signal forwarding for agents, and includes several fixes related to artifact ID generation, API calls, and run logging presentation.
v0.23.1Breaking9 fixes6 featuresThis release adds regex filtering and chart inspection to the LEET TUI, expands the automations API, introduces a beta_history_scan method, deprecates anonymous mode, and includes several performance and bug fixes.
v0.23.05 fixes3 featuresThis release adds an experimental wandb beta leet command, expands the Registry API with member management and a read‑only id property, and includes several bug fixes around artifact handling, upload progress, and VSCode notebook support.
v0.22.37 fixes2 featuresThis release adds configurable console log chunking settings and per_page support for Registry API queries, expands API key length support, and includes numerous bug fixes improving stability, artifact handling, and Jupyter notebook path inference.
v0.22.22 fixesThis release includes bug fixes for missing `output.log` uploads and for excessive data uploads when `run.save()` is called repeatedly on unchanged files.
v0.22.11 fix3 featuresThis release adds checksum caching for artifact downloads, lazy loading for run listings, and a storage_region option for artifacts, while fixing timeout handling in artifact existence checks.
v0.22.0Breaking3 fixesThis release removes macOS 10 support and includes several bug fixes, notably improved run‑resumption handling, API compatibility with older servers, and deadlock prevention in sweep command scripts.
v0.21.41 featureIntroduces DSPy integration for tracking metrics, logging predictions, and saving DSPy programs as W&B artifacts.
v0.21.31 fixThis release updates the click dependency to >=8.0.1 and fixes an extraneous environment-variable warning message.
v0.21.2Breaking2 fixes2 featuresThis update introduces new run-metric limits and a Weave integration, makes linking artifacts raise errors instead of returning `None` (a breaking change), and fixes config dataclass serialization and artifact-link performance.
v0.21.1Breaking8 fixes7 featuresThis release adds new summary options, paginated sweeps, pattern filtering for file listings, and faster large‑artifact uploads, while deprecating the beta workflows module and removing `__bool__` from registry iterators.
v0.21.0Breaking6 fixes3 featuresThis release removes the legacy service implementation and related settings, adds new process‑tracking and custom Vega chart‑preset features, deprecates the `.length` attribute, and includes several bug fixes.
Common Errors
ModuleNotFoundError2 reportsThe "ModuleNotFoundError" in weights-biases (wandb) usually indicates that a required wandb dependency or a related package like gymnasium is not installed in your Python environment. To fix this, identify the missing module from the error message (e.g., 'wandb.errors', 'gymnasium') and install it using pip: `pip install wandb` or `pip install gymnasium` or `pip install` for the specific module shown in the error. Ensure you're installing into the correct Python environment for your project.
TypeError2 reportsTypeError in weights-biases often arises from attempting to apply string operations or access methods meant for strings on non-string data types within a summary object. To fix this, ensure that all data being processed as strings are definitively strings by using `str()` to explicitly convert them before applying string operations or accessing string-specific methods. This will prevent unexpected type mismatches during summary processing.
AttributeError1 reportThe "AttributeError" in weights-biases often arises when outdated or conflicting versions of wandb or its dependencies are installed, particularly in environments like Kaggle notebooks. To fix it, try upgrading wandb to the latest version using `pip install wandb --upgrade` and restarting your kernel or runtime; if the the issue persists, consider specifying the version with `pip install wandb=={known_good_version}` or reinstalling the wandb package and its dependencies to ensure a clean installation and resolve potential conflicts.
CommError1 reportCommError in wandb often arises from network issues or race conditions in the wandb backend during initialization or finalization. Ensure a stable internet connection and consider adding explicit synchronization mechanisms (like locks) around crucial wandb operations such as `wandb.init()` and `wandb.finish()` if running in a multithreaded/multiprocess environment. Check for firewall restrictions or proxy settings potentially interfering with wandb's communication.
LookupError1 reportThe "LookupError: ContextVar 'shell_parent'" in Weights & Biases (wandb) often arises in interactive environments like Jupyter notebooks when wandb's internal process tries to access the shell's parent process information that isn't readily available. To resolve this, explicitly initialize wandb within a defined context; a common fix is to wrap your `wandb.init()` call within an `if __name__ == '__main__':` block, ensuring it's only executed when the script is run directly (not when imported). Alternatively, try setting the environment variable `WANDB_NOTEBOOK_NAME` to bypass the shell parent lookup, for example, `os.environ["WANDB_NOTEBOOK_NAME"] = "my_notebook.ipynb"`.
CppCompileError1 reportCppCompileError in weights-biases often arises when the library tries to compile custom C++ extensions, often required for gradient tracking with certain model architectures or custom operations. Ensure you have a compatible compiler (like g++) and the necessary build tools installed on your system - usually, installing `build-essential` on Debian/Ubuntu-based systems or their equivalent on other OS fixes this. If using a virtual environment, ensure these tools are installed within the environment.
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