- Catch all exceptions when saving Jupyter sessions
- validation_data automatically set in TF >= 2.2
- implements* hooks now implemented in keras callback for TF >= 2.2
- Raw source code saving now disabled by default
- We now support global settings on boot to enable code saving on the server
- New
code_save=Trueargument to wandb.init to enable code saving manually
- Ensure cells don't hang on completion
- Fixed jupyter integration in PyCharm shells
- Made session history saving handle None metadata in outputs
- Save session history in jupyter notebooks
- Kaggle internet enable notification
- Extend wandb.plots.feature_importances to work with more model types, thanks @neomatrix369!
- Code saving for jupyter notebooks restored
- Fixed thread errors in jupyter
- Ensure final history rows aren't dropped in jupyter
- Add default class labels for semantic segmentation
- Enhance bounding box API to be similar to semantic segmentation API
- Increase media table rows to improve ROC/PR curve logging
- Fix issue where pre binned histograms were not being handled properly
- Handle nan values in pytorch histograms
- Fix handling of binary image masks
- Improve semantic segmentation image mask logging
- Close all open files to avoice ResourceWarnings, thanks @CrafterKolyan!
- Parse "tensor" protobufs, fixing issues with tensorboard syncing in 2.1
- Add ROC, precision_recall, HeatMap, explainText, POS, and NER to wandb.plots
- Add wandb.Molecule() logging
- Capture kaggle runs for metrics
- Add ability to watch from run object
- Avoid accidently picking up global debugging logs
- Improve bounding box annotations
- Log active GPU system metrics
- Only writing wandb/settings file if wandb init is called
- Improvements to wandb local command
- Fix GPU logging on some devices without power metrics
- Fix sweep config command handling
- Fix tensorflow string logging
- Added code saving of main python module
- Added ability to specify metadata for bounding boxes and segmentation masks
- Fix situations where uncommited data from wandb.log() is not persisted
- Fix dependency conflict with new versions of six package
- Add best metric and epoch to run summary with Keras callback
- Added wandb.run.config_static for environments required pickled config
- Fixed regression causing failures with wandb.watch() and DataParallel
- Improved compatibility with python 3.8
- Fix model logging under windows
- Fix exception when using wandb.watch() in a notebook
- Improve support for sparse tensor gradient logging on GPUs
- Relax version dependancy for PyYAML for users with old environments
- Added scikit-learn support
- Added ability to specify/exclude specific keys when building wandb.config
- Fix wandb.watch() on sparse tensors
- Fix incompatibilty with ray 0.8.1
- Fix missing pyyaml requirement
- Fix "W&B process failed to launch" problems
- Improved ability to log large model graphs and plots
- Added ability to configure agent commandline from sweep config
- Fix fast.ai prediction logging
- Fix logging of eager tensorflow tensors
- Fix jupyter issues with logging notebook name and wandb.watch()
- Ignore wandb.init() specified project and entity when running a sweep
- Fix agent "flapping" detection
- Fix local controller not starting when sweep is pending
- Added support for LightGBM
- Added local board support (Experimental)
- Added ability to modify sweep configuration
- Added GPU power logging to system metrics
- Prevent sweep agent from failing continously when misconfigured
- Added beta support for ray/tune hyperopt search strategy
- Added ability to specify max runs per agent
- Improve experience starting a sweep without a project already created
- Fix repeated wandb.Api().Run(id).scan_history() calls get updated data
- Fix early_terminate/hyperband in notebook/python environments
- Added min_step and max_step to run.scan_history for grabbing sub-sections of metrics
- wandb.init(reinit=True) now automatically calls wandb.join() to better support multiple runs per process
- wandb.init(sync_tensorboard=True) works again for TensorFlow 2.0
- Handle tags being passed in as a string
- Pin graphql-core < 3.0.0 to fix install errors
- TQDM progress bars update logs properly
- Oversized summary or history logs are now dropped which prevents retry hanging
- Fix regression syncing some versions of Tensorboard since 0.8.13
- Fix network error in Jupyter
- Fix calling wandb.init with sync_tensorboard multiple times in Jupyter
- Fix RuntimeError race when using threads and calling wandb.log
- Don't initialize Sentry when error reporting is disabled
- Added best_run() to wandb.sweep() public Api objects
- Remove internal tracking keys from wandb.config objects in the public Api
- Improve large object warning when values reach maximum size
- Warn when wandb.save isn't passed a string
- Run stopping from the UI works since regressing in 0.8.12
- Restoring a file that already exists locally works
- Fixed TensorBoard incorrectly placing some keys in the wrong step since 0.8.10
- wandb.Video only accepts uint8 instead of incorrectly converting to floats
- SageMaker environment detection is now more robust
- Resuming correctly populates config
- wandb.restore respects root when run.dir is set #658
- Calling wandb.watch multiple times properly namespaces histograms and graphs
- Sweeps now work in Windows!
- Added sweep attribute to Run in the public api
- Added sweep link to Jupyter and terminal output
- TensorBoard logging now stores proper timestamps when importing historic results
- TensorBoard logging now supports configuring rate_limits and filtering event types
- Use simple output mirroring stdout doesn't have a file descriptor
- Write wandb meta files to the system temp directory if the local directory isn't writable
- Added beta api.reports to the public API
- Added wandb.unwatch to remove hooks from pytorch models
- Store the framework used in config._wandb
- Create nested directory when videos are logged from tensorboard namespaces
- Fix race when using wandb.log
async=True - run.summary acts like a proper dictionary
- run.summary sub dictionaries properly render
- handle None when passing class_colors for segmentation masks
- handle tensorflow2 not having a SessionHook
- properly escape args in windows
- fix hanging login when in anonymode
- tf2 keras patch now handles missing callbacks args
- Updates documentation autogenerated from docstrings in /docs
- wandb.init(config=config_dict) does not update sweep specified parameters
- wandb.config object now has a setdefaults method enabling improved sweep support
- Improved terminal and jupyter message incorporating 🚀 emojii!
- Allow wandb.watch to be called multiple times on different models
- Improved support for watching multple tfevent files
- Windows no longer requires
wandb runsimply runpython script_name.py wandb agentnow works on windows.- Nice error message when wandb.log is called without a dict
- Keras callback has a new
log_batch_frequencyfor logging metrics every N batches
- Fix compatibility issue with python 2.7 and old pip dependencies
- Improved onboarding flow when creating new accounts and entering api_key
- Fix public api returning incorrect data when config value is 0 or False
- Resumed runs no longer overwrite run names with run id
- Added recording of spell.run id in config
- wandb magic handles the case of tf.keras and keras being loaded
- tensorboard logging won't drop steps if multiple loggers have different global_steps
- keras gradient logging works in the latest tf.keras
- keras validation_data is properly set in tensorflow 2
- wandb pull command creates directories if they don't exist, thanks @chmod644
- file upload batching now asserts a minimum size
- sweeps works in python2 again
- scan_history now iterates the full set of points
- jupyter will run local mode if credentials can't be obtained
- Sweeps can now be run from within jupyter / directly from python! https://docs.wandb.com/sweeps/python
- New openai gym integration will automatically log videos, enabled with the monitor_gym keyword argument to wandb.init
- Ray Tune logging callback in wandb.ray.WandbLogger
- New global config file in ~/.config/wandb for global settings
- Added tests for fastai, thanks @borisdayma
- Public api performance enhancements
- Deprecated username in favor of enitity in the public api for consistency
- Anonymous login support enabled by default
- New wandb.login method to be used in jupyter enabling anonymous logins
- Better dependency error messages for data frames
- Initial integration with spell.run
- All images are now rendered as PNG to avoid JPEG artifacts
- Public api now has a projects field
- run.summary updates work in jupyter before log is called
- don't require numpy to be installed
- Setting nested keys in summary works
- notebooks in nested directories are properly saved
- Don't retry 404's / better error messaging from the server
- Strip leading slashes when loading paths in the public api
- Small files are batch uploaded as gzipped tarballs
- TensorBoardX gifs are logged to wandb
- wandb.init properly handles network failures on startup
- Keras callback only logs examples if data_type or input_type is set
- Fix edge case PyTorch model logging bug
- Handle patching tensorboard multiple times in jupyter
- Sweep picks up config.yaml from the run directory
- Dataframes handle integer labels
- Handle invalid JSON when querying jupyter servers
- fastai uses a fixed seed for example logging
- increased the max number of images for fastai callback
- new wandb.Video tag for logging video
- sync=False argument to wandb.log moves logging to a thread
- New local sweep controller for custom search logic
- Anonymous login support for easier onboarding
- Calling wandb.init multiple times in jupyter doesn't error out
- keras callback no longer guesses input_type for 2D data
- wandb.Image handles images with 1px height
- wandb Public API now has
run.scan_historyto return all history rows - wandb.config prints helpful errors if used before calling init
- wandb.summary prints helpful errors if used before calling init
- filestream api points to new url on the backend
- fastai callback uses the default monitor instead of assuming val_loss
- notebook introspections handles error cases and doesn't print stacktrace on failure
- Don't print description warning when setting name
- Fixed dataframe logging error with the keras callback
- Fixed line offsets in logs when resuming runs
- wandb.config casts non-builtins before writing to yaml
- vendored backports.tempfile to address missing package on install
- Added
api.sweepto the python export api for querying sweeps - Added
WANDB_NOTEBOOK_NAMEfor specifying the notebook name in cases we can't infer it - Added
WANDB_HOSTto override hostnames - Store if a run was run within jupyter
- Client now supports stopping runs from the web ui
- Handle floats passed as step to
wandb.log - wandb.config has full unicode support
- sync the main file to wandb if code saving is enabled and it's untracked by git
- XGBoost callback: wandb.xgboost.wandb_callback()
- Fixed plotly charts with large numpy arrays not rendering
wandb dockerworks when nvidia is present- Better error when non string keys are sent to log
- Relaxed pyyaml dependency to fix AMI installs
- Magic works in jupyter notebooks.
- New preview release of auto-dataframes for Keras
- Added input_type and output_type to the Keras callback for simpler config
- public api supports retrieving specific keys and custom xaxis
- WANDB_IGNORE_GLOBS is respected on the final scan of files
- Unified run.id, run.name, and run.notes across all apis
- Handle funky terminal sizes when setting up our psuedo tty
- Fixed Jupyter notebook introspection logic
- run.summary.update() persists changes to the server
- tensorboard syncing is robust to invalid histograms and truncated files
- preview release of magic, calling wandb.init(magic=True) should automatically track config and metrics when possible
- cli now supports local installs of the backend
- fastai callback supports logging example images
- image logging works in Windows
- wandb sync handles tfevents with a single timestep
- fix incorrect command in overview page for running runs
- handle histograms with > 512 bins when streaming tensorboard
- better error message when calling wandb sync on a file instead of a directory
- new helper function for handling hyperparameters in sweeps
wandb.config.user_items() - better mocking for improved testing
- entity is persisted on wandb.run when queried from the server
- tmp files always use the temporary directory to avoid syncing
- raise error if file shrinks while uploading
- images log properly in windows
- upgraded pyyaml requirement to address CVE
- no longer store a history of rows to prevent memory leak
- summary now supports new dataframe format
- WANDB_SILENT environment variable writes all wandb messages to debug.log
- Improved error messages for windows and tensorboard logging
- output.log is uploaded at the end of each run
- metadata, requirements, and patches are uploaded at the beginning of a run
- when not running from a git repository, store the main python file
- added WANDB_DISABLE_CODE to prevent diffing and code saving
- when running in jupyter store the name of the notebook
- auto-login support for colab
- store url to colab notebook
- store the version of this library in config
- store sys.executable in metadata
- fastai callback no longer requires path
- wandb.init now accepts a notes argument
- The cli replaced the message argument with notes and name
- wandb sync handles tensorboard embeddings
- wandb sync correctly handles images in tensorboard
- tf.keras correctly handles single input functional models
- wandb.Api().runs returns an iterator that's reusable
- WANDB_DIR within a hidden directory doesn't prevent syncing
- run.files() iterates over all files
- pytorch recurssion too deep error
- wandb sync accepts an --ignore argument with globs to skip files
- run.summary now has an items() method for iterating over all keys
- Better error messages on access denied
- Better error messages when optional packages aren't installed
- Urls printed to the termial are url-escaped
- Namespaced tensorboard events work with histograms
- Public API now retries on failures and re-uses connection pool
- Catch git errors when remotes aren't pushed to origin
- Moved keras graph collection to on_train_begin to handle unbuilt models
- Handle more cases of not being able to save weights
- Updates to summary after resuming are persisted
- PyTorch histc logging fixed in 0.4.1
- Fixed
wandb synctensorboard import
- wandb.init(tensorboard=True) works with Tensorflow 2 and Eager Execution
- wandb.init(tensorboard=True) now works with tb-nightly and PyTorch
- Automatically log examples with tf.keras by adding missing validation_data
- Socket only binds to localhost for improved security and prevents firewall warnings in OSX
- Added user object to public api for getting the source user
- Added run.display_name to the public api
- Show display name in console output
- Added --tags, --job_group, and --job_type to
wandb run - Added environment variable for minimum time to run before considering crashed
- Added flake8 tests to CI, thanks @cclauss!
- wandb-docker-run accepts image digests
- keras callback works in tensorflow2-alpha0
- keras model graph now puts input layer first
- PyTorch log frequency added for gradients and weights
- PyTorch logging performance enhancements
- wandb.init now accepts a name parameter for naming runs
- wandb.run.name reflects custom display names
- Improvements to nested summary values
- Deprecated wandb.Table.add_row in favor of wandb.Table.add_data
- Initial support for a fast.ai callback thanks to @borisdayma!
- run.get_url resolves the default entity if one wasn't specified
- wandb restore accepts run paths with only slashes
- Fixed PyYaml deprecation warnings
- Added entrypoint shell script to manifest
- Strip newlines from cuda version
- handle case insensitive docker credentials
- fix app_url for private cloud login flow
- don't retry 404's when starting sweep agents
- ensure DNS lookup failures can't prevent startup
- centralized debug logging
- wandb agent waits longer to send a SIGKILL after sending SIGINT
- support for logging docker images with the WANDB_DOCKER env var
- WANDB_DOCKER automatically set when run in kubernetes
- new wandb-docker-run command to automatically set env vars and mount code
- wandb.restore supports launching docker for runs that ran with it
- python packages are now recorded and saved in a requirements.txt file
- cpu_count, gpu_count, gpu, os, and python version stored in wandb-metadata.json
- the export api now supports docker-like paths, i.e. username/project:run_id
- better first time user messages and login info
- Improve error reporting for sweeps
- fixed Jupyter logging, don't change logger level
- fixed resuming in Jupyter
- wandb.init now degrades gracefully if a user hasn't logged in to wandb
- added a force flag to wandb.init to require a machine to be logged in
- Tensorboard and TensorboardX logging is now automatically instrumented when enabled
- added a tensorboard to wandb.init which patches tensorboard for logging
- wandb.save handles now accepts a base path to files in sub directories
- wandb.tensorflow and wandb.tensorboard can now be accessed without directly importing
wandb syncwill now traverse a wandb run directory and sync all runs
- Fixed race where wandb process could hang at the end of a run
- Fix resuming in Jupyter on kernel restart
- wandb.save ensures files are pushed regardless of growth
- Added replace=True keyword to init for auto-resuming
- New run.resumed property that can be used to detect if we're resuming
- New run.step property to use for setting an initial epoch on resuming
- Made Keras callback save the best model as it improves
- Really don't require numpy
- Better error message if wandb.log is called before wandb.init
- Prevent calling wandb.watch multiple times
- Handle datetime attributes in logs / plotly
- Add environment to sweeps
- Enable tagging in the public API and in wandb.init
- New media type wandb.Html for logging arbitrary html
- Add Public api.create_run method for custom integrations
- Added glob support to wandb.save, files save as they're written to
- Added wandb.restore for pulling files on resume
- Added a timeout for generating diffs on large repos
- Fixed edge case where file syncing could hang
- Ensure all file changes are captured before exit
- Handle cases of sys.exit where code isn't passed
- Don't require numpy
- New
wandb synccommand that pushes a local directory to the cloud - Support for syncing tfevents file during training
- Detect when running as TFJob and auto group
- New Kubeflow module with initial helpers for pipelines
- Fixed history / summary bug
- Initial support for AWS SageMaker
hook_torchrenamed towatchwith a deprecation warning- Projects are automatically created if they don't exist
- Additional GPU memory_allocated metric added
- Keras Graph stores edges
- PyTorch graph parsing is more robust
- Fixed PyTorch 0.3 support
- File download API supports WANDB_API_KEY authentication
- Sweeps work with new backend (early release).
- Summary tracks all history metrics unless they're overridden by directly writing to summary.
- Files support in data API.
- Show ongoing media file uploads in final upload progress.
- wandb.Audio supports duration
- Pass username header in filestream API
- New wandb.Audio data type.
- New step keyword argument when logging metrics
- Ability to specify run group and job type when calling wandb.init() or via environment variables. This enables automatic grouping of distributed training runs in the UI
- Ability to override username when using a service account API key
- Handle non-tty environments in Python2
- Handle non-existing git binary
- Fix issue where sometimes the same image was logged twice during a Keras step
- PyTorch
- Added a new
wandb.hook_torchmethod which records the graph and logs gradients & parameters of pytorch models wandb.Imagedetects pytorch tensors and uses torchvision.utils.make_grid to render the image.
- Added a new
wandb restorehandles the case of not being run from within a git repo.
- We now open stdout and stderr in raw mode in Python 2 ensuring tools like bpdb work.
- Catastrophic errors are now reported to Sentry unless WANDB_ERROR_REPORTING is set to false
- Improved error handling and messaging on startup
- The first image when calling wandb.log was not being written, now it is
wandb.logandrun.summarynow remove whitespace from keys
- Vendored prompt_toolkit < 1.0.15 because the latest ipython is pinned > 2.0
- Lazy load wandb.h5 only if
summaryis accessed to improve Data API performance
- Jupyter
- Deprecated
wandb.monitorin favor of automatically starting system metrics after the first wandb.log call - Added new %%wandb jupyter magic method to display live results
- Removed jupyter description iframe
- Deprecated
- The Data API now supports
per_pageandorderoptions to theapi.runsmethod - Initial support for wandb.Table logging
- Initial support for matplotlib logging