Release Notes¶
v0.17.6 (2021-04-23)¶
v0.17.5 (2021-04-14)¶
v0.16.4 (2021-01-14)¶
v0.16.3 (2020-12-18)¶
v0.16.2 (2020-12-16)¶
v0.15.9 (2020-11-21)¶
v0.15.8 (2020-11-17)¶
v0.15.6 (2020-10-02)¶
New Features¶
Enhancements¶
- allow custom modules to handle non-PyPI pip-installed packages
- propagate build errors during endpoint updates
- use DirectUpdateStrategy as a default strategy for endpoint updates
- add broader log-requirements support for modules whose PyPI names use dashes instead of underscores
- raise more informative error parsing attributes in CLI
v0.15.4 (2020-09-09)¶
v0.15.1 (2020-08-24)¶
v0.14.16 (2020-08-06)¶
v0.14.15 (2020-07-28)¶
v0.14.14 (2020-07-28)¶
v0.14.12 (2020-07-16)¶
Backwards Incompatibilities¶
New Features¶
- add client.get_project(), get_experiment(), and get_experiment_run()
- add client.projects and client.experiments
- add expt_runs.as_dataframe()
- add list_components() to data versioning blobs
- implement addition for data versioning blobs
- add add() to data versioning blobs
- add run.download_deployment_crd()
- add run.download_docker_context()
Enhancements¶
v0.14.11 (2020-06-26)¶
v0.14.10 (2020-06-22)¶
v0.14.9 (2020-06-22)¶
v0.14.4 (2020-05-04)¶
v0.14.2 (2020-04-01)¶
Bug Fixes¶
- filter out spaCy models from pip freeze
- make dataset.dataset_type friendlier
- enable e.g. Notebook Blobs to be retrieved from a Commit outside of Notebooks
- enable set_repository() without Verta authentication credentials
- validate Client config file against protobuf spec
- add more helpful typechecks on Commit methods
v0.14.0 (2020-02-11)¶
Backwards Incompatibilities¶
New Features¶
- add a workspace parameter to client.set_project() and client.set_dataset()
- enable logging singleton scalar NumPy arrays as metrics
- implement Keras Client integration
- implement PyTorch Client integration
- implement scikit-learn Client integration
- implement TensorFlow Client integration
- implement TensorBoard Client integration
- implement XGBoost Client intergation
v0.13.19 (2020-01-08)¶
v0.13.18 (2019-12-12)¶
v0.13.16 (2019-12-02)¶
v0.13.15 (2019-11-27)¶
v0.13.5 (2019-09-05)¶
v0.13.3 (2019-09-04)¶
Deprecations¶
New Features¶
Bug Fixes¶
- support run.get_*() when the value is None
- fix bug where Project, Experiment, and ExperimentRun objects couldn’t be pickled
- fix bug when Datasets are created in Python 2
- log DatasetVersion timestamps as milliseconds, as expected by the Web App
- fix bug when the working directory is captured by run.log_modules()
- fix bug when run.log_modules() is used in Python 2
- fix bug when querying methods are called from an empty ExperimentRuns
- perform basic key validation in querying methods
v0.13.1 (2019-08-20)¶
v0.13.0 (2019-08-20)¶
v0.12.8 (2019-08-08)¶
Internal Changes¶
- bump patch version to 8, to celebrate August 8th
- handle getting Verta environment variables more consistently
v0.12.5 (2019-07-26)¶
v0.12.4 (2019-07-25)¶
v0.12.3 (2019-07-17)¶
v0.12.2 (2019-07-16)¶
v0.12.1 (2019-07-16)¶
Backwards Incompatibilities¶
New Features¶
Bug Fixes¶
Internal Changes¶
- implement internal Configuration utility struct
- add PyTorch example notebook
- implement internal utility for unwrapping directory paths into contained filepaths
- implement internal utilities for reading Git information from the local filesystem
- implement internal utilities for finding executing Python source files
- implement internal utility for getting the file extension from a filepath
- log file extensions with Artifacts
v0.11.1 (2019-05-29)¶
v0.11.0 (2019-05-29)¶
Backwards Incompatibilities¶
Internal Changes¶
- create an example notebook that downloads our beloved Census data with wget
- rename the “scikit” model type to “sklearn”
- delete old internal model API generation utility
- update demo utility predict function to simply dump the JSON input into the request body
- implement internal utility to check for exact version pins in a requirements.txt
- implement internal utility to obtain the local environment’s Python version number
- update READMEs
- add utils module to API reference
- implement tests for model API generation
- implement property-based tests for model API generation
- add deepdiff to testing requirements
- add hypothesis to testing requirements
v0.10.2 (2019-05-22)¶
no functional changes
v0.10.1 (2019-05-22)¶
v0.10.0 (2019-05-16)¶
Backwards Incompatibilities¶
- log_hyperparameters() now must take a single, whole dictionary as an argument and no longer accepts dictionary unpacking.
- Getting observations from an ExperimentRun now returns tuples pairing observations with their timestamps.
- Passing a string into artifact logging functions now attempts to open a file located at the path represented by that string, rather than simply logging the string itself.
- Attempting to log an unsupported datatype now throws a TypeError instead of a ValueError.
- Logging artifacts now uses cloudpickle by default, instead of pickle.
- The internal logic for getting a Project by name has changed, and will be incompatible with old versions of the Verta Back End.
- The internal logic for handling uploading custom models for deployment has changed, and will be incompatible with old versions of the Verta Back End.
- The internal logic for getting an ExperimentRun by name has changed, and may be incompatible with old versions of the Verta Back End.
New Features¶
- associate user-specified or automatically-generated timestamps with observations
- implement methods on ExperimentRun for logging and getting tags
- implement methods on ExperimentRun for logging multiple attributes, metrics, or hyperparameters in a single transaction
- enable uploading custom model APIs for deployment
- create functions specifically for logging artifact paths without attempting uploads
Internal Changes¶
- convert pandas DataFrames into CSVs when logging for deployment for data monitoring
- implement a secondary predict function in demo utilities that returns the raw HTML response instead of a formatted response
- move our example notebooks from workflows/demos/ to workflows/examples/
- change “unknown” model type to “custom” in model API
- add “keras” deserialization in model API
- add cloudpickle to requirements with the locally pinned version if it was used when logging for deployment
- implement handful of small fixes to maintain Python 2.7 compatibility