Changelog

v0.20.2 (2022-04-27)

Enhancements

• add cluster_config_id parameter to KafkaSettings (#2988)

• bump click version constraint to <9.0 (#3014)

v0.20.1 (2022-04-11)

New Features

• add model monitoring interface to deployment (#2962)

v0.20.0.post0 (2022-04-11)

Bug Fixes

• fix issue where the package couldn’t be built from source (#2986)

v0.20.0 (2022-04-08)

Backwards Incompatibilities

• DeployedModel can no longer be directly instantiated from a run ID (though it usually shouldn’t be directly instantiated anyway) (#2727)

• ExperimentRun.deploy() and related methods have been removed in favor of Endpoint (#2740)

• custom module collection now favors pip-installed libraries over local directories with the same name (#2805)

• verta.monitoring and Client.monitoring have been removed (#2812)

New Features

• support JWT cookies as an authn mechanism (#2716, #2738, #2737, #2928)

• add explicit credentials parameter to get_deployed_model() (#2727)

• add ModelError exception for use in Verta Standard Models (#2735)

• support pip install verta[unit_tests] for optional testing dependencies (#2788)

• add RegisteredModelVersion.log_setup_script() (#2873)

Enhancements

• don’t include spaCy models and anaconda-client in read_pip_environment() (#2709)

• bump PyYAML version constraint to <6.0 (#2718)

• warn instead of error on version mismatch of verta and cloudpickle in environment versioning (#2723)

Bug Fixes

• make sure everything subclasses object (#2748)

• add validation for the artifacts parameter in RegisteredModelVersion.log_model() (#2783)

• support directly deploying models that come from keras rather than tensorflow.python.keras (#2872)

• properly fall back to client config file if an environment variable isn’t set (#2875)

v0.19.3 (2021-10-29)

Backwards Incompatibilities

• validate VertaModelBase.__init__() parameter names in RegisteredModel.create_standard_model() (#2570)

• remove ExperimentRun.log_requirements() (#2584)

• remove ExperimentRun.log_model_for_deployment() (#2584)

• Python.read_pip_file() and Python.read_pip_environment() now include previously-unsupported lines rather than skipping them by default (#2584)

New Features

• add SummarySample.is_aggregate (#2555)

• fall back to logging raw requirements if manual parsing fails (#2584, #2643, #2676)

• support setting custom environment variables in environment objects (#2634)

• add Docker environment object (#2636)

• support Docker in log_environment()/get_environment() (#2637)

• add DockerImage and RegisteredModelVersion.log_docker() (#2641)

• add RegisteredModel.create_containerized_model() (#2648)

• add support for updating endpoints with existing builds (#2685)

Bug Fixes

• return zero-valued samples from profile_point() (#2556)

• avoid divide-by-zero when a histogram’s buckets add to zero (#2554)

Internal Changes

• rename remove_public_version_identifier() to remove_local_version_identifier() (#2601)

• move _get_artifact_msg() to _DeployableEntity interface (#2626)

• add _MODEL_KEY to _DeployableEntity interface (#2628)

• save metadata in Artifact proto when logging model (#2592)

• add ensure_starts_with_slash() (#2640)

v0.19.2 (2021-09-30)

New Features

• add RegisteredModelVersion.change_stage() (#2654)

Bug Fixes

• restrict protobuf dependency version upper constraint from <4.0 to <3.18 for Python 2 compatibility (#2633)

v0.19.1 (2021-08-09)

New Features

• add @verify_io decorator for use with VertaModelBase.predict() (#2540)

Enhancements

• paginate alerts.list() (#2525)

Bug Fixes

• filter out lib32/ and lib64/ when collecting custom modules (#2534)

• pick up environment variables (such as REQUESTS_CA_BUNDLE) for HTTP requests (#2535)

v0.19.0 (2021-08-03)

Backwards Incompatibilities

• remove Repository (#2498)

New Features

• enable extra headers to be passed on requests (#2510)

• add RegisteredModelVersion.log_code_version() (#2523)

• enable Kafka configuration on endpoints (if supported by Verta backend) (#2488, #2520)

Enhancements

• batch requests in log_training_data_profile() for a drastic speedup (#2511)

• allow RegisteredModel.create_version_from_run() to take a run object (#2500)

Bug Fixes

• properly promote naïve datetimes in Python 2 (#2506)

Internal Changes

• include non-public fields in Alert.summary_sample_base (#2519)

v0.18.2 (2021-07-14)

New Features

• add convenience functions to create ready-to-deploy standard Verta model versions (#2397, #2450, #2486)

• add model_version.log_training_data_profile() for deployment monitoring (#2434, #2446, #2457, #2484)

• add profile_point() to profilers

• support specifying start_time and end_time on experiment runs

Enhancements

• propagate model logs when a deployment fails during initialization

• hide internal attributes from histogram __repr__()s

Bug Fixes

• resolve a ParseError for the “UNASSIGNED” model_version stage

• fix bug where NumericValues were deserialized inconsistently when “unit” is empty

• fix bug where ContinuousHistogramProfiler was unable to handle missing data

• allow alerts to properly handle samples of past time windows

Internal Changes

• deprecate verta._internal_utils._histogram_utils

v0.18.1 (2021-06-17)

Enhancements

• raise warning when duplicate attributes are ignored in model versions

Bug Fixes

• fix zip-unzip magic for directories logged with run.log_model()

v0.18.0 (2021-06-11)

Backwards Incompatibilities

• rename with_workspace(workspace_name) parameter to with_workspace(workspace)

• move formerly-private modules and entity classes to public import paths (#2011, #2308, #2313, #2314)

• set upper version bounds on dependency libraries

• add pytimeparse as a dependency

• hide sklearn integration’s internally-used functions

Deprecations

• log_training_data(), being superseded by new monitoring functionality

• log_requirements(), being superseded by log_environment()

• TFSavedModel utility, long-since superseded by Standard Verta Models

New Features

• add monitoring sub-client (#2077, #2096, #2097, #2095, #2091, #2133, #2120, #2126, #2134, #2145, #2159, #2162, #2164, #2182, #2186, #2187, #2184, #2200, #2201, #2212, #2252, #2262, #2263, #2269, #2317, #2318, #2332, #2326, #2348, #2355, #2356, #2360, #2370, #2374, #2399)

• allow setting workspace through environment variable

• add apt_packages to Python()

• add NumericValue and StringValue to verta.data_types

• add verta.registry.VertaModelBase for Standard Verta Models

Enhancements

• add __repr__()s to verta.data_types

Bug Fixes

• return path from download_model()

• support mismatched buckets for discrete histogram data_type

• remove local version identifier from captured library version numbers to unblock deployment

Internal Changes

• use __subclasses__() instead of a manual list

• add client._conn.email

• rewrite __module__s to public import paths

• move verta._dataset_versioning to verta.dataset.entities

v0.17.6 (2021-04-23)

New Features

• add download_artifact() and download_model() to RegisteredModelVersion

v0.17.5 (2021-04-14)

Bug Fixes

• unzip directory models in run.download_model()

v0.17.4 (2021-03-26)

New Features

• support logging structured data types as run attributes

• support getting back structured data type attributes

• enable manually specifying page limit for _LazyList iteration

Bug Fixes

• properly paginate _LazyList requests

v0.17.3 (2021-03-17)

New Features

• set and get lock levels on model versions

Enhancements

• add stage to model version repr

• follow symlinks when collecting custom modules

Bug Fixes

• properly propagate 403s/404s when updating registry entities

Internal Changes

• move verta._registry to verta.registry._entities

• make client’s debug cURL util more readable

v0.17.2 (2021-02-26)

New Features

• enable finding model versions based on stage

v0.17.1 (2021-02-24)

New Features

• add run.log_environment()

• add run.download_model()

Enhancements

• support arbitrary models in run.log_model()

• skip custom modules and model API when logging an arbitrary model

• unzip artifact directories in run.download_artifact()

Internal Changes

• rename artifact key blocklist

• consolidate chunk sizes with named constants

v0.17.0 (2021-02-16)

Backwards Incompatibilities

• in newer backends, an entity created in an organization will use that organization’s permissions settings by default, instead of defaulting to private

New Features

• add client.set_workspace() and client.get_workspace()

• enable new visibility values for newer backends

• enable passing PySpark models to run.log_model()

• add Path.with_spark()

Enhancements

• for custom modules files, grant non-owners read access

• remove “file:” prefix from path datasets

Internal Changes

• consolidate workspace helper methods into Connection

• add Connection methods for personal and default workspace

• prevent test teardowns from resulting in 403s

v0.16.5 (2021-01-26)

New Features

• add parameter to disable autocapture in Git() and run.log_code()

• add is_dirty parameter to Git()

• add is_dirty parameter to run.log_code()

• add public attributes to Git() objects

Internal Changes

• constrain pyyaml to <5.4 to avoid build dependency on C

v0.16.4 (2021-01-14)

Internal Changes

• send Grpc-Metadata-developer-key (hyphen instead of underscore) as an additional auth header

v0.16.3 (2020-12-18)

Bug Fixes

• fix AttributeError when using public_within_org=True

v0.16.2 (2020-12-16)

Bug Fixes

• fix bug where set_registered_model() and set_dataset() unset the client’s active project

v0.16.1 (2020-12-14)

New Features

• enable managed versioning for dataset versions

v0.16.0 (2020-12-09)

Backwards Incompatibilities

• overhaul dataset versioning API

New Features

• add HDFS dataset blob type

v0.15.9 (2020-11-21)

New Features

• enable find() to take *args rather than a single list

Bug Fixes

• remove limitation on searching for runs by tag

• temporarily disable continuing interrupted multipart uploads

v0.15.8 (2020-11-17)

New Features

• add public_within_org param to Client.get_or_create_endpoint()

Enhancements

• propagate HTTP error messages for Client init errors

• display a simpler error message for backend errors

v0.15.7 (2020-11-05)

New Features

• support logging models serialized with torch.save()

• enable continuing previously-interrupted multipart uploads

Enhancements

• ignore folders themselves in old-style S3 dataset versioning

• ignore .git/ for custom modules

• raise warning when metadata are provided to get_or_create_*()

• add print to Dataset.get_latest_version()

• have custom modules ignore libraries in __pycache__/

• catch Keras serialization error with h5py v3.0.0

v0.15.6 (2020-10-02)

New Features

• add ModelVersions.with_workspace()

• add public_within_org param to Client.get_or_create_repository()

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

Bug Fixes

• fix bug where CPU could not be empty for endpoint resources

Internal Changes

• refactor client.set_repository() to attempt get before create

• use backend implementation of experiment run clone

v0.15.4 (2020-09-09)

Bug Fixes

• fix bug where a DatasetVersion could not be retrieved for certain protobuf configurations

v0.15.3 (2020-09-08)

New Features

• add DatasetVersion.list_components() and DatasetVersion.base_path

• enable cloning a run into another experiment

• add delete() to most entities

Enhancements

• support Python 3.8

• add self_contained param to download_docker_context()

Bug Fixes

• fix bug where log_model(overwrite=True) didn’t work with custom model artifacts

• properly raise an error when re-logging an environment to a model version

v0.15.2 (2020-08-28)

Enhancements

• add retries for all client connection errors

v0.15.1 (2020-08-24)

Bug Fixes

• correctly log model artifacts attribute to enable custom models with dependencies

v0.15.0 (2020-08-24)

New Features

• Verta model registry

• Verta endpoints

Enhancements

• expand custom modules virtual environment filter

Bug Fixes

• fix bug where multipart upload loop may have an undefined variable in the event of connection errors

• fix bug where getting a dataset version from another workspace may fail

v0.14.17 (2020-08-13)

New Features

• add DeployedModel.get_curl()

• add ExperimentRun.get_artifact_keys()

Bug Fixes

• fix inability to get others’ shared personal Projects and Datasets

Internal Changes

• add util to convert requests to cURL

v0.14.16 (2020-08-06)

New Features

• add URL to Project.__repr__()

• add client.create_*() for core ModelDB entities

Internal Changes

• make model container paths configurable through environment variables

v0.14.15 (2020-07-28)

Bug Fixes

• nicely display stack traces from prediction errors

v0.14.14 (2020-07-28)

Bug Fixes

• fix error when accessing a shared project in an organization

v0.14.13 (2020-07-24)

Enhancements

• add more possible keys for _LazyList.find()

• print full response body for HTTP errors

Bug Fixes

• fix artifact download when $TMPDIR is in a different filesystem

• clear cache after every ExperimentRun logs

• fix TypeError in _LazyList.with_workspace(None)

Internal Changes

• move verta/deployment.py to verta/deployment/

v0.14.12 (2020-07-16)

Backwards Incompatibilities

• run.log_dataset() now only accepts DatasetVersion objects

• verta.environment.Python now requires its requirements parameter

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

• speed up verta import time by deferring external imports

• cache calls to get metrics and hyperparameters

• include UTC timestamps in HTTPError messages

• attempt torch.load() first in run.get_artifact()

Bug Fixes

• use parent for commit.log() if the commit is unsaved

Internal Changes

• partially refactor versioning blobs

• split ModelDB entities into their own files

• refactor ModelDB entity gets/creates

• add pylint config to repo

v0.14.11 (2020-06-26)

New Features

• add visibility for date created & updated on ExperimentRuns

v0.14.10 (2020-06-22)

Bug Fixes

• use a proper default for VERTA_ARTIFACT_DIR

v0.14.9 (2020-06-22)

Bug Fixes

• expand user directory for clientside artifact storage environment variable

v0.14.8 (2020-06-22)

New Features

• enable clientside artifact storage

• add epoch_num parameter to run.log_observations()

• add run.download_artifact()

Internal Changes

• add fixture for running tests in isolated directories

v0.14.7 (2020-06-18)

New Features

• add workspace parameter to find_datasets()

Enhancements

• print part numbers during multipart upload

• retry part uploads on connection errors

• read bytestreams in chunks

• enable fuzzy find by name in find_datasets()

• raise more informative error on non-JSON response bodies

Bug Fixes

• always set grpc-metadata-source header, even in no-auth

• typecheck tags

• don’t follow 302s

Internal Changes

• add _path_component_blobs attribute for dataset blobs

• handle protos refactor

v0.14.6 (2020-05-29)

New Features

• enable multipart artifact uploads

Bug Fixes

• fix Notebook Blob repr

• support NumPy bool_ and pandas 1.X in ModelAPI

• ignore folders in S3 versioning Blobs

• inject verta and cloudpickle into Python environment Blobs

• blocklist deployment artifact keys

Internal Changes

• remove logic duplication for fixing NFS URLs

• calculate SHA-256 checksums for artifacts in chunks

v0.14.5 (2020-05-13)

New Features

• support logging Keras models in TensorFlow 2.X

• support eagerly-executed TensorFlow Tensors in ModelAPI

Bug Fixes

• filter out spaCy models when versioning pip requirements files

v0.14.4 (2020-05-04)

Backwards Incompatibilities

• run.log_training_data() no longer uploads a “train_data” artifact, and instead directly generates a histogram for deployment data monitoring

v0.14.3 (2020-04-20)

Backwards Incompatibilities

• replace commit.branch() with commit.new_branch()

New Features

• enable passing in ~ as part of filepaths

• enable setting host from $VERTA_HOST

• capture versioning information from S3

Internal Changes

• add click as a dependency in preparation for CLI development

• use back end for commit merges

• use back end for commit reverts

v0.14.2 (2020-04-01)

New Features

• use friendly default messages for merges and reverts

• implement __repr__ for Blobs

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

Internal Changes

• update protobufs for diffapi changes

• fix race condition when running parallel tests

• update tests to delete children Commits first

v0.14.1 (2020-03-17)

New Features

• add complete versioning system

• enable going directly from Client to ExperimentRun using Verta config

• add public_within_org option for set_project() and set_dataset()

• add aliases for Client’s set/get-or-create methods

Bug Fixes

• enable larger sets of ExperimentRuns to be queried

• enable ZIPing files that have invalid timestamps

Internal Changes

• move cloned dependencies to their own submodule

• move internal utils into their own submodule

v0.14.0 (2020-02-11)

Backwards Incompatibilities

• require token in DeployedModel::from_url(), even if it’s None

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

Bug Fixes

• allow negative numbers in Python 3 expt_runs.find() queries

Internal Changes

• interpret HTTP 403s on getProject as project not found

• include gorilla as internal dependency

• explicitly include ModelDB in RPC endpoints

v0.13.19 (2020-01-08)

New Features

• enable overwriting code and dataset versions

• unpack tarballs in run.fetch_artifacts()

Bug Fixes

• include virtual environment-like directories when automatically logging custom modules

v0.13.18 (2019-12-12)

New Features

• add run.clone()

• add a decorator for models’ predict() to handle argument unpacking

Bug Fixes

• properly propagate deployment error messages

• enable calling run.deploy() and run.undeploy() even if the run is already deployed / not deployed

• properly handle Python 2 string types in querying methods

v0.13.17 (2019-12-05)

Deprecations

• utils.TFSavedModel, in favor of the class-as-model system

New Features

• enable passing more datatypes into DeployedModel.predict()

• add overwrite flag to most artifact logging functions

• enable deployment through ExperimentRun objects

• add a decorator for models’ predict() to handle datatype conversion

• only default to https for endpoints hosted by Verta

Internal Changes

• remove external dependency on six

v0.13.16 (2019-12-02)

New Features

• enable logging directories as ZIP archives with log_artifact()

v0.13.15 (2019-11-27)

New Features

• support logging classes as models

• support associating artifact dependencies with Standard Verta Models

• enable downloading artifacts into a local cache for use with Standard Verta Models

v0.13.14 (2019-11-19)

New Features

• enable indefinite retries on prediction 404s

v0.13.13 (2019-11-18)

Backwards Incompatibilities

• ExperimentRun.log_model() now no longer accepts a user-defined key, and is intended for deployment

Deprecations

• ExperimentRun.log_model_for_deployment(), in favor of more modular logging functions

New Features

• implement ExperimentRun.log_requirements()

• implement ExperimentRun.log_training_data()

• make prediction token optional in DeployedModel::from_url()

Bug Fixes

• retry predictions on non-model 502s

v0.13.12 (2019-11-07)

New Features

• enable indefinite retries on prediction 429s

Bug Fixes

• accommodate external 502s on predictions

Internal Changes

• pass host URL scheme to back end

• reduce dataset version name collisions in tests

v0.13.11 (2019-10-30)

Backwards Incompatibilities

• slightly bump dependency versions and remove grpcio

Bug Fixes

• obtain DatasetVersion timestamps robustly for Python 2

Internal Changes

• clean up Datasets generated during tests

• skip tests on missing imports instead of failing

v0.13.10 (2019-10-27)

Bug Fixes

• fix bug with locally-hosted artifact stores

Internal Changes

• update notebooks

v0.13.9 (2019-10-17)

Bug Fixes

• replace json.JSONDecodeError for Python 2

• remove check for Verta credentials from DeployedModel::from_url()

• properly resolve relative paths in deployment for custom modules

• enable uploading non-Python artifacts

• enable consistent retrieval of models for Python 2

Internal Changes

• add retries for HTTP 502s

v0.13.8 (2019-10-03)

New Features

• enable logging a setup script for the beginning of model deployment

• add verta to uploaded requirements if not present

Internal Changes

• revise pytests

v0.13.7 (2019-09-18)

New Features

• accept key prefixes for S3DatasetVersion

• implement verta.deployment.DeployedModel

Bug Fixes

• enable code version to be downloaded as a ZIP archive through the Web App

• fix bug in run.get_dataset_version()

• fix bug in dataset.get_latest_version()

• catch all unpickling-related errors in get_artifact()

Internal Changes

• keep cell execution numbers in example notebooks

v0.13.6 (2019-09-05)

Bug Fixes

• fix small bugs in the _dataset submodule

Internal Changes

• update protos

v0.13.5 (2019-09-05)

Bug Fixes

• fix various bugs in the _dataset submodule

v0.13.3 (2019-09-04)

Deprecations

• client.expt_runs, because its meaning is ambiguous; proj.expt_runs and expt.expt_runs are preferred

• ret_all_info parameter in querying methods, because it returns user-unfriendly objects

New Features

• implement client.set_experiment_run(id=…)

• implement dataset retrieval functions

• propagate error messages from the back end

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

Internal Changes

• create testing fixtures for deterministic input spaces

• fix data versioning tests

• fix non-artifact tests

• fix artifact tests

• implement model logging tests

• implement basic querying method tests

v0.13.2 (2019-08-20)

New Features

• add ExperimentRun.get_dataset_version()

v0.13.1 (2019-08-20)

Bug Fixes

• handle more states in DatasetVersion.__repr__()

v0.13.0 (2019-08-20)

New Features

• enable file extensions on artifacts in the Web App

• support basic data versioning

Bug Fixes

• convert everything to new-style classes for Python 2 compatibility

Internal Changes

• support dynamically fetching custom deployment URLs

• make Pillow an optional dependency

• support potentially handling a 401 on verifyConnection

v0.12.9 (2019-08-13)

New Features

• support passing in a full URL as the host parameter to Client()

Bug Fixes

• fix bugs regarding logging and retrieving datasets

Internal Changes

• propagate more deployment errors to the Client

v0.12.8 (2019-08-08)

Internal Changes

• bump patch version to 8, to celebrate August 8th

• handle getting Verta environment variables more consistently

v0.12.7 (2019-08-08)

New Features

• support logging functions for deployment

• ignore virtual environment directories when logging custom modules for deployment

Bug Fixes

• define source code UTF-8 encoding for Python 2 compatibility

• use new-style classes for Python 2 compatibility

Internal Changes

• implement DeployedModel::from_url() factory method

• propagate runtime errors to the Client during DeployedModel.predict()

• add custom module logging example notebook

v0.12.6 (2019-08-01)

New Features

• implement a compress parameter on demo predict utility to enable request body compression

Internal Changes

• reduce redundancies in demo predict utility

v0.12.5 (2019-07-26)

New Features

• implement a debug parameter and attribute on Client to print verbose debugging information

v0.12.4 (2019-07-25)

New Features

• remove the need for log_modules()’s second argument (search_path)

v0.12.3 (2019-07-17)

Bug Fixes

• ensure ModelAPI value names are cast to str

Internal Changes

• identify model types by superclass

• update example notebooks with proper ModelAPI instantiation

• update demo notebook with log_code()

v0.12.2 (2019-07-16)

Bug Fixes

• move Git repo check from Client init to log_code()

v0.12.1 (2019-07-16)

Backwards Incompatibilities

• The non-public prediction utility now uses our updated REST prediction endpoint

New Features

• implement log_code() and get_code() for code versioning

• allow periods in Artifact get functions

• enable retrieving integers as integers (instead of as floats) from the back end

Bug Fixes

• catch and raise duplicate column name error on ModelAPI initialization

• properly handle daylight saving time when logging observation timestamps

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

v0.12.0 (2019-06-27)

Backwards Incompatibilities

• The dump() and load() functions have been removed from the public utils module.

New Features

• implement ignore_conn_err parameter and attribute to Client

• implement log_modules() for uploading custom Python modules for deployment

Bug Fixes

• enable logging lists, and dictionaries with string keys, as attributes on client.set_*() to match run.log_attribute()

• simplify stack traces by suppressing contexts during handling for a remaining handful of raise statements

• add missing error message to get_observation()

Internal Changes

• use internal Connection utility object for connection configuration

• define Artifact Store bucket names using a checksum of the artifact

• check for dataset CSV existence before wget in census-end-to-end.ipynb

• expand and unify gitignores

v0.11.7 (2019-06-10)

Backwards Incompatibilities

• The constructors for Project, Experiment, ExperimentRun, and ExperimentRuns—as well as with their _get() and _create() functions—now take an additional retry parameter, though these functions are all not intended for public use to begin with.

New Features

• enable logging lists, and dictionaries with string keys, as attributes

• implement a max_retries parameter and attribute on Client to retry requests with exponential backoff on 403s, 503s, and 504s

Internal Changes

• delegate most REST calls to an internal utility function

• implement back end load test

• change Read the Docs sidebar from fixed to static

• fix a bug that matplotlib has with macOS which was restricting testing

v0.11.6 (2019-06-07)

Backwards Incompatibilities

• Providing a cloudpickle version in the requirements for deployment that doesn’t match the version used by the Client now raises an error instead of overwriting the line in the requirements.

New Features

• add ExperimentRun’s Verta WebApp URL to its __repr__()

Bug Fixes

• use cloudpickle.__version__ instead of relying on pip

Internal Changes

• remove internal utility get_env_dependencies()

• update notebooks

v0.11.5 (2019-06-04)

Backwards Incompatibilities

• The dataset_csv parameter for log_model_for_deployment() has been replaced with two parameters for feature and target DataFrames.

Bug Fixes

• properly render lists in docstrings

Internal Changes

• have the upload script clean out build directories after uploading

v0.11.4 (2019-05-31)

Backwards Incompatibilities

• The dataset_df parameter for log_model_for_deployment() has been renamed to dataset_csv.

Bug Fixes

• reset the correct streams in log_model_for_deployment() instead of model_api over and over again

v0.11.3 (2019-05-31)

New Features

• implement __version__ attribute on package

Bug Fixes

• remove unsupported dependency on pandas and NumPy in utils module

Internal Changes

• move package version string from verta/setup.py to verta/verta/__about__.py

• remove old model API tests that have been superseded by property-based tests

• add pandas as a testing dependency

v0.11.2 (2019-05-30)

Backwards Incompatibilities

• Parameters for client.set_* functions have been renamed to name and id, from e.g. proj_name and _proj_id.

• The _id attribute of Project, Experiment, and ExperimentRun have been renamed to id.

• The default generated names for Project, Experiment, and ExperimentRun have been shortened.

Bug Fixes

• fix typos in client.set_* error messages

v0.11.1 (2019-05-29)

Bug Fixes

• fix internal utility get_env_dependencies() for compatibility with Python 3.6 and earlier

v0.11.0 (2019-05-29)

Backwards Incompatibilities

• log_model_for_deployment() now no longer requires a dataset argument, but requires a model API argument. The order of parameters has changed, and dataset_csv has been renamed to dataset_df.

New Features

• implement ModelAPI utility class for generating model APIs

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)

Bug Fixes

• properly expose intermediate subpackages for compatibility with Python 3.2 and earlier

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

Bug Fixes

• reset stream pointer on failed deserialization attempts

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