CHANGELOG.rst

Changelog

1.4.0 (2019-10-30)

MLflow 1.4.0 includes several major features:

• Model Registry (Beta). Adds an experimental model registry feature, where you can manage, version, and keep lineage of your production models. (#1943, @mparkhe, @Zangr, @sueann, @dbczumar, @smurching, @gioa, @clemens-db, @pogil, @mateiz; #1988, #1989, #1995, #2021, @mparkhe; #1983, #1982, #1967, @dbczumar)
• TensorFlow updates
  • MLflow Keras model saving, loading, and logging has been updated to be compatible with TensorFlow 2.0. (#1927, @juntai-zheng)
  • Autologging for tf.estimator and tf.keras models has been updated to be compatible with TensorFlow 2.0. The same functionalities of autologging in TensorFlow 1.x are available in TensorFlow 2.0, namely when fitting tf.keras models and when exporting saved tf.estimator models. (#1910, @juntai-zheng)
  • Examples and READMEs for both TensorFlow 1.X and TensorFlow 2.0 have been added to mlflow/examples/tensorflow. (#1946, @juntai-zheng)

More features and improvements:

• [API] Add functions get_run, get_experiment, get_experiment_by_name to the fluent API (#1923, @fhoering)
• [UI] Use Plotly as artifact image viewer, which allows zooming and panning (#1934, @harupy)
• [UI] Support deleting tags from the run details page (#1933, @harupy)
• [UI] Enable scrolling to zoom in metric and run comparison plots (#1929, @harupy)
• [Artifacts] Add support of viewfs URIs for HDFS federation for artifacts (#1947, @t-henri)
• [Models] Spark UDFs can now be called with struct input if the underlying spark implementation supports it. The data is passed as a pandas DataFrame with column names matching those in the struct. (#1882, @tomasatdatabricks)
• [Models] Spark models will now load faster from DFS by skipping unnecessary copies (#2008, @tomasatdatabricks)

Bug fixes and documentation updates:

• [Projects] Make detection of MLproject files case-insensitive (#1981, @smurching)
• [UI] Fix a bug where viewing metrics containing forward-slashes in the name would break the MLflow UI (#1968, @smurching)
• [CLI] models serve command now works in Windows (#1949, @rboyes)
• [Scoring] Fix a dependency installation bug in Java MLflow model scoring server (#1913, @smurching)

Small bug fixes and doc updates (#1932, #1935, @harupy; #1907, @marnixkoops; #1911, @HackyRoot; #1931, @jmcarp; #2007, @deniskovalenko; #1966, #1955, #1952, @Djailla; #1915, @sueann; #1978, #1894, @smurching; #1940, #1900, #1904, @mparkhe; #1914, @jerrygb; #1857, @mengxr; #2009, @dbczumar)

1.3 (2019-09-30)

MLflow 1.3.0 includes several major features and improvements:

Features:

• The Python client now supports logging & loading models using TensorFlow 2.0 (#1872, @juntai-zheng)
• Significant performance improvements when fetching runs and experiments in MLflow servers that use SQL database-backed storage (#1767, #1878, #1805 @dbczumar)
• New GetExperimentByName REST API endpoint, used in the Python client to speed up set_experiment and get_experiment_by_name (#1775, @smurching)
• New mlflow.delete_run, mlflow.delete_experiment fluent APIs in the Python client(#1396, @MerelTheisenQB)
• New CLI command (mlflow experiments csv) to export runs of an experiment into a CSV (#1705, @jdlesage)
• Directories can now be logged as artifacts via mlflow.log_artifact in the Python fluent API (#1697, @apurva-koti)
• HTML and geojson artifacts are now rendered in the run UI (#1838, @sim-san; #1803, @spadarian)
• Keras autologging support for fit_generator Keras API (#1757, @charnger)
• MLflow models packaged as docker containers can be executed via Google Cloud Run (#1778, @ngallot)
• Artifact storage configurations are propagated to containers when executing docker-based MLflow projects locally (#1621, @nlaille)
• The Python, Java, R clients and UI now retry HTTP requests on 429 (Too Many Requests) errors (#1846, #1851, #1858, #1859 @tomasatdatabricks; #1847, @smurching)

Bug fixes and documentation updates:

• The R mlflow_list_artifact API no longer throws when listing artifacts for an empty run (#1862, @smurching)
• Fixed a bug preventing running the MLflow server against an MS SQL database (#1758, @sifanLV)
• MLmodel files (artifacts) now correctly display in the run UI (#1819, @ankitmathur-db)
• The Python mlflow.start_run API now throws when resuming a run whose experiment ID differs from the active experiment ID set via mlflow.set_experiment (#1820, @mcminnra).
• MlflowClient.log_metric now logs metric timestamps with millisecond (as opposed to second) resolution (#1804, @ustcscgyer)
• Fixed bugs when listing (#1800, @ahutterTA) and downloading (#1890, @jdlesage) artifacts stored in HDFS.
• Fixed a bug preventing Kubernetes Projects from pushing to private Docker repositories (#1788, @dbczumar)
• Fixed a bug preventing deploying Spark models to AzureML (#1769, @Ben-Epstein)
• Fixed experiment id resolution in projects (#1715, @drewmcdonald)
• Updated parallel coordinates plot to show all fields available in compared runs (#1753, @mateiz)
• Streamlined docs for getting started with hosted MLflow (#1834, #1785, #1860 @smurching)

Small bug fixes and doc updates (#1848, @pingsutw; #1868, @iver56; #1787, @apurvakoti; #1741, #1737, @apurva-koti; #1876, #1861, #1852, #1801, #1754, #1726, #1780, #1807 @smurching; #1859, #1858, #1851, @tomasatdatabricks; #1841, @ankitmathur-db; #1744, #1746, #1751, @mateiz; #1821, #1730, @dbczumar; #1727, cfmcgrady; #1716, @axsaucedo; #1714, @fhoering; #1405, @ancasarb; #1502, @jimthompson5802; #1720, jke-zq; #1871, @mehdi254; #1782, @stbof)

1.2 (2019-08-09)

MLflow 1.2 includes the following major features and improvements:

• Experiments now have editable tags and descriptions (#1630, #1632, #1678, @ankitmathur-db)
• Search latency has been significantly reduced in the SQLAlchemyStore (#1660, @t-henri)

More features and improvements

• Backend stores now support run tag values up to 5000 characters in length. Some store implementations may support longer tag values (#1687, @ankitmathur-db)
• Gunicorn options can now be configured for the mlflow models serve CLI with the GUNICORN_CMD_ARGS environment variable (#1557, @LarsDu)
• Jsonnet artifacts can now be previewed in the UI (#1683, @ankitmathur-db)
• Adds an optional python_version argument to mlflow_install for specifying the Python version (e.g. "3.5") to use within the conda environment created for installing the MLflow CLI. If python_version is unspecified, mlflow_install defaults to using Python 3.6. (#1722, @smurching)

Bug fixes and documentation updates

• [Tracking] The Autologging feature is now more resilient to tracking errors (#1690, @apurva-koti)
• [Tracking] The runs field in in the GetExperiment.Response proto has been deprecated & will be removed in MLflow 2.0. Please use the Search Runs API for fetching runs instead (#1647, @dbczumar)
• [Projects] Fixed a bug that prevented docker-based MLflow Projects from logging artifacts to the LocalArtifactRepository (#1450, @nlaille)
• [Projects] Running MLflow projects with the --no-conda flag in R no longer requires Anaconda to be installed (#1650, @spadarian)
• [Models/Scoring] Fixed a bug that prevented Spark UDFs from being loaded on Databricks (#1658, @smurching)
• [UI] AJAX requests made by the MLFlow Server Frontend now specify correct MIME-Types (#1679, @ynotzort)
• [UI] Previews now render correctly for artifacts with uppercase file extensions (e.g., .JSON, .YAML) (#1664, @ankitmathur-db)
• [UI] Fixed a bug that caused search API errors to surface a Niagara Falls page (#1681, @dbczumar)
• [Installation] MLflow dependencies are now selected properly based on the target installation platform (#1643, @akshaya-a)
• [UI] Fixed a bug where the "load more" button in the experiment view did not appear on browsers in Windows (#1718, @Zangr)

Small bug fixes and doc updates (#1663, #1719, @dbczumar; #1693, @max-allen-db; #1695, #1659, @smurching; #1675, @jdlesage; #1699, @ankitmathur-db; #1696, @aarondav; #1710, #1700, #1656, @apurva-koti)

1.1 (2019-07-22)

MLflow 1.1 includes several major features and improvements:

In MLflow Tracking:

• Experimental support for autologging from Tensorflow and Keras. Using mlflow.tensorflow.autolog() will enable automatic logging of metrics and optimizer parameters from TensorFlow to MLflow. The feature will work with TensorFlow versions 1.12 <= v < 2.0. (#1520, #1601, @apurva-koti)
• Parallel coordinates plot in the MLflow compare run UI. Adds out of the box support for a parallel coordinates plot. The plot allows users to observe relationships between a n-dimensional set of parameters to metrics. It visualizes all runs as lines that are color-coded based on the value of a metric (e.g. accuracy), and shows what parameter values each run took on. (#1497, @Zangr)
• Pandas based search API. Adds the ability to return the results of a search as a pandas dataframe using the new mlflow.search_runs API. (#1483, #1548, @max-allen-db)
• Java fluent API. Adds a new set of APIs to create and log to MLflow runs. This API contrasts with the existing low level MlflowClient API which simply wraps the REST APIs. The new fluent API allows you to create and log runs similar to how you would using the Python fluent API. (#1508, @andrewmchen)
• Run tags improvements. Adds the ability to add and edit tags from the run view UI, delete tags from the API, and view tags in the experiment search view. (#1400, #1426, @Zangr; #1548, #1558, @ankitmathur-db)
• Search API improvements. Adds order by and pagination to the search API. Pagination allows you to read a large set of runs in small page sized chunks. This allows clients and backend implementations to handle an unbounded set of runs in a scalable manner. (#1444, @sueann; #1437, #1455, #1482, #1485, #1542, @aarondav; #1567, @max-allen-db; #1217, @mparkhe)
• Windows support for running the MLflow tracking server and UI. (#1080, @akshaya-a)

In MLflow Projects:

• Experimental support to run Docker based MLprojects in Kubernetes. Adds the first fully open source remote execution backend for MLflow projects. With this, you can leverage elastic compute resources managed by kubernetes for their ML training purposes. For example, you can run grid search over a set of hyperparameters by running several instances of an MLproject in parallel. (#1181, @marcusrehm, @tomasatdatabricks, @andrewmchen; #1566, @stbof, @dbczumar; #1574 @dbczumar)

More features and improvements

In MLflow Tracking:

• Paginated “load more” and backend sorting for experiment search view UI. This change allows the UI to scalably display the sorted runs from large experiments. (#1564, @Zangr)
• Search results are encoded in the URL. This allows you to share searches through their URL and to deep link to them. (#1416, @apurva-koti)
• Ability to serve MLflow UI behind jupyter-server-proxy or outside of the root path /. Previous to MLflow 1.1, the UI could only be hosted on / since the Javascript makes requests directly to /ajax-api/.... With this patch, MLflow will make requests to ajax-api/... or a path relative to where the HTML is being served. (#1413, @xhochy)

In MLflow Models:

• Update mlflow.spark.log_model() to accept descendants of pyspark.Model (#1519, @ankitmathur-db)
• Support for saving custom Keras models with custom_objects. This field is semantically equivalent to custom_objects parameter of keras.models.load_model() function (#1525, @ankitmathur-db)
• New more performant split orient based input format for pyfunc scoring server (#1479, @lennon310)
• Ability to specify gunicorn server options for pyfunc scoring server built with mlflow models build-docker. #1428, @lennon310)

Bug fixes and documentation updates

• [Tracking] Fix database migration for MySQL. mlflow db upgrade should now work for MySQL backends. (#1404, @sueann)
• [Tracking] Make CLI mlflow server and mlflow ui commands to work with SQLAlchemy URIs that specify a database driver. (#1411, @sueann)
• [Tracking] Fix usability bugs related to FTP artifact repository. (#1398, @kafendt; #1421, @nlaille)
• [Tracking] Return appropriate HTTP status codes for MLflowException (#1434, @max-allen-db)
• [Tracking] Fix sorting by user ID in the experiment search view. (#1401, @andrewmchen)
• [Tracking] Allow calling log_metric with NaNs and infs. (#1573, @tomasatdatabricks)
• [Tracking] Fixes an infinite loop in downloading artifacts logged via dbfs and retrieved via S3. (#1605, @sueann)
• [Projects] Docker projects should preserve directory structure (#1436, @ahutterTA)
• [Projects] Fix conda activation for newer versions of conda. (#1576, @avinashraghuthu, @smurching)
• [Models] Allow you to log Tensorflow keras models from the tf.keras module. (#1546, @tomasatdatabricks)

Small bug fixes and doc updates (#1463, @mateiz; #1641, #1622, #1418, @sueann; #1607, #1568, #1536, #1478, #1406, #1408, @smurching; #1504, @LizaShak; #1490, @acroz; #1633, #1631, #1603, #1589, #1569, #1526, #1446, #1438, @apurva-koti; #1456, @Taur1ne; #1547, #1495, @aarondav; #1610, #1600, #1492, #1493, #1447, @tomasatdatabricks; #1430, @javierluraschi; #1424, @nathansuh; #1488, @henningsway; #1590, #1427, @Zangr; #1629, #1614, #1574, #1521, #1522, @dbczumar; #1577, #1514, @ankitmathur-db; #1588, #1566, @stbof; #1575, #1599, @max-allen-db; #1592, @abaveja313; #1606, @andrewmchen)

1.0 (2019-06-03)

MLflow 1.0 includes many significant features and improvements. From this version, MLflow is no longer beta, and all APIs except those marked as experimental are intended to be stable until the next major version. As such, this release includes a number of breaking changes.

Major features, improvements, and breaking changes

• Support for recording, querying, and visualizing metrics along a new “step” axis (x coordinate), providing increased flexibility for examining model performance relative to training progress. For example, you can now record performance metrics as a function of the number of training iterations or epochs. MLflow 1.0’s enhanced metrics UI enables you to visualize the change in a metric’s value as a function of its step, augmenting MLflow’s existing UI for plotting a metric’s value as a function of wall-clock time. (#1202, #1237, @dbczumar; #1132, #1142, #1143, @smurching; #1211, #1225, @Zangr; #1372, @stbof)
• Search improvements. MLflow 1.0 includes additional support in both the API and UI for searching runs within a single experiment or a group of experiments. The search filter API supports a simplified version of the SQL WHERE clause. In addition to searching using run's metrics and params, the API has been enhanced to support a subset of run attributes as well as user and system tags. For details see Search syntax and examples for programmatically searching runs. (#1245, #1272, #1323, #1326, @mparkhe; #1052, @Zangr; #1363, @aarondav)
• Logging metrics in batches. MLflow 1.0 now has a runs/log-batch REST API endpoint for logging multiple metrics, params, and tags in a single API request. The endpoint useful for performant logging of multiple metrics at the end of a model training epoch (see example), or logging of many input model parameters at the start of training. You can call this batched-logging endpoint from Python (mlflow.log_metrics, mlflow.log_params, mlflow.set_tags), R (mlflow_log_batch), and Java (MlflowClient.logBatch). (#1214, @dbczumar; see 0.9.1 and 0.9.0 for other changes)
• Windows support for MLflow Tracking. The Tracking portion of the MLflow client is now supported on Windows. (#1171, @eedeleon, @tomasatdatabricks)
• HDFS support for artifacts. Hadoop artifact repository with Kerberos authorization support was added, so you can use HDFS to log and retrieve models and other artifacts. (#1011, @jaroslawk)
• CLI command to build Docker images for serving. Added an mlflow models build-docker CLI command for building a Docker image capable of serving an MLflow model. The model is served at port 8080 within the container by default. Note that this API is experimental and does not guarantee that the arguments nor format of the Docker container will remain the same. (#1329, @smurching, @tomasatdatabricks)
• New onnx model flavor for saving, loading, and evaluating ONNX models with MLflow. ONNX flavor APIs are available in the mlflow.onnx module. (#1127, @avflor, @dbczumar; #1388, @dbczumar)
• Major breaking changes:
  • Some of the breaking changes involve database schema changes in the SQLAlchemy tracking store. If your database instance's schema is not up-to-date, MLflow will issue an error at the start-up of mlflow server or mlflow ui. To migrate an existing database to the newest schema, you can use the mlflow db upgrade CLI command. (#1155, #1371, @smurching; #1360, @aarondav)
  • [Installation] The MLflow Python package no longer depends on scikit-learn, mleap, or boto3. If you want to use the scikit-learn support, the MLeap support, or s3 artifact repository / sagemaker support, you will have to install these respective dependencies explicitly. (#1223, @aarondav)
  • [Artifacts] In the Models API, an artifact's location is now represented as a URI. See the documentation for the list of accepted URIs. (#1190, #1254, @dbczumar; #1174, @dbczumar, @sueann; #1206, @tomasatdatabricks; #1253, @stbof)
    • The affected methods are:
      • Python: <model-type>.load_model, azureml.build_image, sagemaker.deploy, sagemaker.run_local, pyfunc._load_model_env, pyfunc.load_pyfunc, and pyfunc.spark_udf
      • R: mlflow_load_model, mlflow_rfunc_predict, mlflow_rfunc_serve
      • CLI: mlflow models serve, mlflow models predict, mlflow sagemaker, mlflow azureml (with the new --model-uri option)
    • To allow referring to artifacts in the context of a run, MLflow introduces a new URI scheme of the form runs:/<run_id>/relative/path/to/artifact. (#1169, #1175, @sueann)
  • [CLI] mlflow pyfunc and mlflow rfunc commands have been unified as mlflow models (#1257, @tomasatdatabricks; #1321, @dbczumar)
  • [CLI] mlflow artifacts download, mlflow artifacts download-from-uri and mlflow download commands have been consolidated into mlflow artifacts download (#1233, @sueann)
  • [Runs] Expose RunData fields (metrics, params, tags) as dictionaries. Note that the mlflow.entities.RunData constructor still accepts lists of metric/param/tag entities. (#1078, @smurching)
  • [Runs] Rename run_uuid to run_id in Python, Java, and REST API. Where necessary, MLflow will continue to accept run_uuid until MLflow 1.1. (#1187, @aarondav)

Other breaking changes

CLI:

• The --file-store option is deprecated in mlflow server and mlflow ui commands. (#1196, @smurching)
• The --host and --gunicorn-opts options are removed in the mlflow ui command. (#1267, @aarondav)
• Arguments to mlflow experiments subcommands, notably --experiment-name and --experiment-id are now options (#1235, @sueann)
• mlflow sagemaker list-flavors has been removed (#1233, @sueann)

Tracking:

• The user property of Run``s has been moved to tags (similarly, the ``run_name, source_type, source_name properties were moved to tags in 0.9.0). (#1230, @acroz; #1275, #1276, @aarondav)
• In R, the return values of experiment CRUD APIs have been updated to more closely match the REST API. In particular, mlflow_create_experiment now returns a string experiment ID instead of an experiment, and the other APIs return NULL. (#1246, @smurching)
• RunInfo.status's type is now string. (#1264, @mparkhe)
• Remove deprecated RunInfo properties from start_run. (#1220, @aarondav)
• As deprecated in 0.9.1 and before, the RunInfo fields run_name, source_name, source_version, source_type, and entry_point_name and the SearchRuns field anded_expressions have been removed from the REST API and Python, Java, and R tracking client APIs. They are still available as tags, documented in the REST API documentation. (#1188, @aarondav)

Models and deployment:

• In Python, require arguments as keywords in log_model, save_model and add_to_model methods in the tensorflow and mleap modules to avoid breaking changes in the future (#1226, @sueann)
• Remove the unsupported jars argument from `spark.log_model in Python (#1222, @sueann)
• Introduce pyfunc.load_model to be consistent with other Models modules. pyfunc.load_pyfunc will be deprecated in the near future. (#1222, @sueann)
• Rename dst_path parameter in pyfunc.save_model to path (#1221, @aarondav)
• R flavors refactor (#1299, @kevinykuo)
  • mlflow_predict() has been added in favor of mlflow_predict_model() and mlflow_predict_flavor() which have been removed.
  • mlflow_save_model() is now a generic and mlflow_save_flavor() is no longer needed and has been removed.
  • mlflow_predict() takes ... to pass to underlying predict methods.
  • mlflow_load_flavor() now has the signature function(flavor, model_path) and flavor authors should implement mlflow_load_flavor.mlflow_flavor_{FLAVORNAME}. The flavor argument is inferred from the inputs of user-facing mlflow_load_model() and does not need to be explicitly provided by the user.

Projects:

• Remove and rename some projects.run parameters for generality and consistency. (#1222, @sueann)
• In R, the mlflow_run API for running MLflow projects has been modified to more closely reflect the Python mlflow.run API. In particular, the order of the uri and entry_point arguments has been reversed and the param_list argument has been renamed to parameters. (#1265, @smurching)

R:

• Remove mlflow_snapshot and mlflow_restore_snapshot APIs. Also, the r_dependencies argument used to specify the path to a packrat r-dependencies.txt file has been removed from all APIs. (#1263, @smurching)
• The mlflow_cli and crate APIs are now private. (#1246, @smurching)

Environment variables:

• Prefix environment variables with "MLFLOW_" (#1268, @aarondav). Affected variables are:
  • [Tracking] _MLFLOW_SERVER_FILE_STORE, _MLFLOW_SERVER_ARTIFACT_ROOT, _MLFLOW_STATIC_PREFIX
  • [SageMaker] MLFLOW_SAGEMAKER_DEPLOY_IMG_URL, MLFLOW_DEPLOYMENT_FLAVOR_NAME
  • [Scoring] MLFLOW_SCORING_SERVER_MIN_THREADS, MLFLOW_SCORING_SERVER_MAX_THREADS

More features and improvements

• [Tracking] Non-default driver support for SQLAlchemy backends: db+driver is now a valid tracking backend URI scheme (#1297, @drewmcdonald; #1374, @mparkhe)
• [Tracking] Validate backend store URI before starting tracking server (#1218, @luke-zhu, @sueann)
• [Tracking] Add GetMetricHistory client API in Python and Java corresponding to the REST API. (#1178, @smurching)
• [Tracking] Add view_type argument to MlflowClient.list_experiments() in Python. (#1212, @smurching)
• [Tracking] Dictionary values provided to mlflow.log_params and mlflow.set_tags in Python can now be non-string types (e.g., numbers), and they are automatically converted to strings. (#1364, @aarondav)
• [Tracking] R API additions to be at parity with REST API and Python (#1122, @kevinykuo)
• [Tracking] Limit number of results returned from SearchRuns API and UI for faster load (#1125, @mparkhe; #1154, @andrewmchen)
• [Artifacts] To avoid having many copies of large model files in serving, ArtifactRepository.download_artifacts no longer copies local artifacts (#1307, @andrewmchen; #1383, @dbczumar)
• [Artifacts][Projects] Support GCS in download utilities. gs://bucket/path files are now supported by the mlflow artifacts download CLI command and as parameters of type path in MLProject files. (#1168, @drewmcdonald)
• [Models] All Python models exported by MLflow now declare mlflow as a dependency by default. In addition, we introduce a flag --install-mlflow users can pass to mlflow models serve and mlflow models predict methods to force installation of the latest version of MLflow into the model's environment. (#1308, @tomasatdatabricks)
• [Models] Update model flavors to lazily import dependencies in Python. Modules that define Model flavors now import extra dependencies such as tensorflow, scikit-learn, and pytorch inside individual _methods_, ensuring that these modules can be imported and explored even if the dependencies have not been installed on your system. Also, the DEFAULT_CONDA_ENVIRONMENT module variable has been replaced with a get_default_conda_env() function for each flavor.
• [Models] It is now possible to pass extra arguments to mlflow.keras.load_model that will be passed through to keras.load_model. (#1330, @@yorickvP)
• [Serving] For better performance, switch to gunicorn for serving Python models. This does not change the user interface. (#1322, @tomasatdatabricks)
• [Deployment] For SageMaker, use the uniquely-generated model name as the S3 bucket prefix instead of requiring one. (#1183, @dbczumar)
• [REST API] Add support for API paths without the preview component. The preview paths will be deprecated in a future version of MLflow. (#1236, @mparkhe)

Bug fixes and documentation updates

• [Tracking] Log metric timestamps in milliseconds by default (#1177, @smurching; #1333, @dbczumar)
• [Tracking] Fix bug when deserializing integer experiment ID for runs in SQLAlchemyStore (#1167, @smurching)
• [Tracking] Ensure unique constraint names in MLflow tracking database (#1292, @smurching)
• [Tracking] Fix base64 encoding for basic auth in R tracking client (#1126, @freefrag)
• [Tracking] Correctly handle file: URIs for the -—backend-store-uri option in mlflow server and mlflow ui CLI commands (#1171, @eedeleon, @tomasatdatabricks)
• [Artifacts] Update artifact repository download methods to return absolute paths (#1179, @dbczumar)
• [Artifacts] Make FileStore respect the default artifact location (#1332, @dbczumar)
• [Artifacts] Fix log_artifact failures due to existing directory on FTP server (#1327, @kafendt)
• [Artifacts] Fix GCS artifact logging of subdirectories (#1285, @jason-huling)
• [Projects] Fix bug not sharing SQLite database file with Docker container (#1347, @tomasatdatabricks; #1375, @aarondav)
• [Java] Mark sendPost and sendGet as experimental (#1186, @aarondav)
• [Python][CLI] Mark azureml.build_image as experimental (#1222, #1233 @sueann)
• [Docs] Document public MLflow environment variables (#1343, @aarondav)
• [Docs] Document MLflow system tags for runs (#1342, @aarondav)
• [Docs] Autogenerate CLI documentation to include subcommands and descriptions (#1231, @sueann)
• [Docs] Update run selection description in mlflow_get_run in R documentation (#1258, @dbczumar)
• [Examples] Update examples to reflect API changes (#1361, @tomasatdatabricks; #1367, @mparkhe)

Small bug fixes and doc updates (#1359, #1350, #1331, #1301, #1270, #1271, #1180, #1144, #1135, #1131, #1358, #1369, #1368, #1387, @aarondav; #1373, @akarloff; #1287, #1344, #1309, @stbof; #1312, @hchiuzhuo; #1348, #1349, #1294, #1227, #1384, @tomasatdatabricks; #1345, @withsmilo; #1316, @ancasarb; #1313, #1310, #1305, #1289, #1256, #1124, #1097, #1162, #1163, #1137, #1351, @smurching; #1319, #1244, #1224, #1195, #1194, #1328, @dbczumar; #1213, #1200, @Kublai-Jing; #1304, #1320, @andrewmchen; #1311, @Zangr; #1306, #1293, #1147, @mateiz; #1303, @gliptak; #1261, #1192, @eedeleon; #1273, #1259, @kevinykuo; #1277, #1247, #1243, #1182, #1376, @mparkhe; #1210, @vgod-dbx; #1199, @ashtuchkin; #1176, #1138, #1365, @sueann; #1157, @cclauss; #1156, @clemens-db; #1152, @pogil; #1146, @srowen; #875, #1251, @jimthompson5802)

0.9.1 (2019-04-21)

MLflow 0.9.1 is a patch release on top of 0.9.0 containing mostly bug fixes and internal improvements. We have also included a one breaking API change in preparation for additions in MLflow 1.0 and later. This release also includes significant improvements to the Search API.

Breaking changes:

• [Tracking] Generalized experiment_id to string (from a long) to be more permissive of different ID types in different backend stores. While breaking for the REST API, this change is backwards compatible for python and R clients. (#1067, #1034 @eedeleon)

More features and improvements:

• [Search][API] Moving search filters into a query string based syntax, with Java client, Python client, and UI support. This also improves quote, period, and special character handling in query strings and adds the ability to search on tags in filter string. (#1042, #1055, #1063, #1068, #1099, #1106 @mparkhe; #1025 @andrewmchen; #1060 @smurching)
• [Tracking] Limits and validations to batch-logging APIs in OSS server (#958 @smurching)
• [Tracking][Java] Java client API for batch-logging (#1081 @mparkhe)
• [Tracking] Improved consistency of handling multiple metric values per timestamp across tracking stores (#972, #999 @dbczumar)

Bug fixes and documentation updates:

• [Tracking][Python] Reintroduces the parent_run_id argument to MlflowClient.create_run. This API is planned for removal in MLflow 1.0 (#1137 @smurching)
• [Tracking][Python] Provide default implementations of AbstractStore log methods (#1051 @acroz)
• [R] (Released on CRAN as MLflow 0.9.0.1) Small bug fixes with R (#1123 @smurching; #1045, #1017, #1019, #1039, #1048, #1098, #1101, #1107, #1108, #1119 @tomasatdatabricks)

Small bug fixes and doc updates (#1024, #1029 @bayethiernodiop; #1075 @avflor; #968, #1010, #1070, #1091, #1092 @smurching; #1004, #1085 @dbczumar; #1033, #1046 @sueann; #1053 @tomasatdatabricks; #987 @hanyucui; #935, #941 @jimthompson5802; #963 @amilbourne; #1016 @andrewmchen; #991 @jaroslawk; #1007 @mparkhe)

0.9.0.1 (2019-04-09)

Bugfix release (PyPI only) with the following changes:

• Rebuilt MLflow JS assets to fix an issue where form input was broken in MLflow 0.9.0 (identified in #1056, #1113 by @shu-yusa, @timothyjlaurent)

0.9.0 (2019-03-13)

Major features:

• Support for running MLflow Projects in Docker containers. This allows you to include non-Python dependencies in their project environments and provides stronger isolation when running projects. See the Projects documentation for more information. (#555, @marcusrehm; #819, @mparkhe; #970, @dbczumar)
• Database stores for the MLflow Tracking Server. Support for a scalable and performant backend store was one of the top community requests. This feature enables you to connect to local or remote SQLAlchemy-compatible databases (currently supported flavors include MySQL, PostgreSQL, SQLite, and MS SQL) and is compatible with file backed store. See the Tracking Store documentation for more information. (#756, @AndersonReyes; #800, #844, #847, #848, #860, #868, #975, @mparkhe; #980, @dbczumar)
• Simplified custom Python model packaging. You can easily include custom preprocessing and postprocessing logic, as well as data dependencies in models with the python_function flavor using updated mlflow.pyfunc Python APIs. For more information, see the Custom Python Models documentation. (#791, #792, #793, #830, #910, @dbczumar)
• Plugin systems allowing third party libraries to extend MLflow functionality. The proposal document gives the full detail of the three main changes:
  • You can register additional providers of tracking stores using the mlflow.tracking_store entrypoint. (#881, @zblz)
  • You can register additional providers of artifact repositories using the mlflow.artifact_repository entrypoint. (#882, @mociarain)
  • The logic generating run metadata from the run context (e.g. source_name, source_version) has been refactored into an extendable system of run context providers. Plugins can register additional providers using the mlflow.run_context_provider entrypoint, which add to or overwrite tags set by the base library. (#913, #926, #930, #978, @acroz)
• Support for HTTP authentication to the Tracking Server in the R client. Now you can connect to secure Tracking Servers using credentials set in environment variables, or provide custom plugins for setting the credentials. As an example, this release contains a Databricks plugin that can detect existing Databricks credentials to allow you to connect to the Databricks Tracking Server. (#938, #959, #992, @tomasatdatabricks)

Breaking changes:

• [Scoring] The pyfunc scoring server now expects requests with the application/json content type to contain json-serialized pandas dataframes in the split format, rather than the records format. See the documentation on deployment for more detail. (#960, @dbczumar) Also, when reading the pandas dataframes from JSON, the scoring server no longer automatically infers data types as it can result in unintentional conversion of data types (#916, @mparkhe).
• [API] Remove GetMetric & GetParam from the REST API as they are subsumed by GetRun. (#879, @aarondav)

More features and improvements:

• [UI] Add a button for downloading artifacts (#967, @mateiz)
• [CLI] Add CLI commands for runs: now you can list, delete, restore, and describe runs through the CLI (#720, @DorIndivo)
• [CLI] The run command now can take --experiment-name as an argument, as an alternative to the --experiment-id argument. You can also choose to set the _EXPERIMENT_NAME_ENV_VAR environment variable instead of passing in the value explicitly. (#889, #894, @mparkhe)
• [Examples] Add Image classification example with Keras. (#743, @tomasatdatabricks )
• [Artifacts] Add get_artifact_uri() and _download_artifact_from_uri convenience functions (#779)
• [Artifacts] Allow writing Spark models directly to the target artifact store when possible (#808, @smurching)
• [Models] PyTorch model persistence improvements to allow persisting definitions and dependencies outside the immediate scope: - Add a code_paths parameter to mlflow.pytorch.save_model and mlflow.pytorch.log_model to allow external module dependencies to be specified as paths to python files. (#842, @dbczumar) - Improve mlflow.pytorch.save_model to capture class definitions from notebooks and the __main__ scope (#851, #861, @dbczumar)
• [Runs][R] Allow client to infer context info when creating new run in fluent API (#958, @tomasatdatabricks)
• [Runs][UI] Support Git Commit hyperlink for Gitlab and Bitbucket. Previously the clickable hyperlink was generated only for Github pages. (#901)
• [Search][API] Allow param value to have any content, not just alphanumeric characters, ., and - (#788, @mparkhe)
• [Search][API] Support "filter" string in the SearchRuns API. Corresponding UI improvements are planned for the future (#905, @mparkhe)
• [Logging] Basic support for LogBatch. NOTE: The feature is currently experimental and the behavior is expected to change in the near future. (#950, #951, #955, #1001, @smurching)

Bug fixes and documentation updates:

• [Artifacts] Fix empty-file upload to DBFS in log_artifact and log_artifacts (#895, #818, @smurching)
• [Artifacts] S3 artifact store: fix path resolution error when artifact root is bucket root (#928, @dbczumar)
• [UI] Fix a bug with Databricks notebook URL links (#891, @smurching)
• [Export] Fix for missing run name in csv export (#864, @jimthompson5802)
• [Example] Correct missing tensorboardX module error in PyTorch example when running in MLflow Docker container (#809, @jimthompson5802)
• [Scoring][R] Fix local serving of rfunc models (#874, @kevinykuo)
• [Docs] Improve flavor-specific documentation in Models documentation (#909, @dbczumar)

Small bug fixes and doc updates (#822, #899, #787, #785, #780, #942, @hanyucui; #862, #904, #954, #806, #857, #845, @stbof; #907, #872, @smurching; #896, #858, #836, #859, #923, #939, #933, #931, #952, @dbczumar; #880, @zblz; #876, @acroz; #827, #812, #816, #829, @jimthompson5802; #837, #790, #897, #974, #900, @mparkhe; #831, #798, @aarondav; #814, @sueann; #824, #912, @mateiz; #922, #947, @tomasatdatabricks; #795, @KevYuen; #676, @mlaradji; #906, @4n4nd; #777, @tmielika; #804, @alkersan)

0.8.2 (2019-01-28)

MLflow 0.8.2 is a patch release on top of 0.8.1 containing only bug fixes and no breaking changes or features.

Bug fixes:

• [Python API] CloudPickle has been added to the set of MLflow library dependencies, fixing missing import errors when attempting to save models (#777, @tmielika)
• [Python API] Fixed a malformed logging call that prevented mlflow.sagemaker.push_image_to_ecr() invocations from succeeding (#784, @jackblandin)
• [Models] PyTorch models can now be saved with code dependencies, allowing model classes to be loaded successfully in new environments (#842, #836, @dbczumar)
• [Artifacts] Fixed a timeout when logging zero-length files to DBFS artifact stores (#818, @smurching)

Small docs updates (#845, @stbof; #840, @grahamhealy20; #839, @wilderrodrigues)

0.8.1 (2018-12-21)

MLflow 0.8.1 introduces several significant improvements:

• Improved UI responsiveness and load time, especially when displaying experiments containing hundreds to thousands of runs.
• Improved visualizations, including interactive scatter plots for MLflow run comparisons
• Expanded support for scoring Python models as Spark UDFs. For more information, see the updated documentation for this feature.
• By default, saved models will now include a Conda environment specifying all of the dependencies necessary for loading them in a new environment.

Features:

• [API/CLI] Support for running MLflow projects from ZIP files (#759, @jmorefieldexpe)
• [Python API] Support for passing model conda environments as dictionaries to save_model and log_model functions (#748, @dbczumar)
• [Models] Default Anaconda environments have been added to many Python model flavors. By default, models produced by save_model and log_model functions will include an environment that specifies all of the versioned dependencies necessary to load and serve the models. Previously, users had to specify these environments manually. (#705, #707, #708, #749, @dbczumar)
• [Scoring] Support for synchronous deployment of models to SageMaker (#717, @dbczumar)
• [Tracking] Include the Git repository URL as a tag when tracking an MLflow run within a Git repository (#741, @whiletruelearn, @mateiz)
• [UI] Improved runs UI performance by using a react-virtualized table to optimize row rendering (#765, #762, #745, @smurching)
• [UI] Significant performance improvements for rendering run metrics, tags, and parameter information (#764, #747, @smurching)
• [UI] Scatter plots, including run comparsion plots, are now interactive (#737, @mateiz)
• [UI] Extended CSRF support by allowing the MLflow UI server to specify a set of expected headers that clients should set when making AJAX requests (#733, @aarondav)

Bug fixes and documentation updates:

• [Python/Scoring] MLflow Python models that produce Pandas DataFrames can now be evaluated as Spark UDFs correctly. Spark UDF outputs containing multiple columns of primitive types are now supported (#719, @tomasatdatabricks)
• [Scoring] Fixed a serialization error that prevented models served with Azure ML from returning Pandas DataFrames (#754, @dbczumar)
• [Docs] New example demonstrating how the MLflow REST API can be used to create experiments and log run information (#750, kjahan)
• [Docs] R documentation has been updated for clarity and style consistency (#683, @stbof)
• [Docs] Added clarification about user setup requirements for executing remote MLflow runs on Databricks (#736, @andyk)

Small bug fixes and doc updates (#768, #715, @smurching; #728, dodysw; #730, mshr-h; #725, @kryptec; #769, #721, @dbczumar; #714, @stbof)

0.8.0 (2018-11-08)

MLflow 0.8.0 introduces several major features:

• Dramatically improved UI for comparing experiment run results:
  • Metrics and parameters are by default grouped into a single column, to avoid an explosion of mostly-empty columns. Individual metrics and parameters can be moved into their own column to help compare across rows.
  • Runs that are "nested" inside other runs (e.g., as part of a hyperparameter search or multistep workflow) now show up grouped by their parent run, and can be expanded or collapsed altogether. Runs can be nested by calling mlflow.start_run or mlflow.run while already within a run.
  • Run names (as opposed to automatically generated run UUIDs) now show up instead of the run ID, making comparing runs in graphs easier.
  • The state of the run results table, including filters, sorting, and expanded rows, is persisted in browser local storage, making it easier to go back and forth between an individual run view and the table.
• Support for deploying models as Docker containers directly to Azure Machine Learning Service Workspace (as opposed to the previously-recommended solution of Azure ML Workbench).

Breaking changes:

• [CLI] mlflow sklearn serve has been removed in favor of mlflow pyfunc serve, which takes the same arguments but works against any pyfunc model (#690, @dbczumar)

Features:

• [Scoring] pyfunc server and SageMaker now support the pandas "split" JSON format in addition to the "records" format. The split format allows the client to specify the order of columns, which is necessary for some model formats. We recommend switching client code over to use this new format (by sending the Content-Type header application/json; format=pandas-split), as it will become the default JSON format in MLflow 0.9.0. (#690, @dbczumar)
• [UI] Add compact experiment view (#546, #620, #662, #665, @smurching)
• [UI] Add support for viewing & tracking nested runs in experiment view (#588, @andrewmchen; #618, #619, @aarondav)
• [UI] Persist experiments view filters and sorting in browser local storage (#687, @smurching)
• [UI] Show run name instead of run ID when present (#476, @smurching)
• [Scoring] Support for deploying Models directly to Azure Machine Learning Service Workspace (#631, @dbczumar)
• [Server/Python/Java] Add rename_experiment to Tracking API (#570, @aarondav)
• [Server] Add get_experiment_by_name to RestStore (#592, @dmarkhas)
• [Server] Allow passing gunicorn options when starting mlflow server (#626, @mparkhe)
• [Python] Cloudpickle support for sklearn serialization (#653, @dbczumar)
• [Artifacts] FTP artifactory store added (#287, @Shenggan)

Bug fixes and documentation updates:

• [Python] Update TensorFlow integration to match API provided by other flavors (#612, @dbczumar; #670, @mlaradji)
• [Python] Support for TensorFlow 1.12 (#692, @smurching)
• [R] Explicitly loading Keras module at predict time no longer required (#586, @kevinykuo)
• [R] pyfunc serve can correctly load models saved with the R Keras support (#634, @tomasatdatabricks)
• [R] Increase network timeout of calls to the RestStore from 1 second to 60 seconds (#704, @aarondav)
• [Server] Improve errors returned by RestStore (#582, @andrewmchen; #560, @smurching)
• [Server] Deleting the default experiment no longer causes it to be immediately recreated (#604, @andrewmchen; #641, @schipiga)
• [Server] Azure Blob Storage artifact repo supports Windows paths (#642, @marcusrehm)
• [Server] Improve behavior when environment and run files are corrupted (#632, #654, #661, @mparkhe)
• [UI] Improve error page when viewing nonexistent runs or views (#600, @andrewmchen; #560, @andrewmchen)
• [UI] UI no longer throws an error if all experiments are deleted (#605, @andrewmchen)
• [Docs] Include diagram of workflow for multistep example (#581, @dennyglee)
• [Docs] Add reference tags and R and Java APIs to tracking documentation (#514, @stbof)
• [Docs/R] Use CRAN installation (#686, @javierluraschi)

Small bug fixes and doc updates (#576, #594, @javierluraschi; #585, @kevinykuo; #593, #601, #611, #650, #669, #671, #679, @dbczumar; #607, @suzil; #583, #615, @andrewmchen; #622, #681, @aarondav; #625, @pogil; #589, @tomasatdatabricks; #529, #635, #684, @stbof; #657, @mvsusp; #682, @mateiz; #678, vfdev-5; #596, @yutannihilation; #663, @smurching)

0.7.0 (2018-10-01)

MLflow 0.7.0 introduces several major features:

• An R client API (to be released on CRAN soon)
• Support for deleting runs (API + UI)
• UI support for adding notes to a run

The release also includes bugfixes and improvements across the Python and Java clients, tracking UI, and documentation.

Breaking changes:

• [Python] The per-flavor implementation of load_pyfunc has been made private (#539, @tomasatdatabricks)
• [REST API, Java] logMetric now accepts a double metric value instead of a float (#566, @aarondav)

Features:

• [R] Support for R (#370, #471, @javierluraschi; #548 @kevinykuo)
• [UI] Add support for adding notes to Runs (#396, @aadamson)
• [Python] Python API, REST API, and UI support for deleting Runs (#418, #473, #526, #579 @andrewmchen)
• [Python] Set a tag containing the branch name when executing a branch of a Git project (#469, @adrian555)
• [Python] Add a set_experiment API to activate an experiment before starting runs (#462, @mparkhe)
• [Python] Add arguments for specifying a parent run to tracking & projects APIs (#547, @andrewmchen)
• [Java] Add Java set tag API (#495, @smurching)
• [Python] Support logging a conda environment with sklearn models (#489, @dbczumar)
• [Scoring] Support downloading MLflow scoring JAR from Maven during scoring container build (#507, @dbczumar)

Bug fixes:

• [Python] Print errors when the Databricks run fails to start (#412, @andrewmchen)
• [Python] Fix Spark ML PyFunc loader to work on Spark driver (#480, @tomasatdatabricks)
• [Python] Fix Spark ML load_pyfunc on distributed clusters (#490, @tomasatdatabricks)
• [Python] Fix error when downloading artifacts from a run's artifact root (#472, @dbczumar)
• [Python] Fix DBFS upload file-existence-checking logic during Databricks project execution (#510, @smurching)
• [Python] Support multi-line and unicode tags (#502, @mparkhe)
• [Python] Add missing DeleteExperiment, RestoreExperiment implementations in the Python REST API client (#551, @mparkhe)
• [Scoring] Convert Spark DataFrame schema to an MLeap schema prior to serialization (#540, @dbczumar)
• [UI] Fix bar chart always showing in metric view (#488, @smurching)

Small bug fixes and doc updates (#467 @drorata; #470, #497, #508, #518 @dbczumar; #455, #466, #492, #504, #527 @aarondav; #481, #475, #484, #496, #515, #517, #498, #521, #522, #573 @smurching; #477 @parkerzf; #494 @jainr; #501, #531, #532, #552 @mparkhe; #503, #520 @dmatrix; #509, #532 @tomasatdatabricks; #484, #486 @stbof; #533, #534 @javierluraschi; #542 @GCBallesteros; #511 @AdamBarnhard)

0.6.0 (2018-09-10)

MLflow 0.6.0 introduces several major features:

• A Java client API, available on Maven
• Support for saving and serving SparkML models as MLeap for low-latency serving
• Support for tagging runs with metadata, during and after the run completion
• Support for deleting (and restoring deleted) experiments

In addition to these features, there are a host of improvements and bugfixes to the REST API, Python API, tracking UI, and documentation. The examples/ subdirectory has also been revamped to make it easier to jump in, and examples demonstrating multistep workflows and hyperparameter tuning have been added.

Breaking changes:

We fixed a few inconsistencies in the the mlflow.tracking API, as introduced in 0.5.0:

• MLflowService has been renamed MlflowClient (#461, @mparkhe)
• You get an MlflowClient by calling mlflow.tracking.MlflowClient() (previously, this was mlflow.tracking.get_service()) (#461, @mparkhe)
• MlflowService.list_runs was changed to MlflowService.list_run_infos to reflect the information actually returned by the call. It now returns a RunInfo instead of a Run (#334, @aarondav)
• MlflowService.log_artifact and MlflowService.log_artifacts now take a run_id instead of artifact_uri. This now matches list_artifacts and download_artifacts (#444, @aarondav)

Features:

• Java client API added with support for the MLflow Tracking API (analogous to mlflow.tracking), allowing users to create and manage experiments, runs, and artifacts. The release includes a usage example and Javadocs. The client is published to Maven under mlflow:mlflow (#380, #394, #398, #409, #410, #430, #452, @aarondav)
• SparkML models are now also saved in MLeap format (https://github.com/combust/mleap), when applicable. Model serving platforms can choose to serve using this format instead of the SparkML format to dramatically decrease prediction latency. SageMaker now does this by default (#324, #327, #331, #395, #428, #435, #438, @dbczumar)
• [API] Experiments can now be deleted and restored via REST API, Python Tracking API, and MLflow CLI (#340, #344, #367, @mparkhe)
• [API] Tags can now be set via a SetTag API, and they have been moved to RunData from RunInfo (#342, @aarondav)
• [API] Added list_artifacts and download_artifacts to MlflowService to interact with a run's artifactory (#350, @andrewmchen)
• [API] Added get_experiment_by_name to Python Tracking API, and equivalent to Java API (#373, @vfdev-5)
• [API/Python] Version is now exposed via mlflow.__version__.
• [API/CLI] Added mlflow artifacts CLI to list, download, and upload to run artifact repositories (#391, @aarondav)
• [UI] Added icons to source names in MLflow Experiments UI (#381, @andrewmchen)
• [UI] Added support to view .log and .tsv files from MLflow artifacts UI (#393, @Shenggan; #433, @whiletruelearn)
• [UI] Run names can now be edited from within the MLflow UI (#382, @smurching)
• [Serving] Added --host option to mlflow serve to allow listening on non-local addressess (#401, @hamroune)
• [Serving/SageMaker] SageMaker serving takes an AWS region argument (#366, @dbczumar)
• [Python] Added environment variables to support providing HTTP auth (username, password, token) when talking to a remote MLflow tracking server (#402, @aarondav)
• [Python] Added support to override S3 endpoint for S3 artifactory (#451, @hamroune)
• MLflow nightly Python wheel and JAR snapshots are now available and linked from https://github.com/mlflow/mlflow (#352, @aarondav)

Bug fixes and documentation updates:

• [Python] mlflow run now logs default parameters, in addition to explicitly provided ones (#392, @mparkhe)
• [Python] log_artifact in FileStore now requires a relative path as the artifact path (#439, @mparkhe)
• [Python] Fixed string representation of Python entities, so they now display both their type and serialized fields (#371, @smurching)
• [UI] Entry point name is now shown in MLflow UI (#345, @aarondav)
• [Models] Keras model export now includes TensorFlow graph explicitly to ensure the model can always be loaded at deployment time (#440, @tomasatdatabricks)
• [Python] Fixed issue where FileStore ignored provided Run Name (#358, @adrian555)
• [Python] Fixed an issue where any mlflow run failing printed an extraneous exception (#365, @smurching)
• [Python] uuid dependency removed (#351, @antonpaquin)
• [Python] Fixed issues with remote execution on Databricks (#357, #361, @smurching; #383, #387, @aarondav)
• [Docs] Added comprehensive example of doing a multistep workflow, chaining MLflow runs together and reusing results (#338, @aarondav)
• [Docs] Added comprehensive example of doing hyperparameter tuning (#368, @tomasatdatabricks)
• [Docs] Added code examples to mlflow.keras API (#341, @dmatrix)
• [Docs] Significant improvements to Python API documentation (#454, @stbof)
• [Docs] Examples folder refactored to improve readability. The examples now reside in examples/ instead of example/, too (#399, @mparkhe)
• Small bug fixes and doc updates (#328, #363, @ToonKBC; #336, #411, @aarondav; #284, @smurching; #377, @mparkhe; #389, gioa; #408, @aadamson; #397, @vfdev-5; #420, @adrian555; #459, #463, @stbof)

0.5.2 (2018-08-24)

MLflow 0.5.2 is a patch release on top of 0.5.1 containing only bug fixes and no breaking changes or features.

Bug fixes:

• Fix a bug with ECR client creation that caused mlflow.sagemaker.deploy() to fail when searching for a deployment Docker image (#366, @dbczumar)

0.5.1 (2018-08-23)

MLflow 0.5.1 is a patch release on top of 0.5.0 containing only bug fixes and no breaking changes or features.

Bug fixes:

• Fix with mlflow.start_run() as run to actually set run to the created Run (previously, it was None) (#322, @tomasatdatabricks)
• Fixes to DBFS artifactory to throw an exception if logging an artifact fails (#309) and to mimic FileStore's behavior of logging subdirectories (#347, @andrewmchen)
• Fix for Python 3.7 support with tarfiles (#329, @tomasatdatabricks)
• Fix spark.load_model not to delete the DFS tempdir (#335, @aarondav)
• MLflow UI now appropriately shows entrypoint if it's not main (#345, @aarondav)
• Make Python API forward-compatible with newer server versions of protos (#348, @aarondav)
• Improved API docs (#305, #284, @smurching)

0.5.0 (2018-08-17)

MLflow 0.5.0 offers some major improvements, including Keras and PyTorch first-class support as models, SFTP support as an artifactory, a new scatterplot visualization to compare runs, and a more complete Python SDK for experiment and run management.

Breaking changes:

• The Tracking API has been split into two pieces, a "basic logging" API and a "tracking service" API. The "basic logging" API deals with logging metrics, parameters, and artifacts to the currently-active active run, and is accessible in mlflow (e.g., mlflow.log_param). The tracking service API allow managing experiments and runs (especially historical runs) and is available in mlflow.tracking. The tracking service API will look analogous to the upcoming R and Java Tracking Service SDKs. Please be aware of the following breaking changes:
  • mlflow.tracking no longer exposes the basic logging API, only mlflow. So, code that was written like from mlflow.tracking import log_param will have to be from mlflow import log_param (note that almost all examples were already doing this).
  • Access to the service API goes through the mlflow.tracking.get_service() function, which relies on the same tracking server set by either the environment variable MLFLOW_TRACKING_URI or by code with mlflow.tracking.set_tracking_uri(). So code that used to look like mlflow.tracking.get_run() will now have to do mlflow.tracking.get_service().get_run(). This does not apply to the basic logging API.
  • mlflow.ActiveRun has been converted into a lightweight wrapper around mlflow.entities.Run to enable the Python with syntax. This means that there are no longer any special methods on the object returned when calling mlflow.start_run(). These can be converted to the service API.
  • The Python entities returned by the tracking service API are now accessible in mlflow.entities directly. Where previously you may have used mlflow.entities.experiment.Experiment, you would now just use mlflow.entities.Experiment. The previous version still exists, but is deprecated and may be hidden in a future version.
• REST API endpoint /ajax-api/2.0/preview/mlflow/artifacts/get has been moved to $static_prefix/get-artifact. This change is coversioned in the JavaScript, so should not be noticeable unless you were calling the REST API directly (#293, @andremchen)

Features:

• [Models] Keras integration: we now support logging Keras models directly in the log_model API, model format, and serving APIs (#280, @ToonKBC)
• [Models] PyTorch integration: we now support logging PyTorch models directly in the log_model API, model format, and serving APIs (#264, @vfdev-5)
• [UI] Scatterplot added to "Compare Runs" view to help compare runs using any two metrics as the axes (#268, @ToonKBC)
• [Artifacts] SFTP artifactory store added (#260, @ToonKBC)
• [Sagemaker] Users can specify a custom VPC when deploying SageMaker models (#304, @dbczumar)
• Pyfunc serialization now includes the Python version, and warns if the major version differs (can be suppressed by using load_pyfunc(suppress_warnings=True)) (#230, @dbczumar)
• Pyfunc serve/predict will activate conda environment stored in MLModel. This can be disabled by adding --no-conda to mlflow pyfunc serve or mlflow pyfunc predict (#225, @0wu)
• Python SDK formalized in mlflow.tracking. This includes adding SDK methods for get_run, list_experiments, get_experiment, and set_terminated. (#299, @aarondav)
• mlflow run can now be run against projects with no conda.yaml specified. By default, an empty conda environment will be created -- previously, it would just fail. You can still pass --no-conda to avoid entering a conda environment altogether (#218, @smurching)

Bug fixes:

• Fix numpy array serialization for int64 and other related types, allowing pyfunc to return such results (#240, @arinto)
• Fix DBFS artifactory calling log_artifacts with binary data (#295, @aarondav)
• Fix Run Command shown in UI to reproduce a run when the original run is targeted at a subdirectory of a Git repo (#294, @adrian555)
• Filter out ubiquitious dtype/ufunc warning messages (#317, @aarondav)
• Minor bug fixes and documentation updates (#261, @stbof; #279, @dmatrix; #313, @rbang1, #320, @yassineAlouini; #321, @tomasatdatabricks; #266, #282, #289, @smurching; #267, #265, @aarondav; #256, #290, @ToonKBC; #273, #263, @mateiz; #272, #319, @adrian555; #277, @aadamson; #283, #296, @andrewmchen)

0.4.2 (2018-08-07)

Breaking changes: None

Features:

• MLflow experiments REST API and mlflow experiments create now support providing --artifact-location (#232, @aarondav)
• [UI] Runs can now be sorted by columns, and added a Select All button (#227, @ToonKBC)
• Databricks File System (DBFS) artifactory support added (#226, @andrewmchen)
• databricks-cli version upgraded to >= 0.8.0 to support new DatabricksConfigProvider interface (#257, @aarondav)

Bug fixes:

• MLflow client sends REST API calls using snake_case instead of camelCase field names (#232, @aarondav)
• Minor bug fixes (#243, #242, @aarondav; #251, @javierluraschi; #245, @smurching; #252, @mateiz)

0.4.1 (2018-08-03)

Breaking changes: None

Features:

• [Projects] MLflow will use the conda installation directory given by the $MLFLOW_CONDA_HOME if specified (e.g. running conda commands by invoking "$MLFLOW_CONDA_HOME/bin/conda"), defaulting to running "conda" otherwise. (#231, @smurching)
• [UI] Show GitHub links in the UI for projects run from http(s):// GitHub URLs (#235, @smurching)

Bug fixes:

• Fix GCSArtifactRepository issue when calling list_artifacts on a path containing nested directories (#233, @jakeret)
• Fix Spark model support when saving/loading models to/from distributed filesystems (#180, @tomasatdatabricks)
• Add missing mlflow.version import to sagemaker module (#229, @dbczumar)
• Validate metric, parameter and run IDs in file store and Python client (#224, @mateiz)
• Validate that the tracking URI is a remote URI for Databricks project runs (#234, @smurching)
• Fix bug where we'd fetch git projects at SSH URIs into a local directory with the same name as the URI, instead of into a temporary directory (#236, @smurching)

0.4.0 (2018-08-01)

Breaking changes:

• [Projects] Removed the use_temp_cwd argument to mlflow.projects.run() (--new-dir flag in the mlflow run CLI). Runs of local projects now use the local project directory as their working directory. Git projects are still fetched into temporary directories (#215, @smurching)
• [Tracking] GCS artifact storage is now a pluggable dependency (no longer installed by default). To enable GCS support, install google-cloud-storage on both the client and tracking server via pip. (#202, @smurching)
• [Tracking] Clients running MLflow 0.4.0 and above require a server running MLflow 0.4.0 or above, due to a fix that ensures clients no longer double-serialize JSON into strings when sending data to the server (#200, @aarondav). However, the MLflow 0.4.0 server remains backwards-compatible with older clients (#216, @aarondav)

Features:

• [Examples] Add a more advanced tracking example: using MLflow with PyTorch and TensorBoard (#203)
• [Models] H2O model support (#170, @ToonKBC)
• [Projects] Support for running projects in subdirectories of Git repos (#153, @juntai-zheng)
• [SageMaker] Support for specifying a compute specification when deploying to SageMaker (#185, @dbczumar)
• [Server] Added --static-prefix option to serve UI from a specified prefix to MLflow UI and server (#116, @andrewmchen)
• [Tracking] Azure blob storage support for artifacts (#206, @mateiz)
• [Tracking] Add support for Databricks-backed RestStore (#200, @aarondav)
• [UI] Enable productionizing frontend by adding CSRF support (#199, @aarondav)
• [UI] Update metric and parameter filters to let users control column order (#186, @mateiz)

Bug fixes:

• Fixed incompatible file structure returned by GCSArtifactRepository (#173, @jakeret)
• Fixed metric values going out of order on x axis (#204, @mateiz)
• Fixed occasional hanging behavior when using the projects.run API (#193, @smurching)
• Miscellaneous bug and documentation fixes from @aarondav, @andrewmchen, @arinto, @jakeret, @mateiz, @smurching, @stbof

0.3.0 (2018-07-18)

Breaking changes:

• [MLflow Server] Renamed --artifact-root parameter to --default-artifact-root in mlflow server to better reflect its purpose (#165, @aarondav)

Features:

• Spark MLlib integration: we now support logging SparkML Models directly in the log_model API, model format, and serving APIs (#72, @tomasatdatabricks)
• Google Cloud Storage is now supported as an artifact storage root (#152, @bnekolny)
• Support asychronous/parallel execution of MLflow runs (#82, @smurching)
• [SageMaker] Support for deleting, updating applications deployed via SageMaker (#145, @dbczumar)
• [SageMaker] Pushing the MLflow SageMaker container now includes the MLflow version that it was published with (#124, @sueann)
• [SageMaker] Simplify parameters to SageMaker deploy by providing sane defaults (#126, @sueann)
• [UI] One-element metrics are now displayed as a bar char (#118, @cryptexis)

Bug fixes:

• Require gitpython>=2.1.0 (#98, @aarondav)
• Fixed TensorFlow model loading so that columns match the output names of the exported model (#94, @smurching)
• Fix SparkUDF when number of columns >= 10 (#97, @aarondav)
• Miscellaneous bug and documentation fixes from @emres, @dmatrix, @stbof, @gsganden, @dennyglee, @anabranch, @mikehuston, @andrewmchen, @juntai-zheng

0.2.1 (2018-06-28)

This is a patch release fixing some smaller issues after the 0.2.0 release.

• Switch protobuf implementation to C, fixing a bug related to tensorflow/mlflow import ordering (issues #33 and #77, PR #74, @andrewmchen)
• Enable running mlflow server without git binary installed (#90, @aarondav)
• Fix Spark UDF support when running on multi-node clusters (#92, @aarondav)

0.2.0 (2018-06-27)

• Added mlflow server to provide a remote tracking server. This is akin to mlflow ui with new options:
  • --host to allow binding to any ports (#27, @mdagost)
  • --artifact-root to allow storing artifacts at a remote location, S3 only right now (#78, @mateiz)
  • Server now runs behind gunicorn to allow concurrent requests to be made (#61, @mateiz)
• TensorFlow integration: we now support logging TensorFlow Models directly in the log_model API, model format, and serving APIs (#28, @juntai-zheng)
• Added experiments.list_experiments as part of experiments API (#37, @mparkhe)
• Improved support for unicode strings (#79, @smurching)
• Diabetes progression example dataset and training code (#56, @dennyglee)
• Miscellaneous bug and documentation fixes from @Jeffwan, @yupbank, @ndjido, @xueyumusic, @manugarri, @tomasatdatabricks, @stbof, @andyk, @andrewmchen, @jakeret, @0wu, @aarondav

0.1.0 (2018-06-05)

• Initial version of mlflow.