Mlflow

Features:

- Adds a new runs table column view based on `ag-grid` which adds functionality for nested runs, serverside sorting, column reordering, highlighting, and more. (2251, Zangr)
- Adds contour plot to the run comparison page to better support parameter tuning (2225, harupy)
- If you use EarlyStopping with Keras autologging, MLflow now automatically captures the best model trained and the associated metrics (2301, 2219, juntai-zheng)
- Adds autologging functionality for LightGBM and XGBoost flavors to log feature importance, metrics per iteration, the trained model, and more. (2275, 2238, harupy)
- Adds an experimental mlflow.spark.autolog() API for automatic logging of Spark datasource information to the current active run. (2220, smurching)
- Optimizes the file store to load less data from disk for each operation (2339, jonas)
- Upgrades from ubuntu:16.04 to ubuntu:18.04 when building a Docker image with `mlflow models build-docker` (2256, andychow-db)

Bug fixes and documentation updates:

- Fixes bug when running server against database URLs with symbols (2289, hershaw)
- Fixes model Docker image build on Windows (2257, jahas)
- Documents the SQL Server plugin (2320, avflor)
- Adds a help file for the R package (2259, lorenzwalthert)
- Adds an example of using the Search API to find the best performing model (2313, AveshCSingh)
- Documents how to write and use MLflow plugins (2270, smurching)

Small bug fixes and doc updates (2293, 2328, 2244, harupy; 2269, 2332, 2306, 2307, 2292, 2267, 2191, 2231, juntai-zheng; 2325, shubham769; 2291, sueann; 2315, 2249, 2288, 2278, 2253, 2181, smurching; 2342, tomasatdatabricks; 2245, dependabot[bot]; 2338, jcuquemelle; 2285, avflor; 2340, pogil; 2237, 2226, 2243, 2272, 2286, dbczumar; 2281, renaudhager; 2246, avaucher; 2258, lorenzwalthert; 2261, smith-kyle; 2352, dbczumar)

New Model Flavors and Flavor Updates:

- New support for a LightGBM flavor (2136, harupy)
- New support for a XGBoost flavor (2124, harupy)
- New support for a Gluon flavor and autologging (1973, cosmincatalin)
- Runs automatically created by `mlflow.tensorflow.autolog()` and `mlflow.keras.autolog()` (2088) are now automatically ended after training and/or exporting your model. See the [`docs`](https://mlflow.org/docs/latest/tracking.html#automatic-logging-from-tensorflow-and-keras-experimental) for more details (2094, juntai-zheng)

More features and improvements:

- When using the `mlflow server` CLI command, you can now expose metrics on `/metrics` for Prometheus via the optional --activate-parameter argument (2097, t-henri)
- The `mlflow ui` CLI command now has a `--host`/`-h` option to specify user-input IPs to bind to (2176, gandroz)
- MLflow now supports pulling Git submodules while using MLflow Projects (2103, badc0re)
- New `mlflow models prepare-env` command to do any preparation necessary to initialize an environment. This allows distinguishing configuration and user errors during predict/serve time (2040, aarondav)
- TensorFlow.Keras and Keras parameters are now logged by `autolog()` (2119, juntai-zheng)
- MLflow `log_params()` will recognize Spark ML params as keys and will now extract only the name attribute (2064, tomasatdatabricks)
- Exposes `mlflow.tracking.is_tracking_uri_set()` (2026, fhoering)
- The artifact image viewer now displays "Loading..." when it is loading an image (1958, harupy)
- The artifact image view now supports animated GIFs (2070, harupy)
- Adds ability to mount volumes and specify environment variables when using mlflow with docker (1994, nlml)
- Adds run context for detecting job information when using MLflow tracking APIs within Databricks Jobs. The following job types are supported: notebook jobs, Python Task jobs (2205, dbczumar)
- Performance improvement when searching for runs (2030, 2059, jcuquemelle; 2195, rom1504)

Bug fixes and documentation updates:

- Fixed handling of empty directories in FS based artifact repositories (1891, tomasatdatabricks)
- Fixed `mlflow.keras.save_model()` usage with DBFS (2216, andychow-db)
- Fixed several build issues for the Docker image (2107, jimthompson5802)
- Fixed `mlflow_list_artifacts()` (R package) (2200, lorenzwalthert)
- Entrypoint commands of Kubernetes jobs are now shell-escaped (2160, zanitete)
- Fixed project run Conda path issue (2147, Zangr)
- Fixed spark model load from model repository (2175, tomasatdatabricks)
- Stripped "dev" suffix from PySpark versions (2137, dbczumar)
- Fixed note editor on the experiment page (2054, harupy)
- Fixed `models serve`, `models predict` CLI commands against models:/ URIs (2067, smurching)
- Don't unconditionally format values as metrics in generic HtmlTableView component (2068, smurching)
- Fixed remote execution from Windows using posixpath (1996, aestene)
- Add XGBoost and LightGBM examples (2186, harupy)
- Add note about active run instantiation side effect in fluent APIs (2197, andychow-db)
- The tutorial page has been refactored to be be a 'Tutorials and Examples' page (2182, juntai-zheng)
- Doc enhancements for XGBoost and LightGBM flavors (2170, harupy)
- Add doc for XGBoost flavor (2167, harupy)
- Updated `active_run()` docs to clarify it cannot be used accessing current run data (2138, juntai-zheng)
- Document models:/ scheme for URI for load_model methods (2128, stbof)
- Added an example using Prophet via pyfunc (2043, dr3s)
- Added and updated some screenshots and explicit steps for the model registry (2086, stbof)

Small bug fixes and doc updates (2142, 2121, 2105, 2069, 2083, 2061, 2022, 2036, 1972, 2034, 1998, 1959, harupy; 2202, t-henri; 2085, stbof; 2098, AdamBarnhard; 2180, 2109, 1977, 2039, 2062, smurching; 2013, aestene; 2146, joelcthomas; 2161, 2120, 2100, 2095, 2088, 2076, 2057, juntai-zheng; 2077, 2058, 2027, sueann; 2149, zanitete; 2204, 2188, andychow-db; 2110, 2053, jdlesage; 2003, 1953, 2004, Djailla; 2074, nlml; 2116, Silas-Asamoah; 1104, jimthompson5802; 2072, cclauss; 2221, 2207, 2157, 2132, 2114, 2063, 2065, 2055, dbczumar; 2033, cthoyt; 2048, philip-khor; 2002, jspoorta; 2000, christang; 2078, dennyglee; 1986, vguerra; 2020, dependabot[bot])

- 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)

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)

- 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)