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Changelog History
Page 4

  • v1.9.1 Changes

    June 25, 2020

    🚀 MLflow 1.9.1 is a patch release containing a number of bug-fixes and improvements:

    • 🛠 Fixes AttributeError when pickling an instance of the Python MlflowClient class (#2955, @Polyphenolx)
    • 🛠 Fixes bug that prevented updating model-version descriptions in the model registry UI (#2969, @AnastasiaKol)
    • 🛠 Fixes bug where credentials were not properly propagated to artifact CLI commands when logging artifacts from Java to the DatabricksArtifactRepository (#3001, @dbczumar)
    • ✂ Removes use of new Pandas API in new MLflow model-schema functionality, so that it can be used with older Pandas versions (#2988, @aarondav)

  • v1.9.0 Changes

    June 19, 2020

    1.9.0 (2020-06-19)

    MLflow 1.9.0 includes numerous major features and improvements, and a breaking change to
    experimental APIs:

    💥 Breaking Changes:

    • ⚡️ The new_name argument to MlflowClient.update_registered_model
      has been removed. Call MlflowClient.rename_registered_model instead. (#2946, @mparkhe)
    • The stage argument to MlflowClient.update_model_version
      has been removed. Call MlflowClient.transition_model_version_stage instead. (#2946, @mparkhe)

    🔋 Features (MLflow Models and Flavors)

    • log_model and save_model APIs now support saving model signatures (the model's input and output schema)
      and example input along with the model itself (#2698, #2775, @tomasatdatabricks). Model signatures are used
      to reorder and validate input fields when scoring/serving models using the pyfunc flavor, mlflow models
      CLI commands, or mlflow.pyfunc.spark_udf (#2920, @tomasatdatabricks and @aarondav)
    • Introduce fastai model persistence and autologging APIs under mlflow.fastai (#2619, #2689 @antoniomdk)
    • ➕ Add pluggable mlflow.deployments API and CLI for deploying models to custom serving tools, e.g. RedisAI
      (#2327, @hhsecond)
    • Enables loading and scoring models whose conda environments include dependencies in conda-forge (#2797, @dbczumar)
    • ➕ Add support for scoring ONNX-persisted models that return Python lists (#2742, @andychow-db)

    🔋 Features (MLflow Projects)

    • ➕ Add plugin interface for executing MLflow projects against custom backends (#2566, @jdlesage)
    • ➕ Add ability to specify additional cluster-wide Python and Java libraries when executing
      MLflow projects remotely on Databricks (#2845, @pogil)
    • 👍 Allow running MLflow projects against remote artifacts stored in any location with a corresponding
      ArtifactRepository implementation (Azure Blob Storage, GCS, etc) (#2774, @trangevi)
    • 👍 Allow MLflow projects running on Kubernetes to specify a different tracking server to log to via the
      KUBE_MLFLOW_TRACKING_URI for passing a different tracking server to the kubernetes job (#2874, @catapulta)

    🔋 Features (UI)

    • 🐎 Significant performance and scalability improvements to metric comparison and scatter plots in
      💻 the UI (#2447, @mjlbach)
    • 💻 The main MLflow experiment list UI now includes a link to the model registry UI (#2805, @zhidongqu-db),
    • 💻 Enable viewing PDFs logged as artifacts from the runs UI (#2859, @ankmathur96)
    • 💻 UI accessibility improvements: better color contrast (#2872, @Zangr), add child roles to DOM elements (#2871, @Zangr)

    🔋 Features (Tracking Client and Server)

    • ➕ Adds ability to pass client certs as part of REST API requests when using the tracking or model
      registry APIs. (#2843, @PhilipMay)
    • 🆕 New community plugin: support for storing artifacts in Aliyun (Alibaba Cloud) (#2917, @SeaOfOcean)
    • 🌲 Infer and set content type and encoding of objects when logging models and artifacts to S3 (#2881, @hajapy)
    • ➕ Adds support for logging artifacts to HDFS Federation ViewFs (#2782, @fhoering)
    • ➕ Add healthcheck endpoint to the MLflow server at /health (#2725, @crflynn)
    • 👌 Improves performance of default file-based tracking storage backend by using LibYAML (if installed)
      📇 to read experiment and run metadata (#2707, @Higgcz)

    🐛 Bug fixes and documentation updates:

    • 🚚 Several UI fixes: remove margins around icon buttons (#2827, @harupy),
      🛠 fix alignment issues in metric view (#2811, @zhidongqu-db), add handling of NaN
      values in metrics plot (#2773, @dbczumar), truncate run ID in the run name when
      comparing multiple runs (#2508, @harupy)
    • ⬆️ Database engine URLs are no longer logged when running mlflow db upgrade (#2849, @hajapy)
    • 🌲 Updates log_artifact, log_model APIs to consistently use posix paths, rather than OS-dependent
      paths, when computing artifact subpaths. (#2784, @mikeoconnor0308)
    • 🛠 Fix ValueError when scoring tf.keras 1.X models using mlflow.pyfunc.predict (#2762, @juntai-zheng)
    • 🛠 Fixes conda environment activation bug when running MLflow projects on Windows (#2731, @MynherVanKoek)
    • mlflow.end_run will now clear the active run even if the run cannot be marked as
      terminated (e.g. because it's been deleted), (#2693, @ahmed-shariff)
    • ➕ Add missing documentation for mlflow.spacy APIs (#2771, @harupy)

    ⚡️ Small bug fixes and doc updates (#2919, @willzhan-db; #2940, #2942, #2941, #2943, #2927, #2929, #2926, #2914, #2928, #2913, #2852, #2876, #2808, #2810, #2442, #2780, #2758, #2732, #2734, #2431, #2733, #2716, @harupy; #2915, #2897, @jwgwalton; #2856, @jkthompson; #2962, @hhsecond; #2873, #2829, #2582, @dmatrix; #2908, #2865, #2880, #2866, #2833, #2785, #2723, @smurching; #2906, @dependabot[bot]; #2724, @aarondav; #2896, @ezeeetm; #2741, #2721, @mlflow-automation; #2864, @tallen94; #2726, @crflynn; #2710, #2951 @mparkhe; #2935, #2921, @ankitmathur-db; #2963, #2739, @dbczumar; #2853, @stat4jason; #2709, #2792, @juntai-zheng @juntai-zheng; #2749, @HiromuHota; #2957, #2911, #2718, @arjundc-db; #2885, @willzhan-db; #2803, #2761, @pogil; #2392, @jnmclarty; #2794, @Zethson; #2766, #2916 @shubham769)

  • v1.8.0 Changes

    April 16, 2020

    1.8.0 (2020-04-21)

    MLflow 1.8.0 includes several major features and improvements:

    🔋 Features:

    • ➕ Added mlflow.azureml.deploy API for deploying MLflow models to AzureML (#2375 @csteegz, #2711, @akshaya-a)
    • ➕ Added support for case-sensitive LIKE and case-insensitive ILIKE queries (e.g. 'params.framework LIKE '%sklearn%') with the SearchRuns API & UI when running against SQL backends (#2217, @t-henri; #2708, @mparkhe)
    • 👌 Improved line smoothing in MLflow metrics UI using exponential moving averages (#2620, @Valentyn1997)
    • ➕ Added mlflow.spacy module with support for logging and loading spaCy models (#2242, @arocketman)
    • 💻 Parameter values that differ across runs are highlighted in run comparison UI (#2565, @gabrielbretschner)
    • ➕ Added ability to compare source runs associated with model versions from the registered model UI (#2537, @juntai-zheng)
    • ➕ Added support for alphanumerical experiment IDs in the UI. (#2568, @jonas)
    • ➕ Added support for passing arguments to docker run when running docker-based MLflow projects (#2608, @ksanjeevan)
    • ➕ Added Windows support for mlflow sagemaker build-and-push-container CLI & API (#2500, @AndreyBulezyuk)
    • 👌 Improved performance of reading experiment data from local filesystem when LibYAML is installed (#2707, @Higgcz)
    • ➕ Added a healthcheck endpoint to the REST API server at /health that always returns a 200 response status code, to be used to verify health of the server (#2725, @crflynn)
    • 💻 MLflow metrics UI plots now scale to rendering thousands of points using scattergl (#2447, @mjlbach)

    🐛 Bug fixes:

    • 🛠 Fixed CLI summary message in mlflow azureml build_image CLI (#2712, @dbczumar)
    • Updated examples/flower_classifier/score_images_rest.py with multiple bug fixes (#2647, @tfurmston)
    • 🛠 Fixed pip not found error while packaging models via mlflow models build-docker (#2699, @HiromuHota)
    • 🛠 Fixed bug in mlflow.tensorflow.autolog causing erroneous deletion of TensorBoard logging directory (#2670, @dbczumar)
    • 🛠 Fixed a bug that truncated the description of the mlflow gc subcommand in mlflow --help (#2679, @dbczumar)
    • 🛠 Fixed bug where mlflow models build-docker was failing due to incorrect Miniconda download URL (#2685, @michaeltinsley)
    • Fixed a bug in S3 artifact logging functionality where MLFLOW_S3_ENDPOINT_URL was ignored (#2629, @poppash)
    • 🛠 Fixed a bug where Sqlite in-memory was not working as a tracking backend store by modifying DB upgrade logic (#2667, @dbczumar)
    • 🛠 Fixed a bug to allow numerical parameters with values >= 1000 in R mlflow::mlflow_run() API (#2665, @lorenzwalthert)
    • 🛠 Fixed a bug where AWS creds was not found in the Windows platform due path differences (#2634, @AndreyBulezyuk)
    • Fixed a bug to add pip when necessary in _mlflow_conda_env (#2646, @tfurmston)
    • 🛠 Fixed error code to be more meaningful if input to model version is incorrect (#2625, @andychow-db)
    • 🛠 Fixed multiple bugs in model registry (#2638, @aarondav)
    • 🛠 Fixed support for conda env dicts with mlflow.pyfunc.log_model (#2618, @dbczumar)
    • 🛠 Fixed a bug where hiding the start time column in the UI would also hide run selection checkboxes (#2559, @harupy)

    📚 Documentation updates:

    • ➕ Added links to source code to mlflow.org (#2627, @harupy)
    • 🛰 Documented fix for pandas-records payload (#2660, @SaiKiranBurle)
    • 🛠 Fixed documentation bug in TensorFlow load_model utility (#2666, @pogil)
    • ➕ Added the missing Model Registry description and link on the first page (#2536, @dmatrix)
    • ➕ Added documentation for expected datatype for step argument in log_metric to match REST API (#2654, @mparkhe)
    • Added usage of the model registry to the log_model function in sklearn_elasticnet_wine/train.py example (#2609, @netanel246)

    ⚡️ Small bug fixes and doc updates (#2594, @Trollgeir; #2703,#2709, @juntai-zheng; #2538, #2632, @keigohtr; #2656, #2553, @lorenzwalthert; #2622, @pingsutw; #2615, #2600, #2533, @mlflow-automation; #1391, @sueann; #2613, #2598, #2534, #2723, @smurching; #2652, #2710, @mparkhe; #2706, #2653, #2639, @tomasatdatabricks; #2611, @9dogs; #2700, #2705, @aarondav; #2675, #2540, @mengxr; #2686, @RensDimmendaal; #2694, #2695, #2532, @dbczumar; #2733, #2716, @harupy; #2726, @crflynn; #2582, #2687, @dmatrix)

  • v1.7.2 Changes

    March 20, 2020

    🚀 MLflow 1.7.2 is a patch release containing a minor change:

    • 📌 Pin alembic version to 1.4.1 or below to prevent pep517-related installation errors
      (#2612, @smurching)

  • v1.7.1 Changes

    March 17, 2020

    🚀 MLflow 1.7.1 is a patch release containing bug fixes and small changes:

    • ✂ Remove usage of Nonnull annotations and findbugs dependency in Java package (#2583, @mparkhe)
    • ➕ Add version upper bound (<=1.3.13) to sqlalchemy dependency in Python package (#2587, @smurching)

    🛠 Other bugfixes and doc updates (#2595, @mparkhe; #2567, @jdlesage)

  • v1.7.0 Changes

    March 02, 2020

    1.7.0 (2020-03-02)

    MLflow 1.7.0 includes several major features and improvements and some notable breaking changes.

    🛠 MLflow support for Python 2 is now deprecated and will be dropped in a future release. At that point, existing Python 2 workflows that use MLflow will continue to work without modification, but Python 2 users will no longer get access to the latest MLflow features and bugfixes. We recommend that you upgrade to Python 3 - see https://docs.python.org/3/howto/pyporting.html for a migration guide.

    💥 Breaking changes to Model Registry REST APIs:

    🚀 Model Registry REST APIs have been updated to be more consistent with the other MLflow APIs. With this release Model Registry APIs are intended to be stable until the next major version.

    • 🚀 Python and Java client APIs for Model Registry have been updated to use the new REST APIs. When using an MLflow client with a server using updated REST endpoints, you won't need to change any code but will need to upgrade to a new client version. The client APIs contain deprecated arguments, which for this release are backward compatible, but will be dropped in future releases. (#2457, @tomasatdatabricks; #2502, @mparkhe).
    • ⚡️ The Model Registry UI has been updated to use the new REST APIs (#2476 @aarondav; #2507, @mparkhe)

    Other Features:

    • Ability to click through to individual runs from metrics plot (#2295, @harupy)
    • ➕ Added mlflow gc CLI for permanent deletion of runs (#2265, @t-henri)
    • Metric plot state is now captured in page URLs for easier link sharing (#2393, #2408, #2498 @smurching; #2459, @harupy)
    • ➕ Added experiment management to MLflow UI (create/rename/delete experiments) (#2348, @ggliem)
    • 💻 Ability to search for experiments by name in the UI (#2324, @ggliem)
    • 💻 MLflow UI page titles now reflect the content displayed on the page (#2420, @AveshCSingh)
    • ➕ Added a new LogModel REST API endpoint for capturing model metadata, and call it from the Python and R clients (#2369, #2430, #2468 @tomasatdatabricks)
    • Java Client API to download model artifacts from Model Registry (#2308, @andychow-db)

    🐛 Bug fixes and documentation updates:

    • 📚 Updated Model Registry documentation page with code snippets and examples (#2493, @dmatrix; #2517, @harupy)
    • 👍 Better error message for Model Registry, when using incompatible backend server (#2456, @aarondav)
    • matplotlib is no longer required to use XGBoost and LightGBM autologging (#2423, @harupy)
    • 🛠 Fixed bug where matplotlib figures were not closed in XGBoost and LightGBM autologging (#2386, @harupy)
    • 🛠 Fixed parameter reading logic to support param values with newlines in FileStore (#2376, @dbczumar)
    • 👌 Improve readability of run table column selector nodes (#2388, @dbczumar)
    • ⚡️ Validate experiment name supplied to UpdateExperiment REST API endpoint (#2357, @ggliem)
    • 🛠 Fixed broken MLflow DB README link in CLI docs (#2377, @dbczumar)
    • 🔄 Change copyright year across docs to 2020 (#2349, @ParseThis)

    ⚡️ Small bug fixes and doc updates (#2378, #2449, #2402, #2397, #2391, #2387, #2523, #2527 @harupy; #2314, @juntai-zheng; #2404, @andychow-db; #2343, @pogil; #2366, #2370, #2364, #2356, @AveshCSingh; #2373, #2365, #2363, @smurching; #2358, @jcuquemelle; #2490, @RensDimmendaal; #2506, @dbczumar; #2234 @Zangr; #2359 @lbernickm; #2525, @mparkhe)

  • v1.6.0 Changes

    January 29, 2020

    1.6.0 (2020-01-29)

    🌲 MLflow 1.6.0 includes several new features, including a better runs table interface, a utility for easier parameter tuning, and automatic logging from XGBoost, LightGBM, and Spark. It also implements a long-awaited fix allowing @ symbols in database URLs. A complete list is below:

    🔋 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 comparsion 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)

  • v1.5.0 Changes

    December 19, 2019

    1.5.0 (2019-12-19)

    MLflow 1.5.0 includes several major features and improvements:

    🆕 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])

  • v1.4.0 Changes

    October 30, 2019

    MLflow 1.4.0 includes several major features:

    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)

  • v1.3 Changes

    September 30, 2019

    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)