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61
Latest Version
Avg Release Cycle
28 days
Latest Release
523 days ago

Changelog History
Page 3

  • v1.16.0 Changes

    April 22, 2021

    MLflow 1.16.0 includes several major features and improvements:

    🔋 Features:

    • ➕ Add mlflow.pyspark.ml.autolog() API for autologging of pyspark.ml estimators (#4228, @WeichenXu123)
    • Add mlflow.catboost.log_model, mlflow.catboost.save_model, mlflow.catboost.load_model APIs for CatBoost model persistence (#2417, @harupy)
    • 0️⃣ Enable mlflow.pyfunc.spark_udf to use column names from model signature by default (#4236, @Loquats)
    • ➕ Add datetime data type for model signatures (#4241, @vperiyasamy)
    • Add mlflow.sklearn.eval_and_log_metrics API that computes and logs metrics for the given scikit-learn model and labeled dataset. (#4218, @alkispoly-db)

    🐛 Bug fixes and documentation updates:

    • 🛠 Fix a database migration error for PostgreSQL (#4211, @dolfinus)
    • 🛠 Fix autologging silent mode bugs (#4231, @dbczumar)

    ⚡️ Small bug fixes and doc updates (#4255, #4252, #4254, #4253, #4242, #4247, #4243, #4237, #4233, @harupy; #4225, @dmatrix; #4206, @mlflow-automation; #4207, @shrinath-suresh; #4264, @WeichenXu123; #3884, #3866, #3885, @ankan94; #4274, #4216, @dbczumar)

  • v1.15.0 Changes

    March 26, 2021

    🛠 MLflow 1.15.0 includes several features, bug fixes and improvements. Notably, it includes a number of improvements to MLflow autologging:

    🔋 Features:

    • ➕ Add silent=False option to all autologging APIs, to allow suppressing MLflow warnings and logging statements during autologging setup and training (#4173, @dbczumar)
    • Add disable_for_unsupported_versions=False option to all autologging APIs, to disable autologging for versions of ML frameworks that have not been explicitly tested against the current version of the MLflow client (#4119, @WeichenXu123)

    🐛 Bug fixes:

    • Autologged runs are now terminated when execution is interrupted via SIGINT (#4200, @dbczumar)
    • The R mlflow_get_experiment API now returns the same tag structure as mlflow_list_experiments and mlflow_get_run (#4017, @lorenzwalthert)
    • 🛠 Fix bug where mlflow.tensorflow.autolog would previously mutate the user-specified callbacks list when fitting tf.keras models (#4195, @dbczumar)
    • 🛠 Fix bug where SQL-backed MLflow tracking server initialization failed when using the MLflow skinny client (#4161, @eedeleon)
    • Model version creation (e.g. via mlflow.register_model) now fails if the model version status is not READY (#4114, @ankit-db)

    ⚡️ Small bug fixes and doc updates (#4191, #4149, #4162, #4157, #4155, #4144, #4141, #4138, #4136, #4133, #3964, #4130, #4118, @harupy; #4152, @mlflow-automation; #4139, @WeichenXu123; #4193, @smurching; #4029, @architkulkarni; #4134, @xhochy; #4116, @wenleix; #4160, @wentinghu; #4203, #4184, #4167, @dbczumar)

  • v1.14.1 Changes

    March 01, 2021

    🚀 MLflow 1.14.1 is a patch release containing the following bug fix:

    • 🛠 Fix issues in handling flexible numpy datatypes in TensorSpec (#4147, @arjundc-db)

  • v1.14.0 Changes

    February 18, 2021

    MLflow 1.14.0 includes several major features and improvements:

    • 🚀 MLflow's model inference APIs (mlflow.pyfunc.predict), built-in model serving tools (mlflow models serve), and model signatures now support tensor inputs. In particular, MLflow now provides built-in support for scoring PyTorch, TensorFlow, Keras, ONNX, and Gluon models with tensor inputs. For more information, see https://mlflow.org/docs/latest/models.html#deploy-mlflow-models (#3808, #3894, #4084, #4068 @wentinghu; #4041 @tomasatdatabricks, #4099, @arjundc-db)
    • Add new mlflow.shap.log_explainer, mlflow.shap.load_explainer APIs for logging and loading shap.Explainer instances (#3989, @vivekchettiar)
    • 📦 The MLflow Python client is now available with a reduced dependency set via the mlflow-skinny PyPI package (#4049, @eedeleon)
    • ➕ Add new RequestHeaderProvider plugin interface for passing custom request headers with REST API requests made by the MLflow Python client (#4042, @jimmyxu-db)
    • 0️⃣ mlflow.keras.log_model now saves models in the TensorFlow SavedModel format by default instead of the older Keras H5 format (#4043, @harupy)
    • 🌲 mlflow_log_model now supports logging MLeap models in R (#3819, @yitao-li)
    • Add mlflow.pytorch.log_state_dict, mlflow.pytorch.load_state_dict for logging and loading PyTorch state dicts (#3705, @shrinath-suresh)
    • mlflow gc can now garbage-collect artifacts stored in S3 (#3958, @sklingel)

    🐛 Bug fixes and documentation updates:

    • Enable autologging for TensorFlow estimators that extend tensorflow.compat.v1.estimator.Estimator (#4097, @mohamad-arabi)
    • 🛠 Fix for universal autolog configs overriding integration-specific configs (#4093, @dbczumar)
    • 👍 Allow mlflow.models.infer_signature to handle dataframes containing pandas.api.extensions.ExtensionDtype (#4069, @caleboverman)
    • ⏪ Fix bug where mlflow_restore_run doesn't propagate the client parameter to mlflow_get_run (#4003, @yitao-li)
    • 🛠 Fix bug where scoring on served model fails when request data contains a string that looks like URL and pandas version is later than 1.1.0 (#3921, @Secbone)
    • Fix bug causing mlflow_list_experiments to fail listing experiments with tags (#3942, @lorenzwalthert)
    • 🛠 Fix bug where metrics plots are computed from incorrect target values in scikit-learn autologging (#3993, @mtrencseni)
    • ✂ Remove redundant / verbose Python event logging message in autologging (#3978, @dbczumar)
    • Fix bug where mlflow_load_model doesn't load metadata associated to MLflow model flavor in R (#3872, @yitao-li)
    • Fix mlflow.spark.log_model, mlflow.spark.load_model APIs on passthrough-enabled environments against ACL'd artifact locations (#3443, @smurching)

    ⚡️ Small bug fixes and doc updates (#4102, #4101, #4096, #4091, #4067, #4059, #4016, #4054, #4052, #4051, #4038, #3992, #3990, #3981, #3949, #3948, #3937, #3834, #3906, #3774, #3916, #3907, #3938, #3929, #3900, #3902, #3899, #3901, #3891, #3889, @harupy; #4014, #4001, @dmatrix; #4028, #3957, @dbczumar; #3816, @lorenzwalthert; #3939, @pauldj54; #3740, @jkthompson; #4070, #3946, @jimmyxu-db; #3836, @t-henri; #3982, @neo-anderson; #3972, #3687, #3922, @eedeleon; #4044, @WeichenXu123; #4063, @yitao-li; #3976, @whiteh; #4110, @tomasatdatabricks; #4050, @apurva-koti; #4100, #4084, @wentinghu; #3947, @vperiyasamy; #4021, @trangevi; #3773, @ankan94; #4090, @jinzhang21; #3918, @danielfrg)

  • v1.13.1 Changes

    December 30, 2020

    🚀 MLflow 1.13.1 is a patch release containing bug fixes and small changes:

    • 🛠 Fix bug causing Spark autologging to ignore configuration options specified by mlflow.autolog() (#3917, @dbczumar)
    • 🛠 Fix bugs causing metrics to be dropped during TensorFlow autologging (#3913, #3914, @dbczumar)
    • 🛠 Fix incorrect value of optimizer name parameter in autologging PyTorch Lightning (#3901, @harupy)
    • Fix model registry database allow_null_for_run_id migration failure affecting MySQL databases (#3836, @t-henri)
    • Fix failure in transition_model_version_stage when uncanonical stage name is passed (#3929, @harupy)
    • 🛠 Fix an undefined variable error causing AzureML model deployment to fail (#3922, @eedeleon)
    • Reclassify scikit-learn as a pip dependency in MLflow Model conda environments (#3896, @harupy)
    • 🛠 Fix experiment view crash and artifact view inconsistency caused by artifact URIs with redundant slashes (#3928, @dbczumar)

  • v1.13 Changes

    December 22, 2020

    MLflow 1.13 includes several major features and improvements:

    🔋 Features:

    🆕 New fluent APIs for logging in-memory objects as artifacts:

    • ➕ Add mlflow.log_text which logs text as an artifact (#3678, @harupy)
    • ➕ Add mlflow.log_dict which logs a dictionary as an artifact (#3685, @harupy)
    • ➕ Add mlflow.log_figure which logs a figure object as an artifact (#3707, @harupy)
    • ➕ Add mlflow.log_image which logs an image object as an artifact (#3728, @harupy)

    ⚡️ UI updates / fixes (#3867, @smurching):

    • ➕ Add model version link in compact experiment table view
    • ➕ Add logged/registered model links in experiment runs page view
    • ✨ Enhance artifact viewer for MLflow models
    • 💻 Model registry UI settings are now persisted across browser sessions
    • ➕ Add model version description field to model version table

    Autologging enhancements:

    • 👌 Improve robustness of autologging integrations to exceptions (#3682, #3815, dbczumar; #3860, @mohamad-arabi; #3854, #3855, #3861, @harupy)
    • ➕ Add disable configuration option for autologging (#3682, #3815, dbczumar; #3838, @mohamad-arabi; #3854, #3855, #3861, @harupy)
    • ➕ Add exclusive configuration option for autologging (#3851, @apurva-koti; #3869, @dbczumar)
    • ➕ Add log_models configuration option for autologging (#3663, @mohamad-arabi)
    • Set tags on autologged runs for easy identification (and add tags to start_run) (#3847, @dbczumar)

    More features and improvements:

    • 👍 Allow Keras models to be saved with SavedModel format (#3552, @skylarbpayne)
    • ➕ Add support for statsmodels flavor (#3304, @olbapjose)
    • ➕ Add support for nested-run in mlflow R client (#3765, @yitao-li)
    • 🚀 Deploying a model using mlflow.azureml.deploy now integrates better with the AzureML tracking/registry. (#3419, @trangevi)
    • ⚡️ Update schema enforcement to handle integers with missing values (#3798, @tomasatdatabricks)

    🐛 Bug fixes and documentation updates:

    • When running an MLflow Project on Databricks, the version of MLflow installed on the Databricks cluster will now match the version used to run the Project (#3880, @FlorisHoogenboom)
    • 🛠 Fix bug where metrics are not logged for single-epoch tf.keras training sessions (#3853, @dbczumar)
    • 🌲 Reject boolean types when logging MLflow metrics (#3822, @HCoban)
    • 🛠 Fix alignment of Keras / tf.Keras metric history entries when initial_epoch is different from zero. (#3575, @garciparedes)
    • 🛠 Fix bugs in autologging integrations for newer versions of TensorFlow and Keras (#3735, @dbczumar)
    • ⬇️ Drop global filterwwarnings module at import time (#3621, @jogo)
    • 🛠 Fix bug that caused preexisting Python loggers to be disabled when using MLflow with the SQLAlchemyStore (#3653, @arthury1n)
    • 🛠 Fix h5py library incompatibility for exported Keras models (#3667, @tomasatdatabricks)

    ⚡️ Small changes, bug fixes and doc updates (#3887, #3882, #3845, #3833, #3830, #3828, #3826, #3825, #3800, #3809, #3807, #3786, #3794, #3731, #3776, #3760, #3771, #3754, #3750, #3749, #3747, #3736, #3701, #3699, #3698, #3658, #3675, @harupy; #3723, @mohamad-arabi; #3650, #3655, @shrinath-suresh; #3850, #3753, #3725, @dmatrix; ##3867, #3670, #3664, @smurching; #3681, @sueann; #3619, @andrewnitu; #3837, @javierluraschi; #3721, @szczeles; #3653, @arthury1n; #3883, #3874, #3870, #3877, #3878, #3815, #3859, #3844, #3703, @dbczumar; #3768, @wentinghu; #3784, @HCoban; #3643, #3649, @arjundc-db; #3864, @AveshCSingh, #3756, @yitao-li)

  • v1.12.1 Changes

    November 19, 2020

    🚀 MLflow 1.12.1 is a patch release containing bug fixes and small changes:

    • 🛠 Fix run_link for cross-workspace model versions (#3681, @sueann)
    • ✂ Remove hard dependency on matplotlib for sklearn autologging (#3703, @dbczumar)
    • Do not disable existing loggers when initializing alembic (#3653, @arthury1n)

  • v1.12.0 Changes

    November 10, 2020

    MLflow 1.12.0 includes several major features and improvements, in particular a number of improvements to autologging and MLflow's Pytorch integrations:

    🔋 Features:

    PyTorch:

    • mlflow.pytorch.log_model, mlflow.pytorch.load_model now support logging/loading TorchScript models (#3557, @shrinath-suresh)
    • 👍 mlflow.pytorch.log_model supports passing requirements_file & extra_files arguments to log additional artifacts along with a model (#3436, @shrinath-suresh)

    Autologging:

    • ➕ Add universal mlflow.autolog which enables autologging for all supported integrations (#3561, #3590, @andrewnitu)
    • ➕ Add mlflow.pytorch.autolog API for automatic logging of metrics, params, and models from Pytorch Lightning training (#3601, @shrinath-suresh, #3636, @karthik-77). This API is also enabled by mlflow.autolog.
    • 🌲 Scikit-learn, XGBoost, and LightGBM autologging now support logging model signatures and input examples (#3386, #3403, #3449, @andrewnitu)
    • 🌲 mlflow.sklearn.autolog now supports logging metrics (e.g. accuracy) and plots (e.g. confusion matrix heat map) (#3423, #3327, @willzhan-db, @harupy)

    More features and improvements:

    • ➕ Add mlflow.shap.log_explanation for logging model explanations generated by SHAP (#3513, @harupy)
    • log_model and create_model_version now supports an await_creation_for argument (#3376, @andychow-db)
    • Put preview paths before non-preview paths for backwards compatibility (#3648, @sueann)
    • Clean up model registry endpoint and client method definitions (#3610, @sueann)
    • 🚀 MLflow deployments plugin now supports 'predict' CLI command (#3597, @shrinath-suresh)
    • 👌 Support H2O for R (#3416, @yitao-li)
    • Add MLFLOW_S3_IGNORE_TLS environment variable to enable skipping TLS verification of S3 endpoint (#3345, @dolfinus)

    🐛 Bug fixes and documentation updates:

    • 🔀 Ensure that results are synced across distributed processes if ddp enabled (no-op else) (#3651, @SeanNaren)
    • ✂ Remove optimizer step override to ensure that all accelerator cases are covered by base module (#3635, @SeanNaren)
    • 🛠 Fix AttributeError in keras autologgging (#3611, @sephib)
    • Scikit-learn autologging: Exclude feature extraction / selection estimator (#3600, @dbczumar)
    • Scikit-learn autologging: Fix behavior when a child and its parent are both patched (#3582, @dbczumar)
    • 🛠 Fix a bug where lightgbm.Dataset(None) fails after running mlflow.lightgbm.autolog (#3594, @harupy)
    • 🛠 Fix a bug where xgboost.DMatrix(None) fails after running mlflow.xgboost.autolog (#3584, @harupy)
    • 🐳 Pass docker_args in non-synchronous mlflow project runs (#3563, @alfozan)
    • Fix a bug of FTPArtifactRepository.log_artifacts with artifact_path keyword argument (issue #3388) (#3391, @kzm4269)
    • Exclude preprocessing & imputation steps from scikit-learn autologging (#3491, @dbczumar)
    • 🛠 Fix duplicate stderr logging during artifact logging and project execution in the R client (#3145, @yitao-li)
    • Don't call atexit.register(_flush_queue) in __main__ scope of mlflow/tensorflow.py (#3410, @harupy)
    • 🛠 Fix for restarting terminated run not setting status correctly (#3329, @apurva-koti)
    • 🛠 Fix model version run_link URL for some Databricks regions (#3417, @sueann)
    • Skip JSON validation when endpoint is not MLflow REST API (#3405, @harupy)
    • 🔌 Document mlflow-torchserve plugin (#3634, @karthik-77)
    • ➕ Add mlflow-elasticsearchstore to the doc (#3462, @AxelVivien25)
    • ➕ Add code snippets for fluent and MlflowClient APIs (#3385, #3437, #3489 #3573, @dmatrix)
    • Document mlflow-yarn backend (#3373, @fhoering)
    • 🛠 Fix a breakage in loading Tensorflow and Keras models (#3667, @tomasatdatabricks)

    ⚡️ Small bug fixes and doc updates:

    #3607, #3616, #3534, #3598, #3542, #3568, #3349, #3554, #3544, #3541, #3533, #3535, #3516, #3512, #3497, #3522, #3521, #3492, #3502, #3434, #3422, #3394, #3387, #3294, #3324, #3654, @harupy; #3451, @jgc128; #3638, #3632, #3608, #3452, #3399, @shrinath-suresh; #3495, #3459, #3662, #3668, #3670 @smurching; #3488, @edgan8; #3639, @karthik-77; #3589, #3444, #3276, @lorenzwalthert; #3538, #3506, #3509, #3507, #3510, #3508, @rahulporuri; #3504, @sbrugman; #3486, #3466, @apurva-koti; #3477, @juntai-zheng; #3617, #3609, #3605, #3603, #3560, @dbczumar; #3411, @danielvdende; #3377, @willzhan-db; #3420, #3404, @andrewnitu; #3591, @mateiz; #3465, @abawchen; #3543, @emptalk; #3302, @bramrodenburg; #3468, @ghisvail; #3496, @extrospective; #3549, #3501, #3435, @yitao-li; #3243, @OlivierBondu; #3439, @andrewnitu; #3651, #3635 @SeanNaren, #3470, @ankit-db

  • v1.11.0 Changes

    August 31, 2020

    MLflow 1.11.0 includes several major features and improvements:

    🔋 Features:

    • 🆕 New mlflow.sklearn.autolog() API for automatic logging of metrics, params, and models from scikit-learn model training (#3287, @harupy; #3323, #3358 @dbczumar)
    • 👍 Registered model & model version creation APIs now support specifying an initial description (#3271, @sueann)
    • 🌲 The R mlflow_log_model and mlflow_load_model APIs now support XGBoost models (#3085, @lorenzwalthert)
    • ⚙ New mlflow.list_run_infos fluent API for listing run metadata (#3183, @trangevi)
    • ➕ Added section for visualizing and comparing model schemas to model version and model-version-comparison UIs (#3209, @zhidongqu-db)
    • ✨ Enhanced support for using the model registry across Databricks workspaces: support for registering models to a Databricks workspace from outside the workspace (#3119, @sueann), tracking run-lineage of these models (#3128, #3164, @ankitmathur-db; #3187, @harupy), and calling mlflow.<flavor>.load_model against remote Databricks model registries (#3330, @sueann)
    • 💻 UI support for setting/deleting registered model and model version tags (#3187, @harupy)
    • 💻 UI support for archiving existing staging/production versions of a model when transitioning a new model version to staging/production (#3134, @harupy)

    🐛 Bug fixes and documentation updates:

    • 🛠 Fixed parsing of MLflow project parameter values containing'=' (#3347, @dbczumar)
    • 🛠 Fixed a bug preventing listing of WASBS artifacts on the latest version of Azure Blob Storage (12.4.0) (#3348, @dbczumar)
    • 🛠 Fixed a bug where artifact locations become malformed when using an SFTP file store in Windows (#3168, @harupy)
    • 🛠 Fixed bug where list_artifacts returned incorrect results on GCS, preventing e.g. loading SparkML models from GCS (#3242, @santosh1994)
    • Writing and reading artifacts via MlflowClient to a DBFS location in a Databricks tracking server specified through the tracking_uri parameter during the initialization of MlflowClient now works properly (#3220, @sueann)
    • 🛠 Fixed bug where FTPArtifactRepository returned artifact locations as absolute paths, rather than paths relative to the artifact repository root (#3210, @shaneing), and bug where calling log_artifacts against an FTP artifact location copied the logged directory itself into the FTP location, rather than the contents of the directory.
    • 🛠 Fixed bug where Databricks project execution failed due to passing of GET request params as part of the request body rather than as query parameters (#2947, @cdemonchy-pro)
    • 🛠 Fix bug where artifact viewer did not correctly render PDFs in MLflow 1.10 (#3172, @ankitmathur-db)
    • 🛠 Fixed parsing of order_by arguments to MLflow search APIs when ordering by fields whose names contain spaces (#3118, @jdlesage)
    • 🛠 Fixed bug where MLflow model schema enforcement raised exceptions when validating string columns using pandas >= 1.0 (#3130, @harupy)
    • 🛠 Fixed bug where mlflow.spark.log_model did not save model signature and input examples (#3151, @harupy)
    • 🛠 Fixed bug in runs UI where tags table did not reflect deletion of tags. (#3135, @ParseDark)
    • ➕ Added example illustrating the use of RAPIDS with MLFlow (#3028, @drobison00)

    ⚡️ Small bug fixes and doc updates (#3326, #3344, #3314, #3289, #3225, #3288, #3279, #3265, #3263, #3260, #3255, #3267, #3266, #3264, #3256, #3253, #3231, #3245, #3191, #3238, #3192, #3188, #3189, #3180, #3178, #3166, #3181, #3142, #3165, #2960, #3129, #3244, #3359 @harupy; #3236, #3141, @AveshCSingh; #3295, #3163, @arjundc-db; #3241, #3200, @zhidongqu-db; #3338, #3275, @sueann; #3020, @magnus-m; #3322, #3219, @dmatrix; #3341, #3179, #3355, #3360, #3363 @smurching; #3124, @jdlesage; #3232, #3146, @ankitmathur-db; #3140, @andreakress; #3174, #3133, @mlflow-automation; #3062, @cafeal; #3193, @tomasatdatabricks; 3115, @fhoering; #3328, @apurva-koti; #3046, @OlivierBondu; #3194, #3158, @dmatrix; #3250, @shivp950; #3259, @simonhessner; #3357 @dbczumar)

  • v1.10.0 Changes

    July 20, 2020

    🚀 MLflow 1.10.0 includes several major features and improvements, in particular the release of several new model registry Python client APIs.

    🔋 Features:

    • MlflowClient.transition_model_version_stage now supports an archive_existing_versions argument for archiving existing staging or production model versions when transitioning a new model version to staging or production (#3095, @harupy)
    • Added set_registry_uri, get_registry_uri APIs. Setting the model registry URI causes fluent APIs like mlflow.register_model to communicate with the model registry at the specified URI (#3072, @sueann)
    • Added paginated MlflowClient.search_registered_models API (#2939, #3023, #3027 @ankitmathur-db; #2966, @mparkhe)
    • ➕ Added syntax highlighting when viewing text files (YAML etc) in the MLflow runs UI (#3041, @harupy)
    • ➕ Added REST API and Python client support for setting and deleting tags on model versions and registered models, via the MlflowClient.create_registered_model, MlflowClient.create_model_version, MlflowClient.set_registered_model_tag, MlflowClient.set_model_version_tag, MlflowClient.delete_registered_model_tag, and MlflowClient.delete_model_version_tag APIs (#3094, @zhidongqu-db)

    🐛 Bug fixes and documentation updates:

    • ✂ Removed usage of deprecated aws ecr get-login command in mlflow.sagemaker (#3036, @mrugeles)
    • 🛠 Fixed bug where artifacts could not be viewed and downloaded from the artifact UI when using Azure Blob Storage (#3014, @Trollgeir)
    • Databricks credentials are now propagated to the project subprocess when running MLflow projects within a notebook (#3035, @smurching)
    • ➕ Added docs explaining how to fetching an MLflow model from the model registry (#3000, @andychow-db)

    ⚡️ Small bug fixes and doc updates (#3112, #3102, #3089, #3103, #3096, #3090, #3049, #3080, #3070, #3078, #3083, #3051, #3050, #2875, #2982, #2949, #3121 @harupy; #3082, @ankitmathur-db; #3084, #3019, @smurching)