mlflow-skinny

We are happy to announce the availability of MLflow 1.29.0!

MLflow 1.29.0 includes several major features and improvements

Features:

[Pipelines] Improve performance and fidelity of dataset profiling in the scikit-learn regression Pipeline (#6792, @sunishsheth2009) [Pipelines] Add an mlflow pipelines get-artifact CLI for retrieving Pipeline artifacts (#6517, @prithvikannan) [Pipelines] Introduce an option for skipping dataset profiling to the scikit-learn regression Pipeline (#6456, @apurva-koti) [Pipelines / UI] Display an mlflow pipelines CLI command for reproducing a Pipeline run in the MLflow UI (#6376, @hubertzub-db) [Tracking] Automatically generate friendly names for Runs if not supplied by the user (#6736, @BenWilson2) [Tracking] Add load_text(), load_image() and load_dict() fluent APIs for convenient artifact loading (#6475, @subramaniam02) [Tracking] Add creation_time and last_update_time attributes to the Experiment class (#6756, @subramaniam02) [Tracking] Add official MLflow Tracking Server Dockerfiles to the MLflow repository (#6731, @oojo12) [Tracking] Add searchExperiments API to Java client and deprecate listExperiments (#6561, @dbczumar) [Tracking] Add mlflow_search_experiments API to R client and deprecate mlflow_list_experiments (#6576, @dbczumar) [UI] Make URLs clickable in the MLflow Tracking UI (#6526, @marijncv) [UI] Introduce support for csv data preview within the artifact viewer pane (#6567, @nnethery) [Model Registry / Models] Introduce mlflow.models.add_libraries_to_model() API for adding libraries to an MLflow Model (#6586, @arjundc-db) [Models] Add model validation support to mlflow.evaluate() (#6582, @zhe-db, @jerrylian-db) [Models] Introduce sample_weights support to mlflow.evaluate() (#6806, @dbczumar) [Models] Add pos_label support to mlflow.evaluate() for identifying the positive class (#6696, @harupy) [Models] Make the metric name prefix and dataset info configurable in mlflow.evaluate() (#6593, @dbczumar) [Models] Add utility for validating the compatibility of a dataset with a model signature (#6494, @serena-ruan) [Models] Add predict_proba() support to the pyfunc representation of scikit-learn models (#6631, @skylarbpayne) [Models] Add support for Decimal type inference to MLflow Model schemas (#6600, @shitaoli-db) [Models] Add new CLI command for generating Dockerfiles for model serving (#6591, @anuarkaliyev23) [Scoring] Add /health endpoint to scoring server (#6574, @gabriel-milan) [Scoring] Support specifying a variant_name during Sagemaker deployment (#6486, @nfarley-soaren) [Scoring] Support specifying a data_capture_config during SageMaker deployment (#6423, @jonwiggins)

Bug fixes:

[Tracking] Make Run and Experiment deletion and restoration idempotent (#6641, @dbczumar) [UI] Fix an alignment bug affecting the Experiments list in the MLflow UI (#6569, @sunishsheth2009) [Models] Fix a regression in the directory path structure of logged Spark Models that occurred in MLflow 1.28.0 (#6683, @gwy1995) [Models] No longer reload the main module when loading model code (#6647, @Jooakim) [Artifacts] Fix an mlflow server compatibility issue with HDFS when running in --serve-artifacts mode (#6482, @shidianshifen) [Scoring] Fix an inference failure with 1-dimensional tensor inputs in TensorFlow and Keras (#6796, @LiamConnell)

Documentation updates:

[Tracking] Mark the SearchExperiments API as stable (#6551, @dbczumar) [Tracking / Model Registry] Deprecate the ListExperiments, ListRegisteredModels, and list_run_infos() APIs (#6550, @dbczumar) [Scoring] Deprecate mlflow.sagemaker.deploy() in favor of SageMakerDeploymentClient.create() (#6651, @dbczumar) Small bug fixes and documentation updates:

#6803, #6804, #6801, #6791, #6772, #6745, #6762, #6760, #6761, #6741, #6725, #6720, #6666, #6708, #6717, #6704, #6711, #6710, #6706, #6699, #6700, #6702, #6701, #6685, #6664, #6644, #6653, #6629, #6639, #6624, #6565, #6558, #6557, #6552, #6549, #6534, #6533, #6516, #6514, #6506, #6509, #6505, #6492, #6490, #6478, #6481, #6464, #6463, #6460, #6461, @harupy; #6810, #6809, #6727, #6648, @BenWilson2; #6808, #6766, #6729, @jerrylian-db; #6781, #6694, @marijncv; #6580, #6661, @bbarnes52; #6778, #6687, #6623, @shraddhafalane; #6662, #6737, #6612, #6595, @sunishsheth2009; #6777, @aviralsharma07; #6665, #6743, #6573, @liangz1; #6784, @apurva-koti; #6753, #6751, @mingyu89; #6690, #6455, #6484, @kriscon-db; #6465, #6689, @hubertzub-db; #6721, @WeichenXu123; #6722, #6718, #6668, #6663, #6621, #6547, #6508, #6474, #6452, @dbczumar; #6555, #6584, #6543, #6542, #6521, @dsgibbons; #6634, #6596, #6563, #6495, @prithvikannan; #6571, @smurching; #6630, #6483, @serena-ruan; #6642, @thinkall; #6614, #6597, @jinzhang21; #6457, @cnphil; #6570, #6559, @kumaryogesh17; #6560, #6540, @iamthen0ise; #6544, @Monkero; #6438, @ahlag; #3292, @dolfinus; #6637, @ninabacc-db; #6632, @arpitjasa-db

MLflow 1.28.0 includes several major features and improvements:

Features:

• [Pipelines] Log the full Pipeline runtime configuration to MLflow Tracking during Pipeline execution (#6359, @jinzhang21)
• [Pipelines] Add pipeline.yaml configurations to specify the Model Registry backend used for model registration (#6284, @sunishsheth2009)
• [Pipelines] Support optionally skipping the transform step of the scikit-learn regression pipeline (#6362, @sunishsheth2009)
• [Pipelines] Add UI links to Runs and Models in Pipeline Step Cards on Databricks (#6294, @dbczumar)
• [Tracking] Introduce mlflow.search_experiments() API for searching experiments by name and by tags (#6333, @WeichenXu123; #6227, #6172, #6154, @harupy)
• [Tracking] Increase the maximum parameter value length supported by File and SQL backends to 500 characters (#6358, @johnyNJ)
• [Tracking] Introduce an --older-than flag to mlflow gc for removing runs based on deletion time (#6354, @Jason-CKY)
• [Tracking] Add MLFLOW_SQLALCHEMYSTORE_POOL_RECYCLE environment variable for recycling SQLAlchemy connections (#6344, @postrational)
• [UI] Display deeply nested runs in the Runs Table on the Experiment Page (#6065, @tospe)
• [UI] Add box plot visualization for metrics to the Compare Runs page (#6308, @ahlag)
• [UI] Display tags on the Compare Runs page (#6164, @CaioCavalcanti)
• [UI] Use scientific notation for axes when viewing metric plots in log scale (#6176, @RajezMariner)
• [UI] Add button to Metrics page for downloading metrics as CSV (#6048, @rafaelvp-db)
• [UI] Include NaN and +/- infinity values in plots on the Metrics page (#6422, @hubertzub-db)
• [Tracking / Model Registry] Introduce environment variables to control retry behavior and timeouts for REST API requests (#5745, @peterdhansen)
• [Tracking / Model Registry] Make MlflowClient importable as mlflow.MlflowClient (#6085, @subramaniam02)
• [Model Registry] Add support for searching registered models and model versions by tags (#6413, #6411, #6320, @WeichenXu123)
• [Model Registry] Add stage parameter to set_model_version_tag() (#6185, @subramaniam02)
• [Model Registry] Add --registry-store-uri flag to mlflow server for specifying the Model Registry backend URI (#6142, @Secbone)
• [Models] Improve performance of Spark Model logging on Databricks (#6282, @bbarnes52)
• [Models] Include Pandas Series names in inferred model schemas (#6361, @RynoXLI)
• [Scoring] Make model_uri optional in mlflow models build-docker to support building generic model serving images (#6302, @harupy)
• [R] Support logging of NA and NaN parameter values (#6263, @nathaneastwood)

Bug fixes and documentation updates:

• [Pipelines] Improve scikit-learn regression pipeline latency by limiting dataset profiling to the first 100 columns (#6297, @sunishsheth2009)
• [Pipelines] Use xdg-open instead of open for viewing Pipeline results on Linux systems (#6326, @strangiato)
• [Pipelines] Fix a bug that skipped Step Card rendering in Jupyter Notebooks (#6378, @apurva-koti)
• [Tracking] Use the 401 HTTP response code in authorization failure REST API responses, instead of 500 (#6106, @balvisio)
• [Tracking] Correctly classify artifacts as files and directories when using Azure Blob Storage (#6237, @nerdinand)
• [Tracking] Fix a bug in the File backend that caused run metadata to be lost in the event of a failed write (#6388, @dbczumar)
• [Tracking] Adjust mlflow.pyspark.ml.autolog() to only log model signatures for supported input / output data types (#6365, @harupy)
• [Tracking] Adjust mlflow.tensorflow.autolog() to log TensorFlow early stopping callback info when log_models=False is specified (#6170, @WeichenXu123)
• [Tracking] Fix signature and input example logging errors in mlflow.sklearn.autolog() for models containing transformers (#6230, @dbczumar)
• [Tracking] Fix a failure in mlflow gc that occurred when removing a run whose artifacts had been previously deleted (#6165, @dbczumar)
• [Tracking] Add missing sqlparse library to MLflow Skinny client, which is required for search support (#6174, @dbczumar)
• [Tracking / Model Registry] Fix an mlflow server bug that rejected parameters and tags with empty string values (#6179, @dbczumar)
• [Model Registry] Fix a failure preventing model version schemas from being downloaded with --serve-arifacts enabled (#6355, @abbas123456)
• [Scoring] Patch the Java Model Server to support MLflow Models logged on recent versions of the Databricks Runtime (#6337, @dbczumar)
• [Scoring] Verify that either the deployment name or endpoint is specified when invoking the mlflow deployments predict CLI (#6323, @dbczumar)
• [Scoring] Properly encode datetime columns when performing batch inference with mlflow.pyfunc.spark_udf() (#6244, @harupy)
• [Projects] Fix an issue where local directory paths were misclassified as Git URIs when running Projects (#6218, @ElefHead)
• [R] Fix metric logging behavior for +/- infinity values (#6271, @nathaneastwood)
• [Docs] Move Python API docs for MlflowClient from mlflow.tracking to mlflow.client (#6405, @dbczumar)
• [Docs] Document that MLflow Pipelines requires Make (#6216, @dbczumar)
• [Docs] Improve documentation for developing and testing MLflow JS changes in CONTRIBUTING.rst (#6330, @ahlag)

Small bug fixes and doc updates (#6322, #6321, #6213, @KarthikKothareddy; #6409, #6408, #6396, #6402, #6399, #6398, #6397, #6390, #6381, #6386, #6385, #6373, #6375, #6380, #6374, #6372, #6363, #6353, #6352, #6350, #6351, #6349, #6347, #6287, #6341, #6342, #6340, #6338, #6319, #6314, #6316, #6317, #6318, #6315, #6313, #6311, #6300, #6292, #6291, #6289, #6290, #6278, #6279, #6276, #6272, #6252, #6243, #6250, #6242, #6241, #6240, #6224, #6220, #6208, #6219, #6207, #6171, #6206, #6199, #6196, #6191, #6190, #6175, #6167, #6161, #6160, #6153, @harupy; #6193, @jwgwalton; #6304, #6239, #6234, #6229, @sunishsheth2009; #6258, @xanderwebs; #6106, @balvisio; #6303, @bbarnes52; #6117, @wenfeiy-db; #6389, #6214, @apurva-koti; #6412, #6420, #6277, #6266, #6260, #6148, @WeichenXu123; #6120, @ameya-parab; #6281, @nathaneastwood; #6426, #6415, #6417, #6418, #6257, #6182, #6157, @dbczumar; #6189, @shrinath-suresh; #6309, @SamirPS; #5897, @temporaer; #6251, @herrmann; #6198, @sniafas; #6368, #6158, @jinzhang21; #6236, @subramaniam02; #6036, @serena-ruan; #6430, @ninabacc-db)

Note: Version 1.28.0 of the MLflow R package has not yet been released. It will be available on CRAN within the next week.

MLflow 1.27.0 includes several major features and improvements:

• [Pipelines] With MLflow 1.27.0, we are excited to announce the release of MLflow Pipelines, an opinionated framework for structuring MLOps workflows that simplifies and standardizes machine learning application development and productionization. MLflow Pipelines makes it easy for data scientists to follow best practices for creating production-ready ML deliverables, allowing them to focus on developing excellent models. MLflow Pipelines also enables ML engineers and DevOps teams to seamlessly deploy models to production and incorporate them into applications. To get started with MLflow Pipelines, check out the docs at https://mlflow.org/docs/latest/pipelines.html. (#6115)

• [UI] Introduce UI support for searching and comparing runs across multiple Experiments (#5971, @r3stl355)

More features:

• [Tracking] When using batch logging APIs, automatically split large sets of metrics, tags, and params into multiple requests (#6052, @nzw0301)
• [Tracking] When an Experiment is deleted, SQL-based backends also move the associate Runs to the "deleted" lifecycle stage (#6064, @AdityaIyengar27)
• [Tracking] Add support for logging single-element ndarray and tensor instances as metrics via the mlflow.log_metric() API (#5756, @ntakouris)
• [Models] Add support for CatBoostRanker models to the mlflow.catboost flavor (#6032, @danielgafni)
• [Models] Integrate SHAP's KernelExplainer with mlflow.evaluate(), enabling model explanations on categorical data (#6044, #5920, @WeichenXu123)
• [Models] Extend mlflow.evaluate() to automatically log the score() outputs of scikit-learn models as metrics (#5935, #5903, @WeichenXu123)

Bug fixes and documentation updates:

• [UI] Fix broken model links in the Runs table on the MLflow Experiment Page (#6014, @hctpbl)
• [Tracking/Installation] Require sqlalchemy>=1.4.0 upon MLflow installation, which is necessary for usage of SQL-based MLflow Tracking backends (#6024, @sniafas)
• [Tracking] Fix a regression that caused mlflow server to reject LogParam API requests containing empty string values (#6031, @harupy)
• [Tracking] Fix a failure in scikit-learn autologging that occurred when matplotlib was not installed on the host system (#5995, @fa9r)
• [Tracking] Fix a failure in TensorFlow autologging that occurred when training models on tf.data.Dataset inputs (#6061, @dbczumar)
• [Artifacts] Address artifact download failures from SFTP locations that occurred due to mismanaged concurrency (#5840, @rsundqvist)
• [Models] Fix a bug where MLflow Models did not restore bundled code properly if multiple models use the same code module name (#5926, @BFAnas)
• [Models] Address an issue where mlflow.sklearn.model() did not properly restore bundled model code (#6037, @WeichenXu123)
• [Models] Fix a bug in mlflow.evaluate() that caused input data objects to be mutated when evaluating certain scikit-learn models (#6141, @dbczumar)
• [Models] Fix a failure in mlflow.pyfunc.spark_udf that occurred when the UDF was invoked on an empty RDD partition (#6063, @WeichenXu123)
• [Models] Fix a failure in mlflow models build-docker that occurred when env-manager=local was specified (#6046, @bneijt)
• [Projects] Improve robustness of the git repository check that occurs prior to MLflow Project execution (#6000, @dkapur17)
• [Projects] Address a failure that arose when running a Project that does not have a master branch (#5889, @harupy)
• [Docs] Correct several typos throughout the MLflow docs (#5959, @ryanrussell)

Small bug fixes and doc updates (#6041, @drsantos89; #6138, #6137, #6132, @sunishsheth2009; #6144, #6124, #6125, #6123, #6057, #6060, #6050, #6038, #6029, #6030, #6025, #6018, #6019, #5962, #5974, #5972, #5957, #5947, #5907, #5938, #5906, #5932, #5919, #5914, #5888, #5890, #5886, #5873, #5865, #5843, @harupy; #6113, @comojin1994; #5930, @yashaswikakumanu; #5837, @shrinath-suresh; #6067, @deepyaman; #5997, @idlefella; #6021, @BenWilson2; #5984, @Sumanth077; #5929, @krunal16-c; #5879, @kugland; #5875, @ognis1205; #6006, @ryanrussell; #6140, @jinzhang21; #5983, @elk15; #6022, @apurva-koti; #5982, @EB-Joel; #5981, #5980, @punitkashyup; #6103, @ikrizanic; #5988, #5969, @SaumyaBhushan; #6020, #5991, @WeichenXu123; #5910, #5912, @Dark-Knight11; #6005, @Asinsa; #6023, @subramaniam02; #5999, @Regis-Caelum; #6007, @CaioCavalcanti; #5943, @kvaithin; #6017, #6002, @NeoKish; #6111, @T1b4lt; #5986, @seyyidibrahimgulec; #6053, @Zohair-coder; #6146, #6145, #6143, #6139, #6134, #6136, #6135, #6133, #6071, #6070, @dbczumar; #6026, @rotate2050)

MLflow 1.26.1 is a patch release containing the following bug fixes:

• [Installation] Fix compatibility issue with protobuf >= 4.21.0 (#5945, @harupy)
• [Models] Fix get_model_dependencies behavior for models: URIs containing artifact paths (#5921, @harupy)
• [Models] Revert a problematic change to artifacts persistence in mlflow.pyfunc.log_model() that was introduced in MLflow 1.25.0 (#5891, @kyle-jarvis)
• [Models] Close associated image files when EvaluationArtifact outputs from mlflow.evaluate() are garbage collected (#5900, @WeichenXu123)

Small bug fixes and updates (#5874, #5942, #5941, #5940, #5938, @harupy; #5893, @PrajwalBorkar; #5909, @yashaswikakumanu; #5937, @BenWilson2)

MLflow 1.26.0 includes several major features and improvements:

Features:

• [CLI] Add endpoint naming and options configuration to the deployment CLI (#5731, @trangevi)
• [Build,Doc] Add development environment setup script for Linux and MacOS x86 Operating Systems (#5717, @BenWilson2)
• [Tracking] Update mlflow.set_tracking_uri to add support for paths defined as pathlib.Path in addition to existing str path declarations (#5824, @cacharle)
• [Scoring] Add custom timeout override option to the scoring server CLI to support high latency models (#5663, @sniafas)
• [UI] Add sticky header to experiment run list table to support column name visibility when scrolling beyond page fold (#5818, @hubertzub-db)
• [Artifacts] Add GCS support for MLflow garbage collection (#5811, @aditya-iyengar-rtl-de)
• [Evaluate] Add pos_label argument for eval_and_log_metrics API to support accurate binary classifier evaluation metrics (#5807, @yxiong)
• [UI] Add fields for latest, minimum and maximum metric values on metric display page (#5574, @adamreeve)
• [Models] Add support for input_example and signature logging for pyspark ml flavor when using autologging (#5719, @bali0019)
• [Models] Add virtualenv environment manager support for mlflow models docker-build CLI (#5728, @harupy)
• [Models] Add support for wildcard module matching in log_model_allowlist for PySpark models (#5723, @serena-ruan)
• [Projects] Add virtualenv environment manager support for MLflow projects (#5631, @harupy)
• [Models] Add virtualenv environment manager support for MLflow Models (#5380, @harupy)
• [Models] Add virtualenv environment manager support for mlflow.pyfunc.spark_udf (#5676, @WeichenXu123)
• [Models] Add support for input_example and signature logging for tensorflow flavor when using autologging (#5510, @bali0019)
• [Server-infra] Add JSON Schema Type Validation to enable raising 400 errors on malformed requests to REST API endpoints (#5458, @mrkaye97)
• [Scoring] Introduce abstract endpoint interface for mlflow deployments (#5378, @trangevi)
• [UI] Add End Time and Duration fields to run comparison page (#3378, @RealArpanBhattacharya)
• [Serving] Add schema validation support when parsing input csv data for model serving (#5531, @vvijay-bolt)

Bug fixes and documentation updates:

• [Models] Fix REPL ID propagation from datasource listener to publisher for Spark data sources (#5826, @dbczumar)
• [UI] Update ag-grid and implement getRowId to improve performance in the runs table visualization (#5725, @adamreeve)
• [Serving] Fix tf-serving parsing to support columnar-based formatting (#5825, @arjundc-db)
• [Artifacts] Update log_artifact to support models larger than 2GB in HDFS (#5812, @hitchhicker)
• [Models] Fix autologging to support lightgbm metric names with "@" symbols within their names (#5785, @mengchendd)
• [Models] Pyfunc: Fix code directory resolution of subdirectories (#5806, @dbczumar)
• [Server-Infra] Fix mlflow-R server starting failure on windows (#5767, @serena-ruan)
• [Docs] Add documentation for virtualenv environment manager support for MLflow projects (#5727, @harupy)
• [UI] Fix artifacts display sizing to support full width rendering in preview pane (#5606, @szczeles)
• [Models] Fix local hostname issues when loading spark model by binding driver address to localhost (#5753, @WeichenXu123)
• [Models] Fix autologging validation and batch_size calculations for tensorflow flavor (#5683, @MarkYHZhang)
• [Artifacts] Fix SqlAlchemyStore.log_batch implementation to make it log data in batches (#5460, @erensahin)

Small bug fixes and doc updates (#5858, #5859, #5853, #5854, #5845, #5829, #5842, #5834, #5795, #5777, #5794, #5766, #5778, #5765, #5763, #5768, #5769, #5760, #5727, #5748, #5726, #5721, #5711, #5710, #5708, #5703, #5702, #5696, #5695, #5669, #5670, #5668, #5661, #5638, @harupy; #5749, @arpitjasa-db; #5675, @Davidswinkels; #5803, #5797, @ahlag; #5743, @kzhang01; #5650, #5805, #5724, #5720, #5662, @BenWilson2; #5627, @cterrelljones; #5646, @kutal10; #5758, @davideli-db; #5810, @rahulporuri; #5816, #5764, @shrinath-suresh; #5869, #5715, #5737, #5752, #5677, #5636, @WeichenXu123; #5735, @subramaniam02; #5746, @akaigraham; #5734, #5685, @lucalves; #5761, @marcelatoffernet; #5707, @aashish-khub; #5808, @ketangangal; #5730, #5700, @shaikmoeed; #5775, @dbczumar; #5747, @zhixuanevelynwu)

Note: Version 1.26.0 of the MLflow R package has not yet been released. It will be available on CRAN within the next week.

MLflow 1.25.1 is a patch release containing the following bug fixes:

• [Models] Fix a pyfunc artifact overwrite bug when multiple artifacts are saved in sub-directories (#5657, @kyle-jarvis)
• [Scoring] Fix permissions issue for Spark workers accessing model artifacts from a temp directory created by the driver (#5684, @WeichenXu123)

Note: Version 1.25.1 of the MLflow R package has not yet been released. It will be available on CRAN within the next week.

MLflow 1.25.0 includes several major features and improvements:

Features:

• [Tracking] Introduce a new fluent API mlflow.last_active_run() that provides the most recent fluent active run (#5584, @MarkYHZhang)
• [Tracking] Add experiment_names argument to the mlflow.search_runs() API to support searching runs by experiment names (#5564, @r3stl355)
• [Tracking] Add a description parameter to mlflow.start_run() (#5534, @dogeplusplus)
• [Tracking] Add log_every_n_step parameter to mlflow.pytorch.autolog() to control metric logging frequency (#5516, @adamreeve)
• [Tracking] Log pyspark.ml.param.Params values as MLflow parameters during PySpark autologging (#5481, @serena-ruan)
• [Tracking] Add support for pyspark.ml.Transformers to PySpark autologging (#5466, @serena-ruan)
• [Tracking] Add input example and signature autologging for Keras models (#5461, @bali0019)
• [Models] Introduce mlflow.diviner flavor for large-scale time series forecasting (#5553, @BenWilson2)
• [Models] Add pyfunc.get_model_dependencies() API to retrieve reproducible environment specifications for MLflow Models with the pyfunc flavor (#5503, @WeichenXu123)
• [Models] Add code_paths argument to all model flavors to support packaging custom module code with MLflow Models (#5448, @stevenchen-db)
• [Models] Support creating custom artifacts when evaluating models with mlflow.evaluate() (#5405, #5476 @MarkYHZhang)
• [Models] Add mlflow_version field to MLModel specification (#5515, #5576, @r3stl355)
• [Models] Add support for logging models to preexisting destination directories (#5572, @akshaya-a)
• [Scoring / Projects] Introduce --env-manager configuration for specifying environment restoration tools (e.g. conda) and deprecate --no-conda (#5567, @harupy)
• [Scoring] Support restoring model dependencies in mlflow.pyfunc.spark_udf() to ensure accurate predictions (#5487, #5561, @WeichenXu123)
• [Scoring] Add support for numpy.ndarray type inputs to the TensorFlow pyfunc predict() function (#5545, @WeichenXu123)
• [Scoring] Support deployment of MLflow Models to Sagemaker Serverless (#5610, @matthewmayo)
• [UI] Add MLflow version to header beneath logo (#5504, @adamreeve)
• [Artifacts] Introduce a mlflow.artifacts.download_artifacts() API mirroring the functionality of the mlflow artifacts download CLI (#5585, @dbczumar)
• [Artifacts] Introduce environment variables for controlling GCS artifact upload/download chunk size and timeouts (#5438, #5483, @mokrueger)

Bug fixes and documentation updates:

• [Tracking/SQLAlchemy] Create an index on run_uuid for PostgreSQL to improve query performance (#5446, @harupy)
• [Tracking] Remove client-side validation of metric, param, tag, and experiment fields (#5593, @BenWilson2)
• [Projects] Support setting the name of the MLflow Run when executing an MLflow Project (#5187, @bramrodenburg)
• [Scoring] Use pandas split orientation for DataFrame inputs to SageMaker deployment predict() API to preserve column ordering (#5522, @dbczumar)
• [Server-Infra] Fix runs search compatibility bugs with PostgreSQL, MySQL, and MSSQL (#5540, @harupy)
• [CLI] Fix a bug in the mlflow-skinny client that caused mlflow --version to fail (#5573, @BenWilson2)
• [Docs] Update guidance and examples for model deployment to AzureML to recommend using the mlflow-azureml package (#5491, @santiagxf)

Small bug fixes and doc updates (#5591, #5629, #5597, #5592, #5562, #5477, @BenWilson2; #5554, @juntai-zheng; #5570, @tahesse; #5605, @guelate; #5633, #5632, #5625, #5623, #5615, #5608, #5600, #5603, #5602, #5596, #5587, #5586, #5580, #5577, #5568, #5290, #5556, #5560, #5557, #5548, #5547, #5538, #5513, #5505, #5464, #5495, #5488, #5485, #5468, #5455, #5453, #5454, #5452, #5445, #5431, @harupy; #5640, @nchittela; #5520, #5422, @Ark-kun; #5639, #5604, @nishipy; #5543, #5532, #5447, #5435, @WeichenXu123; #5502, @singankit; #5500, @Sohamkayal4103; #5449, #5442, @apurva-koti; #5552, @vinijaiswal; #5511, @adamreeve; #5428, @jinzhang21; #5309, @sunishsheth2009; #5581, #5559, @Kr4is; #5626, #5618, #5529, @sisp; #5652, #5624, #5622, #5613, #5509, #5459, #5437, @dbczumar; #5616, @liangz1)

MLflow 1.24.0 includes several major features and improvements:

Features:

• [Tracking] Support uploading, downloading, and listing artifacts through the MLflow server via mlflow server --serve-artifacts (#5320, @BenWilson2, @harupy)
• [Tracking] Add the registered_model_name argument to mlflow.autolog() for automatic model registration during autologging (#5395, @WeichenXu123)
• [UI] Improve and restructure the Compare Runs page. Additions include "show diff only" toggles and scrollable tables (#5306, @WeichenXu123)
• [Models] Introduce mlflow.pmdarima flavor for pmdarima models (#5373, @BenWilson2)
• [Models] When loading an MLflow Model, print a warning if a mismatch is detected between the current environment and the Model's dependencies (#5368, @WeichenXu123)
• [Models] Support computing custom scalar metrics during model evaluation with mlflow.evaluate() (#5389, @MarkYHZhang)
• [Scoring] Add support for deploying and evaluating SageMaker models via the MLflow Deployments API (#4971, #5396, @jamestran201)

Bug fixes and documentation updates:

• [Tracking / UI] Fix artifact listing and download failures that occurred when operating the MLflow server in --serve-artifacts mode (#5409, @dbczumar)
• [Tracking] Support environment-variable-based authentication when making artifact requests to the MLflow server in --serve-artifacts mode (#5370, @TimNooren)
• [Tracking] Fix bugs in hostname and path resolution when making artifacts requests to the MLflow server in --serve-artifacts mode (#5384, #5385, @mert-kirpici)
• [Tracking] Fix an import error that occurred when mlflow.log_figure() was used without matplotlib.figure imported (#5406, @WeichenXu123)
• [Tracking] Correctly log XGBoost metrics containing the @ symbol during autologging (#5403, @maxfriedrich)
• [Tracking] Fix a SQL Server database error that occurred during Runs search (#5382, @dianacarvalho1)
• [Tracking] When downloading artifacts from HDFS, store them in the user-specified destination directory (#5210, @DimaClaudiu)
• [Tracking / Model Registry] Improve performance of large artifact and model downloads (#5359, @mehtayogita)
• [Models] Fix fast.ai PyFunc inference behavior for models with 2D outputs (#5411, @santiagxf)
• [Models] Record Spark model information to the active run when mlflow.spark.log_model() is called (#5355, @szczeles)
• [Models] Restore onnxruntime execution providers when loading ONNX models with mlflow.pyfunc.load_model() (#5317, @ecm200)
• [Projects] Increase Docker image push timeout when using Projects with Docker (#5363, @zanitete)
• [Python] Fix a bug that prevented users from enabling DEBUG-level Python log outputs (#5362, @dbczumar)
• [Docs] Add a developer guide explaining how to build custom plugins for mlflow.evaluate() (#5333, @WeichenXu123)

Small bug fixes and doc updates (#5298, @wamartin-aml; #5399, #5321, #5313, #5307, #5305, #5268, #5284, @harupy; #5329, @Ark-kun; #5375, #5346, #5304, @dbczumar; #5401, #5366, #5345, @BenWilson2; #5326, #5315, @WeichenXu123; #5236, @singankit; #5302, @timvink; #5357, @maitre-matt; #5347, #5344, @mehtayogita; #5367, @apurva-koti; #5348, #5328, #5310, @liangz1; #5267, @sunishsheth2009)

Note: Version 1.24.0 of the MLflow R package has not yet been released. It will be available on CRAN within the next week.

MLflow 1.23.1 is a patch release containing the following bug fixes:

• [Models] Fix a directory creation failure when loading PySpark ML models (#5299, @arjundc-db)
• [Model Registry] Revert to using case-insensitive validation logic for stage names in models:/ URIs (#5312, @lichenran1234)
• [Projects] Fix a race condition during Project tar file creation (#5303, @dbczumar)

Note: Version 1.23.1 of the MLflow R package has not yet been released. It will be available on CRAN within the next week.

MLflow 1.23.0 includes several major features and improvements:

Note: Version 1.23.0 of the MLflow R package has not yet been released. It will be available on CRAN within the next week.

Features:

• [Models] Introduce an mlflow.evaluate() API for evaluating MLflow Models, providing performance and explainability insights. For an overview, see https://mlflow.org/docs/latest/models.html#model-evaluation (#5069, #5092, #5256, @WeichenXu123)
• [Models] log_model() APIs now return information about the logged MLflow Model, including artifact location, flavors, and schema (#5230, @liangz1)
• [Models] Introduce an mlflow.models.Model.load_input_example() Python API for loading MLflow Model input examples (#5212, @maitre-matt)
• [Models] Add a UUID field to the MLflow Model specification. MLflow Models now have a unique identifier (#5149, #5167, @WeichenXu123)
• [Models] Support passing SciPy CSC and CSR matrices as MLflow Model input examples (#5016, @WeichenXu123)
• [Model Registry] Support specifying latest in model URI to get the latest version of a model regardless of the stage (#5027, @lichenran1234)
• [Tracking] Add support for LightGBM scikit-learn models to mlflow.lightgbm.autolog() (#5130, #5200, #5271 @jwyyy)
• [Tracking] Improve S3 artifact download speed by caching boto clients (#4695, @Samreay)
• [UI] Automatically update metric plots for in-progress runs (#5017, @cedkoffeto, @harupy)

Bug fixes and documentation updates:

• [Models] Fix a bug in MLflow Model schema enforcement where strings were incorrectly cast to Pandas objects (#5134, @stevenchen-db)
• [Models] Fix a bug where keyword arguments passed to mlflow.pytorch.load_model() were not applied for scripted models (#5163, @schmidt-jake)
• [Model Registry][r] Fix bug in R client mlflow_create_model_version() API that caused model source to be set incorrectly (#5185, @bramrodenburg)
• [Projects] Fix parsing behavior for Project URIs containing quotes (#5117, @dinaldoap)
• [Scoring] Use the correct 400-level error code for malformed MLflow Model Server requests (#5003, @abatomunkuev)
• [Tracking] Fix a bug where mlflow.start_run() modified user-supplied tags dictionary (#5191, @matheusMoreno)
• [UI] Fix a bug causing redundant scroll bars to be displayed on the Experiment Page (#5159, @sunishsheth2009)

Small bug fixes and doc updates (#5275, #5264, #5244, #5249, #5255, #5248, #5243, #5240, #5239, #5232, #5234, #5235, #5082, #5220, #5219, #5226, #5217, #5194, #5188, #5132, #5182, #5183, #5180, #5177, #5165, #5164, #5162, #5015, #5136, #5065, #5125, #5106, #5127, #5120, @harupy; #5045, @BenWilson2; #5156, @pbezglasny; #5202, @jwyyy; #3863, @JoshuaAnickat; #5205, @abhiramr; #4604, @OSobky; #4256, @einsmein; #5140, @AveshCSingh; #5273, #5186, #5176, @WeichenXu123; #5260, #5229, #5206, #5174, #5160, @liangz1)

1.22.0 (2021-11-29)

MLflow 1.22.0 includes several major features and improvements:

Features:

• [UI] Add a share button to the Experiment page (#4936, @marijncv)
• [UI] Improve readability of column sorting dropdown on Experiment page (#5022, @WeichenXu123; #5018, @NieuweNils, @coder-freestyle)
• [Tracking] Mark all autologging integrations as stable by removing @experimental decorators (#5028, @liangz1)
• [Tracking] Add optional experiment_id parameter to mlflow.set_experiment() (#5012, @dbczumar)
• [Tracking] Add support for XGBoost scikit-learn models to mlflow.xgboost.autolog() (#5078, @jwyyy)
• [Tracking] Improve statsmodels autologging performance by removing unnecessary metrics (#4942, @WeichenXu123)
• [Tracking] Update R client to tag nested runs with parent run ID (#4197, @yitao-li)
• [Models] Support saving and loading all XGBoost model types (#4954, @jwyyy)
• [Scoring] Support specifying AWS account and role when deploying models to SageMaker (#4923, @andresionek91)
• [Scoring] Support serving MLflow models with MLServer (#4963, @adriangonz)

Bug fixes and documentation updates:

• [UI] Fix bug causing Metric Plot page to crash when metric values are too large (#4947, @ianshan0915)
• [UI] Fix bug causing parallel coordinate curves to vanish (#5087, @harupy)
• [UI] Remove Creator field from Model Version page if user information is absent (#5089, @jinzhang21)
• [UI] Fix model loading instructions for non-pyfunc models in Artifact Viewer (#5006, @harupy)
• [Models] Fix a bug that added mlflow to conda.yaml even if a hashed version was already present (#5058, @maitre-matt)
• [Docs] Add Python documentation for metric, parameter, and tag key / value length limits (#4991, @westford14)
• [Examples] Update Python version used in Prophet example to fix installation errors (#5101, @BenWilson2)
• [Examples] Fix Kubernetes resources specification in MLflow Projects + Kubernetes example (#4948, @jianyuan)

Small bug fixes and doc updates (#5119, #5107, #5105, #5103, #5085, #5088, #5051, #5081, #5039, #5073, #5072, #5066, #5064, #5063, #5060, #4718, #5053, #5052, #5041, #5043, #5047, #5036, #5037, #5029, #5031, #5032, #5030, #5007, #5019, #5014, #5008, #4998, #4985, #4984, #4970, #4966, #4980, #4967, #4978, #4979, #4968, #4976, #4975, #4934, #4956, #4938, #4950, #4946, #4939, #4913, #4940, #4935, @harupy; #5095, #5070, #5002, #4958, #4945, @BenWilson2; #5099, @chaosddp; #5005, @you-n-g; #5042, #4952, @shrinath-suresh; #4962, #4995, @WeichenXu123; #5010, @lichenran1234; #5000, @wentinghu; #5111, @alexott; #5102, #5024, #5011, #4959, @dbczumar; #5075, #5044, #5026, #4997, #4964, #4989, @liangz1; #4999, @stevenchen-db)

MLflow 1.21.0 includes several major features and improvements:

Features:

• [UI] Add a diff-only toggle to the runs table for filtering out columns with constant values (#4862, @marijncv)
• [UI] Add a duration column to the runs table (#4840, @marijncv)
• [UI] Display the default column sorting order in the runs table (#4847, @marijncv)
• [UI] Add start_time and duration information to exported runs CSV (#4851, @marijncv)
• [UI] Add lifecycle stage information to the run page (#4848, @marijncv)
• [UI] Collapse run page sections by default for space efficiency, limit artifact previews to 50MB (#4917, @dbczumar)
• [Tracking] Introduce autologging capabilities for PaddlePaddle model training (#4751, @jinminhao)
• [Tracking] Add an optional tags field to the CreateExperiment API (#4788, @dbczumar; #4795, @apurva-koti)
• [Tracking] Add support for deleting artifacts from SFTP stores via the mlflow gc CLI (#4670, @afaul)
• [Tracking] Support AzureDefaultCredential for authenticating with Azure artifact storage backends (#4002, @marijncv)
• [Models] Upgrade the fastai model flavor to support fastai V2 (>=2.4.1) (#4715, @jinzhang21)
• [Models] Introduce an mlflow.prophet model flavor for Prophet time series models (#4773, @BenWilson2)
• [Models] Introduce a CLI for publishing MLflow Models to the SageMaker Model Registry (#4669, @jinnig)
• [Models] Print a warning when inferred model dependencies are not available on PyPI (#4891, @dbczumar)
• [Models, Projects] Add MLFLOW_CONDA_CREATE_ENV_CMD for customizing Conda environment creation (#4746, @giacomov)

Bug fixes and documentation updates:

• [UI] Fix an issue where column selections made in the runs table were persisted across experiments (#4926, @sunishsheth2009)
• [UI] Fix an issue where the text null was displayed in the runs table column ordering dropdown (#4924, @harupy)
• [UI] Fix a bug causing the metric plot view to display NaN values upon click (#4858, @arpitjasa-db)
• [Tracking] Fix a model load failure for paths containing spaces or special characters on UNIX systems (#4890, @BenWilson2)
• [Tracking] Correct a migration issue that impacted usage of MLflow Tracking with SQL Server (#4880, @marijncv)
• [Tracking] Spark datasource autologging tags now respect the maximum allowable size for MLflow Tracking (#4809, @dbczumar)
• [Model Registry] Add previously-missing certificate sources for Model Registry REST API requests (#4731, @ericgosno91)
• [Model Registry] Throw an exception when users supply invalid Model Registry URIs for Databricks (#4877, @yunpark93)
• [Scoring] Fix a schema enforcement error that incorrectly cast date-like strings to datetime objects (#4902, @wentinghu)
• [Docs] Expand the documentation for the MLflow Skinny Client (#4113, @eedeleon)

Small bug fixes and doc updates (#4928, #4919, #4927, #4922, #4914, #4899, #4893, #4894, #4884, #4864, #4823, #4841, #4817, #4796, #4797, #4767, #4768, #4757, @harupy; #4863, #4838, @marijncv; #4834, @ksaur; #4772, @louisguitton; #4801, @twsl; #4929, #4887, #4856, #4843, #4789, #4780, @WeichenXu123; #4769, @Ark-kun; #4898, #4756, @apurva-koti; #4784, @lakshikaparihar; #4855, @ianshan0915; #4790, @eedeleon; #4931, #4857, #4846, 4777, #4748, @dbczumar)

MLflow 1.20.2 is a patch release containing the following features and bug fixes:

Features:

• Enabled auto dependency inference in spark flavor in autologging (#4759, @harupy)

Bug fixes and documentation updates:

• Increased MLflow client HTTP request timeout from 10s to 120s (#4764, @jinzhang21)
• Fixed autologging compatibility bugs with TensorFlow and Keras version 2.6.0 (#4766, @dbczumar)

Small bug fixes and doc updates (#4770, @WeichenXu123)

Note: The MLflow R package for 1.20.1 is not yet available but will be in a week because CRAN's submission system will be offline until September 1st.

MLflow 1.20.1 is a patch release for the MLflow Python and R packages containing the following bug fixes:

• Avoid calling importlib_metadata.packages_distributions upon mlflow.utils.requirements_utils import (#4741, @dbczumar)
• Avoid depending on importlib_metadata==4.7.0 (#4740, @dbczumar)

Note: The MLflow R package for 1.20.0 is not yet available but will be in a week because CRAN's submission system will be offline until September 1st.

MLflow 1.20.0 includes several major features and improvements:

Features:

• Autologging for scikit-learn now records post training metrics when scikit-learn evaluation APIs, such as sklearn.metrics.mean_squared_error, are called (#4491, #4628 #4638, @WeichenXu123)
• Autologging for PySpark ML now records post training metrics when model evaluation APIs, such as Evaluator.evaluate(), are called (#4686, @WeichenXu123)
• Add pip_requirements and extra_pip_requirements to mlflow.*.log_model and mlflow.*.save_model for directly specifying the pip requirements of the model to log / save (#4519, #4577, #4602, @harupy)
• Added stdMetrics entries to the training metrics recorded during PySpark CrossValidator autologging (#4672, @WeichenXu123)
• MLflow UI updates:
  1. Improved scalability of the parallel coordinates plot for run performance comparison,
  2. Added support for filtering runs based on their start time on the experiment page,
  3. Added a dropdown for runs table column sorting on the experiment page,
  4. Upgraded the AG Grid plugin, which is used for runs table loading on the experiment page, to version 25.0.0,
  5. Fixed a bug on the experiment page that caused the metrics section of the runs table to collapse when selecting columns from other table sections (#4712, @dbczumar)
• Added support for distributed execution to autologging for PyTorch Lightning (#4717, @dbczumar)
• Expanded R support for Model Registry functionality (#4527, @bramrodenburg)
• Added model scoring server support for defining custom prediction response wrappers (#4611, @Ark-kun)
• mlflow.*.log_model and mlflow.*.save_model now automatically infer the pip requirements of the model to log / save based on the current software environment (#4518, @harupy)
• Introduced support for running Sagemaker Batch Transform jobs with MLflow Models (#4410, #4589, @YQ-Wang)

Bug fixes and documentation updates:

• Deprecate requirements_file argument for mlflow.*.save_model and mlflow.*.log_model (#4620, @harupy)
• set nextPageToken to null (#4729, @harupy)
• Fix a bug in MLflow UI where the pagination token for run search is not refreshed when switching experiments (#4709, @harupy)
• Fix a bug in the model scoring server that rejected requests specifying a valid Content-Type header with the charset parameter (#4609, @Ark-kun)
• Fixed a bug that caused SQLAlchemy backends to exhaust DB connections. (#4663, @arpitjasa-db)
• Improve docker build procedures to raise exceptions if docker builds fail (#4610, @Ark-kun)
• Disable autologging for scikit-learn cross_val_* APIs, which are incompatible with autologging (#4590, @WeichenXu123)
• Deprecate MLflow Models support for fast.ai V1 (#4728, @dbczumar)
• Deprecate the old Azure ML deployment APIs mlflow.azureml.cli.build_image and mlflow.azureml.build_image (#4646, @trangevi)
• Deprecate MLflow Models support for TensorFlow < 2.0 and Keras < 2.3 (#4716, @harupy)

Small bug fixes and doc updates (#4730, #4722, #4725, #4723, #4703, #4710, #4679, #4694, #4707, #4708, #4706, #4705, #4625, #4701, #4700, #4662, #4699, #4682, #4691, #4684, #4683, #4675, #4666, #4648, #4653, #4651, #4641, #4649, #4627, #4637, #4632, #4634, #4621, #4619, #4622, #4460, #4608, #4605, #4599, #4600, #4581, #4583, #4565, #4575, #4564, #4580, #4572, #4570, #4574, #4576, #4568, #4559, #4537, #4542, @harupy; #4698, #4573, @Ark-kun; #4674, @kvmakes; #4555, @vagoston; #4644, @zhengjxu; #4690, #4588, @apurva-koti; #4545, #4631, #4734, @WeichenXu123; #4633, #4292, @shrinath-suresh; #4711, @jinzhang21; #4688, @murilommen; #4635, @ryan-duve; #4724, #4719, #4640, #4639, #4629, #4612, #4613, #4586, @dbczumar)

MLflow 1.19.0 includes several major features and improvements:

Features:

• Add support for plotting per-class feature importance computed on linear boosters in XGBoost autologging (#4523, @dbczumar)

• Add mlflow_create_registered_model and mlflow_delete_registered_model for R to create/delete registered models.

• Add support for setting tags while resuming a run (#4497, @dbczumar)

• MLflow UI updates (#4490, @sunishsheth2009)

  • Add framework for internationalization support.
  • Move metric columns before parameter and tag columns in the runs table.
  • Change the display format of run start time to elapsed time (e.g. 3 minutes ago) from timestamp (e.g. 2021-07-14 14:02:10) in the runs table.

Bug fixes and documentation updates:

• Fix a bug causing MLflow UI to crash when sorting a column containing both NaN and empty values (#3409, @harupy)

Small bug fixes and doc updates (#4541, #4534, #4533, #4517, #4508, #4513, #4512, #4509, #4503, #4486, #4493, #4469, @harupy; #4458, @KasirajanA; #4501, @jimmyxu-db; #4521, #4515, @jerrylian-db; #4359, @shrinath-suresh; #4544, @WeichenXu123; #4549, @smurching; #4554, @derkomai; #4506, @tomasatdatabricks; #4551, #4516, #4494, @dbczumar; #4511, @keypointt)

MLflow 1.18.0 includes the following features and improvements:

Features:

• Autologging performance improvements for XGBoost, LightGBM, and scikit-learn (#4416, #4473, @dbczumar)
• Add new PaddlePaddle flavor to MLflow Models (#4406, #4439, @jinminhao)
• Introduce paginated ListExperiments API (#3881, @wamartin-aml)
• Include Runtime version for MLflow Models logged on Databricks (#4421, @stevenchen-db)
• MLflow Models now log dependencies in pip requirements.txt format, in addition to existing conda format (#4409, #4422, @stevenchen-db)
• Add support for limiting the number child runs created by autologging for scikit-learn hyperparameter search models (#4382, @mohamad-arabi)
• Improve artifact upload / download performance on Databricks (#4260, @dbczumar)
• Migrate all model dependencies from conda to "pip" section (#4393, @WeichenXu123)

Bug fixes and documentation updates:

• Fix an MLflow UI bug that caused git source URIs to be rendered improperly (#4403, @takabayashi)
• Fix a bug that prevented reloading of MLflow Models based on the TensorFlow SavedModel format (#4223) (#4319, @saschaschramm)
• Fix a bug in the behavior of KubernetesSubmittedRun.get_status() for Kubernetes MLflow Project runs (#3962) (#4159, @jcasse)
• Fix a bug in TLS verification for MLflow artifact operations on S3 (#4047, @PeterSulcs)
• Fix a bug causing the MLflow server to crash after deletion of the default experiment (#4352, @asaf400)
• Fix a bug causing mlflow models serve to crash on Windows 10 (#4377, @simonvanbernem)
• Fix a crash in runs search when ordering by metric values against the MSSQL backend store (#2551) (#4238, @naor2013)
• Fix an autologging incompatibility issue with TensorFlow 2.5 (#4371, @dbczumar)
• Fix a bug in the disable_for_unsupported_versions autologging argument that caused library versions to be incorrectly compared (#4303, @WeichenXu123)

Small bug fixes and doc updates (#4405, @mohamad-arabi; #4455, #4461, #4459, #4464, #4453, #4444, #4449, #4301, #4424, #4418, #4417, #3759, #4398, #4389, #4386, #4385, #4384, #4380, #4373, #4378, #4372, #4369, #4348, #4364, #4363, #4349, #4350, #4174, #4285, #4341, @harupy; #4446, @kHarshit; #4471, @AveshCSingh; #4435, #4440, #4368, #4360, @WeichenXu123; #4431, @apurva-koti; #4428, @stevenchen-db; #4467, #4402, #4261, @dbczumar)

MLflow 1.17.0 includes the following major features and improvements:

Features:

• Add support for hyperparameter-tuning models to mlflow.pyspark.ml.autolog() (#4270, @WeichenXu123)

Bug fixes and documentation updates:

• Fix PyTorch Lightning callback definition for compatibility with PyTorch Lightning 1.3.0 (#4333, @dbczumar)
• Fix a bug in scikit-learn autologging that omitted artifacts for unsupervised models (#4325, @dbczumar)
• Support logging datetime.date objects as part of model input examples (#4313, @vperiyasamy)
• Implement HTTP request retries in the MLflow Java client for 500-level responses (#4311, @dbczumar)
• Include a community code of conduct (#4310, @dennyglee)

Small bug fixes and doc updates (#4276, #4263, @WeichenXu123; #4289, #4302, #3599, #4287, #4284, #4265, #4266, #4275, #4268, @harupy; #4335, #4297, @dbczumar; #4324, #4320, @tleyden)

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)
• 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)

1.15.0 (2021-03-26)

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)

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)

We are happy to announce the availability of MLflow 1.14.0!

In addition to bug and documentation fixes, MLflow 1.14.0 includes the following features and improvements:

Python 3.5 has been deprecated

MLflow support for Python 3.5 is deprecated and will be dropped in an upcoming release. At that point, existing Python 3.5 workflows that use MLflow will continue to work without modification, but Python 3.5 users will no longer get access to the latest MLflow features and bugfixes. We recommend that you upgrade to Python 3.6 or newer.

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)
• 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)