MLflow 1.26.1

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

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

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

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

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

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

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)

MLflow 1.22.0

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

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

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)