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Latest Version
Avg Release Cycle
29 days
Latest Release
45 days ago

Changelog History
Page 1

  • v1.25.1 Changes

    April 13, 2022

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

    • [Models] Fix a pyfunc artifact overwrite bug for 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)

  • v1.25.0 Changes

    April 11, 2022

    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)

  • v1.24.0 Changes

    February 27, 2022

    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)

  • v1.23.1 Changes

    January 27, 2022

    🚀 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)

  • v1.23.0 Changes

    January 17, 2022

    MLflow 1.23.0 includes several major features and improvements:

    🔋 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)

  • v1.22.0 Changes

    November 29, 2021

    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)

  • v1.21.0 Changes

    October 23, 2021

    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)
    • 0️⃣ [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)
    • 0️⃣ [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)
    • 0️⃣ [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)

  • v1.20.2 Changes

    September 03, 2021

    🚀 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)

  • v1.20.1 Changes

    August 26, 2021

    🚀 MLflow 1.20.1 is a patch release 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)

  • v1.20.0 Changes

    August 25, 2021

    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)