All Versions
47
Latest Version
Avg Release Cycle
27 days
Latest Release
51 days ago

Changelog History
Page 1

  • 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)

  • v1.19.0 Changes

    July 14, 2021

    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)

  • v1.18.0 Changes

    June 18, 2021

    MLflow 1.18.0 includes several major 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)

  • v1.17.0 Changes

    May 07, 2021

    MLflow 1.17.0 includes several 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)

  • v1.16.0 Changes

    April 22, 2021

    MLflow 1.16.0 includes several major features and improvements:

    🔋 Features:

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

    🐛 Bug fixes and documentation updates:

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

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

  • v1.15.0 Changes

    March 26, 2021

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

    🔋 Features:

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

    🐛 Bug fixes:

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

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

  • v1.14.1 Changes

    March 01, 2021

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

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

  • v1.14.0 Changes

    February 18, 2021

    MLflow 1.14.0 includes several major features and improvements:

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

    🐛 Bug fixes and documentation updates:

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

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