Update dependency mlflow to v1.30.1 #8

renovate · 2022-07-08T12:44:39Z

This PR contains the following updates:

Package	Change	Age	Confidence
mlflow	`==1.20.0` -> `==1.30.1`

Release Notes

mlflow/mlflow (mlflow)

`v1.30.0`

Compare Source

MLflow 1.30.0 includes several major features and improvements

Features:

[Pipelines] Introduce hyperparameter tuning support to MLflow Pipelines (#6859, @prithvikannan)
[Pipelines] Introduce support for prediction outlier comparison to training data set (#6991, @jinzhang21)
[Pipelines] Introduce support for recording all training parameters for reproducibility (#7026, #7094, @prithvikannan)
[Pipelines] Add support for Delta tables as a datasource in the ingest step (#7010, @sunishsheth2009)
[Pipelines] Add expanded support for data profiling up to 10,000 columns (#7035, @prithvikanna)
[Pipelines] Add support for AutoML in MLflow Pipelines using FLAML (#6959, @mshtelma)
[Pipelines] Add support for simplified transform step execution by allowing for unspecified configuration (#6909, @apurva-koti)
[Pipelines] Introduce a data preview tab to the transform step card (#7033, @prithvikannan)
[Tracking] Introduce run_name attribute for create_run, get_run and update_run APIs (#6782, #6798 @apurva-koti)
[Tracking] Add support for searching by creation_time and last_update_time for the search_experiments API (#6979, @harupy)
[Tracking] Add support for search terms run_id IN and run ID NOT IN for the search_runs API (#6945, @harupy)
[Tracking] Add support for searching by user_id and end_time for the search_runs API (#6881, #6880 @subramaniam02)
[Tracking] Add support for searching by run_name and run_id for the search_runs API (#6899, @harupy; #6952, @alexacole)
[Tracking] Add support for synchronizing run name attribute and mlflow.runName tag (#6971, @BenWilson2)
[Tracking] Add support for signed tracking server requests using AWSSigv4 and AWS IAM (#7044, @pdifranc)
[Tracking] Introduce the update_run() API for modifying the status and name attributes of existing runs (#7013, @gabrielfu)
[Tracking] Add support for experiment deletion in the mlflow gc cli API (#6977, @shaikmoeed)
[Models] Add support for environment restoration in the evaluate() API (#6728, @jerrylian-db)
[Models] Remove restrictions on binary classification labels in the evaluate() API (#7077, @dbczumar)
[Scoring] Add support for BooleanType to mlflow.pyfunc.spark_udf() (#6913, @BenWilson2)
[SQLAlchemy] Add support for configurable Pool class options for SqlAlchemyStore (#6883, @mingyu89)

Bug fixes:

[Pipelines] Enable Pipeline subprocess commands to create a new SparkSession if one does not exist (#6846, @prithvikannan)
[Pipelines] Fix a rendering issue with bool column types in Step Card data profiles (#6907, @sunishsheth2009)
[Pipelines] Add validation and an exception if required step files are missing (#7067, @mingyu89)
[Pipelines] Change step configuration validation to only be performed during runtime execution of a step (#6967, @prithvikannan)
[Tracking] Fix infinite recursion bug when inferring the model schema in mlflow.pyspark.ml.autolog() (#6831, @harupy)
[UI] Remove the browser error notification when failing to fetch artifacts (#7001, @kevingreer)
[Models] Allow mlflow-skinny package to serve as base requirement in MLmodel requirements (#6974, @BenWilson2)
[Models] Fix an issue with code path resolution for loading SparkML models (#6968, @dbczumar)
[Models] Fix an issue with dependency inference in logging SparkML models (#6912, @BenWilson2)
[Models] Fix an issue involving potential duplicate downloads for SparkML models (#6903, @serena-ruan)
[Models] Add missing pos_label to sklearn.metrics.precision_recall_curve in mlflow.evaluate() (#6854, @dbczumar)
[SQLAlchemy] Fix a bug in SqlAlchemyStore where set_tag() updates the incorrect tags (#7027, @gabrielfu)

Documentation updates:

[Models] Update details regarding the default Keras serialization format (#7022, @balvisio)

Small bug fixes and documentation updates:

#7093, #7095, #7092, #7064, #7049, #6921, #6920, #6940, #6926, #6923, #6862, @jerrylian-db; #6946, #6954, #6938, @mingyu89; #7047, #7087, #7056, #6936, #6925, #6892, #6860, #6828, @sunishsheth2009; #7061, #7058, #7098, #7071, #7073, #7057, #7038, #7029, #6918, #6993, #6944, #6976, #6960, #6933, #6943, #6941, #6900, #6901, #6898, #6890, #6888, #6886, #6887, #6885, #6884, #6849, #6835, #6834, @harupy; #7094, #7065, #7053, #7026, #7034, #7021, #7020, #6999, #6998, #6996, #6990, #6989, #6934, #6924, #6896, #6895, #6876, #6875, #6861, @prithvikannan; #7081, #7030, #7031, #6965, #6750, @bbarnes52; #7080, #7069, #7051, #7039, #7012, #7004, @dbczumar; #7054, @jinzhang21; #7055, #7037, #7036, #6949, #6951, @apurva-koti; #6815, @michaguenther; #6897, @chaturvedakash; #7025, #6981, #6950, #6948, #6937, #6829, #6830, @BenWilson2; #6982, @vadim; #6985, #6927, @kriscon-db; #6917, #6919, #6872, #6855, @WeichenXu123; #6980, @utkarsh867; #6973, #6935, @wentinghu; #6930, @mingyangge-db; #6956, @RohanBha1; #6916, @av-maslov; #6824, @shrinath-suresh; #6732, @oojo12; #6807, @ikrizanic; #7066, @subramaniam20jan; #7043, @AvikantSrivastava; #6879, @jspablo

`v1.29.0`

Compare Source

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, @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

`v1.28.0`

Compare Source

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)

`v1.27.0`

Compare Source

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 ([#&#

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renovate bot added renovate dependencies labels Jul 8, 2022

renovate bot force-pushed the renovate/all-minor-patch branch from e810ac2 to ee3c426 Compare August 11, 2022 10:45

renovate bot changed the title ~~Update dependency mlflow to v1.27.0~~ Update dependency mlflow to v1.28.0 Aug 11, 2022

renovate bot force-pushed the renovate/all-minor-patch branch from ee3c426 to 65a53d8 Compare September 25, 2022 18:59

renovate bot changed the title ~~Update dependency mlflow to v1.28.0~~ Update dependency mlflow to v1.29.0 Sep 25, 2022

renovate bot force-pushed the renovate/all-minor-patch branch from 65a53d8 to 2e2aa35 Compare November 20, 2022 16:28

renovate bot changed the title ~~Update dependency mlflow to v1.29.0~~ Update dependency mlflow to v1.30.0 Nov 20, 2022

Update dependency mlflow to v1.30.1

06677d8

renovate bot force-pushed the renovate/all-minor-patch branch from 2e2aa35 to 06677d8 Compare May 30, 2023 23:49

renovate bot changed the title ~~Update dependency mlflow to v1.30.0~~ Update dependency mlflow to v1.30.1 May 30, 2023

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Release Notes

v1.30.0

v1.29.0

v1.28.0

v1.27.0

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`v1.30.0`

`v1.29.0`

`v1.28.0`

`v1.27.0`