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Update dependency mlflow to v1.30.1 #8

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@renovate renovate bot commented Jul 8, 2022

This PR contains the following updates:

Package Change Age Adoption Passing Confidence
mlflow ==1.20.0 -> ==1.30.1 age adoption passing confidence

Release Notes

mlflow/mlflow (mlflow)

v1.30.0

Compare Source

MLflow 1.30.0 includes several major features and improvements

Features:

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:

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:

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:

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 (#​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, [@​Weiche

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@renovate renovate bot force-pushed the renovate/all-minor-patch branch from e810ac2 to ee3c426 Compare August 11, 2022 10:45
@renovate renovate bot changed the title Update dependency mlflow to v1.27.0 Update dependency mlflow to v1.28.0 Aug 11, 2022
@renovate renovate bot force-pushed the renovate/all-minor-patch branch from ee3c426 to 65a53d8 Compare September 25, 2022 18:59
@renovate renovate bot changed the title Update dependency mlflow to v1.28.0 Update dependency mlflow to v1.29.0 Sep 25, 2022
@renovate renovate bot force-pushed the renovate/all-minor-patch branch from 65a53d8 to 2e2aa35 Compare November 20, 2022 16:28
@renovate renovate bot changed the title Update dependency mlflow to v1.29.0 Update dependency mlflow to v1.30.0 Nov 20, 2022
@renovate renovate bot force-pushed the renovate/all-minor-patch branch from 2e2aa35 to 06677d8 Compare May 30, 2023 23:49
@renovate 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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