build(deps): bump mlflow from 1.28.0 to 1.29.0

Bumps mlflow from 1.28.0 to 1.29.0.

Release notes

MLflow 1.29.0

We are happy to announce the availability of MLflow 1.29.0!

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

Changelog

Sourced from mlflow's changelog.

1.29.0 (2022-09-16)

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

Commits

2487179 Integrating step card with pandas_renderer (#6792)
b23e481 Update MLflow version to 1.29.0 (#6816)
13245a5 Run python3 dev/update_ml_package_versions.py (#6814)
9441c35 Run python3 dev/update_pypi_package_index.py (#6813)
8230a8b Add category (#6803)
6e993f7 Revert "Add setuptools for packaging dependency resolution (#6809)" (#6810)
8461284 Retry conda env creation in pytest session (#6804)
8f73d58 Introduce sample_weights arg to mlflow.evaluate() (#6806)
6639509 Add setuptools for packaging dependency resolution (#6809)
305c6ed Model Validation polishing (#6808)
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getindata / kedro-kubeflow

build(deps): bump mlflow from 1.28.0 to 1.29.0 #183

MLflow 1.29.0

1.29.0 (2022-09-16)