Bump mlflow[extras] from 1.28.0 to 1.29.0

Bumps mlflow[extras] from 1.28.0 to 1.29.0.

Release notes

Sourced from mlflow[extras]'s releases.

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[extras]'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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neuro-inc / neuro-base-environment

Bump mlflow[extras] from 1.28.0 to 1.29.0 #522

MLflow 1.29.0

1.29.0 (2022-09-16)