MLflow v1.10.0 Release Notes
Release Date: 2020-07-20 // almost 4 years ago-
๐ MLflow 1.10.0 includes several major features and improvements, in particular the release of several new model registry Python client APIs.
๐ Features:
MlflowClient.transition_model_version_stage
now supports anarchive_existing_versions
argument for archiving existing staging or production model versions when transitioning a new model version to staging or production (#3095, @harupy)- Added
set_registry_uri
,get_registry_uri
APIs. Setting the model registry URI causes fluent APIs likemlflow.register_model
to communicate with the model registry at the specified URI (#3072, @sueann) - Added paginated
MlflowClient.search_registered_models
API (#2939, #3023, #3027 @ankitmathur-db; #2966, @mparkhe) - โ Added syntax highlighting when viewing text files (YAML etc) in the MLflow runs UI (#3041, @harupy)
- โ Added REST API and Python client support for setting and deleting tags on model versions and registered models,
via the
MlflowClient.create_registered_model
,MlflowClient.create_model_version
,MlflowClient.set_registered_model_tag
,MlflowClient.set_model_version_tag
,MlflowClient.delete_registered_model_tag
, andMlflowClient.delete_model_version_tag
APIs (#3094, @zhidongqu-db)
๐ Bug fixes and documentation updates:
- โ Removed usage of deprecated
aws ecr get-login
command inmlflow.sagemaker
(#3036, @mrugeles) - ๐ Fixed bug where artifacts could not be viewed and downloaded from the artifact UI when using Azure Blob Storage (#3014, @Trollgeir)
- Databricks credentials are now propagated to the project subprocess when running MLflow projects within a notebook (#3035, @smurching)
- โ Added docs explaining how to fetching an MLflow model from the model registry (#3000, @andychow-db)
โก๏ธ Small bug fixes and doc updates (#3112, #3102, #3089, #3103, #3096, #3090, #3049, #3080, #3070, #3078, #3083, #3051, #3050, #2875, #2982, #2949, #3121 @harupy; #3082, @ankitmathur-db; #3084, #3019, @smurching)