We are happy to announce the availability of MLflow 1.30.0!
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 forcreate_run
,get_run
andupdate_run
APIs (#6782, #6798 @apurva-koti) - [Tracking] Add support for searching by
creation_time
andlast_update_time
for thesearch_experiments
API (#6979, @harupy) - [Tracking] Add support for search terms
run_id IN
andrun ID NOT IN
for thesearch_runs
API (#6945, @harupy) - [Tracking] Add support for searching by
user_id
andend_time
for thesearch_runs
API (#6881, #6880 @subramaniam02) - [Tracking] Add support for searching by
run_name
andrun_id
for thesearch_runs
API (#6899, @harupy; #6952, @alexacole) - [Tracking] Add support for synchronizing run
name
attribute andmlflow.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 thestatus
andname
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
tomlflow.pyfunc.spark_udf()
(#6913, @BenWilson2) - [SQLAlchemy] Add support for configurable
Pool
class options forSqlAlchemyStore
(#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 inMLmodel
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
tosklearn.metrics.precision_recall_curve
inmlflow.evaluate()
(#6854, @dbczumar) - [SQLAlchemy] Fix a bug in
SqlAlchemyStore
whereset_tag()
updates the incorrect tags (#7027, @gabrielfu)
Documentation updates:
For a comprehensive list of changes, see the release change log, and check out the latest documentation on mlflow.org.