We are happy to announce the availability of MLflow 1.26.0!

MLflow 1.26.0 includes several major features and improvements:

Features:

  • [CLI] Add endpoint naming and options configuration to the deployment CLI (#5731, @trangevi)
  • [Build,Doc] Add development environment setup script for Linux and MacOS x86 Operating Systems (#5717, @BenWilson2)
  • [Tracking] Update mlflow.set_tracking_uri to add support for paths defined as pathlib.Path in addition to existing str path declarations (#5824, @cacharle)
  • [Scoring] Add custom timeout override option to the scoring server CLI to support high latency models (#5663, @sniafas)
  • [UI] Add sticky header to experiment run list table to support column name visibility when scrolling beyond page fold (#5818, @hubertzub-db)
  • [Artifacts] Add GCS support for MLflow garbage collection (#5811, @aditya-iyengar-rtl-de)
  • [Evaluate] Add pos_label argument for eval_and_log_metrics API to support accurate binary classifier evaluation metrics (#5807, @yxiong)
  • [UI] Add fields for latest, minimum and maximum metric values on metric display page (#5574, @adamreeve)
  • [Models] Add support for input_example and signature logging for pyspark ml flavor when using autologging (#5719, @bali0019)
  • [Models] Add virtualenv environment manager support for mlflow models docker-build CLI (#5728, @harupy)
  • [Models] Add support for wildcard module matching in log_model_allowlist for PySpark models (#5723, @serena-ruan)
  • [Projects] Add virtualenv environment manager support for MLflow projects (#5631, @harupy)
  • [Models] Add virtualenv environment manager support for MLflow Models (#5380, @harupy)
  • [Models] Add virtualenv environment manager support for mlflow.pyfunc.spark_udf (#5676, @WeichenXu123)
  • [Models] Add support for input_example and signature logging for tensorflow flavor when using autologging (#5510, @bali0019)
  • [Server-infra] Add JSON Schema Type Validation to enable raising 400 errors on malformed requests to REST API endpoints (#5458, @mrkaye97)
  • [Scoring] Introduce abstract endpoint interface for mlflow deployments (#5378, @trangevi)
  • [UI] Add End Time and Duration fields to run comparison page (#3378, @RealArpanBhattacharya)
  • [Serving] Add schema validation support when parsing input csv data for model serving (#5531, @vvijay-bolt)

Bug fixes and documentation updates:

  • [Models] Fix REPL ID propagation from datasource listener to publisher for Spark data sources (#5826, @dbczumar)
  • [UI] Update ag-grid and implement getRowId to improve performance in the runs table visualization (#5725, @adamreeve)
  • [Serving] Fix tf-serving parsing to support columnar-based formatting (#5825, @arjundc-db)
  • [Artifacts] Update log_artifact to support models larger than 2GB in HDFS (#5812, @hitchhicker)
  • [Models] Fix autologging to support lightgbm metric names with “@” symbols within their names (#5785, @mengchendd)
  • [Models] Pyfunc: Fix code directory resolution of subdirectories (#5806, @dbczumar)
  • [Server-Infra] Fix mlflow-R server starting failure on windows (#5767, @serena-ruan)
  • [Docs] Add documentation for virtualenv environment manager support for MLflow projects (#5727, @harupy)
  • [UI] Fix artifacts display sizing to support full width rendering in preview pane (#5606, @szczeles)
  • [Models] Fix local hostname issues when loading spark model by binding driver address to localhost (#5753, @WeichenXu123)
  • [Models] Fix autologging validation and batch_size calculations for tensorflow flavor (#5683, @MarkYHZhang)
  • [Artifacts] Fix SqlAlchemyStore.log_batch implementation to make it log data in batches (#5460, @erensahin)

For a comprehensive list of changes, see the release change log, and check out the latest documentation on mlflow.org.