MLflow v1.9.0

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MLflow v1.9.0 Release Notes

• 1.9.0 (2020-06-19)

  MLflow 1.9.0 includes numerous major features and improvements, and a breaking change to
  experimental APIs:

  💥 Breaking Changes:

  • ⚡️ The new_name argument to MlflowClient.update_registered_model
    has been removed. Call MlflowClient.rename_registered_model instead. (#2946, @mparkhe)
  • The stage argument to MlflowClient.update_model_version
    has been removed. Call MlflowClient.transition_model_version_stage instead. (#2946, @mparkhe)

  🔋 Features (MLflow Models and Flavors)

  • log_model and save_model APIs now support saving model signatures (the model's input and output schema)
    and example input along with the model itself (#2698, #2775, @tomasatdatabricks). Model signatures are used
    to reorder and validate input fields when scoring/serving models using the pyfunc flavor, mlflow models
    CLI commands, or mlflow.pyfunc.spark_udf (#2920, @tomasatdatabricks and @aarondav)
  • Introduce fastai model persistence and autologging APIs under mlflow.fastai (#2619, #2689 @antoniomdk)
  • ➕ Add pluggable mlflow.deployments API and CLI for deploying models to custom serving tools, e.g. RedisAI
    (#2327, @hhsecond)
  • Enables loading and scoring models whose conda environments include dependencies in conda-forge (#2797, @dbczumar)
  • ➕ Add support for scoring ONNX-persisted models that return Python lists (#2742, @andychow-db)

  🔋 Features (MLflow Projects)

  • ➕ Add plugin interface for executing MLflow projects against custom backends (#2566, @jdlesage)
  • ➕ Add ability to specify additional cluster-wide Python and Java libraries when executing
    MLflow projects remotely on Databricks (#2845, @pogil)
  • 👍 Allow running MLflow projects against remote artifacts stored in any location with a corresponding
    ArtifactRepository implementation (Azure Blob Storage, GCS, etc) (#2774, @trangevi)
  • 👍 Allow MLflow projects running on Kubernetes to specify a different tracking server to log to via the
    KUBE_MLFLOW_TRACKING_URI for passing a different tracking server to the kubernetes job (#2874, @catapulta)

  🔋 Features (UI)

  • 🐎 Significant performance and scalability improvements to metric comparison and scatter plots in
    💻 the UI (#2447, @mjlbach)
  • 💻 The main MLflow experiment list UI now includes a link to the model registry UI (#2805, @zhidongqu-db),
  • 💻 Enable viewing PDFs logged as artifacts from the runs UI (#2859, @ankmathur96)
  • 💻 UI accessibility improvements: better color contrast (#2872, @Zangr), add child roles to DOM elements (#2871, @Zangr)

  🔋 Features (Tracking Client and Server)

  • ➕ Adds ability to pass client certs as part of REST API requests when using the tracking or model
    registry APIs. (#2843, @PhilipMay)
  • 🆕 New community plugin: support for storing artifacts in Aliyun (Alibaba Cloud) (#2917, @SeaOfOcean)
  • 🌲 Infer and set content type and encoding of objects when logging models and artifacts to S3 (#2881, @hajapy)
  • ➕ Adds support for logging artifacts to HDFS Federation ViewFs (#2782, @fhoering)
  • ➕ Add healthcheck endpoint to the MLflow server at /health (#2725, @crflynn)
  • 👌 Improves performance of default file-based tracking storage backend by using LibYAML (if installed)
    📇 to read experiment and run metadata (#2707, @Higgcz)

  🐛 Bug fixes and documentation updates:

  • 🚚 Several UI fixes: remove margins around icon buttons (#2827, @harupy),
    🛠 fix alignment issues in metric view (#2811, @zhidongqu-db), add handling of NaN
    values in metrics plot (#2773, @dbczumar), truncate run ID in the run name when
    comparing multiple runs (#2508, @harupy)
  • ⬆️ Database engine URLs are no longer logged when running mlflow db upgrade (#2849, @hajapy)
  • 🌲 Updates log_artifact, log_model APIs to consistently use posix paths, rather than OS-dependent
    paths, when computing artifact subpaths. (#2784, @mikeoconnor0308)
  • 🛠 Fix ValueError when scoring tf.keras 1.X models using mlflow.pyfunc.predict (#2762, @juntai-zheng)
  • 🛠 Fixes conda environment activation bug when running MLflow projects on Windows (#2731, @MynherVanKoek)
  • mlflow.end_run will now clear the active run even if the run cannot be marked as
    terminated (e.g. because it's been deleted), (#2693, @ahmed-shariff)
  • ➕ Add missing documentation for mlflow.spacy APIs (#2771, @harupy)

  ⚡️ Small bug fixes and doc updates (#2919, @willzhan-db; #2940, #2942, #2941, #2943, #2927, #2929, #2926, #2914, #2928, #2913, #2852, #2876, #2808, #2810, #2442, #2780, #2758, #2732, #2734, #2431, #2733, #2716, @harupy; #2915, #2897, @jwgwalton; #2856, @jkthompson; #2962, @hhsecond; #2873, #2829, #2582, @dmatrix; #2908, #2865, #2880, #2866, #2833, #2785, #2723, @smurching; #2906, @dependabot[bot]; #2724, @aarondav; #2896, @ezeeetm; #2741, #2721, @mlflow-automation; #2864, @tallen94; #2726, @crflynn; #2710, #2951 @mparkhe; #2935, #2921, @ankitmathur-db; #2963, #2739, @dbczumar; #2853, @stat4jason; #2709, #2792, @juntai-zheng @juntai-zheng; #2749, @HiromuHota; #2957, #2911, #2718, @arjundc-db; #2885, @willzhan-db; #2803, #2761, @pogil; #2392, @jnmclarty; #2794, @Zethson; #2766, #2916 @shubham769)

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