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MLflow v1.20.0 Release Notes

Release Date: 2021-08-25 // over 2 years ago

  • MLflow 1.20.0 includes several major features and improvements:

    🔋 Features:

    • Autologging for scikit-learn now records post training metrics when scikit-learn evaluation APIs, such as sklearn.metrics.mean_squared_error, are called (#4491, #4628 #4638, @WeichenXu123)
    • Autologging for PySpark ML now records post training metrics when model evaluation APIs, such as Evaluator.evaluate(), are called (#4686, @WeichenXu123)
    • Add pip_requirements and extra_pip_requirements to mlflow.*.log_model and mlflow.*.save_model for directly specifying the pip requirements of the model to log / save (#4519, #4577, #4602, @harupy)
    • ➕ Added stdMetrics entries to the training metrics recorded during PySpark CrossValidator autologging (#4672, @WeichenXu123)
    • ⚡️ MLflow UI updates:
      1. Improved scalability of the parallel coordinates plot for run performance comparison,
      2. Added support for filtering runs based on their start time on the experiment page,
      3. Added a dropdown for runs table column sorting on the experiment page,
      4. Upgraded the AG Grid plugin, which is used for runs table loading on the experiment page, to version 25.0.0,
      5. Fixed a bug on the experiment page that caused the metrics section of the runs table to collapse when selecting columns from other table sections (#4712, @dbczumar)
    • ➕ Added support for distributed execution to autologging for PyTorch Lightning (#4717, @dbczumar)
    • 👍 Expanded R support for Model Registry functionality (#4527, @bramrodenburg)
    • ➕ Added model scoring server support for defining custom prediction response wrappers (#4611, @Ark-kun)
    • 🌲 mlflow.*.log_model and mlflow.*.save_model now automatically infer the pip requirements of the model to log / save based on the current software environment (#4518, @harupy)
    • 👷 Introduced support for running Sagemaker Batch Transform jobs with MLflow Models (#4410, #4589, @YQ-Wang)

    🐛 Bug fixes and documentation updates:

    • Deprecate requirements_file argument for mlflow.*.save_model and mlflow.*.log_model (#4620, @harupy)
    • set nextPageToken to null (#4729, @harupy)
    • 🛠 Fix a bug in MLflow UI where the pagination token for run search is not refreshed when switching experiments (#4709, @harupy)
    • 🛠 Fix a bug in the model scoring server that rejected requests specifying a valid Content-Type header with the charset parameter (#4609, @Ark-kun)
    • 🛠 Fixed a bug that caused SQLAlchemy backends to exhaust DB connections. (#4663, @arpitjasa-db)
    • 👌 Improve docker build procedures to raise exceptions if docker builds fail (#4610, @Ark-kun)
    • Disable autologging for scikit-learn cross_val_* APIs, which are incompatible with autologging (#4590, @WeichenXu123)
    • 🗄 Deprecate MLflow Models support for fast.ai V1 (#4728, @dbczumar)
    • 🏗 Deprecate the old Azure ML deployment APIs mlflow.azureml.cli.build_image and mlflow.azureml.build_image (#4646, @trangevi)
    • 🗄 Deprecate MLflow Models support for TensorFlow < 2.0 and Keras < 2.3 (#4716, @harupy)

    ⚡️ Small bug fixes and doc updates (#4730, #4722, #4725, #4723, #4703, #4710, #4679, #4694, #4707, #4708, #4706, #4705, #4625, #4701, #4700, #4662, #4699, #4682, #4691, #4684, #4683, #4675, #4666, #4648, #4653, #4651, #4641, #4649, #4627, #4637, #4632, #4634, #4621, #4619, #4622, #4460, #4608, #4605, #4599, #4600, #4581, #4583, #4565, #4575, #4564, #4580, #4572, #4570, #4574, #4576, #4568, #4559, #4537, #4542, @harupy; #4698, #4573, @Ark-kun; #4674, @kvmakes; #4555, @vagoston; #4644, @zhengjxu; #4690, #4588, @apurva-koti; #4545, #4631, #4734, @WeichenXu123; #4633, #4292, @shrinath-suresh; #4711, @jinzhang21; #4688, @murilommen; #4635, @ryan-duve; #4724, #4719, #4640, #4639, #4629, #4612, #4613, #4586, @dbczumar)