CHANGELOG.rst

Changelog

0.5.2 (2018-08-24)

MLflow 0.5.2 is a patch release on top of 0.5.1 containing only bug fixes and no breaking changes or features.

Bug fixes:

• Fix a bug with ECR client creation that caused mlflow.sagemaker.deploy() to fail when searching for a deployment Docker image (#366, @dbczumar)

0.5.1 (2018-08-23)

MLflow 0.5.1 is a patch release on top of 0.5.0 containing only bug fixes and no breaking changes or features.

Bug fixes:

• Fix with mlflow.start_run() as run to actually set run to the created Run (previously, it was None) (#322, @tomasatdatabricks)
• Fixes to DBFS artifactory to throw an exception if logging an artifact fails (#309) and to mimic FileStore's behavior of logging subdirectories (#347, @andrewmchen)
• Fix for Python 3.7 support with tarfiles (#329, @tomasatdatabricks)
• Fix spark.load_model not to delete the DFS tempdir (#335, @aarondav)
• MLflow UI now appropriately shows entrypoint if it's not main (#345, @aarondav)
• Make Python API forward-compatible with newer server versions of protos (#348, @aarondav)
• Improved API docs (#305, #284, @smurching)

0.5.0 (2018-08-17)

MLflow 0.5.0 offers some major improvements, including Keras and PyTorch first-class support as models, SFTP support as an artifactory, a new scatterplot visualization to compare runs, and a more complete Python SDK for experiment and run management.

Breaking changes:

• The Tracking API has been split into two pieces, a "basic logging" API and a "tracking service" API. The "basic logging" API deals with logging metrics, parameters, and artifacts to the currently-active active run, and is accessible in mlflow (e.g., mlflow.log_param). The tracking service API allow managing experiments and runs (especially historical runs) and is available in mlflow.tracking. The tracking service API will look analogous to the upcoming R and Java Tracking Service SDKs. Please be aware of the following breaking changes:
  • mlflow.tracking no longer exposes the basic logging API, only mlflow. So, code that was written like from mlflow.tracking import log_param will have to be from mlflow import log_param (note that almost all examples were already doing this).
  • Access to the service API goes through the mlflow.tracking.get_service() function, which relies on the same tracking server set by either the environment variable MLFLOW_TRACKING_URI or by code with mlflow.tracking.set_tracking_uri(). So code that used to look like mlflow.tracking.get_run() will now have to do mlflow.tracking.get_service().get_run(). This does not apply to the basic logging API.
  • mlflow.ActiveRun has been converted into a lightweight wrapper around mlflow.entities.Run to enable the Python with syntax. This means that there are no longer any special methods on the object returned when calling mlflow.start_run(). These can be converted to the service API.
  • The Python entities returned by the tracking service API are now accessible in mlflow.entities directly. Where previously you may have used mlflow.entities.experiment.Experiment, you would now just use mlflow.entities.Experiment. The previous version still exists, but is deprecated and may be hidden in a future version.
• REST API endpoint /ajax-api/2.0/preview/mlflow/artifacts/get has been moved to $static_prefix/get-artifact. This change is coversioned in the JavaScript, so should not be noticeable unless you were calling the REST API directly (#293, @andremchen)

Features:

• [Models] Keras integration: we now support logging Keras models directly in the log_model API, model format, and serving APIs (#280, @ToonKBC)
• [Models] PyTorch integration: we now support logging PyTorch models directly in the log_model API, model format, and serving APIs (#264, @vfdev-5)
• [UI] Scatterplot added to "Compare Runs" view to help compare runs using any two metrics as the axes (#268, @ToonKBC)
• [Artifacts] SFTP artifactory store added (#260, @ToonKBC)
• [Sagemaker] Users can specify a custom VPC when deploying SageMaker models (#304, @dbczumar)
• Pyfunc serialization now includes the Python version, and warns if the major version differs (can be suppressed by using load_pyfunc(suppress_warnings=True)) (#230, @dbczumar)
• Pyfunc serve/predict will activate conda environment stored in MLModel. This can be disabled by adding --no-conda to mlflow pyfunc serve or mlflow pyfunc predict (#225, @0wu)
• Python SDK formalized in mlflow.tracking. This includes adding SDK methods for get_run, list_experiments, get_experiment, and set_terminated. (#299, @aarondav)
• mlflow run can now be run against projects with no conda.yaml specified. By default, an empty conda environment will be created -- previously, it would just fail. You can still pass --no-conda to avoid entering a conda environment altogether (#218, @smurching)

Bug fixes:

• Fix numpy array serialization for int64 and other related types, allowing pyfunc to return such results (#240, @arinto)
• Fix DBFS artifactory calling log_artifacts with binary data (#295, @aarondav)
• Fix Run Command shown in UI to reproduce a run when the original run is targeted at a subdirectory of a Git repo (#294, @adrian555)
• Filter out ubiquitious dtype/ufunc warning messages (#317, @aarondav)
• Minor bug fixes and documentation updates (#261, @stbof; #279, @dmatrix; #313, @rbang1, #320, @yassineAlouini; #321, @tomasatdatabricks; #266, #282, #289, @smurching; #267, #265, @aarondav; #256, #290, @ToonKBC; #273, #263, @mateiz; #272, #319, @adrian555; #277, @aadamson; #283, #296, @andrewmchen)

0.4.2 (2018-08-07)

Breaking changes: None

Features:

• MLflow experiments REST API and mlflow experiments create now support providing --artifact-location (#232, @aarondav)
• [UI] Runs can now be sorted by columns, and added a Select All button (#227, @ToonKBC)
• Databricks File System (DBFS) artifactory support added (#226, @andrewmchen)
• databricks-cli version upgraded to >= 0.8.0 to support new DatabricksConfigProvider interface (#257, @aarondav)

Bug fixes:

• MLflow client sends REST API calls using snake_case instead of camelCase field names (#232, @aarondav)
• Minor bug fixes (#243, #242, @aarondav; #251, @javierluraschi; #245, @smurching; #252, @mateiz)

0.4.1 (2018-08-03)

Breaking changes: None

Features:

• [Projects] MLflow will use the conda installation directory given by the $MLFLOW_CONDA_HOME if specified (e.g. running conda commands by invoking "$MLFLOW_CONDA_HOME/bin/conda"), defaulting to running "conda" otherwise. (#231, @smurching)
• [UI] Show GitHub links in the UI for projects run from http(s):// GitHub URLs (#235, @smurching)

Bug fixes:

• Fix GCSArtifactRepository issue when calling list_artifacts on a path containing nested directories (#233, @jakeret)
• Fix Spark model support when saving/loading models to/from distributed filesystems (#180, @tomasatdatabricks)
• Add missing mlflow.version import to sagemaker module (#229, @dbczumar)
• Validate metric, parameter and run IDs in file store and Python client (#224, @mateiz)
• Validate that the tracking URI is a remote URI for Databricks project runs (#234, @smurching)
• Fix bug where we'd fetch git projects at SSH URIs into a local directory with the same name as the URI, instead of into a temporary directory (#236, @smurching)

0.4.0 (2018-08-01)

Breaking changes:

• [Projects] Removed the use_temp_cwd argument to mlflow.projects.run() (--new-dir flag in the mlflow run CLI). Runs of local projects now use the local project directory as their working directory. Git projects are still fetched into temporary directories (#215, @smurching)
• [Tracking] GCS artifact storage is now a pluggable dependency (no longer installed by default). To enable GCS support, install google-cloud-storage on both the client and tracking server via pip. (#202, @smurching)
• [Tracking] Clients running MLflow 0.4.0 and above require a server running MLflow 0.4.0 or above, due to a fix that ensures clients no longer double-serialize JSON into strings when sending data to the server (#200, @aarondav). However, the MLflow 0.4.0 server remains backwards-compatible with older clients (#216, @aarondav)

Features:

• [Examples] Add a more advanced tracking example: using MLflow with PyTorch and TensorBoard (#203)
• [Models] H2O model support (#170, @ToonKBC)
• [Projects] Support for running projects in subdirectories of Git repos (#153, @juntai-zheng)
• [SageMaker] Support for specifying a compute specification when deploying to SageMaker (#185, @dbczumar)
• [Server] Added --static-prefix option to serve UI from a specified prefix to MLflow UI and server (#116, @andrewmchen)
• [Tracking] Azure blob storage support for artifacts (#206, @mateiz)
• [Tracking] Add support for Databricks-backed RestStore (#200, @aarondav)
• [UI] Enable productionizing frontend by adding CSRF support (#199, @aarondav)
• [UI] Update metric and parameter filters to let users control column order (#186, @mateiz)

Bug fixes:

• Fixed incompatible file structure returned by GCSArtifactRepository (#173, @jakeret)
• Fixed metric values going out of order on x axis (#204, @mateiz)
• Fixed occasional hanging behavior when using the projects.run API (#193, @smurching)
• Miscellaneous bug and documentation fixes from @aarondav, @andrewmchen, @arinto, @jakeret, @mateiz, @smurching, @stbof

0.3.0 (2018-07-18)

Breaking changes:

• [MLflow Server] Renamed --artifact-root parameter to --default-artifact-root in mlflow server to better reflect its purpose (#165, @aarondav)

Features:

• Spark MLlib integration: we now support logging SparkML Models directly in the log_model API, model format, and serving APIs (#72, @tomasatdatabricks)
• Google Cloud Storage is now supported as an artifact storage root (#152, @bnekolny)
• Support asychronous/parallel execution of MLflow runs (#82, @smurching)
• [SageMaker] Support for deleting, updating applications deployed via SageMaker (#145, @dbczumar)
• [SageMaker] Pushing the MLflow SageMaker container now includes the MLflow version that it was published with (#124, @sueann)
• [SageMaker] Simplify parameters to SageMaker deploy by providing sane defaults (#126, @sueann)
• [UI] One-element metrics are now displayed as a bar char (#118, @cryptexis)

Bug fixes:

• Require gitpython>=2.1.0 (#98, @aarondav)
• Fixed TensorFlow model loading so that columns match the output names of the exported model (#94, @smurching)
• Fix SparkUDF when number of columns >= 10 (#97, @aarondav)
• Miscellaneous bug and documentation fixes from @emres, @dmatrix, @stbof, @gsganden, @dennyglee, @anabranch, @mikehuston, @andrewmchen, @juntai-zheng

0.2.1 (2018-06-28)

This is a patch release fixing some smaller issues after the 0.2.0 release.

• Switch protobuf implementation to C, fixing a bug related to tensorflow/mlflow import ordering (issues #33 and #77, PR #74, @andrewmchen)
• Enable running mlflow server without git binary installed (#90, @aarondav)
• Fix Spark UDF support when running on multi-node clusters (#92, @aarondav)

0.2.0 (2018-06-27)

• Added mlflow server to provide a remote tracking server. This is akin to mlflow ui with new options:
  • --host to allow binding to any ports (#27, @mdagost)
  • --artifact-root to allow storing artifacts at a remote location, S3 only right now (#78, @mateiz)
  • Server now runs behind gunicorn to allow concurrent requests to be made (#61, @mateiz)
• Tensorflow integration: we now support logging Tensorflow Models directly in the log_model API, model format, and serving APIs (#28, @juntai-zheng)
• Added experiments.list_experiments as part of experiments API (#37, @mparkhe)
• Improved support for unicode strings (#79, @smurching)
• Diabetes progression example dataset and training code (#56, @dennyglee)
• Miscellaneous bug and documentation fixes from @Jeffwan, @yupbank, @ndjido, @xueyumusic, @manugarri, @tomasatdatabricks, @stbof, @andyk, @andrewmchen, @jakeret, @0wu, @aarondav

0.1.0 (2018-06-05)

• Initial version of mlflow.