Mlflow

- A Java client API, available on Maven
- Support for saving and serving SparkML models as MLeap for low-latency serving
- Support for tagging runs with metadata, during and after the run completion
- Support for deleting (and restoring deleted) experiments

In addition to these features, there are a host of improvements and bugfixes to the REST API, Python API, tracking UI, and documentation. The [examples](https://github.com/mlflow/mlflow/tree/master/examples) subdirectory has also been revamped to make it easier to jump in, and examples demonstrating multistep workflows and hyperparameter tuning have been added.

Breaking changes:

We fixed a few inconsistencies in the the `mlflow.tracking` API, as introduced in 0.5.0:

- `MLflowService` has been renamed `MlflowClient` (461, mparkhe)
- You get an `MlflowClient` by calling `mlflow.tracking.MlflowClient()` (previously, this was `mlflow.tracking.get_service()`) (461, mparkhe)
- `MlflowService.list_runs` was changed to `MlflowService.list_run_infos` to reflect the information actually returned by the call. It now returns a `RunInfo` instead of a `Run` (334, aarondav)
- `MlflowService.log_artifact` and `MlflowService.log_artifacts` now take a `run_id` instead of `artifact_uri`. This now matches `list_artifacts` and `download_artifacts` (444, aarondav)

Features:

- Java client API added with support for the MLflow Tracking API (analogous to `mlflow.tracking`), allowing users to create and manage experiments, runs, and artifacts. The release includes a [usage example](https://github.com/mlflow/mlflow/blob/master/mlflow/java/client/src/main/java/org/mlflow/tracking/samples/QuickStartDriver.java>)and [Javadocs](https://mlflow.org/docs/latest/java_api/index.html). The client is published to Maven under `mlflow:mlflow` (#380, 394, 398, 409, 410, 430, 452, aarondav)
- SparkML models are now also saved in MLeap format (https://github.com/combust/mleap), when applicable. Model serving platforms can choose to serve using this format instead of the SparkML format to dramatically decrease prediction latency. SageMaker now does this by default (#324, 327, 331, 395, 428, 435, 438, dbczumar)
- [API] Experiments can now be deleted and restored via REST API, Python Tracking API, and MLflow CLI (340, 344, 367, mparkhe)
- [API] Tags can now be set via a SetTag API, and they have been moved to `RunData` from `RunInfo` (342, aarondav)
- [API] Added `list_artifacts` and `download_artifacts` to `MlflowService` to interact with a run's artifactory (350, andrewmchen)
- [API] Added `get_experiment_by_name` to Python Tracking API, and equivalent to Java API (373, vfdev-5)
- [API/Python] Version is now exposed via `mlflow.__version__`.
- [API/CLI] Added `mlflow artifacts` CLI to list, download, and upload to run artifact repositories (391, aarondav)
- [UI] Added icons to source names in MLflow Experiments UI (381, andrewmchen)
- [UI] Added support to view `.log` and `.tsv` files from MLflow artifacts UI (393, Shenggan; 433, whiletruelearn)
- [UI] Run names can now be edited from within the MLflow UI (382, smurching)
- [Serving] Added `--host` option to `mlflow serve` to allow listening on non-local addressess (401, hamroune)
- [Serving/SageMaker] SageMaker serving takes an AWS region argument (366, dbczumar)
- [Python] Added environment variables to support providing HTTP auth (username, password, token) when talking to a remote MLflow tracking server (402, aarondav)
- [Python] Added support to override S3 endpoint for S3 artifactory (451, hamroune)
- MLflow nightly Python wheel and JAR snapshots are now available and linked from https://github.com/mlflow/mlflow (#352, aarondav)

Bug fixes and documentation updates:

- [Python] `mlflow run` now logs default parameters, in addition to explicitly provided ones (392, mparkhe)
- [Python] `log_artifact` in FileStore now requires a relative path as the artifact path (439, mparkhe)
- [Python] Fixed string representation of Python entities, so they now display both their type and serialized fields (371, smurching)
- [UI] Entry point name is now shown in MLflow UI (345, aarondav)
- [Models] Keras model export now includes TensorFlow graph explicitly to ensure the model can always be loaded at deployment time (440, tomasatdatabricks)
- [Python] Fixed issue where FileStore ignored provided Run Name (358, adrian555)
- [Python] Fixed an issue where any `mlflow run` failing printed an extraneous exception (365, smurching)
- [Python] uuid dependency removed (351, antonpaquin)
- [Python] Fixed issues with remote execution on Databricks (357, 361, smurching; 383, 387, aarondav)
- [Docs] Added [comprehensive example](https://github.com/mlflow/mlflow/tree/master/examples/multistep_workflow) of doing a multistep workflow, chaining MLflow runs together and reusing results (#338, aarondav)
- [Docs] Added [comprehensive example](https://github.com/mlflow/mlflow/tree/master/examples/hyperparam) of doing hyperparameter tuning (#368, tomasatdatabricks)
- [Docs] Added code examples to `mlflow.keras` API (341, dmatrix)
- [Docs] Significant improvements to Python API documentation (454, stbof)
- [Docs] Examples folder refactored to improve readability. The examples now reside in `examples/` instead of `example/`, too (399, mparkhe)
- Small bug fixes and doc updates (328, 363, ToonKBC; 336, 411, aarondav; 284, smurching; 377, mparkhe; 389, gioa; 408, aadamson; 397, vfdev-5; 420, adrian555; 459, 463, stbof)

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)

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)

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)

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)

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)