New Features in MLflow v0.6.0

Today, we’re excited to announce MLflow v0.6.0, released early in the week with new features. Now available on PyPI and Maven, the docs are updated. You can install the recent release with pip install mlflow as described in the MLflow quickstart guide.

MLflow v0.6.0 introduces a number of major features:

  • A Java client API, available on Maven
  • Support for saving and serving Spark MLlib 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 this post, we’ll describe new features, enhancements, and bug fixes in this release. In particular, we will focus on two features: A new Java MLflow client API and Spark MLlib and MLeap model integration.

Java Client API

To give developers a choice of programming languages, we have included a Java client tracking API, similar in functionality to Python client tracking API. Both offer CRUD interface to MLflow experiments and runs. This Java client is available on Maven.

Through the primary Java class constructor MlflowClient() and its instance methods, you create, list, delete, log or access runs and its artifacts. By default, it connects to the tracking server set in the environment variable MLFLOW_TRACKING_URI, unless instantiated explicitly with MlflowClient(tracking_server_ui) constructor.

If you have used the new MLflow Python tracking and experiment API, introduced in MLflow v0.5.2, it’s no different in functionality. As always, some code snippet will illustrate its usage. A full example, though, can be found in the sample directory of the Java client source code:

import java.util.List;
import java.util.Optional;
import org.apache.log4j.Level;
import org.apache.log4j.LogManager;
import org.mlflow.api.proto.Service.*;
import org.mlflow.tracking.MlflowClient;

 * This is an example application which uses the MLflow Tracking API to create and manage
* experiments and runs.
public class QuickStartDriver {
 public static void main(String[] args) throws Exception {
   (new QuickStartDriver()).process(args);

 void process(String[] args) throws Exception {
   MlflowClient client;
   if (args.length < 1) {
     client = new MlflowClient();
   } else {
     client = new MlflowClient(args[0]);

   String expName = "Exp_" + System.currentTimeMillis();
   long expId = client.createExperiment(expName);
   System.out.println("createExperiment: expId=" + expId);

   GetExperiment.Response exp = client.getExperiment(expId);
   System.out.println("getExperiment: " + exp);

   List<Experiment> exps = client.listExperiments();
   System.out.println("#experiments: " + exps.size());
   exps.forEach(e -> System.out.println("  Exp: " + e));
   //create a new experiment
   createRun(client, expId);

   System.out.println("====== getExperiment again");
   GetExperiment.Response exp2 = client.getExperiment(expId);
   System.out.println("getExperiment: " + exp2);

  System.out.println("====== getExperiment by name");
   Optional<Experiment> exp3 = client.getExperimentByName(expName);
   System.out.println("getExperimentByName: " + exp3);

 void createRun(MlflowClient client, long expId) {
   System.out.println("====== createRun");

   // Create run
   String sourceFile = "";
 RunInfo runCreated = client.createRun(expId, sourceFile);
   System.out.println("CreateRun: " + runCreated);
   String runId = runCreated.getRunUuid();

   // Log parameters
   client.logParam(runId, "min_samples_leaf", "2");
   client.logParam(runId, "max_depth", "3");

   // Log metrics
   client.logMetric(runId, "auc", 2.12F);
   client.logMetric(runId, "accuracy_score", 3.12F);
   client.logMetric(runId, "zero_one_loss", 4.12F);

   // Update finished run
   client.setTerminated(runId, RunStatus.FINISHED);

   // Get run details
   Run run = client.getRun(runId);
   System.out.println("GetRun: " + run);

Spark MLlib and MLeap Model Integration

True to the MLflow’s design goal of “open platform,” supporting popular ML libraries and model flavors, we have added yet another model flavor: mlflow.mleap. Spark MLlib models can be optionally saved in the MLeap format. This new MLeap format allows deploying Spark MLlib models for low-latency production serving.

For real-time serving, the MLeap framework is far more performant than Spark MLlib for a number of reasons. First, it employs a lighter weight, performant DataFrame representation. Second, unlike the Spark MLlib Pipeline model, it does not require a SparkContext while evaluating MLlib Pipelines in Scala. And, finally, it has serialization and deserialization mechanisms to convert PySpark Pipeline models into Scala objects.

From the above graph, you can see that MLeap can serve predictions in the single-digit millisecond range, whereas Spark MLlib reaches in the 100-millisecond range.

Saving Spark MLib Models in MLeap Flavor

For this functionality, we have extended the mlflow.spark API’s save_model(...) to optionally save a Spark MLib model in MLeap format too, giving you the option to deploy a performant model for real-time serving. An example will illustrate how to save this model in both formats.

Let’s create a simple Spark MLlib model, log model, some parameters, and persist it in both a Spark MLlib and MLeap model format. An additional argument to mlflow.spark.save_model(...) will persist in both formats: Spark MLlib and MLeap.

import mlflow
import mlflow.spark
from import Pipeline
from import LogisticRegression
from import HashingTF, Tokenizer
# training DataFrame
training = spark.createDataFrame([
    (0, "a b c d e spark", 1.0),
    (1, "b d", 0.0),
    (2, "spark f g h", 1.0),
    (3, "hadoop mapreduce", 0.0) ], ["id", "text", "label"])
# testing DataFrame
test_df = spark.createDataFrame([
    (4, "spark i j k"),
    (5, "l m n"),
    (6, "spark hadoop spark"),
    (7, "apache hadoop")], ["id", "text"])
#Create an MLlib pipeline 
tokenizer = Tokenizer(inputCol="text", outputCol="words")
hashingTF = HashingTF(inputCol=tokenizer.getOutputCol(), outputCol="features")
lr = LogisticRegression(maxIter=10, regParam=0.001)
pipeline = Pipeline(stages=[tokenizer, hashingTF, lr])
model =
#log parameters
mlflow.log_parameter(“max_iter”, 10)
mlflow.log_parameter(“reg_param”, 0.001)
#log the model in mleap format 
mlflow.mleap.log_model(model, test_df, "mleap-model")
# This call with an added test_df argument will save 
# in both formats. 
# Now let’s persist it. This API call will save both flavors
# of the model: Spark MLlib and MLeap, which both can be used 
# in deployment with pyfunc call, if we provide MLeap flavor 
# arguments, such as DataFrame input, it will save both flavors

mlflow.spark.save_model(model, test_df, “mleap_models”)

Other Features and Bug Fixes

In addition to these features, other items, bugs and documentation fixes are included in this release. Some items worthy of note are:

  • [API] Support for tagging runs with metadata, during and after the run completion
  • [API] Experiments can now be deleted and restored via REST API, Python Tracking API, and MLflow CLI (#340, #344, #367, @mparkhe)
  • [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)
    *[API/CLI] Added mlflow artifacts CLI to list, download, and upload to run artifact repositories (#391, @aarondav)
  • [API] Added get_experiment_by_name to Python Tracking API, and equivalent to Java API (#373, @vfdev-5)
  • [Serving/SageMaker] SageMaker serving takes an AWS region argument (#366, @dbczumar)
  • [UI] Added icons to source names in MLflow Experiments UI (#381, @andrewmchen)
  • [Docs] Added comprehensive example of doing a multi-step workflow, chaining MLflow runs together and reusing results (#338, @aarondav)
  • [Docs] Added comprehensive example 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)

The full list of changes and contributions from the community can be found in the 0.6.0 Changelog. We welcome more input on [email protected] or by filing issues or submitting patches on GitHub. For real-time questions about MLflow, we have a Slack channel for MLflow as well as you can follow @MLflow on Twitter.

Read More

For an overview of what we’re working on next, take a look at the roadmap slides in our presentation.


MLflow 0.6.0 includes patches, bug fixes, and doc changes from from Aaron Davidson, Adrian Zhuang, Alex Adamson, Andrew Chen, Corey Zumar, Hamroune Zahir, Joy Gioa, Jules Damji, Krishna Sangeeth, Matei Zaharia, Siddharth Murching, Shenggan, Stephanie Bodoff, Tomas Nykodym, Toon Baeyens, and VFDev.

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