Version: Next

Install SynapseML

Choose the artifact that matches your Spark runtime

SynapseML installation has two parts:

language wrappers such as the synapseml Python package; and
JVM artifacts loaded by Spark.

Installing the Python package does not add the JVM artifacts. A Python wrapper can import successfully while its JVM class is missing. In particular, using a _2.12 artifact with Spark 4 can cause errors such as LightGBMClassifier does not exist in the JVM.

Choose one complete published build from the Spark runtime. master is the canonical Spark 3.5 development line; Spark 4.0 and Spark 4.1 are maintained on their corresponding branches.

Code line	Spark runtime	Scala	Python baseline	Release tag	Python package	Maven coordinate
`master`	Spark 3.5.x	2.12	Python 3.11	`v1.1.3`	`synapseml==1.1.3`	`com.microsoft.azure:synapseml_2.12:1.1.3`
`spark4.0`	Spark 4.0.1+ (`<4.1`)	2.13	Python 3.12	`v1.1.3-spark4.0`	`synapseml==1.1.3`	`com.microsoft.azure:synapseml_2.13:1.1.3-spark4.0`
`spark4.1`	Spark 4.1.x	2.13	Python 3.13	`v1.1.3-spark4.1`	`synapseml==1.1.3`	`com.microsoft.azure:synapseml_2.13:1.1.3-spark4.1`

Always add the SynapseML repository:

https://mmlspark.blob.core.windows.net/maven

Latest master snapshot

The latest successful master build targets Spark 3.5 and Scala 2.12. This copy-ready command reads the current snapshot version published by CI and starts Spark with that exact JVM build:

MASTER_VERSION="$(
  curl -fsSL https://mmlspark.blob.core.windows.net/icons/badges/master_version3.svg |
    sed -n 's/.*aria-label="master version: \([^"]*\)".*/\1/p'
)"
test -n "$MASTER_VERSION"
spark-shell \
  --repositories "https://mmlspark.blob.core.windows.net/maven" \
  --packages "com.microsoft.azure:synapseml_2.12:${MASTER_VERSION}"

The PyPI package contains released Python wrappers. If you need Python APIs that are new on master, build the matching wheel from source.

Microsoft Fabric

SynapseML is already installed in Microsoft Fabric notebooks. The following copy-ready override targets a Spark 4.1 / Scala 2.13 runtime:

%%configure -f
{
  "name": "synapseml",
  "conf": {
      "spark.jars.packages": "com.microsoft.azure:synapseml_2.13:1.1.3-spark4.1",
      "spark.jars.repositories": "https://mmlspark.blob.core.windows.net/maven",
      "spark.jars.excludes": "org.scala-lang:scala-reflect,org.apache.spark:spark-tags_2.13,org.scalactic:scalactic_2.13,org.scalatest:scalatest_2.13,com.fasterxml.jackson.core:jackson-databind",
      "spark.yarn.user.classpath.first": "true",
      "spark.sql.parquet.enableVectorizedReader": "false"
  }
}

Synapse

Current Synapse Analytics pools use Spark 3.5. To override the preinstalled version, place the following in the first cell of your notebook:

%%configure -f
{
  "name": "synapseml",
  "conf": {
      "spark.jars.packages": "com.microsoft.azure:synapseml_2.12:1.1.3",
      "spark.jars.repositories": "https://mmlspark.blob.core.windows.net/maven",
      "spark.jars.excludes": "org.scala-lang:scala-reflect,org.apache.spark:spark-tags_2.12,org.scalactic:scalactic_2.12,org.scalatest:scalatest_2.12,com.fasterxml.jackson.core:jackson-databind",
      "spark.yarn.user.classpath.first": "true",
      "spark.sql.parquet.enableVectorizedReader": "false"
  }
}

Python

To try out SynapseML on a Python (or Conda) installation, you can get Spark installed via pip. Choose exactly one complete runtime variant below, then start Spark with that variant's JVM artifact.

Spark 4.1 / Python 3.13

python -m pip install "synapseml==1.1.3" "pyspark>=4.1,<4.2"

Spark 4.0 / Python 3.12

python -m pip install "synapseml==1.1.3" "pyspark>=4.0.1,<4.1"

Spark 3.5 / Python 3.11

python -m pip install "synapseml==1.1.3" "pyspark>=3.5,<3.6"

from pyspark.sql import SparkSession

# Spark 4.1. Select the coordinate matching the PySpark command used above.
synapseml_coordinate = "com.microsoft.azure:synapseml_2.13:1.1.3-spark4.1"
# Spark 4.0:
# synapseml_coordinate = "com.microsoft.azure:synapseml_2.13:1.1.3-spark4.0"
# Spark 3.5:
# synapseml_coordinate = "com.microsoft.azure:synapseml_2.12:1.1.3"

spark = (
    SparkSession.builder.appName("MyApp")
    .config("spark.jars.packages", synapseml_coordinate)
    .config(
        "spark.jars.repositories",
        "https://mmlspark.blob.core.windows.net/maven",
    )
    .getOrCreate()
)
import synapse.ml

SBT

If you're building a Spark application in Scala, add the following lines to your build.sbt. Choose the dependency matching your Spark runtime.

Spark 4.1

resolvers += "SynapseML" at "https://mmlspark.blob.core.windows.net/maven"
libraryDependencies +=
  "com.microsoft.azure" % "synapseml_2.13" % "1.1.3-spark4.1"

Spark 4.0

resolvers += "SynapseML" at "https://mmlspark.blob.core.windows.net/maven"
libraryDependencies +=
  "com.microsoft.azure" % "synapseml_2.13" % "1.1.3-spark4.0"

Spark 3.5

resolvers += "SynapseML" at "https://mmlspark.blob.core.windows.net/maven"
libraryDependencies +=
  "com.microsoft.azure" % "synapseml_2.12" % "1.1.3"

Spark package

SynapseML can be conveniently installed on existing Spark clusters via the --packages option. Each example below is independently copyable.

# Spark 4.1
pyspark --repositories "https://mmlspark.blob.core.windows.net/maven" \
  --packages "com.microsoft.azure:synapseml_2.13:1.1.3-spark4.1"

# Spark 4.0
pyspark --repositories "https://mmlspark.blob.core.windows.net/maven" \
  --packages "com.microsoft.azure:synapseml_2.13:1.1.3-spark4.0"

# Spark 3.5
pyspark --repositories "https://mmlspark.blob.core.windows.net/maven" \
  --packages "com.microsoft.azure:synapseml_2.12:1.1.3"

A similar technique can be used in other Spark contexts too. For example, you can use SynapseML in AZTK by adding it to the .aztk/spark-defaults.conf file.

Databricks

To install SynapseML on the Databricks cloud, create a new library from Maven coordinates in your workspace.

Use one of these exact Maven coordinates:

Spark 4.1 / Scala 2.13: com.microsoft.azure:synapseml_2.13:1.1.3-spark4.1
Spark 4.0 / Scala 2.13: com.microsoft.azure:synapseml_2.13:1.1.3-spark4.0
Spark 3.5 / Scala 2.12: com.microsoft.azure:synapseml_2.12:1.1.3

Add the resolver https://mmlspark.blob.core.windows.net/maven, attach the library to the target cluster, and restart it before importing synapse.ml.

You can use SynapseML in both your Scala and PySpark notebooks. To get started with our example notebooks, import the following databricks archive:

https://mmlspark.blob.core.windows.net/dbcs/SynapseMLExamplesv1.1.3.dbc

Apache Livy and HDInsight

To install SynapseML from within a Jupyter notebook served by Apache Livy, the following Spark 3.5 / Scala 2.12 configure magic can be used. You'll need to start a new session after this configure cell is executed.

Excluding certain packages from the library may be necessary due to current issues with Livy 0.5

%%configure -f
{
    "name": "synapseml",
    "conf": {
        "spark.jars.packages": "com.microsoft.azure:synapseml_2.12:1.1.3",
        "spark.jars.repositories": "https://mmlspark.blob.core.windows.net/maven",
        "spark.jars.excludes": "org.scala-lang:scala-reflect,org.apache.spark:spark-tags_2.12,org.scalactic:scalactic_2.12,org.scalatest:scalatest_2.12,com.fasterxml.jackson.core:jackson-databind"
    }
}

In Azure Synapse, spark.yarn.user.classpath.first should be set to true to override the existing SynapseML packages:

%%configure -f
{
    "name": "synapseml",
    "conf": {
        "spark.jars.packages": "com.microsoft.azure:synapseml_2.12:1.1.3",
        "spark.jars.repositories": "https://mmlspark.blob.core.windows.net/maven",
        "spark.jars.excludes": "org.scala-lang:scala-reflect,org.apache.spark:spark-tags_2.12,org.scalactic:scalactic_2.12,org.scalatest:scalatest_2.12,com.fasterxml.jackson.core:jackson-databind",
        "spark.yarn.user.classpath.first": "true"
    }
}

Docker

The easiest way to evaluate SynapseML is via our pre-built Docker container. To do so, run the following command:

docker run -it -p 8888:8888 -e ACCEPT_EULA=yes mcr.microsoft.com/mmlspark/release

Navigate to http://localhost:8888/ in your web browser to run the sample notebooks. See the [documentation](../../Reference/Docker Setup.md) for more on Docker use.

To read the EULA for using the docker image, run

docker run -it -p 8888:8888 mcr.microsoft.com/mmlspark/release eula

Building from source

SynapseML has recently transitioned to a new build infrastructure. For detailed developer docs, see the Developer Readme

If you're an existing SynapseML developer, you'll need to reconfigure your development setup. We now support platform independent development and better integrate with intellij and SBT. If you encounter issues, reach out to our support email!

R (Beta)

To try out SynapseML using the R autogenerated wrappers, see our instructions. Note: This feature is still under development and some necessary custom wrappers may be missing.

Choose the artifact that matches your Spark runtime​

Latest master snapshot​

Microsoft Fabric​

Synapse​

Python​

SBT​

Spark package​

Databricks​

Apache Livy and HDInsight​

Docker​

Building from source​

R (Beta)​