Installing Hail


Regardless of installation method, you will need:

  • Java 8 JDK Note: it must be version eight. Hail does not support Java versions nine, ten, or eleven due to our dependency on Spark.

  • Python 3.6 or later, we recommend Anaconda’s Python 3

For all methods other than using pip, you will additionally need Spark 2.4.x.


Installing Hail on Mac OS X or GNU/Linux with pip

If you have Mac OS X, this is the recommended installation method for running Hail locally (i.e. not on a cluster).

Create a conda enviroment named hail and install the Hail python library in that environment:

conda create -n hail python==3.6
conda activate hail
pip install hail

To try Hail out, open iPython or a Jupyter notebook and run:

>>> import hail as hl
>>> mt = hl.balding_nichols_model(n_populations=3, n_samples=50, n_variants=100)
>>> mt.count()

You’re now all set to run the tutorials locally!

Building your own JAR

To build your own JAR, you will need a C++ compiler and lz4. Debian users might try:

sudo apt-get install g++ liblz4-dev

On Mac OS X, you might try:

xcode-select --install
brew install lz4

To build the Hail JAR compatible with Spark 2.3.0, execute this:

./gradlew -Dspark.version=2.3.0 releaseJar

The Spark version in this command should match whichever version of Spark you would like to build against.

Running on a Spark cluster

Hail can run on any Spark 2.4 cluster. For example, Google and Amazon make it possible to rent Spark clusters with many thousands of cores on-demand, providing for the elastic compute requirements of scientific research without an up-front capital investment.

For more about computing on the cloud, see Hail on the cloud.

For Cloudera-specific instructions, see Running on a Cloudera cluster.

For all other Spark clusters, you will need to build Hail from the source code.

Hail should be built on the master node of the Spark cluster. Follow the instructions in the “Building your own JAR” section and then additionally run:

./gradlew archiveZip

Python and IPython need a few environment variables to correctly find Spark and the Hail jar. We recommend you set these environment variables in the relevant profile file for your shell (e.g. ~/.bash_profile).

export SPARK_HOME=/path/to/spark-2.4.0/
export HAIL_HOME=/path/to/hail/
export PYTHONPATH="${PYTHONPATH:+$PYTHONPATH:}$HAIL_HOME/build/distributions/"
export PYTHONPATH="$PYTHONPATH:$SPARK_HOME/python/lib/py4j-*"
## PYSPARK_SUBMIT_ARGS is used by ipython and jupyter
  --jars $HAIL_HOME/build/libs/hail-all-spark.jar \
  --conf spark.driver.extraClassPath=\"$HAIL_HOME/build/libs/hail-all-spark.jar\" \
  --conf spark.executor.extraClassPath=./hail-all-spark.jar \
  --conf spark.serializer=org.apache.spark.serializer.KryoSerializer \
  --conf spark.kryo.registrator=is.hail.kryo.HailKryoRegistrator

If the previous environment variables are set correctly, an IPython shell which can run Hail backed by the cluster can be started with the following command:


When using ipython, you can import hail and start interacting directly:

>>> import hail as hl
>>> mt = hl.balding_nichols_model(n_populations=3, n_samples=50, n_variants=100)
>>> mt.count()

You can also interact with hail via a pyspark session, but you will need to pass the configuration from PYSPARK_SUBMIT_ARGS directly as well as adding extra configuration parameters specific to running Hail through pyspark:

pyspark \
  --jars $HAIL_HOME/build/libs/hail-all-spark.jar \
  --conf spark.driver.extraClassPath=$HAIL_HOME/build/libs/hail-all-spark.jar \
  --conf spark.executor.extraClassPath=./hail-all-spark.jar \
  --conf spark.serializer=org.apache.spark.serializer.KryoSerializer \
  --conf spark.kryo.registrator=is.hail.kryo.HailKryoRegistrator

Moreover, unlike in ipython, pyspark provides a Spark Context via the global variable sc. For Hail to interact properly with the Spark cluster, you must tell hail about this special Spark Context

>>> import hail as hl
>>> hl.init(sc) 

After this initialization step, you can interact as you would in ipython

>>> mt = hl.balding_nichols_model(n_populations=3, n_samples=50, n_variants=100)
>>> mt.count()

It is also possible to run Hail non-interactively, by passing a Python script to spark-submit. Again, you will need to explicitly pass several configuration parameters to spark-submit:

spark-submit \
  --jars "$HAIL_HOME/build/libs/hail-all-spark.jar" \
  --py-files "$HAIL_HOME/build/distributions/" \
  --conf spark.driver.extraClassPath="$HAIL_HOME/build/libs/hail-all-spark.jar" \
  --conf spark.executor.extraClassPath=./hail-all-spark.jar \
  --conf spark.serializer=org.apache.spark.serializer.KryoSerializer \
  --conf spark.kryo.registrator=is.hail.kryo.HailKryoRegistrator \

Running on a Cloudera cluster

These instructions explain how to install Spark 2 on a Cloudera cluster. You should work on a gateway node on the cluster that has the Hadoop and Spark packages installed on it.

Once Spark is installed, building and running Hail on a Cloudera cluster is exactly the same as above, except:

  • On a Cloudera cluster, when building a Hail JAR, you must specify a Cloudera version of Spark and the associated py4j version. The following example builds a Hail JAR for Cloudera’s 2.4.0 version of Spark:

    ./gradlew releaseJar -Dspark.version=2.4.0.cloudera -Dpy4j.version=0.10.7
  • On a Cloudera cluster, SPARK_HOME should be set as: SPARK_HOME=/opt/cloudera/parcels/SPARK2/lib/spark2,

  • On Cloudera, you can create an interactive Python shell using pyspark:

    pyspark --jars build/libs/hail-all-spark.jar \
            --py-files build/distributions/ \
            --conf spark.driver.extraClassPath="build/libs/hail-all-spark.jar" \
            --conf spark.executor.extraClassPath=./hail-all-spark.jar \
            --conf spark.serializer=org.apache.spark.serializer.KryoSerializer \
            --conf spark.kryo.registrator=is.hail.kryo.HailKryoRegistrator \

Common Installation Issues


Hail uses BLAS and LAPACK optimized linear algebra libraries. These should load automatically on recent versions of Mac OS X and Google Dataproc. On Linux, these must be explicitly installed; on Ubuntu 14.04, run:

apt-get install libatlas-base-dev

If natives are not found, hail.log will contain these warnings:

Failed to load implementation from: com.github.fommil.netlib.NativeSystemLAPACK
Failed to load implementation from: com.github.fommil.netlib.NativeSystemBLAS

See netlib-java for more information.