Spark NLP Cheat Sheet

# Install Spark NLP from PyPI
pip install spark-nlp==3.0.1

# Install Spark NLP from Anacodna/Conda
conda install -c johnsnowlabs spark-nlp

# Load Spark NLP with Spark Shell
spark-shell --packages com.johnsnowlabs.nlp:spark-nlp_2.12:3.0.1

# Load Spark NLP with PySpark
pyspark --packages com.johnsnowlabs.nlp:spark-nlp_2.12:3.0.1

# Load Spark NLP with Spark Submit
spark-submit --packages com.johnsnowlabs.nlp:spark-nlp_2.12:3.0.1

# Load Spark NLP as external JAR after compiling and building Spark NLP by `sbt assembly`
spark-shell --jar spark-nlp-assembly-3.0.1


Quick Install

Let’s create a new Conda environment to manage all the dependencies there. You can use Python Virtual Environment if you prefer or not have any enviroment.

$ java -version
# should be Java 8 (Oracle or OpenJDK)
$ conda create -n sparknlp python=3.7 -y
$ conda activate sparknlp
$ pip install spark-nlp==3.0.1 pyspark==3.1.1

Of course you will need to have jupyter installed in your system:

pip install jupyter

Now you should be ready to create a jupyter notebook running from terminal:

jupyter notebook

Start Spark NLP Session from python

If you need to manually start SparkSession because you have other configuraations and sparknlp.start() is not including them, you can manually start the SparkSession:

spark = SparkSession.builder \
    .appName("Spark NLP")\
    .config("spark.driver.maxResultSize", "0") \
    .config("spark.kryoserializer.buffer.max", "2000M")\
    .config("spark.jars.packages", "com.johnsnowlabs.nlp:spark-nlp_2.12:3.0.1")\    

Scala and Java


Our package is deployed to maven central. In order to add this package as a dependency in your application:

spark-nlp on Apache Spark 3.x:

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spark-nlp on Apache Spark 2.4.x:

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spark-nlp on Apache Spark 2.3.x:

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spark-nlp on Apache Spark 3.x.x:

libraryDependencies += "com.johnsnowlabs.nlp" %% "spark-nlp" % "3.0.1"


libraryDependencies += "com.johnsnowlabs.nlp" %% "spark-nlp-gpu" % "3.0.1"

spark-nlp on Apache Spark 2.4.x:

libraryDependencies += "com.johnsnowlabs.nlp" %% "spark-nlp-spark24" % "3.0.1"


libraryDependencies += "com.johnsnowlabs.nlp" %% "spark-nlp-gpu-spark24" % "3.0.1"

spark-nlp on Apache Spark 2.3.x:

libraryDependencies += "com.johnsnowlabs.nlp" %% "spark-nlp-spark23" % "3.0.1"


libraryDependencies += "com.johnsnowlabs.nlp" %% "spark-nlp-gpu-spark23" % "3.0.1"

Maven Central:

Google Colab Notebook

Google Colab is perhaps the easiest way to get started with spark-nlp. It requires no installation or setup other than having a Google account.

Run the following code in Google Colab notebook and start using spark-nlp right away.

# This is only to setup PySpark and Spark NLP on Colab
!wget -O - | bash

This script comes with the two options to define pyspark and spark-nlp versions via options:

# -p is for pyspark
# -s is for spark-nlp
# by default they are set to the latest
!bash -p 3.1.1 -s 3.0.1

Spark NLP quick start on Google Colab is a live demo on Google Colab that performs named entity recognitions and sentiment analysis by using Spark NLP pretrained pipelines.

Kaggle Kernel

Run the following code in Kaggle Kernel and start using spark-nlp right away.

# Let's setup Kaggle for Spark NLP and PySpark
!wget -O - | bash

Spark NLP quick start on Kaggle Kernel is a live demo on Kaggle Kernel that performs named entity recognitions by using Spark NLP pretrained pipeline.

Databricks Support

Spark NLP 3.0.1 has been tested and is compatible with the following runtimes:

  • 5.5 LTS
  • 5.5 LTS ML & GPU
  • 6.4
  • 6.4 ML & GPU
  • 7.3
  • 7.3 ML & GPU
  • 7.4
  • 7.4 ML & GPU
  • 7.5
  • 7.5 ML & GPU
  • 7.6
  • 7.6 ML & GPU
  • 8.0
  • 8.0 ML
  • 8.1 Beta

NOTE: The Databricks 8.1 Beta ML with GPU is not supported in Spark NLP 3.0.1 due to its default CUDA 11.x incompatibility

Install Spark NLP on Databricks

  1. Create a cluster if you don’t have one already

  2. On a new cluster or existing one you need to add the following to the Advanced Options -> Spark tab:

     spark.kryoserializer.buffer.max 2000M
     spark.serializer org.apache.spark.serializer.KryoSerializer
  3. In Libraries tab inside your cluster you need to follow these steps:

    3.1. Install New -> PyPI -> spark-nlp -> Install

    3.2. Install New -> Maven -> Coordinates -> com.johnsnowlabs.nlp:spark-nlp_2.12:3.0.1 -> Install

  4. Now you can attach your notebook to the cluster and use Spark NLP!

Databricks Notebooks

You can view all the Databricks notebooks from this address:

Note: You can import these notebooks by using their URLs.

EMR Support

Spark NLP 3.0.1 has been tested and is compatible with the following EMR releases:

  • emr-5.20.0
  • emr-5.21.0
  • emr-5.21.1
  • emr-5.22.0
  • emr-5.23.0
  • emr-5.24.0
  • emr-5.24.1
  • emr-5.25.0
  • emr-5.26.0
  • emr-5.27.0
  • emr-5.28.0
  • emr-5.29.0
  • emr-5.30.0
  • emr-5.30.1
  • emr-5.31.0
  • emr-5.32.0
  • emr-6.1.0
  • emr-6.2.0

Full list of Amazon EMR 5.x releases Full list of Amazon EMR 6.x releases

NOTE: The EMR 6.0.0 is not supported by Spark NLP 3.0.1

How to create EMR cluster via CLI

To lanuch EMR cluster with Apache Spark/PySpark and Spark NLP correctly you need to have bootstrap and software configuration.

A sample of your bootstrap script

set -x -e

echo -e 'export PYSPARK_PYTHON=/usr/bin/python3 
export HADOOP_CONF_DIR=/etc/hadoop/conf 
export SPARK_JARS_DIR=/usr/lib/spark/jars 
export SPARK_HOME=/usr/lib/spark' >> $HOME/.bashrc && source $HOME/.bashrc

sudo python3 -m pip install awscli boto spark-nlp

set +x
exit 0

A sample of your software configuration in JSON on S3 (must be public access):

  "Classification": "spark-env",
  "Configurations": [{
    "Classification": "export",
    "Properties": {
      "PYSPARK_PYTHON": "/usr/bin/python3"
  "Classification": "spark-defaults",
    "Properties": {
      "spark.yarn.stagingDir": "hdfs:///tmp",
      "spark.yarn.preserve.staging.files": "true",
      "spark.kryoserializer.buffer.max": "2000M",
      "spark.serializer": "org.apache.spark.serializer.KryoSerializer",
      "spark.driver.maxResultSize": "0",
      "spark.jars.packages": "com.johnsnowlabs.nlp:spark-nlp_2.12:3.0.1"

A sample of AWS CLI to launch EMR cluster:

aws emr create-cluster \
--name "Spark NLP 3.0.1" \
--release-label emr-6.2.0 \
--applications Name=Hadoop Name=Spark Name=Hive \
--instance-type m4.4xlarge \
--instance-count 3 \
--use-default-roles \
--log-uri "s3://<S3_BUCKET>/" \
--bootstrap-actions Path=s3://<S3_BUCKET>/,Name=custome \
--configurations "https://<public_access>/sparknlp-config.json" \
--ec2-attributes KeyName=<your_ssh_key>,EmrManagedMasterSecurityGroup=<security_group_with_ssh>,EmrManagedSlaveSecurityGroup=<security_group_with_ssh> \
--profile <aws_profile_credentials>

Docker Support

For having Spark NLP, PySpark, Jupyter, and other ML/DL dependencies as a Docker image you can use the following template:

#Download base image ubuntu 18.04
FROM ubuntu:18.04

ENV NB_USER jovyan


RUN apt-get update && apt-get install -y \
    tar \
    wget \
    bash \
    rsync \
    gcc \
    libfreetype6-dev \
    libhdf5-serial-dev \
    libpng-dev \
    libzmq3-dev \
    python3 \ 
    python3-dev \
    python3-pip \
    unzip \
    pkg-config \
    software-properties-common \

RUN adduser --disabled-password \
    --gecos "Default user" \
    --uid ${NB_UID} \

# Install OpenJDK-8
RUN apt-get update && \
    apt-get install -y openjdk-8-jdk && \
    apt-get install -y ant && \
    apt-get clean;

# Fix certificate issues
RUN apt-get update && \
    apt-get install ca-certificates-java && \
    apt-get clean && \
    update-ca-certificates -f;
# Setup JAVA_HOME -- useful for docker commandline
ENV JAVA_HOME /usr/lib/jvm/java-8-openjdk-amd64/

RUN echo "export JAVA_HOME=/usr/lib/jvm/java-8-openjdk-amd64/" >> ~/.bashrc

RUN apt-get clean && rm -rf /var/lib/apt/lists/* /tmp/* /var/tmp/*

RUN pip3 install --upgrade pip
# You only need pyspark and spark-nlp paclages to use Spark NLP
# The rest of the PyPI packages are here as examples
RUN pip3 install --no-cache-dir pyspark==3.1.1 spark-nlp==3.0.1 notebook==5.* numpy pandas mlflow Keras scikit-spark scikit-learn scipy matplotlib pydot tensorflow==2.3.1 graphviz

# Make sure the contents of our repo are in ${HOME}
RUN mkdir -p /home/jovyan/tutorials
RUN mkdir -p /home/jovyan/jupyter

COPY data ${HOME}/data
COPY jupyter ${HOME}/jupyter
COPY tutorials ${HOME}/tutorials
RUN jupyter notebook --generate-config
COPY jupyter_notebook_config.json /home/jovyan/.jupyter/jupyter_notebook_config.json
USER root
RUN chown -R ${NB_UID} ${HOME}


# Specify the default command to run
CMD ["jupyter", "notebook", "--ip", ""]

Finally, use jupyter_notebook_config.json for the password:

  "NotebookApp": {
    "password": "sha1:65adaa6ffb9c:36df1c2086ef294276da703667d1b8ff38f92614"

Windows Support

In order to fully take advantage of Spark NLP on Windows (8 or 10), you need to setup/install Apache Spark, Apache Hadoop, and Java correctly by following the following instructions:

How to correctly install Spark NLP on Windows 8 and 10

Follow the below steps:

  1. Download OpenJDK from here:;
    • Make sure it is 64-bit
    • Make sure you install it in the root C:\java Windows .
    • During installation after changing the path, select setting Path
  2. Download winutils and put it in C:\hadoop\bin;

  3. Download Anaconda 3.6 from Archive:;

  4. Download Apache Spark 3.1.1 and extract it in C:\spark

  5. Set the env for HADOOP_HOME to C:\hadoop and SPARK_HOME to C:\spark

  6. Set Paths for %HADOOP_HOME%\bin and %SPARK_HOME%\bin

  7. Install C++

  8. Create C:\temp and C:\temp\hive

  9. Fix permissions:
  • C:\Users\maz>%HADOOP_HOME%\bin\winutils.exe chmod 777 /tmp/hive
  • C:\Users\maz>%HADOOP_HOME%\bin\winutils.exe chmod 777 /tmp/

Either create a conda env for python 3.6, install pyspark==3.1.1 spark-nlp numpy and use Jupyter/python console, or in the same conda env you can go to spark bin for pyspark –packages com.johnsnowlabs.nlp:spark-nlp_2.12:3.0.1.

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