Installation

Spark NLP Cheat Sheet

# Install Spark NLP from PyPI
$ pip install spark-nlp==2.2.1

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

# Load Spark NLP with Spark Shell
$ spark-shell --packages JohnSnowLabs:spark-nlp:2.2.1

# Load Spark NLP with PySpark
$ pyspark --packages JohnSnowLabs:spark-nlp:2.2.1

# Load Spark NLP with Spark Submit
$ spark-submit --packages JohnSnowLabs:spark-nlp:2.2.1

# Load Spark NLP as external JAR after comiling and bulding Spark NLP by `sbt assembly`
$ spark-shell --jar spark-nlp-assembly-2.2.1

Python

Setup Jupyter Notebook

While Jupyter runs code in many programming languages, Python is a requirement (Python 3.3 or greater, or Python 2.7) for installing the Jupyter Notebook itself.

Installing Jupyter using Anaconda

You can install Python and Jupyter using the Anaconda Distribution which includes Python, the Jupyter Notebook, and other commonly used packages for scientific computing and data science.

First, download Anaconda. We recommend downloading Anaconda’s latest Python 3 version.

Second, install the version of Anaconda which you downloaded, following the instructions on the download page.

Congratulations, you have installed Jupyter Notebook! To run the notebook, run the following command at the Terminal (Mac/Linux) or Command Prompt (Windows):

jupyter notebook

Installing Jupyter using pip

As an existing or experienced Python user, you may wish to install Jupyter using Python’s package manager, pip, instead of Anaconda.

If you have Python 3 installed (which is recommended):

python3 -m pip install --upgrade pip
python3 -m pip install jupyter

Congratulations, you have installed Jupyter Notebook! To run the notebook, run the following command at the Terminal (Mac/Linux) or Command Prompt (Windows):

jupyter notebook

Install Apache Spark using pyspark

Pip package Be sure that you have the required python library (pyspark 2.4.3) installed in your python environment by running:

pip list

If not there you can install by using:

pip install --ignore-installed pyspark==2.4.3

Conda package You can also install pyspark 2.4.3 using conda by running in your terminal:

conda install pyspark=2.4.3

Install Spark NLP Opensource

Pip package To install Spark NLP Opensource version you can just run:

pip install --ignore-installed spark-nlp==2.2.1

The –ignore-installed parameter is to overwrite your previous pip package version if already installed.

Conda package If you are using Anaconda/Conda for managing Python packages, you can install Spark NLP Opensource as follow:

conda install -c johnsnowlabs spark-nlp=2.2.1

Install Licensed Spark NLP

You can also install the licensed package with extra functionalities and pretrained models by using:

pip install spark-nlp-jsl==2.2.1 --extra-index-url #### --ignore-installed

The #### is a secret url only avaliable for users with license, if you have not received it please contact us at info@johnsnowlabs.com.

At the moment there is no conda package for Licensed Spark NLP version.

Setup AWS-CLI Credentials for licensed pretrained models

From Licensed version 2.2.1 in order to access private JohnSnowLabs models repository you need first to setup your AWS credentials. This access is done via Amazon aws command line interface (AWSCLI).

Instructions about how to install awscli are available at:

https://docs.aws.amazon.com/cli/latest/userguide/cli-chap-install.html

Make sure you configure your credentials with aws configure following the instructions at:

https://docs.aws.amazon.com/cli/latest/userguide/cli-chap-configure.html

Please substitute the ACCESS_KEY and SECRET_KEY with the credentials you have recived. If you need your credentials contact us at info@johnsnowlabs.com

Start Spark NLP Session from python

The following will initialize the spark session in case you have run the jupyter notebook directly. If you have started the notebook using pyspark this cell is just ignored.

spark = SparkSession.builder \
    .appName("Spark NLP")\
    .master("local[*]")\
    .config("spark.driver.memory","8G")\
    .config("spark.driver.maxResultSize", "2G") \
    .config("spark.jars.packages", "JohnSnowLabs:spark-nlp:2.2.1")\
    .config("spark.kryoserializer.buffer.max", "500m")\
    .getOrCreate()

If using local jars, you can use spark.jars instead for a comma delimited jar files. For cluster setups, of course you’ll have to put the jars in a reachable location for all driver and executor nodes.

Start Licensed Spark NLP Session from python

The following will initialize the spark session in case you have run the jupyter notebook directly. If you have started the notebook using pyspark this cell is just ignored.

Initializing the spark session takes some seconds (usually less than 1 minute) as the jar from the server needs to be loaded.

We will be using version 2.2.1 of Spark NLP Open Source and 2.2.1 of Spark NLP Enterprise Edition.

The #### in .config(“spark.jars”, “####”) is a secret code, if you have not received it please contact us at info@johnsnowlabs.com.

from pyspark.sql import SparkSession

spark = SparkSession.builder \
    .appName("Global DEMO - Spark NLP Enterprise 2.2.1") \
    .master("local[*]") \
    .config("spark.driver.memory","4G") \
    .config("spark.driver.maxResultSize", "2G") \
    .config("spark.jars.packages", "JohnSnowLabs:spark-nlp:2.2.1") \
    .config("spark.jars", "####") \
    .getOrCreate()

Scala

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

Maven

<!-- https://mvnrepository.com/artifact/com.johnsnowlabs.nlp/spark-nlp -->
<dependency>
    <groupId>com.johnsnowlabs.nlp</groupId>
    <artifactId>spark-nlp_2.11</artifactId>
    <version>2.2.1</version>
</dependency>

and

<!-- https://mvnrepository.com/artifact/com.johnsnowlabs.nlp/spark-nlp-ocr -->
<dependency>
    <groupId>com.johnsnowlabs.nlp</groupId>
    <artifactId>spark-nlp-ocr_2.11</artifactId>
    <version>2.2.1</version>
</dependency>

SBT

// https://mvnrepository.com/artifact/com.johnsnowlabs.nlp/spark-nlp
libraryDependencies += "com.johnsnowlabs.nlp" %% "spark-nlp" % "2.2.1"

and

// https://mvnrepository.com/artifact/com.johnsnowlabs.nlp/spark-nlp-ocr
libraryDependencies += "com.johnsnowlabs.nlp" %% "spark-nlp-ocr" % "2.2.1"

Maven Central: https://mvnrepository.com/artifact/com.johnsnowlabs.nlp

Spark-NLP Databricks

Databricks Notebooks

You can view all the Databricks notebooks from this address:

https://johnsnowlabs.github.io/spark-nlp-workshop/databricks/index.html

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

How to use Spark-NLP library in Databricks

1- Right-click the Workspace folder where you want to store the library.

2- Select Create > Library.

3- Select where you would like to create the library in the Workspace, and open the Create Library dialog:

Databricks

4- From the Source drop-down menu, select Maven Coordinate: Databricks

5- Now, all available Spark Packages are at your fingertips! Just search for JohnSnowLabs:spark-nlp:version where version stands for the library version such as: 1.8.4 or 2.2.1 Databricks

6- Select spark-nlp package and we are good to go!

More info about how to use 3rd Party Libraries in Databricks

PREVIOUSQuick Start

NEXTExamples