Installation

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=3.6 -y
$ conda activate sparknlp
$ pip install spark-nlp==2.7.1 pyspark==2.4.7

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")\
    .master("local[4]")\
    .config("spark.driver.memory","16G")\
    .config("spark.driver.maxResultSize", "0") \
    .config("spark.jars.packages", "com.johnsnowlabs.nlp:spark-nlp_2.11:2.7.1")\
    .config("spark.kryoserializer.buffer.max", "1000M")\
    .getOrCreate()

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

spark-nlp on Apacahe Spark 2.4.x:

<!-- https://mvnrepository.com/artifact/com.johnsnowlabs.nlp/spark-nlp -->
<dependency>
    <groupId>com.johnsnowlabs.nlp</groupId>
    <artifactId>spark-nlp_2.11</artifactId>
    <version>2.7.1</version>
</dependency>

spark-nlp-gpu:

<!-- https://mvnrepository.com/artifact/com.johnsnowlabs.nlp/spark-nlp-gpu -->
<dependency>
    <groupId>com.johnsnowlabs.nlp</groupId>
    <artifactId>spark-nlp-gpu_2.11</artifactId>
    <version>2.7.1</version>
</dependency>

spark-nlp on Apacahe Spark 2.3.x:

<!-- https://mvnrepository.com/artifact/com.johnsnowlabs.nlp/spark-nlp-spark23 -->
<dependency>
    <groupId>com.johnsnowlabs.nlp</groupId>
    <artifactId>spark-nlp-spark23_2.11</artifactId>
    <version>2.7.1</version>
</dependency>

spark-nlp-gpu:

<!-- https://mvnrepository.com/artifact/com.johnsnowlabs.nlp/spark-nlp-gpu-spark23 -->
<dependency>
    <groupId>com.johnsnowlabs.nlp</groupId>
    <artifactId>spark-nlp-gpu-spark23_2.11</artifactId>
    <version>2.7.1</version>
</dependency>

SBT

spark-nlp on Apacahe Spark 2.4.x:

// https://mvnrepository.com/artifact/com.johnsnowlabs.nlp/spark-nlp
libraryDependencies += "com.johnsnowlabs.nlp" %% "spark-nlp" % "2.7.1"

spark-nlp-gpu:

// https://mvnrepository.com/artifact/com.johnsnowlabs.nlp/spark-nlp-gpu
libraryDependencies += "com.johnsnowlabs.nlp" %% "spark-nlp-gpu" % "2.7.1"

spark-nlp on Apacahe Spark 2.3.x:

// https://mvnrepository.com/artifact/com.johnsnowlabs.nlp/spark-nlp-spark23
libraryDependencies += "com.johnsnowlabs.nlp" %% "spark-nlp-spark23" % "2.7.1"

spark-nlp-gpu:

// https://mvnrepository.com/artifact/com.johnsnowlabs.nlp/spark-nlp-gpu-spark23
libraryDependencies += "com.johnsnowlabs.nlp" %% "spark-nlp-gpu-spark23" % "2.7.1"

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

Databricks Support

Spark NLP 2.7.1 has been tested and is compatible with the following runtimes: 6.2, 6.2 ML, 6.3, 6.3 ML, 6.4, 6.4 ML, 6.5, 6.5 ML

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 1000M
spark.serializer org.apache.spark.serializer.KryoSerializer

1. Check Enable autoscaling local storage box to have persistent local storage

2. In Libraries tab inside your cluster you need to follow these steps:

   4.1. Insatll New -> PyPI -> spark-nlp -> Install

   4.2. Install New -> Maven -> Coordinates -> com.johnsnowlabs.nlp:spark-nlp_2.11:2.7.1 -> Install

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