Enterprise Spark NLP Installation

AWS Marketplace

The entire suite of John Snow Labs NLP and Visual NLP libraries are offered as a pay-as-you-go product on AWS Marketplace, pre-installed and ready to use. 30+ Notebooks are included in the AWS product to allow you to start experimenting on your own data right away. To subscribe to the pay-as-you-go product on AWS Marketplace navigate to the product page and follow the instructions in the video below.

Note: 30-day free trial are available for AWS and Azure subscriptions.

Azure Marketplace

John Snow Labs NLP and Visual NLP libraries are also offered as a pay-as-you-go product on Azure Marketplace, pre-installed and ready to use. 30+ Notebooks are included in the Azure product to allow you to start experimenting on your own data right away. To subscribe to the pay-as-you-go product on Azure Marketplace navigate to the product page and follow the instructions in the video below.

Note: 30-day free trial are available for AWS and Azure subscriptions.

Private Endpoints - SageMaker, Snowflake, Databricks

Some of the most popular Medical Language Models we offer are now available as private endpoints on platforms like AWS SageMaker, Snowflake, or Databricks, providing organizations with powerful tools to enhance their data capabilities while ensuring security and control.

Deploying these models as private endpoints offers several advantages. First, it allows for enhanced data privacy, as sensitive information remains within your own private network and infrastructure, reducing exposure to potential breaches. This facilitates compliance with industry regulations by enabling organizations to manage their data according to specific governance policies.

Working with John Snow Labs API endpoints is straightforward. You get rapid access to advanced language models with minimal setup, while eliminating the limitations of relying on specific programming languages. This flexibility allows you to work across different platforms and languages, unlocking valuable insights, automating text processing tasks, and driving innovation—all while maintaining high standards of security and operational efficiency.

Subscribing, deploying, and managing these API endpoints is also effortless. You can easily adjust resources according to your processing needs. With just a few clicks, you can subscribe to the desired Language Models, deploy them as private endpoints on your preferred cloud platform, and easily scale or stop them as your workloads demand. This flexibility ensures optimal resource utilization while keeping costs in check, without the hassle of long-term commitments, complex configurations, and without compromising on speed or reliability.

AWS SageMaker

You can discover the language models available on AWS SageMaker by visiting this page.

The following video illustrates the steps to discover the language models available as API endpoints on AWS Sagemaker via the NLP Models Hub, how to subscribe to those, how to deploy them, how to query them and how to stop them.

Snowflake

You can discover the language models available on Snowflake Marketplace by visiting this page.

The following video illustrates the steps to discover the language models available as API endpoints on Snowflake via the NLP Models Hub, how to subscribe to those, how to deploy them, how to query them and how to stop them.

Databricks

You can discover the language models available on Databricks Marketplace by visiting this page.

The following video illustrates the steps to discover the language models available as API endpoints on Databricks via the NLP Models Hub, how to subscribe to those, how to deploy them, how to query them and how to stop them.

Installation with johnsnowlabs

On Oct 4th, 2022 we released johnsnowlabs library, which eases the installation and session starting processes in an almost transparent way for the user.

Finance NLP and Legal NLP are built on the top of a new John Snow Labs library, called johnsnowlabs. If you are a former user of Spark NLP or Spark NLP for Healthcare, you will find this new way of deploying your Spark NLP clusters much more user-friendly! Clinical NLP (former Spark NLP for Healthcare) still can be run without johnsnowlabs library, although we highly recommend to install it with this new method.

For advanced installation options, please check johnsnowlabs webpage.

1. Installing johnsnowlabs

The first step you need to carry out is installing johnsnowlabs library. This is as easy as doing:

!pip install johnsnowlabs

2. Installing Enterprise NLP (Finance, Legal, Healthcare)

Import johnsnowlabs and use our one-liner nlp.install() to install all the dependencies, downloading the jars (yes, Spark NLP runs on top of the Java Virtual Machine!), preparing the cluster environment variables, licenses, etc!

from johnsnowlabs import *

nlp.install(force_browser=True)

The force_browser=True command gets rid of you uploading a license. It will open a popup to connect to our license server at my.johnsnowlabs.com retrieve the license for you, and install everything your license allows you to use! If you are a user of Financial NLP, you will get that installed. If you are a Legal user, then Legal NLP will be installed, or Clinical! Everything will be taken care on your behalf!

Optional: Uploading the license manually

We still have the way of downloading manually the license, in case the connection with my.johnsnowlabs.com is not an option for you. Just put your license json in the same folder of the notebook, and run:

nlp.install()

In colab, you can use this fancy widget to upload a file to your environment:

from google.colab import files

print('Please Upload your John Snow Labs License using the button below')
license_keys = files.upload()

And then do:

nlp.install()

3. Starting an Enterprise NLP cluster

Another one-liner can be used to start your Enterprise Spark NLP cluster:

spark = nlp.start()

It will take into account the previous steps and your license and return a Spark Session.

4. Ready to go!

And you are done! Simple, isn’t it? Find hundreds of notebooks using johnsnowlabs library here:

• Finance NLP notebooks
• Legal NLP notebooks
• Clinical NLP notebooks

Finance, Legal, Clinical NLP on Databricks

• List of tested runtimes.
• Recommended instance type Standard_F8s_v2 (16 GB Memory, 8 Cores) or higher.
• The installation takes around 15 minutes.

Connection via Databricks Partner connect

Databricks has an integration of Spark NLP libraries via Partner Connect. If you are eligible, you can connect your Databricks workspace to John Snow Labs. The Partner Connect wizard will redirect you to John Snow Labs portal. After you fill-in/validate your information a 30-day trial license will be automatically generated for you. A new Databricks cluster will also be created, and all necessary resources to run the library on your account will be installed on your new cluster. Furthermore, a set of ready to use notebooks will be copied to your workspace, so you can start experimenting on your data right away. The trial license file will also be deployed to your environment and made available to your cluster.

The trial period is 30 days. You can use the trial period only once. After the trial period, we will contact you with a licensing offer.

• Start exploring preloaded notebooks Workspace -> Shared -> John Snow Labs

Automatic deployment of John Snow Labs NLP libraries from www.johnsnowlabs.com/databricks

Alternatively, you can automatically deploy John Snow Labs libraries on Databricks by filling in the form available here. This will allow you to start a 30-day free trial with no limit on the amount of processed data. You just need to provide a Databricks Access Token that is used by our deployment script to connect to your Databricks instance and install John Snow Labs NLP libraries on a cluster of your choice.

• Start exploring preloaded notebooks Workspace -> Shared -> John Snow Labs

Automatic deployment via my.JohnSnowLabs.com

Login to your account on my.JohnSnowLabs.com, navigate to ‘My Subscriptions’ page, and identify your license for Databricks. Click on the three dots as illustrated in the image below, then select the Install On Cluster option.

On the install form, provide an access token for this account and then select the cluster where you want to install the libraries. Once it is done, you will get an email with information on the status of your deployment and on how to get started with the libraries.

Automatic deployment or upgrade from the Databricks workspace

If you have already deployed the libraries in the past, you have a script Workspace -> Shared -> John Snow Labs -> Install JohnSnowLabs NLP. If you attach it to any cluster and run it, it will reinstall the libraries on the respective cluster.

• This is also the recommended way to upgrade to the latest versions of the libraries.

Manual deployment of Enterprise Spark NLP

Automatic deployment is the preferred option.

1. Create a cluster with one of the supported runtimes 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, in Spark.Config box:

    spark.kryoserializer.buffer.max 1000M
    spark.serializer org.apache.spark.serializer.KryoSerializer
   

   • Please add the following to the Advanced Options -> Spark tab, in Environment Variables box:

    AWS_ACCESS_KEY_ID=xxx
    AWS_SECRET_ACCESS_KEY=yyy
    SPARK_NLP_LICENSE=zzz
   

   Note: Enterprise Spark NLP also support reading the license from the Databricks DFS, on the fixed location, dbfs:/FileStore/johnsnowlabs/license.key. The precedence for that location is the highest, so make sure that file is not containing any outdated license key.

   • (OPTIONAL) If the environment variables used to setup the AWS Access/Secret keys are conflicting with the credential provider chain in Databricks, you may not be able to access to other s3 buckets. To access both JSL repos with JSL AWS keys as well as your own s3 bucket with your own AWS keys), you need to use the following script, copy that to dbfs folder, then go to the Databricks console (init scripts menu) to add the init script for your cluster as follows:

    %scala
    val script = """
    #!/bin/bash
   
    echo "******** Inject Spark NLP AWS Profile Credentials ******** "
   
    mkdir ~/.aws/
   
    cat << EOF > ~/.aws/credentials
    [spark_nlp]
    aws_access_key_id=<YOUR_AWS_ACCESS_KEY>
    aws_secret_access_key=<YOUR_AWS_SECRET_KEY>
    EOF
   
    echo "******** End Inject Spark NLP AWS Profile Credentials  ******** "
   
    """
   

3. In Libraries tab inside your cluster you need to follow these steps:
   • Lookup the version of Healhcare NLP vs. Spark NLP you will install.
   • Install Spark NLP (Public): New -> PyPI -> spark-nlp==${x.y.z_public_version} -> Install
   • Install: New -> Maven -> Coordinates -> com.johnsnowlabs.nlp:spark-nlp_2.12:${x.y.z_public_version} -> Install
   • Please add following jars:
     • Install: New -> Python Whl -> upload https://pypi.johnsnowlabs.com/${secret.code}/spark-nlp-jsl/spark_nlp_jsl-${x.y.z_healthcare_version}-py3-none-any.whl
     • Install: New -> Jar -> upload https://pypi.johnsnowlabs.com/${secret.code}/spark-nlp-jsl-${x.y.z_healthcare_version}.jar
   • (For Legal and Finance NLP) Install: New -> PyPI -> johnsnowlabs-for-databricks==${x.y.z_healthcare_version} -> Install
4. Now you can attach your notebook to the cluster and use Spark NLP!

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, Java and a Pyton environment correctly by following the following instructions: https://github.com/JohnSnowLabs/spark-nlp/discussions/1022

How to correctly install Spark NLP on Windows

Follow the below steps to set up Spark NLP with Spark 3.1.2:

1. Download Adopt OpenJDK 1.8
   • Make sure it is 64-bit
   • Make sure you install it in the root of your main drive C:\java.
   • During installation after changing the path, select setting Path
2. Download the pre-compiled Hadoop binaries winutils.exe, hadoop.dll and put it in a folder called C:\hadoop\bin from https://github.com/cdarlint/winutils/tree/master/hadoop-3.2.0/bin
   • Note: The version above is for Spark 3.1.2, which was built for Hadoop 3.2.0. You might have to change the hadoop version in the link, depending on which Spark version you are using.

3. Download Apache Spark 3.1.2 and extract it to C:\spark.

4. Set/add environment variables for HADOOP_HOME to C:\hadoop and SPARK_HOME to C:\spark.

5. Add %HADOOP_HOME%\bin and %SPARK_HOME%\bin to the PATH environment variable.

6. Install Microsoft Visual C++ 2010 Redistributed Package (x64).

7. Create folders C:\tmp and C:\tmp\hive
   • If you encounter issues with permissions to these folders, you might need to change the permissions by running the following commands:

     %HADOOP_HOME%\bin\winutils.exe chmod 777 /tmp/hive
     %HADOOP_HOME%\bin\winutils.exe chmod 777 /tmp/
     

Requisites for PySpark

We recommend using conda to manage your python environment on Windows.

• Download Miniconda for python 3.8
• See Quick Install on how to set up a conda environment with Spark NLP.
• The following environment variables need to be set:
  • PYSPARK_python=python
  • Optionally, if you want to use the Jupyter Notebook runtime of Spark:
    • first install it in the environment with conda install notebook
    • then set PYSPARK_DRIVER_python=jupyter, PYSPARK_DRIVER_python_OPTS=notebook
  • The environment variables can either be directly set in windows, or if only the conda env will be used, with conda env config vars set PYSPARK_python=python. After setting the variable with conda, you need to deactivate and re-activate the environment.

Now you can use the downloaded binary by navigating to %SPARK_HOME%\bin and running

Either create a conda env for python 3.6, install pyspark==3.1.2 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_2.12:3.4.4.

Windows Server

1. Download and Install JAVA 8

   i) Download and Install JAVA 8 from https://adoptium.net/temurin/releases/?version=8

   ii) Once installed , We can check if java is installed by opening cmd and type java -version command

   

2. Install Microsoft Visual C++ 2010

   i) Install Microsoft Visual C++ 2010 from Microsoft Visual C++ 2010 Service Pack 1 Redistributable Package MFC Security Update

3. Download the pre-compiled Hadoop binaries winutils.exe, hadoop.dll

   i) Download the pre-compiled Hadoop binaries winutils.exe, hadoop.dll from winutils/hadoop-3.2.0/bin at master · cdarlint/winutils

   ii) Copy files into a folder called C:\hadoop\bin

4. Configure Hadoop ENV variables

   • Windows Explorer → This PC -> Right Click select properties -> Click on Advanced system settings -> Click on Environment Variables Under system variables

   

   • Add HADOOP_HOME as below

   Under system variables -> Click on new

   VARIABLE Name: HADOOP_HOME

   VARIABLE Value: C:\hadoop

   

   • Select the Path (from variable) -> Click on edit → Click on New → add %HADOOP_HOME%\bin

   

   

5. Dowload and Install Conda and set Conda ENV variables

   i) Download Miniconda for python 3.8 from https://repo.anaconda.com/miniconda/Miniconda3-py38_4.11.0-Windows-x86_64.exe

   ii) Install miniconda exe file.

   iii) Under system variables -> Select the Path (from variable) -> Click on edit -> (ADD the miniconda install location)/bin (Same steps as above)

   

6. Configure conda env

   i) Open cmd and execute the following commands

conda --version
java -version
conda create -n sparknlp python=3.8 -y
conda activate sparknlp
pip install spark-nlp==5.1.2 pyspark==3.2.1
pip install jupyter
conda env config vars set PYSPARK_PYTHON=python
conda activate sparknlp
conda env config vars set PYSPARK_DRIVER_PYTHON=jupyter
conda activate sparknlp
conda env config vars set PYSPARK_DRIVER_python_OPTS=notebook
conda activate sparknlp
jupyter notebook

Apple Silicon Support

Installation for Apple Silicon (M1, M2, M3): Starting from version 4.0.0, Spark NLP has experimental support for apple silicon.

Make sure the following prerequisites are set:

1. Installing SDKMAN, you can also follow the official documentation at https://sdkman.io/install
   • $ curl -s "https://get.sdkman.io" | bash
   • source "$HOME/.sdkman/bin/sdkman-init.sh"
   • sdk list java
     list available java libraries:

   

2. Installing Java
   • sdk install java 8.0.402-amzn
   • whereis java
   • java -version
3. Installing MiniConda, you can also follow the official documentation at https://docs.anaconda.com/free/miniconda/#quick-command-line-install
   • mkdir -p ~/miniconda3
   • curl https://repo.anaconda.com/miniconda/Miniconda3-py39_23.11.0-2-MacOSX-arm64.sh -o ~/miniconda3/miniconda.sh PS: you can change python version to 3.10 or 3.11
   • bash ~/miniconda3/miniconda.sh -b -u -p ~/miniconda3
   • ~/miniconda3/bin/conda init bash
   • ~/miniconda3/bin/conda init zsh
   • source miniconda3/bin/activate

4. Installing jupyter environments or you can install it via VSCode

    # use base environment
    conda --version
    java -version
    conda activate
    pip install pyspark==3.4.0
    pip install jupyter
    conda env config vars set PYSPARK_PYTHON=python
    conda activate 
    conda env config vars set PYSPARK_DRIVER_PYTHON=jupyter
    conda activate 
    conda env config vars set PYSPARK_DRIVER_python_OPTS=notebook
    conda activate 
    jupyter notebook
   

    # or create new sparknlp environment
    conda --version
    java -version
    conda create -n sparknlp python=3.9 -y
    conda activate sparknlp
    pip install pyspark==3.4.0
    pip install jupyter
    conda env config vars set PYSPARK_PYTHON=python
    conda activate sparknlp
    conda env config vars set PYSPARK_DRIVER_PYTHON=jupyter
    conda activate sparknlp
    conda env config vars set PYSPARK_DRIVER_python_OPTS=notebook
    conda activate sparknlp
    jupyter notebook
   

5. Installing Spark NLP Healthcare

   please see the Spark NLP Healthcare Installation Notebook

Non-johnsnowlabs Healthcare NLP on Ubuntu

These instructions use non-johnsnowlabs installation syntax. For simplified installation with johnsnowlabs library, check first section.

For installing John Snow Labs NLP libraries on an Ubuntu machine/VM please run the following command:

wget https://setup.johnsnowlabs.com/nlp/install.sh -O - | sudo bash -s -- -a PATH_TO_LICENSE_JSON_FILE -i -r

This script will install Spark NLP, Enterprise Spark NLP, Spark OCR, NLU and Spark NLP Display on the specified virtual environment. It will also create a special folder, ./JohnSnowLabs, dedicated to all resources necessary for using the libraries. Under ./JohnSnowLabs/example_notebooks you will find some ready to use example notebooks that you can use to test the libraries on your data.

For a complete step-by-step guide on how to install NLP Libraries check the video below:

The install script offers several options:

• -h show brief help
• -i install mode: create a virtual environment and install the library
• -r run mode: start jupyter after installation of the library
• -v path to virtual environment (default: ./sparknlp_env)
• -j path to license json file for Enterprise Spark NLP
• -o path to license json file for Spark OCR
• -a path to a single license json file for both Spark OCR and Spark NLP
• -s specify pyspark version
• -p specify port of jupyter notebook

Use the -i flag for installing the libraries in a new virtual environment.

You can provide the desired path for virtual env using -v flag, otherwise a default location of ./sparknlp_env will be selected.

The PATH_TO_LICENSE_JSON_FILE parameter must be replaced with the path where the license file is available on the local machine. According to the libraries you want to use different flags are available: -j, -o or -a. The license files can be easily downloaded from My Subscription section in your my.JohnSnowLabs.com account.

To start using Jupyter Notebook after the installation of the libraries use the -r flag.

The install script downloads a couple of example notebooks that you can use to start experimenting with the libraries. Those will be availabe under ./JohnSnowLabs/example_notebooks folder.

Non-johnsnowlabs Healthcare NLP via Docker

These instructions use non-johnsnowlabs installation syntax. For simplified installation with johnsnowlabs library, check first section.

A docker image that contains all the required libraries for installing and running Enterprise Spark NLP libraries is also available. However, it does not contain the library itself, as it is licensed, and requires installation credentials.

Make sure you have a valid license for Enterprise Spark NLP libraries (in case you do not have one, you can ask for a trial here), and follow the instructions below:

Docker Image for running Spark NLP for Healthcare inside Jupyter Notebook

The Image contains all the required libraries for installing and running Spark NLP for Healthcare. However, it does not contain the library itself, as it is licensed, and requires installation credentials.

• Please download the necessary files from the here or just get them with commandline as the following:

curl -o Dockerfile https://raw.githubusercontent.com/JohnSnowLabs/spark-nlp-workshop/master/jupyter/docker_enterprise/docker_image_nlp_hc/sparknlp_for_healthcare_jupyter/Dockerfile
curl -o entrypoint.sh https://raw.githubusercontent.com/JohnSnowLabs/spark-nlp-workshop/master/jupyter/docker_enterprise/docker_image_nlp_hc/sparknlp_for_healthcare_jupyter/entrypoint.sh
curl -o requirements.txt https://raw.githubusercontent.com/JohnSnowLabs/spark-nlp-workshop/master/jupyter/docker_enterprise/docker_image_nlp_hc/sparknlp_for_healthcare_jupyter/requirements.txt

• Download your license key in json format from my.JohnSnowLabs.com and put the same folder
• Make sure you have valid license for Spark NLP for Healthcare, and run the following command:

docker run -v /home/jsl_keys.json:/notebooks/sparknlp_keys.json -p 8888:8888 -d johnsnowlabs/sparknlp:sparknlp_for_healthcare_jupyter

• Please replace values inside tags. For instance, replace /home/jsl_keys.json with the correct license json absolute path.
• Make sure docker is installed on your system.
• Run docker ps to validate the container is running.
• If your container is not running, look at docker logs to identify issue.
• If the default port 8888 is already occupied by another process, please change the mapping. Only change values inside the tags.

Alternative Docker Image Instractions

• Run the following commands to download the docker-compose.yml and the sparknlp_keys.txt files on your local machine:

  curl -o docker-compose.yaml https://raw.githubusercontent.com/JohnSnowLabs/spark-nlp-workshop/master/jupyter/docker_enterprise/docker_image_nlp_hc/sparknlp_for_healthcare_image/docker-compose.yaml
  curl -o sparknlp_keys.txt https://raw.githubusercontent.com/JohnSnowLabs/spark-nlp-workshop/master/jupyter/docker_image_nlp_hc/sparknlp_keys.txt
  

• Download your license key in json format from my.JohnSnowLabs.com
• Update License keys in sparknlp_keys.txt file.
• Run the following command to run the container in detached mode:

 docker-compose up -d

• By default, the jupyter notebook runs on port 8888 - you can access it by typing localhost:8888 in your browser
• Create a new jupyter notebook and start coding

Troubleshooting

• Make sure docker is installed on your system.
• If you face any error while importing the lib inside jupyter, make sure all the credentials are correct in the key files and restart the service again.
• If the default port 8888 is already occupied by another process, please change the mapping.
• You can change/adjust volume and port mapping in the docker-compose.yaml file.
• You don’t have a license key? Ask for a trial license here.

Non-johnsnowlabs Healthcare NLP on python

These instructions use non-johnsnowlabs installation syntax. For simplified installation with johnsnowlabs library, check first section.

You can install the Healthcare NLP by using:

pip install -q spark-nlp-jsl==${version} --extra-index-url https://pypi.johnsnowlabs.com/${secret.code} --upgrade

{version} is the version part of the {secret.code} ({secret.code}.split('-')[0]) (i.e. 2.6.0)

The {secret.code} is a secret code that is only available to users with valid/trial license.

You can ask for a free trial for Enterprise Spark NLP libraries here. Then, you can obtain the secret code by visiting your account on my.JohnSnowLabs.com. Read more on how to get a license here.

Setup AWS-CLI Credentials for licensed pretrained models

You need to first set up your AWS credentials to be able to access the private repository for John Snow Labs Pretrained Models. You can do this setup via Amazon AWS Command Line Interface (AWSCLI).

Instructions about how to install AWSCLI are available at:

Installing the AWS CLI

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

Configuring the AWS CLI

Please substitute the ACCESS_KEY and SECRET_KEY with the credentials available on your license json file. This is available on your account from my.JohnSnowLabs.com. Read this for more information.

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.

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

The {secret.code} is a secret code that is only available to users with valid/trial license.

You can ask for a free trial for Enterprise Spark NLP here. Then, you can obtain the secret code by visiting your account on my.JohnSnowLabs.com. Read more on how to get a license here.

You can either use our convenience function to start your Spark Session that will use standard configuration arguments:

import sparknlp_jsl
spark = sparknlp_jsl.start(SECRET)

Or use the SparkSession module for more flexibility:

from pyspark.sql import SparkSession

def start(SECRET):
    builder = SparkSession.builder \
        .appName("Spark NLP Licensed") \
        .master("local[*]") \
        .config("spark.driver.memory", "16G") \
        .config("spark.serializer", "org.apache.spark.serializer.KryoSerializer") \
        .config("spark.kryoserializer.buffer.max", "2000M") \
        .config("spark.jars.packages", "com.johnsnowlabs.nlp:spark-nlp_2.12:"+PUBLIC_VERSION) \
        .config("spark.jars", "https://pypi.johnsnowlabs.com/"+SECRET+"/spark-nlp-jsl-"+JSL_VERSION+".jar")
      
    return builder.getOrCreate()

spark = start(SECRET)

If you want to download the source files (jar and whl files) locally, you can follow the instructions here.

Cheatsheet

# Install Spark NLP from PyPI
pip install spark-nlp==${public_version}

#install Spark NLP Healthcare

pip install spark-nlp-jsl==${version} --extra-index-url https://pypi.johnsnowlabs.com/${secret.code} --upgrade

# Load Spark NLP with Spark Shell
spark-shell --packages com.johnsnowlabs.nlp:spark-nlp_2.12:${public_version} --jars spark-nlp-jsl-${version}.jar

# Load Spark NLP with PySpark
pyspark --packages com.johnsnowlabs.nlp:spark-nlp_2.12:${public_version} --jars spark-nlp-jsl-${version}.jar

# Load Spark NLP with Spark Submit
spark-submit --packages com.johnsnowlabs.nlp:spark-nlp_2.12:${public_version} --jars spark-nlp-jsl-${version}.jar

Non-johnsnowlabs Healthcare NLP for Scala

These instructions use non-johnsnowlabs installation syntax, since johnsnowlabs is a Python library.

Use Spark NLP in Spark shell

1.Download the fat jar for Enterprise Spark NLP

aws s3 cp --region us-east-2 s3://pypi.johnsnowlabs.com/$jsl_secret/spark-nlp-jsl-$jsl_version.jar spark-nlp-jsl-$jsl_version.jar

2.Set up the Environment Variables box:

    AWS_ACCESS_KEY_ID=xxx
    AWS_SECRET_ACCESS_KEY=yyy
    SPARK_NLP_LICENSE=zzz

3.The preferred way to use the library when running Spark programs is using the --packagesand --jar option as specified in the spark-packages section.

spark-shell --packages com.johnsnowlabs.nlp:spark-nlp_2.12:${public-version} --jars /spark-nlp-jsl-${version}.jar

Non-johnsnowlabs Healthcare NLP in Sbt project

These instructions use non-johnsnowlabs installation syntax. For simplified installation with johnsnowlabs library, check first section.

1.Download the fat jar for Enterprise Spark NLP.

aws s3 cp --region us-east-2 s3://pypi.johnsnowlabs.com/$jsl_secret/spark-nlp-jsl-$jsl_version.jar spark-nlp-jsl-$jsl_version.jar

2.Set up the Environment Variables box:

    AWS_ACCESS_KEY_ID=xxx
    AWS_SECRET_ACCESS_KEY=yyy
    SPARK_NLP_LICENSE=zzz

3.Add the spark-nlp jar in your build.sbt project

libraryDependencies += "com.johnsnowlabs.nlp" %% "spark-nlp" % "{public-version}"

4.You need to create the /lib folder and paste the spark-nlp-jsl-${version}.jar file.

5.Add the fat spark-nlp-healthcare in your classpath. You can do it by adding this line in your build.sbt

unmanagedJars in Compile += file("lib/sparknlp-jsl.jar")

Non-johnsnowlabs Healthcare NLP on Colab

This is the way to run Clinical NLP in Google Colab if you don’t use johnsnowlabs library.

Run the following code in Google Colab notebook and start using Spark NLP right away.

The first thing that you need is to create the json file with the credentials and the configuration in your local system.

{
  "PUBLIC_VERSION": "{public-version}",
  "JSL_VERSION": "{version}",
  "SECRET": "{version}-{secret.code}",
  "SPARK_NLP_LICENSE": "xxxxx",
  "AWS_ACCESS_KEY_ID": "yyyy",
  "AWS_SECRET_ACCESS_KEY": "zzzz"
}

If you have a valid floating license, the license json file can be downloaded from your account on my.JohnSnowLabs.com on My Subscriptions section. To get a trial license please visit

Then you need to write that piece of code to load the credentials that you created before.

import json
import os

from google.colab import files

license_keys = files.upload()

with open(list(license_keys.keys())[0]) as f:
  license_keys = json.load(f)

# Defining license key-value pairs as local variables
locals().update(license_keys)

# Adding license key-value pairs to environment variables
os.environ.update(license_keys)

# This is only to setup PySpark and Spark NLP on Colab
!wget https://raw.githubusercontent.com/JohnSnowLabs/spark-nlp-workshop/master/jsl_colab_setup.sh

# -p is for pyspark (by default 3.1.1)
!bash jsl_colab_setup.sh

Spark NLP quick start on Google Colab is a live demo on Google Colab that performs named entity recognitions for HealthCare.

Non-johnsnowlabs Healthcare NLP on GCP Dataproc

These instructions use non-johnsnowlabs installation syntax. For simplified installation with johnsnowlabs library, check first section.

• You can follow the steps here for installation via IU

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

At gcloud shell:

gcloud services enable dataproc.googleapis.com \
  compute.googleapis.com \
  storage-component.googleapis.com \
  bigquery.googleapis.com \
  bigquerystorage.googleapis.com

REGION=<region>

BUCKET_NAME=<bucket_name>
gsutil mb -c standard -l ${REGION} gs://${BUCKET_NAME}

REGION=<region>
ZONE=<zone>
CLUSTER_NAME=<cluster_name>
BUCKET_NAME=<bucket_name>

You can set image-version, master-machine-type, worker-machine-type, master-boot-disk-size, worker-boot-disk-size, num-workers as your needs. If you use the previous image-version from 2.0, you should also add ANACONDA to optional-components. And, you should enable gateway. As noticed below, you should explicitly write JSL_SECRET and JSL_VERSION at metadata param inside the quotes. This will start the pip installation using the wheel file of Licensed SparkNLP!

gcloud dataproc clusters create ${CLUSTER_NAME} \
  --region=${REGION} \
  --network=${NETWORK} \
  --zone=${ZONE} \
  --image-version=2.0 \
  --master-machine-type=n1-standard-4 \
  --worker-machine-type=n1-standard-2 \
  --master-boot-disk-size=128GB \
  --worker-boot-disk-size=128GB \
  --num-workers=2 \
  --bucket=${BUCKET_NAME} \
  --optional-components=JUPYTER \
  --enable-component-gateway \
  --metadata 'PIP_PACKAGES=google-cloud-bigquery google-cloud-storage spark-nlp-display
  https://s3.eu-west-1.amazonaws.com/pypi.johnsnowlabs.com/JSL_SECRET/spark-nlp-jsl/spark_nlp_jsl-JSL_VERSION-py3-none-any.whl' \
  --initialization-actions gs://goog-dataproc-initialization-actions-${REGION}/python/pip-install.sh

1. On an existing one, you need to install spark-nlp and spark-nlp-display packages from PyPI.

2. Now, you can attach your notebook to the cluster and use Spark NLP via following the instructions. The key part of this usage is how to start SparkNLP sessions using Apache Hadoop YARN cluster manager.

3.1. Read license file from the notebook using GCS.

3.2. Set the right path of the Java Home Path.

3.3. Use the start function to start the SparkNLP JSL version such as follows:

def start(secret):
    builder = SparkSession.builder \
        .appName("Spark NLP Licensed") \
        .config("spark.serializer", "org.apache.spark.serializer.KryoSerializer") \
        .config("spark.kryoserializer.buffer.max", "2000M") \
        .config("spark.jars.packages", "com.johnsnowlabs.nlp:spark-nlp_2.12:"+PUBLIC_VERSION) \
        .config("spark.jars", "https://pypi.johnsnowlabs.com/"+SECRET+"/spark-nlp-jsl-"+JSL_VERSION+".jar")

    return builder.getOrCreate()

spark = start(SECRET)

As you see, we did not set .master('local[*]') explicitly to let YARN manage the cluster. Or you can set .master('yarn').

Non-johnsnowlabs Healthcare NLP on AWS SageMaker

These instructions use non-johnsnowlabs installation syntax. For simplified installation with johnsnowlabs library, check first section.

1. Access AWS Sagemaker in AWS.
2. Go to Notebook -> Notebook Instances.
3. Create a new Notebook Instance, follow this Instructions Steps
4. Minimum requirement 16G RAM and 50G Volume. This is the configuration we have used, although most of the interesting models will require a ml.t3.xlarge instance or more. Reserve at least 50GB of memory
5. Once created, open JupyterLab and use Conda python 3 kernel.
6. Upload license key and set Environment Variables.

import json
import os

with open('spark_nlp_for_healthcare.json', 'r') as f:
    for k, v in json.load(f).items():
        %set_env $k=$v

%set_env PYSPARK=3.2.2
%set_env SPARK_HOME=/home/ec2-user/SageMaker/spark-3.2.2-bin-hadoop2.7

1. Download and install libraries

!wget https://raw.githubusercontent.com/JohnSnowLabs/spark-nlp-workshop/master/jsl_sagemaker_setup.sh
!bash jsl_sagemaker_setup.sh

1. Import libraries and start session

import sparknlp
import sparknlp_jsl
from pyspark.sql import SparkSession

spark = sparknlp_jsl.start(license_keys['SECRET'])

Non-johnsnowlabs Healthcare NLP with Poetry

These instructions use non-johnsnowlabs installation syntax. For simplified installation with johnsnowlabs library, check first section.

This is a sample project.toml file which you can use with poetry install to setup spark NLP + the Healthcare python library spark-nlp-jsl

You need to point it to either the tar.gz or .whl file which are hosted at https://pypi.johnsnowlabs.com/<SECRET>/spark-nlp-jsl/

NOTE You must update the url whenever you are upgrading your spark-nlp-jsl version

[tool.poetry]
name = "poertry_demo"
version = "0.1.0"
description = ""
authors = ["person <person@gmail.com>"]

[tool.poetry.dependencies]
python = "^3.7"

[tool.poetry.dev-dependencies]
spark-nlp = "5.1.2"
spark-nlp-jsl = { url = "https://pypi.johnsnowlabs.com/SECRET/spark-nlp-jsl/spark_nlp_jsl-tar.gz_OR_.whl" }

[build-system]
requires = ["poetry-core>=1.0.0"]
build-backend = "poetry.core.masonry.api"

Non-johnsnowlabs Healthcare NLP on AWS EMR

These instructions use non-johnsnowlabs installation syntax. For simplified installation with johnsnowlabs library, check first section.

In this page we explain how to setup Spark-NLP + Spark-NLP Healthcare in AWS EMR, using the AWS console.

Steps

1. Go to AWS services, and select EMR

2. Press Create Cluster and start:

   • Name your cluster
   • select EMR version
   • select required applications

• Specify EC2 instances for the cluster, as primary/master node and cores/workers
• Specify the storage/ EBS volume

• Choose Cluster scaling and provisioning
• Choose Networking / VPC

• Choose Security Groups/Firewall for primary/master node and cores/workers/slaves

• If you have add steps , that will be executed after cluster is provisioned
• Specify the S3 location for logs
• Under Tags section, please add a KEY: VALUE pair with for-use-with-amazon-emr-managed-policies true

Important

• Specify the Bootstrap Action

jsl_emr_bootstrap.sh

Put this sample shell script in a S3 location and specify it in the form: You will have spark-nlp and spark-nlp-jsl and spark-ocr installed by bootstrap action, this file is executed during the cluster provisioning. Version of Libraries and other credentials provided by Johnsnowlabs will be in this file.

Important

• Specify the Configuration for spark: Here is a sample configuration, you can copy/paste into Software settings tab or load from S3. You can change spark configuration according to your needs.

[
  {
    "Classification": "spark-env",
    "Configurations": [
      {
        "Classification": "export",
        "Properties": {
          "JSL_EMR": "1",
          "PYSPARK_PYTHON": "/usr/bin/python3",
          "SPARK_NLP_LICENSE": "XYXYXYXYXY"
        }
      }
    ],
    "Properties": {}
  },
  {
    "Classification": "yarn-env",
    "Configurations": [
      {
        "Classification": "export",
        "Properties": {
          "JSL_EMR": "1",
          "SPARK_NLP_LICENSE": "XYXYXYXYXY"
        }
      }
    ],
    "Properties": {}
  },
  {
    "Classification": "spark-defaults",
    "Properties": {
      "spark.driver.maxResultSize": "0",
      "spark.driver.memory": "64G",
      "spark.dynamicAllocation.enabled": "true",
      "spark.executor.memory": "64G",
      "spark.executorEnv.SPARK_NLP_LICENSE": "XYXYXYXYXY",
      "spark.jsl.settings.aws.credentials.access_key_id": "XYXYXYXYXY",
      "spark.jsl.settings.aws.credentials.secret_access_key": "XYXYXYXYXY",
      "spark.jsl.settings.aws.region": "us-east-1",
      "spark.jsl.settings.pretrained.credentials.access_key_id": "XYXYXYXYXY",
      "spark.jsl.settings.pretrained.credentials.secret_access_key": "XYXYXYXYXY",
      "spark.kryoserializer.buffer.max": "2000M",
      "spark.rpc.message.maxSize": "1024",
      "spark.serializer": "org.apache.spark.serializer.KryoSerializer",
      "spark.yarn.appMasterEnv.SPARK_NLP_LICENSE": "XYXYXYXYXY",
      "spark.yarn.preserve.staging.files": "true",
      "spark.yarn.stagingDir": "hdfs:///tmp"
    }
  }
]

• There’s not much additional setup you need to perform. So just start a notebook server, connect it to the cluster you just created(be patient, it takes a while), and test with the jsl_test_notebook_for_emr.ipynb test notebook.
  

Non-johnsnowlabs Healthcare NLP on Amazon Linux 2

These instructions use non-johnsnowlabs installation syntax. For simplified installation with johnsnowlabs library, check first section.

# Update Package List & Install  Required Packages
sudo yum update
sudo yum install -y amazon-linux-extras
sudo yum -y install python3-pip

# Create python virtual environment and activate it:
python3 -m venv .sparknlp-env
source .sparknlp-env/bin/activate

Check JAVA version:

• For Sparknlp versions above 3.x, please use JAVA-11
• For Sparknlp versions below 3.x and SparkOCR, please use JAVA-8

Checking Java versions installed on your machine:

sudo alternatives --config java

You can pick the index number (I am using java-8 as default - index 2):

If you dont have java-11 or java-8 in you system, you can easily install via:

sudo yum install java-1.8.0-openjdk

Now, we can start installing the required libraries:

pip install jupyter

We can start jupyter notebook via:

jupyter notebook

### Now we are in the jupyter notebook cell:
import json
import os

with open('sparknlp_for_healthcare.json) as f:
    license_keys = json.load(f)

# Defining license key-value pairs as local variables
locals().update(license_keys)

# Adding license key-value pairs to environment variables
os.environ.update(license_keys)

# Installing pyspark and spark-nlp
! pip install --upgrade -q pyspark==3.1.2 spark-nlp==$PUBLIC_VERSION

# Installing Spark NLP Healthcare
! pip install --upgrade -q spark-nlp-jsl==$JSL_VERSION  --extra-index-url https://pypi.johnsnowlabs.com/$SECRET

Deploying Spark NLP Healthcare on Kubernetes

This guide will walk you through the deployment of a Spark NLP Healthcare application on a Kubernetes cluster using kind.

Prerequisites

Installing Necessary Tools:

1. Docker:
   • Install from Docker Desktop(https://www.docker.com/products/docker-desktop/).
   • Ensure Kubernetes is enabled in Docker Desktop settings.
2. kubectl:
   • Install using the instructions from Kubernetes official documentation(https://kubernetes.io/docs/tasks/tools/).
3. kind:
   • Install using the instructions from Kubernetes official documentation(https://kubernetes.io/docs/tasks/tools/).
4. Docker Hub Account:
   • If you don’t have one, create your account at Docker Hub(https://hub.docker.com/signup).
5. Install JohnSnow Labs licence key file to the project directory(https://my.johnsnowlabs.com/subscriptions).

Project Structure:

.
├── Dockerfile
├── main.py
├── README.md
├── requirements.txt
├── spark-nlp-healthcare-deployment.yaml
└── spark_nlp_for_healthcare_spark_ocr_8204.json (licence key filename)

Application Details

The main application script, main.py, is as follows:

from johnsnowlabs import nlp, medical
import pandas as pd
from pyspark.sql import DataFrame
import pyspark.sql.functions as F
import pyspark.sql.types as T
import pyspark.sql as SQL
from pyspark import keyword_only
from pyspark.ml import PipelineModel
import os

class NLPProcessor:
    def __init__(self):
        """Initialize and set up NLP tools."""
        # Install all licensed Python Wheels and pre-download Jars the Spark Session JVM
        nlp.install()
        
        # Automatically load license data and start a session with all jars user has access to
        self.spark = nlp.start()
        
        # Set up the NLP pipeline
        self.model = self.setup_pipeline()

    def setup_pipeline(self):
        """Set up the NLP pipeline using John Snow Labs library."""

        # Annotator that transforms a text column from dataframe into an Annotation ready for NLP
        documentAssembler = nlp.DocumentAssembler()\
            .setInputCol("text")\
            .setOutputCol("document")

        # Sentence detector specific to healthcare data
        sentenceDetector = nlp.SentenceDetectorDLModel.pretrained("sentence_detector_dl_healthcare", "en", "clinical/models")\
            .setInputCols(["document"])\
            .setOutputCol("sentence")

        # Tokenizer splits words in a relevant format for NLP
        tokenizer = nlp.Tokenizer()\
            .setInputCols(["sentence"])\
            .setOutputCol("token")

        # Clinical word embeddings trained on PubMED dataset
        word_embeddings = nlp.WordEmbeddingsModel.pretrained("embeddings_clinical", "en", "clinical/models")\
            .setInputCols(["sentence", "token"])\
            .setOutputCol("embeddings")

        # NER model trained on i2b2 (sampled from MIMIC) dataset
        jsl_ner = medical.NerModel.pretrained("ner_jsl", "en", "clinical/models")\
            .setInputCols(["sentence", "token", "embeddings"])\
            .setOutputCol("jsl_ner")

        # Converter to transform NER results
        jsl_ner_converter = nlp.NerConverter()\
            .setInputCols(["sentence", "token", "jsl_ner"])\
            .setOutputCol("jsl_ner_chunk")

        # Combine all the stages of the pipeline
        nlpPipeline = nlp.Pipeline(stages=[
            documentAssembler,
            sentenceDetector,
            tokenizer,
            word_embeddings,
            jsl_ner,
            jsl_ner_converter
        ])

        # Fit an empty dataframe to initialize the pipeline
        return nlpPipeline.fit(self.spark.createDataFrame([[""]]).toDF("text"))

    def annotate_text(self, text):
        """Annotate the provided text using the NLP pipeline."""
        light_model = nlp.LightPipeline(self.model)
        return light_model.annotate(text)

def main():
    """Main function to run the NLP annotation."""
    processor = NLPProcessor()
    
    sample_text = '''A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years prior to presentation and subsequent type two diabetes mellitus ( T2DM )'''
    
    result = processor.annotate_text(sample_text)
    
    print(result)

if __name__ == "__main__":
    main()

Step-by-step Guide

1. Containerizing the Spark NLP Healthcare Application

Dockerfile:

# Use Ubuntu 20.04 as the base image
FROM ubuntu:20.04

# Update and install necessary packages
RUN apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y openjdk-8-jdk python3-pip curl

# Set JAVA_HOME
ENV JAVA_HOME /usr/lib/jvm/java-8-openjdk-amd64/

# Copy the base requirements and main application into the image
COPY requirements.txt /app/requirements.txt
COPY <licence_filename> /app/<licence_filename>
WORKDIR /app

# Install Python packages
RUN pip3 install -r requirements.txt

# Copy the main application
COPY main.py /app/main.py

CMD ["python3", "main.py"]

Note: Before building the Docker image, replace in the Dockerfile with your actual Spark NLP Healthcare license key file name and set to the appropriate version number (e.g., 5.0.1). You can find the value in your licence key json file.

Logging in to Docker Hub: Run the command:docker login -u <your-docker-hub-username> -p <your-docker-hub-password> This will authenticate you with Docker Hub, allowing you to push and pull private images.

Build the Docker image with the specific tag: docker build -t <your-docker-hub-username>/spark-nlp-healthcare:<JSL_VERSION> .

2. Pushing Docker Image to Docker Hub

Tag the image with your Docker Hub username: docker tag spark-nlp-healthcare:<JSL_VERSION> <your-docker-hub-username>/spark-nlp-healthcare:<JSL_VERSION>

Push the image to Docker Hub:

docker push <your-docker-hub-username>/spark-nlp-healthcare:<JSL_VERSION>

3. Setting Up the Kubernetes Cluster with kind

Before deploying the application, you’ll need to set up a local Kubernetes cluster using kind. Run the following command: kind create cluster

4. Setting up Secrets in Kubernetes

Make sure your Spark NLP Healthcare license key file (e.g., ) is present in the project directory.

Replace with your actual license key file name in the below command:

kubectl create secret generic spark-nlp-healthcare-secret --from-file=license=<licence_filename>

5. Deploying the Spark NLP Healthcare Application

Before proceeding, ensure that you replace the placeholders and in the spark-nlp-healthcare-deployment.yaml with your Docker Hub username and the appropriate Spark NLP version respectively. Use the following content for spark-nlp-healthcare-deployment.yaml:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: spark-nlp-healthcare-deployment
spec:
  replicas: 1
  selector:
    matchLabels:
      app: spark-nlp-healthcare
  template:
    metadata:
      labels:
        app: spark-nlp-healthcare
    spec:
      containers:
      - name: spark-nlp-healthcare
        image: <your-docker-hub-username>/spark-nlp-healthcare:<JSL_VERSION>
        ports:
        - containerPort: 8888
        env:
        - name: SPARK_NLP_LICENSE
          valueFrom:
            secretKeyRef:
              name: spark-nlp-healthcare-secret
              key: license

Apply the deployment: kubectl apply -f spark-nlp-healthcare-deployment.yaml

To verify, run commands below:

kubectl get deployments
kubectl get pods

The output will look like as following;

kubectl get deployments
NAME                              READY   UP-TO-DATE   AVAILABLE   AGE
spark-nlp-healthcare-deployment   0/1     1            0           2m42s

kubectl get pods
NAME                                               READY   STATUS              RESTARTS   AGE
spark-nlp-healthcare-deployment-7fc4c6b4ff-rdj97   0/1     ContainerCreating   0          2m50s

Wait until the output becomes as following;

kubectl get deployments                                              
NAME                              READY   UP-TO-DATE   AVAILABLE   AGE
spark-nlp-healthcare-deployment   1/1     1            1           8m46s

kubectl get pods                                                     
NAME                                               READY   STATUS    RESTARTS   AGE
spark-nlp-healthcare-deployment-7fc4c6b4ff-rdj97   1/1     Running   0          8m54s

Now the pod is ready and running.

6. Validating the Deployment

To get the name of the pod:

kubectl get pods -l app=spark-nlp-healthcare -o jsonpath="{.items[0].metadata.name}"

You can verify if the application is running properly within the Kubernetes cluster by executing a shell within the pod:

kubectl exec -it <kubernetes_pod_name> -- /bin/bash

This command will open a bash shell and the program can be run with python3 main.py command. It will output the following;

[OK!]
{'document': ['A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years prior to presentation and subsequent type two diabetes mellitus ( T2DM )'], 'jsl_ner_chunk': ['28-year-old', 'female', 'gestational diabetes mellitus', 'eight years prior', 'type two diabetes mellitus', 'T2DM'], 'jsl_ner': ['O', 'B-Age', 'B-Gender', 'O', 'O', 'O', 'O', 'B-Diabetes', 'I-Diabetes', 'I-Diabetes', 'O', 'B-RelativeDate', 'I-RelativeDate', 'I-RelativeDate', 'O', 'O', 'O', 'O', 'B-Diabetes', 'I-Diabetes', 'I-Diabetes', 'I-Diabetes', 'O', 'B-Diabetes', 'O'], 'token': ['A', '28-year-old', 'female', 'with', 'a', 'history', 'of', 'gestational', 'diabetes', 'mellitus', 'diagnosed', 'eight', 'years', 'prior', 'to', 'presentation', 'and', 'subsequent', 'type', 'two', 'diabetes', 'mellitus', '(', 'T2DM', ')'], 'embeddings': ['A', '28-year-old', 'female', 'with', 'a', 'history', 'of', 'gestational', 'diabetes', 'mellitus', 'diagnosed', 'eight', 'years', 'prior', 'to', 'presentation', 'and', 'subsequent', 'type', 'two', 'diabetes', 'mellitus', '(', 'T2DM', ')'], 'sentence': ['A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years prior to presentation and subsequent type two diabetes mellitus ( T2DM )']}

If you have any questions or face any issues during the deployment process, please feel free to reach out to me at burhan@johnsnowlabs.com. I’m here to help!

Fancy trying?

You can ask for a free trial for Enterprise Spark NLP here. This will automatically create a new account for you on my.JohnSnowLabs.com. Login in to your new account and from My Subscriptions section, you can download your license key as a json file.

The license json file contains:

• the secrets for installing the Enterprise Spark NLP and Spark OCR libraries,
• the license key as well as
• AWS credentials that you need to access the s3 bucket where the healthcare models and pipelines are published.

If you have asked for a trial license, but you cannot access your account on my.JohnSnowLabs.com and you did not receive the license information via email, please contact us at support@johnsnowlabs.com.

Azure Synapse Analytics Support

Step 1: Sign in to Azure portal

• Sign in to the Azure portal at https://portal.azure.com.

Step 2: Create a new Resource Group

• On the left-hand menu, click on “Resource groups”.
• In the new window, click “Create”.
• Provide a unique name for the Resource Group and select the Region where you want to create it.
• Click “Review + Create” and then “Create”.

Step 3: Create a Storage Account

• On the left-hand menu, click on “Create a resource”.
• In the “New” window, search for “Storage Account”.
• In the search results, select “Storage Account” and then click “Create”.
• In the new window, select the Resource Group you just created, provide a unique name for your Storage Account, and select the Region.
• Select the Performance, Account kind, Replication, and Access tier according to your requirements.
• Click “Review + Create” and then “Create”.

Step 4: Create a Synapse workspace

• On the left-hand menu, click on “Create a resource”.
• In the “New” window, search for “Azure Synapse Analytics”.
• In the search results, select “Azure Synapse Analytics” and then click “Create”.
• In the new window, select the Resource Group you just created, provide a unique name for your Synapse Workspace, select the Region, and provide the Storage Account you created earlier.
• You also need to create a new file system in your storage account for Synapse workspace, provide a unique name for it.
• Fill the Security & networking details as per your requirements.
• Click “Review + Create” and then “Create”.

Step 5: Configuring the Synapse Studio

• Once your workspace is created, open the Azure Synapse Studio.
• Navigate to the “Manage” section within Azure Synapse Studio.
• Under the “Workspace settings” section, find and select “Workspace Packages”.
• Click “Upload” to upload the necessary JAR and wheel files.

• For running licensed models, navigate to the “Apache Spark configurations” under the “Manage” section.
• Click on “New” to add a new configuration.
• For licensed Healthcare models, Add the following properties:
  • spark.hadoop.fs.s3a.access.key :
  • spark.hadoop.fs.s3a.secret.key :
  • spark.yarn.appMasterEnv.SPARK_NLP_LICENSE :

After adding these properties, the Apache Spark configuration is ready.

• Navigate to “Apache Spark pools” under the “Analytics pools” section.
• Click on “New” to create a new Spark pool. Configure the pool settings as required, selecting a “Medium” Node size under “Performance Settings”. Under “Additional settings”, allow “session level packages”. Add the Apache Spark configuration created above (this is needed for licensed models only). Review your settings, then click “Create”.
• Navigate to the “Develop” section in Azure Synapse Studio.
• Create a new notebook or import an existing one. Attach the notebook to the Apache Spark pool created above.

Now, all the necessary licenses and JARs are ready to be used. You can proceed to run your notebook.

For running OCR models, upload the following JAR and wheel files to the Workspace packages.

• For licensed OCR models, Add the following properties:
  • spark.hadoop.fs.s3a.access.key :
  • spark.hadoop.fs.s3a.secret.key :
  • spark.yarn.appMasterEnv.SPARK_OCR_LICENSE :
  • spark.driver.extraJavaOptions : -Dorg.fluentd.logger.sender.NullSender=org.fluentd.logger.sender.NullSender
  • spark.executor.extraJavaOptions : -Dorg.fluentd.logger.sender.NullSender=org.fluentd.logger.sender.NullSender
  • spark.sql.legacy.allowUntypedScalaUDF : True

Now, you can proceed to run your OCR models and notebooks.

Azure AI Studio instructions

Go to https://ai.azure.com/

To create a project in Azure AI Studio, adhere to the following structured approach:

1. Navigate to the Build Tab
   • Access the Azure AI Studio webpage and locate the Build tab positioned at the top of the interface.
2. Initiate New Project Creation
   • Click on the + New project button to start the process of creating a new project.
3. Specify Project Name
   • Enter a name for your project that reflects its purpose or content.
4. Select an Azure AI Hub Resource
   • Choose an existing Azure AI hub resource from the dropdown list to associate with your project. If you do not possess an existing resource, select the option to create a new one.
5. Permissions for New Azure AI Hub Resource
   • If opting to create a new Azure AI hub resource, ensure you have either Owner or Contributor permissions on the chosen resource group.
6. Collaboration and Resource Sharing
   • For collaborative projects, it is beneficial to share an Azure AI hub resource with your team. This promotes shared configurations and centralized resource management.
7. Naming the New Azure AI Hub Resource
   • Assign a name to the new Azure AI hub resource if you’re creating one.
8. Select Azure Subscription
   • From the dropdown list, pick your Azure subscription to manage billing and access.
9. Resource Group Decision
   • Decide whether to create a new Resource group or use an existing one for your project. Creating a new group can facilitate easier management of the project’s resources.
10. Location Selection
    • Choose the Location for your Azure AI hub resource, which inherently decides your project’s geographical location. The choice of location is crucial as the availability of Azure AI services may differ across regions.
11. Review AI Services Provider
    • On the Review and finish page, scrutinize the AI Services provider details to ensure access to Azure AI services like Azure OpenAI is correctly configured.
12. Finalize Project Creation
    • After reviewing all the details for accuracy, click on Create a project to complete the setup process.

1. Then go to All Azure AI dropdown and choose Azure Machine Learning Studio.

1. Such a window will Appear. Go to Add compute.

1. Choose the compute type and enable auto-shutdown as well.

1. Then go to the Notebooks Section.

In NB section, you can create your own NB or load existing NBs.

1. Here I uploaded a NB and attached the compute we created above, with Python 3.8 Azure ML as kernel.

1. Once the session starts, you just need to install the johnsnowlabs library and update the environment with license keys in this way. Then using nlp.install() and nlp.start() you can install the required jars and wheels and start the spark session.

1. Once spark session is created. Then you can run the johnsnowlabs models and pipelines there (licensed as well as open source).

Microsoft Fabric Instructions

Step 1: Log into MS Fabric

Navigate to MS Fabric and sign in with your MS Fabric Account credentials.

Step 2: Create a Lakehouse

• Go to the Data Science section.
• Navigate to the Create section.
• Create a new lakehouse, (for instance let us name it jsl_workspace.)

Step 3: Create a Notebook

• Similarly, create a new notebook ( for instance let us name it JSL_Notebook.)

Step 4: Attach the Lakehouse

Attach the newly created lakehouse (jsl_workspace) to your notebook.

Step 5: Upload Files

Upload the necessary .jar and .whl files to the attached lakehouse.

After uploading is complete, you can configure and run the notebook.

Step 6: Configure the Notebook Session

Configure the session within the notebook as follows:

%%configure -f
{
  "conf": {
    "spark.jsl.settings.aws.credentials.access_key_id": {
      "parameterName": "awsAccessKey",
      "defaultValue": "<AWS_ACCESS_KEY_ID>"
    },
    "spark.jsl.settings.aws.credentials.secret_access_key": {
      "parameterName": "awsSecretKey",
      "defaultValue": "<AWS_SECRET_ACCESS_KEY>"
    },

    "spark.yarn.appMasterEnv.SPARK_NLP_LICENSE": {
      "parameterName": "sparkNlpLicense",
      "defaultValue": "<SPARK_NLP_LICENSE>"
    },
    "spark.jars": {
      "parameterName": "sparkJars",
      "defaultValue": "abfss://&&&&&&/Files/spark-nlp-assembly-5.5.0.jar, abfss://&&&&&&/Files/spark-nlp-jsl-5.5.0.jar"
    },
    "spark.jsl.settings.pretrained.cache_folder": {
      "parameterName": "cacheFolder",
      "defaultValue": "abfss://&&&&&&/Files/unzip_files"
    },
    "spark.extraListeners": {
      "parameterName": "extraListener",
      "defaultvalue": "com.johnsnowlabs.license.LicenseLifeCycleManager"
    }
  }
}

Step 7: Install Spark NLP Libraries

Install the required Spark NLP libraries using pip commands:

%pip install <johnsnowlabs whl File API path>
%pip install <spark-nlp whl File API path>
%pip install <spark-nlp-jsl whl File API path>

Example Usage:

Step 8: Make Necessary Imports

Import the necessary Python and Spark libraries:

import functools
import numpy as np
import pandas as pd
from scipy import spatial

import pyspark.sql.types as T
from pyspark.ml import Pipeline
from pyspark.sql import SparkSession
from pyspark.sql import functions as F

import sparknlp
import sparknlp_jsl
from sparknlp.base import *
from sparknlp.annotator import *
from sparknlp_jsl.annotator import *

Step 9: Explore Available Licensed Models

Download and explore the list of available licensed models:

! pip install -q boto3
! wget https://nlp.johnsnowlabs.com/models.json

import json
with open("models.json", "r", encoding="utf-8") as f:
    full_list = json.load(f)

Example Usage:

Step 10: Download and Extract Required Models

Set up AWS credentials and use Boto3 to handle files:

import shutil
import boto3

# Setup AWS credentials
ACCESS_KEY = "<AWS-ACCESS-KEY>"
SECRET_KEY = "<AWS-SECRET-KEY>"

# Connect to S3
s3 = boto3.resource('s3', aws_access_key_id=ACCESS_KEY, aws_secret_access_key=SECRET_KEY)
buck_auxdata = s3.Bucket('auxdata.johnsnowlabs.com')

!mkdir -p /lakehouse/default/Files/zip_files /lakehouse/default/Files/

# Download and extract model
import shutil
from sparknlp.pretrained import PretrainedPipeline

zip_file_path = '/lakehouse/default/Files/zip_files/<Model-ZIP>'
unzip_dir_path = '/lakehouse/default/Files/<MODEL-NAME>'

try:
    buck_auxdata.download_file('clinical/models/<Model-ZIP>', zip_file_path)
    shutil.unpack_archive(zip_file_path, unzip_dir_path, 'zip')
    print("Model downloaded and extracted successfully.")
except Exception as e:
    print(f"An error occurred: {e}")

Step 11: Load the Model and Make Predictions

Load the model and perform predictions on the desired text:

pipeline = PretrainedPipeline.from_disk("Files/<MODEL-NAME>")
text = "<INPUT-TEXT>"
result = pipeline.annotate(text)

Example Usage:

Step 12: Run the pipeline with .pretrained() method

You can also run the pipelines without using the .load() or .from_disk() methods