Starting a Spark Session

To use most features you must start a Spark Session with nlp.start()first.
This will launch a Java Virtual Machine(JVM) process on your machine which has all of John Snow Labs and Sparks Scala/Java Libraries(JARs) you have access to loaded into memory.

The nlp.start() method downloads loads and caches all jars for which credentials are provided if they are missing into ~/.jsl_home/java_installs.
If you have installed via nlp.install() you can most likely skip the rest of this page, since your secrets have been cached in ~/.jsl_home and will be re-used.
If you disabled license caching while installing or if you want to tweak settings about your spark session continue reading this section further.

Outputs of running nlp.start() tell you which jars are loaded and versions of all relevant libraries.

Authorization Flow Parameters

Most of the authorization Flows and Parameters of nlp.install() are supported.
Review detailed docs here

Parameter	Description	Example	Default
None	Load license automatically via one of the Auto-Detection Mechanisms	nlp.start()	False
browser_login	Browser based authorization, Button to click on Notebooks and Browser Pop-Up otherwise.	nlp.start(browser_login=True)	False
access_token	Vist my.johnsnowlabs.com to extract a token which you can provide to enable license access. See Access Token Example	nlp.start(access_token='myToken')	None
secrets_file	Define JSON license file with keys defined by License Variable Overview and provide file path	nlp.start(secrets_file='path/to/license.json')	None
store_in_jsl_home	Disable caching of new licenses to ~./jsl_home	nlp.start(store_in_jsl_home=False)	True
local_license_number	Specify which license to use, if you have access to multiple locally cached	nlp.start(license_number=5)	0
remote_license_number	Specify which license to use, if you have access to multiple via OAUTH on my.jsl.com	nlp.start(license_number=5)	0

Manually specify License Parameters

These can be omitted according to the License Variable Overview

Parameter	Description
aws_access_key	Corresponds to AWS_ACCESS_KEY_ID
aws_key_id	Corresponds to AWS_SECRET_ACCESS_KEY
enterprise_nlp_secret	Corresponds to HC_SECRET
ocr_secret	Corresponds to OCR_SECRET
hc_license	Corresponds to HC_LICENSE
ocr_license	Corresponds to OCR_LICENSE
fin_license	Corresponds to JSL_LEGAL_LICENSE
leg_license	Corresponds to JSL_FINANCE_LICENSE

Sparksession Parameters

These parameters configure how your spark Session is started up.
See Spark Configuration for a comprehensive overview of all spark settings

Parameter	Default	Description	Example
spark_conf	None	Dictionary Key/Value pairs of Spark Configurations for the Spark Session	nlp.start(spark_conf={'spark.executor.memory':'6g'})
master_url	local[*]	URL to Spark Cluster master	nlp.start(master_url='spark://my.master')
jar_paths	None	List of paths to jars which should be loaded into the Spark Session	nlp.start(jar_paths=['my/jar_folder/jar1.zip','my/jar_folder/jar2.zip'] )
exclude_nlp	False	Whether to include Spark NLP jar in Session or not. This will always load the jar if available, unless set to True.	nlp.start(exclude_nlp=True)
exclude_healthcare	False	Whether to include licensed NLP Jar for Legal,Finance or Healthcare. This will always load the jar if available using your provided license, unless set to True.	nlp.start(exclude_healthcare=True)
exclude_ocr	False	Whether to include licensed OCR Jar for Legal,Finance or Healthcare. This will always load the jar if available using your provided license, unless set to True.	nlp.start(exclude_ocr=True)
hardware_target	cpu	Specify for which hardware Jar should be optimized. Valid values are gpu,cpu,m1,aarch	nlp.start(hardware_target='m1')
model_cache_folder	None	Specify where models should be downloaded to when using model.pretrained()	nlp.start(model_cache_folder=True)