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### Step 2: Create a Lakehouse -- Go to the **Synapse Data Science** section. +- Go to the **Data Science** section. - Navigate to the **Create** section. - Create a new lakehouse, (for instance let us name it `jsl_workspace`.) -![image](/assets/images/installation/355920557-2c5f778c-4c33-4a54-af21-71f4486f5e4b.webp) +![Create a Lakehouse](/assets/images/installation/Fabric_1.png)
### Step 3: Create a Notebook - Similarly, create a new notebook ( for instance let us name it `JSL_Notebook`.) -![image](/assets/images/installation/355920928-697cac4b-29ff-4f23-beaa-5aaa32569ff0.webp) +![Create a Notebook in Fabric](/assets/images/installation/Fabric_2.png)
### Step 4: Attach the Lakehouse Attach the newly created lakehouse (`jsl_workspace`) to your notebook. -![image](/assets/images/installation/355921285-63996c40-4cd6-4aa2-925f-a1ad886914f4.webp) +![Attach the Lakehouse](/assets/images/installation/355921285-63996c40-4cd6-4aa2-925f-a1ad886914f4.webp) -![image](/assets/images/installation/355921392-b711eef6-55ed-4073-b974-14b565cd40be.webp) +![Attach the Lakehouse](/assets/images/installation/355921392-b711eef6-55ed-4073-b974-14b565cd40be.webp)
### Step 5: Upload Files Upload the necessary `.jar` and `.whl` files to the attached lakehouse. -![image](/assets/images/installation/355921637-a275d80d-768f-4402-bdab-d95864e73690.webp) +![Upload Files to Fabric](/assets/images/installation/355921637-a275d80d-768f-4402-bdab-d95864e73690.webp) -![image](/assets/images/installation/360943582-53bc84ae-40dc-41dc-9522-e87bf70d4fba.webp) +![Upload Files to Fabric](/assets/images/installation/Fabric_3.png) After uploading is complete, you can configure and run the notebook. @@ -1631,21 +1631,30 @@ Configure the session within the notebook as follows: %%configure -f { "conf": { - "spark.hadoop.fs.s3a.access.key": { + "spark.jsl.settings.aws.credentials.access_key_id": { "parameterName": "awsAccessKey", - "defaultValue": "" + "defaultValue": "" }, - "spark.hadoop.fs.s3a.secret.key": { + "spark.jsl.settings.aws.credentials.secret_access_key": { "parameterName": "awsSecretKey", - "defaultValue": "" + "defaultValue": "" }, + "spark.yarn.appMasterEnv.SPARK_NLP_LICENSE": { "parameterName": "sparkNlpLicense", - "defaultValue": "" + "defaultValue": "" }, "spark.jars": { "parameterName": "sparkJars", - "defaultValue": "," + "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" } } } @@ -1658,6 +1667,7 @@ Configure the session within the notebook as follows: Install the required Spark NLP libraries using pip commands: ```bash +%pip install %pip install %pip install ``` @@ -1754,4 +1764,9 @@ result = pipeline.annotate(text) ![Load the Model and Make Predictions](/assets/images/installation/355924362-f62b4bc5-96ee-41d5-a80b-887766b252c9.webp) +### Step 12: Run the pipeline with `.pretrained()` method +You can also run the pipelines without using the `.load()` or `.from_disk()` methods + +![Run the pipeline with `.pretrained()` method](/assets/images/installation/Fabric_4.png) +![Run the pipeline with `.pretrained()` method](/assets/images/installation/Fabric_5.png)
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