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[SPARKNLP-961] Adding ONNX configs to README #14111

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[SPARKNLP-961] Adding ONNX configs to README #14111

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24 changes: 14 additions & 10 deletions README.md
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Expand Up @@ -915,16 +915,20 @@ gcloud dataproc clusters create ${CLUSTER_NAME} \

You can change the following Spark NLP configurations via Spark Configuration:

| Property Name | Default | Meaning |
|--------------------------------------------------------|----------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| `spark.jsl.settings.pretrained.cache_folder` | `~/cache_pretrained` | The location to download and extract pretrained `Models` and `Pipelines`. By default, it will be in User's Home directory under `cache_pretrained` directory |
| `spark.jsl.settings.storage.cluster_tmp_dir` | `hadoop.tmp.dir` | The location to use on a cluster for temporarily files such as unpacking indexes for WordEmbeddings. By default, this locations is the location of `hadoop.tmp.dir` set via Hadoop configuration for Apache Spark. NOTE: `S3` is not supported and it must be local, HDFS, or DBFS |
| `spark.jsl.settings.annotator.log_folder` | `~/annotator_logs` | The location to save logs from annotators during training such as `NerDLApproach`, `ClassifierDLApproach`, `SentimentDLApproach`, `MultiClassifierDLApproach`, etc. By default, it will be in User's Home directory under `annotator_logs` directory |
| `spark.jsl.settings.aws.credentials.access_key_id` | `None` | Your AWS access key to use your S3 bucket to store log files of training models or access tensorflow graphs used in `NerDLApproach` |
| `spark.jsl.settings.aws.credentials.secret_access_key` | `None` | Your AWS secret access key to use your S3 bucket to store log files of training models or access tensorflow graphs used in `NerDLApproach` |
| `spark.jsl.settings.aws.credentials.session_token` | `None` | Your AWS MFA session token to use your S3 bucket to store log files of training models or access tensorflow graphs used in `NerDLApproach` |
| `spark.jsl.settings.aws.s3_bucket` | `None` | Your AWS S3 bucket to store log files of training models or access tensorflow graphs used in `NerDLApproach` |
| `spark.jsl.settings.aws.region` | `None` | Your AWS region to use your S3 bucket to store log files of training models or access tensorflow graphs used in `NerDLApproach` |
| Property Name | Default | Meaning |
|---------------------------------------------------------|----------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| `spark.jsl.settings.pretrained.cache_folder` | `~/cache_pretrained` | The location to download and extract pretrained `Models` and `Pipelines`. By default, it will be in User's Home directory under `cache_pretrained` directory |
| `spark.jsl.settings.storage.cluster_tmp_dir` | `hadoop.tmp.dir` | The location to use on a cluster for temporarily files such as unpacking indexes for WordEmbeddings. By default, this locations is the location of `hadoop.tmp.dir` set via Hadoop configuration for Apache Spark. NOTE: `S3` is not supported and it must be local, HDFS, or DBFS |
| `spark.jsl.settings.annotator.log_folder` | `~/annotator_logs` | The location to save logs from annotators during training such as `NerDLApproach`, `ClassifierDLApproach`, `SentimentDLApproach`, `MultiClassifierDLApproach`, etc. By default, it will be in User's Home directory under `annotator_logs` directory |
| `spark.jsl.settings.aws.credentials.access_key_id` | `None` | Your AWS access key to use your S3 bucket to store log files of training models or access tensorflow graphs used in `NerDLApproach` |
| `spark.jsl.settings.aws.credentials.secret_access_key` | `None` | Your AWS secret access key to use your S3 bucket to store log files of training models or access tensorflow graphs used in `NerDLApproach` |
| `spark.jsl.settings.aws.credentials.session_token` | `None` | Your AWS MFA session token to use your S3 bucket to store log files of training models or access tensorflow graphs used in `NerDLApproach` |
| `spark.jsl.settings.aws.s3_bucket` | `None` | Your AWS S3 bucket to store log files of training models or access tensorflow graphs used in `NerDLApproach` |
| `spark.jsl.settings.aws.region` | `None` | Your AWS region to use your S3 bucket to store log files of training models or access tensorflow graphs used in `NerDLApproach` |
| `spark.jsl.settings.onnx.gpuDeviceId` | `0` | Constructs CUDA execution provider options for the specified non-negative device id. |
| `spark.jsl.settings.onnx.intraOpNumThreads` | `6` | Sets the size of the CPU thread pool used for executing a single graph, if executing on a CPU. |
| `spark.jsl.settings.onnx.optimizationLevel` | `ALL_OPT` | Sets the optimization level of this options object, overriding the old setting. |
| `spark.jsl.settings.onnx.executionMode` | `SEQUENTIAL` | Sets the execution mode of this options object, overriding the old setting. |

### How to set Spark NLP Configuration

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