GraphGrid SDK

The GraphGrid Python SDK is a python-based software development kit that can be used to programmatically interact with GraphGrid services.

Currently, its primary purpose is to provide a flexible way to train NLP models on a variety of tasks.

This README covers setting up the SDK object and method overview. For further documentation and tutorials please visit https://docs.graphgrid.com/ to learn about the GraphGrid SDK and the GraphGrid CDP platform.

Set Up the SDK object

The first step in using the SDK is setting up the GraphGridSdk python object.

# Setup bootstrap configbootstrap_conf=SdkBootstrapConfig(
access_key='a3847750f486bd931de26c6e683b1dc4',
secret_key='81a62cea53883f4a163a96355d47656e',
url_base='localhost',
is_docker_context=False)
# Initialize the SDKsdk=GraphGridSdk(bootstrap_conf)

You create a SdkBootstrapConfig object that provides the basic configuration the SDK needs. This example uses the default access_key and secret_key associated with GraphGrid CDP.

You can initialize your GraphGridSdk object with that configuration and begin using the SDK.

For details on usage please see the docs on GraphGrid SDK Usage.

GraphGrid SDK Methods

There are currently seven SDK methods available for use:

Method	Description
nmt_train	Kick off training job
nmt_status	Status and results of a training job
job_run	Kick off a custom job
job_status	Status of a custom job
save_dataset	Save a dataset for training
promote_model	Promote an NLP model, swapping it in for use
nmt_train_pipeline	Kick off NLP model training pipeline

The nmt_train and nmt_status methods are provided to trigger, monitor, and retrieve results from a nlp-model-training job run. In contrast, the methods job_run and job_status are provided to trigger and monitor custom jobs.

The nmt_train_pipeline method is specifically for kicking off NLP model training pipeline, it runs training jobs, monitors them, and can promote the newly trained models.

For details on specific methods please see the docs on GraphGrid SDK Method Reference.

Configuring a TrainRequestBody

The TrainRequestBody is necessary for kicking of NLP model training via the nmt_train and nmt_train_pipeline SDK methods. TrainRequestBody has the following attributes:

Attribute	Type	Description	Required
model	NlpModel	the type of model to train	True
dataset_id	str	id of dataset for training	True
no_cache	bool	flag for whether caching should be disabled	False (defaults to False)
gpu	bool	flag for whether gpu should be used for training	False (defaults to False)

Below is an example for how defining a TrainRequestBody might look:

request_body=TrainRequestBody(model=NlpModel.NAMED_ENTITY_RECOGNITION,
dataset_id="9tb98wJhuQCoPSJEDKys3WRfrUfpp3tkFpAYexGVMzGc",
no_cache=False,
gpu=True)

The value of dataset_id can be retrieved from the response of a successful call of the save_dataset SDK method. If the dataset has already been saved, the dataset_id can also be found as an attribute of the node representing that dataset within the graph.