How to invoke Sagemaker XGBoost endpoint post model creation?

Question

I have been following along with this really helpful XGBoost tutorial on Medium (code used towards bottom of article): https://medium.com/analytics-vidhya/random-forest-and-xgboost-on-amazon-sagemaker-and-aws-lambda-29abd9467795 .

To-date, I've been able to get data appropriately formatted for ML purposes, a model created based on training data, and then test data fed through the model to give useful results.

Whenever I leave and come back to work more on the model or feed in new test data however, I find I need to re-run all model creation steps in order to make any further predictions. Instead I would like to just call my already created model endpoint based on the Image_URI and feed in new data.

Current steps performed:

Model Training

xgb = sagemaker.estimator.Estimator(containers[my_region],
                                    role, 
                                    train_instance_count=1, 
                                    train_instance_type='ml.m4.xlarge',
                                    output_path='s3://{}/{}/output'.format(bucket_name, prefix),
                                    sagemaker_session=sess)
xgb.set_hyperparameters(eta=0.06,
                        alpha=0.8,
                        lambda_bias=0.8,
                        gamma=50,
                        min_child_weight=6,
                        subsample=0.5,
                        silent=0,
                        early_stopping_rounds=5,
                        objective='reg:linear',
                        num_round=1000)

xgb.fit({'train': s3_input_train})

xgb_predictor = xgb.deploy(initial_instance_count=1,instance_type='ml.m4.xlarge')

Evaluation

test_data_array = test_data.drop([ 'price','id','sqft_above','date'], axis=1).values #load the data into an array

xgb_predictor.serializer = csv_serializer # set the serializer type

predictions = xgb_predictor.predict(test_data_array).decode('utf-8') # predict!
predictions_array = np.fromstring(predictions[1:], sep=',') # and turn the prediction into an array
print(predictions_array.shape)

from sklearn.metrics import r2_score
print("R2 score : %.2f" % r2_score(test_data['price'],predictions_array))

It seems that this particular line:

predictions = xgb_predictor.predict(test_data_array).decode('utf-8') # predict!

needs to be re-written in order to not reference xgb.predictor but instead reference the model location.

I have tried the following

trained_model = sagemaker.model.Model(
    model_data='s3://{}/{}/output/xgboost-2020-11-10-00-00/output/model.tar.gz'.format(bucket_name, prefix),
    image_uri='XXXXXXXXXX.dkr.ecr.us-east-1.amazonaws.com/xgboost:latest',
    role=role)  # your role here; could be different name

trained_model.deploy(initial_instance_count=1, instance_type='ml.m4.xlarge')

and then replaced

xgb_predictor.serializer = csv_serializer # set the serializer type
predictions = xgb_predictor.predict(test_data_array).decode('utf-8') # predict!

with

trained_model.serializer = csv_serializer # set the serializer type
predictions = trained_model.predict(test_data_array).decode('utf-8') # predict!

but I get the following error:

AttributeError: 'Model' object has no attribute 'predict'

Answer 1

that's a good question :) I agree, many of the official tutorials tend to show the full train-to-invoke pipeline and don't emphasize enough that each step can be done separately. In your specific case, when you want to invoke an already-deployed endpoint, you can either: (A) use the invoke API call in one of the numerous SDKs (example in CLI , boto3 ) or (B) or instantiate a predictor with the high-level Python SDK, either the generic sagemaker.model.Model class or its XGBoost-specific child: sagemaker.xgboost.model.XGBoostPredictor as illustrated below:

from sagemaker.xgboost.model import XGBoostPredictor
    
predictor = XGBoostPredictor(endpoint_name='your-endpoint')
predictor.predict('<payload>')

similar question How to use a pretrained model from s3 to predict some data?

Note:

• If you want the model.deploy() call to return a predictor, your model must be instantiated with a predictor_cls . This is optional, you can also first deploy a model, and then invoke it as a separate step with the above technique
• Endpoints create charges even if you don't invoke them; they are charged per uptime. So if you don't need an always-on endpoint, don't hesitate to shut it down to minimize costs.