无法在 AWS Sagemaker 上部署本地训练的逻辑回归 model

Question

I have trained a Logistic Regression model on my local machine. Saved the model using Joblib and tried deploying it on Aws Sagemaker using "Linear-Learner" image.

Facing issues while deployment as the deployment process keeps continuing and the Status is always as "Creating" and does not turn to "InService".

endpoint_name = "DEMO-LogisticEndpoint" + strftime("%Y-%m-%d-%H-%M-%S", gmtime())
print(endpoint_name)
create_endpoint_response = sm_client.create_endpoint(
    EndpointName=endpoint_name, EndpointConfigName=endpoint_config_name
)
print(create_endpoint_response["EndpointArn"])

resp = sm_client.describe_endpoint(EndpointName=endpoint_name)
status = resp["EndpointStatus"]
print("Status: " + status)

while status == "Creating":
    time.sleep(60)
    resp = sm_client.describe_endpoint(EndpointName=endpoint_name)
    status = resp["EndpointStatus"]
    print("Status: " + status)

The while loop keeps executing and the status never change.

Answer 1

Background: What is important to understand is that the endpoint runs a container that includes the serving software. Each container expects a certain type of model. You need to make sure you're model and how you package it matches what the container expects.

Two easy paths forward:

1. Linear-learner is a SageMaker built-in algorithm, so a straight forward path would be to train it in the cloud. See example , making it very easy to deploy.
2. Use Scikit-learn Logistic Regression] 2 , which you can train locally and deploy to SageMaker using the scikit-learn container ( XGBoost is another easy path).

Otherwise, you can always go more advanced and use any custom algorithm by bringing your own custom algorithm/framework by bringing your own container . Google for existing implementations (eg, CatBoost/SageMaker ).