# Invoke your endpoint
<a name="canvas-deploy-model-invoke"></a>

**Note**  
We recommend that you [test your model deployment in Amazon SageMaker Canvas](canvas-deploy-model-test.md) before invoking a SageMaker AI endpoint programmatically.

You can use your Amazon SageMaker Canvas models that you've deployed to a SageMaker AI endpoint in production with your applications. Invoke the endpoint programmatically the same way that you invoke any other [SageMaker AI real-time endpoint](https://docs.amazonaws.cn/sagemaker/latest/dg/realtime-endpoints.html). Invoking an endpoint programmatically returns a response object which contains the same fields described in [Test your deployment](canvas-deploy-model-test.md).

For more detailed information about how to programmatically invoke endpoints, see [Invoke models for real-time inference](realtime-endpoints-test-endpoints.md).

The following Python examples show you how to invoke your endpoint based on the model type.

## JumpStart foundation models
<a name="canvas-invoke-js-example"></a>

The following example shows you how to invoke a JumpStart foundation model that you've deployed to an endpoint.

```
import boto3
import pandas as pd

client = boto3.client("runtime.sagemaker")
body = pd.DataFrame(
    [['feature_column1', 'feature_column2'], 
    ['feature_column1', 'feature_column2']]
).to_csv(header=False, index=False).encode("utf-8")
    
response = client.invoke_endpoint(
    EndpointName="endpoint_name",
    ContentType="text/csv",
    Body=body,
    Accept="application/json"
)
```

## Numeric and categorical prediction models
<a name="canvas-invoke-tabular-example"></a>

The following example shows you how to invoke numeric or categorical prediction models.

```
import boto3
import pandas as pd

client = boto3.client("runtime.sagemaker")
body = pd.DataFrame(['feature_column1', 'feature_column2'], ['feature_column1', 'feature_column2']).to_csv(header=False, index=False).encode("utf-8")
    
response = client.invoke_endpoint(
    EndpointName="endpoint_name",
    ContentType="text/csv",
    Body=body,
    Accept="application/json"
)
```

## Time series forecasting models
<a name="canvas-invoke-forecast-example"></a>

The following example shows you how to invoke time series forecasting models. For a complete example of how to test invoke a time series forecasting model, see [ Time-Series Forecasting with Amazon SageMaker Autopilot](https://github.com/aws/amazon-sagemaker-examples/blob/eef13dae197a6e588a8bc111aba3244f99ee0fbb/autopilot/autopilot_time_series.ipynb).

```
import boto3
import pandas as pd

csv_path = './real-time-payload.csv'
data = pd.read_csv(csv_path)

client = boto3.client("runtime.sagemaker")

body = data.to_csv(index=False).encode("utf-8")
    
response = client.invoke_endpoint(
    EndpointName="endpoint_name",
    ContentType="text/csv",
    Body=body,
    Accept="application/json"
)
```

## Image prediction models
<a name="canvas-invoke-cv-example"></a>

The following example shows you how to invoke image prediction models.

```
import boto3
client = boto3.client("runtime.sagemaker")
with open("example_image.jpg", "rb") as file:
    body = file.read()
    response = client.invoke_endpoint(
        EndpointName="endpoint_name",
        ContentType="application/x-image",
        Body=body,
        Accept="application/json"
    )
```

## Text prediction models
<a name="canvas-invoke-nlp-example"></a>

The following example shows you how to invoke text prediction models.

```
import boto3
import pandas as pd

client = boto3.client("runtime.sagemaker")
body = pd.DataFrame([["Example text 1"], ["Example text 2"]]).to_csv(header=False, index=False).encode("utf-8")
    
response = client.invoke_endpoint(
    EndpointName="endpoint_name",
    ContentType="text/csv",
    Body=body,
    Accept="application/json"
)
```