# Code example for batch inference
<a name="batch-inference-example"></a>

The code example in this chapter shows how to create a batch inference job, view information about it, and stop it. This example uses the `InvokeModel` API format. For information about using the `Converse` API format, see [Format and upload your batch inference data](batch-inference-data.md).

Select a language to see a code example for it:

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#### [ Python ]

Create a JSONL file named *abc.jsonl* and include a JSON object for each record that contains at least the minimum number of records (see the **Minimum number of records per batch inference job for *\$1Model\$1*** [Quotas for Amazon Bedrock](quotas.md)). In this example, you'll use the Anthropic Claude 3 Haiku model. The following example shows the first input JSON in the file:

```
{
    "recordId": "CALL0000001", 
    "modelInput": {
        "anthropic_version": "bedrock-2023-05-31", 
        "max_tokens": 1024,
        "messages": [ 
            { 
                "role": "user", 
                "content": [
                    {
                        "type": "text", 
                        "text": "Summarize the following call transcript: ..." 
                    } 
                ]
            }
        ]
    }
}
... 
# Add records until you hit the minimum
```

Create an S3 bucket called *amzn-s3-demo-bucket-input* and upload the file to it. Then create an S3 bucket called *amzn-s3-demo-bucket-output* to write your output files to. Run the following code snippet to submit a job and get the *jobArn* from the response:

```
import boto3

bedrock = boto3.client(service_name="bedrock")

inputDataConfig=({
    "s3InputDataConfig": {
        "s3Uri": "s3://amzn-s3-demo-bucket-input/abc.jsonl"
    }
})

outputDataConfig=({
    "s3OutputDataConfig": {
        "s3Uri": "s3://amzn-s3-demo-bucket-output/"
    }
})

response=bedrock.create_model_invocation_job(
    roleArn="arn:aws:iam::123456789012:role/MyBatchInferenceRole",
    modelId="anthropic.claude-3-haiku-20240307-v1:0",
    jobName="my-batch-job",
    inputDataConfig=inputDataConfig,
    outputDataConfig=outputDataConfig
)

jobArn = response.get('jobArn')
```

Return the `status` of the job.

```
bedrock.get_model_invocation_job(jobIdentifier=jobArn)['status']
```

List batch inference jobs that *Failed*.

```
bedrock.list_model_invocation_jobs(
    maxResults=10,
    statusEquals="Failed",
    sortOrder="Descending"
)
```

Stop the job that you started.

```
bedrock.stop_model_invocation_job(jobIdentifier=jobArn)
```

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