# MXNet Framework Processor
<a name="processing-job-frameworks-mxnet"></a>

Apache MXNet is an open-source deep learning framework commonly used for training and deploying neural networks. The `MXNetProcessor` in the Amazon SageMaker Python SDK provides you with the ability to run processing jobs with MXNet scripts. When you use the `MXNetProcessor`, you can leverage an Amazon-built Docker container with a managed MXNet environment so that you don't need to bring your own container.

The following code example shows how you can run your Processing job using a Docker image provided and maintained by SageMaker AI. Note that when you run the job, you can specify a directory containing your scripts and dependencies in the `source_dir` argument, and you can have a `requirements.txt` file located inside your `source_dir` directory that specifies the dependencies for your processing script(s). SageMaker Processing installs the dependencies in `requirements.txt` in the container for you.

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#### [ SageMaker Python SDK v3 ]

```
from sagemaker.core.resources import ProcessingJob
from sagemaker.core.helper.session_helper import get_execution_role

# Create a processing job with an MXNet container
processing_job = ProcessingJob.create(
    processing_job_name='frameworkprocessor-mxnet',
    role_arn=get_execution_role(),
    app_specification={
        "image_uri": "{{mxnet-processing-image-uri}}",
        "container_entrypoint": ["python3", "/opt/ml/processing/input/code/{{processing-script.py}}"]
    },
    processing_resources={
        "cluster_config": {"instance_count": 1, "instance_type": "ml.c5.xlarge", "volume_size_in_gb": 30}
    },
    processing_inputs=[
        {"input_name": "data", "s3_input": {"s3_uri": f"{{s3://{{BUCKET}}/{{S3_INPUT_PATH}}}}", "local_path": "/opt/ml/processing/input/data/", "s3_data_type": "S3Prefix", "s3_input_mode": "File"}},
        {"input_name": "code", "s3_input": {"s3_uri": "{{s3://path/to/scripts/}}", "local_path": "/opt/ml/processing/input/code", "s3_data_type": "S3Prefix", "s3_input_mode": "File"}}
    ],
    processing_output_config={
        "outputs": [
            {"output_name": "processed_data", "s3_output": {"s3_uri": f"{{s3://{{BUCKET}}/{{S3_OUTPUT_PATH}}}}", "local_path": "/opt/ml/processing/output/", "s3_upload_mode": "EndOfJob"}}
        ]
    }
)
```

------
#### [ SageMaker Python SDK v2 (Legacy) ]

```
from sagemaker.mxnet import MXNetProcessor
from sagemaker.processing import ProcessingInput, ProcessingOutput
from sagemaker import get_execution_role

#Initialize the MXNetProcessor
mxp = MXNetProcessor(
    framework_version='1.8.0',
    py_version='py37',
    role=get_execution_role(), 
    instance_count=1,
    instance_type='ml.c5.xlarge',
    base_job_name='frameworkprocessor-mxnet'
)

#Run the processing job
mxp.run(
    code='{{processing-script.py}}',
    source_dir='{{scripts}}',
    inputs=[
        ProcessingInput(
            input_name='data',
            source=f'{{s3://{BUCKET}/{S3_INPUT_PATH}}}',
            destination='/opt/ml/processing/input/data/'
        )
    ],
    outputs=[
        ProcessingOutput(
            output_name='processed_data',
            source='/opt/ml/processing/output/',
            destination=f'{{s3://{BUCKET}/{S3_OUTPUT_PATH}}}'
        )
    ]
)
```

------

If you have a `requirements.txt` file, it should be a list of libraries you want to install in the container. The path for `source_dir` can be a relative, absolute, or Amazon S3 URI path. However, if you use an Amazon S3 URI, then it must point to a tar.gz file. You can have multiple scripts in the directory you specify for `source_dir`. To learn more about the `MXNetProcessor` class, see [MXNet Estimator](https://sagemaker.readthedocs.io/en/stable/api/sagemaker_train.html) in the *Amazon SageMaker Python SDK*.