# Create the Amazon IoT Greengrass V2 Components Amazon IoT Greengrass uses *components*, a software module that is deployed to and runs on a Amazon IoT Greengrass core device. You need (at a minimum) three components: 1. *A public Edge Manager Agent Amazon IoT Greengrass component* which deploys the Edge Manager agentbinary. 1. *A model component* that is autogenerated when you package your machine learning model with either the Amazon SDK for Python (Boto3) API or with the SageMaker AI console. For information, see [Create an autogenerated component](#edge-greengrass-autogenerate-component-how). 1. *A private, custom component* to implement the Edge Manager agent client application, and do any preprocessing and post-processing of the inference results. For more information about how to create a custom component, see [Create an autogenerated component](#edge-greengrass-autogenerate-component-how) or [Create custom Amazon IoT Greengrass components](https://docs.amazonaws.cn/greengrass/v2/developerguide/create-components.html). ## Create an autogenerated component Generate the model component with the [https://docs.amazonaws.cn/sagemaker/latest/APIReference/API_CreateEdgePackagingJob.html](https://docs.amazonaws.cn/sagemaker/latest/APIReference/API_CreateEdgePackagingJob.html) API and specify `GreengrassV2Component` for the SageMaker Edge Manager packaging job API field `PresetDeploymentType`. When you call the `CreateEdgePackagingJob` API, Edge Manager takes your SageMaker AI Neo–compiled model in Amazon S3 and creates a model component. The model component is automatically stored in your account. You can view any of your components by navigating to the Amazon IoT console [https://console.amazonaws.cn/iot/](https://console.amazonaws.cn/greengrass/). Select **Greengrass** and then select **Core** devices. The page has a list of Amazon IoT Greengrass core devices associated with your account. If a model component name is not specified in `PresetDeploymentConfig`, the default name generated consists of `"SagemakerEdgeManager"` and the name of your Edge Manager agent packaging job. The following example demonstrates how to specify to Edge Manager to create a Amazon IoT Greengrass V2 component with the `CreateEdgePackagingJob` API. ``` import sagemaker import boto3 # Create a SageMaker client object to make it easier to interact with other AWS services. sagemaker_client = boto3.client('sagemaker', region={{}}) # Replace with your IAM Role ARN sagemaker_role_arn = "arn:aws:iam::{{:role/*}}" # Replace string with the name of your already created S3 bucket. bucket = 'amzn-s3-demo-bucket-edge-manager' # Specify a name for your edge packaging job. edge_packaging_name = "edge_packag_job_demo" # Replace the following string with the name you used for the SageMaker Neo compilation job. compilation_job_name = "getting-started-demo" # The name of the model and the model version. model_name = "sample-model" model_version = "1.1" # Output directory in S3 where you want to store the packaged model. packaging_output_dir = 'packaged_models' packaging_s3_output = 's3://{}/{}'.format(bucket, packaging_output_dir) # The name you want your Greengrass component to have. component_name = "SagemakerEdgeManager" + edge_packaging_name sagemaker_client.create_edge_packaging_job( EdgePackagingJobName=edge_packaging_name, CompilationJobName=compilation_job_name, RoleArn=sagemaker_role_arn, ModelName=model_name, ModelVersion=model_version, OutputConfig={ "S3OutputLocation": packaging_s3_output, "PresetDeploymentType":"GreengrassV2Component", "PresetDeploymentConfig":"{\"ComponentName\":\"sample-component-name\", \"ComponentVersion\":\"1.0.2\"}" } ) ``` You can also create the autogenerated component with the SageMaker AI console. Follow steps 1-6 in [Package a Model (Amazon SageMaker AI Console)](edge-packaging-job-console.md) Enter the Amazon S3 bucket URI where you want to store the output of the packaging job and optional encrytion key. Complete the following to create the model component: 1. Choose **Preset deployment**. 1. Specify the name of the component for the **Component name** field. 1. Optionally, provide a description of the component, a component version, the platform OS, or the platform architecture for the **Component description**, **Component version**, **Platform OS**, and **Platform architecture**, respectively. 1. Choose **Submit**. ## Create a Hello World custom component The custom application component is used to perform inference on the edge device. The component is responsible for loading models to SageMaker Edge Manager, invoking the Edge Manager agent for inference, and unloading the model when the component is shut down. Before you create your component, ensure the agent and application can communicate with Edge Manager. To do this, configure [gRPC](https://grpc.io/). The Edge Manager agent uses methods defined in Protobuf Buffers and the gRPC server to establish communication with the client application on the edge device and the cloud. To use gRPC, you must: 1. Create a gRPC stub using the .proto file provided when you download the Edge Manager agent from Amazon S3 release bucket. 1. Write client code with the language you prefer. You do not need to define the service in a .proto file. The service .proto files are included in the compressed TAR file when you download the Edge Manager agent release binary from the Amazon S3 release bucket. Install gRPC and other necessary tools on your host machine and create the gRPC stubs `agent_pb2_grpc.py` and `agent_pb2.py` in Python. Make sure you have `agent.proto` in your local directory. ``` %%bash pip install grpcio pip install grpcio-tools python3 -m grpc_tools.protoc --proto_path=. --python_out=. --grpc_python_out=. agent.proto ``` The preceding code generates the gRPC client and server interfaces from your .proto service definition. In other words, it creates the gRPC model in Python. The API directory contains the Protobuf specification for communicating with the agent. Next, use the gRPC API to write a client and server for your service (2). The following example script, `edge_manager_python_example.py`, uses Python to load, list, and unload a `yolov3` model to the edge device. ``` import grpc from PIL import Image import agent_pb2 import agent_pb2_grpc import os model_path = '{{}}' agent_socket = 'unix:///tmp/aws.greengrass.SageMakerEdgeManager.sock' agent_channel = grpc.insecure_channel(agent_socket, options=(('grpc.enable_http_proxy', 0),)) agent_client = agent_pb2_grpc.AgentStub(agent_channel) def list_models(): return agent_client.ListModels(agent_pb2.ListModelsRequest()) def list_model_tensors(models): return { model.name: { 'inputs': model.input_tensor_metadatas, 'outputs': model.output_tensor_metadatas } for model in list_models().models } def load_model(model_name, model_path): load_request = agent_pb2.LoadModelRequest() load_request.url = model_path load_request.name = model_name return agent_client.LoadModel(load_request) def unload_model(name): unload_request = agent_pb2.UnLoadModelRequest() unload_request.name = name return agent_client.UnLoadModel(unload_request) def predict_image(model_name, image_path): image_tensor = agent_pb2.Tensor() image_tensor.byte_data = Image.open(image_path).tobytes() image_tensor_metadata = list_model_tensors(list_models())[model_name]['inputs'][0] image_tensor.tensor_metadata.name = image_tensor_metadata.name image_tensor.tensor_metadata.data_type = image_tensor_metadata.data_type for shape in image_tensor_metadata.shape: image_tensor.tensor_metadata.shape.append(shape) predict_request = agent_pb2.PredictRequest() predict_request.name = model_name predict_request.tensors.append(image_tensor) predict_response = agent_client.Predict(predict_request) return predict_response def main(): try: unload_model('your-model') except: pass print('LoadModel...', end='') try: load_model('your-model', model_path) print('done.') except Exception as e: print() print(e) print('Model already loaded!') print('ListModels...', end='') try: print(list_models()) print('done.') except Exception as e: print() print(e) print('List model failed!') print('Unload model...', end='') try: unload_model('your-model') print('done.') except Exception as e: print() print(e) print('unload model failed!') if __name__ == '__main__': main() ``` Ensure `model_path` points to the name of the Amazon IoT Greengrass component containing the model if you use the same client code example. You can create your Amazon IoT Greengrass V2 Hello World component once you have generated your gRPC stubs and you have your Hello World code ready. To do so: + Upload your `edge_manager_python_example.py`, `agent_pb2_grpc.py`, and `agent_pb2.py` to your Amazon S3 bucket and note down their Amazon S3 path. + Create a private component in the Amazon IoT Greengrass V2 console and define the recipe for your component. Specify the Amazon S3 URI to your Hello World application and gRPC stub in the following recipe. ``` --- RecipeFormatVersion: 2020-01-25 ComponentName: com.sagemaker.edgePythonExample ComponentVersion: 1.0.0 ComponentDescription: Sagemaker Edge Manager Python example ComponentPublisher: Amazon Web Services, Inc. ComponentDependencies: aws.greengrass.SageMakerEdgeManager: VersionRequirement: '>=1.0.0' DependencyType: HARD Manifests: - Platform: os: linux architecture: "/amd64|x86/" Lifecycle: install: |- apt-get install python3-pip pip3 install grpcio pip3 install grpcio-tools pip3 install protobuf pip3 install Pillow run: script: |- python3 {{{artifacts:path}}}/edge_manager_python_example.py Artifacts: - URI: {{}} - URI: {{}} - URI: {{}} ``` For detailed information about creating a Hello World recipe, see [Create your first component](https://docs.amazonaws.cn/greengrass/v2/developerguide/getting-started.html#create-first-component) in the Amazon IoT Greengrass documentation.