Deploy machine learning models using C++: best practices for containers and cloud

Deploying machine learning models using C++: Best practices for containers and cloud

Containerization and cloud deployment have become best practices for deploying machine learning models, and they can Provide portability, scalability and maintainability. This article dives into best practices for deploying machine learning models in containers and the cloud using C++, and provides a practical example.

Using Containers

Benefits of Containers

• Portability: Containers package code and its dependencies together and can be used anywhere environment.
• Isolation: Containers isolate the model from the host system, ensuring that the model is protected from potential problems.
• Lightweight: Containers are lighter than virtual machines and start faster.

Create a container image

Build a container image using Docker:

FROM tensorflow/tensorflow:latest
COPY model.pb /model
CMD ["tensorflow_model_server", "--port=9000", "--model_name=my_model", "--model_base_path=/model"]

Deploy in the cloud

Select a cloud platform

Choose the cloud platform that best suits your needs, such as AWS, Azure or Google Cloud Platform.

Deploy to Kubernetes

Kubernetes is a container orchestration system that can be used to deploy and manage models in the cloud.

apiVersion: v1
kind: Deployment
metadata:
  name: my-model-deployment
spec:
  selector:
    matchLabels:
      app: my-model
  template:
    metadata:
      labels:
        app: my-model
    spec:
      containers:
        - name: my-model
          image: my-model-image
          ports:
            - containerPort: 9000

Practical case

Model inference service

Developed a machine learning model inference service using C++:

#include <tensorflow/c/c_api.h>
...
TF_Tensor* tensor = TF_NewTensor(TF_FLOAT, shape, dims, data, data_len);
TF_Status* status = TF_NewStatus();
TF_SessionOptions* opts = TF_NewSessionOptions();
TF_Graph* graph = TF_NewGraph();
TF_Session* session = TF_NewSession(graph, opts, status);
TF_InferenceContext* ic = TF_LoadSessionFromTensorFlowModel(
  session, "path/to/model.pb",
  status);
...

Deployment service

Containerize the service using Docker and deploy it in Kubernetes.

docker build -t my-model-image .
kubectl apply -f deployment.yaml

Conclusion

Using C++ to deploy machine learning models in containers and the cloud offers a range of advantages. By following best practices, you can deploy portable, scalable, and maintainable models in any environment.

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