关于在GCP上通过gRPC请求创建MNIST分类模型的技术咨询

Hey there! Let's break this down clearly since you already have HTTP basics under your belt—gRPC can feel a bit opaque at first, but it’s just another way to interact with Google’s APIs that builds on some concepts you already know.

First off: the POST https://ml.googleapis.com/v1/{parent=projects/*}/models URL you’re looking at is for Google’s REST API, not pure gRPC. That’s probably where the confusion is coming from! gRPC uses protocol buffers (protobuf) for defining API interfaces and communication, rather than manually constructing HTTP request strings. Google does map gRPC methods to REST endpoints (via a process called "HTTP transcoding"), but you don’t need to handle that directly when using gRPC.

Let’s walk through how to actually use gRPC to create your MNIST model on Google Cloud (note: Google has moved from ML Engine to Vertex AI as their primary ML service, so we’ll use the current Vertex AI gRPC API here):

1. Prep Your Environment #

• Make sure you have the Google Cloud SDK installed and authenticated: run gcloud auth application-default login to set up credentials for your local environment.
• Install the necessary gRPC client libraries for your language. For Python, that would be:

  pip install google-cloud-aiplatform grpcio
  

2. Understand the gRPC Service Structure #

Google’s Vertex AI has a ModelService gRPC service with a CreateModel method. The proto definition (simplified) looks like this:

service ModelService {
  rpc CreateModel(CreateModelRequest) returns (Model) {
    option (google.api.http) = {
      post: "/v1/{parent=projects/*/locations/*}/models"
      body: "model"
    };
  }
}

That google.api.http option is what maps the gRPC method to the REST endpoint you saw earlier—but with gRPC, you don’t need to build that URL yourself. You’ll use a client library to call the CreateModel method directly with a structured protobuf request.

3. Example Code (Python) #

Here’s a working snippet to create your MNIST model via gRPC:

import grpc
from google.cloud.aiplatform_v1.types import model_service, model

# Replace these with your project details
PROJECT_ID = "your-project-id-here"
REGION = "us-central1"  # Use the region where your bucket is hosted
PARENT = f"projects/{PROJECT_ID}/locations/{REGION}"

# Create a gRPC channel to the Vertex AI service
channel = grpc.insecure_channel("aiplatform.googleapis.com:443")
client = model_service.ModelServiceStub(channel)

# Define your model request using protobuf messages
create_request = model_service.CreateModelRequest(
    parent=PARENT,
    model=model.Model(
        display_name="mnist-classifier",
        description="Trained MNIST digit classification model",
        # Point to your model files stored in GCS
        model_source_info=model.ModelSourceInfo(
            gcs_source=model.GcsSource(uris=["gs://your-bucket-name/mnist-model-files/"])
        )
    )
)

# Send the gRPC request and get the response
response = client.CreateModel(create_request)
print(f"Successfully created model: {response.name}")

4. Key Differences from HTTP Requests #

• No manual URL/body building: Instead of crafting a JSON body and POST URL, you use strongly-typed protobuf messages (like CreateModelRequest) that the client library serializes for you.
• HTTP/2 under the hood: gRPC uses HTTP/2 for transport, which is more efficient, but the client handles all the low-level details (like connection pooling, streaming, etc.).
• Simplified auth: Credentials are pulled from your Google Cloud SDK setup automatically—no need to manually generate and attach bearer tokens to requests.

Since you know HTTP basics, you can absolutely use the REST endpoint instead of gRPC! Here’s a quick breakdown:

1. Replace {parent=projects/*} in the URL with your project path: https://ml.googleapis.com/v1/projects/your-project-id/models
2. Generate an auth token with gcloud auth print-access-token
3. Send a POST request with a JSON body defining your model (matching the structure of the protobuf Model message) and include the auth token in the Authorization: Bearer <token> header.

• Ensure your model files are stored in a Google Cloud Storage bucket that your authenticated service account has read access to.
• For Vertex AI (the current service), the parent path includes the region (projects/{project}/locations/{region})—the old ML Engine didn’t require this, so don’t skip it!

内容的提问来源于stack exchange，提问作者Matt123