Hey there! Let's tackle your needs together—setting up Intel OpenVINO Toolkit on Google Colab, and converting your cloud-stored model to IR format since your local CPU doesn't support AVX instructions. Here's a straightforward, step-by-step guide:

Step 1: Install OpenVINO Toolkit in Google Colab #

Colab's cloud runtime uses CPUs that support AVX, so we don't have to worry about your local hardware limitations here. Let's get the toolkit installed:

• First, confirm your Colab runtime uses Python 3.7+ (it usually does by default, but double-check with):

  !python --version
  

• Install the toolkit via pip. Include the optional extensions if you need support for specific model formats like ONNX or TensorFlow:

  !pip install openvino-dev[onnx,tensorflow2]
  

• Verify the installation worked by checking the Model Optimizer (our conversion tool) version:

  !mo --version
  

Step 2: Mount Your Cloud Drive to Access the Model #

Since your model lives in cloud storage, we'll mount it to Colab to access the files directly. I'll assume you're using Google Drive (the most common with Colab):

• Run this snippet and follow the authorization prompt to link your drive:

  from google.colab import drive
  drive.mount('/content/drive')
  

• Confirm your model is accessible by listing the contents of its folder:

  !ls /content/drive/MyDrive/[your-model-folder-path]
  

Step 3: Convert Your Model to IR Format #

The Model Optimizer (mo) converts pre-trained models to OpenVINO's IR format. The exact command depends on your model type—here are common examples:

For TensorFlow SavedModel #

!mo --saved_model_dir /content/drive/MyDrive/path/to/your/saved_model --output_dir /content/drive/MyDrive/path/to/save/ir_model

For ONNX Model #

!mo --input_model /content/drive/MyDrive/path/to/your/model.onnx --output_dir /content/drive/MyDrive/path/to/save/ir_model

For PyTorch Model (via ONNX) #

First export your PyTorch model to ONNX, then use the ONNX conversion command above:

import torch
# Import your model class (adjust this to match your code)
from your_model_module import YourModel

# Load and prepare the model
model = YourModel()
model.load_state_dict(torch.load('/content/drive/MyDrive/path/to/your/model.pth'))
model.eval()

# Export to ONNX (adjust dummy input shape to match your model's input)
dummy_input = torch.randn(1, 3, 224, 224)
torch.onnx.export(model, dummy_input, '/content/drive/MyDrive/path/to/exported_model.onnx', opset_version=11)

Quick Tips:

• Replace all paths with your actual cloud drive paths.
• Add the --input_shape flag if your model uses dynamic input shapes (e.g., --input_shape [1,3,224,224]).
• If you hit errors, check OpenVINO's supported model list—most popular pre-trained models (ResNet, BERT, etc.) work seamlessly.

Step 4: Verify the Converted IR Model #

Once conversion is done, test the model to make sure it works:

from openvino.runtime import Core

# Load and compile the IR model
ie = Core()
model = ie.read_model(model='/content/drive/MyDrive/path/to/save/ir_model/model.xml')
compiled_model = ie.compile_model(model=model, device_name='CPU')

# Run a quick inference test
input_layer = compiled_model.input(0)
output_layer = compiled_model.output(0)
import numpy as np
sample_input = np.random.randn(*input_layer.shape)
result = compiled_model([sample_input])[output_layer]

print("Inference successful! Output shape:", result.shape)

内容的提问来源于stack exchange，提问作者user11585758