This document is relevant for: Inf1

The default DataParallel use mode will replicate the model on all available NeuronCores in the current process. The inputs will be split on dim=0.

import torch
import torch_neuron
from torchvision import models

# Load the model and set it to evaluation mode
model = models.resnet50(pretrained=True)
model.eval()

# Compile with an example input
image = torch.rand([1, 3, 224, 224])
model_neuron = torch.neuron.trace(model, image)

# Create the DataParallel module
model_parallel = torch.neuron.DataParallel(model_neuron)

# Create a batched input
batch_size = 5
image_batched = torch.rand([batch_size, 3, 224, 224])

# Run inference with a batched input
output = model_parallel(image_batched)

This document is relevant for: Inf1