This document is relevant for: Inf2, Trn1, Trn2

The following example uses the device_ids argument to use the first three NeuronCores for DataParallel inference.

import torch
import torch_neuronx
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_neuronx.trace(model, image)

# Create the DataParallel module, run on the first two NeuronCores
# Equivalent to model_parallel = torch.neuron.DataParallel(model_neuron, device_ids=[0, 1])
model_parallel = torch_neuronx.DataParallel(model_neuron, device_ids=['nc:0', 'nc:1'])

# 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: Inf2, Trn1, Trn2