AWS Neuron SDK

Master the AWS Neuron SDK — the software stack that enables you to compile, optimize, and run ML models on Inferentia and Trainium hardware.

Neuron SDK Components

The Neuron SDK is a comprehensive software stack with several key components:

PyTorch Integration (torch-neuronx)

The most popular framework integration. Key APIs for inference:

import torch
import torch_neuronx
from transformers import AutoModelForSequenceClassification, AutoTokenizer

# Load model and tokenizer
model = AutoModelForSequenceClassification.from_pretrained("bert-base-uncased")
tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")

# Create example input for compilation
example_input = tokenizer("Hello world", return_tensors="pt")

# Compile for Neuron (trace the model)
model_neuron = torch_neuronx.trace(model, example_input["input_ids"])

# Save compiled model
torch.jit.save(model_neuron, "model_neuron.pt")

# Load and run inference
model_loaded = torch.jit.load("model_neuron.pt")
output = model_loaded(example_input["input_ids"])

Training with Neuron

import torch
import torch_xla.core.xla_model as xm
from torch_neuronx.distributed import parallel_model_trace

# Get the Neuron device
device = xm.xla_device()

# Move model and data to Neuron
model = model.to(device)
inputs = inputs.to(device)

# Training loop (similar to standard PyTorch)
optimizer = torch.optim.AdamW(model.parameters(), lr=5e-5)
for epoch in range(num_epochs):
    outputs = model(**inputs)
    loss = outputs.loss
    loss.backward()
    xm.optimizer_step(optimizer)  # Neuron-aware optimizer step
    optimizer.zero_grad()

Neuron Monitoring Tools