TorchNeuron Tutorial: Run PyTorch Models on AWS Trainium

If your AI project is written in PyTorch, moving it to a different accelerator has traditionally meant more than changing a hardware setting. On October 20, 2026, Amazon Web Services (AWS) released the public beta of TorchNeuron, an open-source backend that lets developers run PyTorch models on its Trainium AI chips using familiar PyTorch APIs. This tutorial explains how to prepare a compatible environment, move a model onto Trainium, test its output, and decide whether compilation is worth the extra setup.

What TorchNeuron changes for PyTorch developers

PyTorch is a machine-learning framework used to define neural networks, train models, and run predictions. A backend connects that framework to the hardware that executes its calculations. TorchNeuron registers AWS Trainium as a device called neuron, allowing supported PyTorch operations to run on the accelerator rather than requiring developers to rewrite their model around a completely different programming interface.

The practical change can be small. A model that previously used model.to("cuda") to run on a compatible NVIDIA graphics processing unit can use model.to("neuron") on supported Trainium hardware. AWS also supports eager execution, which runs operations as Python executes them, and torch.compile, which compiles model operations into a more optimized execution path. Compatibility still depends on the operations, libraries, model architecture, and software versions involved.

TorchNeuron supports Amazon EC2 Trainium2 and Trainium3 instances in its public beta. AWS provides the software as open source, but running it still requires access to compatible cloud hardware. Installing the Python package on an ordinary desktop or laptop does not turn its processor into a Trainium accelerator.

Check the hardware and software requirements first

Before installing anything, confirm that your development environment matches the current support requirements. TorchNeuron's documented setup targets Linux on x86-64 processors, with supported Ubuntu 24.04 and Ubuntu 26.04 environments and compatible Trainium instances. The documented Python versions are 3.12, 3.13, and 3.14. A supported Neuron Deep Learning Amazon Machine Image (DLAMI) or container is a practical starting point because it can provide the required drivers, runtime libraries, and development tools together.

• Hardware: An AWS EC2 Trainium2 or Trainium3 instance.
• Operating system: A supported Linux x86-64 environment.
• Python: Version 3.12, 3.13, or 3.14.
• Framework: A PyTorch version matched to the installed TorchNeuron package.
• Runtime: The AWS Neuron driver and runtime components required by the instance.

Version matching deserves particular attention. The current documentation lists TorchNeuron package families for PyTorch 2.13 and 2.14, including torch-neuronx==2.14.* for PyTorch 2.14. The name can be misleading because earlier releases of torch-neuronx used the PyTorch/XLA architecture. Follow the current TorchNeuron installation instructions rather than copying commands from an older Neuron tutorial.

Install TorchNeuron in a compatible environment

The simplest installation route is to start with an AWS Neuron Deep Learning AMI or a compatible deep-learning container that already includes TorchNeuron. Activate the environment supplied with that image, then verify that the installed framework and device runtime are available. This approach avoids manually assembling system drivers and accelerator libraries, which can otherwise introduce version conflicts before you run your first model.

If you are configuring a compatible Ubuntu instance yourself, first follow the official AWS Neuron installation instructions for the operating system and instance type. Once the required driver and runtime packages are installed, create a Python virtual environment and install the exact compatible package versions from the AWS Neuron package repository.

python3 -m venv ~/neuron_venv
source ~/neuron_venv/bin/activate

pip install torch==2.14.0 \
 --index-url https://download.pytorch.org/whl/cpu

pip install "torch-neuronx==2.14.*" \
 "neuronx-cc==2.*" "nki>=0.6" \
 --extra-index-url https://pip.repos.neuron.amazonaws.com

This example uses the documented PyTorch 2.14 package family; it is not a universal installation command for every Trainium environment. The driver, compiler, runtime, and operating-system support matrix must also match. If your chosen AWS image already provides a tested installation, do not replace its packages with these example pins without checking compatibility.

After installation, run the environment diagnostic:

python -m torch_neuronx.collect_env

Check the output for the line indicating whether the device is usable from PyTorch. It should report yes. If it does not, resolve the runtime, driver, or package mismatch before attempting to train a model. A successful Python import alone does not prove that the accelerator is ready.

Move a small PyTorch model onto Trainium

Start with a small model rather than a large language model. A single linear layer is enough to verify that PyTorch can create a model, allocate its parameters on the Neuron device, and execute a forward pass. The following example uses the standard PyTorch API and does not require a dataset or a training loop.

import torch

model = torch.nn.Linear(16, 8).to("neuron")
model.eval()

inputs = torch.randn(4, 16, device="neuron")

with torch.no_grad():
 outputs = model(inputs)

print(tuple(outputs.cpu().shape))

Save the example as test_torchneuron.py and execute it inside the configured environment on your Trainium instance. The expected output shape is (4, 8): four input examples, each containing 16 values, produce four output vectors containing eight values each. Moving the output to the CPU before printing its shape also makes it straightforward to inspect the result without trying to print an accelerator-resident tensor directly.

This test confirms that a basic model can execute on the device, but it does not establish that your production model is fully supported or faster than its existing implementation. Test representative operations, input shapes, numerical outputs, and memory requirements before moving a real workload.

Compile the model when repeated execution justifies it

Eager execution is useful for debugging because operations run as the program reaches them. Compilation adds a preparation stage in which the backend translates supported operations into an executable form. That preparation can make repeated inference or training steps more efficient, but the first invocation may take longer while compilation occurs. For a script that runs only once, this initial cost may outweigh the benefit.

You can compare eager execution with the compiled backend using the same model and inputs:

import torch

model = torch.nn.Linear(16, 8).to("neuron").eval()
inputs = torch.randn(4, 16, device="neuron")

with torch.no_grad():
 eager_outputs = model(inputs)

 compiled_model = torch.compile(
 model,
 backend="neuron",
 dynamic=False
 )

 compiled_outputs = compiled_model(inputs)

print(tuple(eager_outputs.cpu().shape))
print(tuple(compiled_outputs.cpu().shape))

Both executions should produce an output shape of (4, 8) when the environment and operations are supported. The dynamic=False setting tells the compiler to target a fixed input shape, which is appropriate for this example. If your application receives variable batch sizes or other changing dimensions, review the backend's dynamic-shape support before using this configuration.

Do not judge the compiled version by timing only its first call. Compilation time and execution time are different costs, so a fair comparison measures initial setup separately from repeated runs. Use the same input shapes, model weights, hardware, and measurement procedure, and check that both implementations produce acceptably similar numerical results.

Load a Hugging Face model with the Neuron device

Developers using Hugging Face Transformers can also try a pretrained language model, provided its architecture and required operations are supported by the installed backend. The library's from_pretrained method loads the model configuration and weights, while device_map determines where those weights are placed. TorchNeuron documentation includes support for loading models onto the neuron device.

from transformers import AutoModelForCausalLM

model = AutoModelForCausalLM.from_pretrained(
 "Qwen/Qwen3-0.6B",
 device_map="neuron"
)

This example uses the Qwen3 0.6-billion-parameter model as a loading demonstration. It is not a guarantee that every Transformers model will run without additional configuration, or that the example will fit every available Trainium instance. The model's tokenizer, input preparation, generation settings, supported operators, and available device memory still matter.

For a real inference application, load the tokenizer as well, prepare an input prompt, and test generation with a small batch before increasing the workload. Measure end-to-end latency, memory use, and output quality. A model that loads successfully can still encounter unsupported operations or performance bottlenecks during generation.

Know when eager execution is enough and when to scale

TorchNeuron offers more than a device-placement change. Its documented capabilities include standard distributed-training APIs, such as Distributed Data Parallel (DDP), Fully Sharded Data Parallel (FSDP), distributed tensors, and tensor parallelism. These approaches help divide computation or model parameters across accelerator devices when a single device is insufficient. The Neuron Kernel Interface (NKI) also allows developers to create custom accelerator kernels for operations that need specialized optimization.

Those features are useful when a project has moved beyond a simple inference test, but they add complexity. Distributed training requires careful configuration of processes, devices, communication, and data loading. Custom kernels require additional knowledge of the hardware and numerical behavior. Start with one supported instance and a working single-device example before adding distributed execution or low-level optimization.

There is also a distinction between source-code compatibility and performance portability. Reusing familiar PyTorch APIs can reduce migration effort, but it does not guarantee that every model runs unchanged or that Trainium will outperform another accelerator. Actual results depend on the workload, software support, memory behavior, compilation overhead, instance cost, and how efficiently the model uses the hardware.

Troubleshoot the problems most likely to stop a first run

If a model fails before executing, check the environment diagnostic and package versions first. A mismatch between PyTorch and TorchNeuron can prevent the backend from loading correctly, while missing drivers or runtime libraries can make the device unavailable. If the model runs eagerly but fails during compilation, isolate the failing operation and consult the documented API compatibility information rather than assuming the entire model is unsupported.

If the first compiled run is slow, repeat the same workload before concluding that performance is poor. Compilation overhead can dominate short tests, and subsequent executions may behave differently. If eager and compiled outputs disagree, compare the input tensors, model mode, data types, and numerical tolerances, then reduce the example to the smallest operation that reproduces the difference.

Finally, distinguish a functional problem from a capacity or cost problem. A model may be valid but exceed available device memory, require a different parallelism strategy, or run too infrequently to justify compilation. Keep a baseline on your current hardware so you can compare correctness, throughput, latency, and total cost rather than relying on a successful launch as the measure of success.

Decide whether TorchNeuron fits your next AI project

TorchNeuron is worth evaluating if your team already uses PyTorch and wants to test AWS Trainium without immediately rebuilding its model code around a new framework. The most sensible starting point is a small, representative workload that can be validated on one supported instance. Once that works, compare eager and compiled execution, test the model's actual dependencies, and estimate the cost of the complete training or inference job.

The public beta is a reason to test compatibility, not to assume that migration is finished. Before committing a production workload, review the current support matrix, monitor changes to the backend, and run your own benchmark against the hardware you already use. That evidence will tell you whether the easier device switch translates into a practical improvement for your particular AI application.