If you’re working with deep learning models in PyTorch, chances are you’ve stumbled upon a puzzling error message like:

RuntimeError: CUDA error: device-side assert triggered

This error can be incredibly frustrating, especially when you’re not exactly sure what’s causing it. Unlike many programming errors that give you a helpful stack trace pointing to the problem, this one can feel more like your GPU is raising its eyebrows and quietly walking away. But don’t worry — by the end of this guide, you’ll not only understand why this happens, but also how to methodically fix it.

What Does This Error Actually Mean?

This error occurs when a CUDA kernel running on your GPU encounters an assertion failure. This is usually due to invalid input or unexpected behavior that you might not catch in CPU mode. Because it’s a GPU-level issue, it tends to crash without the detailed debug messages we’re used to seeing from Python exceptions.

Common causes include:

  • Indexing errors (e.g., trying to use a class index that doesn’t exist)
  • Invalid tensor shapes
  • Incorrect loss function usage
  • Data that breaks expectations (like empty tensors)

Luckily, with the right approach, you can track down and fix this issue—let’s walk through how.

Step-by-Step Process to Diagnose and Fix

1. Run Your Model on the CPU

The first diagnostic step is to disable CUDA and run everything on the CPU. Often, when running on the CPU, PyTorch provides clearer error messages because device-side asserts are now thrown as Python exceptions with full context.

To switch to CPU mode, modify your code as follows:

device = torch.device("cpu")
model.to(device)

If an assertion fails, you should now get a much more informative stack trace.

2. Check Your Target Labels

One of the most frequent causes of this error is improper class labels — especially when using nn.CrossEntropyLoss. This loss function expects your target tensor to include class indices between 0 and num_classes - 1. So if your model outputs 10 classes, the targets must be integers from 0 to 9.

Common mistake:

# Target contains 10 instead of 0–9 range
target = torch.tensor([10])

If these indices are out of bounds, you’ll run into an assert on the GPU. To validate this, use:

assert target.max().item() < num_classes

If you’re doing image classification, also ensure that the shape of your target is appropriate. For CrossEntropyLoss it should be of shape [batch_size], not one-hot encoded!

# Incorrect (for CrossEntropyLoss)
target = torch.tensor([[0, 0, 1], [1, 0, 0]])

# Correct
target = torch.tensor([2, 0])

3. Inspect the DataLoader for Errors

Sometimes the error comes from your dataset or DataLoader, especially when used in batch training. If some labels are corrupted or inconsistent, they may break your model on the GPU.

Double check your dataset like this:

for i, (x, y) in enumerate(loader):
    assert y.dtype == torch.long
    assert y.max().item() < num_classes
    assert x.shape[0] == y.shape[0]

This is particularly useful if your dataset is constructed from a CSV file or custom processing logic that might silently introduce invalid labels.

introspective developer, inspecting code, laptop, debugging

Other Common Pitfalls

4. Mismatched Batch Sizes

Sometimes the model or loss function expects inputs to be of certain shapes. Mismatches can lead to subtle problems. Ensure your batch size in inputs and targets align:

# torchvision models usually expect [N, 3, 224, 224]
assert inputs.shape[1:] == (3, 224, 224)

This especially matters when using DataLoader with drop_last=False — the last batch might be smaller depending on your dataset size. Your model or operations like BatchNorm must handle it properly or explicitly check for smaller batches.

5. Accidental Tensors on Different Devices

Ensure that both your input features and model are on the same device. If you send your model to CUDA but leave your inputs on the CPU, things will fail unexpectedly, often without helpful errors.

Always double check with:

assert inputs.device == model.device

Advanced Tip: Enable Full Error Reporting

If running on CPU doesn’t help, or you’re working in a mixed CPU/GPU setup and still not getting useful errors — try setting:

CUDA_LAUNCH_BLOCKING=1 python my_script.py

This tells PyTorch to run GPU code synchronously, so it will crash at the exact point of failure. It may slow down execution a bit, but it provides a much clearer traceback.

In Python only, without modifying the shell:

import os
os.environ["CUDA_LAUNCH_BLOCKING"] = "1"

Now the runtime should offer more specific information about where the CUDA assert occurred.

Fix by Example

Let’s look at a practical example. Suppose you’re building a model for digit classification on MNIST and define your final model layer as follows:

self.fc = nn.Linear(128, 10)

In the training loop, you have:

criterion = nn.CrossEntropyLoss()
output = model(images)        # Output shape: [batch_size, 10]
loss = criterion(output, labels)

But your labels are like:

labels = torch.tensor([[0], [1], [2]])

This shape is incorrect. CrossEntropyLoss expects labels as a 1D vector of class indices:

labels = torch.tensor([0, 1, 2])

Fixing this shape alone could resolve the issue.

pytorch model, loss function, gpu error, fix

Summary: Checklist to Fix the Error

Before you start pulling out your hair, follow this checklist:

  1. Switch to CPU mode and try again — the error message might be more descriptive.
  2. Verify class labels: Make sure they’re within the valid range and correct format.
  3. Inspect data coming from the DataLoader — iterate through batches and check for anomalies.
  4. Ensure proper tensor shapes and dimensions, especially for outputs and targets.
  5. Use CUDA_LAUNCH_BLOCKING=1 to get a detailed, synchronous traceback from CUDA.

Conclusion

While the device-side assert triggered error can feel vague and opaque at first, it’s ultimately your model or data’s way of waving a red flag at you. By systematically checking your labels, data shapes, and making use of CPU mode and launch blocking, you can almost always isolate the issue.

Next time, instead of reacting with confusion, you’ll be armed with knowledge and a diagnostic toolkit. Happy debugging!