How to Fix RuntimeError: CUDA Error: Device-Side Assert Triggered on NVIDIA (JavaScript)

Rick Bowen (JavaScript)

2 months ago

RuntimeError: CUDA Error: Device-Side Assert Triggered

You’re coding in JavaScript, you’ve got your GPU (an NVIDIA card) humming away, and suddenly you hit the dreaded error: RuntimeError: CUDA error: device-side assert triggered Ugh. Frustrating, right? It halts your flow, gives you a vague message, and leaves you scratching your head. If you’re using JavaScript, maybe via Node.js or a WebGPU/ CUDA bridge, things feel even tougher most posts talk about Python.

JavaScript + NVIDIA GPU

Let’s define the coding project we’ll use as our example this helps ground the debugging steps.

Project Description:

Imagine you’re building a image-classification demo in Node.js, using an NVIDIA GPU via a CUDA backend (for example via a library that wraps CUDA for JavaScript). Your code loads image data, converts it to GPU tensors, feeds into a model (pretrained or custom) and trains or infers labels.

What goes wrong:

During training, after some batches, your code crashes with:

RuntimeError: CUDA error: device-side assert triggered
CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.

You’re stuck. It works for a bit (say 50 batches) and then boom.

What the Error Really

Before we fix it, let’s clearly understand what this error indicates.

Device side assert triggered In plain language:

On the GPU (“device” side) some code (kernel) had an assert() check and the check failed. In C++/CUDA code the developer puts assert(condition); if condition is false, the program aborts that kernel. When that happens in a CUDA kernel, you get this error.
Because GPU kernels are asynchronous, the actual failure might be earlier, while the error shows later at some API call.

Why stack trace is unreliable:

The error message even says:

“CUDA kernel errors might be asynchronously reported at some other API call”
This means you can’t trust the line number or trace as exactly where you messed up. You need to find the root cause manually.

Common root causes:

Invalid indexing/out-of-bounds. Eg: label index = 10 but classes = 10 (0-9) → index=10 is invalid.
Mismatch between number of classes and output units/layers.
Wrong input type for loss function (e.g., using BCELoss with values >1) causing assertions inside loss kernel.
Memory corruption or prior error state: once device assert triggers, CUDA context might be corrupted and further GPU operations fail.

JavaScript + NVIDIA GPU Adds Extra Complexity

Because JS frameworks often wrap native CUDA libraries, you might not see the full native stack. Additional problems unique to JS include:

Using plain JS arrays (rather than typed arrays) to feed GPU buffers -> memory misalignment or weird types.
Running in browser or Electron vs Node: some abstractions hide error details.
Reusing GPU tensors and not freeing memory in JS wrappers -> hidden memory corruption.
Bridging libraries may do asynchronous work and buffering differently, making the “where” even harder to pin down.

Fix Process for JavaScript + NVIDIA GPU

Here’s a structured process to debug and fix the error in our JS + NVIDIA GPU project.

Switch to CPU mode / disable GPU temporarily:

First, force your code to run on CPU instead of GPU. This helps you get more descriptive errors (often Python frameworks show “IndexError: out of range” on CPU instead of generic device-side assert).
Even in JS, if your library allows you to set backend = CPU (or disable GPU acceleration), switch it. That gives clearer location of the bug.

Rerun on CPU and inspect error:

When you run on CPU, you may see something like:
“IndexError: label index 10 is out of bounds for dimension 0 with size 10”
That gives you the exact issue: labels vs classes mismatch. On GPU you only saw “device‐side assert triggered”. See posts where switching helped.
In JS you’ll see a more meaningful exception (depending on the library) once you disable GPU.

Check your labels, classes, dtypes and shapes:

In your image-classification example:

Ensure your labels are in range [0 … numClasses-1].
Make sure your model’s output layer has exactly numClasses units.
Confirm the loss function you use matches the setup (e.g., crossEntropy for multi-class, binaryCrossEntropy for 2-class).
In JS: log the typed array you use for labels: console.log(Math.min(...labels), Math.max(...labels)).
Check shapes: Do your data tensors match expected input shape? Eg: [batchSize, height, width, channels] etc.

Enable synchronous GPU error reporting / debug mode:

In Python you’d set CUDA_LAUNCH_BLOCKING=1. In JS bridging libraries there may be a debug or synchronous mode. This ensures the GPU kernel error is reported right where it happens rather than later.
If your library supports environment vars or init config, enable “GPU debug” or “sync mode” so you pin the line.
Check the documentation of your JS CUDA library (for example if you use node-cuda or WebGPU bindings).

Restart GPU context / clear memory:

Once a device-side assert occurs, further GPU ops may misbehave because the context is corrupted. In JS environment you might need to:

Dispose all GPU tensors / buffers.
Exit the process and restart (in Node).
If in browser, refresh page or reload WebGPU context.
This clears state and ensures your fix is applied cleanly.

Fix the bug in your code:

Based on what you found on CPU run + label/shape check:
For example: your labels array had values [0,1,2,10] but you have only 10 classes (0-9). Fix by filtering/mapping such labels.
Or your output layer had 9 units though you had 10 classes; adjust it.
In JS code sample:

const numClasses = 10;
const labels = new Int32Array([...]); // ensure all < numClasses
const maxLabel = Math.max(...labels);
console.log('Max label =', maxLabel);
if (maxLabel >= numClasses) {
  throw new Error(`Label out of bounds: ${maxLabel} >= ${numClasses}`);
}

Switch back to GPU mode and test:

Once you’ve fixed the bug on CPU mode, re-enable GPU mode and run again. If everything’s valid, your code should run without error. If the device-side assert still appears, repeat above steps (shape/dtype/typed-array check).

Add safeguards / preventive checks in code:

To avoid future issues:

In JS, assert label ranges early.
Check tensor shapes/dtypes after conversion from typed arrays.
Release/dispose GPU buffers when done.
Add logging around the batch that fails (keep track of batch index).
If your project uses multiple GPUs or async tasks, include a catch for GPU failure state and restart session.

Type Arrays and Memory Alignment in JS GPU Context

JavaScript uses Float32Array, Int32Array, etc. GPU libraries expect aligned buffers. If you accidentally pass a plain Array or mismatched typed buffer, the GPU kernel might get garbage values, causing an assertion. So always convert:

const featureArray = new Float32Array(featureData);
const tensor = gpuLib.tensor(featureArray, [batchSize, features], 'float32');

Browser vs Node.js GPU Compute Differences:

If you run in browser via WebGPU or some CUDA-bridge, the stack trace and error reporting might hide the native kernel info. In Node.js with native bindings you may get better trace. So if you’re stuck, try switching platform (browser → Node) to get more debug info.

GPU memory cleanup in JS wrappers:

Because JS garbage collection doesn’t necessarily free GPU memory immediately, you might get “ghost” memory issues that lead to device asserts unrelated to your code logic. Always call disposal methods of your library, e.g.:

tensor.dispose();
gpuLib.cleanup();

If you don’t cleanup, the GPU context may get into a bad state later, triggering unexpected asserts.

Multi-gpu / batch size interplay in JS:

If you use multiple GPUs or large batch sizes, sometimes the error shows later (after many batches). A smaller batch size might appear to “work” but hide the root issue. In JS environments people often ramp up batch size too early. Instead, start with small batch and monitor first. Also log the batch index where error happens.

Logging GPU memory usage from JS:

In Node.js you can execute a child process to call nvidia-smi and log memory usage before each epoch. This helps correlate whether the device-side assert was due to out-of-memory vs logic bug. Example snippet:

const { execSync } = require('child_process');
console.log(execSync('nvidia-smi --query-gpu=memory.used --format=csv').toString());

This extra info gives you a richer debugging context which many posts don’t mention.

Full Example in JavaScript

Here’s a simplified JS code skeleton for our project with debugging checks built in.

// 1. Setup environment and optionally choose backend
const useGPU = true;
const backend = useGPU ? 'cuda-backend' : 'cpu-backend';
await gpuLib.init({ backend });

const numClasses = 10;

async function train(dataset) {
  for (let batchIndex = 0; batchIndex < dataset.length; batchIndex++) {
    const { features, labels } = dataset[batchIndex];
    // Convert to typed arrays
    const featureArray = new Float32Array(features);
    const labelArray   = new Int32Array(labels);

    // Quick check
    const maxLabel = Math.max(...labelArray);
    if (maxLabel >= numClasses) {
      throw new Error(`Label out of bounds in batch ${batchIndex}: ${maxLabel} >= ${numClasses}`);
    }

    // Create tensors
    const inputTensor = gpuLib.tensor(featureArray, [labels.length, featureSize], 'float32');
    const labelTensor = gpuLib.tensor(labelArray,   [labels.length],           'int32');

    // Forward pass
    const logits = model.forward(inputTensor);
    const loss   = gpuLib.crossEntropy(logits, labelTensor);
    loss.backward();
    optimizer.step();

    // Dispose tensors
    inputTensor.dispose();
    labelTensor.dispose();
    logits.dispose();
  }
}

// Run on CPU first for debugging
await train(datasetCpuVersion);

// Once that works, switch to GPU
if (useGPU) {
  await gpuLib.setBackend('cuda-backend');
  await train(datasetGpuVersion);
}

You’ll notice we added a manual label check, converted arrays to typed arrays, dispose tensors, and separate CPU vs GPU runs. These patterns help prevent or catch the device-side assert early.

Final Thoughts

If you’re facing How to Fix RuntimeError: CUDA Error: Device-Side Assert Triggered on NVIDIA (JavaScript), remember: it’s not some mysterious GPU bug it usually comes down to invalid data, mismatched shapes, or memory/context issues. In a JS + NVIDIA GPU setup you need to be extra careful with typed arrays, backends, tensor disposal, and debugging modes.