August 14, 2026 · 22 min read

NeuroCUDA Google Colab Guide

Use a free hosted GPU to convert a trained PyTorch model into a spiking network without installing local NVIDIA drivers. This is the notebook path: runtime, pip, Drive, disconnect limits.

To run NeuroCUDA Google Colab: Runtime - Change runtime type - GPU, then !pip install neurocuda, load a ReLU PyTorch checkpoint, and call snn, meta = neurocuda.convert(model, calib_loader). Compile with target="gpu" and evaluate. Save weights to Drive; Colab sessions disconnect. This page is not a replacement for the pip install neurocuda guide.

This page vs nearby pages: this URL is the hosted-notebook HowTo. pip install neurocuda guide is local venv and CUDA wheels. Convert PyTorch to SNN is the full API walkthrough. QCFS ANN to SNN explains calibration. Google should index this page for the Colab query, not as a clone of the install guide.

People search neurocuda google colab when they have a PyTorch checkpoint and no spare GPU workstation. They want a browser tab, a T4, and a convert call that returns spikes. They do not want to debug Windows CUDA wheels at midnight. That is a real job. It is also a different job from installing NeuroCUDA on a lab machine, and treating the two as the same page is how install docs and notebook docs cannibalize each other.

NeuroCUDA is the open-source compiler that turns a trained ANN into an SNN with QCFS calibration and BPTT fine-tuning. Install from PyPI with pip install neurocuda. The method, backends, and published scores live in the technical report (PDF). This post only answers: how do you run that compiler inside Google Colab without lying about hardware, accuracy, or session lifetime.

Why NeuroCUDA Google Colab exists

A local install is still the right default for papers, CI, and robotics. Colab is the right default when you need CUDA today and you do not own a driver stack. Students, reviewers, and engineers who only need to confirm that convert() runs all land here. The hosted VM already has a recent PyTorch CUDA wheel. You skip the matrix of driver versus wheel versus torch.cuda.is_available() that dominates the pip install neurocuda guide.

Colab is not a neuromorphic chip. It is not Intel Loihi. It is not Manchester SpiNNaker. It is an NVIDIA GPU in a Google data center, rented by the session. NeuroCUDA's GPU backend is the correct target. If your question is "what is neuromorphic CUDA as a field," start at neuromorphic CUDA. If your question is "paste cells until spikes print," stay on this page.

Three constraints make NeuroCUDA Google Colab different from a desktop run. First, the disk under /content dies with the VM. Second, idle notebooks disconnect and long jobs get preempted on the free tier. Third, you reinstall packages after every restart. Ignore those three and you will "lose" a converted ResNet that actually converted fine, then vanished with the runtime.

Step 1: Enable the GPU runtime

Open Google Colab, create a new notebook, then:

1. Menu: Runtime then Change runtime type
2. Hardware accelerator: GPU (free tier is usually a T4)
3. Save, then run the check cell below

import torch
print("torch", torch.__version__)
print("cuda", torch.cuda.is_available())
print("device", torch.cuda.get_device_name(0) if torch.cuda.is_available() else "cpu")

You want cuda True and a device name that starts with Tesla T4, T4, L4, or A100. If you see CPU, you did not enable the accelerator, or Colab could not assign a GPU (common when free-tier capacity is exhausted). Do not proceed to conversion on CPU unless you are only testing a tiny MLP. BPTT inside convert() is the slow step; a CPU Colab session will sit there until the idle timer wins.

Do not select TPU. NeuroCUDA talks to PyTorch CUDA, not XLA TPU kernels. A TPU runtime is a clean way to waste an afternoon on a false "CUDA not available" error.

Step 2: pip install neurocuda

Colab images already include PyTorch. You still need the compiler package. Put this in the first code cell so a restart is one click:

!pip install -q neurocuda
import neurocuda
print("neurocuda", getattr(neurocuda, "__version__", "imported"))

That is the entire install for the notebook path. Optional extras exist for NIR and simulator backends:

!pip install -q "neurocuda[all]"

Source and issues: github.com/Krishnav1/neurocuda. If pip cannot see PyPI from a restricted network, Colab is the wrong environment; go back to a local venv and the install guide. If import fails with No module named 'torch', the runtime is broken in a way that is not NeuroCUDA's fault - factory-reset the runtime and pick GPU again.

Re-run the pip cell after Runtime - Restart runtime and after every disconnect. Colab does not persist site-packages. Putting install in a markdown comment instead of a cell is the most common reason a shared notebook "doesn't work" for the next reader.

Step 3: Mount Drive for checkpoints

Convert can take minutes for ResNet-18. Free Colab will not wait politely if you walk away. Mount Drive before you spend GPU time:

from google.colab import drive
drive.mount("/content/drive")
CKPT = "/content/drive/MyDrive/neurocuda_colab"
import os
os.makedirs(CKPT, exist_ok=True)
print("checkpoints ->", CKPT)

Store three artifacts, not one: the ANN .pth, the converted SNN object (or its state_dict), and a tiny JSON of the evaluate numbers. If the VM dies during BPTT, you still have the ANN and you can convert again. If you only keep files in /content, you keep nothing.

Do not commit secrets into the notebook. Drive mount will prompt for Google OAuth in the cell output. That is expected. Sharing the notebook with "include Drive paths" is fine; sharing a notebook that embeds a personal service-account key is not.

Step 4: Load a trained ReLU model

NeuroCUDA converts conventional ANNs. You need ReLU (or ReLU-like) activations and a checkpoint that already classifies. Training that ANN can happen on Colab too, but it is out of scope here. For a smoke test, a small MLP on random tensors is enough to prove the runtime. For a real score, load CIFAR-10 ResNet-18 or an N-MNIST CNN as in the published protocol.

import torch
import torch.nn as nn
from torch.utils.data import DataLoader, TensorDataset

device = torch.device("cuda")
model = nn.Sequential(
    nn.Flatten(),
    nn.Linear(3 * 32 * 32, 256),
    nn.ReLU(),
    nn.Linear(256, 10),
).to(device)
model.eval()

# Fake in-distribution batches for the smoke test only
x = torch.randn(256, 3, 32, 32)
y = torch.randint(0, 10, (256,))
calib_loader = DataLoader(TensorDataset(x, y), batch_size=32)
test_loader = DataLoader(TensorDataset(x[:64], y[:64]), batch_size=32)

Replace the fake tensors with a real DataLoader before you quote accuracy. Calibration data must use the same resize, crop, and normalize as training. A loader that forgets CIFAR mean and std will look like a conversion bug and is actually a preprocessing bug. See SNN accuracy drop after conversion if the number collapses to chance.

For ResNet-18 on CIFAR-10, follow the architecture notes in the ResNet-18 SNN conversion tutorial rather than torchvision's ImageNet-sized stem. For event data, use the N-MNIST SNN conversion path instead of stuffing frames into a CIFAR pipeline.

Step 5: Convert on the Colab GPU

import neurocuda

snn, meta = neurocuda.convert(model, calib_loader)
torch.save(snn, f"{CKPT}/snn_smoke.pt")
print("converted", type(snn), "meta", type(meta))

The call runs QCFS calibration, folds BatchNorm when present, replaces activations with integrate-and-fire neurons, then fine-tunes with BPTT and an atan surrogate. That is the same pipeline described in QCFS ANN to SNN explained. You do not implement those stages by hand in the notebook. If thresholds look frozen after a real conversion, that is the optimizer bug on QCFS threshold not learning, not a Colab-specific failure.

Pass timesteps=32 when you want the published ResNet-18 protocol. The smoke MLP above can use a smaller T to finish in seconds. Do not mix a T=8 smoke test with a claim that you matched 94.61%. Accuracy claims need the documented T, dataset, and seeds from reproduce NeuroCUDA results.

meta is the conversion-side record (thresholds, timestep config, diagnostics). Log it. Do not invent extra fields in a blog cell. If you need a paper trail, print meta and store the string next to the checkpoint on Drive.

Step 6: Compile and evaluate

neurocuda.compile(snn, target="gpu")
snn_acc = neurocuda.evaluate(snn, test_loader, device=device)
print(f"SNN acc={snn_acc:.4f}")

On a real CIFAR-10 test set, the published software-backend result is SNN 94.61% ± 0.14% versus ANN 95.56% ± 0.11% at T=32. On N-MNIST the published SNN is 99.88% ± 0.02% versus ANN 99.70%. Those numbers come from the technical report. A Colab run that uses fake tensors, a different stem, or T=8 is not a failed reproduction; it is a different experiment. Quote the report, or quote your own test-set number with the protocol attached. Do not invent a third accuracy.

Optional sparsity and NIR cells, still writing to Drive:

sparsity = neurocuda.measure_sparsity(snn, test_loader, device=device)
print(f"sparsity={sparsity:.3f}")
nir_path = f"{CKPT}/snn_smoke.nir"
neurocuda.to_nir(snn, nir_path)
print("NIR ->", nir_path)

NIR is the vendor-neutral graph. How to inspect round-trips is export PyTorch to NIR. Theory of conversion versus from-scratch SNN libraries is NeuroCUDA vs SpikingJelly and the hub at ANN-to-SNN.

Full NeuroCUDA Google Colab notebook cells

Copy the block below into a fresh GPU notebook if you want a single paste. It is a smoke test. Swap in a real checkpoint before you publish a number.

# Cell 1 - GPU check
import torch
assert torch.cuda.is_available(), "Runtime - Change runtime type - GPU"
print(torch.cuda.get_device_name(0))

# Cell 2 - install (re-run after every restart)
!pip install -q neurocuda

# Cell 3 - Drive
from google.colab import drive
drive.mount("/content/drive")

# Cell 4 - convert + evaluate
import torch, torch.nn as nn, neurocuda
from torch.utils.data import DataLoader, TensorDataset
device = torch.device("cuda")
model = nn.Sequential(nn.Flatten(), nn.Linear(3072, 128), nn.ReLU(), nn.Linear(128, 10)).to(device)
x, y = torch.randn(128, 3, 32, 32), torch.randint(0, 10, (128,))
calib_loader = DataLoader(TensorDataset(x, y), batch_size=32)
snn, meta = neurocuda.convert(model, calib_loader)
neurocuda.compile(snn, target="gpu")
print(neurocuda.evaluate(snn, calib_loader, device=device), meta)

Keep cells separate in the saved notebook even if you first tested them as one paste. A failed pip then hides the convert traceback if they share a cell. Reviewers who open your gist should be able to Run all after picking GPU.

Session disconnect limits

Google Colab is not a batch queue. Treat the VM as rented and impatient.

If convert is still running when the session dies, you do not get a partial SNN you can resume. You get an ANN checkpoint (if you saved it) and a need to convert again. That is why Drive comes before convert, not after you "see if it works."

Colab Pro and Pro+ change idle policy and GPU class. They do not change NeuroCUDA's published accuracy. A faster GPU shortens BPTT wall-clock. It does not create a new 99.9% CIFAR number. If a paid plan is what you have, still follow the same cells and the same honest labels.

GPU memory on a free T4

A typical free-tier T4 has 16 GB of device memory. That is enough for ResNet-18/CIFAR-10 conversion at batch 64 for many setups, and tight if you also keep the ANN, the SNN, and a large calibration cache. Out-of-memory during convert is almost always batch size, not a missing NeuroCUDA flag.

• Drop batch_size on the calibration loader first (64 to 32 to 16)
• Delete unused tensors and call torch.cuda.empty_cache() after loading the ANN
• Do not open a second notebook on the same GPU runtime
• Avoid logging every activation tensor "for debugging" on the device

If Colab still OOMs, the experiment belongs on a local GPU or a cloud VM you control, with the install steps from the pip guide. Colab is the try-it path, not the only path.

What NeuroCUDA Google Colab is not

This page is not a replacement for /blog/pip-install-neurocuda-guide. That URL owns virtualenv, CUDA 11.8 versus 12.x wheels, Windows, CPU-only PyTorch, and corporate SSL. Copying those sections here would split the install query across two URLs.

This page is not the conversion theory primer. /ann-to-snn owns ANN-to-SNN as a topic. /blog/qcfs-ann-to-snn owns Quantized Clip-Floor-Shift as a method. /blog/convert-pytorch-to-snn owns the eight-step API HowTo for any machine.

This page is not silicon. Label the backend when you paste a screenshot into a paper or a pull request:

The SpiNNaker confirmation post is NeuroCUDA SpiNNaker physical silicon. Do not caption a Colab T4 screenshot as SpiNNaker. Do not caption it as Loihi. The field already has too many slides that blur simulator, GPU, and wafer.

Colab versus local versus Docker

If you already have a workstation GPU, skip Colab. The extra failure modes (disconnect, pip amnesia, Drive paths) are not worth it when pip install neurocuda on the metal is one command. If you do not have a GPU, Colab is the honest answer, with the limits written in the same paragraph as the cells.

Common Colab errors

CUDA: False after you picked GPU

Factory-reset the runtime, pick GPU again, and re-run the torch check before pip. Occasionally the free pool has no GPU. Wait and retry. Installing a CPU-only torch wheel on top of Colab's GPU image is a self-inflicted downgrade; do not pip install torch unless Colab's image is actually missing it.

ModuleNotFoundError: neurocuda after you already installed it

You restarted. Re-run !pip install neurocuda. If you used a second Python kernel or %%bash in a way that missed the notebook kernel, switch to a plain !pip cell.

Accuracy near 10% on CIFAR-10

That is chance for 10 classes. Check membrane reset, T, QCFS thresholds, and BatchNorm folding on the accuracy-drop page. Also check that Colab downloaded the real CIFAR test set and that you did not evaluate on the fake smoke tensors from this guide.

Drive mount hangs

Complete the OAuth prompt in the cell output. If you run Colab from an account that cannot use Drive, save to GitHub or download the .pth through the Colab files pane before the session dies. The files pane is still ephemeral; download means actually download.

Conversion slower than the blog promised

This page does not promise a wall-clock. T4 versus A100 versus your lab 4090 will differ. The published accuracy is not a function of which NVIDIA SKU Colab assigned. If convert has been running for hours on a tiny MLP, you are on CPU. Re-check the runtime.

Sharing a NeuroCUDA Google Colab notebook

When you share, pin a comment at the top: enable GPU, run the pip cell, mount Drive or skip Drive for the smoke test. Link the product hub quantaracore.in/neurocuda and the GitHub repo so the notebook is not an orphan gist. If the notebook demonstrates ResNet-18, link the tutorial and the PDF rather than pasting a 94.61% figure into a markdown cell with no protocol.

Do not paste API keys. Do not paste other people's unpublished checkpoints. Do not claim the notebook ran on Loihi because you imported an extra. The share dialog should be enough; a private Drive file that collaborators cannot read is not a public reproduction.

After the notebook works

Move off Colab when you need overnight jobs, ROS2, or silicon. GPU validation is step one in the convert guide. Loihi 2 equation simulation is a different backend with a simulator label (PyTorch to Loihi 2 without Lava). Physical SpiNNaker is a different post with job IDs. Robotics glue lives under NeuroCUDA ROS2, not in a Colab cell.

If you came here from a search for neuromorphic CUDA frameworks (GeNN, Brian2, GPU-RANC), you may be in the wrong HowTo. Those tools simulate neuron equations. NeuroCUDA converts a trained PyTorch model. The field map is neuromorphic CUDA. The compiler map versus SpikingJelly is NeuroCUDA vs SpikingJelly.

Frequently asked questions

Start now: enable GPU · !pip install neurocuda · snn, meta = neurocuda.convert(model, calib_loader) · Product page · PDF report