How can I tell if PyTorch is using my GPU?

GPU
1

I’m working on a deep learning project using PyTorch, and I want to ensure that my model is utilizing the GPU for training. I suspect it might still be running on the CPU because the training feels slow. How do I check if PyTorch is actually using the GPU?

2

(Comparison reviewer)

From your description, it seems like training speed could definitely be an indicator that your model isn’t utilizing the GPU. You’d expect a visible difference in training times if the GPU is active, especially with larger models or data sets.

First off, in PyTorch, you can explicitly check whether a GPU is available and ensure your tensors and model are transferred to the GPU. Here’s a simple way of checking:

import torch
print(torch.cuda.is_available())

If this returns True, then PyTorch recognizes your GPU. Next, you need to move your model and data to the GPU like so:

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model.to(device)

For your data, remember to transfer your tensors as well:

inputs, labels = inputs.to(device), labels.to(device)

If you’re using a DataLoader, make sure each batch is moved to the GPU.

for inputs, labels in dataloader:
    inputs, labels = inputs.to(device), labels.to(device)
    # continue with forward pass and loss calculation

On another note, if you’re still noticing slow performance despite the above changes, you might want to check GPU utilization directly. Tools like nvidia-smi can show you real-time GPU usage. Run nvidia-smi in your terminal during training and look for your GPU usage. It should increase when your model is training if the GPU is being used.

watch -n 1 nvidia-smi

This updates GPU stats every second.

Another potential speedup could be ensuring PyTorch tensors are not being inadvertently transferred back to the CPU with operations that don’t respect device consistency. This can sometimes happen during data transformations or saving/loading model states improperly.

As a side note, TensorFlow users sometimes compare the easy checking of GPU usage via tf.test.is_gpu_available(), so if you have experience there, the PyTorch equivalent is similar—but remember to explicitly move everything to the GPU.

Lastly, not all speed issues are GPU-related. Sometimes data pipeline bottlenecks or other factors slow down training. Profilers and memory checks can help diagnose further.

That should cover the basics. If you still encounter issues or odd behaviors, more specific code may be needed for debugging.

3

Nah, I think some of those suggestions might be overkill, tbh. You probably don’t need to go through every single step mentioned by @codecrafter just to check if your GPU is being utilized. The simplest way to see immediate results is checking the nvidia-smi without involving any PyTorch code changes first. If you open your terminal and run:

nvidia-smi

If you see your PyTorch script running, the problem ain’t PyTorch not using the GPU, it’s likely something else, like a bottleneck somewhere in your data pipeline or disk I/O.

Also, moving tensors might not be your issue. It’s tedious to keep track of moving everything to ‘cuda’. Sometimes it’s better to just track it overall. Simpler checking, see if you just have something like this in your setup:

if torch.cuda.is_available():
    device = torch.device('cuda')
else:
    device = torch.device('cpu')
model = MyModel().to(device)

This should be enough for a simple project. Other factors like batch size, poorly optimized network, or even your GPU capacity could be culprits here. With those over-the-top steps you usually end up wasting time if the root cause is, say, a slow data loader.

And okay, comparing with TensorFlow doesn’t add much value here. They have their own eccentricities and troubles. Trust me, I’ve had my share of phantom bugs there too.

Bottom line: Start with nvidia-smi, and maybe just check your device setup. If they’re fine, look elsewhere.

4

You know, you guys both bring up good points. The immediate run to nvidia-smi can indeed give a quick diagnosis, @techchizkid, and avoiding overly complex steps at first check makes sense. But @codecrafter’s focus on ensuring tensors and the model are moved to the GPU shouldn’t be brushed off, either. Sometimes it’s these meticulous steps we struggle with due to forgetting a small piece of the larger puzzle.

But there’s more to it. Just confirming GPU usage through nvidia-smi or torch.cuda.is_available() isn’t where it should end. Even after verifying GPU usage, slow training times could mean numerous things. Here’s some additional steps you might overlook:

  1. CUDA Version & PyTorch Compatibility:
    Make sure the CUDA version compatible with the version of PyTorch you’re using is installed. Inconsistencies here can affect performance or even result in the GPU not being used properly.

    print(torch.__version__)
print(torch.version.cuda)

    Cross-check the above prints with the PyTorch site to ensure you’re aligned.

  2. DataLoader Optimizations:
    If your DataLoader isn’t prefetching or effectively utilizing multiple workers, your GPU will sit idle, waiting for data, regardless of the GPU usage being high. This has a direct impact on training speed.

    train_loader = torch.utils.data.DataLoader(
    train_dataset, batch_size=batch_size, shuffle=True, num_workers=4)

    The num_workers should generally be the number of CPU cores available.

  3. Batch Size:
    Too small a batch size can reduce GPU utilization efficiency. GPUs work better with larger batch sizes due to parallel computation, so increase batch size gradually until you see no performance benefit to find an optimal number.

  4. Bottleneck Analysis:
    Use PyTorch’s built-in profiler for a detailed look into bottlenecks:

    import torch.autograd.profiler as profiler

with profiler.profile() as prof:
    with profiler.record_function("model_inference"):
        output = model(input)
print(prof.key_averages().table(sort_by="cpu_time_total"))

    This can reveal whether the slowdown is due to certain operations being slow.

  5. Pin Memory:
    Ensure your data loader uses pinned memory which makes data transfer from CPU to GPU faster.

    train_loader = torch.utils.data.DataLoader(
    train_dataset, batch_size=batch_size, shuffle=True, num_workers=4, pin_memory=True)

  6. Mixed Precision Training:
    If your GPU supports Tensor Cores (Volta, Turing, and newer), using mixed precision can significantly speed up training. Use PyTorch’s AMP (Automatic Mixed Precision).

    from torch.cuda.amp import autocast, GradScaler

scaler = GradScaler()

for inputs, labels in train_loader:
    inputs, labels = inputs.to(device), labels.to(device)

    # Sets the gradients of all optimized tensors to zero
    optimizer.zero_grad()

    with autocast():
        outputs = model(inputs)
        loss = criterion(outputs, labels)

    scaler.scale(loss).backward()
    scaler.step(optimizer)
    scaler.update()

  7. Check GPU Memory Usage:
    Even if the GPU is recognized and in use, having insufficient memory for your batch size can cause swapping, which degrades performance. Monitor GPU memory:

    watch -n 1 nvidia-smi

  8. Ensure No Data Augmentation Bottlenecks:
    If you’re applying complex real-time data augmentations, it can bog down PyTorch, leading to a slow data pipeline. Offload such tasks to GPUs if possible or pre-process data.

  9. Check for Computational Graph Issues:
    Sometimes, inefficient computation or dynamic computational graph recreations can cause slowness. Ensure your PyTorch script is streamlined for efficiency and check if you’re inadvertently creating computation overhead in each batch.

  10. Python Environment Tidiness:
    Finally, stupid as it might sound, ensure your Python environment is clean and doesn’t contain conflicting packages which might affect efficiency. Conda virtual environments usually help in isolating dependencies effectively.

@codecrafter’s point about operational inefficiencies that can cause inappropriate device transfers cannot be stressed enough. It’s more common than anticipated, especially during early stages of debugging.

To synthesize, start with the basics from @techchizkid’s recommendation (like using nvidia-smi), then move towards ensuring all parts of your training pipeline are optimized for GPU usage. If you’ve followed the basic checks and still face issues, dive deeper using the profiling tools and optimizing data pipelines. Remember, it’s not just about checking GPU usage, but optimizing entire data flow to make sure no bottleneck is holding you back.