Torch not compiled with cuda enabled windows

Keep in mind that we might have to run the command twice to confirm uninstallation.

Note: Once we see the warning: skipping torch as it is not installed warning, we will know that we have completely uninstalled PyTorch.

Installing CUDA Toolkit

The next approach is to install the NVIDIA CUDA Toolkit before installing PyTorch with CUDA support.

To accomplish this, we need to check the compatibility of our GPU with CUDA before installing the CUDA Toolkit. This is to make sure that our GPU is compatible with CUDA. We can check the list of CUDA-compatible GPUs on the NVIDIA website.

Note: To learn more about CUDA installation on your machine, visit the CUDA official documentation..

We can also install CUDA Toolkit using a Python package manager like Miniconda on Linux. We start off by downloading the Miniconda installer script for Linux from the official conda website and running the following command:

The “AssertionError: torch not compiled with CUDA enabled” can be a significant hurdle when working with PyTorch, a renowned open-source machine learning library known for its proficiency in training deep neural networks. It relies on CUDA, a parallel computing platform by NVIDIA, for efficient GPU acceleration.

In this article, we’ll explore the origins of this error, present illustrative examples, and provide a systematic approach to rectify it.

The error message “AssertionError: torch not compiled with CUDA enabled” serves as a clear indicator that the existing PyTorch installation is devoid of the necessary framework for CUDA support.

This essentially means that the PyTorch library has been set up in a way that it lacks the ability to offload certain computations to the GPU, which is a significant aspect of accelerating the training and inference processes of deep neural networks.

What Causes of the AssertionError: torch not compiled with CUDA?

The “AssertionError: torch not compiled with CUDA enabled” can be caused due to any of the reasons mentioned below.

Incorrect Installation:

This is one of the most common reasons for encountering the error. When PyTorch is initially installed, users must specify whether or not they want CUDA support. If CUDA support is not selected during installation, PyTorch will default to a CPU-only build.

Missing GPU Drivers:

Even if PyTorch is installed with CUDA support, it relies on NVIDIA GPU drivers to function properly. If these drivers are missing, outdated, or incompatible, it can lead to the assertion error.

Incompatible PyTorch and CUDA Versions:

Using incompatible versions of PyTorch and CUDA can also trigger this error. It’s important to ensure that the PyTorch version you’re using is compatible with the CUDA version installed on your system.

Below are few examples mentioned to explain the AssertionError: torch not compiled with CUDA more properly.

Example 1: Basic Assertion Error

In this example, a Python script (likely named example.py) is being executed. At line 5, a tensor is being created using torch.tensor([1.0, 2.0]), and then an attempt is made to move it to the GPU using .cuda().

However, an ‘AssertionError: torch not compiled with CUDA enabled’ is raised, stating that PyTorch was not compiled with CUDA support.

Traceback (most recent call last):
File "example.py", line 5, in <module>
x = torch.tensor([1.0, 2.0]).cuda()

This error indicates that the PyTorch installation being used lacks the necessary components for CUDA support. As a result, it’s unable to utilize the GPU for accelerated computations.

Example 2: Importing torch.cuda

In this example, the script starts by trying to import torch.cuda. However, it encounters an AssertionError, stating that PyTorch was not compiled with CUDA support.

Syntax:

Traceback (most recent call last):
File "example.py", line 2, in <module>
import torch.cuda

This error is caused when the code attempts to directly access torch.cuda, indicating that the PyTorch installation lacks CUDA support.

How to resolve such error?

The AssertionError: torch not compiled with CUDA more properly. can be resolved by using one of the

Verifying GPU and Drivers

Begin by confirming that your system has an NVIDIA GPU installed and check that the necessary drivers are properly installed. You can do this by running the command nvidia-smi in your terminal.

Reinstall PyTorch with CUDA Support 

To rectify this issue, you’ll need to reinstall PyTorch with CUDA enabled. Use the following pip command, replacing the version numbers with the appropriate ones for your system:

pip install torch==1.9.0+cu111 torchvision==0.10.0+cu111 torchaudio==0.9.0 -f https://download.pytorch.org/whl/torch_stable.html

This will ensure that you have a PyTorch installation with CUDA support.

Verify Installation 

After reinstalling PyTorch, you can verify if CUDA is working correctly by running a short Python script:

import torch

print(torch.cuda.is_available())

print(torch.cuda.device_count())

If CUDA is set up correctly, the script will print True for torch.cuda.is_available() and indicate the number of available GPUs.

Set the Device 

In your Python code, it’s essential to specify the device (CPU or GPU) on which tensors and models should operate. This can be done as follows:

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

Картинка с сайта: www.pythonclear.com

This line of code checks if CUDA is available and assigns the appropriate device. You can then use tensor.to(device) to move tensors to the specified device.

Passing the pin_memory value as false

Another way to resolve this error is to pass the pin_memory value in a code while using the get_iterator function. This function is used to call upon an iterator. In this process of using this function ano,ther function called pin_memory gets enabled, which usually passes the dataset while training to the GPU. Thus it should always remain true, but in some instances, it may raise the AssertionError: Torch Not Compiled With CUDA Enabled. To avoid this error, you need to pass the pin_memory value as false while calling for the get_iterator function.

Syntax:

import torch
from your_module import create_batches, get_coco_data  # Import necessary functions and modules

def get_iterator(data, batch_size=32, max_length=30, shuffle=True, num_workers=4, pin_memory=True):
    cap, vocab = data
    return torch.utils.data.DataLoader(
        cap,
        batch_size=batch_size, shuffle=shuffle,
        collate_fn=create_batches(vocab, max_length),
        num_workers=num_workers, pin_memory=pin_memory
    )


train_data = get_iterator(get_coco_data(vocab, train=True), batch_size=args.batch_size, pin_memory=False)

Solution For Mac

While working on Mac, facing this error might be different as the Mac system does not have CUDA. Thus, in the case of Mac, the best solution is to not use a .cuda() function on Mac. Instead, you can externally add NVIDIA CUDA, but it will be incompatible with the integrated intel; thus, using platforms like Google Collaborate will be beneficial.

Solution For Detectron2

For platforms like Detectron2 any of the above solutions won’t work but instead you can use the following code which can help you resolve the AssertionError: Torch Not Compiled With CUDA Enabled.

Syntax:

import torch

# Assuming cfg is a configuration object
cfg = torch.hub.load('facebookresearch/detectron2:main', 'config', 'COCO-Detection/faster_rcnn_R_50_FPN_3x.yaml')

# Set the device to CPU
cfg.MODEL.DEVICE = "cpu"

# Now you can create and use the model with CPU computation
model = torch.hub.load('facebookresearch/detectron2:main', 'model', config=cfg)

FAQs

Conclusion

In conclusion, The “AssertionError: torch not compiled with CUDA enabled” error is a common stumbling block for those looking to harness the GPU-accelerated power of PyTorch. By ensuring a proper installation and configuration, you can unlock the full potential of your system for deep learning tasks.

Always remember to check for GPU compatibility, install the correct drivers, and verify your PyTorch installation with CUDA support. With these steps, you’ll be on your way to seamless GPU-accelerated machine learning workflows.

References

1. AssertionError
2. CUDA

To learn more about some common errors follow Python Clear’s errors section.

Torch not compiled with cuda enabled is an error you will encounter while using a PyTorch version that does not support CUDA. If PyTorch is not compiled with CUDA enabled, it means that it cannot utilize GPUs for computation acceleration.

Картинка с сайта: www.positioniseverything.net

In this article, you will delve into the various causes of this error and provide practical solutions for overcoming it. By the end of this article, you will have a clear understanding of how to ensure that PyTorch is properly configured to utilize CUDA acceleration.

Why Am I Triggering Torch Not Compiled With Cuda Enabled?

You are triggering the torch not compiled with CUDA enabled error because you have installed the wrong PyTorch that does not support CUDA. If you do not specify the version of PyTorch built with CUDA support, you will end up with a version without CUDA support.

PyTorch is a popular deep-learning framework that provides support for computation acceleration using Graphics Processing Units (GPUs). To use GPUs for computation acceleration in PyTorch, you need to have the CUDA Toolkit installed on your system, as well as a version of PyTorch that has been compiled with CUDA support.

However, if you install PyTorch without specifying that you want a version with CUDA support, you will end up with a version of PyTorch that does not have CUDA support. This means that PyTorch will not be able to use GPUs for computation acceleration, and you will trigger this error.

The reason for this error is that PyTorch has different versions that are compiled with or without CUDA support. For example, there is a version of PyTorch that has been built with CUDA support for CUDA 11.0 and another version that has been built with CUDA support for CUDA 10.1.

– Lack of Cuda Toolkit Installation – Negative Consequences

The error can occur if you have not installed the CUDA Toolkit on your system. CUDA is a parallel computing platform and programming model developed by NVIDIA that enables computationally intensive applications to run on GPUs (graphics processing units) for faster performance.

PyTorch requires the CUDA Toolkit to run on GPUs. If you don’t have the CUDA Toolkit installed, you’ll get the error when you try to run PyTorch code that uses CUDA.

If you have an NVIDIA GPU in your machine, but have not installed the NVIDIA CUDA Toolkit and a version of PyTorch with CUDA support. In that case, you may encounter this error when you run a PyTorch script that uses CUDA acceleration.

On the other hand, if your machine does not have an NVIDIA GPU, you cannot use CUDA with PyTorch. Attempting to run a PyTorch script with CUDA acceleration will trigger the error.

As well, if you have created a virtual environment for your PyTorch project, and you have not installed the NVIDIA CUDA Toolkit and a version of PyTorch with CUDA support in the virtual environment, you may encounter this error when you run a PyTorch script that uses CUDA acceleration.

In addition, if you are running a PyTorch script in a cloud-based platform such as Amazon AWS, Google Cloud, or Microsoft Azure, and you have not enabled CUDA support in the platform, you may encounter this error when you run a PyTorch script that uses CUDA acceleration.

– Dark Side of the Cpuonly Package and Its Impact on Pytorch

The cpuonly package is a PyTorch package that is specifically designed to run on CPUs. This package is designed to prevent CUDA-specific features from being installed or enabled, as CUDA is not supported in this case.

Картинка с сайта: www.positioniseverything.net

The error under consideration arises when users try to run CUDA-based code while having the cpuonly package installed, as this package is effectively disabling CUDA support. As a result, the code will raise the AssertionError torch not compiled with CUDA enabled error.

– Common Scenarios and Platforms That Can Trigger the Error

Here are a few examples of scenarios and platforms where you may trigger the error:

• Torch not compiled with cuda enabled mac: This error in the context of a Mac refers to the same issue as in any other context. It indicates that the version of PyTorch installed on the Mac does not have CUDA support.
• Assertionerror: torch not compiled with cuda enabled conda: This error in the conda environment indicates that the version of PyTorch installed in the conda environment does not have CUDA support.
• Torch not compiled with cuda enabled AMD: The error in the context of an AMD system shows that the version of PyTorch installed on the AMD system does not have CUDA support.
• Torch not compiled with cuda enabled kaggle: The error in the context of Kaggle indicates that the version of PyTorch installed on the Kaggle environment does not have CUDA support, which is required for acceleration of computations using the GPU.
• Torch not compiled with cuda enabled mac m1: The error in a Mac M1 indicates that the version of PyTorch installed on the Mac M1 device does not have CUDA support.
• Torch not compiled with cuda enabled stable diffusion: The error in the context of Stable Diffusion indicates that the version of PyTorch installed on the system does not have CUDA support, which is necessary for acceleration of computations using the GPU.
• Assertionerror: torch not compiled with cuda enabled jetson: This error in the context of Jetson shows that the version of PyTorch installed on the Jetson device does not have CUDA support.
• Assertionerror: torch not compiled with cuda enabled detectron2: The error in the context of Detectron2 indicates that the version of PyTorch installed on your system does not have CUDA support. Detectron2 is a computer vision toolkit built on top of PyTorch, and it requires that PyTorch be installed with CUDA support.

How To Solve the Torch Not Compiled With Cuda Enabled Error

You can solve torch not compiled with cuda enabled error by completely reinstalling PyTorch with CUDA support. To accomplish this, you need to completely remove the current version of PyTorch. You can do this by running the following command in your terminal or command prompt: pip uninstall torch.



Keep in mind that you may have to run the command several times (usually twice). Once you see the warning: skipping torch as it is not installed, you will know that you have completely uninstalled the torch.

Next, you need to Install the NVIDIA CUDA Toolkit before installing PyTorch with CUDA support. You can find a free download of the latest version of the CUDA Toolkit from the NVIDIA website and follow the installation instructions.

After installing the NVIDIA CUDA Toolkit, you now need to install PyTorch with CUDA support by running the following command in your terminal or command prompt:

pip install torch torchvision cudatoolkit=<CUDA version>

Replace “<CUDA version>” with the version of CUDA that you have installed. Lastly, you need to verify that PyTorch has been installed with CUDA support.

To do this run the following code snippet in your Python environment:

If the output of the code snippet is True, it means that PyTorch has been installed with CUDA support.

Reinstalling PyTorch with CUDA support will solve the error, but it’s important to make sure that your system is set up correctly, and that you have the necessary hardware and software components installed and configured properly.

– Installing the Cuda Toolkit: The Missing Piece to Pytorch

Another way you can resolve this issue is by installing the CUDA Toolkit. To accomplish this, you need to check the compatibility of your GPU with CUDA before installing the CUDA Toolkit. This is to make sure that your GPU is compatible with CUDA. You can check the list of CUDA-compatible GPUs on the NVIDIA website.

Картинка с сайта: www.positioniseverything.net

Next, download the CUDA Toolkit from the NVIDIA website. Make sure to download the version that’s compatible with your operating system and GPU. After downloading the toolkit, install it. Follow the instructions provided by NVIDIA to install the CUDA Toolkit on your system.

After installing the CUDA Toolkit, you need to set up the environment variables to make sure that PyTorch can find the CUDA libraries.

The exact steps to set up the environment variables depend on your operating system and the version of the CUDA Toolkit you have installed. Once you have installed the CUDA Toolkit, you can reinstall PyTorch with CUDA support.

Conclusion

In this article, you have explored the various causes and solutions to this error, highlighting the importance of CUDA Toolkit installation and compatibility with the GPU and platform you are using. Here is a quick recap of what you have learned:

• The main cause of this error is installing incorrect PyTorch package.
• Other causes include lack of CUDA toolkit and installing the cpuonly package.
• To solve this error, you have to completely reinstall PyTorch with CUDA.
• Other solutions include installing the CUDA Toolkit and removing the cpuonly package.

With this information, you can resolve this issue and take full advantage of PyTorch and CUDA in your projects.