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n v3.8\)") ) – If ` True  ` , and the source is in pinned memory and destination is on the GPU or vice versa, the copy is performed asynchronously with respect to the host. Otherwise, the argument has no effect. 

  * ****kwargs** – For compatibility, may contain the key ` async  ` in place of the ` non_blocking  ` argument. The ` async  ` arg is deprecated. 

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on. ` ` is_ninja_available ` (  )  [ [source]
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#  How to Solve 'CUDA out of memory' in PyTorch

If you’ve ever worked with large datasets in PyTorch, chances are you’ve
encountered the dreaded ‘CUDA out of memory’ error. This error message occurs
when your GPU runs out of memory while trying to allocate space for tensors in
your PyTorch model. Out-of-memory errors can be frustrating, especially when
you’ve spent much time fine-tuning your model and optimizing your code. In
this blog post, we’ll explore some common causes of this error and provide
solutions to help you solve it.

By **Saturn Cloud** |  Monday, October 23, 2023  | [ Data Science & ML ](https://saturncloud.io/blog/categories/data-science-ml/)

![](/images/blog/how-to-solve-cuda-out-of-memory-in-pytorch.webp)

If you’ve ever worked with large datasets in [ PyTorch
](https://saturncloud.io/glossary/pytorch/) , chances are you’ve encountered
the dreaded ‘CUDA out of memory’ error. This error message occurs when your [
GPU ](https://saturncloud.io/glossary/gpu/) runs out of memory while trying to
allocate space for tensors in your PyTorch model. Out-of-memory errors can be
frustrating, especially when you’ve spent much time fine-tuning your model and
optimizing your code.

🚀 Streamline your model training with Saturn Cloud from monitoring memory to
leveraging multiple GPUs. Scale efficiently and dive into large-scale model
training without the frustration. Start for free [ here
](http://www.saturncloud.io) .

In this blog post, we’ll explore some common causes of this error and provide
solutions to help you solve it.
ones(4))
    __main__:1: UserWarning: self and other do not have the same shape, but are broadcastable, and have the same number of elements.
    Changing behavior in a backwards incompatible manner to broadcasting rather than viewing as 1-dimensional.
    

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    * General semantics 
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(class in torch.nn) ](generated/torch.nn.ZeroPad2d.html#torch.nn.ZeroPad2d)
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