How to free GPU memory for a specific tensor in PyTorch?

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I’m currently running a deep learning program using PyTorch and wanted to free the GPU memory for a specific tensor.

I’ve thought of methods like del and torch.cuda.empty_cache(), but del doesn’t seem to work properly (I’m not even sure if it frees memory at all) and torch.cuda.empty_cache() seems to free all unused memory, but I want to free memory for just a specific tensor.

Is there any functionality in PyTorch that provides this?

Thanks in advance.

del operator works but you won't see a decrease in the GPU memory used as the memory is not returned to the cuda device. It is an optimization technique and from the user's perspective, the memory has been "freed". That is, the memory is available for making new tensors now.

Source: Pytorch forum

How to delete a Tensor in GPU to free up memory, How to delete a Tensor in GPU to free up memory? I can get a Tensor in GPU by Tensor.cuda(), but it just returns a copy in GPU. I wonder how can I delete this� However, it won't be reflected in torch.cuda.memory_allocated because that memory would still be managed by pytorch's memory allocator, and would be reported as allocated to cuda driver, but this memory is still available for pytorch process to use.

Both obj = None or del obj are similar, except the del will remove the reference.

However, you need to call gc.collect() to free Python memory without restarting the notebook.

If you would like to clear the obj from PyTorch cache also run:

torch.cuda.empty_cache()

After the last command Nvidea smi or nvtop will notice your did.

How to free GPU memory for a specific tensor in PyTorch?, del operator works but you won't see a decrease in the GPU memory used as the memory is not returned to the cuda device. It is an� The memory usage in PyTorch is extremely efficient compared to Torch or some of the alternatives. PyTorch's creators have written custom memory allocators for the GPU to make sure that your deep learning models are maximally memory efficient. This enables you to train bigger deep learning models than before.

PyTorch will store the tensors in the computation graph (if it was initialized with requires_grad = True) in case you want to perform automatic differentiation later on. If you don't want to use a specific tensor any longer for gradient computation, you can use the detach method to tell PyTorch that it doesn't need to store the values of that tensor anymore for gradient computation. This will help free up some memory (only removing that specific tensor and not deleting the entire computation graph).

eg - my_tensor.detach()

How to release gpu memory of intermediate result tensor � Issue , In the example below, after calling torch.matmul, the gpu memory How to release gpu memory of intermediate result tensor #29802 that memory would still be managed by pytorch's memory allocator, is possible (in your particular configuration) to implement this matmul without an intermediate matrix. Hi PyTorch Forum, I have access to a server with a NVIDIA K80. Problem is, there are about 5 people using this server alongside me. Most of the others use Tensorflow with standard settings, which means that their processes allocate the full gpu memory at startup. I use PyTorch, which dynamically allocates the memory it needs to do the calculation. Here the problem scenario: 1.) I start my

Memory Management, Optimisation and Debugging with PyTorch, PyTorch 101, Part 4: Memory Management and Using Multiple GPUs You can also move a tensor to a certain GPU by giving it's index as the As soon as a variable goes out of scope, the garbage collection will free it. In cpp, I would just read the file directly into a cpu tensor instead of a vector. Then send that Tensor on the GPU. Note that you can always get the pointer to the gpu data from the pytorch cuda tensor to use in your own kernel. Some example code for the cpp extensions include working with cuda Tensors here. You can ignore the part that

7 Tips To Maximize PyTorch Performance, Throughout the last 10 months, while working on PyTorch Lightning, the team and I have You know how sometimes your GPU memory shows that it's full but you're pretty sure t = tensor.rand(2,2, device=torch.device('cuda:0')) Certain GPUs (V100, 2080Ti) give you automatic speed-ups (3x-8x faster)� Calling empty_cache() releases all unused cached memory from PyTorch so that those can be used by other GPU applications. However, the occupied GPU memory by tensors will not be freed so it can not increase the amount of GPU memory available for PyTorch. For more advanced users, we offer more comprehensive memory benchmarking via memory_stats().

[PDF] Estimating GPU Memory Consumption of Deep Learning , lyzes the liveness of tensors to handle GPU memory deallocation. DNNMem is general eters under 3 mainstream DL frameworks (TensorFlow, PyTorch, and MXNet). under certain circumstances (e.g., in model-parallel training). (3) Ephemeral two release policies: RELEASE_ON_EXIT (at the finalizing phase) and. Hello I am new in pytorch. Now I am trying to run my network in GPU. Some of the articles recommend me to use torch.cuda.set_device(0) as long as my GPU ID is 0. However some articles also tell me to convert all of the computation to Cuda, so every operation should be followed by .cuda() . My questions are: -) Is there any simple way to set mode of pytorch to GPU, without using .cuda() per

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  • Is there any way to release the memory and also return it to the device?