# CUDA semantics¶

torch.cuda is used to set up and run CUDA operations. It keeps track of the currently selected GPU, and all CUDA tensors you allocate will by default be created on that device. The selected device can be changed with a torch.cuda.device context manager.

However, once a tensor is allocated, you can do operations on it irrespective of the selected device, and the results will be always placed in on the same device as the tensor.

Cross-GPU operations are not allowed by default, with the only exception of copy_(). Unless you enable peer-to-peer memory access, any attempts to launch ops on tensors spread across different devices will raise an error.

Below you can find a small example showcasing this:

x = torch.cuda.FloatTensor(1)
# x.get_device() == 0
y = torch.FloatTensor(1).cuda()
# y.get_device() == 0

with torch.cuda.device(1):
# allocates a tensor on GPU 1
a = torch.cuda.FloatTensor(1)

# transfers a tensor from CPU to GPU 1
b = torch.FloatTensor(1).cuda()
# a.get_device() == b.get_device() == 1

c = a + b
# c.get_device() == 1

z = x + y
# z.get_device() == 0

# even within a context, you can give a GPU id to the .cuda call
d = torch.randn(2).cuda(2)
# d.get_device() == 2


## Memory management¶

PyTorch use a caching memory allocator to speed up memory allocations. This allows fast memory deallocation without device synchronizations. However, the unused memory managed by the allocator will still show as if used in nvidia-smi. Calling empty_cache() can release all unused cached memory from PyTorch so that those can be used by other GPU applications.

## Best practices¶

### Device-agnostic code¶

Due to the structure of PyTorch, you may need to explicitly write device-agnostic (CPU or GPU) code; an example may be creating a new tensor as the initial hidden state of a recurrent neural network.

The first step is to determine whether the GPU should be used or not. A common pattern is to use Python’s argparse module to read in user arguments, and have a flag that can be used to disable CUDA, in combination with is_available(). In the following, args.cuda results in a flag that can be used to cast tensors and modules to CUDA if desired:

import argparse
import torch

parser = argparse.ArgumentParser(description='PyTorch Example')
parser.add_argument('--disable-cuda', action='store_true',
help='Disable CUDA')
args = parser.parse_args()
args.cuda = not args.disable_cuda and torch.cuda.is_available()


If modules or tensors need to be sent to the GPU, args.cuda can be used as follows:

x = torch.Tensor(8, 42)
net = Network()
if args.cuda:
x = x.cuda()
net.cuda()


When creating tensors, an alternative to the if statement is to have a default datatype defined, and cast all tensors using that. An example when using a dataloader would be as follows:

dtype = torch.cuda.FloatTensor
for i, x in enumerate(train_loader):
x = Variable(x.type(dtype))


When working with multiple GPUs on a system, you can use the CUDA_VISIBLE_DEVICES environment flag to manage which GPUs are available to PyTorch. As mentioned above, to manually control which GPU a tensor is created on, the best practice is to use a torch.cuda.device context manager:

print("Outside device is 0")  # On device 0 (default in most scenarios)
with torch.cuda.device(1):
print("Inside device is 1")  # On device 1
print("Outside device is still 0")  # On device 0


If you have a tensor and would like to create a new tensor of the same type on the same device, then you can use the new() method, which acts the same as a normal tensor constructor. Whilst the previously mentioned methods depend on the current GPU context, new() preserves the device of the original tensor.

This is the recommended practice when creating modules in which new tensors/variables need to be created internally during the forward pass:

x_cpu = torch.FloatTensor(1)
x_gpu = torch.cuda.FloatTensor(1)
x_cpu_long = torch.LongTensor(1)

y_cpu = x_cpu.new(8, 10, 10).fill_(0.3)
y_gpu = x_gpu.new(x_gpu.size()).fill_(-5)
y_cpu_long = x_cpu_long.new([[1, 2, 3]])


If you want to create a tensor of the same type and size of another tensor, and fill it with either ones or zeros, ones_like() or zeros_like() are provided as convenient helper functions (which also preserve device):

x_cpu = torch.FloatTensor(1)
x_gpu = torch.cuda.FloatTensor(1)

y_cpu = torch.ones_like(x_cpu)
y_gpu = torch.zeros_like(x_gpu)


### Use pinned memory buffers¶

Host to GPU copies are much faster when they originate from pinned (page-locked) memory. CPU tensors and storages expose a pin_memory() method, that returns a copy of the object, with data put in a pinned region.

Also, once you pin a tensor or storage, you can use asynchronous GPU copies. Just pass an additional async=True argument to a cuda() call. This can be used to overlap data transfers with computation.

You can make the DataLoader return batches placed in pinned memory by passing pin_memory=True to its constructor.

### Use nn.DataParallel instead of multiprocessing¶

Most use cases involving batched inputs and multiple GPUs should default to using DataParallel to utilize more than one GPU. Even with the GIL, a single Python process can saturate multiple GPUs.

As of version 0.1.9, large numbers of GPUs (8+) might not be fully utilized. However, this is a known issue that is under active development. As always, test your use case.

There are significant caveats to using CUDA models with multiprocessing; unless care is taken to meet the data handling requirements exactly, it is likely that your program will have incorrect or undefined behavior.