Multiprocessing best practices¶
torch.multiprocessing is a drop in replacement for Python’s
multiprocessing module. It supports the exact same operations,
but extends it, so that all tensors sent through a
multiprocessing.Queue, will have their data moved into shared
memory and will only send a handle to another process.
Tensor is sent to another process, the
Tensor data is shared. If
None, it is also shared. After a
torch.Tensor.grad field is sent to the other process, it
creates a standard process-specific
is not automatically shared across all processes, unlike how the
Tensor’s data has been shared.
This allows to implement various training methods, like Hogwild, A3C, or any others that require asynchronous operation.
CUDA in multiprocessing¶
The CUDA runtime does not support the
fork start method; either the
forkserver start method are
required to use CUDA in subprocesses.
The start method can be set via either creating a context with
multiprocessing.get_context(...) or directly using
Unlike CPU tensors, the sending process is required to keep the original tensor as long as the receiving process retains a copy of the tensor. It is implemented under the hood but requires users to follow the best practices for the program to run correctly. For example, the sending process must stay alive as long as the consumer process has references to the tensor, and the refcounting can not save you if the consumer process exits abnormally via a fatal signal. See this section.
Best practices and tips¶
Avoiding and fighting deadlocks¶
There are a lot of things that can go wrong when a new process is spawned, with
the most common cause of deadlocks being background threads. If there’s any
thread that holds a lock or imports a module, and
fork is called, it’s very
likely that the subprocess will be in a corrupted state and will deadlock or
fail in a different way. Note that even if you don’t, Python built in
libraries do - no need to look further than
multiprocessing.Queue is actually a very complex class, that
spawns multiple threads used to serialize, send and receive objects, and they
can cause aforementioned problems too. If you find yourself in such situation
try using a
SimpleQueue, that doesn’t
use any additional threads.
We’re trying our best to make it easy for you and ensure these deadlocks don’t happen but some things are out of our control. If you have any issues you can’t cope with for a while, try reaching out on forums, and we’ll see if it’s an issue we can fix.
Reuse buffers passed through a Queue¶
Remember that each time you put a
Tensor into a
multiprocessing.Queue, it has to be moved into shared memory.
If it’s already shared, it is a no-op, otherwise it will incur an additional
memory copy that can slow down the whole process. Even if you have a pool of
processes sending data to a single one, make it send the buffers back - this
is nearly free and will let you avoid a copy when sending next batch.
Asynchronous multiprocess training (e.g. Hogwild)¶
torch.multiprocessing, it is possible to train a model
asynchronously, with parameters either shared all the time, or being
periodically synchronized. In the first case, we recommend sending over the whole
model object, while in the latter, we advise to only send the
We recommend using
multiprocessing.Queue for passing all kinds
of PyTorch objects between processes. It is possible to e.g. inherit the tensors
and storages already in shared memory, when using the
fork start method,
however it is very bug prone and should be used with care, and only by advanced
users. Queues, even though they’re sometimes a less elegant solution, will work
properly in all cases.
You should be careful about having global statements, that are not guarded
if __name__ == '__main__'. If a different start method than
fork is used, they will be executed in all subprocesses.
A concrete Hogwild implementation can be found in the examples repository, but to showcase the overall structure of the code, there’s also a minimal example below as well:
import torch.multiprocessing as mp from model import MyModel def train(model): # Construct data_loader, optimizer, etc. for data, labels in data_loader: optimizer.zero_grad() loss_fn(model(data), labels).backward() optimizer.step() # This will update the shared parameters if __name__ == '__main__': num_processes = 4 model = MyModel() # NOTE: this is required for the ``fork`` method to work model.share_memory() processes =  for rank in range(num_processes): p = mp.Process(target=train, args=(model,)) p.start() processes.append(p) for p in processes: p.join()
CPU in multiprocessing¶
Inappropriate multiprocessing can lead to CPU oversubscription, causing different processes to compete for CPU resources, resulting in low efficiency.
This tutorial will explain what CPU oversubscription is and how to avoid it.
CPU oversubscription is a technical term that refers to a situation where the total number of vCPUs allocated to a system exceeds the total number of vCPUs available on the hardware.
This leads to severe contention for CPU resources. In such cases, there is frequent switching between processes, which increases processes switching overhead and decreases overall system efficiency.
See CPU oversubscription with the code examples in the Hogwild implementation found in the example repository.
When running the training example with the following command on CPU using 4 processes:
python main.py --num-processes 4
Assuming there are N vCPUs available on the machine, executing the above command will generate 4 subprocesses. Each subprocess will allocate N vCPUs for itself, resulting in a requirement of 4*N vCPUs. However, the machine only has N vCPUs available. Consequently, the different processes will compete for resources, leading to frequent process switching.
The following observations indicate the presence of CPU over subscription:
High CPU Utilization: By using the
htopcommand, you can observe that the CPU utilization is consistently high, often reaching or exceeding its maximum capacity. This indicates that the demand for CPU resources exceeds the available physical cores, causing contention and competition among processes for CPU time.
Frequent Context Switching with Low System Efficiency: In an oversubscribed CPU scenario, processes compete for CPU time, and the operating system needs to rapidly switch between different processes to allocate resources fairly. This frequent context switching adds overhead and reduces the overall system efficiency.
Avoid CPU oversubscription¶
A good way to avoid CPU oversubscription is proper resource allocation. Ensure that the number of processes or threads running concurrently does not exceed the available CPU resources.
In this case, a solution would be to specify the appropriate number of
threads in the subprocesses. This can be achieved by setting the number
of threads for each process using the
function in subprocess.
Assuming there are N vCPUs on the machine and M processes will be
generated, the maximum
num_threads value used by each process would
floor(N/M). To avoid CPU oversubscription in the mnist_hogwild
example, the following changes are needed for the file
def train(rank, args, model, device, dataset, dataloader_kwargs): torch.manual_seed(args.seed + rank) #### define the num threads used in current sub-processes torch.set_num_threads(floor(N/M)) train_loader = torch.utils.data.DataLoader(dataset, **dataloader_kwargs) optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum) for epoch in range(1, args.epochs + 1): train_epoch(epoch, args, model, device, train_loader, optimizer)
num_thread for each process using
torch.set_num_threads(floor(N/M)). where you replace N with the
number of vCPUs available and M with the chosen number of processes. The
num_thread value will vary depending on the specific
task at hand. However, as a general guideline, the maximum value for the
num_thread should be
floor(N/M) to avoid CPU oversubscription.
In the mnist_hogwild training example, after avoiding CPU over
subscription, you can achieve a 30x performance boost.