.. _sphx_glr_beginner_blitz_data_parallel_tutorial.py: Optional: Data Parallelism ========================== **Authors**: `Sung Kim `_ and `Jenny Kang `_ In this tutorial, we will learn how to use multiple GPUs using ``DataParallel``. It's very easy to use GPUs with PyTorch. You can put the model on a GPU: .. code:: python model.gpu() Then, you can copy all your tensors to the GPU: .. code:: python mytensor = my_tensor.gpu() Please note that just calling ``mytensor.gpu()`` won't copy the tensor to the GPU. You need to assign it to a new tensor and use that tensor on the GPU. It's natural to execute your forward, backward propagations on multiple GPUs. However, Pytorch will only use one GPU by default. You can easily run your operations on multiple GPUs by making your model run parallelly using ``DataParallel``: .. code:: python model = nn.DataParallel(model) That's the core behind this tutorial. We will explore it in more detail below. Imports and parameters ---------------------- Import PyTorch modules and define parameters. .. code-block:: python import torch import torch.nn as nn from torch.autograd import Variable from torch.utils.data import Dataset, DataLoader # Parameters and DataLoaders input_size = 5 output_size = 2 batch_size = 30 data_size = 100 Dummy DataSet ------------- Make a dummy (random) dataset. You just need to implement the getitem .. code-block:: python class RandomDataset(Dataset): def __init__(self, size, length): self.len = length self.data = torch.randn(length, size) def __getitem__(self, index): return self.data[index] def __len__(self): return self.len rand_loader = DataLoader(dataset=RandomDataset(input_size, 100), batch_size=batch_size, shuffle=True) Simple Model ------------ For the demo, our model just gets an input, performs a linear operation, and gives an output. However, you can use ``DataParallel`` on any model (CNN, RNN, Capsule Net etc.) We've placed a print statement inside the model to monitor the size of input and output tensors. Please pay attention to what is printed at batch rank 0. .. code-block:: python class Model(nn.Module): # Our model def __init__(self, input_size, output_size): super(Model, self).__init__() self.fc = nn.Linear(input_size, output_size) def forward(self, input): output = self.fc(input) print(" In Model: input size", input.size(), "output size", output.size()) return output Create Model and DataParallel ----------------------------- This is the core part of the tutorial. First, we need to make a model instance and check if we have multiple GPUs. If we have multiple GPUs, we can wrap our model using ``nn.DataParallel``. Then we can put our model on GPUs by ``model.gpu()`` .. code-block:: python model = Model(input_size, output_size) if torch.cuda.device_count() > 1: print("Let's use", torch.cuda.device_count(), "GPUs!") # dim = 0 [30, xxx] -> [10, ...], [10, ...], [10, ...] on 3 GPUs model = nn.DataParallel(model) if torch.cuda.is_available(): model.cuda() Run the Model ------------- Now we can see the sizes of input and output tensors. .. code-block:: python for data in rand_loader: if torch.cuda.is_available(): input_var = Variable(data.cuda()) else: input_var = Variable(data) output = model(input_var) print("Outside: input size", input_var.size(), "output_size", output.size()) .. rst-class:: sphx-glr-script-out Out:: In Model: input size torch.Size([30, 5]) output size torch.Size([30, 2]) Outside: input size torch.Size([30, 5]) output_size torch.Size([30, 2]) In Model: input size torch.Size([30, 5]) output size torch.Size([30, 2]) Outside: input size torch.Size([30, 5]) output_size torch.Size([30, 2]) In Model: input size torch.Size([30, 5]) output size torch.Size([30, 2]) Outside: input size torch.Size([30, 5]) output_size torch.Size([30, 2]) In Model: input size torch.Size([10, 5]) output size torch.Size([10, 2]) Outside: input size torch.Size([10, 5]) output_size torch.Size([10, 2]) Results ------- When we batch 30 inputs and 30 outputs, the model gets 30 and outputs 30 as expected. But if you have GPUs, then you can get results like this. 2 GPUs ~~~~~~ If you have 2, you will see: .. code:: bash # on 2 GPUs Let's use 2 GPUs! In Model: input size torch.Size([15, 5]) output size torch.Size([15, 2]) In Model: input size torch.Size([15, 5]) output size torch.Size([15, 2]) Outside: input size torch.Size([30, 5]) output_size torch.Size([30, 2]) In Model: input size torch.Size([15, 5]) output size torch.Size([15, 2]) In Model: input size torch.Size([15, 5]) output size torch.Size([15, 2]) Outside: input size torch.Size([30, 5]) output_size torch.Size([30, 2]) In Model: input size torch.Size([15, 5]) output size torch.Size([15, 2]) In Model: input size torch.Size([15, 5]) output size torch.Size([15, 2]) Outside: input size torch.Size([30, 5]) output_size torch.Size([30, 2]) In Model: input size torch.Size([5, 5]) output size torch.Size([5, 2]) In Model: input size torch.Size([5, 5]) output size torch.Size([5, 2]) Outside: input size torch.Size([10, 5]) output_size torch.Size([10, 2]) 3 GPUs ~~~~~~ If you have 3 GPUs, you will see: .. code:: bash Let's use 3 GPUs! In Model: input size torch.Size([10, 5]) output size torch.Size([10, 2]) In Model: input size torch.Size([10, 5]) output size torch.Size([10, 2]) In Model: input size torch.Size([10, 5]) output size torch.Size([10, 2]) Outside: input size torch.Size([30, 5]) output_size torch.Size([30, 2]) In Model: input size torch.Size([10, 5]) output size torch.Size([10, 2]) In Model: input size torch.Size([10, 5]) output size torch.Size([10, 2]) In Model: input size torch.Size([10, 5]) output size torch.Size([10, 2]) Outside: input size torch.Size([30, 5]) output_size torch.Size([30, 2]) In Model: input size torch.Size([10, 5]) output size torch.Size([10, 2]) In Model: input size torch.Size([10, 5]) output size torch.Size([10, 2]) In Model: input size torch.Size([10, 5]) output size torch.Size([10, 2]) Outside: input size torch.Size([30, 5]) output_size torch.Size([30, 2]) In Model: input size torch.Size([4, 5]) output size torch.Size([4, 2]) In Model: input size torch.Size([4, 5]) output size torch.Size([4, 2]) In Model: input size torch.Size([2, 5]) output size torch.Size([2, 2]) Outside: input size torch.Size([10, 5]) output_size torch.Size([10, 2]) 8 GPUs ~~~~~~~~~~~~~~ If you have 8, you will see: .. code:: bash Let's use 8 GPUs! In Model: input size torch.Size([4, 5]) output size torch.Size([4, 2]) In Model: input size torch.Size([4, 5]) output size torch.Size([4, 2]) In Model: input size torch.Size([2, 5]) output size torch.Size([2, 2]) In Model: input size torch.Size([4, 5]) output size torch.Size([4, 2]) In Model: input size torch.Size([4, 5]) output size torch.Size([4, 2]) In Model: input size torch.Size([4, 5]) output size torch.Size([4, 2]) In Model: input size torch.Size([4, 5]) output size torch.Size([4, 2]) In Model: input size torch.Size([4, 5]) output size torch.Size([4, 2]) Outside: input size torch.Size([30, 5]) output_size torch.Size([30, 2]) In Model: input size torch.Size([4, 5]) output size torch.Size([4, 2]) In Model: input size torch.Size([4, 5]) output size torch.Size([4, 2]) In Model: input size torch.Size([4, 5]) output size torch.Size([4, 2]) In Model: input size torch.Size([4, 5]) output size torch.Size([4, 2]) In Model: input size torch.Size([4, 5]) output size torch.Size([4, 2]) In Model: input size torch.Size([4, 5]) output size torch.Size([4, 2]) In Model: input size torch.Size([2, 5]) output size torch.Size([2, 2]) In Model: input size torch.Size([4, 5]) output size torch.Size([4, 2]) Outside: input size torch.Size([30, 5]) output_size torch.Size([30, 2]) In Model: input size torch.Size([4, 5]) output size torch.Size([4, 2]) In Model: input size torch.Size([4, 5]) output size torch.Size([4, 2]) In Model: input size torch.Size([4, 5]) output size torch.Size([4, 2]) In Model: input size torch.Size([4, 5]) output size torch.Size([4, 2]) In Model: input size torch.Size([4, 5]) output size torch.Size([4, 2]) In Model: input size torch.Size([4, 5]) output size torch.Size([4, 2]) In Model: input size torch.Size([4, 5]) output size torch.Size([4, 2]) In Model: input size torch.Size([2, 5]) output size torch.Size([2, 2]) Outside: input size torch.Size([30, 5]) output_size torch.Size([30, 2]) In Model: input size torch.Size([2, 5]) output size torch.Size([2, 2]) In Model: input size torch.Size([2, 5]) output size torch.Size([2, 2]) In Model: input size torch.Size([2, 5]) output size torch.Size([2, 2]) In Model: input size torch.Size([2, 5]) output size torch.Size([2, 2]) In Model: input size torch.Size([2, 5]) output size torch.Size([2, 2]) Outside: input size torch.Size([10, 5]) output_size torch.Size([10, 2]) Summary ------- DataParallel splits your data automatically and sends job orders to multiple models on several GPUs. After each model finishes their job, DataParallel collects and merges the results before returning it to you. For more information, please check out http://pytorch.org/tutorials/beginner/former\_torchies/parallelism\_tutorial.html. **Total running time of the script:** ( 0 minutes 0.001 seconds) .. only :: html .. container:: sphx-glr-footer .. container:: sphx-glr-download :download:`Download Python source code: data_parallel_tutorial.py ` .. container:: sphx-glr-download :download:`Download Jupyter notebook: data_parallel_tutorial.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_