PyTorch with CUDA QUIZ (MCQ QUESTIONS AND ANSWERS)

It scatters keyword arguments across multiple GPUs.

It scatters the model parameters.

It scatters the loss function.

It scatters data across multiple GPUs.

torch.cuda.set_default_device()

torch.set_default_gpu()

torch.set_default_device()

torch.cuda.set_default_gpu()

It returns the total GPU memory.

It returns the amount of GPU memory currently allocated.

It deallocates the GPU memory.

It checks if the GPU memory is available.

tensor.set_gpu()

tensor.cuda()

tensor.set_device()

tensor.to_device()

They serve the same purpose and are interchangeable.

torch.nn.parallel.DataParallel is deprecated, and torch.nn.DataParallel should be used.

torch.nn.DataParallel supports distributed training, while torch.nn.parallel.DataParallel does not.

torch.nn.parallel.DataParallel supports distributed training, while torch.nn.DataParallel does not.

tensor.to_device()

tensor.move_to_gpu()

tensor.to_gpu()

tensor.to("cuda:0")

torch.print_gpu_info()

torch.cuda.device_info()

torch.gpu_info()

torch.cuda.get_device_properties()

It scatters keyword arguments across multiple GPUs.

It scatters the model parameters.

It scatters the loss function.

It scatters data across multiple GPUs.

tensor.contiguous()

tensor.is_contiguous()

tensor.check_contiguous()

tensor.verify_contiguous()

It releases the GPU memory held by unused variables.

It clears the GPU cache for faster computations.

It deallocates the GPU device.

It resets the GPU to its initial state.

torch.nn.parallel.DistributedDataParallel

torch.nn.parallel.DataParallel

torch.nn.parallel.parallel_apply

torch.nn.parallel.replicate

Moves the tensor to the CPU.

Copies the tensor to a remote server.

Converts the tensor to a NumPy array.

Changes the data type of the tensor.

torch.cuda.default_device()

torch.get_default_gpu()

torch.cuda.current_device()

torch.default_gpu_index()

It applies a function to each element of a tensor in parallel.

It applies a function to each GPU in parallel.

It applies a function to multiple models in parallel.

It applies a function to each batch of data in parallel.

tensor.on_gpu()

tensor.cuda()

tensor.is_cuda()

tensor.is_on_gpu()

torch.device()

torch.cuda.is_available()

torch.gpu_status()

torch.is_cuda_supported()

It replicates the model on multiple GPUs.

It replicates the data on multiple GPUs.

It replicates the training process on multiple GPUs.

It replicates the loss function on multiple GPUs.

It detaches the tensor from the computation graph.

It detaches the tensor from the GPU.

It detaches the tensor from the CPU.

It detaches the tensor from the memory.

torch.get_gpu_name()

torch.cuda.get_device_name()

torch.gpu_device_name()

torch.selected_gpu_name()

It applies a function to each element of a tensor in parallel.

It applies a function to each GPU in parallel.

It applies a function to multiple models in parallel.

It applies a function to each batch of data in parallel.

tensor.to_gpu()

tensor.cuda()

tensor.to("cuda")

tensor.to_default_gpu()

torch.list_gpu_devices()

torch.cuda.device_count()

torch.gpu_list()

torch.get_gpu_devices()

It scatters data across multiple GPUs.

It scatters the model parameters.

It scatters the loss function.

It scatters the gradient updates.

tensor.copy()

tensor.clone()

tensor.copyto()

tensor.duplicate()

torch.current_gpu()

torch.selected_device()

torch.cuda.current_device()

torch.get_selected_gpu()

It enables distributed training across multiple nodes.

It allows parallel processing of data on a single GPU.

It optimizes the model architecture for GPU acceleration.

It simplifies data loading for parallel processing.

Moves the tensor to a remote server.

Converts the tensor to a NumPy array.

Copies the tensor to the CPU.

Reshapes the tensor.

torch.set_gpu()

torch.device()

torch.cuda.set_device()

torch.select_gpu()

It enables distributed training across multiple nodes.

It allows parallel processing of data on multiple GPUs.

It manages the data loading pipeline efficiently.

It optimizes the model architecture for GPU acceleration.

tensor.gpu()

tensor.to_gpu()

tensor.cuda()

tensor.to("cuda")