Data (you can either download them or use the aicrowd cli). 2021. 7. 18. · Syntax: Tensor.to (device_name): Returns new instance of ‘Tensor’ on the device specified by ‘device_name’: ‘cpu’ for CPU and ‘cuda’ for CUDA enabled GPU. Tensor.cpu (): Transfers ‘Tensor’ to CPU from it’s current device. To demonstrate the above functions, we’ll be creating a test tensor and do the following operations:. torch.cuda.seed() [source] Sets the seed for generating random numbers to a random number for the current GPU. It's safe to call this function if CUDA is not available; in that case, it is silently ignored. Warning If you are working with a multi-GPU model, this function will only initialize the seed on one GPU.

2019. 5. 14. · Arguments: device (torch.device or int, optional): device for which to return the device capability. This function is a no-op if this argument is a negative integer. Uses the current device, given by :meth:`~torch.cuda.current_device`, if :attr:`device` is ``None`` (default). Returns: tuple (int, int): the major and minor cuda capability of the. 20 hours ago · There are three steps involved in training the PyTorch model in GPU using CUDA methods. First, we should code a neural network, allocate a model with GPU and start the training in the system. Initially, we can check whether the model is present in GPU or not by running the code. next (net.parameters ()).is_cuda. Seems like with the external GPU the random seed is not working. When device="cuda" its automatically uses the RTX 3070 and no reproducibility. I am working with num_workers=0 and worker_init_fn=np.random.seed(1) in the dataloder. So practically changing the executor GPU has effect on the random seed.
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