PyTorch framework for cryptographically secure random number generation, torchcsprng, now available

August 24, 2020

PyTorch framework for cryptographically secure random number generation, torchcsprng, now available

One of the key components of modern cryptography is the pseudorandom number generator. Katz and Lindell stated, “The use of badly designed or inappropriate random number generators can often leave a good cryptosystem vulnerable to attack. Particular care must be taken to use a random number generator that is designed for cryptographic use, rather than a ‘general-purpose’ random number generator which may be fine for some applications but not ones that are required to be cryptographically secu...

August 18, 2020

PyTorch 1.6 now includes Stochastic Weight Averaging

Do you use stochastic gradient descent (SGD) or Adam? Regardless of the procedure you use to train your neural network, you can likely achieve significantly better generalization at virtually no additional cost with a simple new technique now natively supported in PyTorch 1.6, Stochastic Weight Averaging (SWA) [1]. Even if you have already trained your model, it’s easy to realize the benefits of SWA by running SWA for a small number of epochs starting with a pre-trained model.

Efficient PyTorch I/O library for Large Datasets, Many Files, Many GPUs

Data sets are growing bigger every day and GPUs are getting faster. This means there are more data sets for deep learning researchers and engineers to train and validate their models.