You can use torch.manual_seed() to seed the RNG for all devices (both CPU and CUDA):
Furthermore, if you are using CUDA tensors, and your CUDA version is 10.2 or greater, you should set the environment variable CUBLAS_WORKSPACE_CONFIG according to CUDA documentation: https://docs.nvidia.com/cuda/cublas/index.html#cublasApi_reproducibility
PyTorch random number generator¶
Completely reproducible results are not guaranteed across PyTorch releases, individual commits, or different platforms. Furthermore, results may not be reproducible between CPU and GPU executions, even when using identical seeds.
While disabling CUDA convolution benchmarking (discussed above) ensures that CUDA selects the same algorithm each time an application is run, that algorithm itself may be nondeterministic, unless either torch.use_deterministic_algorithms(True) or torch.backends.cudnn.deterministic = True is set. The latter setting controls only this behavior, unlike torch.use_deterministic_algorithms() which will make other PyTorch operations behave deterministically, too.
For example, running the nondeterministic CUDA implementation of torch.Tensor.index_add_() will throw an error:
3. Money from your personal account can be used to get your business going.
Accelerators and Incubators may require equity from a company in exchange for their services. Check out our guide to learn more about accelerators vs incubators.
Friends and family are loaning—not gifting—money most of the time; they will expect to be paid back.
Once a company acquires Seed Capital, they can use it for a variety of purposes:
Trying to fund your early stage business through personal funds, or working on it as a side-hustle, can be a recipe for disaster. Companies typically need time before they obtain reliable customer bases, which makes it extremely difficult to finance a startup through early-stage revenue.
Sometimes it is useful to be able to reproduce the sequences given by a pseudo-random number generator. By re-using a seed value, the same sequence should be reproducible from run to run as long as multiple threads are not running.
random. expovariate ( lambd ) ¶
random. getrandbits ( k ) ¶
New in version 3.9.
random. vonmisesvariate ( mu, kappa ) ¶
Returns a new list containing elements from the population while leaving the original population unchanged. The resulting list is in selection order so that all sub-slices will also be valid random samples. This allows raffle winners (the sample) to be partitioned into grand prize and second place winners (the subslices).
Changed in version 3.2: randrange() is more sophisticated about producing equally distributed values. Formerly it used a style like int(random()*n) which could produce slightly uneven distributions.