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get seed

This module implements pseudo-random number generators for various distributions.

Most of the random module’s algorithms and seeding functions are subject to change across Python versions, but two aspects are guaranteed not to change:

Bookkeeping functions¶

random. betavariate ( alpha, beta ) ¶

Changed in version 3.9: Raises a ValueError if all weights are zero.

Return a random floating point number N such that a <= N <= b for a <= b and b <= N <= a for b < a .

torch.use_deterministic_algorithms() lets you configure PyTorch to use deterministic algorithms instead of nondeterministic ones where available, and to throw an error if an operation is known to be nondeterministic (and without a deterministic alternative).

If you or any of the libraries you are using rely on NumPy, you can seed the global NumPy RNG with:

Disabling the benchmarking feature with torch.backends.cudnn.benchmark = False causes cuDNN to deterministically select an algorithm, possibly at the cost of reduced performance.

Avoiding nondeterministic algorithms¶

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.

The cuDNN library, used by CUDA convolution operations, can be a source of nondeterminism across multiple executions of an application. When a cuDNN convolution is called with a new set of size parameters, an optional feature can run multiple convolution algorithms, benchmarking them to find the fastest one. Then, the fastest algorithm will be used consistently during the rest of the process for the corresponding set of size parameters. Due to benchmarking noise and different hardware, the benchmark may select different algorithms on subsequent runs, even on the same machine.

For example, running the nondeterministic CUDA implementation of torch.Tensor.index_add_() will throw an error:

However, there are some steps you can take to limit the number of sources of nondeterministic behavior for a specific platform, device, and PyTorch release. First, you can control sources of randomness that can cause multiple executions of your application to behave differently. Second, you can configure PyTorch to avoid using nondeterministic algorithms for some operations, so that multiple calls to those operations, given the same inputs, will produce the same result.

To address the problem, RentCheck built a web app for property managers that they believe also benefits tenants. The company’s digital platform works by providing a way for property managers to facilitate and conduct remote, guided property inspections. For obvious reasons, the company saw increased demand upon the onset of the COVID-19 pandemic, considering that the platform was automated and contactless. It saw 1,000% — mostly organic — growth in terms of the number of properties on the platform.

“It was an injustice for me not to pursue it,” she told TechCrunch. “I took meticulous photos of the move-out condition of my apartment. The process took 18 months. But not everyone has the time or knowledge to fight in court.”

Image Credits: RentCheck; Co-founders Marco Nelson and Lydia Winkler

You’re getting ready to vacate a property you’ve rented, only to be told by the landlord that you won’t be getting your security deposit back.

And so New Orleans-based RentCheck was born.

Everything is done within the app so that users can’t upload photos that were previously on their camera roll “to ensure the integrity of the inspection” and that everything is time stamped. Once the inspection is complete, whoever does it signs off on it that they completed it accurately and honestly. Then the property manager can also sign off on it so both parties can agree on the move-out condition.

This happened to me the first time I ever rented a place in the late 90s. I was shocked, but more than anything, I was angry at the injustice because I knew that what the landlord claimed was not true. It was her word against mine and my roommate’s. Still, we took her to small claims court, not so much over the $800 she was trying to keep but more to prove her wrong. In the end, we won.