pyPeriod
Python package implementing various periodicity transforms in Python: Sethares and Staley, Muresan and Parks, my own. (by woolgathering)
framework-reproducibility
Providing reproducibility in deep learning frameworks (by NVIDIA)
| pyPeriod | framework-reproducibility | |
|---|---|---|
| 1 | 5 | |
| 2 | 420 | |
| - | 0.7% | |
| 2.9 | 2.8 | |
| 6 months ago | about 1 month ago | |
| Python | Python | |
| MIT License | Apache License 2.0 |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
pyPeriod
Posts with mentions or reviews of pyPeriod.
We have used some of these posts to build our list of alternatives
and similar projects.
framework-reproducibility
Posts with mentions or reviews of framework-reproducibility.
We have used some of these posts to build our list of alternatives
and similar projects.
-
Tensorflow: I'm getting different results from the same code depending on where I run it. [D]
Even with a fixed seed there's no guarantee that you'll get the exact same results due to the fact that most floating operations are not deterministic when parallelized. You can enable determinism flags in your framework to try and mitigate that, but results may still vary depending on your model and how you're running it.
- Same seed, different images
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Dealing with non-deterministic result
Setting the seed alone is not enough because there will be a randomness resulted from GPU operations (there is some way to eliminate randomness due to GPU operations like https://github.com/NVIDIA/framework-determinism, but I cannot make it work with the current latest version of TF). Another workaround is not using GPU, but the training time does not make sense as I need to iterate fast, trying new idea.
- No Bee, it's you...
-
[D] Do you yourself write 100% reproducible ML code?
check out https://github.com/NVIDIA/framework-determinism, which should allow you to make fully reproducible to the bit code that runs on GPU. i've contributed to this repo and the author is extremely helpful.
What are some alternatives?
When comparing pyPeriod and framework-reproducibility you can also consider the following projects:
einops - Flexible and powerful tensor operations for readable and reliable code (for pytorch, jax, TF and others)
Real-Time-Voice-Cloning - Clone a voice in 5 seconds to generate arbitrary speech in real-time
horovod - Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet.