ScrapMechanicSeedTool
A tool that allows you to modify the seed of scrap mechanic save files. (by FxMorin)
framework-reproducibility
Providing reproducibility in deep learning frameworks (by NVIDIA)
| ScrapMechanicSeedTool | framework-reproducibility | |
|---|---|---|
| 5 | 5 | |
| 3 | 419 | |
| - | 1.4% | |
| 0.0 | 2.8 | |
| almost 4 years ago | 8 days ago | |
| Python | Python | |
| GNU General Public License v3.0 only | 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.
ScrapMechanicSeedTool
Posts with mentions or reviews of ScrapMechanicSeedTool.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2021-04-08.
-
Edit tiles survival
YES! There is: https://github.com/fxmorin/ScrapMechanicSeedTool
-
Ability to set the world seed when creating a world.
I figured there was a way to check a seed and use it to generate a world - there is this Scrap Mechanic Seed Tool made by user fxmorin. No clue if it still works, considering it hasn't been updated in a while.
- The perfect Survival map?
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
-
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 ScrapMechanicSeedTool and framework-reproducibility you can also consider the following projects:
VW_Flash - Flashing tools for VW AG control units over UDS. Compression, encryption, RSA bypass, and checksums are supported for Simos18.1/6/10, DQ250-MQB, DQ381-MQB, and Haldex4Motion-Gen5-MQB.
einops - Flexible and powerful tensor operations for readable and reliable code (for pytorch, jax, TF and others)
doublerandom - Randomness mixers, offline seed generator, BIP39 seed generator, random numbers in range algorithm, random words from a dictionary.
Real-Time-Voice-Cloning - Clone a voice in 5 seconds to generate arbitrary speech in real-time
seed-labs - SEED Labs developed in the last 20 years.
horovod - Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet.