koila VS thrash-protect

Compare koila vs thrash-protect and see what are their differences.

thrash-protect

Simple-Stupid user-space program doing "kill -STOP" and "kill -CONT" to protect from thrashing. It works a bit like the ABS break on the car. (by tobixen)
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koila thrash-protect
7 1
1,817 160
- -
6.8 10.0
20 days ago about 2 years ago
Python Python
Apache License 2.0 GPL-3.0
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.

koila

Posts with mentions or reviews of koila. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-04-17.

thrash-protect

Posts with mentions or reviews of thrash-protect. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-10-12.

What are some alternatives?

When comparing koila and thrash-protect you can also consider the following projects:

TorchGA - Train PyTorch Models using the Genetic Algorithm with PyGAD

bustd - Process killer daemon for out-of-memory scenarios

torchsynth - A GPU-optional modular synthesizer in pytorch, 16200x faster than realtime, for audio ML researchers.

nohang - A sophisticated low memory handler for Linux

bittensor - Internet-scale Neural Networks

le9-patch - [PATCH] mm: Protect the working set under memory pressure to prevent thrashing, avoid high latency and prevent livelock in near-OOM conditions

gpt-j-api-huggingface

tributary - Streaming reactive and dataflow graphs in Python

merged_depth - Monocular Depth Estimation - Weighted-average prediction from multiple pre-trained depth estimation models

ADOP