phi-accrual-failure-detector
hivemind
| phi-accrual-failure-detector | hivemind | |
|---|---|---|
| 1 | 40 | |
| 7 | 1,840 | |
| - | 1.5% | |
| 0.0 | 5.4 | |
| about 3 years ago | about 1 month ago | |
| Python | Python | |
| MIT License | MIT License |
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.
phi-accrual-failure-detector
-
Detecting node failures and the Phi accrual failure detector
This post goes through some concepts of the ϕ Accrual failure detector paper, and it describes a concrete python implementation available at the following link: phi-accrual-failure-detector. The code is using a fixed value (phi_value < threshold) to decide if a node/process is available or not. Still, the resulting φ value is dynamic, and the implementation can eventually consider assigning different values of availability depending on the resulting φ value.
hivemind
-
You can now train a 70B language model at home
https://github.com/learning-at-home/hivemind is also relevant
-
Would anyone be interested in contributing to some group projects?
I really hope you'll join me, for the Petals support, at least! A single docker-compose.yml file is all we need, for now. If we are able to find enough people willing to host some smaller models, perhaps we could expand into the Hivemind, and create our own, custom foundation model one day?
- Hive mind:Train deep learning models on thousands of volunteers across the world
-
Could a model not be trained by a decentralized network? Like Seti @ home or kinda-sorta like bitcoin. Petals accomplishes this somewhat, but if raw computer power is the only barrier to open-source I'd be happy to try organizing decentalized computing efforts
Decentralized deep learning: https://github.com/learning-at-home/hivemind
-
Orca (built on llama13b) looks like the new sheriff in town
https://github.com/learning-at-home/hivemind - same people behind it, was made before petals I think.
-
Do you think that AI research will slow down to a halt because of regulation?
not if we rise to meet that challenge. here's a few tools that facilitate AI research in the face of an advanced persistent threat: Hivemind- a distributed Pytorch framework
-
LLM@home
yeah, there's Hivemind. and there's research wrt how to chunk out training workload so it can be scaled up. not sure why there's commentary that latency issues would limit this sort of enterprise, the architecture typically isn't designed for liveness. other subfields of distributed training/inference include zero-knowledge machine learning. besides all of that, there's also adversarial computation like SafetyNets and refereed delegation of computation.
-
[D] Google "We Have No Moat, And Neither Does OpenAI": Leaked Internal Google Document Claims Open Source AI Will Outcompete Google and OpenAI
We already have the software for it. There are some projects, but the one I'm most familiar with is https://github.com/learning-at-home/hivemind for training and it's sister project https://petals.ml/ for running large models distributed.
-
Run 100B+ language models at home, BitTorrent‑style
I'm not entirely how the approach they're using works [0], but I study federated learning and one of the highly-cited survey papers has several chapters (5 and 6 in particular) addressing potential attacks, failure modes, and bias [1].
0: https://github.com/learning-at-home/hivemind
1: https://arxiv.org/abs/1912.04977
-
SETI Home Is in Hibernation
The Hivemind project is just that
https://github.com/learning-at-home/hivemind
What are some alternatives?
Akka - Build highly concurrent, distributed, and resilient message-driven applications on the JVM
replika-research - Replika.ai Research Papers, Posters, Slides & Datasets
Thespian Actor Library - Python Actor concurrency library
GLM-130B - GLM-130B: An Open Bilingual Pre-Trained Model (ICLR 2023)
paper_nava_2023_icra_fault-control-ironcub - Repository associated with the paper "Failure Detection and Fault Tolerant Control of a Jet-Powered Flying Humanoid Robot", published in IEEE ICRA 2023.
Super-SloMo - PyTorch implementation of Super SloMo by Jiang et al.
alpa - Training and serving large-scale neural networks with auto parallelization.
mesh-transformer-jax - Model parallel transformers in JAX and Haiku
HiveMind-core - Join the OVOS collective, utils for OpenVoiceOS mesh networking
FedML - FEDML - The unified and scalable ML library for large-scale distributed training, model serving, and federated learning. FEDML Launch, a cross-cloud scheduler, further enables running any AI jobs on any GPU cloud or on-premise cluster. Built on this library, FEDML Nexus AI (https://fedml.ai) is your generative AI platform at scale.
Open-Assistant - OpenAssistant is a chat-based assistant that understands tasks, can interact with third-party systems, and retrieve information dynamically to do so.
mixture-of-experts - PyTorch Re-Implementation of "The Sparsely-Gated Mixture-of-Experts Layer" by Noam Shazeer et al. https://arxiv.org/abs/1701.06538