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5 лет назад | |
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.circleci | 5 лет назад | |
docs | 5 лет назад | |
tesseract | 5 лет назад | |
tests | 5 лет назад | |
.gitignore | 5 лет назад | |
CONTRIBUTING.md | 5 лет назад | |
LICENSE | 5 лет назад | |
README.md | 5 лет назад | |
requirements.txt | 5 лет назад | |
setup.py | 5 лет назад |
Distributed training of large neural networks across volunteer computers.
[WIP] - this branch is a work in progress. If you're interested in supplementary code for Learning@home paper, you can find it at https://github.com/mryab/learning-at-home .
Currently, there is no way to do it easily. There are some tests (you can check ./tests/benchmark_throughput.py
or look into CI logs) and we want to expand them. If you want to
do something complex with it, please contact us by opening an issue (less preferred: telegram).
tesseract
quick tourTrainer process:
RemoteExpert
(tesseract/client/remote_expert.py
) behaves like a pytorch
module with autograd support but actually sends request to a remote runtime.GatingFunction
(tesseract/client/gating_function.py
) finds best experts
for a given input and either returns them as RemoteExpert
or applies them
right away.Runtime process:
TesseractRuntime
(tesseract/runtime/__init__.py
) aggregates batches
and performs inference/training of experts according to their priority.TesseractServer
(tesseract/server/__init__.py
) wraps runtime and
periodically uploads experts into TesseractNetwork
.DHT:
TesseractNetwork
(tesseract/network/__init__.py
) is a node of
Kademlia-based DHT that stores metadata used by trainer and runtime.DHT:
Runtime: