Skip to main content

DASH

Project description

DASH (wip)

Implementation of DASH, Warm-Starting Neural Network Training in Stationary Settings without Loss of Plasticity

Install

$ pip install DASH-pytorch

Usage

import torch
from torch.nn import Linear

from DASH.DASH import AdamW

net = Linear(10, 5)
optim = AdamW(net.parameters(), lr = 3e-4)

loss = net(torch.randn(10)).sum()
loss.backward()

optim.step()
optim.zero_grad()

optim.shrink_params()
optim.clear_grad_ema()

Citations

@misc{shin2024dashwarmstartingneuralnetwork,
    title   = {DASH: Warm-Starting Neural Network Training in Stationary Settings without Loss of Plasticity}, 
    author  = {Baekrok Shin and Junsoo Oh and Hanseul Cho and Chulhee Yun},
    year    = {2024},
    eprint  = {2410.23495},
    archivePrefix = {arXiv},
    primaryClass = {cs.LG},
    url     = {https://arxiv.org/abs/2410.23495}, 
}

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

dash_pytorch-0.0.2.tar.gz (105.6 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

dash_pytorch-0.0.2-py3-none-any.whl (5.3 kB view details)

Uploaded Python 3

File details

Details for the file dash_pytorch-0.0.2.tar.gz.

File metadata

  • Download URL: dash_pytorch-0.0.2.tar.gz
  • Upload date:
  • Size: 105.6 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.9.24

File hashes

Hashes for dash_pytorch-0.0.2.tar.gz
Algorithm Hash digest
SHA256 1f65d2933d9470f4f1281e4084a2702388a4d9ec77eb1e1ffc5ffa96e5513395
MD5 098478f478bef9313fac3790678b26c5
BLAKE2b-256 7019a915eed51f384e13fcbb5627329f338be0fbad0abf103e71c7c4efbbffec

See more details on using hashes here.

File details

Details for the file dash_pytorch-0.0.2-py3-none-any.whl.

File metadata

  • Download URL: dash_pytorch-0.0.2-py3-none-any.whl
  • Upload date:
  • Size: 5.3 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.9.24

File hashes

Hashes for dash_pytorch-0.0.2-py3-none-any.whl
Algorithm Hash digest
SHA256 e0ebcfcc8aa7f7ba97ad6ca4fc40918991b0ceabee29e59bfcbf49c8331a7500
MD5 fa9d9b9586a2d521a86105b6551e91af
BLAKE2b-256 fc82e145a2916ec9d221480a1d4ca5959418e9ff88bd37e8d87f350b498f1bf4

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page