- act2vec, trace2vec, log2vec, model2vec https://link.springer.com/chapter/10.1007/978-3-319-98648-7_18
- apk2vec https://arxiv.org/abs/1809.05693
- app2vec http://paul.rutgers.edu/~qma/research/ma_app2vec.pdf
- ast2vec https://arxiv.org/abs/2103.11614
- attribute2vec https://arxiv.org/abs/2004.01375
- author2vec http://dl.acm.org/citation.cfm?id=2889382
- baller2vec https://arxiv.org/abs/2102.03291
- bb2vec https://arxiv.org/abs/1809.09621
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""" | |
Modification version of https://github.com/optuna/optuna/pull/2303 with nccl backend | |
Optuna example that optimizes multi-layer perceptrons using PyTorch distributed. | |
In this example, we optimize the validation accuracy of hand-written digit recognition using | |
PyTorch distributed data parallel and MNIST. We optimize the neural network architecture as well | |
as the optimizer configuration. As it is too time consuming to use the whole MNIST dataset, we | |
here use a small subset of it. |
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# CIFAR-100 | |
import numpy as np | |
import torchvision.transforms as transforms | |
from torch.utils.data import DataLoader | |
from torchvision.datasets import CIFAR100 | |
train_transform = transforms.Compose( | |
[ |
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# -*- coding: utf-8 -*- | |
require 'twitter' | |
consumer_key = '' | |
consumer_secret = '' | |
access_token = '' | |
access_token_secret = '' | |
client = Twitter::REST::Client.new do |config| |
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Just run:
sh ./ubuntu14.04-emacs-24.4-install.sh
This will install ncurses which is dependency of emacs24.4. It may requires another dependencies depending on your environment. Note that configure options in emacs installation are for disabling GUI features.
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