| # Author: Denis A. Engemann <[email protected]> | |
| # | |
| # License: BSD (3-clause) | |
| """ Profile fast_dot versus np.dot | |
| Dependencies | |
| ------------ | |
| scikit-learn | |
| https://github.com/fabianp/memory_profiler |
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| module Calc | |
| ( Expr(..) | |
| , parse | |
| , calculate | |
| ) where | |
| import Control.Applicative | |
| import Parser | |
| data Expr = Add Expr Expr |
| from bs4 import BeautifulSoup | |
| import requests | |
| start_url = 'https://en.wikipedia.org/wiki/Transhumanism' | |
| domain = 'https://en.wikipedia.org' | |
| ''' get soup ''' | |
| def get_soup(url): | |
| # get contents from url | |
| content = requests.get(url).content |
| For troubleshooting, two things to first try: | |
| run `git config --global gpg.program gpg2`, to make sure git uses gpg2 and not gpg | |
| run `echo "test" | gpg2 --clearsign`, to make sure gpg2 itself is working | |
| If that all looks all right, one next thing to try: | |
| run `brew install pinentry` to ensure you have a good tool installed for passphrase entry | |
| If after that install and you re-try git commit and still get the "failed to sign the data" error: | |
| run `gpgconf --kill gpg-agent` to kill any running agent that might be hung |
| # Based on the code in https://github.com/tristandeleu/pytorch-meta/tree/master/examples/maml | |
| # Basically, we only use the dataset loaders/helpers from TorchMeta and replace usage of MetaModules | |
| # with normal pytorch nn.Modules, letting higher deal with making the inner loop unrollable and the | |
| # optimizers differentiable. This makes it easier to use another optimizer than SGD, or any arbitrary | |
| # third-party model, when doing MAML using this codebase. | |
| import os | |
| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F |
| import os; import psutil; import timeit | |
| from datasets import load_dataset | |
| mem_before = psutil.Process(os.getpid()).memory_info().rss >> 20 | |
| wiki = load_dataset("wikipedia", "20200501.en", split='train') | |
| mem_after = psutil.Process(os.getpid()).memory_info().rss >> 20 | |
| print(f"RAM memory used: {(mem_after - mem_before)} MB") | |
| s = """batch_size = 1000 | |
| for i in range(0, len(wiki), batch_size): |
