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Graduate Student @ SCS, CMU

Ketan Todi todiketan

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Graduate Student @ SCS, CMU
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@goldsborough
goldsborough / install-gcc.sh
Last active January 1, 2025 17:14
Instructions for installing GCC >= 4.9 for PyTorch Extensions
# Instructions for installing GCC 4.9 on various platforms.
# The commands show instructions for GCC 4.9, but any higher version will also work!
# Ubuntu (https://askubuntu.com/questions/466651/how-do-i-use-the-latest-gcc-on-ubuntu/581497#581497)
sudo apt-get install software-properties-common
sudo add-apt-repository ppa:ubuntu-toolchain-r/test
sudo apt-get update
sudo apt-get install gcc-4.9 g++-4.9
sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-4.9 60 --slave /usr/bin/g++ g++ /usr/bin/g++-4.9
@yuzeh
yuzeh / masked_softmax.py
Last active September 14, 2020 15:17
A PyTorch implementation of a softmax function where support of the underlying categorical distribution is given as input. Useful for, e.g., learning discrete policies where certain actions are known a-priori to be invalid.
# MIT License
#
# Copyright (c) 2018 Yuze Huang (hi@yuzeh.com)
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
@Tushar-N
Tushar-N / pad_packed_demo.py
Last active October 27, 2024 15:17
How to use pad_packed_sequence in pytorch<1.1.0
import torch
import torch.nn as nn
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
seqs = ['gigantic_string','tiny_str','medium_str']
# make <pad> idx 0
vocab = ['<pad>'] + sorted(set(''.join(seqs)))
# make model
@MaximumEntropy
MaximumEntropy / padded_rnn.py
Last active July 23, 2018 12:46
Padded RNN PyTorch
import torch
import torch.nn as nn
from torch.autograd import Variable
from torch.nn.utils.rnn import pad_packed_sequence, pack_padded_sequence
x = Variable(torch.randn(10, 20, 30)).cuda()
lens = range(10)
x = pack_padded_sequence(x, lens[::-1], batch_first=True)
@kenzotakahashi
kenzotakahashi / pytorch_gru.py
Last active February 4, 2020 17:35
PyTorch GRU example with a Keras-like interface.
import numpy as np
from sklearn.model_selection import train_test_split
import torch
import torch.nn as nn
from torch.autograd import Variable
np.random.seed(1337)
MAX_LEN = 30
@cbaziotis
cbaziotis / AttentionWithContext.py
Last active April 25, 2022 14:37
Keras Layer that implements an Attention mechanism, with a context/query vector, for temporal data. Supports Masking. Follows the work of Yang et al. [https://www.cs.cmu.edu/~diyiy/docs/naacl16.pdf] "Hierarchical Attention Networks for Document Classification"
def dot_product(x, kernel):
"""
Wrapper for dot product operation, in order to be compatible with both
Theano and Tensorflow
Args:
x (): input
kernel (): weights
Returns:
"""
if K.backend() == 'tensorflow':
@0xjac
0xjac / private_fork.md
Last active March 19, 2026 09:14
Create a private fork of a public repository

The repository for the assignment is public and Github does not allow the creation of private forks for public repositories.

The correct way of creating a private frok by duplicating the repo is documented here.

For this assignment the commands are:

  1. Create a bare clone of the repository. (This is temporary and will be removed so just do it wherever.) 
    

    2. 



git clone --bare git@github.com:usi-systems/easytrace.git