peterjc123

# Prerequisites
# 1. MSVC 2017 C++ Build Tools
# 2. CMAKE 3.0 or up
# 3. 64 bits of Windows
# 4. Anaconda / MiniConda 64 bits

# Prerequisites for CUDA
# 1. CUDA 8.0 or up
# 2. NVTX( in CUDA as Visual Studio Integration. if fail to install, you can extract
#     the CUDA installer exe and found the NVTX installer under the CUDAVisualStudioIntegration)

lucaswiman

digraph  {
	0 -> "*" [key=0,
	label="[b]"];
1 -> "*" [key=0,
label="[d]"];
enter -> "*" [key=0,
label=ε];
"*" -> 0 [key=0,
label="[a]"];
"*" -> 1 [key=0,

tmdavid

"""
Visualize word embeddings, using tsne.
First computes cosine distance of the 100 closests words, and then shows a clustering graph
of the first 11 closest words (the first one is always the word)
IT REQUIRES GLOVE MODEL.txt
line 31: glove_file = '../TBIR/glove.840B.300d.txt' MODIFY with the appropiate path

To Use it, you can just type: python word_embedding_vis.py <list of words space separated>
                              e.g: python word_embedding_vis.py cake word embedding music
"""

junyann

from transformers import GPT2Tokenizer, GPT2LMHeadModel
import torch
from torch.nn import CrossEntropyLoss
from tqdm import trange


max_length = 24
batch_size = 200

thousandlemons

This setup guide has been moved to my blog:

https://nathaniel.blog/tutorials/shadowsocks/

WeiTang114

#!/bin/bash
# Show username after each process in nvidia-smi
# like:
# ...
# +------------------------------------------------------+
# | Processes:                                GPU Memory |
# |  GPU       PID  Type  Process name        Usage      |
# |======================================================|
# |    0    150752    C   python                  830MiB | User: user1
# |    1      2185    C   /usr/bin/python        1090MiB | User: user2

abhishekcs10

#!/bin/bash

# this script installs GCC 5.4.0 
# to use it navigate to your home directory and type:
# sh install-gcc-5.4.0.sh

# download and install gcc 4.9.3
wget https://github.com/gcc-mirror/gcc/archive/gcc-5_4_0-release.tar.gz
tar xzf gcc-5_4_0-release.tar.gz
cd gcc-5_4_0-release

Tushar-N

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

yuchenlin

import torch
from transformers import OpenAIGPTTokenizer, OpenAIGPTLMHeadModel
from transformers import GPT2Tokenizer, GPT2LMHeadModel
import numpy as np
from scipy.special import softmax
 
def model_init(model_string, cuda):
    if model_string.startswith("gpt2"):
        tokenizer = GPT2Tokenizer.from_pretrained(model_string)
        model = GPT2LMHeadModel.from_pretrained(model_string)

misberner

# LaTeX temporary files
*.aux
*.log
*.toc

# PDF output - usually a bad idea to keep this in Git
*.pdf

# Latexmk
*.fdb_latexmk