sway

% FontAwesome (http://fortawesome.github.com/Font-Awesome/) bindings for (Xe)LaTeX
% Author: Honza Ustohal <honza@egoistic.biz>
%
% Translation of FontAwesome's private range characters into XeTeX symbols. All icons are camel-cased and prefixed with 'fa', i.e. what was .icon-align-center the CSS version of FontAwesome becomes \faAlignCenter
% This might be reworked into a full blown package in the near future
%
% Prerequisite:
% XeLaTeX, FontAwesome installed as a system font accessible by XeLaTeX
%
% Usage:

developius

Create a new repository, or reuse an existing one.

Generate a new SSH key:

ssh-keygen -t rsa -C "your_email@example.com"

Copy the contents of the file ~/.ssh/id_rsa.pub to your SSH keys in your GitHub account settings (https://github.com/settings/keys).

Test SSH key:

karpathy

"""
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
BSD License
"""
import numpy as np

# data I/O
data = open('input.txt', 'r').read() # should be simple plain text file
chars = list(set(data))
data_size, vocab_size = len(data), len(chars)

mrocklin

omoindrot

"""
Example TensorFlow script for finetuning a VGG model on your own data.
Uses tf.contrib.data module which is in release v1.2
Based on PyTorch example from Justin Johnson
(https://gist.github.com/jcjohnson/6e41e8512c17eae5da50aebef3378a4c)

Required packages: tensorflow (v1.2)
Download the weights trained on ImageNet for VGG:
```
wget http://download.tensorflow.org/models/vgg_16_2016_08_28.tar.gz

danisfermi

Here are the steps to installing and setting up GDB on Mac OS Sierra/High Sierra. Run brew install gdb. On starting gdb, you will get the following error:

Unable to find Mach task port for process-id 2133: (os/kern) failure (0x5).
 (please check gdb is codesigned - see taskgated(8))

To fix this error, follow the following steps:

Hasil-Sharma

import numpy as np

def softmax(x):
  """ Compute the softmax for each row of the input x
  Arguments:
  x -- A N dimensional veector or M X N dimensional numpy matrix.
  Return:
  x -- modified x in-place
  """
  

lukmanr

def inference_tfserving(eval_data, batch=BATCH_SIZE, 
                        repeat=1000, signature='predict'):
  url = 'http://localhost:8501/v1/models/mnist_classifier:predict'

  instances = [[float(i) for i in list(eval_data[img])] for img in range(batch)]

  request_data = {'signature_name': signature, 
                  'instances': instances}
  time_start = datetime.utcnow() 
  for i in range(repeat):

VibhuJawa

mcarilli

# This isn't supposed to run as a bash script, i named it with ".sh" for syntax highlighting.

# https://developer.nvidia.com/nsight-systems
# https://docs.nvidia.com/nsight-systems/profiling/index.html

# My preferred nsys (command line executable used to create profiles) commands
#
# In your script, write
# torch.cuda.nvtx.range_push("region name")
# ...