Allan Costa allanino

## simple_docker_api.py
import requests

class DockerAPI(object):
    """ Helper for using Fleet API to deploy services to a CoreOS cluster."""

    def __init__(self, docker_endpoint):
        self.docker_endpoint = docker_endpoint

    def containers(self):
        """ Return a list of pairs with container's names and ids """

## min-char-rnn.py
"""
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)

## tensorflow_docker.sh
#!/bin/bash

# Run this
docker run -it -p 8888:8888 b.gcr.io/tensorflow/tensorflow-full /run_jupyter.sh --notebook-dir=/tensorflow/tensorflow/tools/docker/notebooks/

# Go to http://localhost:8888

## keras_silly_example.py
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation
from keras.optimizers import SGD
import numpy as np
from keras.utils import np_utils

# Create a random matrix with 1000 rows (data points) and 15 columns (features)
train_rows = 1000
X_train = np.random.rand(train_rows, 15)

## gist:8f8eb50a62e450980cbc
"""
This is a batched LSTM forward and backward pass
"""
import numpy as np
import code

class LSTM:

  @staticmethod
  def init(input_size, hidden_size, fancy_forget_bias_init = 3):

## get_ip.sh
#!/bin/bash

wget -qO- http://ipecho.net/plain ; echo

## modular_inversion.c
#include "stdio.h"

// Compute the solution x to x*n % m == 1 using the Generalized Euclidean Agorithm
int inverse(int n, int m){
	int t0 = 0, t1 = 1;
	int s0 = 1, s1 = 0;
	int r = m - 1; // Just to get started
	int a = m;
	int b = n;
	int q, s, t;

## generate.rb
def character(i)
    return i < 10 ? (i + 48).chr : (i + 87).chr
end

def generate(n)
    i1 = n % 36
    i3 = n/(36*36)
    i2 = n/36 - i3*36
    return "#{character(i3)}#{character(i2)}#{character(i1)}"
end

## pr-fetch.md

      
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                allanino
                / pr-fetch.md
            
            
              Created
              October 17, 2014 17:50
            
              
                Fetch a PR
              
          
    git fetch origin pull/1234/head:pr-1234

Here:
1234 is the PR number.
pr-1234 is your local branch name.

  
## Dockerfile
FROM ubuntu:14.04

RUN apt-get update

# Install curl (for downloading pip) and other dependencies
RUN apt-get install -y curl clang cmake git python2.7-dev python2.7 python-numpy

# Install pip
RUN curl --silent --show-error --retry 5 https://bootstrap.pypa.io/get-pip.py | sudo python2.7
	import requests

	class DockerAPI(object):
	""" Helper for using Fleet API to deploy services to a CoreOS cluster."""

	def __init__(self, docker_endpoint):
	self.docker_endpoint = docker_endpoint

	def containers(self):
	""" Return a list of pairs with container's names and ids """
	"""
	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)
	#!/bin/bash

	# Run this
	docker run -it -p 8888:8888 b.gcr.io/tensorflow/tensorflow-full /run_jupyter.sh --notebook-dir=/tensorflow/tensorflow/tools/docker/notebooks/

	# Go to http://localhost:8888
	from keras.models import Sequential
	from keras.layers.core import Dense, Dropout, Activation
	from keras.optimizers import SGD
	import numpy as np
	from keras.utils import np_utils

	# Create a random matrix with 1000 rows (data points) and 15 columns (features)
	train_rows = 1000
	X_train = np.random.rand(train_rows, 15)
	"""
	This is a batched LSTM forward and backward pass
	"""
	import numpy as np
	import code

	class LSTM:

	@staticmethod
	def init(input_size, hidden_size, fancy_forget_bias_init = 3):
	#include "stdio.h"

	// Compute the solution x to x*n % m == 1 using the Generalized Euclidean Agorithm
	int inverse(int n, int m){
	int t0 = 0, t1 = 1;
	int s0 = 1, s1 = 0;
	int r = m - 1; // Just to get started
	int a = m;
	int b = n;
	int q, s, t;
	def character(i)
	return i < 10 ? (i + 48).chr : (i + 87).chr
	end

	def generate(n)
	i1 = n % 36
	i3 = n/(36*36)
	i2 = n/36 - i3*36
	return "#{character(i3)}#{character(i2)}#{character(i1)}"
	end
	FROM ubuntu:14.04

	RUN apt-get update

	# Install curl (for downloading pip) and other dependencies
	RUN apt-get install -y curl clang cmake git python2.7-dev python2.7 python-numpy

	# Install pip
	RUN curl --silent --show-error --retry 5 https://bootstrap.pypa.io/get-pip.py \| sudo python2.7