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client = boto3.client('ec2')

instance_id = 'my instance id'

def rotate_ip(last_eip=None):
    if last_eip:
        client.release_address(AllocationId=last_eip['AllocationId'])
    eip = client.allocate_address(Domain='vpc')
    r = client.associate_address(AllocationId=eip['AllocationId'], InstanceId=instance_id)
    return eip

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# modified from http://scikit-image.org/docs/stable/auto_examples/transform/plot_pyramid.html#sphx-glr-auto-examples-transform-plot-pyramid-py
import numpy as np
import matplotlib.pyplot as plt

from skimage import data
from skimage.transform import pyramid_gaussian


image = data.astronaut()

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%matplotlib inline
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
from skimage import data

# get image
img = data.astronaut()
print('image size', img.shape)
fig, ax = plt.subplots()
ax.imshow(img)

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%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from skimage.draw import (line, polygon, circle,
                          circle_perimeter,
                          ellipse, ellipse_perimeter,
                          bezier_curve)

# Draw a rectangle
img = np.ones((10, 10), dtype=np.double) # a gray image

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class Pair:
    def __init__(self, key, value):
        self.key = key
        self.value = value
        
    def __str__(self):
        return "({}, {})".format(str(self.key), str(self.value))
    
    def __repr__(self):
        return self.__str__()

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#include <stdio.h>


void deviceQuery ()
{
  cudaDeviceProp prop;
  int nDevices=0, i;
  cudaError_t ierr;

  ierr = cudaGetDeviceCount(&nDevices);

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#include <stdio.h>

#define N (32)


__global__ void increment(int* time) {
    __shared__ float s[1024];
    for (int i = 0; i < 1024; i++) {
        s[i] = 1.0f;
    }

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from datetime import datetime
import json

class Timer:
    def __init__(self, outputFile):
        self.fout = open(outputFile, 'w')
        self.cache = []
        self.records = {}
    
    def start(self, event="time"):

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import numpy as np

def compute_average_precision(test_results, num_true_cases):
    """
    It computes average precision based on the definition of Pascal Competition. It computes the under curve area
    of precision and recall. Recall follows the normal definition. Precision is a variant. 
    pascal_precision[i] = typical_precision[i:].max()
    
    Usage:
        test_results = np.array([True, False, True, False, True, False, False, False, False, True])

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r"""Convert an ImageNet like dataset into tfRecord files, provide a method get_dataset to read the created files.

It has similar functions as ImageFolder in Pytorch.

Modified from
https://raw.githubusercontent.com/tensorflow/models/master/research/slim/datasets/download_and_convert_flowers.py
https://github.com/tensorflow/models/blob/master/research/slim/datasets/flowers.py
"""

from __future__ import absolute_import