esc

c['change_source'].append(changes.GitPoller(
        'git://github.com/esc/numba.git',
        workdir='gitpoller-workdir-esc',
        branches=True,
        pollInterval=300,
        pollAtLaunch=True,
))
c['schedulers'] = []
c['schedulers'].append(schedulers.SingleBranchScheduler(
                            name="all",

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(torch) root@7396071ace7e:/home/rgoyal/GulpIO-benchmarks# CUDA_VISIBLE_DEVICES=0,1 python train_gulp.py --config configs/config_gulpio.json
=> active GPUs: 0,1
=> Output folder for this run -- jester_conv_example
 > Using 10 processes for data loader.
 > Training is getting started...
 > Training takes 999999 epochs.
 > Current LR : 0.001
Epoch: [0][0/1852]      Time 12.410 (12.410)    Data 9.079 (9.079)      Loss 3.2856 (3.2856)    Prec@1 9.375 (9.375)    Prec@5 17.188 (17.188)
Epoch: [0][100/1852]    Time 0.457 (0.854)      Data 0.000 (0.429)      Loss 2.5770 (3.0944)    Prec@1 26.562 (12.655)  Prec@5 68.750 (37.036)
Epoch: [0][200/1852]    Time 0.456 (0.818)      Data 0.000 (0.432)      Loss 2.6250 (2.8789)    Prec@1 25.000 (18.043)  Prec@5 56.250 (47.528)

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(torch) root@7396071ace7e:/home/rgoyal/GulpIO-benchmarks# CUDA_VISIBLE_DEVICES=0,1 python train_jpeg.py --config configs/config_jpeg.json -g 0,1
=> active GPUs: 0,1
=> Output folder for this run -- jester_conv_example
 > Using 10 processes for data loader.
 > Training is getting started...
 > Training takes 999999 epochs.
 > Current LR : 0.001
Epoch: [0][0/1852]      Time 77.101 (77.101)    Data 73.654 (73.654)    Loss 3.3249 (3.3249)    Prec@1 0.000 (0.000)    Prec@5 21.875 (21.875)
Epoch: [0][100/1852]    Time 36.009 (7.519)     Data 35.798 (7.183)     Loss 2.6882 (3.0521)    Prec@1 21.875 (13.784)  Prec@5 54.688 (40.161)
Epoch: [0][200/1852]    Time 13.762 (6.756)     Data 13.562 (6.433)     Loss 2.5396 (2.8169)    Prec@1 20.312 (19.652)  Prec@5 62.500 (50.451)

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adapter = Custom20BNCsvJpegAdapter(input_csv, jpeg_path, output_folder,
                                   shuffle=shuffle,
                                   frame_size=img_size,
                                   shm_dir_path=shm_dir
                                   )
ingestor = GulpIngestor(adapter, output_folder, videos_per_chunk,
                        num_workers=num_workers)
ingestor()

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class AbstractDatasetAdapter(ABC):
    """ Base class adapter for gulping (video) datasets.
    Inherit from this class and implement the `iter_data` method. This method
    should iterate over your entire dataset and for each element return a
    dictionary with the following fields:
        id     : a unique(?) ID for the element.
        frames : a list of frames (PIL images, numpy arrays..)
        meta   : a dictionary with arbitrary metadata (labels, start_time...)
    For examples, see the custom adapters below.
    """

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from gulpio.dataset import GulpImageDataset
from gulpio.loader import DataLoader
from gulpio.transforms import Scale, CenterCrop, Compose, UnitNorm

# define data augmentations. Notice that there are different functions for videos and images
transforms = Compose([
                      Scale(28),  # resize image by the shortest edge
                      CenterCrop(28),
                      UnitNorm(),  # instance wise mean and std norm
                    ])

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# import the main interface for reading
from gulpio import GulpDirectory
# instantiate the GulpDirectory
gulp_directory = GulpDirectory('/tmp/something_something_gulps')
# iterate over all chunks
for chunk in gulp_directory:
    # for each 'video' get the metadata and all frames
    for frames, meta in chunk:
        # do something with the metadata
        for i, f in enumerate(frames):

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import boto3
import moto


@moto.mock_ec2
def expose():
    ec2 = boto3.resource('ec2')
    blue, green = ec2.create_instances(
        ImageId='ANY_ID', MinCount=2, MaxCount=2)
    ec2.create_tags(Resources=[blue.instance_id],

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zsh» git init example
Initialized empty Git repository in /tmp/example/.git/
zsh» cd example
zsh» cp ../objektmodell-beispiel/hello.py .
zsh» cat hello.py
#! /usr/bin/env python

""" Hello World! """

print 'Hello World!'