Chainer MNIST Model DeploymentΒΆ

  • Wrap a Chainer MNIST python model for use as a prediction microservice in seldon-core
  • Run locally on Docker to test
  • Deploy on seldon-core running on minikube

DependenciesΒΆ

pip install seldon-core
pip install chainer==6.2.0

Train locallyΒΆ

[1]:
#!/usr/bin/env python
import argparse

import chainer
import chainer.functions as F
import chainer.links as L
from chainer import training
from chainer.training import extensions
import chainerx


# Network definition
class MLP(chainer.Chain):

    def __init__(self, n_units, n_out):
        super(MLP, self).__init__()
        with self.init_scope():
            # the size of the inputs to each layer will be inferred
            self.l1 = L.Linear(None, n_units)  # n_in -> n_units
            self.l2 = L.Linear(None, n_units)  # n_units -> n_units
            self.l3 = L.Linear(None, n_out)  # n_units -> n_out

    def forward(self, x):
        h1 = F.relu(self.l1(x))
        h2 = F.relu(self.l2(h1))
        return self.l3(h2)


def main():
    parser = argparse.ArgumentParser(description='Chainer example: MNIST')
    parser.add_argument('--batchsize', '-b', type=int, default=100,
                        help='Number of images in each mini-batch')
    parser.add_argument('--epoch', '-e', type=int, default=20,
                        help='Number of sweeps over the dataset to train')
    parser.add_argument('--frequency', '-f', type=int, default=-1,
                        help='Frequency of taking a snapshot')
    parser.add_argument('--device', '-d', type=str, default='-1',
                        help='Device specifier. Either ChainerX device '
                        'specifier or an integer. If non-negative integer, '
                        'CuPy arrays with specified device id are used. If '
                        'negative integer, NumPy arrays are used')
    parser.add_argument('--out', '-o', default='result',
                        help='Directory to output the result')
    parser.add_argument('--resume', '-r', type=str,
                        help='Resume the training from snapshot')
    parser.add_argument('--unit', '-u', type=int, default=1000,
                        help='Number of units')
    parser.add_argument('--noplot', dest='plot', action='store_false',
                        help='Disable PlotReport extension')
    group = parser.add_argument_group('deprecated arguments')
    group.add_argument('--gpu', '-g', dest='device',
                       type=int, nargs='?', const=0,
                       help='GPU ID (negative value indicates CPU)')
    args = parser.parse_args(args=[])

    device = chainer.get_device(args.device)

    print('Device: {}'.format(device))
    print('# unit: {}'.format(args.unit))
    print('# Minibatch-size: {}'.format(args.batchsize))
    print('# epoch: {}'.format(args.epoch))
    print('')

    # Set up a neural network to train
    # Classifier reports softmax cross entropy loss and accuracy at every
    # iteration, which will be used by the PrintReport extension below.
    model = L.Classifier(MLP(args.unit, 10))
    model.to_device(device)
    device.use()

    # Setup an optimizer
    optimizer = chainer.optimizers.Adam()
    optimizer.setup(model)

    # Load the MNIST dataset
    train, test = chainer.datasets.get_mnist()

    train_iter = chainer.iterators.SerialIterator(train, args.batchsize)
    test_iter = chainer.iterators.SerialIterator(test, args.batchsize,
                                                 repeat=False, shuffle=False)

    # Set up a trainer
    updater = training.updaters.StandardUpdater(
        train_iter, optimizer, device=device)
    trainer = training.Trainer(updater, (args.epoch, 'epoch'), out=args.out)

    # Evaluate the model with the test dataset for each epoch
    trainer.extend(extensions.Evaluator(test_iter, model, device=device))

    # Dump a computational graph from 'loss' variable at the first iteration
    # The "main" refers to the target link of the "main" optimizer.
    # TODO(niboshi): Temporarily disabled for chainerx. Fix it.
    if device.xp is not chainerx:
        trainer.extend(extensions.DumpGraph('main/loss'))

    # Take a snapshot for each specified epoch
    frequency = args.epoch if args.frequency == -1 else max(1, args.frequency)
    trainer.extend(extensions.snapshot(), trigger=(frequency, 'epoch'))

    # Write a log of evaluation statistics for each epoch
    trainer.extend(extensions.LogReport())

    # Save two plot images to the result dir
    if args.plot and extensions.PlotReport.available():
        trainer.extend(
            extensions.PlotReport(['main/loss', 'validation/main/loss'],
                                  'epoch', file_name='loss.png'))
        trainer.extend(
            extensions.PlotReport(
                ['main/accuracy', 'validation/main/accuracy'],
                'epoch', file_name='accuracy.png'))

    # Print selected entries of the log to stdout
    # Here "main" refers to the target link of the "main" optimizer again, and
    # "validation" refers to the default name of the Evaluator extension.
    # Entries other than 'epoch' are reported by the Classifier link, called by
    # either the updater or the evaluator.
    trainer.extend(extensions.PrintReport(
        ['epoch', 'main/loss', 'validation/main/loss',
         'main/accuracy', 'validation/main/accuracy', 'elapsed_time']))

    # Print a progress bar to stdout
    trainer.extend(extensions.ProgressBar())

    if args.resume is not None:
        # Resume from a snapshot
        chainer.serializers.load_npz(args.resume, trainer)

    # Run the training
    trainer.run()


if __name__ == '__main__':
    main()
/Users/dtaniwaki/.pyenv/versions/3.7.4/lib/python3.7/site-packages/chainer/_environment_check.py:41: UserWarning: Accelerate has been detected as a NumPy backend library.
vecLib, which is a part of Accelerate, is known not to work correctly with Chainer.
We recommend using other BLAS libraries such as OpenBLAS.
For details of the issue, please see
https://docs.chainer.org/en/stable/tips.html#mnist-example-does-not-converge-in-cpu-mode-on-mac-os-x.

Please be aware that Mac OS X is not an officially supported OS.

  ''')  # NOQA
Device: @numpy
# unit: 1000
# Minibatch-size: 100
# epoch: 20

/Users/dtaniwaki/.pyenv/versions/3.7.4/lib/python3.7/site-packages/chainer/training/extensions/plot_report.py:32: UserWarning: matplotlib is not installed on your environment, so nothing will be plotted at this time. Please install matplotlib to plot figures.

  $ pip install matplotlib

  warnings.warn('matplotlib is not installed on your environment, '
epoch       main/loss   validation/main/loss  main/accuracy  validation/main/accuracy  elapsed_time
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1           0.191134    0.104015              0.942833       0.9664                    14.3463
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2           0.0770754   0.0721484             0.9757         0.9764                    30.3365
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3           0.0491765   0.0686589             0.984117       0.9783                    47.5777
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4           0.0344382   0.0732416             0.988733       0.9793                    66.0447
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6           0.0221732   0.0800123             0.992683       0.9783                    101.278
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7           0.0197829   0.0768317             0.9938         0.9784                    119.732
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8           0.016657    0.113716              0.994683       0.9749                    137.569
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9           0.0196976   0.0879474             0.993633       0.9807                    155.458
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10          0.0135036   0.094506              0.9956         0.9788                    174.195
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11          0.0174658   0.118295              0.9947         0.9766                    193.345
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12          0.0110422   0.092281              0.996467       0.9804                    213.149
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13          0.0131023   0.112165              0.996183       0.9795                    233.983
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14          0.0107402   0.106344              0.99645        0.9813                    255.572
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     total [###################################...............] 70.83%
this epoch [########..........................................] 16.67%
      8500 iter, 14 epoch / 20 epochs
    32.686 iters/sec. Estimated time to finish: 0:01:47.081025.
     total [###################################...............] 71.67%
this epoch [################..................................] 33.33%
      8600 iter, 14 epoch / 20 epochs
    32.641 iters/sec. Estimated time to finish: 0:01:44.163313.
     total [####################################..............] 72.50%
this epoch [#########################.........................] 50.00%
      8700 iter, 14 epoch / 20 epochs
    32.589 iters/sec. Estimated time to finish: 0:01:41.262226.
     total [####################################..............] 73.33%
this epoch [#################################.................] 66.67%
      8800 iter, 14 epoch / 20 epochs
    32.542 iters/sec. Estimated time to finish: 0:01:38.335180.
     total [#####################################.............] 74.17%
this epoch [#########################################.........] 83.33%
      8900 iter, 14 epoch / 20 epochs
    32.489 iters/sec. Estimated time to finish: 0:01:35.416948.
15          0.0125309   0.0853632             0.996467       0.9835                    277.066
     total [#####################################.............] 75.00%
this epoch [..................................................]  0.00%
      9000 iter, 15 epoch / 20 epochs
    32.402 iters/sec. Estimated time to finish: 0:01:32.586512.
     total [#####################################.............] 75.83%
this epoch [########..........................................] 16.67%
      9100 iter, 15 epoch / 20 epochs
    32.348 iters/sec. Estimated time to finish: 0:01:29.649633.
     total [######################################............] 76.67%
this epoch [################..................................] 33.33%
      9200 iter, 15 epoch / 20 epochs
    32.302 iters/sec. Estimated time to finish: 0:01:26.683121.
     total [######################################............] 77.50%
this epoch [#########################.........................] 50.00%
      9300 iter, 15 epoch / 20 epochs
    32.247 iters/sec. Estimated time to finish: 0:01:23.728150.
     total [#######################################...........] 78.33%
this epoch [#################################.................] 66.67%
      9400 iter, 15 epoch / 20 epochs
    32.181 iters/sec. Estimated time to finish: 0:01:20.793071.
     total [#######################################...........] 79.17%
this epoch [#########################################.........] 83.33%
      9500 iter, 15 epoch / 20 epochs
    32.114 iters/sec. Estimated time to finish: 0:01:17.848522.
16          0.0076524   0.0845158             0.9977         0.9833                    299.077
     total [########################################..........] 80.00%
this epoch [..................................................]  0.00%
      9600 iter, 16 epoch / 20 epochs
    32.021 iters/sec. Estimated time to finish: 0:01:14.951347.
     total [########################################..........] 80.83%
this epoch [########..........................................] 16.67%
      9700 iter, 16 epoch / 20 epochs
    31.966 iters/sec. Estimated time to finish: 0:01:11.951340.
     total [########################################..........] 81.67%
this epoch [################..................................] 33.33%
      9800 iter, 16 epoch / 20 epochs
    31.917 iters/sec. Estimated time to finish: 0:01:08.928651.
     total [#########################################.........] 82.50%
this epoch [#########################.........................] 50.00%
      9900 iter, 16 epoch / 20 epochs
    31.865 iters/sec. Estimated time to finish: 0:01:05.903967.
     total [#########################################.........] 83.33%
this epoch [#################################.................] 66.67%
     10000 iter, 16 epoch / 20 epochs
    31.812 iters/sec. Estimated time to finish: 0:01:02.869297.
     total [##########################################........] 84.17%
this epoch [#########################################.........] 83.33%
     10100 iter, 16 epoch / 20 epochs
    31.743 iters/sec. Estimated time to finish: 0:00:59.854943.
17          0.0125142   0.0967522             0.996583       0.9808                    321.412
     total [##########################################........] 85.00%
this epoch [..................................................]  0.00%
     10200 iter, 17 epoch / 20 epochs
    31.571 iters/sec. Estimated time to finish: 0:00:57.014005.
     total [##########################################........] 85.83%
this epoch [########..........................................] 16.67%
     10300 iter, 17 epoch / 20 epochs
    31.412 iters/sec. Estimated time to finish: 0:00:54.119356.
     total [###########################################.......] 86.67%
this epoch [################..................................] 33.33%
     10400 iter, 17 epoch / 20 epochs
    31.253 iters/sec. Estimated time to finish: 0:00:51.194843.
     total [###########################################.......] 87.50%
this epoch [#########################.........................] 50.00%
     10500 iter, 17 epoch / 20 epochs
    31.052 iters/sec. Estimated time to finish: 0:00:48.305561.
     total [############################################......] 88.33%
this epoch [#################################.................] 66.67%
     10600 iter, 17 epoch / 20 epochs
    30.971 iters/sec. Estimated time to finish: 0:00:45.203923.
     total [############################################......] 89.17%
this epoch [#########################################.........] 83.33%
     10700 iter, 17 epoch / 20 epochs
    30.835 iters/sec. Estimated time to finish: 0:00:42.159730.
18          0.00738255  0.112494              0.997783       0.9795                    345.352
     total [#############################################.....] 90.00%
this epoch [..................................................]  0.00%
     10800 iter, 18 epoch / 20 epochs
    30.674 iters/sec. Estimated time to finish: 0:00:39.121586.
     total [#############################################.....] 90.83%
this epoch [########..........................................] 16.67%
     10900 iter, 18 epoch / 20 epochs
    30.563 iters/sec. Estimated time to finish: 0:00:35.991122.
     total [#############################################.....] 91.67%
this epoch [################..................................] 33.33%
     11000 iter, 18 epoch / 20 epochs
    30.458 iters/sec. Estimated time to finish: 0:00:32.831952.
     total [##############################################....] 92.50%
this epoch [#########################.........................] 50.00%
     11100 iter, 18 epoch / 20 epochs
    30.334 iters/sec. Estimated time to finish: 0:00:29.669973.
     total [##############################################....] 93.33%
this epoch [#################################.................] 66.67%
     11200 iter, 18 epoch / 20 epochs
    30.257 iters/sec. Estimated time to finish: 0:00:26.440229.
     total [###############################################...] 94.17%
this epoch [#########################################.........] 83.33%
     11300 iter, 18 epoch / 20 epochs
    30.178 iters/sec. Estimated time to finish: 0:00:23.195403.
19          0.0121511   0.0853964             0.9964         0.9833                    368.794
     total [###############################################...] 95.00%
this epoch [..................................................]  0.00%
     11400 iter, 19 epoch / 20 epochs
    30.066 iters/sec. Estimated time to finish: 0:00:19.956411.
     total [###############################################...] 95.83%
this epoch [########..........................................] 16.67%
     11500 iter, 19 epoch / 20 epochs
    29.966 iters/sec. Estimated time to finish: 0:00:16.685483.
     total [################################################..] 96.67%
this epoch [################..................................] 33.33%
     11600 iter, 19 epoch / 20 epochs
    29.849 iters/sec. Estimated time to finish: 0:00:13.400864.
     total [################################################..] 97.50%
this epoch [#########################.........................] 50.00%
     11700 iter, 19 epoch / 20 epochs
    29.753 iters/sec. Estimated time to finish: 0:00:10.082910.
     total [#################################################.] 98.33%
this epoch [#################################.................] 66.67%
     11800 iter, 19 epoch / 20 epochs
    29.675 iters/sec. Estimated time to finish: 0:00:06.739601.
     total [#################################################.] 99.17%
this epoch [#########################################.........] 83.33%
     11900 iter, 19 epoch / 20 epochs
    29.581 iters/sec. Estimated time to finish: 0:00:03.380585.
20          0.00638073  0.098195              0.997883       0.9814                    392.74
     total [##################################################] 100.00%
this epoch [..................................................]  0.00%
     12000 iter, 20 epoch / 20 epochs
    29.484 iters/sec. Estimated time to finish: 0:00:00.

Wrap model using s2i

[2]:
!s2i build . seldonio/seldon-core-s2i-python3:0.10 chainer-mnist:0.1
---> Installing application source...
---> Installing dependencies ...
Looking in links: /whl
Collecting chainer==6.2.0 (from -r requirements.txt (line 1))
  WARNING: Url '/whl' is ignored. It is either a non-existing path or lacks a specific scheme.
Downloading https://files.pythonhosted.org/packages/2c/5a/86c50a0119a560a39d782c4cdd9b72927c090cc2e3f70336e01b19a5f97a/chainer-6.2.0.tar.gz (873kB)
Requirement already satisfied: setuptools in /usr/local/lib/python3.6/site-packages (from chainer==6.2.0->-r requirements.txt (line 1)) (41.0.1)
Collecting typing<=3.6.6 (from chainer==6.2.0->-r requirements.txt (line 1))
  WARNING: Url '/whl' is ignored. It is either a non-existing path or lacks a specific scheme.
Downloading https://files.pythonhosted.org/packages/4a/bd/eee1157fc2d8514970b345d69cb9975dcd1e42cd7e61146ed841f6e68309/typing-3.6.6-py3-none-any.whl
Collecting typing_extensions<=3.6.6 (from chainer==6.2.0->-r requirements.txt (line 1))
  WARNING: Url '/whl' is ignored. It is either a non-existing path or lacks a specific scheme.
Downloading https://files.pythonhosted.org/packages/62/4f/392a1fa2873e646f5990eb6f956e662d8a235ab474450c72487745f67276/typing_extensions-3.6.6-py3-none-any.whl
Collecting filelock (from chainer==6.2.0->-r requirements.txt (line 1))
  WARNING: Url '/whl' is ignored. It is either a non-existing path or lacks a specific scheme.
Downloading https://files.pythonhosted.org/packages/93/83/71a2ee6158bb9f39a90c0dea1637f81d5eef866e188e1971a1b1ab01a35a/filelock-3.0.12-py3-none-any.whl
Requirement already satisfied: numpy>=1.9.0 in /usr/local/lib/python3.6/site-packages (from chainer==6.2.0->-r requirements.txt (line 1)) (1.16.4)
Collecting protobuf<3.8.0rc1,>=3.0.0 (from chainer==6.2.0->-r requirements.txt (line 1))
  WARNING: Url '/whl' is ignored. It is either a non-existing path or lacks a specific scheme.
Downloading https://files.pythonhosted.org/packages/5a/aa/a858df367b464f5e9452e1c538aa47754d467023850c00b000287750fa77/protobuf-3.7.1-cp36-cp36m-manylinux1_x86_64.whl (1.2MB)
Requirement already satisfied: six>=1.9.0 in /usr/local/lib/python3.6/site-packages (from chainer==6.2.0->-r requirements.txt (line 1)) (1.12.0)
Building wheels for collected packages: chainer
Building wheel for chainer (setup.py): started
Building wheel for chainer (setup.py): finished with status 'done'
Stored in directory: /root/.cache/pip/wheels/2e/be/c5/6ee506abcaa4a53106f7d7671bbee8b4e5243bc562a9d32ad1
Successfully built chainer
Installing collected packages: typing, typing-extensions, filelock, protobuf, chainer
Found existing installation: protobuf 3.8.0
Uninstalling protobuf-3.8.0:
Successfully uninstalled protobuf-3.8.0
Successfully installed chainer-6.2.0 filelock-3.0.12 protobuf-3.7.1 typing-3.6.6 typing-extensions-3.6.6
WARNING: Url '/whl' is ignored. It is either a non-existing path or lacks a specific scheme.
WARNING: You are using pip version 19.1, however version 19.2.2 is available.
You should consider upgrading via the 'pip install --upgrade pip' command.
Build completed successfully
[3]:
!docker run --name "mnist_predictor" -d --rm -p 5000:5000 chainer-mnist:0.1
b03f58f82ca07e25261be34b75be4a0ffbbfa1ad736d3866790682bf0d8202a3

Send some random features that conform to the contract

[6]:
!seldon-core-tester contract.json 0.0.0.0 5000 -p
/Users/dtaniwaki/.pyenv/versions/3.7.4/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:516: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_qint8 = np.dtype([("qint8", np.int8, 1)])
/Users/dtaniwaki/.pyenv/versions/3.7.4/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:517: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_quint8 = np.dtype([("quint8", np.uint8, 1)])
/Users/dtaniwaki/.pyenv/versions/3.7.4/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:518: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_qint16 = np.dtype([("qint16", np.int16, 1)])
/Users/dtaniwaki/.pyenv/versions/3.7.4/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:519: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_quint16 = np.dtype([("quint16", np.uint16, 1)])
/Users/dtaniwaki/.pyenv/versions/3.7.4/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:520: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_qint32 = np.dtype([("qint32", np.int32, 1)])
/Users/dtaniwaki/.pyenv/versions/3.7.4/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  np_resource = np.dtype([("resource", np.ubyte, 1)])
/Users/dtaniwaki/.pyenv/versions/3.7.4/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:541: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_qint8 = np.dtype([("qint8", np.int8, 1)])
/Users/dtaniwaki/.pyenv/versions/3.7.4/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:542: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_quint8 = np.dtype([("quint8", np.uint8, 1)])
/Users/dtaniwaki/.pyenv/versions/3.7.4/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:543: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_qint16 = np.dtype([("qint16", np.int16, 1)])
/Users/dtaniwaki/.pyenv/versions/3.7.4/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:544: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_quint16 = np.dtype([("quint16", np.uint16, 1)])
/Users/dtaniwaki/.pyenv/versions/3.7.4/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:545: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_qint32 = np.dtype([("qint32", np.int32, 1)])
/Users/dtaniwaki/.pyenv/versions/3.7.4/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:550: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  np_resource = np.dtype([("resource", np.ubyte, 1)])
----------------------------------------
SENDING NEW REQUEST:

[[0.997 0.039 0.778 0.59  0.526 0.591 0.659 0.423 0.404 0.302 0.322 0.453
  0.54  0.852 0.268 0.564 0.163 0.032 0.934 0.317 0.395 0.122 0.056 0.729
  0.106 0.443 0.334 0.784 0.646 0.296 0.524 0.855 0.503 0.727 0.326 0.491
  0.385 0.042 0.82  0.715 0.972 0.699 0.431 0.618 0.096 0.849 0.224 0.187
  0.145 0.357 0.187 0.779 0.009 0.775 0.775 0.584 0.897 0.674 0.01  0.775
  0.095 0.081 0.089 0.351 0.985 0.878 0.906 0.396 0.499 0.646 0.127 0.966
  0.087 0.668 0.314 0.853 0.55  0.345 0.95  0.792 0.797 0.037 0.18  0.592
  0.941 0.662 0.101 0.388 0.902 0.868 0.505 0.824 0.8   0.855 0.568 0.368
  0.605 0.224 0.214 0.582 0.365 0.44  0.389 0.922 0.028 0.142 0.525 0.843
  0.706 0.61  0.215 0.962 0.334 0.273 0.365 0.075 0.929 0.693 0.382 0.76
  0.75  0.403 0.344 0.218 0.831 0.431 0.469 0.527 0.755 0.048 0.407 0.953
  0.468 0.186 0.589 0.839 0.513 0.307 0.251 0.738 0.173 0.185 0.499 0.797
  0.264 0.149 0.547 0.699 0.935 0.071 0.145 0.853 0.884 0.195 0.944 0.775
  0.523 0.627 0.729 0.826 0.894 0.117 0.935 0.363 0.03  0.16  0.435 0.579
  0.954 0.487 0.133 0.348 0.12  0.741 0.203 0.103 0.334 0.009 0.898 0.597
  0.375 0.241 0.27  0.094 0.819 0.737 0.147 0.715 0.138 0.801 0.427 0.602
  0.336 0.796 0.691 0.415 0.329 0.155 0.17  0.152 0.237 0.957 0.298 0.837
  0.982 0.805 0.972 0.125 0.916 0.101 0.054 0.347 0.566 0.232 0.885 0.864
  0.049 0.205 0.361 0.767 0.099 0.634 0.359 0.975 0.56  0.289 0.49  0.359
  0.901 0.39  0.197 0.985 0.141 0.232 0.336 0.932 0.923 0.032 0.126 0.51
  0.571 0.743 0.831 0.999 0.972 0.649 0.527 0.909 0.071 0.539 0.676 0.851
  0.104 0.103 0.392 0.641 0.838 0.333 0.453 0.573 0.199 0.924 0.588 0.955
  0.866 0.085 0.985 0.803 0.386 0.713 0.056 0.972 0.489 0.623 0.108 0.904
  0.746 0.986 0.824 0.996 0.161 0.738 0.24  0.153 0.935 0.782 0.393 0.098
  0.449 0.24  0.621 0.293 0.569 0.196 0.893 0.605 0.608 0.114 0.383 0.038
  0.573 0.373 0.474 0.006 0.292 0.738 0.943 0.65  0.553 0.684 0.3   0.587
  0.183 0.521 0.211 0.074 0.696 0.672 0.206 0.694 0.129 0.81  0.415 0.56
  0.994 0.686 0.807 0.514 0.215 0.096 0.295 0.233 0.625 0.663 0.794 0.16
  0.837 0.194 0.07  0.939 0.965 0.142 0.66  0.152 0.249 0.995 0.892 0.265
  0.865 0.742 0.19  0.03  0.42  0.807 0.15  0.163 0.529 0.23  0.59  0.676
  0.121 0.474 0.329 0.383 0.534 0.093 0.861 0.058 0.019 0.212 0.296 0.947
  0.879 0.445 0.357 0.021 0.551 0.362 0.653 0.258 0.146 0.453 0.373 0.448
  0.339 0.974 0.266 0.656 0.036 0.698 0.651 0.91  0.438 0.767 0.716 0.267
  0.871 0.781 0.13  0.912 0.13  0.332 0.647 0.31  0.171 0.323 0.703 0.197
  0.918 0.803 0.43  0.103 0.606 0.955 0.733 0.902 0.139 0.471 0.994 0.393
  0.95  0.485 0.782 0.213 0.994 0.206 0.938 0.019 0.429 0.135 0.811 0.209
  0.991 0.93  0.878 0.742 0.859 0.397 0.128 0.087 0.447 0.392 0.61  0.18
  0.087 0.641 0.31  0.033 0.211 0.431 0.051 0.639 0.461 0.466 0.171 0.736
  0.727 0.183 0.542 0.416 0.524 0.251 0.513 0.087 0.395 0.164 0.25  0.384
  0.705 0.683 0.827 0.188 0.163 0.325 0.256 0.904 0.161 0.334 0.639 0.728
  0.267 0.463 0.373 0.111 0.585 0.794 0.972 0.281 0.984 0.564 0.671 0.868
  0.741 0.638 0.702 0.778 0.667 0.372 0.818 0.49  0.102 0.403 0.187 0.283
  0.492 0.937 0.643 0.657 0.514 0.492 0.042 0.809 0.088 0.018 0.631 0.731
  0.516 0.625 0.597 0.629 0.798 0.907 0.861 0.439 0.777 0.014 0.771 0.152
  0.16  0.997 0.699 0.127 0.038 0.503 0.572 0.878 0.901 0.215 0.606 0.686
  0.847 0.007 0.976 0.895 0.357 0.374 0.989 0.544 0.317 0.043 0.718 0.788
  0.121 0.432 0.16  0.485 0.553 0.048 0.003 0.375 0.592 0.207 0.853 0.81
  0.043 0.554 0.084 0.584 0.73  0.766 0.738 0.038 0.56  0.475 0.763 0.002
  0.382 0.49  0.302 0.873 0.141 0.023 0.341 0.113 0.197 0.948 0.088 0.294
  0.778 0.807 0.935 0.712 0.466 0.885 0.815 0.843 0.745 0.217 0.664 0.142
  0.421 0.371 0.536 0.009 0.036 0.352 0.916 0.161 0.345 0.348 0.688 0.806
  0.434 0.413 0.567 0.043 0.934 0.072 0.54  0.347 0.817 0.321 0.85  0.478
  0.832 0.899 0.283 0.34  0.304 0.955 0.915 0.934 0.452 0.423 0.75  0.013
  0.5   0.691 0.854 0.453 0.959 0.843 0.698 0.756 0.918 0.992 0.663 0.608
  0.756 0.7   0.347 0.427 0.198 0.37  0.837 0.362 0.291 0.126 0.695 0.777
  0.318 0.88  0.859 0.958 0.075 0.332 0.321 0.179 0.834 0.027 0.332 0.799
  0.504 0.274 0.819 0.081 0.337 0.02  0.598 0.727 0.159 0.937 0.199 0.639
  0.063 0.75  0.637 0.686 0.677 0.102 0.135 0.264 0.091 0.837 0.562 0.453
  0.503 0.884 0.147 0.966 0.118 0.293 0.327 0.859 0.958 0.498 0.369 0.123
  0.354 0.812 0.163 0.96  0.64  0.596 0.029 0.84  0.159 0.717 0.025 0.394
  0.185 0.29  0.554 0.646 0.432 0.197 0.668 0.531 0.206 0.599 0.842 0.579
  0.836 0.889 0.797 0.891 0.1   0.087 0.825 0.952 0.781 0.295 0.819 0.038
  0.34  0.476 0.08  0.784 0.556 0.282 0.699 0.954 0.5   0.332 0.213 0.618
  0.92  0.776 0.147 0.749 0.597 0.191 0.957 0.47  0.324 0.352 0.837 0.263
  0.536 0.48  0.997 0.417 0.08  0.464 0.886 0.019 0.307 0.164 0.36  0.638
  0.46  0.803 0.139 0.575]]
Traceback (most recent call last):
  File "/Users/dtaniwaki/.pyenv/versions/3.7.4/lib/python3.7/site-packages/urllib3/connection.py", line 160, in _new_conn
    (self._dns_host, self.port), self.timeout, **extra_kw)
  File "/Users/dtaniwaki/.pyenv/versions/3.7.4/lib/python3.7/site-packages/urllib3/util/connection.py", line 80, in create_connection
    raise err
  File "/Users/dtaniwaki/.pyenv/versions/3.7.4/lib/python3.7/site-packages/urllib3/util/connection.py", line 70, in create_connection
    sock.connect(sa)
ConnectionRefusedError: [Errno 61] Connection refused

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/Users/dtaniwaki/.pyenv/versions/3.7.4/lib/python3.7/site-packages/urllib3/connectionpool.py", line 603, in urlopen
    chunked=chunked)
  File "/Users/dtaniwaki/.pyenv/versions/3.7.4/lib/python3.7/site-packages/urllib3/connectionpool.py", line 355, in _make_request
    conn.request(method, url, **httplib_request_kw)
  File "/Users/dtaniwaki/.pyenv/versions/3.7.4/lib/python3.7/http/client.py", line 1244, in request
    self._send_request(method, url, body, headers, encode_chunked)
  File "/Users/dtaniwaki/.pyenv/versions/3.7.4/lib/python3.7/http/client.py", line 1290, in _send_request
    self.endheaders(body, encode_chunked=encode_chunked)
  File "/Users/dtaniwaki/.pyenv/versions/3.7.4/lib/python3.7/http/client.py", line 1239, in endheaders
    self._send_output(message_body, encode_chunked=encode_chunked)
  File "/Users/dtaniwaki/.pyenv/versions/3.7.4/lib/python3.7/http/client.py", line 1026, in _send_output
    self.send(msg)
  File "/Users/dtaniwaki/.pyenv/versions/3.7.4/lib/python3.7/http/client.py", line 966, in send
    self.connect()
  File "/Users/dtaniwaki/.pyenv/versions/3.7.4/lib/python3.7/site-packages/urllib3/connection.py", line 183, in connect
    conn = self._new_conn()
  File "/Users/dtaniwaki/.pyenv/versions/3.7.4/lib/python3.7/site-packages/urllib3/connection.py", line 169, in _new_conn
    self, "Failed to establish a new connection: %s" % e)
urllib3.exceptions.NewConnectionError: <urllib3.connection.HTTPConnection object at 0x1232a2050>: Failed to establish a new connection: [Errno 61] Connection refused

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/Users/dtaniwaki/.pyenv/versions/3.7.4/lib/python3.7/site-packages/requests/adapters.py", line 449, in send
    timeout=timeout
  File "/Users/dtaniwaki/.pyenv/versions/3.7.4/lib/python3.7/site-packages/urllib3/connectionpool.py", line 641, in urlopen
    _stacktrace=sys.exc_info()[2])
  File "/Users/dtaniwaki/.pyenv/versions/3.7.4/lib/python3.7/site-packages/urllib3/util/retry.py", line 399, in increment
    raise MaxRetryError(_pool, url, error or ResponseError(cause))
urllib3.exceptions.MaxRetryError: HTTPConnectionPool(host='0.0.0.0', port=5000): Max retries exceeded with url: /predict (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x1232a2050>: Failed to establish a new connection: [Errno 61] Connection refused'))

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/Users/dtaniwaki/.pyenv/versions/3.7.4/bin/seldon-core-tester", line 10, in <module>
    sys.exit(main())
  File "/Users/dtaniwaki/.pyenv/versions/3.7.4/lib/python3.7/site-packages/seldon_core/microservice_tester.py", line 258, in main
    run_predict(args)
  File "/Users/dtaniwaki/.pyenv/versions/3.7.4/lib/python3.7/site-packages/seldon_core/microservice_tester.py", line 225, in run_predict
    response = sc.microservice(data=batch, transport=transport, method="predict", payload_type=payload_type, names=feature_names)
  File "/Users/dtaniwaki/.pyenv/versions/3.7.4/lib/python3.7/site-packages/seldon_core/seldon_client.py", line 395, in microservice
    return microservice_api_rest_seldon_message(**k)
  File "/Users/dtaniwaki/.pyenv/versions/3.7.4/lib/python3.7/site-packages/seldon_core/seldon_client.py", line 534, in microservice_api_rest_seldon_message
    data={"json": json.dumps(payload)})
  File "/Users/dtaniwaki/.pyenv/versions/3.7.4/lib/python3.7/site-packages/requests/api.py", line 116, in post
    return request('post', url, data=data, json=json, **kwargs)
  File "/Users/dtaniwaki/.pyenv/versions/3.7.4/lib/python3.7/site-packages/requests/api.py", line 60, in request
    return session.request(method=method, url=url, **kwargs)
  File "/Users/dtaniwaki/.pyenv/versions/3.7.4/lib/python3.7/site-packages/requests/sessions.py", line 533, in request
    resp = self.send(prep, **send_kwargs)
  File "/Users/dtaniwaki/.pyenv/versions/3.7.4/lib/python3.7/site-packages/requests/sessions.py", line 646, in send
    r = adapter.send(request, **kwargs)
  File "/Users/dtaniwaki/.pyenv/versions/3.7.4/lib/python3.7/site-packages/requests/adapters.py", line 516, in send
    raise ConnectionError(e, request=request)
requests.exceptions.ConnectionError: HTTPConnectionPool(host='0.0.0.0', port=5000): Max retries exceeded with url: /predict (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x1232a2050>: Failed to establish a new connection: [Errno 61] Connection refused'))
[7]:
!docker rm mnist_predictor --force
Error: No such container: mnist_predictor

Test using MinikubeΒΆ

Due to a `minikube/s2i issue <https://github.com/SeldonIO/seldon-core/issues/253>`__ you will need `s2i >= 1.1.13 <https://github.com/openshift/source-to-image/releases/tag/v1.1.13>`__

[14]:
!minikube start --memory 4096
πŸ˜„  minikube v1.2.0 on darwin (amd64)
πŸ”₯  Creating virtualbox VM (CPUs=2, Memory=4096MB, Disk=20000MB) ...
🐳  Configuring environment for Kubernetes v1.15.0 on Docker 18.09.6
🚜  Pulling images ...
πŸš€  Launching Kubernetes ...
βŒ›  Verifying: apiserver proxy etcd scheduler controller dns
πŸ„  Done! kubectl is now configured to use "minikube"
[15]:
!kubectl create clusterrolebinding kube-system-cluster-admin --clusterrole=cluster-admin --serviceaccount=kube-system:default
clusterrolebinding.rbac.authorization.k8s.io/kube-system-cluster-admin created
[16]:
!helm init
$HELM_HOME has been configured at /Users/dtaniwaki/.helm.

Tiller (the Helm server-side component) has been installed into your Kubernetes Cluster.

Please note: by default, Tiller is deployed with an insecure 'allow unauthenticated users' policy.
To prevent this, run `helm init` with the --tiller-tls-verify flag.
For more information on securing your installation see: https://docs.helm.sh/using_helm/#securing-your-helm-installation
[17]:
!kubectl rollout status deploy/tiller-deploy -n kube-system
Waiting for deployment "tiller-deploy" rollout to finish: 0 of 1 updated replicas are available...
deployment "tiller-deploy" successfully rolled out
[18]:
!helm install ../../../helm-charts/seldon-core-operator --name seldon-core --set usageMetrics.enabled=true --namespace seldon-system
NAME:   seldon-core
LAST DEPLOYED: Fri Aug 16 22:53:14 2019
NAMESPACE: seldon-system
STATUS: DEPLOYED

RESOURCES:
==> v1/ClusterRole
NAME                          AGE
seldon-operator-manager-role  1s

==> v1/ClusterRoleBinding
NAME                                 AGE
seldon-operator-manager-rolebinding  1s

==> v1/ConfigMap
NAME                     DATA  AGE
seldon-config            1     1s
seldon-spartakus-config  1     1s

==> v1/Pod(related)
NAME                                         READY  STATUS             RESTARTS  AGE
seldon-operator-controller-manager-0         0/1    ContainerCreating  0         1s
seldon-spartakus-volunteer-7d6dd98f89-p6tcn  0/1    ContainerCreating  0         1s

==> v1/Secret
NAME                                   TYPE    DATA  AGE
seldon-operator-webhook-server-secret  Opaque  0     1s

==> v1/Service
NAME                                        TYPE       CLUSTER-IP     EXTERNAL-IP  PORT(S)  AGE
seldon-operator-controller-manager-service  ClusterIP  10.108.43.223  <none>       443/TCP  1s
webhook-server-service                      ClusterIP  10.104.128.63  <none>       443/TCP  1s

==> v1/ServiceAccount
NAME                              SECRETS  AGE
seldon-core-seldon-core-operator  1        1s
seldon-spartakus-volunteer        1        1s

==> v1/StatefulSet
NAME                                READY  AGE
seldon-operator-controller-manager  0/1    1s

==> v1beta1/ClusterRole
NAME                        AGE
seldon-spartakus-volunteer  1s

==> v1beta1/ClusterRoleBinding
NAME                        AGE
seldon-spartakus-volunteer  1s

==> v1beta1/CustomResourceDefinition
NAME                                         AGE
seldondeployments.machinelearning.seldon.io  1s

==> v1beta1/Deployment
NAME                        READY  UP-TO-DATE  AVAILABLE  AGE
seldon-spartakus-volunteer  0/1    1           0          1s


NOTES:
NOTES: TODO


[19]:
!kubectl rollout status statefulset.apps/seldon-operator-controller-manager -n seldon-system
Waiting for 1 pods to be ready...
partitioned roll out complete: 1 new pods have been updated...

Setup IngressΒΆ

There are gRPC issues with the latest Ambassador, so we rewcommend 0.40.2 until these are fixed.

[20]:
!helm install stable/ambassador --name ambassador --set crds.keep=false
NAME:   ambassador
LAST DEPLOYED: Fri Aug 16 22:57:51 2019
NAMESPACE: default
STATUS: DEPLOYED

RESOURCES:
==> v1/Deployment
NAME        READY  UP-TO-DATE  AVAILABLE  AGE
ambassador  0/3    3           0          1s

==> v1/Pod(related)
NAME                        READY  STATUS             RESTARTS  AGE
ambassador-5f7fd859b-96ktm  0/1    ContainerCreating  0         1s
ambassador-5f7fd859b-qv5x9  0/1    ContainerCreating  0         1s
ambassador-5f7fd859b-zcrb2  0/1    ContainerCreating  0         1s

==> v1/Service
NAME              TYPE          CLUSTER-IP     EXTERNAL-IP  PORT(S)                     AGE
ambassador        LoadBalancer  10.97.72.34    <pending>    80:30535/TCP,443:32073/TCP  1s
ambassador-admin  ClusterIP     10.108.82.228  <none>       8877/TCP                    1s

==> v1/ServiceAccount
NAME        SECRETS  AGE
ambassador  1        1s

==> v1beta1/ClusterRole
NAME             AGE
ambassador       1s
ambassador-crds  1s

==> v1beta1/ClusterRoleBinding
NAME             AGE
ambassador       1s
ambassador-crds  1s

==> v1beta1/CustomResourceDefinition
NAME                                          AGE
authservices.getambassador.io                 1s
consulresolvers.getambassador.io              1s
kubernetesendpointresolvers.getambassador.io  1s
kubernetesserviceresolvers.getambassador.io   1s
mappings.getambassador.io                     1s
modules.getambassador.io                      1s
ratelimitservices.getambassador.io            1s
tcpmappings.getambassador.io                  1s
tlscontexts.getambassador.io                  1s
tracingservices.getambassador.io              1s


NOTES:
Congratuations! You've successfully installed Ambassador.

For help, visit our Slack at https://d6e.co/slack or view the documentation online at https://www.getambassador.io.

To get the IP address of Ambassador, run the following commands:
NOTE: It may take a few minutes for the LoadBalancer IP to be available.
     You can watch the status of by running 'kubectl get svc -w  --namespace default ambassador'

  On GKE/Azure:
  export SERVICE_IP=$(kubectl get svc --namespace default ambassador -o jsonpath='{.status.loadBalancer.ingress[0].ip}')

  On AWS:
  export SERVICE_IP=$(kubectl get svc --namespace default ambassador -o jsonpath='{.status.loadBalancer.ingress[0].hostname}')

  echo http://$SERVICE_IP:

[21]:
!kubectl rollout status deployment.apps/ambassador
Waiting for deployment "ambassador" rollout to finish: 0 of 3 updated replicas are available...
Waiting for deployment "ambassador" rollout to finish: 1 of 3 updated replicas are available...
Waiting for deployment "ambassador" rollout to finish: 2 of 3 updated replicas are available...
deployment "ambassador" successfully rolled out
[22]:
!eval $(minikube docker-env) && s2i build . seldonio/seldon-core-s2i-python3:0.10 chainer-mnist:0.1
---> Installing application source...
---> Installing dependencies ...
Looking in links: /whl
Collecting chainer==6.2.0 (from -r requirements.txt (line 1))
  WARNING: Url '/whl' is ignored. It is either a non-existing path or lacks a specific scheme.
Downloading https://files.pythonhosted.org/packages/2c/5a/86c50a0119a560a39d782c4cdd9b72927c090cc2e3f70336e01b19a5f97a/chainer-6.2.0.tar.gz (873kB)
Requirement already satisfied: setuptools in /usr/local/lib/python3.6/site-packages (from chainer==6.2.0->-r requirements.txt (line 1)) (41.0.1)
Collecting typing<=3.6.6 (from chainer==6.2.0->-r requirements.txt (line 1))
  WARNING: Url '/whl' is ignored. It is either a non-existing path or lacks a specific scheme.
Downloading https://files.pythonhosted.org/packages/4a/bd/eee1157fc2d8514970b345d69cb9975dcd1e42cd7e61146ed841f6e68309/typing-3.6.6-py3-none-any.whl
Collecting typing_extensions<=3.6.6 (from chainer==6.2.0->-r requirements.txt (line 1))
  WARNING: Url '/whl' is ignored. It is either a non-existing path or lacks a specific scheme.
Downloading https://files.pythonhosted.org/packages/62/4f/392a1fa2873e646f5990eb6f956e662d8a235ab474450c72487745f67276/typing_extensions-3.6.6-py3-none-any.whl
Collecting filelock (from chainer==6.2.0->-r requirements.txt (line 1))
  WARNING: Url '/whl' is ignored. It is either a non-existing path or lacks a specific scheme.
Downloading https://files.pythonhosted.org/packages/93/83/71a2ee6158bb9f39a90c0dea1637f81d5eef866e188e1971a1b1ab01a35a/filelock-3.0.12-py3-none-any.whl
Requirement already satisfied: numpy>=1.9.0 in /usr/local/lib/python3.6/site-packages (from chainer==6.2.0->-r requirements.txt (line 1)) (1.16.4)
Collecting protobuf<3.8.0rc1,>=3.0.0 (from chainer==6.2.0->-r requirements.txt (line 1))
  WARNING: Url '/whl' is ignored. It is either a non-existing path or lacks a specific scheme.
Downloading https://files.pythonhosted.org/packages/5a/aa/a858df367b464f5e9452e1c538aa47754d467023850c00b000287750fa77/protobuf-3.7.1-cp36-cp36m-manylinux1_x86_64.whl (1.2MB)
Requirement already satisfied: six>=1.9.0 in /usr/local/lib/python3.6/site-packages (from chainer==6.2.0->-r requirements.txt (line 1)) (1.12.0)
Building wheels for collected packages: chainer
Building wheel for chainer (setup.py): started
Building wheel for chainer (setup.py): finished with status 'done'
Stored in directory: /root/.cache/pip/wheels/2e/be/c5/6ee506abcaa4a53106f7d7671bbee8b4e5243bc562a9d32ad1
Successfully built chainer
Installing collected packages: typing, typing-extensions, filelock, protobuf, chainer
Found existing installation: protobuf 3.8.0
Uninstalling protobuf-3.8.0:
Successfully uninstalled protobuf-3.8.0
Successfully installed chainer-6.2.0 filelock-3.0.12 protobuf-3.7.1 typing-3.6.6 typing-extensions-3.6.6
WARNING: Url '/whl' is ignored. It is either a non-existing path or lacks a specific scheme.
WARNING: You are using pip version 19.1, however version 19.2.2 is available.
You should consider upgrading via the 'pip install --upgrade pip' command.
Build completed successfully
[23]:
!kubectl create -f chainer_mnist_deployment.json
seldondeployment.machinelearning.seldon.io/seldon-deployment-example created
[24]:
!kubectl rollout status deploy/chainer-mnist-deployment-chainer-mnist-predictor-76478b2
Waiting for deployment "chainer-mnist-deployment-chainer-mnist-predictor-76478b2" rollout to finish: 0 of 1 updated replicas are available...
deployment "chainer-mnist-deployment-chainer-mnist-predictor-76478b2" successfully rolled out
[25]:
!seldon-core-api-tester contract.json `minikube ip` `kubectl get svc ambassador -o jsonpath='{.spec.ports[0].nodePort}'` \
    seldon-deployment-example --namespace default -p
/Users/dtaniwaki/.pyenv/versions/3.7.4/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:516: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_qint8 = np.dtype([("qint8", np.int8, 1)])
/Users/dtaniwaki/.pyenv/versions/3.7.4/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:517: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_quint8 = np.dtype([("quint8", np.uint8, 1)])
/Users/dtaniwaki/.pyenv/versions/3.7.4/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:518: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_qint16 = np.dtype([("qint16", np.int16, 1)])
/Users/dtaniwaki/.pyenv/versions/3.7.4/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:519: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_quint16 = np.dtype([("quint16", np.uint16, 1)])
/Users/dtaniwaki/.pyenv/versions/3.7.4/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:520: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_qint32 = np.dtype([("qint32", np.int32, 1)])
/Users/dtaniwaki/.pyenv/versions/3.7.4/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  np_resource = np.dtype([("resource", np.ubyte, 1)])
/Users/dtaniwaki/.pyenv/versions/3.7.4/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:541: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_qint8 = np.dtype([("qint8", np.int8, 1)])
/Users/dtaniwaki/.pyenv/versions/3.7.4/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:542: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_quint8 = np.dtype([("quint8", np.uint8, 1)])
/Users/dtaniwaki/.pyenv/versions/3.7.4/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:543: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_qint16 = np.dtype([("qint16", np.int16, 1)])
/Users/dtaniwaki/.pyenv/versions/3.7.4/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:544: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_quint16 = np.dtype([("quint16", np.uint16, 1)])
/Users/dtaniwaki/.pyenv/versions/3.7.4/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:545: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_qint32 = np.dtype([("qint32", np.int32, 1)])
/Users/dtaniwaki/.pyenv/versions/3.7.4/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:550: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  np_resource = np.dtype([("resource", np.ubyte, 1)])
----------------------------------------
SENDING NEW REQUEST:

[[0.64  0.213 0.028 0.604 0.586 0.076 0.629 0.568 0.806 0.931 0.266 0.098
  0.526 0.336 0.569 0.965 0.157 0.401 0.15  0.405 0.594 0.21  0.699 0.085
  0.314 0.467 0.303 0.384 0.788 0.135 0.349 0.467 0.025 0.525 0.767 0.819
  0.275 0.212 0.784 0.448 0.808 0.582 0.939 0.165 0.761 0.272 0.332 0.321
  0.005 0.921 0.285 0.181 0.161 0.948 0.148 0.788 0.664 0.65  0.795 0.548
  0.754 0.407 0.057 0.429 0.569 0.538 0.295 0.4   0.581 0.569 0.299 0.066
  0.456 0.118 0.983 0.93  0.316 0.865 0.492 0.048 0.505 0.573 0.595 0.13
  0.595 0.595 0.474 0.334 0.708 0.25  0.183 0.391 0.268 0.252 0.366 0.029
  0.676 0.869 0.12  0.737 0.502 0.868 0.846 0.891 0.578 0.598 0.984 0.543
  0.515 0.081 0.998 0.976 0.611 0.492 0.494 0.985 0.443 0.246 0.252 0.871
  0.615 0.885 0.903 0.254 0.651 0.412 0.645 0.608 0.921 0.5   0.18  0.845
  0.91  0.601 0.782 0.27  0.643 0.671 0.273 0.37  0.454 0.08  0.854 0.439
  0.912 0.709 0.703 0.817 0.381 0.963 0.057 0.015 0.126 0.686 0.284 0.463
  0.231 0.332 0.932 0.804 0.538 0.039 0.12  0.992 0.436 0.791 0.261 0.842
  0.901 0.208 0.578 0.423 0.657 0.293 0.633 0.45  0.609 0.715 0.149 0.244
  0.026 0.332 0.525 0.157 0.749 0.88  0.713 0.405 0.473 0.01  0.038 0.807
  0.934 0.157 0.141 0.155 0.124 0.781 0.738 0.018 0.42  0.635 0.867 0.925
  0.398 0.505 0.695 0.429 0.174 0.327 0.123 0.967 0.378 0.224 0.393 0.053
  0.344 0.731 0.02  0.848 0.079 0.814 0.023 0.087 0.578 0.642 0.18  0.563
  0.276 0.491 0.021 0.719 0.85  0.156 0.031 0.506 0.271 0.095 0.186 0.002
  0.799 0.138 0.734 0.925 0.881 0.187 0.559 0.946 0.826 0.488 0.744 0.322
  0.333 0.322 0.665 0.032 0.663 0.754 0.495 0.569 0.917 0.167 0.168 0.409
  0.369 0.363 0.23  0.961 0.201 0.463 0.565 0.834 0.431 0.848 0.742 0.436
  0.061 0.656 0.3   0.128 0.485 0.78  0.617 0.082 0.396 0.416 0.673 0.961
  0.727 0.986 0.222 0.909 0.898 0.144 0.639 0.046 0.101 0.546 0.782 0.069
  0.672 0.824 0.861 0.981 0.003 0.591 0.303 0.384 0.67  0.7   0.834 0.475
  0.932 0.949 0.938 0.945 0.368 0.522 0.833 0.045 0.452 0.068 0.165 0.569
  0.44  0.702 0.727 0.069 0.686 0.262 0.891 0.547 0.994 0.454 0.947 0.364
  0.154 0.322 0.571 0.19  0.476 0.925 0.871 0.605 0.442 0.585 0.544 0.316
  0.915 0.253 0.973 0.501 0.402 0.96  0.206 0.501 0.37  0.463 0.904 0.981
  0.969 0.877 0.724 0.5   0.447 0.499 0.443 0.349 0.79  0.051 0.384 0.27
  0.094 0.774 0.742 0.16  0.517 0.266 0.908 0.796 0.862 0.987 0.939 0.909
  0.962 0.587 0.964 0.159 0.029 0.952 0.416 0.72  0.346 0.257 0.152 0.233
  0.862 0.457 0.153 0.076 0.105 0.634 0.652 0.435 0.757 0.985 0.487 0.114
  0.95  0.217 0.877 0.483 0.302 0.929 0.856 0.768 0.223 0.006 0.841 0.565
  0.611 0.407 0.71  0.588 0.654 0.197 0.506 0.938 0.779 0.387 0.007 0.482
  0.523 0.993 0.671 0.044 0.497 0.71  0.418 0.06  0.114 0.082 0.811 0.083
  0.773 0.134 0.87  0.414 0.787 0.972 0.132 0.047 0.593 0.502 0.15  0.042
  0.363 0.311 0.17  0.895 0.569 0.774 0.006 0.408 0.92  0.753 0.543 0.279
  0.911 0.314 0.195 0.538 0.977 0.606 0.954 0.378 0.397 0.261 0.085 0.656
  0.978 0.598 0.216 0.832 0.105 0.958 0.185 0.81  0.444 0.308 0.013 0.176
  0.603 0.383 0.671 0.436 0.981 0.072 0.713 0.349 0.962 0.055 0.315 0.417
  0.052 0.076 0.198 0.786 0.397 0.757 0.145 0.539 0.671 0.583 0.42  0.575
  0.563 0.286 0.788 0.481 0.403 0.85  0.864 0.945 0.427 0.511 0.268 0.091
  0.049 0.611 0.137 0.58  0.281 0.057 0.453 0.461 0.895 0.701 0.662 0.599
  0.967 0.562 0.295 0.6   0.742 0.909 0.69  0.383 0.553 0.078 0.949 0.109
  0.771 0.083 0.712 0.514 0.549 0.403 0.575 0.494 0.31  0.307 0.091 0.874
  0.591 0.315 0.199 0.372 0.131 0.905 0.32  0.284 0.516 0.055 0.832 0.042
  0.927 0.667 0.273 0.426 0.054 0.799 0.356 0.564 0.223 0.772 0.79  0.628
  0.893 0.512 0.523 0.518 0.48  0.869 0.49  0.416 0.775 0.864 0.921 0.968
  0.109 0.812 0.943 0.042 0.179 0.943 0.324 0.079 0.017 0.226 0.848 0.803
  0.873 0.834 0.696 0.582 0.125 0.042 0.917 0.909 0.491 0.5   0.101 0.779
  0.65  0.424 0.94  0.582 0.706 0.935 0.286 0.057 0.544 0.198 0.893 0.537
  0.405 0.91  0.908 0.297 0.288 0.368 0.654 0.347 0.002 0.677 0.32  0.691
  0.17  0.133 0.586 0.857 0.001 0.639 0.223 0.164 0.689 0.97  0.913 0.947
  0.962 0.44  0.201 0.343 0.493 0.662 0.728 0.295 0.445 0.739 0.764 0.955
  0.206 0.298 0.996 0.835 0.983 0.033 0.801 0.284 0.621 0.941 0.293 0.865
  0.158 0.788 0.681 0.613 0.705 0.753 0.006 0.175 0.414 0.299 0.116 0.67
  0.66  0.845 0.905 0.369 0.11  0.841 0.717 0.348 0.537 0.116 0.024 0.575
  0.211 0.427 0.84  0.447 0.056 0.427 0.39  0.424 0.48  0.738 0.698 0.377
  0.143 0.242 0.877 0.238 0.188 0.786 0.965 0.112 0.952 0.679 0.916 0.13
  0.882 0.353 0.433 0.608 0.297 0.558 0.663 0.646 0.185 0.91  0.131 0.217
  0.549 0.759 0.087 0.96  0.11  0.613 0.643 0.218 0.126 0.535 0.751 0.097
  0.681 0.782 0.367 0.197 0.05  0.742 0.623 0.763 0.625 0.317 0.364 0.879
  0.445 0.751 0.87  0.727 0.879 0.035 0.412 0.907 0.895 0.923 0.373 0.22
  0.21  0.176 0.182 0.821]]
RECEIVED RESPONSE:
meta {
  puid: "c88qkq0g5nsdtkero34a1i56dv"
  requestPath {
    key: "chainer-mnist-classifier"
    value: "chainer-mnist:0.1"
  }
}
data {
  names: "t:0"
  names: "t:1"
  names: "t:2"
  names: "t:3"
  names: "t:4"
  names: "t:5"
  names: "t:6"
  names: "t:7"
  names: "t:8"
  names: "t:9"
  ndarray {
    values {
      list_value {
        values {
          number_value: 2.4670965671539307
        }
        values {
          number_value: -4.471328258514404
        }
        values {
          number_value: -12.0973482131958
        }
        values {
          number_value: -8.344386100769043
        }
        values {
          number_value: -1.4862585067749023
        }
        values {
          number_value: -2.065153121948242
        }
        values {
          number_value: 1.480709195137024
        }
        values {
          number_value: -6.679415702819824
        }
        values {
          number_value: -7.224794864654541
        }
        values {
          number_value: -7.008320331573486
        }
      }
    }
  }
}


[ ]:
!minikube delete