This page was generated from examples/models/deep_mnist/deep_mnist.ipynb.
Tensorflow MNIST Model¶
- Wrap a Tensorflow 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
Train locally¶
[1]:
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot = True)
import tensorflow as tf
if __name__ == '__main__':
x = tf.placeholder(tf.float32, [None,784], name="x")
W = tf.Variable(tf.zeros([784,10]))
b = tf.Variable(tf.zeros([10]))
y = tf.nn.softmax(tf.matmul(x,W) + b, name="y")
y_ = tf.placeholder(tf.float32, [None, 10])
cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1]))
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
init = tf.initialize_all_variables()
sess = tf.Session()
sess.run(init)
for i in range(1000):
batch_xs, batch_ys = mnist.train.next_batch(100)
sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})
correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
print(sess.run(accuracy, feed_dict = {x: mnist.test.images, y_:mnist.test.labels}))
saver = tf.train.Saver()
saver.save(sess, "model/deep_mnist_model")
WARNING:tensorflow:From <ipython-input-1-b7995d30f035>:2: read_data_sets (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.
Instructions for updating:
Please use alternatives such as official/mnist/dataset.py from tensorflow/models.
WARNING:tensorflow:From /home/clive/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:260: maybe_download (from tensorflow.contrib.learn.python.learn.datasets.base) is deprecated and will be removed in a future version.
Instructions for updating:
Please write your own downloading logic.
WARNING:tensorflow:From /home/clive/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:262: extract_images (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.
Instructions for updating:
Please use tf.data to implement this functionality.
Extracting MNIST_data/train-images-idx3-ubyte.gz
WARNING:tensorflow:From /home/clive/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:267: extract_labels (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.
Instructions for updating:
Please use tf.data to implement this functionality.
Extracting MNIST_data/train-labels-idx1-ubyte.gz
WARNING:tensorflow:From /home/clive/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:110: dense_to_one_hot (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.
Instructions for updating:
Please use tf.one_hot on tensors.
Extracting MNIST_data/t10k-images-idx3-ubyte.gz
Extracting MNIST_data/t10k-labels-idx1-ubyte.gz
WARNING:tensorflow:From /home/clive/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:290: DataSet.__init__ (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.
Instructions for updating:
Please use alternatives such as official/mnist/dataset.py from tensorflow/models.
WARNING:tensorflow:From /home/clive/anaconda3/lib/python3.6/site-packages/tensorflow/python/util/tf_should_use.py:118: initialize_all_variables (from tensorflow.python.ops.variables) is deprecated and will be removed after 2017-03-02.
Instructions for updating:
Use `tf.global_variables_initializer` instead.
0.9159
Wrap model using s2i
[2]:
!s2i build . seldonio/seldon-core-s2i-python36:0.5.1 deep-mnist:0.1
---> Installing application source...
---> Installing dependencies ...
Looking in links: /whl
Requirement already satisfied: tensorflow>=1.12.0 in /usr/local/lib/python3.6/site-packages (from -r requirements.txt (line 1)) (1.12.0)
Requirement already satisfied: keras-applications>=1.0.6 in /usr/local/lib/python3.6/site-packages (from tensorflow>=1.12.0->-r requirements.txt (line 1)) (1.0.7)
Requirement already satisfied: tensorboard<1.13.0,>=1.12.0 in /usr/local/lib/python3.6/site-packages (from tensorflow>=1.12.0->-r requirements.txt (line 1)) (1.12.2)
Requirement already satisfied: gast>=0.2.0 in /usr/local/lib/python3.6/site-packages (from tensorflow>=1.12.0->-r requirements.txt (line 1)) (0.2.2)
Requirement already satisfied: protobuf>=3.6.1 in /usr/local/lib/python3.6/site-packages (from tensorflow>=1.12.0->-r requirements.txt (line 1)) (3.6.1)
Requirement already satisfied: grpcio>=1.8.6 in /usr/local/lib/python3.6/site-packages (from tensorflow>=1.12.0->-r requirements.txt (line 1)) (1.18.0)
Requirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.6/site-packages (from tensorflow>=1.12.0->-r requirements.txt (line 1)) (1.12.0)
Requirement already satisfied: wheel>=0.26 in /usr/local/lib/python3.6/site-packages (from tensorflow>=1.12.0->-r requirements.txt (line 1)) (0.33.1)
Requirement already satisfied: numpy>=1.13.3 in /usr/local/lib/python3.6/site-packages (from tensorflow>=1.12.0->-r requirements.txt (line 1)) (1.16.1)
Requirement already satisfied: keras-preprocessing>=1.0.5 in /usr/local/lib/python3.6/site-packages (from tensorflow>=1.12.0->-r requirements.txt (line 1)) (1.0.9)
Requirement already satisfied: astor>=0.6.0 in /usr/local/lib/python3.6/site-packages (from tensorflow>=1.12.0->-r requirements.txt (line 1)) (0.7.1)
Requirement already satisfied: absl-py>=0.1.6 in /usr/local/lib/python3.6/site-packages (from tensorflow>=1.12.0->-r requirements.txt (line 1)) (0.7.0)
Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.6/site-packages (from tensorflow>=1.12.0->-r requirements.txt (line 1)) (1.1.0)
Requirement already satisfied: h5py in /usr/local/lib/python3.6/site-packages (from keras-applications>=1.0.6->tensorflow>=1.12.0->-r requirements.txt (line 1)) (2.9.0)
Requirement already satisfied: werkzeug>=0.11.10 in /usr/local/lib/python3.6/site-packages (from tensorboard<1.13.0,>=1.12.0->tensorflow>=1.12.0->-r requirements.txt (line 1)) (0.14.1)
Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.6/site-packages (from tensorboard<1.13.0,>=1.12.0->tensorflow>=1.12.0->-r requirements.txt (line 1)) (3.0.1)
Requirement already satisfied: setuptools in /usr/local/lib/python3.6/site-packages (from protobuf>=3.6.1->tensorflow>=1.12.0->-r requirements.txt (line 1)) (40.8.0)
Url '/whl' is ignored. It is either a non-existing path or lacks a specific scheme.
Build completed successfully
[3]:
!docker run --name "mnist_predictor" -d --rm -p 5000:5000 deep-mnist:0.1
13b5515bb7074825056c4795cacd4348b6d28272def928380116a1fe9ea30659
Send some random features that conform to the contract
[4]:
!seldon-core-tester contract.json 0.0.0.0 5000 -p
----------------------------------------
SENDING NEW REQUEST:
RECEIVED RESPONSE:
Success:True message:
Request:
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values {
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values {
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values {
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values {
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values {
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values {
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values {
number_value: 0.987
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values {
number_value: 0.663
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values {
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values {
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values {
number_value: 0.556
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values {
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values {
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values {
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values {
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values {
number_value: 0.618
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values {
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values {
number_value: 0.349
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values {
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values {
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values {
number_value: 0.306
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values {
number_value: 0.898
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values {
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}
values {
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values {
number_value: 0.009
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values {
number_value: 0.364
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values {
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values {
number_value: 0.224
}
values {
number_value: 0.431
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values {
number_value: 0.642
}
values {
number_value: 0.607
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values {
number_value: 0.9
}
values {
number_value: 0.598
}
values {
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values {
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values {
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values {
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values {
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values {
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values {
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values {
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values {
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values {
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values {
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values {
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values {
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values {
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values {
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values {
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values {
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values {
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values {
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values {
number_value: 0.092
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values {
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values {
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values {
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values {
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values {
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values {
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values {
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values {
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values {
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values {
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values {
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values {
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values {
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values {
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values {
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values {
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values {
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values {
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values {
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values {
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values {
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values {
number_value: 0.365
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values {
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values {
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values {
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values {
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values {
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values {
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}
values {
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}
values {
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}
values {
number_value: 0.814
}
values {
number_value: 0.234
}
values {
number_value: 0.076
}
values {
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}
values {
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}
values {
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values {
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}
values {
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}
values {
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values {
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values {
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values {
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}
values {
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}
values {
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values {
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}
values {
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}
values {
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}
values {
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values {
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values {
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values {
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values {
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values {
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values {
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values {
number_value: 0.391
}
values {
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}
values {
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values {
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values {
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values {
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values {
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values {
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}
values {
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}
values {
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values {
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}
values {
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}
values {
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}
values {
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}
values {
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values {
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}
values {
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values {
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values {
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values {
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}
values {
number_value: 0.201
}
values {
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values {
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values {
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values {
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}
values {
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values {
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values {
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values {
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values {
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values {
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values {
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values {
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values {
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values {
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values {
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values {
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values {
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values {
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values {
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values {
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values {
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values {
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values {
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values {
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values {
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}
values {
number_value: 0.342
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values {
number_value: 0.449
}
values {
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values {
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values {
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values {
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values {
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values {
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values {
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}
values {
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values {
number_value: 0.715
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values {
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}
values {
number_value: 0.292
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values {
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values {
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values {
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values {
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values {
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values {
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values {
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}
values {
number_value: 0.752
}
values {
number_value: 0.485
}
values {
number_value: 0.111
}
values {
number_value: 0.163
}
values {
number_value: 0.794
}
values {
number_value: 0.65
}
values {
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}
values {
number_value: 0.364
}
values {
number_value: 0.34
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values {
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values {
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values {
number_value: 0.812
}
values {
number_value: 0.422
}
values {
number_value: 0.412
}
values {
number_value: 0.292
}
values {
number_value: 0.171
}
values {
number_value: 0.058
}
values {
number_value: 0.041
}
values {
number_value: 0.785
}
values {
number_value: 0.065
}
values {
number_value: 0.105
}
values {
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}
values {
number_value: 0.905
}
values {
number_value: 0.409
}
values {
number_value: 0.21
}
values {
number_value: 0.25
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values {
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values {
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values {
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values {
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values {
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values {
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values {
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values {
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values {
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values {
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values {
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values {
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values {
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values {
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values {
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values {
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values {
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values {
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values {
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values {
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values {
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values {
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values {
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values {
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values {
number_value: 0.137
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values {
number_value: 0.725
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values {
number_value: 0.098
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values {
number_value: 0.841
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values {
number_value: 0.13
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values {
number_value: 0.149
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values {
number_value: 0.586
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values {
number_value: 0.858
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values {
number_value: 0.688
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values {
number_value: 0.43
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values {
number_value: 0.094
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values {
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values {
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values {
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values {
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values {
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values {
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values {
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values {
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values {
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values {
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values {
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values {
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values {
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values {
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values {
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values {
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values {
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values {
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values {
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values {
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values {
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values {
number_value: 0.422
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values {
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values {
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values {
number_value: 0.107
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values {
number_value: 0.954
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values {
number_value: 0.738
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values {
number_value: 0.204
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values {
number_value: 0.896
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values {
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values {
number_value: 0.685
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values {
number_value: 0.801
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values {
number_value: 0.037
}
values {
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values {
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values {
number_value: 0.639
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values {
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values {
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values {
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values {
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values {
number_value: 0.797
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values {
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values {
number_value: 0.829
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values {
number_value: 0.384
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values {
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values {
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values {
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values {
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values {
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values {
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values {
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values {
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values {
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values {
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values {
number_value: 0.905
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values {
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values {
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values {
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values {
number_value: 0.459
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values {
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values {
number_value: 0.52
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values {
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values {
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values {
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values {
number_value: 0.259
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values {
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values {
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values {
number_value: 0.909
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values {
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values {
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values {
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values {
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values {
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values {
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values {
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values {
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values {
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values {
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values {
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values {
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values {
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values {
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values {
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values {
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values {
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values {
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values {
number_value: 0.875
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values {
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values {
number_value: 0.194
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values {
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}
values {
number_value: 0.786
}
values {
number_value: 0.265
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values {
number_value: 0.364
}
values {
number_value: 0.003
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values {
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}
values {
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}
values {
number_value: 0.145
}
values {
number_value: 0.179
}
values {
number_value: 0.255
}
values {
number_value: 0.842
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values {
number_value: 0.338
}
values {
number_value: 0.223
}
values {
number_value: 0.311
}
values {
number_value: 0.079
}
values {
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values {
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}
values {
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values {
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}
values {
number_value: 0.092
}
values {
number_value: 0.203
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values {
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values {
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values {
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values {
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values {
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values {
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values {
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values {
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values {
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values {
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values {
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values {
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values {
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values {
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}
values {
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values {
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values {
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values {
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values {
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values {
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values {
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values {
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values {
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values {
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values {
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values {
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values {
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values {
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values {
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}
values {
number_value: 0.271
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values {
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}
values {
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}
values {
number_value: 0.031
}
values {
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}
values {
number_value: 0.874
}
values {
number_value: 0.034
}
values {
number_value: 0.029
}
values {
number_value: 0.771
}
values {
number_value: 0.906
}
values {
number_value: 0.326
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values {
number_value: 0.662
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values {
number_value: 0.668
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values {
number_value: 0.31
}
values {
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values {
number_value: 0.559
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values {
number_value: 0.592
}
values {
number_value: 0.647
}
values {
number_value: 0.471
}
values {
number_value: 0.089
}
values {
number_value: 0.267
}
values {
number_value: 0.759
}
values {
number_value: 0.512
}
values {
number_value: 0.693
}
values {
number_value: 0.24
}
values {
number_value: 0.505
}
values {
number_value: 0.646
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values {
number_value: 0.751
}
values {
number_value: 0.552
}
values {
number_value: 0.746
}
values {
number_value: 0.251
}
values {
number_value: 0.621
}
values {
number_value: 0.495
}
values {
number_value: 0.571
}
values {
number_value: 0.497
}
values {
number_value: 0.843
}
values {
number_value: 0.155
}
values {
number_value: 0.038
}
values {
number_value: 0.4
}
values {
number_value: 0.749
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values {
number_value: 0.217
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values {
number_value: 0.907
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values {
number_value: 0.605
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values {
number_value: 0.965
}
values {
number_value: 0.781
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values {
number_value: 0.548
}
values {
number_value: 0.346
}
values {
number_value: 0.875
}
values {
number_value: 0.2
}
values {
number_value: 0.529
}
values {
number_value: 0.499
}
values {
number_value: 0.858
}
values {
number_value: 0.569
}
values {
number_value: 0.597
}
values {
number_value: 0.241
}
}
}
}
}
Response:
meta {
}
data {
names: "class:0"
names: "class:1"
names: "class:2"
names: "class:3"
names: "class:4"
names: "class:5"
names: "class:6"
names: "class:7"
names: "class:8"
names: "class:9"
ndarray {
values {
list_value {
values {
number_value: 0.001586819882504642
}
values {
number_value: 5.388684485296835e-07
}
values {
number_value: 0.0681319385766983
}
values {
number_value: 0.39322274923324585
}
values {
number_value: 2.890761606977321e-06
}
values {
number_value: 0.4975513219833374
}
values {
number_value: 0.0009588279062882066
}
values {
number_value: 0.0003235357580706477
}
values {
number_value: 0.037815213203430176
}
values {
number_value: 0.00040610713767819107
}
}
}
}
}
[5]:
!docker rm mnist_predictor --force
mnist_predictor
Test using Minikube¶
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>`__
[6]:
!minikube start --memory 4096
😄 minikube v0.34.1 on linux (amd64)
🔥 Creating virtualbox VM (CPUs=2, Memory=4096MB, Disk=20000MB) ...
📶 "minikube" IP address is 192.168.99.100
🐳 Configuring Docker as the container runtime ...
✨ Preparing Kubernetes environment ...
🚜 Pulling images required by Kubernetes v1.13.3 ...
🚀 Launching Kubernetes v1.13.3 using kubeadm ...
🔑 Configuring cluster permissions ...
🤔 Verifying component health .....
💗 kubectl is now configured to use "minikube"
🏄 Done! Thank you for using minikube!
[7]:
!kubectl create clusterrolebinding kube-system-cluster-admin --clusterrole=cluster-admin --serviceaccount=kube-system:default
clusterrolebinding.rbac.authorization.k8s.io/kube-system-cluster-admin created
[8]:
!helm init
$HELM_HOME has been configured at /home/clive/.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
Happy Helming!
[9]:
!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
[10]:
!helm install ../../../helm-charts/seldon-core-crd --name seldon-core-crd --set usage_metrics.enabled=true
!helm install ../../../helm-charts/seldon-core --name seldon-core
NAME: seldon-core-crd
LAST DEPLOYED: Wed Mar 13 09:35:37 2019
NAMESPACE: default
STATUS: DEPLOYED
RESOURCES:
==> v1/ServiceAccount
NAME SECRETS AGE
seldon-spartakus-volunteer 1 0s
==> v1beta1/ClusterRole
NAME AGE
seldon-spartakus-volunteer 0s
==> v1beta1/ClusterRoleBinding
NAME AGE
seldon-spartakus-volunteer 0s
==> v1/ConfigMap
NAME DATA AGE
seldon-spartakus-config 3 5s
==> v1beta1/CustomResourceDefinition
NAME AGE
seldondeployments.machinelearning.seldon.io 1s
==> v1beta1/Deployment
NAME DESIRED CURRENT UP-TO-DATE AVAILABLE AGE
seldon-spartakus-volunteer 1 0 0 0 1s
NOTES:
NOTES: TODO
NAME: seldon-core
LAST DEPLOYED: Wed Mar 13 09:35:42 2019
NAMESPACE: default
STATUS: DEPLOYED
RESOURCES:
==> v1/Role
NAME AGE
seldon-local 0s
==> v1/RoleBinding
NAME AGE
seldon 0s
==> v1/Service
NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE
seldon-core-seldon-apiserver NodePort 10.107.198.43 <none> 8080:31692/TCP,5000:30651/TCP 0s
seldon-core-redis ClusterIP 10.104.104.7 <none> 6379/TCP 0s
==> v1beta1/Deployment
NAME DESIRED CURRENT UP-TO-DATE AVAILABLE AGE
seldon-core-seldon-apiserver 1 1 1 0 0s
seldon-core-seldon-cluster-manager 1 1 1 0 0s
seldon-core-redis 1 1 1 0 0s
==> v1/Pod(related)
NAME READY STATUS RESTARTS AGE
seldon-core-seldon-apiserver-7c9898d988-s8rq9 0/1 ContainerCreating 0 0s
seldon-core-seldon-cluster-manager-68ff4ccfcf-jq6l4 0/1 ContainerCreating 0 0s
seldon-core-redis-7d64dc686b-wfmhp 0/1 ContainerCreating 0 0s
==> v1/ServiceAccount
NAME SECRETS AGE
seldon 1 0s
NOTES:
Thank you for installing Seldon Core.
Documentation can be found at https://github.com/SeldonIO/seldon-core
[11]:
!eval $(minikube docker-env) && s2i build . seldonio/seldon-core-s2i-python2:0.5.1 deep-mnist:0.1
---> Installing application source...
---> Installing dependencies ...
DEPRECATION: Python 2.7 will reach the end of its life on January 1st, 2020. Please upgrade your Python as Python 2.7 won't be maintained after that date. A future version of pip will drop support for Python 2.7.
Looking in links: /whl
Requirement already satisfied: tensorflow>=1.12.0 in /usr/local/lib/python2.7/site-packages (from -r requirements.txt (line 1)) (1.12.0)
Requirement already satisfied: astor>=0.6.0 in /usr/local/lib/python2.7/site-packages (from tensorflow>=1.12.0->-r requirements.txt (line 1)) (0.7.1)
Requirement already satisfied: keras-preprocessing>=1.0.5 in /usr/local/lib/python2.7/site-packages (from tensorflow>=1.12.0->-r requirements.txt (line 1)) (1.0.9)
Requirement already satisfied: gast>=0.2.0 in /usr/local/lib/python2.7/site-packages (from tensorflow>=1.12.0->-r requirements.txt (line 1)) (0.2.2)
Requirement already satisfied: enum34>=1.1.6 in /usr/local/lib/python2.7/site-packages (from tensorflow>=1.12.0->-r requirements.txt (line 1)) (1.1.6)
Requirement already satisfied: protobuf>=3.6.1 in /usr/local/lib/python2.7/site-packages (from tensorflow>=1.12.0->-r requirements.txt (line 1)) (3.6.1)
Requirement already satisfied: six>=1.10.0 in /usr/local/lib/python2.7/site-packages (from tensorflow>=1.12.0->-r requirements.txt (line 1)) (1.12.0)
Requirement already satisfied: absl-py>=0.1.6 in /usr/local/lib/python2.7/site-packages (from tensorflow>=1.12.0->-r requirements.txt (line 1)) (0.7.0)
Requirement already satisfied: backports.weakref>=1.0rc1 in /usr/local/lib/python2.7/site-packages (from tensorflow>=1.12.0->-r requirements.txt (line 1)) (1.0.post1)
Requirement already satisfied: wheel in /usr/local/lib/python2.7/site-packages (from tensorflow>=1.12.0->-r requirements.txt (line 1)) (0.33.1)
Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python2.7/site-packages (from tensorflow>=1.12.0->-r requirements.txt (line 1)) (1.1.0)
Requirement already satisfied: tensorboard<1.13.0,>=1.12.0 in /usr/local/lib/python2.7/site-packages (from tensorflow>=1.12.0->-r requirements.txt (line 1)) (1.12.2)
Requirement already satisfied: numpy>=1.13.3 in /usr/local/lib/python2.7/site-packages (from tensorflow>=1.12.0->-r requirements.txt (line 1)) (1.16.1)
Requirement already satisfied: mock>=2.0.0 in /usr/local/lib/python2.7/site-packages (from tensorflow>=1.12.0->-r requirements.txt (line 1)) (2.0.0)
Requirement already satisfied: keras-applications>=1.0.6 in /usr/local/lib/python2.7/site-packages (from tensorflow>=1.12.0->-r requirements.txt (line 1)) (1.0.7)
Requirement already satisfied: grpcio>=1.8.6 in /usr/local/lib/python2.7/site-packages (from tensorflow>=1.12.0->-r requirements.txt (line 1)) (1.18.0)
Requirement already satisfied: setuptools in /usr/local/lib/python2.7/site-packages (from protobuf>=3.6.1->tensorflow>=1.12.0->-r requirements.txt (line 1)) (40.8.0)
Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python2.7/site-packages (from tensorboard<1.13.0,>=1.12.0->tensorflow>=1.12.0->-r requirements.txt (line 1)) (3.0.1)
Requirement already satisfied: futures>=3.1.1; python_version < "3" in /usr/local/lib/python2.7/site-packages (from tensorboard<1.13.0,>=1.12.0->tensorflow>=1.12.0->-r requirements.txt (line 1)) (3.2.0)
Requirement already satisfied: werkzeug>=0.11.10 in /usr/local/lib/python2.7/site-packages (from tensorboard<1.13.0,>=1.12.0->tensorflow>=1.12.0->-r requirements.txt (line 1)) (0.14.1)
Requirement already satisfied: funcsigs>=1; python_version < "3.3" in /usr/local/lib/python2.7/site-packages (from mock>=2.0.0->tensorflow>=1.12.0->-r requirements.txt (line 1)) (1.0.2)
Requirement already satisfied: pbr>=0.11 in /usr/local/lib/python2.7/site-packages (from mock>=2.0.0->tensorflow>=1.12.0->-r requirements.txt (line 1)) (5.1.2)
Requirement already satisfied: h5py in /usr/local/lib/python2.7/site-packages (from keras-applications>=1.0.6->tensorflow>=1.12.0->-r requirements.txt (line 1)) (2.9.0)
Url '/whl' is ignored. It is either a non-existing path or lacks a specific scheme.
Build completed successfully
[12]:
!kubectl create -f deep_mnist.json
seldondeployment.machinelearning.seldon.io/deep-mnist created
[14]:
!kubectl rollout status deploy/deep-mnist-single-model-8969cc0
Waiting for deployment "deep-mnist-single-model-8969cc0" rollout to finish: 0 of 1 updated replicas are available...
deployment "deep-mnist-single-model-8969cc0" successfully rolled out
[15]:
!seldon-core-api-tester contract.json \
`minikube ip` `kubectl get svc -l app=seldon-apiserver-container-app -o jsonpath='{.items[0].spec.ports[0].nodePort}'` \
--oauth-key oauth-key --oauth-secret oauth-secret -p
RECEIVED RESPONSE:
Success:True message:
Request:
data {
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}
}
Response:
meta {
puid: "ivo4qrm59vikikiu5h852lsanv"
requestPath {
key: "classifier"
value: "deep-mnist:0.1"
}
}
data {
names: "class:0"
names: "class:1"
names: "class:2"
names: "class:3"
names: "class:4"
names: "class:5"
names: "class:6"
names: "class:7"
names: "class:8"
names: "class:9"
tensor {
shape: 1
shape: 10
values: 0.000305197638226673
values: 2.2940020016903873e-07
values: 0.17768406867980957
values: 0.3751635253429413
values: 1.6084664821391925e-05
values: 0.42158570885658264
values: 0.0020625267643481493
values: 0.0028135962784290314
values: 0.019332798197865486
values: 0.001036234200000763
}
}
[16]:
!minikube delete
🔥 Deleting "minikube" from virtualbox ...
💔 The "minikube" cluster has been deleted.
[ ]: