fix example for enas

Author: tenzen-y

cmd/metricscollector/v1beta1/tfevent-metricscollector/Dockerfile.ppc64le

@@ -1,5 +1,4 @@
 FROM ibmcom/tensorflow-ppc64le:2.2.0-py3
-RUN pip install rfc3339 grpcio googleapis-common-protos
 ADD . /usr/src/app/github.com/kubeflow/katib
 WORKDIR /usr/src/app/github.com/kubeflow/katib/cmd/metricscollector/v1beta1/tfevent-metricscollector/
 RUN pip install --no-cache-dir -r requirements.txt

examples/v1beta1/trial-images/enas-cnn-cifar10/Dockerfile.cpu

@@ -5,6 +5,7 @@ ENV TARGET_DIR /opt/enas-cnn-cifar10
 ADD examples/v1beta1/trial-images/enas-cnn-cifar10 ${TARGET_DIR}
 WORKDIR  ${TARGET_DIR}
 
+RUN pip3 install --no-cache-dir -r requirements.txt
 ENV PYTHONPATH ${TARGET_DIR}
 
 RUN chgrp -R 0 ${TARGET_DIR} \

examples/v1beta1/trial-images/enas-cnn-cifar10/RunTrial.py

@@ -1,12 +1,10 @@
-import keras
-import numpy as np
+from tensorflow import keras
 from keras.datasets import cifar10
 from ModelConstructor import ModelConstructor
 from tensorflow.keras.utils import to_categorical
 from tensorflow.python.keras.utils.multi_gpu_utils import multi_gpu_model
 from keras.preprocessing.image import ImageDataGenerator
 import argparse
-import time
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser(description='TrainingContainer')
@@ -46,7 +44,7 @@
 
     test_model.summary()
     test_model.compile(loss=keras.losses.categorical_crossentropy,
-                       optimizer=keras.optimizers.Adam(lr=1e-3, decay=1e-4),
+                       optimizer=keras.optimizers.Adam(learning_rate=1e-3, decay=1e-4),
                        metrics=['accuracy'])
 
     (x_train, y_train), (x_test, y_test) = cifar10.load_data()
@@ -67,12 +65,12 @@
 
     print(">>> Data Loaded. Training starts.")
     for e in range(num_epochs):
-        print("\nTotal Epoch {}/{}".format(e+1, num_epochs))
-        history = test_model.fit_generator(generator=aug_data_flow,
-                                           steps_per_epoch=int(len(x_train)/128)+1,
-                                           epochs=1, verbose=1,
-                                           validation_data=(x_test, y_test))
-        print("Training-Accuracy={}".format(history.history['acc'][-1]))
+        print("\nTotal Epoch {}/{}".format(e + 1, num_epochs))
+        history = test_model.fit(aug_data_flow,
+                                 steps_per_epoch=int(len(x_train) / 128) + 1,
+                                 epochs=1, verbose=1,
+                                 validation_data=(x_test, y_test))
+        print("Training-Accuracy={}".format(history.history['accuracy'][-1]))
         print("Training-Loss={}".format(history.history['loss'][-1]))
-        print("Validation-Accuracy={}".format(history.history['val_acc'][-1]))
+        print("Validation-Accuracy={}".format(history.history['val_accuracy'][-1]))
         print("Validation-Loss={}".format(history.history['val_loss'][-1]))

examples/v1beta1/trial-images/enas-cnn-cifar10/requirements.txt

@@ -0,0 +1 @@
+scipy>=1.7.2

examples/v1beta1/trial-images/tf-mnist-with-summaries/README.md

@@ -8,4 +8,4 @@ If you want to read more about this example, visit the official
 GitHub repository.
 
 Katib uses this training container in some Experiments, for instance in the
-[TF Event Metrics Collector](../../metrics-collector/tfevent-metrics-collector.yaml#L55-L64).
+[TF Event Metrics Collector](../../metrics-collector/tfevent-metrics-collector.yaml#L42-L49).

test/e2e/v1beta1/argo_workflow.py

@@ -59,7 +59,7 @@
     "earlystopping-medianstop":      "cmd/earlystopping/medianstop/v1beta1/Dockerfile",
     "trial-mxnet-mnist":             "examples/v1beta1/trial-images/mxnet-mnist/Dockerfile",
     "trial-pytorch-mnist":           "examples/v1beta1/trial-images/pytorch-mnist/Dockerfile",
-#     "trial-tf-mnist-with-summaries": "examples/v1beta1/trial-images/tf-mnist-with-summaries/Dockerfile",
+    "trial-tf-mnist-with-summaries": "examples/v1beta1/trial-images/tf-mnist-with-summaries/Dockerfile",
     "trial-enas-cnn-cifar10-gpu":    "examples/v1beta1/trial-images/enas-cnn-cifar10/Dockerfile.gpu",
     "trial-enas-cnn-cifar10-cpu":    "examples/v1beta1/trial-images/enas-cnn-cifar10/Dockerfile.cpu",
     "trial-darts-cnn-cifar10":       "examples/v1beta1/trial-images/darts-cnn-cifar10/Dockerfile",
@@ -80,7 +80,7 @@
     "pytorchjob":               "examples/v1beta1/kubeflow-training-operator/pytorchjob-mnist.yaml",
     "tfjob":                    "examples/v1beta1/kubeflow-training-operator/tfjob-mnist-with-summaries.yaml",
     "file-metricscollector":    "examples/v1beta1/metrics-collector/file-metrics-collector.yaml",
-#     "tfevent-metricscollector": "examples/v1beta1/metrics-collector/tfevent-metrics-collector.yaml",
+    "tfevent-metricscollector": "examples/v1beta1/metrics-collector/tfevent-metrics-collector.yaml",
     "never-resume":             "examples/v1beta1/resume-experiment/never-resume.yaml",
     "from-volume-resume":       "examples/v1beta1/resume-experiment/from-volume-resume.yaml",
     "median-stop":              "examples/v1beta1/early-stopping/median-stop.yaml"