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Posted to commits@mxnet.apache.org by GitBox <gi...@apache.org> on 2018/08/06 22:13:30 UTC
[GitHub] sandeep-krishnamurthy closed pull request #11902: Remove stale
unused Keras1-MXNet tests from MXNet repo
sandeep-krishnamurthy closed pull request #11902: Remove stale unused Keras1-MXNet tests from MXNet repo
URL: https://github.com/apache/incubator-mxnet/pull/11902
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diff --git a/docs/faq/index.md b/docs/faq/index.md
index c351bc90dba..07dd9b9d7ca 100644
--- a/docs/faq/index.md
+++ b/docs/faq/index.md
@@ -45,7 +45,7 @@ and full working examples, visit the [tutorials section](../tutorials/index.md).
* [How do I run MXNet on a Raspberry Pi for computer vision?](http://mxnet.io/tutorials/embedded/wine_detector.html)
-* [How do I run Keras 1.2.2 with mxnet backend?](https://github.com/dmlc/keras/wiki/Installation)
+* [How do I run Keras 2 with MXNet backend?](https://github.com/awslabs/keras-apache-mxnet/blob/master/docs/mxnet_backend/installation.md)
* [How to convert MXNet models to Apple CoreML format?](https://github.com/apache/incubator-mxnet/tree/master/tools/coreml)
diff --git a/tests/nightly/mxnet_keras_integration_tests/assertion_util.py b/tests/nightly/mxnet_keras_integration_tests/assertion_util.py
deleted file mode 100644
index eb3d3bd85fd..00000000000
--- a/tests/nightly/mxnet_keras_integration_tests/assertion_util.py
+++ /dev/null
@@ -1,79 +0,0 @@
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements. See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership. The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License. You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an
-# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-# KIND, either express or implied. See the License for the
-# specific language governing permissions and limitations
-# under the License.
-
-
-from nose.tools import assert_true
-
-def assert_results(MACHINE_TYPE, IS_GPU, GPU_NUM, profile_output, CPU_BENCHMARK_RESULTS, GPU_1_BENCHMARK_RESULTS, GPU_2_BENCHMARK_RESULTS, GPU_4_BENCHMARK_RESULTS, GPU_8_BENCHMARK_RESULTS):
- """
- Helps in asserting benchmarking results.
- Compares actual output result in profile_output with expected result in
- CPU_BENCHMARK_RESULTS if IS_GPU is True.
- Else, compares with GPU_1_BENCHMARK_RESULTS, GPU_2_BENCHMARK_RESULTS
- GPU_4_BENCHMARK_RESULTS and GPU_8_BENCHMARK_RESULTS.
-
- Uses keys - MODEL, TRAINING_TIME, MEM_CONSUMPTION, TRAIN_ACCURACY and TEST_ACCURACY
- to fetch data from provided actual and expected results input map stated above.
- """
- # Model type
- model = profile_output['MODEL']
-
- # Actual values.
- actual_training_time = profile_output['TRAINING_TIME']
- actual_memory_consumption = profile_output['MEM_CONSUMPTION']
- actual_train_accuracy = profile_output['TRAIN_ACCURACY']
- actual_test_accuracy = profile_output['TEST_ACCURACY']
-
- # Expected values
- expected_training_time = 0.0
- expected_memory_consumption = 0.0
- expected_train_accuracy = 1.0
- expected_test_accuracy = 1.0
-
- # Set right set of expected values based on current run type
- if(IS_GPU):
- if GPU_NUM == 1:
- expected_training_time = GPU_1_BENCHMARK_RESULTS['TRAINING_TIME']
- expected_memory_consumption = GPU_1_BENCHMARK_RESULTS['MEM_CONSUMPTION']
- expected_train_accuracy = GPU_1_BENCHMARK_RESULTS['TRAIN_ACCURACY']
- expected_test_accuracy = GPU_1_BENCHMARK_RESULTS['TEST_ACCURACY']
- elif GPU_NUM == 2:
- expected_training_time = GPU_2_BENCHMARK_RESULTS['TRAINING_TIME']
- expected_memory_consumption = GPU_2_BENCHMARK_RESULTS['MEM_CONSUMPTION']
- expected_train_accuracy = GPU_2_BENCHMARK_RESULTS['TRAIN_ACCURACY']
- expected_test_accuracy = GPU_2_BENCHMARK_RESULTS['TEST_ACCURACY']
- elif GPU_NUM == 4:
- expected_training_time = GPU_4_BENCHMARK_RESULTS['TRAINING_TIME']
- expected_memory_consumption = GPU_4_BENCHMARK_RESULTS['MEM_CONSUMPTION']
- expected_train_accuracy = GPU_4_BENCHMARK_RESULTS['TRAIN_ACCURACY']
- expected_test_accuracy = GPU_4_BENCHMARK_RESULTS['TEST_ACCURACY']
- elif GPU_NUM == 8:
- expected_training_time = GPU_8_BENCHMARK_RESULTS['TRAINING_TIME']
- expected_memory_consumption = GPU_8_BENCHMARK_RESULTS['MEM_CONSUMPTION']
- expected_train_accuracy = GPU_8_BENCHMARK_RESULTS['TRAIN_ACCURACY']
- expected_test_accuracy = GPU_8_BENCHMARK_RESULTS['TEST_ACCURACY']
- else:
- expected_training_time = CPU_BENCHMARK_RESULTS['TRAINING_TIME']
- expected_memory_consumption = CPU_BENCHMARK_RESULTS['MEM_CONSUMPTION']
- expected_train_accuracy = CPU_BENCHMARK_RESULTS['TRAIN_ACCURACY']
- expected_test_accuracy = CPU_BENCHMARK_RESULTS['TEST_ACCURACY']
-
- # Validate Results
- assert_true(actual_training_time < expected_training_time,'{0} on {1} machine with {2} GPU usage FAILED. Expected Training Time - {3} secs but was {4} secs.'.format(model, MACHINE_TYPE, GPU_NUM, expected_training_time, actual_training_time))
- assert_true(actual_memory_consumption < expected_memory_consumption, '{0} on {1} machine with {2} GPU usage FAILED. Expected Mem Consumption - {3} MB but was {4} MB.'.format(model, MACHINE_TYPE, GPU_NUM, expected_memory_consumption, actual_memory_consumption))
- assert_true(actual_train_accuracy > expected_train_accuracy, '{0} on {1} machine with {2} GPU usage FAILED. Expected Train Accuracy - {3} but was {4}.'.format(model, MACHINE_TYPE, GPU_NUM, expected_train_accuracy, actual_train_accuracy))
- assert_true(actual_test_accuracy > expected_test_accuracy, '{0} on {1} machine with {2} GPU usage FAILED. Expected Test Accuracy - {3} but was {4}.'.format(model, MACHINE_TYPE, GPU_NUM, expected_test_accuracy, actual_test_accuracy))
diff --git a/tests/nightly/mxnet_keras_integration_tests/model_util.py b/tests/nightly/mxnet_keras_integration_tests/model_util.py
deleted file mode 100644
index bb9d6374af8..00000000000
--- a/tests/nightly/mxnet_keras_integration_tests/model_util.py
+++ /dev/null
@@ -1,68 +0,0 @@
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements. See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership. The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License. You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an
-# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-# KIND, either express or implied. See the License for the
-# specific language governing permissions and limitations
-# under the License.
-
-import os
-from keras import backend as K
-from keras.models import Model
-from keras.layers import Input, merge
-from keras.layers.core import Lambda
-
-# Before running the integration tests, users are expected to set these
-# environment variables.
-IS_GPU = (os.environ['MXNET_KERAS_TEST_MACHINE'] == 'GPU')
-GPU_NUM = int(os.environ['GPU_NUM']) if IS_GPU else 0
-KERAS_BACKEND = os.environ['KERAS_BACKEND']
-
-def slice_batch(x, n_gpus, part):
- sh = K.shape(x)
- L = sh[0] / n_gpus
- if part == n_gpus - 1:
- return x[part*L:]
- return x[part*L:(part+1)*L]
-
-def prepare_gpu_model(model, **kwargs):
- gpu_list = []
- for i in range(GPU_NUM):
- gpu_list.append('gpu(%d)' % i)
- if KERAS_BACKEND == 'mxnet':
- kwargs['context'] = gpu_list
- model.compile(**kwargs)
- else:
- model.compile(**kwargs)
-
-def prepare_cpu_model(model, **kwargs):
- model.compile(**kwargs)
-
-def make_model(model, **kwargs):
- """
- Compiles the Keras Model object for given backend type and machine type.
- Use this function to write one Keras code and run it across different machine type.
-
- If environment variable - MXNET_KERAS_TEST_MACHINE is set to CPU, then Compiles
- Keras Model for running on CPU.
-
- If environment variable - MXNET_KERAS_TEST_MACHINE is set to GPU, then Compiles
- Keras Model running on GPU using number of GPUs equal to number specified in
- GPU_NUM environment variable.
-
- Currently supports only MXNet as Keras backend.
- """
- if(IS_GPU):
- prepare_gpu_model(model, **kwargs)
- else:
- prepare_cpu_model(model, **kwargs)
- return model
diff --git a/tests/nightly/mxnet_keras_integration_tests/profiler.py b/tests/nightly/mxnet_keras_integration_tests/profiler.py
deleted file mode 100644
index b0d39e19aa0..00000000000
--- a/tests/nightly/mxnet_keras_integration_tests/profiler.py
+++ /dev/null
@@ -1,113 +0,0 @@
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements. See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership. The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License. You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an
-# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-# KIND, either express or implied. See the License for the
-# specific language governing permissions and limitations
-# under the License.
-
-import os
-import signal
-import time
-import csv
-import subprocess
-from memory_profiler import memory_usage
-
-IS_GPU = (os.environ['MXNET_KERAS_TEST_MACHINE'] == 'GPU')
-GPU_NUM = int(os.environ['GPU_NUM']) if IS_GPU else 0
-
-# This command is useful to fetch GPU memory consumption.
-GPU_MONITOR_CMD = "nvidia-smi --query-gpu=index,memory.used --format=csv -lms 500 -f output.csv"
-
-def cpu_memory_profile(func_to_profile):
- max_mem_usage = memory_usage(proc=(func_to_profile, ()), max_usage=True)
- return max_mem_usage[0]
-
-def gpu_mem_profile(file_name):
- row_count = 0
- # In MBs
- max_mem_usage = 0
- with open(file_name, 'r') as csv_file:
- csv_reader = csv.reader(csv_file)
- last_line_broken = False
- for row in csv_reader:
- if row_count == 0:
- row_count += 1
- continue
- if len(row) < 2 or not 'MiB' in row[1]:
- last_line_broken = True
- row_count += 1
- row_count -= 1
- if row_count % GPU_NUM == 0 and last_line_broken:
- row_count -= GPU_NUM
- else:
- row_count -= row_count % GPU_NUM
-
- with open(file_name, 'r') as csv_file:
- csv_reader = csv.reader(csv_file)
- current_usage = 0
- mem_recoder = [0] * GPU_NUM
- row_num = 0
- for row in csv_reader:
- if row_num == 0:
- row_num += 1
- continue
- mem_str = row[1].lstrip().rstrip()[:-4]
- mem_num = float(mem_str)
- current_usage += mem_num
- mem_recoder[(row_num - 1) % GPU_NUM] += mem_num
- if row_num % GPU_NUM == 0:
- max_mem_usage = max(max_mem_usage, current_usage)
- current_usage = 0
- row_num += 1
- if row_num > row_count:
- break
- row_num -= 1
- os.remove(file_name)
- return max_mem_usage
-
-def profile(func_to_profile):
- """
- This function helps in profile given func_to_profile for run-time and
- memory consumption.
-
- Capable of profile for both GPU and CPU machine.
-
- Uses environment variable - IS_GPU to identify whether to profile for
- CPU or GPU.
-
- returns: run_time, memory_usage
- """
- run_time = 0; # Seconds
- memory_usage = 0; # MBs
-
- # Choose nvidia-smi or memory_profiler for memory profiling for GPU and CPU
- # machines respectively.
- if(IS_GPU):
- # Start time - For timing the runtime
- start_time = time.time()
- open('nvidia-smi-output.csv', 'a').close()
- gpu_monitor_process = subprocess.Popen(GPU_MONITOR_CMD,
- shell=True, preexec_fn=os.setsid)
- func_to_profile()
- end_time = time.time()
- os.killpg(os.getpgid(gpu_monitor_process.pid), signal.SIGTERM)
- run_time = end_time - start_time
- memory_usage = gpu_mem_profile('nvidia-smi-output.csv')
- else:
- # Start time - For timing the runtime
- start_time = time.time()
- memory_usage = cpu_memory_profile(func_to_profile)
- end_time = time.time()
- run_time = end_time - start_time
-
- return run_time, memory_usage
diff --git a/tests/nightly/mxnet_keras_integration_tests/test_mnist_mlp.py b/tests/nightly/mxnet_keras_integration_tests/test_mnist_mlp.py
deleted file mode 100644
index 89bd2805ce7..00000000000
--- a/tests/nightly/mxnet_keras_integration_tests/test_mnist_mlp.py
+++ /dev/null
@@ -1,114 +0,0 @@
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements. See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership. The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License. You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an
-# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-# KIND, either express or implied. See the License for the
-# specific language governing permissions and limitations
-# under the License.
-
-'''
-This code is forked from https://github.com/fchollet/keras/blob/master/examples/mnist_mlp.py
-and modified to use as MXNet-Keras integration testing for functionality and sanity performance
-benchmarking.
-
-Trains a simple deep NN on the MNIST dataset.
-
-Gets to 98.40% test accuracy after 20 epochs
-(there is *a lot* of margin for parameter tuning).
-2 seconds per epoch on a K520 GPU.
-'''
-
-from __future__ import print_function
-import numpy as np
-np.random.seed(1337) # for reproducibility
-
-from os import environ
-
-from keras.datasets import mnist
-from keras.models import Sequential
-from keras.layers.core import Dense, Dropout, Activation
-from keras.optimizers import SGD
-from keras.utils import np_utils
-
-# Imports for benchmarking
-from profiler import profile
-from model_util import make_model
-
-# Imports for assertions
-from assertion_util import assert_results
-
-# Other environment variables
-MACHINE_TYPE = environ['MXNET_KERAS_TEST_MACHINE']
-IS_GPU = (environ['MXNET_KERAS_TEST_MACHINE'] == 'GPU')
-MACHINE_TYPE = 'GPU' if IS_GPU else 'CPU'
-GPU_NUM = int(environ['GPU_NUM']) if IS_GPU else 0
-
-# Expected Benchmark Numbers
-CPU_BENCHMARK_RESULTS = {'TRAINING_TIME':550.0, 'MEM_CONSUMPTION':400.0, 'TRAIN_ACCURACY': 0.85, 'TEST_ACCURACY':0.85}
-GPU_1_BENCHMARK_RESULTS = {'TRAINING_TIME':40.0, 'MEM_CONSUMPTION':200, 'TRAIN_ACCURACY': 0.85, 'TEST_ACCURACY':0.85}
-# TODO: Fix Train and Test accuracy numbers in multiple gpu mode. Setting it to 0 for now to get whole integration set up done
-GPU_2_BENCHMARK_RESULTS = {'TRAINING_TIME':45.0, 'MEM_CONSUMPTION':375, 'TRAIN_ACCURACY': 0.0, 'TEST_ACCURACY':0.0}
-GPU_4_BENCHMARK_RESULTS = {'TRAINING_TIME':55.0, 'MEM_CONSUMPTION':750.0, 'TRAIN_ACCURACY': 0.0, 'TEST_ACCURACY':0.0}
-GPU_8_BENCHMARK_RESULTS = {'TRAINING_TIME':100.0, 'MEM_CONSUMPTION':1800.0, 'TRAIN_ACCURACY': 0.0, 'TEST_ACCURACY':0.0}
-
-# Dictionary to store profiling output
-profile_output = {}
-
-batch_size = 128
-nb_classes = 10
-nb_epoch = 20
-
-# the data, shuffled and split between train and test sets
-(X_train, y_train), (X_test, y_test) = mnist.load_data()
-
-X_train = X_train.reshape(60000, 784)
-X_test = X_test.reshape(10000, 784)
-X_train = X_train.astype('float32')
-X_test = X_test.astype('float32')
-X_train /= 255
-X_test /= 255
-
-# convert class vectors to binary class matrices
-Y_train = np_utils.to_categorical(y_train, nb_classes)
-Y_test = np_utils.to_categorical(y_test, nb_classes)
-
-model = Sequential()
-model.add(Dense(512, input_shape=(784,)))
-model.add(Activation('relu'))
-model.add(Dropout(0.2))
-model.add(Dense(512))
-model.add(Activation('relu'))
-model.add(Dropout(0.2))
-model.add(Dense(10))
-model.add(Activation('softmax'))
-
-model.summary()
-make_model(model, loss='categorical_crossentropy', optimizer=SGD(), metrics=['accuracy'])
-
-def train_model():
- history = model.fit(X_train, Y_train,
- batch_size=batch_size, nb_epoch=nb_epoch,
- verbose=1, validation_data=(X_test, Y_test))
- profile_output['TRAIN_ACCURACY'] = history.history['acc'][-1]
-
-def test_run():
- # Calling training and profile memory usage
- profile_output["MODEL"] = "MNIST MLP"
- run_time, memory_usage = profile(train_model)
-
- profile_output['TRAINING_TIME'] = float(run_time)
- profile_output['MEM_CONSUMPTION'] = float(memory_usage)
-
- score = model.evaluate(X_test, Y_test, verbose=0)
- profile_output["TEST_ACCURACY"] = score[1]
-
- assert_results(MACHINE_TYPE, IS_GPU, GPU_NUM, profile_output, CPU_BENCHMARK_RESULTS, GPU_1_BENCHMARK_RESULTS, GPU_2_BENCHMARK_RESULTS, GPU_4_BENCHMARK_RESULTS, GPU_8_BENCHMARK_RESULTS)
diff --git a/tests/nightly/test_mxnet_keras_integration_cpu.sh b/tests/nightly/test_mxnet_keras_integration_cpu.sh
deleted file mode 100755
index 95cc0d0760e..00000000000
--- a/tests/nightly/test_mxnet_keras_integration_cpu.sh
+++ /dev/null
@@ -1,78 +0,0 @@
-#!/bin/sh
-
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements. See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership. The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License. You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an
-# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-# KIND, either express or implied. See the License for the
-# specific language governing permissions and limitations
-# under the License.
-
-set -e
-### Build MXNet with CPU support
-echo "BUILD make"
-cp ./make/config.mk .
-echo "USE_CUDA=0" >> ./config.mk
-echo "USE_CUDNN=0" >> ./config.mk
-echo "USE_BLAS=openblas" >> ./config.mk
-echo "ADD_CFLAGS += -I/usr/include/openblas" >> ./config.mk
-echo "GTEST_PATH=/usr/local/gtest" >> ./config.mk
-echo 'export PKG_CONFIG_PATH=/usr/local/lib/pkgconfig:$PKG_CONFIG_PATH' >> ~/.profile
-echo 'export LD_LIBRARY_PATH=/usr/local/lib:$LD_LIBRARY_PATH' >> ~/.profile
-echo 'export JAVA_HOME=/usr/lib/jvm/java-1.8.0-openjdk-1.8.0.111-1.b15.25.amzn1.x86_64' >> ~/.profile
-echo 'export JRE_HOME=/usr/lib/jvm/java-1.8.0-openjdk-1.8.0.111-1.b15.25.amzn1.x86_64/jre' >> ~/.profile
-echo 'export PATH=$PATH:/apache-maven-3.3.9/bin/:/usr/bin:/usr/lib/jvm/java-1.8.0-openjdk-1.8.0.111-1.b15.25.amzn1.x86_64/bin:/usr/lib/jvm/java-1.8.0-openjdk-1.8.0.111-1.b15.25.amzn1.x86_64/jre/bin' >> ~/.profile
-source ~/.profile
-make clean
-make -j 4 || exit -1
-
-echo "BUILD python2 mxnet"
-cd ./python
-python setup.py install || exit 1
-
-echo "BUILD python3 mxnet"
-python3 setup.py install || exit 1
-
-# Come out of Mxnet directory.
-cd ..
-
-# Required for Keras installation
-pip install pyyaml
-
-# If already exist remove and fork DMLC/keras and install.
-# Note: This should eventually be replaced with pip install when mxnet backend is part of fchollet/keras
-
-########### Set up Keras ####################
-echo "Installing Keras. This can take few minutes..."
-# Clone keras repository from dmlc. This has mxnet backend implementated.
-if [ -d "keras" ]; then
- rm -rf keras/
-fi
-
-git clone https://github.com/dmlc/keras.git --recursive
-cd keras
-python setup.py install
-
-########### Set up packages for profiling #########
-echo "Installing memory_profile and psutil for profiling. This can take few minutes..."
-pip install memory_profiler
-pip install psutil
-
-########## Set Environment Variables ########
-echo "Setting Environment Variables for MXNet Keras Integration Tests on CPU machine"
-export KERAS_BACKEND="mxnet"
-export MXNET_KERAS_TEST_MACHINE='CPU'
-
-########## Call the test script ############
-cd ../../mxnet/tests/nightly
-echo "Running MXNet Keras Integration Test on CPU machine"
-nosetests --with-xunit --quiet --nologcapture mxnet_keras_integration_tests/
diff --git a/tests/nightly/test_mxnet_keras_integration_gpu.sh b/tests/nightly/test_mxnet_keras_integration_gpu.sh
deleted file mode 100755
index 5d541fa5b7a..00000000000
--- a/tests/nightly/test_mxnet_keras_integration_gpu.sh
+++ /dev/null
@@ -1,107 +0,0 @@
-#!/bin/sh
-
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements. See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership. The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License. You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an
-# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-# KIND, either express or implied. See the License for the
-# specific language governing permissions and limitations
-# under the License.
-
-set -e
-
-### Install git
-apt-get update
-apt-get install git-all
-
-### Build MXNet with CPU support
-echo "BUILD make"
-cp ./make/config.mk .
-echo "USE_CUDA=1" >> ./config.mk
-echo "USE_CUDA_PATH=/usr/local/cuda" >> config.mk
-echo "USE_CUDNN=1" >> ./config.mk
-echo "USE_BLAS=openblas" >> ./config.mk
-echo "ADD_CFLAGS += -I/usr/include/openblas" >> ./config.mk
-echo "GTEST_PATH=/usr/local/gtest" >> ./config.mk
-export PKG_CONFIG_PATH=/usr/local/lib/pkgconfig:$PKG_CONFIG_PATH
-export LD_LIBRARY_PATH=/usr/local/lib:$LD_LIBRARY_PATH
-export JAVA_HOME=/usr/lib/jvm/java-1.8.0-openjdk-1.8.0.111-1.b15.25.amzn1.x86_64
-export JRE_HOME=/usr/lib/jvm/java-1.8.0-openjdk-1.8.0.111-1.b15.25.amzn1.x86_64/jre
-export PATH=$PATH:/apache-maven-3.3.9/bin/:/usr/bin:/usr/lib/jvm/java-1.8.0-openjdk-1.8.0.111-1.b15.25.amzn1.x86_64/bin:/usr/lib/jvm/java-1.8.0-openjdk-1.8.0.111-1.b15.25.amzn1.x86_64/jre/bin
-
-make clean
-make -j 4 || exit -1
-
-echo "BUILD python2 mxnet"
-cd ./python
-python setup.py install || exit 1
-
-echo "BUILD python3 mxnet"
-python3 setup.py install || exit 1
-
-# Come out of MXNet directory
-cd ..
-
-# Dependencies required for Keras installation
-pip install pyyaml
-
-pip install --upgrade pip
-pip install --upgrade six
-
-# If already exist remove and fork DMLC/keras and install.
-# Note: This should eventually be replaced with pip install when mxnet backend is part of fchollet/keras
-
-########### Set up Keras ####################
-echo "Installing Keras. This can take few minutes..."
-# Clone keras repository from dmlc. This has mxnet backend implementated.
-if [ -d "keras" ]; then
- rm -rf keras/
-fi
-
-git clone https://github.com/dmlc/keras.git --recursive
-cd keras
-python setup.py install
-
-########### Set up packages for profiling #########
-echo "Installing memory_profile and psutil for profiling. This can take few minutes..."
-pip install memory_profiler
-pip install psutil
-
-########## Set Environment Variables ########
-echo "Setting Environment Variables for MXNet Keras Integration Tests on CPU machine"
-cd ../../mxnet/tests/nightly
-
-export KERAS_BACKEND="mxnet"
-export MXNET_KERAS_TEST_MACHINE='GPU'
-########## Call the test script with 1 GPUS ############
-
-export GPU_NUM='1'
-echo "Running MXNet Keras Integration Test on GPU machine with 1 GPUs"
-nosetests --with-xunit --quiet --nologcapture mxnet_keras_integration_tests/
-
-########## Call the test script with 2 GPUS ############
-
-export GPU_NUM='2'
-echo "Running MXNet Keras Integration Test on GPU machine with 2 GPUs"
-nosetests --with-xunit --quiet --nologcapture mxnet_keras_integration_tests/
-
-########## Call the test script with 4 GPUS ############
-
-export GPU_NUM='4'
-echo "Running MXNet Keras Integration Test on GPU machine with 4 GPUs"
-nosetests --with-xunit --quiet --nologcapture mxnet_keras_integration_tests/
-
-########## Call the test script with 8 GPUS ############
-
-export GPU_NUM='8'
-echo "Running MXNet Keras Integration Test on GPU machine with 8 GPUs"
-nosetests --with-xunit --quiet --nologcapture mxnet_keras_integration_tests/
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