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# 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. | ||
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import mxnet as mx | ||
from mxnet.gluon import nn | ||
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def simple_forward(): | ||
ctx = mx.gpu() | ||
mx.profiler.set_config(profile_all=True) | ||
mx.profiler.set_state('run') | ||
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# define simple gluon network with random weights | ||
net = nn.Sequential() | ||
with net.name_scope(): | ||
net.add(nn.Dense(128, activation='relu')) | ||
net.add(nn.Dense(64, activation='relu')) | ||
net.add(nn.Dense(10)) | ||
net.initialize(mx.init.Xavier(magnitude=2.24), ctx=ctx) | ||
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input = mx.nd.zeros((128,), ctx=ctx) | ||
predictions = net(input) | ||
print('Ran simple NN forward, results:') | ||
print(predictions.asnumpy()) | ||
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if __name__ == '__main__': | ||
simple_forward() |
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# 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 unittest | ||
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import mxnet as mx | ||
import sys | ||
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from subprocess import Popen, PIPE | ||
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def test_nvtx_ranges_present_in_profile(): | ||
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if not mx.test_utils.list_gpus(): | ||
unittest.skip('Test only applicable to machines with GPUs') | ||
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# Build a system independent wrapper to execute simple_forward with nvprof | ||
# This requires nvprof to be on your path (which should be the case for most GPU workstations with cuda installed). | ||
simple_forward_path = os.path.realpath(__file__) | ||
simple_forward_path = simple_forward_path.replace('test_nvtx', 'simple_forward') | ||
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process = Popen(["nvprof", sys.executable, simple_forward_path], stdout=PIPE, stderr=PIPE) | ||
(output, profiler_output) = process.communicate() | ||
process.wait() | ||
profiler_output = profiler_output.decode('ascii') | ||
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# Verify that some of the NVTX ranges we should have created are present | ||
# Verify that we have NVTX ranges for our simple operators. | ||
assert "Range \"FullyConnected\"" in profiler_output | ||
assert "Range \"_zeros\"" in profiler_output | ||
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# Verify that we have some expected output from the engine. | ||
assert "Range \"WaitForVar\"" in profiler_output | ||
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if __name__ == '__main__': | ||
import nose | ||
nose.runmodule() |