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<!DOCTYPE html>
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<title>Performance — OpenPifPaf Guide</title>
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Performance
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Profile Training
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<h1>Performance</h1>
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<li class="toc-h2 nav-item toc-entry">
<a class="reference internal nav-link" href="#profile-decoder">
Profile Decoder
</a>
</li>
<li class="toc-h2 nav-item toc-entry">
<a class="reference internal nav-link" href="#profile-training">
Profile Training
</a>
</li>
</ul>
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</div>
</div>
</div>
<main id="main-content" role="main">
<div>
<section class="tex2jax_ignore mathjax_ignore" id="performance">
<h1>Performance<a class="headerlink" href="#performance" title="Permalink to this headline">#</a></h1>
<section id="profile-decoder">
<h2>Profile Decoder<a class="headerlink" href="#profile-decoder" title="Permalink to this headline">#</a></h2>
<p>The default Python and Cython decoder can be profiled with Python’s standard <code class="docutils literal notranslate"><span class="pre">cprofile</span></code>. The output can be a sorted table and a flame graph. Both are generated below:</p>
<div class="cell docutils container">
<div class="cell_input docutils container">
<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">%%</span><span class="k">bash</span>
python -m openpifpaf.predict coco/000000081988.jpg --no-download-progress --debug --profile-decoder
</pre></div>
</div>
</div>
<div class="cell_output docutils container">
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>INFO:__main__:neural network device: cpu (CUDA available: False, count: 0)
DEBUG:openpifpaf.show.painters:color connections = False, lw = 6, marker = 3
DEBUG:openpifpaf.network.factory:Shell(
(base_net): ShuffleNetV2K(
(input_block): Sequential(
(0): Sequential(
(0): Conv2d(3, 24, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(1): BatchNorm2d(24, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
)
)
(stage2): Sequential(
(0): InvertedResidualK(
(branch1): Sequential(
(0): Conv2d(24, 24, kernel_size=(5, 5), stride=(2, 2), padding=(2, 2), groups=24, bias=False)
(1): BatchNorm2d(24, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(2): Conv2d(24, 174, kernel_size=(1, 1), stride=(1, 1), bias=False)
(3): BatchNorm2d(174, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(4): ReLU(inplace=True)
)
(branch2): Sequential(
(0): Conv2d(24, 174, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(174, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
(3): Conv2d(174, 174, kernel_size=(5, 5), stride=(2, 2), padding=(2, 2), groups=174, bias=False)
(4): BatchNorm2d(174, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(5): Conv2d(174, 174, kernel_size=(1, 1), stride=(1, 1), bias=False)
(6): BatchNorm2d(174, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(7): ReLU(inplace=True)
)
)
(1): InvertedResidualK(
(branch2): Sequential(
(0): Conv2d(174, 174, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(174, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
(3): Conv2d(174, 174, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=174, bias=False)
(4): BatchNorm2d(174, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(5): Conv2d(174, 174, kernel_size=(1, 1), stride=(1, 1), bias=False)
(6): BatchNorm2d(174, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(7): ReLU(inplace=True)
)
)
(2): InvertedResidualK(
(branch2): Sequential(
(0): Conv2d(174, 174, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(174, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
(3): Conv2d(174, 174, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=174, bias=False)
(4): BatchNorm2d(174, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(5): Conv2d(174, 174, kernel_size=(1, 1), stride=(1, 1), bias=False)
(6): BatchNorm2d(174, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(7): ReLU(inplace=True)
)
)
(3): InvertedResidualK(
(branch2): Sequential(
(0): Conv2d(174, 174, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(174, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
(3): Conv2d(174, 174, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=174, bias=False)
(4): BatchNorm2d(174, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(5): Conv2d(174, 174, kernel_size=(1, 1), stride=(1, 1), bias=False)
(6): BatchNorm2d(174, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(7): ReLU(inplace=True)
)
)
)
(stage3): Sequential(
(0): InvertedResidualK(
(branch1): Sequential(
(0): Conv2d(348, 348, kernel_size=(5, 5), stride=(2, 2), padding=(2, 2), groups=348, bias=False)
(1): BatchNorm2d(348, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(2): Conv2d(348, 348, kernel_size=(1, 1), stride=(1, 1), bias=False)
(3): BatchNorm2d(348, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(4): ReLU(inplace=True)
)
(branch2): Sequential(
(0): Conv2d(348, 348, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(348, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
(3): Conv2d(348, 348, kernel_size=(5, 5), stride=(2, 2), padding=(2, 2), groups=348, bias=False)
(4): BatchNorm2d(348, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(5): Conv2d(348, 348, kernel_size=(1, 1), stride=(1, 1), bias=False)
(6): BatchNorm2d(348, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(7): ReLU(inplace=True)
)
)
(1): InvertedResidualK(
(branch2): Sequential(
(0): Conv2d(348, 348, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(348, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
(3): Conv2d(348, 348, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=348, bias=False)
(4): BatchNorm2d(348, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(5): Conv2d(348, 348, kernel_size=(1, 1), stride=(1, 1), bias=False)
(6): BatchNorm2d(348, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(7): ReLU(inplace=True)
)
)
(2): InvertedResidualK(
(branch2): Sequential(
(0): Conv2d(348, 348, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(348, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
(3): Conv2d(348, 348, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=348, bias=False)
(4): BatchNorm2d(348, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(5): Conv2d(348, 348, kernel_size=(1, 1), stride=(1, 1), bias=False)
(6): BatchNorm2d(348, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(7): ReLU(inplace=True)
)
)
(3): InvertedResidualK(
(branch2): Sequential(
(0): Conv2d(348, 348, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(348, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
(3): Conv2d(348, 348, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=348, bias=False)
(4): BatchNorm2d(348, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(5): Conv2d(348, 348, kernel_size=(1, 1), stride=(1, 1), bias=False)
(6): BatchNorm2d(348, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(7): ReLU(inplace=True)
)
)
(4): InvertedResidualK(
(branch2): Sequential(
(0): Conv2d(348, 348, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(348, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
(3): Conv2d(348, 348, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=348, bias=False)
(4): BatchNorm2d(348, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(5): Conv2d(348, 348, kernel_size=(1, 1), stride=(1, 1), bias=False)
(6): BatchNorm2d(348, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(7): ReLU(inplace=True)
)
)
(5): InvertedResidualK(
(branch2): Sequential(
(0): Conv2d(348, 348, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(348, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
(3): Conv2d(348, 348, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=348, bias=False)
(4): BatchNorm2d(348, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(5): Conv2d(348, 348, kernel_size=(1, 1), stride=(1, 1), bias=False)
(6): BatchNorm2d(348, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(7): ReLU(inplace=True)
)
)
(6): InvertedResidualK(
(branch2): Sequential(
(0): Conv2d(348, 348, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(348, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
(3): Conv2d(348, 348, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=348, bias=False)
(4): BatchNorm2d(348, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(5): Conv2d(348, 348, kernel_size=(1, 1), stride=(1, 1), bias=False)
(6): BatchNorm2d(348, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(7): ReLU(inplace=True)
)
)
(7): InvertedResidualK(
(branch2): Sequential(
(0): Conv2d(348, 348, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(348, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
(3): Conv2d(348, 348, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=348, bias=False)
(4): BatchNorm2d(348, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(5): Conv2d(348, 348, kernel_size=(1, 1), stride=(1, 1), bias=False)
(6): BatchNorm2d(348, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(7): ReLU(inplace=True)
)
)
)
(stage4): Sequential(
(0): InvertedResidualK(
(branch1): Sequential(
(0): Conv2d(696, 696, kernel_size=(5, 5), stride=(2, 2), padding=(2, 2), groups=696, bias=False)
(1): BatchNorm2d(696, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(2): Conv2d(696, 696, kernel_size=(1, 1), stride=(1, 1), bias=False)
(3): BatchNorm2d(696, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(4): ReLU(inplace=True)
)
(branch2): Sequential(
(0): Conv2d(696, 696, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(696, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
(3): Conv2d(696, 696, kernel_size=(5, 5), stride=(2, 2), padding=(2, 2), groups=696, bias=False)
(4): BatchNorm2d(696, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(5): Conv2d(696, 696, kernel_size=(1, 1), stride=(1, 1), bias=False)
(6): BatchNorm2d(696, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(7): ReLU(inplace=True)
)
)
(1): InvertedResidualK(
(branch2): Sequential(
(0): Conv2d(696, 696, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(696, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
(3): Conv2d(696, 696, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=696, bias=False)
(4): BatchNorm2d(696, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(5): Conv2d(696, 696, kernel_size=(1, 1), stride=(1, 1), bias=False)
(6): BatchNorm2d(696, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(7): ReLU(inplace=True)
)
)
(2): InvertedResidualK(
(branch2): Sequential(
(0): Conv2d(696, 696, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(696, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
(3): Conv2d(696, 696, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=696, bias=False)
(4): BatchNorm2d(696, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(5): Conv2d(696, 696, kernel_size=(1, 1), stride=(1, 1), bias=False)
(6): BatchNorm2d(696, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(7): ReLU(inplace=True)
)
)
(3): InvertedResidualK(
(branch2): Sequential(
(0): Conv2d(696, 696, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(696, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
(3): Conv2d(696, 696, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=696, bias=False)
(4): BatchNorm2d(696, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(5): Conv2d(696, 696, kernel_size=(1, 1), stride=(1, 1), bias=False)
(6): BatchNorm2d(696, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(7): ReLU(inplace=True)
)
)
)
(conv5): Sequential(
(0): Conv2d(1392, 1392, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(1392, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
)
)
(head_nets): ModuleList(
(0): CompositeField4(
(dropout): Dropout2d(p=0.0, inplace=False)
(conv): Conv2d(1392, 340, kernel_size=(1, 1), stride=(1, 1))
(upsample_op): PixelShuffle(upscale_factor=2)
)
(1): CompositeField4(
(dropout): Dropout2d(p=0.0, inplace=False)
(conv): Conv2d(1392, 608, kernel_size=(1, 1), stride=(1, 1))
(upsample_op): PixelShuffle(upscale_factor=2)
)
)
)
DEBUG:openpifpaf.decoder.factory:head names = ['cif', 'caf']
DEBUG:openpifpaf.signal:subscribe to eval_reset
DEBUG:openpifpaf.decoder.pose_similarity:valid keypoints = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]
DEBUG:openpifpaf.visualizer.base:cif: indices = []
DEBUG:openpifpaf.show.painters:color connections = True, lw = 2, marker = 6
DEBUG:openpifpaf.show.painters:color connections = False, lw = 6, marker = 3
DEBUG:openpifpaf.visualizer.base:cif: indices = []
DEBUG:openpifpaf.visualizer.base:caf: indices = []
DEBUG:openpifpaf.show.painters:color connections = True, lw = 2, marker = 6
DEBUG:openpifpaf.show.painters:color connections = False, lw = 6, marker = 3
DEBUG:openpifpaf.visualizer.base:cif: indices = []
DEBUG:openpifpaf.show.painters:color connections = True, lw = 2, marker = 6
DEBUG:openpifpaf.show.painters:color connections = False, lw = 6, marker = 3
DEBUG:openpifpaf.visualizer.base:cif: indices = []
DEBUG:openpifpaf.visualizer.base:caf: indices = []
DEBUG:openpifpaf.show.painters:color connections = True, lw = 2, marker = 6
DEBUG:openpifpaf.show.painters:color connections = False, lw = 6, marker = 3
DEBUG:openpifpaf.decoder.factory:created 2 decoders
INFO:openpifpaf.decoder.factory:No specific decoder requested. Using the first one from:
--decoder=cifcaf:0
--decoder=posesimilarity:0
Use any of the above arguments to select one or multiple decoders and to suppress this message.
INFO:openpifpaf.predictor:neural network device: cpu (CUDA available: False, count: 0)
DEBUG:openpifpaf.transforms.pad:valid area before pad: [ 0. 0. 639. 426.], image size = (640, 427)
DEBUG:openpifpaf.transforms.pad:pad with [0, 3, 1, 3]
DEBUG:openpifpaf.transforms.pad:valid area after pad: [ 0. 3. 639. 426.], image size = (641, 433)
DEBUG:openpifpaf.decoder.decoder:nn processing time: 752.8ms
DEBUG:openpifpaf.decoder.decoder:parallel execution with worker <openpifpaf.decoder.decoder.DummyPool object at 0x7ff66aa51ca0>
DEBUG:openpifpaf.decoder.multi:task 0
DEBUG:openpifpaf.decoder.cifcaf:cpp annotations = 5 (12.7ms)
INFO:openpifpaf.decoder.cifcaf:annotations 5: [16, 14, 13, 12, 12]
INFO:openpifpaf.profiler:writing profile file profile_decoder.prof
386 function calls in 0.015 seconds
Ordered by: internal time
ncalls tottime percall cumtime percall filename:lineno(function)
1 0.013 0.013 0.015 0.015 /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/openpifpaf/decoder/cifcaf.py:224(__call__)
5 0.000 0.000 0.000 0.000 {method 'tolist' of 'numpy.ndarray' objects}
5 0.000 0.000 0.001 0.000 /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/openpifpaf/annotation.py:17(__init__)
10 0.000 0.000 0.000 0.000 /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/numpy/core/fromnumeric.py:2188(sum)
15 0.000 0.000 0.000 0.000 {method 'numpy' of 'torch._C._TensorBase' objects}
10 0.000 0.000 0.000 0.000 {method 'reduce' of 'numpy.ufunc' objects}
10 0.000 0.000 0.000 0.000 {built-in method numpy.asarray}
2 0.000 0.000 0.000 0.000 /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/logging/__init__.py:288(__init__)
2 0.000 0.000 0.000 0.000 {method 'flush' of '_io.TextIOWrapper' objects}
1 0.000 0.000 0.000 0.000 /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/openpifpaf/visualizer/cif.py:47(predicted)
1 0.000 0.000 0.000 0.000 /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/openpifpaf/decoder/cifcaf.py:276(<listcomp>)
2 0.000 0.000 0.000 0.000 /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/logging/__init__.py:1689(isEnabledFor)
10 0.000 0.000 0.000 0.000 /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/numpy/core/fromnumeric.py:69(_wrapreduction)
2 0.000 0.000 0.000 0.000 {method 'unbind' of 'torch._C._TensorBase' objects}
1 0.000 0.000 0.000 0.000 /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/openpifpaf/visualizer/caf.py:47(predicted)
15 0.000 0.000 0.000 0.000 /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/torch/_tensor.py:952(__array__)
10 0.000 0.000 0.000 0.000 {built-in method numpy.zeros}
2 0.000 0.000 0.000 0.000 /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/logging/__init__.py:364(getMessage)
1 0.000 0.000 0.000 0.000 /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/torch/_tensor.py:890(__len__)
2 0.000 0.000 0.000 0.000 /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/torch/_tensor.py:906(__iter__)
5 0.000 0.000 0.000 0.000 /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/site-packages/openpifpaf/visualizer/base.py:114(indices)
2 0.000 0.000 0.000 0.000 /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/logging/__init__.py:1514(findCaller)
10 0.000 0.000 0.000 0.000 <__array_function__ internals>:177(sum)
2 0.000 0.000 0.000 0.000 /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/posixpath.py:140(basename)
10 0.000 0.000 0.000 0.000 {built-in method numpy.core._multiarray_umath.implement_array_function}
24 0.000 0.000 0.000 0.000 {built-in method builtins.len}
19 0.000 0.000 0.000 0.000 {built-in method builtins.isinstance}
2 0.000 0.000 0.000 0.000 /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/logging/__init__.py:1073(emit)
2 0.000 0.000 0.000 0.000 /opt/hostedtoolcache/Python/3.8.16/x64/lib/python3.8/logging/__init__.py:1565(_log)
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DEBUG:openpifpaf.decoder.decoder:time: nn = 753.1ms, dec = 16.8ms
INFO:openpifpaf.predictor:batch 0: coco/000000081988.jpg
src/openpifpaf/csrc/src/cif_hr.cpp:102: UserInfo: resizing cifhr buffer
src/openpifpaf/csrc/src/occupancy.cpp:53: UserInfo: resizing occupancy buffer
</pre></div>
</div>
</div>
</div>
<div class="cell docutils container">
<div class="cell_input docutils container">
<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">!</span>flameprof<span class="w"> </span>profile_decoder.prof<span class="w"> </span>><span class="w"> </span>profile_decoder_flame.svg
</pre></div>
</div>
</div>
</div>
<p><img alt="flame" src="_images/profile_decoder_flame.svg" /></p>
<p>There is a second output that is generated from the Autograd Profiler. This can only be viewed in the Chrome browser:</p>
<ul class="simple">
<li><p>open <code class="docutils literal notranslate"><span class="pre">chrome://tracing</span></code></p></li>
<li><p>click “Load” in the top left corner</p></li>
<li><p>select <code class="docutils literal notranslate"><span class="pre">decoder_profile.1.json</span></code></p></li>
</ul>
<p>This is the same type of plot that is used to trace the training of a batch. An example of such a plot is shown below.</p>
</section>
<section id="profile-training">
<h2>Profile Training<a class="headerlink" href="#profile-training" title="Permalink to this headline">#</a></h2>
<p>For a training batch, the Chrome trace looks like this:</p>
<p><img alt="train_trace" src="_images/chrome_trace_train.jpg" /></p>
</section>
</section>
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