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50 lines
1.6 KiB
Markdown
50 lines
1.6 KiB
Markdown
# Kata Containers Tensorflow Metrics
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Kata Containers provides a series of performance tests using the
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TensorFlow reference benchmarks (tf_cnn_benchmarks).
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The tf_cnn_benchmarks containers TensorFlow implementations of several
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popular convolutional models https://github.com/tensorflow/benchmarks/tree/master/scripts/tf_cnn_benchmarks.
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Currently the TensorFlow benchmark on Kata Containers includes test for
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the `AxelNet` and `ResNet50` models.
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## Running the test
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Individual tests can be run by hand, for example:
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```
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$ cd metrics/machine_learning
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$ ./tensorflow.sh 25 60
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```
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# Kata Containers Pytorch Metrics
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Based on a suite of Python high performance computing benchmarks that
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uses various popular Python HPC libraries using Python
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https://github.com/dionhaefner/pyhpc-benchmarks.
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## Running the Pytorch test
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Individual tests can be run by hand, for example:
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```
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$ cd metrics/machine_learning
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$ ./tensorflow.sh 40 100
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```
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# Kata Containers Tensorflow `MobileNet` Metrics
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`MobileNets` are small, low-latency, low-power models parameterized to meet the resource
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constraints of a variety of use cases. They can be built upon for classification, detection,
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embeddings and segmentation similar to how other popular large scale models, such as Inception, are used.
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`MobileNets` can be run efficiently on mobile devices with `Tensorflow` Lite.
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Kata Containers provides a test for running `MobileNet V1` inference using Intel-Optimized `Tensorflow`.
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## Running the `Tensorflow` `MobileNet` test
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Individual test can be run by hand, for example:
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```
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$ cd metrics/machine_learning
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$ ./tensorflow_mobilenet_benchmark.sh 25 60
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```
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