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This PR adds the oneDNN benchmark information to the machine learning metrics README. Signed-off-by: Gabriela Cervantes <gabriela.cervantes.tellez@intel.com>
89 lines
2.6 KiB
Markdown
89 lines
2.6 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_nhwc.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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$ ./pytorch.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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# Kata Containers TensorFlow `ResNet50` Metrics
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`ResNet50` is an image classification model pre-trained on the `ImageNet` dataset.
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Kata Containers provides a test for running `ResNet50` inference using Intel-Optimized
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`TensorFlow`.
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## Running the `TensorFlow` `ResNet50` 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_resnet50_int8.sh 25 60
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```
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# Kata Containers OpenVINO Benchmark
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This is a toolkit around neural networks using its built-in benchmarking support
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and analyzing the throughput and latency for various models.
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## Running the `OpenVINO` 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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$ ./openvino.sh
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```
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# Kata Containers `oneDNN` Benchmark
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This is a test of the Intel `oneDNN` as an Intel optimized library for Deep Neural Networks
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and making use of its built-in `benchdnn` functionality.
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## Running the `oneDNN` 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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$ ./onednn.sh
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```
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