18a7fd8e4e
This PR renames the tensorflow scripts to include the data format that is being used as we will have multiple tests with different data and model formats for tensorflow so this will help us to distinguish them. Fixes #7645 Signed-off-by: Gabriela Cervantes <gabriela.cervantes.tellez@intel.com>
Kata Containers Tensorflow Metrics
Kata Containers provides a series of performance tests using the TensorFlow reference benchmarks (tf_cnn_benchmarks). The tf_cnn_benchmarks containers TensorFlow implementations of several popular convolutional models https://github.com/tensorflow/benchmarks/tree/master/scripts/tf_cnn_benchmarks.
Currently the TensorFlow benchmark on Kata Containers includes test for
the AxelNet and ResNet50 models.
Running the test
Individual tests can be run by hand, for example:
$ cd metrics/machine_learning
$ ./tensorflow.sh 25 60
Kata Containers Pytorch Metrics
Based on a suite of Python high performance computing benchmarks that uses various popular Python HPC libraries using Python https://github.com/dionhaefner/pyhpc-benchmarks.
Running the Pytorch test
Individual tests can be run by hand, for example:
$ cd metrics/machine_learning
$ ./tensorflow.sh 40 100
Kata Containers Tensorflow MobileNet Metrics
MobileNets are small, low-latency, low-power models parameterized to meet the resource
constraints of a variety of use cases. They can be built upon for classification, detection,
embeddings and segmentation similar to how other popular large scale models, such as Inception, are used.
MobileNets can be run efficiently on mobile devices with Tensorflow Lite.
Kata Containers provides a test for running MobileNet V1 inference using Intel-Optimized Tensorflow.
Running the Tensorflow MobileNet test
Individual test can be run by hand, for example:
$ cd metrics/machine_learning
$ ./tensorflow_mobilenet_benchmark.sh 25 60