kata-containers/tests/metrics/machine_learning

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