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19412cd29c
This is part of a series of changes to add metric charts in performance benchmarks. All benchmarks now auto-detect if profiling metrics are enabled in the test runtime. If profiling metrics data is present, it will generate charts of the data. PiperOrigin-RevId: 633094699
105 lines
3.1 KiB
Go
105 lines
3.1 KiB
Go
// Copyright 2020 The gVisor Authors.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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package ml
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import (
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"context"
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"os"
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"testing"
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"gvisor.dev/gvisor/pkg/test/dockerutil"
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"gvisor.dev/gvisor/test/benchmarks/harness"
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"gvisor.dev/gvisor/test/benchmarks/tools"
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"gvisor.dev/gvisor/test/metricsviz"
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)
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func BenchmarkTensorflowDashboard(b *testing.B) {
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workloads := map[string]string{
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"ConvolutionalNetwork": "3_NeuralNetworks/convolutional_network.py",
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"LogisticRegression": "2_BasicModels/logistic_regression.py",
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"NeuralNetwork": "3_NeuralNetworks/neural_network.py",
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}
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doTensorflowTest(b, workloads)
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}
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// BenchmarkTensorflow runs workloads from a TensorFlow tutorial.
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// See: https://github.com/aymericdamien/TensorFlow-Examples
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func BenchmarkTensorflow(b *testing.B) {
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workloads := map[string]string{
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"Kmeans": "2_BasicModels/kmeans.py",
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"LogisticRegression": "2_BasicModels/logistic_regression.py",
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"NearestNeighbor": "2_BasicModels/nearest_neighbor.py",
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"RandomForest": "2_BasicModels/random_forest.py",
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"ConvolutionalNetwork": "3_NeuralNetworks/convolutional_network.py",
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"MultilayerPerceptron": "3_NeuralNetworks/multilayer_perceptron.py",
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"NeuralNetwork": "3_NeuralNetworks/neural_network.py",
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}
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doTensorflowTest(b, workloads)
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}
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func doTensorflowTest(b *testing.B, workloads map[string]string) {
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machine, err := harness.GetMachine()
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if err != nil {
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b.Fatalf("failed to get machine: %v", err)
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}
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defer machine.CleanUp()
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for name, file := range workloads {
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runName, err := tools.ParametersToName(tools.Parameter{
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Name: "operation",
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Value: name,
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})
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if err != nil {
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b.Fatalf("Failed to parse param: %v", err)
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}
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b.Run(runName, func(b *testing.B) {
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ctx := context.Background()
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b.ResetTimer()
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b.StopTimer()
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for i := 0; i < b.N; i++ {
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func() {
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container := machine.GetContainer(ctx, b)
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defer container.CleanUp(ctx)
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if i == 0 {
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defer metricsviz.FromContainerLogs(ctx, b, container)
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}
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if err := harness.DropCaches(machine); err != nil {
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b.Skipf("failed to drop caches: %v. You probably need root.", err)
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}
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// Run tensorflow.
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b.StartTimer()
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if out, err := container.Run(ctx, dockerutil.RunOpts{
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Image: "benchmarks/tensorflow",
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Env: []string{"PYTHONPATH=$PYTHONPATH:/TensorFlow-Examples/examples"},
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WorkDir: "/TensorFlow-Examples/examples",
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}, "python", file); err != nil {
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b.Errorf("failed to run container: %v logs: %s", err, out)
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}
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b.StopTimer()
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}()
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}
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})
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}
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}
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func TestMain(m *testing.M) {
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harness.Init()
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harness.SetFixedBenchmarks()
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os.Exit(m.Run())
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}
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