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-# Benchmark tools
-
-These scripts are tools for collecting performance data for Docker-based tests.
-
-## Setup
-
-The scripts assume the following:
-
-* There are two sets of machines: one where the scripts will be run
- (controller) and one or more machines on which docker containers will be run
- (environment).
-* The controller machine must have bazel installed along with this source
- code. You should be able to run a command like `bazel run //benchmarks --
- --list`
-* Environment machines must have docker and the required runtimes installed.
- More specifically, you should be able to run a command like: `docker run
- --runtime=$RUNTIME your/image`.
-* The controller has ssh private key which can be used to login to environment
- machines and run docker commands without using `sudo`. This is not required
- if running locally via the `run-local` command.
-* The docker daemon on each of your environment machines is listening on
- `unix:///var/run/docker.sock` (docker's default).
-
-For configuring the environment manually, consult the
-[dockerd documentation][dockerd].
-
-## Running benchmarks
-
-### Locally
-
-The tool is built to, by default, use Google Cloud Platform to run benchmarks,
-but it does support GCP workflows. To run locally, run the following from the
-benchmarks directory:
-
-```bash
-bazel run --define gcloud=off //benchmarks -- run-local startup
-
-...
-method,metric,result
-startup.empty,startup_time_ms,652.5772
-startup.node,startup_time_ms,1654.4042000000002
-startup.ruby,startup_time_ms,1429.835
-```
-
-The above command ran the startup benchmark locally, which consists of three
-benchmarks (empty, node, and ruby). Benchmark tools ran it on the default
-runtime, runc. Running on another installed runtime, like say runsc, is as
-simple as:
-
-```bash
-bazel run --define gcloud=off //benchmarks -- run-local startup --runtime=runsc
-```
-
-There is help:
-
-```bash
-bazel run --define gcloud=off //benchmarks -- --help
-bazel run --define gcloud=off //benchmarks -- run-local --help
-```
-
-To list available benchmarks, use the `list` commmand:
-
-```bash
-bazel --define gcloud=off run //benchmarks -- list
-
-...
-Benchmark: sysbench.cpu
-Metrics: events_per_second
- Run sysbench CPU test. Additional arguments can be provided for sysbench.
-
- :param max_prime: The maximum prime number to search.
-```
-
-You can choose benchmarks by name or regex like:
-
-```bash
-bazel run --define gcloud=off //benchmarks -- run-local startup.node
-...
-metric,result
-startup_time_ms,1671.7178000000001
-
-```
-
-or
-
-```bash
-bazel run --define gcloud=off //benchmarks -- run-local s
-...
-method,metric,result
-startup.empty,startup_time_ms,1792.8292
-startup.node,startup_time_ms,3113.5274
-startup.ruby,startup_time_ms,3025.2424
-sysbench.cpu,cpu_events_per_second,12661.47
-sysbench.memory,memory_ops_per_second,7228268.44
-sysbench.mutex,mutex_time,17.4835
-sysbench.mutex,mutex_latency,3496.7
-sysbench.mutex,mutex_deviation,0.04
-syscall.syscall,syscall_time_ns,2065.0
-```
-
-You can run parameterized benchmarks, for example to run with different
-runtimes:
-
-```bash
-bazel run --define gcloud=off //benchmarks -- run-local --runtime=runc --runtime=runsc sysbench.cpu
-```
-
-Or with different parameters:
-
-```bash
-bazel run --define gcloud=off //benchmarks -- run-local --max_prime=10 --max_prime=100 sysbench.cpu
-```
-
-### On Google Compute Engine (GCE)
-
-Benchmarks may be run on GCE in an automated way. The default project configured
-for `gcloud` will be used.
-
-An additional parameter `installers` may be provided to ensure that the latest
-runtime is installed from the workspace. See the files in `tools/installers` for
-supported install targets.
-
-```bash
-bazel run //benchmarks -- run-gcp --installers=head --runtime=runsc sysbench.cpu
-```
-
-When running on GCE, the scripts generate a per run SSH key, which is added to
-your project. The key is set to expire in GCE after 60 minutes and is stored in
-a temporary directory on the local machine running the scripts.
-
-## Writing benchmarks
-
-To write new benchmarks, you should familiarize yourself with the structure of
-the repository. There are three key components.
-
-## Harness
-
-The harness makes use of the [docker py SDK][docker-py]. It is advisable that
-you familiarize yourself with that API when making changes, specifically:
-
-* clients
-* containers
-* images
-
-In general, benchmarks need only interact with the `Machine` objects provided to
-the benchmark function, which are the machines defined in the environment. These
-objects allow the benchmark to define the relationships between different
-containers, and parse the output.
-
-## Workloads
-
-The harness requires workloads to run. These are all available in the
-`workloads` directory.
-
-In general, a workload consists of a Dockerfile to build it (while these are not
-hermetic, in general they should be as fixed and isolated as possible), some
-parsers for output if required, parser tests and sample data. Provided the test
-is named after the workload package and contains a function named `sample`, this
-variable will be used to automatically mock workload output when the `--mock`
-flag is provided to the main tool.
-
-## Writing benchmarks
-
-Benchmarks define the tests themselves. All benchmarks have the following
-function signature:
-
-```python
-def my_func(output) -> float:
- return float(output)
-
-@benchmark(metrics = my_func, machines = 1)
-def my_benchmark(machine: machine.Machine, arg: str):
- return "3.4432"
-```
-
-Each benchmark takes a variable amount of position arguments as
-`harness.Machine` objects and some set of keyword arguments. It is recommended
-that you accept arbitrary keyword arguments and pass them through when
-constructing the container under test.
-
-To write a new benchmark, open a module in the `suites` directory and use the
-above signature. You should add a descriptive doc string to describe what your
-benchmark is and any test centric arguments.
-
-[dockerd]: https://docs.docker.com/engine/reference/commandline/dockerd/
-[docker-py]: https://docker-py.readthedocs.io/en/stable/