OSU#

This workload (test_template_name is OSUBench) allows you to execute OSU Micro Benchmarks within the CloudAI framework.

Usage Example#

Test example:

name = "osu_example"
test_template_name = "OSUBench"
description = "OSU Benchmark example"

[cmd_args]
"docker_image_url" = "<docker container url here>"
"benchmarks_dir" = "/directory/with/osu/binaries/in/container"
"benchmark" = ["osu_allreduce", "osu_allgather"]
"iterations" = 10
"message_size" = "1024"

Test Scenario example:

name = "osu_example"

[[Tests]]
id = "Tests.1"
test_name = "osu_example"
num_nodes = "2"
time_limit = "00:20:00"

Test-in-Scenario example:

name = "osu-test"

[[Tests]]
id = "Tests.osu_allreduce"
num_nodes = 2
time_limit = "00:05:00"

name = "osu_example"
description = "OSU allreduce 1KB"
test_template_name = "OSUBench"

    [Tests.cmd_args]
    docker_image_url = "<docker container url here>"
    benchmarks_dir = "/directory/with/osu/binaries/in/container"
    benchmark = "osu_allreduce"
    iterations = 10
    message_size = "1024"

API Documentation#

Command Arguments#

class cloudai.workloads.osu_bench.osu_bench.OSUBenchCmdArgs(

*,

docker_image_url: str,

benchmarks_dir: str,

benchmark: str | List[str],

message_size: str | List[str] | None = None,

iterations: int | None = None,

warmup: int | None = None,

mem_limit: int | None = None,

full: bool = True,

**extra_data: Any,

)[source]#

Bases: CmdArgs

Command line arguments for a OSU Benchmark test.

field docker_image_url: str [Required]#: URL of the Docker image to use for the test.

field benchmarks_dir: str [Required]#: Directory with the OSU Benchmark binaries inside the container.

field benchmark: str | List[str] [Required]#: Name of the benchmark to run.

field message_size: str | List[str] | None = None#

Message size for the benchmark.

Examples:

  // min = default, max = 128
128  // min = 2, max = 128
    // min 2, max = default

field iterations: int | None = None#: Number of iterations for the benchmark.

field warmup: int | None = None#: Number of warmup iterations to skip before timing.

field mem_limit: int | None = None#: Per-process maximum memory consumption in bytes.

field full: bool = True#: Print full format listing of results.

Test Definition#

class cloudai.workloads.osu_bench.osu_bench.OSUBenchTestDefinition(*, name: str, description: str, test_template_name: str, cmd_args: ~cloudai.workloads.osu_bench.osu_bench.OSUBenchCmdArgs, dse_excluded_args: list[str] = <factory>, extra_env_vars: dict[str, str | ~typing.List[str]] = {}, extra_cmd_args: dict[str, str] = {}, extra_container_mounts: list[str] = [], git_repos: list[~cloudai._core.installables.git_repo.GitRepo] = [], nsys: ~cloudai.models.workload.NsysConfiguration | None = None, predictor: ~cloudai.models.workload.PredictorConfig | None = None, agent: str = 'grid_search', agent_steps: int = 1, agent_metrics: list[str] = ['default'], agent_reward_function: str = 'inverse', agent_config: dict[str, ~typing.Any] | None = None, env_params: dict[str, ~cloudai.configurator.env_params.EnvParamSpec] = <factory>)[source]#

Bases: TestDefinition

Test definition for OSU Benchmark test.

property is_domain_randomization_enabled: bool#

at least one env_params annotation.

Type:: Whether the config declares domain randomization

is_dse_excluded_arg(path: str) → bool#: Return whether a dot-separated cmd_args path should be ignored by DSE.

is_env_sampled(cmd_args_path: str) → bool#: Whether a cmd_args field is env-sampled (env draws it per trial, not the agent).

validator validate_env_params » all fields#

Validate env_params annotations against cmd_args.

env_params is an annotation: each key names a cmd_args field whose value is the candidate set (the single source of truth), and the entry carries only how to sample. So each key must name a real cmd_args field whose value is a candidate list; a scalar is already fixed, so annotating it is a meaningless label and is rejected here. When weights are declared, the list needs >= 2 values and the weights must align 1:1 with it. Sampling, persistence, the per-trial cmd_args overlay, and the cache key all live in CloudAIGymEnv; keeping this shape check in core lets the overlay stay agent- and workload-agnostic rather than re-implemented per workload.