OSU

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

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"
        

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:

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128    // min = default, max = 128
2:128  // min = 2, max = 128
2:     // 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, training_report: ~cloudai.models.workload.TrainingReportConfig | 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.