NCCL

This workload (test_template_name is NcclTest) allows users to execute NCCL benchmarks within the CloudAI framework.

Test TOML example:

            

            
name = "my_nccl_test"
description = "Example NCCL test"
test_template_name = "NcclTest"

[cmd_args]
docker_image_url = "nvcr.io#nvidia/pytorch:25.06-py3"
        

Test Scenario example:

            

            
name = "nccl-test"

[[Tests]]
id = "nccl.1"
num_nodes = 1
time_limit = "00:05:00"

test_name = "my_nccl_test"
        

Test-in-Scenario example:

            

            
name = "nccl-test"

[[Tests]]
id = "nccl.1"
num_nodes = 1
time_limit = "00:05:00"

name = "my_nccl_test"
description = "Example NCCL test"
test_template_name = "NcclTest"

  [Tests.cmd_args]
  docker_image_url = "nvcr.io#nvidia/pytorch:25.06-py3"
  subtest_name = "all_reduce_perf_mpi"
  iters = 100
        

Command Arguments

class cloudai.workloads.nccl_test.nccl.NCCLCmdArgs(*, docker_image_url: str, subtest_name: Literal['all_reduce_perf_mpi', 'all_gather_perf_mpi', 'alltoall_perf_mpi', 'alltoallv_perf_mpi', 'broadcast_perf_mpi', 'gather_perf_mpi', 'hypercube_perf_mpi', 'reduce_perf_mpi', 'reduce_scatter_perf_mpi', 'scatter_perf_mpi', 'sendrecv_perf_mpi', 'bisection_perf_mpi', 'all_reduce_perf', 'all_gather_perf', 'alltoall_perf', 'alltoallv_perf', 'broadcast_perf', 'gather_perf', 'hypercube_perf', 'reduce_perf', 'reduce_scatter_perf', 'scatter_perf', 'sendrecv_perf', 'bisection_perf'] | list[Literal['all_reduce_perf_mpi', 'all_gather_perf_mpi', 'alltoall_perf_mpi', 'alltoallv_perf_mpi', 'broadcast_perf_mpi', 'gather_perf_mpi', 'hypercube_perf_mpi', 'reduce_perf_mpi', 'reduce_scatter_perf_mpi', 'scatter_perf_mpi', 'sendrecv_perf_mpi', 'bisection_perf_mpi', 'all_reduce_perf', 'all_gather_perf', 'alltoall_perf', 'alltoallv_perf', 'broadcast_perf', 'gather_perf', 'hypercube_perf', 'reduce_perf', 'reduce_scatter_perf', 'scatter_perf', 'sendrecv_perf', 'bisection_perf']] = 'all_reduce_perf_mpi', nthreads: int | list[int] = 1, ngpus: int | list[int] = 1, minbytes: str | list[str] = '32M', maxbytes: str | list[str] = '32M', stepbytes: str | list[str] = '1M', op: Literal['sum', 'prod', 'min', 'max', 'avg', 'all'] | list[Literal['sum', 'prod', 'min', 'max', 'avg', 'all']] = 'sum', datatype: Literal['uint8', 'float'] | list[Literal['uint8', 'float']] = 'float', root: int | list[int] = 0, iters: int | list[int] = 20, warmup_iters: int | list[int] = 5, agg_iters: int | list[int] = 1, average: int | list[int] = 1, parallel_init: int | list[int] = 0, check: int | list[int] = 1, blocking: int | list[int] = 0, cudagraph: int | list[int] = 0, stepfactor: int | list[int] | None = None, use_deepep_matrix: bool = False, alltoallv_matrix_container_path: str = '/tmp/traffic_matrix.txt', min_busbw: float | None = None, **extra_data: Any)[source]

Bases: CmdArgs

NCCL test command arguments.

Test Definition

class cloudai.workloads.nccl_test.nccl.NCCLTestDefinition(*, name: str, description: str, test_template_name: str, cmd_args: ~cloudai.workloads.nccl_test.nccl.NCCLCmdArgs, 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 object for NCCL.

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.