NIXL CTPerf

This workload (test_template_name is NixlPerftest) runs NIXL performance testing suite for comprehensive network performance evaluation.

Test TOML example:

            

            
name = "my_nixl_perftest_test"
description = "Example NIXL Perftest test"
test_template_name = "NixlPerftest"

[cmd_args]
docker_image_url = "<docker container url here>"
subtest = "sequential-ct-perftest"
num_user_requests = 1
batch_size = 1
num_prefill_nodes = 1
num_decode_nodes = 1
prefill_tp = 4
decode_tp = 4
isl_mean = 10000
isl_scale = 3000
model = "deepseek-r1-distill-llama-70b"
        

Test Scenario example:

            

            
name = "nixl-perftest-test"

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

test_name = "my_nixl_perftest_test"
        

Test-in-Scenario example:

            

            
name = "nixl-perftest-test"

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

name = "my_nixl_perftest_test"
description = "Example NIXL Perftest test"
test_template_name = "NixlPerftest"

  [Tests.cmd_args]
  docker_image_url = "<docker container url here>"
  subtest = "sequential-ct-perftest"
  num_user_requests = 100
  batch_size = 1
  num_prefill_nodes = 1
  num_decode_nodes = 1
  prefill_tp = 8
  decode_tp = 8
  model = "deepseek-r1-distill-llama-70b"

  [Tests.extra_env_vars]
  CUDA_VISIBLE_DEVICES = "$SLURM_LOCALID"
        

Command Arguments

pydantic model cloudai.workloads.nixl_perftest.nixl_perftest.NixlPerftestCmdArgs[source]

CmdArgs for NIXL Perftest.

field subtest: Literal['sequential-ct-perftest'] [Required]

field perftest_script: str = '/workspace/nixl/benchmark/kvbench/main.py'

field matgen_script: str = '/workspace/nixl/benchmark/kvbench/test/inference_workload_matgen.py'

field python_executable: str = 'python'

field num_user_requests: int | list[int] [Required]

field batch_size: int | list[int] [Required]

field num_prefill_nodes: int | list[int] [Required]

field num_decode_nodes: int | list[int] [Required]

field isl_mean: int | list[int] | None = None

field isl_scale: int | list[int] | None = None

field prefill_tp: int | list[int] = 1

field prefill_pp: int | list[int] = 1

field prefill_cp: int | list[int] = 1

field decode_tp: int | list[int] = 1

field decode_pp: int | list[int] = 1

field decode_cp: int | list[int] = 1

field model: str | list[str] | None = None

field hidden_size: int | None = None

field num_layers: int | None = None

field num_heads: int | None = None

field num_kv_heads: int | None = None

field dtype_size: int | None = None

field matgen_args: MatgenCmdArgs [Optional]

field docker_image_url: str [Required]

URL of the Docker image to use for the benchmark.

field etcd_path: str = 'etcd'

Path to the etcd executable.

field wait_etcd_for: int = 60

Number of seconds to wait for etcd to become healthy.

field etcd_image_url: str | None = None

Optional URL of the Docker image to use for etcd, by default etcd will be run from the same image as the benchmark.

Test Definition

class cloudai.workloads.nixl_perftest.nixl_perftest.NixlPerftestTestDefinition(*, name: str, description: str, test_template_name: str, cmd_args: ~cloudai.workloads.nixl_perftest.nixl_perftest.NixlPerftestCmdArgs, 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: NIXLBaseTestDefinition[NixlPerftestCmdArgs]

TestDefinition for NixlPerftest.

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.