Use the benchmark API

Use the benchmark API#

This guide is for automation authors who need to run supported benchmarks from Python, consume typed results, or add a benchmark producer. For day-to-day benchmarking, start with the CLI workflows and interpretation guidance in Run benchmarks. To isolate one asset method, cached property, or sensor update, use Write micro-benchmarks.

Run one workflow#

Use a typed request for a supported workflow. It applies the same launch, task, timing, schema, and output behavior as the CLI. The following runtime example measures 1000 steps after 50 warm-up steps. It disables visualizers and requests both stable schema and human-readable summary output:

from pathlib import Path

from isaaclab.benchmark import (
    BenchmarkLauncherConfig,
    BenchmarkOutputConfig,
    BenchmarkRuntimeRequest,
    run_runtime_benchmark,
)

result = run_runtime_benchmark(
    BenchmarkRuntimeRequest(
        task="Isaac-Cartpole-Direct",
        num_envs=4096,
        num_steps=1000,
        warmup_steps=50,
        seed=42,
        presets=("newton_mjwarp",),
        output=BenchmarkOutputConfig(
            path=Path("results/runtime"),
            formatters=("schema", "summary"),
        ),
        launcher=BenchmarkLauncherConfig(visualizers=()),
    )
)

print(f"Total FPS: {result.bundle.runtime.total_fps.mean:,.0f}")
for output_path in result.output_paths:
    print(output_path)

Run the file through the Isaac Lab Python wrapper:

./isaaclab.sh -p runtime_benchmark.py

The command prints the summary report. The paths in result.output_paths identify the schema and summary JSON files that were written. Use these paths in automation instead of reconstructing the timestamped names.

Choose a request#

Choose the request and dedicated runner that match the measurement question. run_benchmark() accepts any request type when generic dispatch is more convenient.

Question	Request	Runner
Environment-step capacity	`BenchmarkRuntimeRequest`	`run_runtime_benchmark()`
Application and task startup	`BenchmarkStartupRequest`	`run_startup_benchmark()`
RL training and learning	`BenchmarkTrainingRequest`	`run_training_benchmark()`
Trained-policy playback	`BenchmarkPlayRequest`	`run_play_benchmark()`

The examples below reuse the runtime workflow’s output and launcher objects.

Configure a request#

All requests share the task identifier, optional environment count and seed, task configuration, output configuration, and launcher configuration.

Shared request fields#
Field	Purpose
`task`	Registered Gym task identifier, for example `Isaac-Cartpole-Direct`.
`num_envs` and `seed`	Environment count and random seed. Keep both fixed for comparisons.
`presets`	Typed task presets, such as `("newton_mjwarp",)`.
`hydra_args`	Additional Hydra overrides, represented as a tuple of strings.
`output`	`BenchmarkOutputConfig` controlling the output directory and formatter set.
`launcher`	`BenchmarkLauncherConfig` controlling device, cameras, visualizers, Kit, livestream, and logging options.

Workflow-specific fields#
Workflow	Fields	Meaning
Runtime	`num_steps`, `warmup_steps`	Measured environment steps and preceding excluded steps.
Startup	`top_n`, `whitelist_config`	Number and optional filter of profiled functions retained per phase.
Training	`backend`, `max_iterations`, `warmup_steps`	RL library, learning iterations, and initial environment steps excluded from environment-step timing.
Play	`backend`, `checkpoint`, `num_steps`, `warmup_steps`	RL library, policy, measured inference steps, and initial excluded steps.

Training exposes additional learning, video, sensor capture, and convergence options. Training and play also expose backend_args for options that truly belong to one RL library. Use typed fields, presets, and hydra_args for supported options. backend_args bypasses the common request contract. See the isaaclab.benchmark API reference for the exhaustive field list.

An empty visualizers tuple explicitly disables every visualizer. None preserves task and environment defaults. Enabling cameras, rendering, livestreaming, deterministic behavior, or animation recording changes the workload and must be kept identical across compared runs.

Choose output formats#

Select one or more formatter names in BenchmarkOutputConfig:

Formatter	Contract
`schema`	Writes the stable typed bundle. Use this for programmatic comparison and long-lived result storage.
`summary`	Prints a compact terminal report and writes a flat metrics JSON file.
`json`	Writes the lower-level phase, measurement, and metadata representation.
`osmo`	Writes phase-oriented KPI files for Osmo consumers.
`omniperf`	Writes the OmniPerf KPI representation.

Use ("schema", "summary") for results intended for review or publication. The summary gives immediate feedback. The schema preserves typed data for analysis. With multiple formatters, each output filename includes its formatter name, so JSON outputs cannot overwrite each other.

Read the result#

Every workflow returns BenchmarkResult with two members:

bundle is a RuntimeBundle, StartupBundle, TrainingBundle, or PlayBundle.
output_paths is the exact tuple of pathlib.Path objects written by the selected formatters.

Consume typed fields rather than scraping terminal text or depending on keys in bundle.extra:

# Runtime or play
total_fps = result.bundle.runtime.total_fps.mean
step_fps = result.bundle.runtime.environment_step_timing.environment_step_fps.mean

# Startup
scene_creation_s = result.bundle.phases["env_creation"].total_time_s

# Training
collection_fps = result.bundle.runtime.collection_fps.mean
final_reward = result.bundle.learning.reward.final_raw
checkpoint = result.bundle.checkpoint_path

# Play, when at least one episode completed
mean_return = result.bundle.reward.mean if result.bundle.reward is not None else None

The common run, versions, and hardware fields carry the comparison context. runtime separates collection throughput, total throughput, startup time, and environment-step timing. For measurement definitions, see Run benchmarks. See the public API reference for every schema field.

Handle errors and process lifetime#

If a workflow parser rejects a typed request, the API raises ValueError. If a workflow exits without returning a result, the API raises RuntimeError. Invalid output configuration also raises an error. Examples include an empty formatter tuple and an unknown formatter.

Run each workflow in a separate process. This gives each benchmark a clean simulator lifecycle and prevents state from carrying over between workflows. It also matches the CLI execution model. Process startup is reported separately from steady-state throughput. Save result.bundle.checkpoint_path before starting playback.

Warning

measure_synchronized_step_breakdown=True inserts device synchronizations around environment and simulation step boundaries. This serializes work and changes the schedule being measured, especially on Newton. Every rate from that run is diagnostic and must not be reported as throughput. Time outside SimulationContext.step() includes required action, actuator, state, manager, reset, wrapper, and synchronization work. It is not equivalent to removable Isaac Lab overhead.

Add a custom producer#

Use BaseIsaacLabBenchmark when a producer’s lifecycle cannot be represented by a supported workflow. The framework provides phases, typed measurement and metadata records, system recorders, and formatters. You must define the timing boundary.

Measurement and metadata types#
Type	Use
`SingleMeasurement`	One scalar with a unit, such as total duration or completed operations.
`StatisticalMeasurement`	Mean, standard deviation, sample count, and unit for repeated samples.
`BooleanMeasurement`	A status or correctness outcome.
`DictMeasurement`	Structured measurement data that is not a scalar sample.
`ListMeasurement`	A retained sample or learning series.
`StringMetadata`, `IntMetadata`, `FloatMetadata`, `DictMetadata`	Configuration and identity needed to compare the measurement.

This executable example measures a CPU workload, records the sample distribution and configuration, updates system recorders, and always finalizes the benchmark:

import statistics
import time

from isaaclab.benchmark import (
    BaseIsaacLabBenchmark,
    IntMetadata,
    StatisticalMeasurement,
)

num_iterations = 100
benchmark = BaseIsaacLabBenchmark(
    benchmark_name="custom_cpu_workload",
    formatter_type=["json", "summary"],
    output_path="results/custom",
    output_prefix="custom_cpu_workload",
    use_recorders=True,
    workflow_metadata={
        "metadata": [{"name": "num_iterations", "data": num_iterations}]
    },
)

output_paths = ()
try:
    for _ in range(10):
        sum(value * value for value in range(10_000))

    samples_ms = []
    for _ in range(num_iterations):
        start_ns = time.perf_counter_ns()
        sum(value * value for value in range(10_000))
        samples_ms.append((time.perf_counter_ns() - start_ns) / 1.0e6)

    benchmark.add_measurement(
        "workload",
        measurement=StatisticalMeasurement(
            name="duration",
            mean=statistics.fmean(samples_ms),
            std=statistics.stdev(samples_ms),
            n=len(samples_ms),
            unit="ms",
        ),
        metadata=IntMetadata(name="warmup_iterations", data=10),
    )
    benchmark.update_manual_recorders()
finally:
    output_paths = benchmark.finalize()

for output_path in output_paths:
    print(output_path)

Choose a meaningful phase name and state units explicitly. Warm up before sampling. Report the sample count with the mean and standard deviation. For GPU work, state whether the metric is submission latency or synchronized completion latency. Place synchronization at deliberate boundaries.

When a custom producer creates one of the stable workflow bundles, call attach_bundle(bundle) before finalize() and select schema. Otherwise, use the lower-level json representation. finalize() stops optional Kit frametime recorders and gathers recorder data. It writes every selected formatter and returns the paths written.

With use_recorders=True (the default), the base class captures CPU, GPU, memory, and version metadata. Call update_manual_recorders() at least once before finalize(), and update during long workloads when current utilization samples are needed.

Set frametime_recorders=True only when a Kit application is running and the workload needs physics, render, application, or GPU frametime data. The framework enables the available Isaac Sim benchmark services and skips unavailable recorders. Frametime instrumentation is part of the workload. Keep its setting fixed across comparisons.

Choose a lower-level runner#

MethodBenchmarkRunner repeats one method or property across input modes, warm-up steps, and instance counts. Use measure_latency() to define paired host-submission and device-synchronized timing boundaries. Use LatencyBenchmarkRunner to report those structured latency samples. Neither isolated result predicts end-to-end environment or training throughput. The command matrices and extension protocol are in Write micro-benchmarks.

Troubleshooting#

Run scripts through ./isaaclab.sh -p so the Isaac Lab and simulator Python environment is active. If output is missing, inspect returned output_paths and make sure custom producers call finalize(). Formatter names are lowercase and case-sensitive. Valid names are schema, summary, json, osmo, and omniperf.

Missing GPU metadata usually means nvidia-smi or CUDA device discovery is unavailable. Missing Kit frametime measurements can instead mean the relevant benchmark service is not present. It does not invalidate non-frametime phases.

For a new supported end-to-end workflow, keep typed request construction, dispatch, schema output, summary output, and CLI behavior aligned. For an isolated operation, follow Write micro-benchmarks before adding a new runner or timing convention.