.. _testing_benchmark_framework: Benchmark Framework API ======================= This advanced 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 :ref:`testing_benchmarks`. .. seealso:: To isolate one asset method, cached property, or sensor update, see :ref:`testing_micro_benchmarks`. Those workloads use the framework described here, but have a different timing protocol and interpretation. Choose The Supported API ------------------------ Use the typed workflow API whenever an existing workflow answers the question. It applies the same launch, task, timing, schema, and output behavior as the CLI. .. list-table:: :header-rows: 1 :widths: 25 37 38 * - Question - Request - Runner * - Environment-step capacity - :class:`~isaaclab.benchmark.BenchmarkRuntimeRequest` - :func:`~isaaclab.benchmark.run_runtime_benchmark` * - Application and task startup - :class:`~isaaclab.benchmark.BenchmarkStartupRequest` - :func:`~isaaclab.benchmark.run_startup_benchmark` * - RL training and learning - :class:`~isaaclab.benchmark.BenchmarkTrainingRequest` - :func:`~isaaclab.benchmark.run_training_benchmark` * - Trained-policy playback - :class:`~isaaclab.benchmark.BenchmarkPlayRequest` - :func:`~isaaclab.benchmark.run_play_benchmark` :func:`~isaaclab.benchmark.run_benchmark` accepts any of these request types when generic dispatch is more convenient. Use :class:`~isaaclab.benchmark.BaseIsaacLabBenchmark` only when implementing a measurement workflow that the supported requests do not cover. Run A Workflow From Python -------------------------- The following complete runtime example measures 1000 steps after 50 warm-up steps, disables visualizers, and requests both the stable schema and the human-readable summary: .. code-block:: python 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: .. code-block:: bash ./isaaclab.sh -p runtime_benchmark.py The terminal receives the summary report. The two paths returned in ``result.output_paths`` identify the schema and summary JSON files actually written; do not reconstruct their timestamped names in automation. The other workflows use the same output and launcher objects. These focused examples show their workflow-specific inputs and results. Startup profiling records wall time and the most expensive functions for each phase: .. code-block:: python from pathlib import Path from isaaclab.benchmark import ( BenchmarkLauncherConfig, BenchmarkOutputConfig, BenchmarkStartupRequest, run_startup_benchmark, ) result = run_startup_benchmark( BenchmarkStartupRequest( task="Isaac-Cartpole-Direct", num_envs=4096, top_n=20, presets=("newton_mjwarp",), output=BenchmarkOutputConfig( path=Path("results/startup"), formatters=("schema", "summary"), ), launcher=BenchmarkLauncherConfig(visualizers=()), ) ) print(f"Environment creation: {result.bundle.phases['env_creation'].total_time_s:.3f} s") Training returns the checkpoint produced by the selected RL library: .. code-block:: python from pathlib import Path from isaaclab.benchmark import ( BenchmarkLauncherConfig, BenchmarkOutputConfig, BenchmarkTrainingRequest, run_training_benchmark, ) result = run_training_benchmark( BenchmarkTrainingRequest( backend="rsl_rl", task="Isaac-Cartpole-Direct", num_envs=4096, max_iterations=500, warmup_steps=50, seed=42, presets=("newton_mjwarp",), output=BenchmarkOutputConfig( path=Path("results/training"), formatters=("schema", "summary"), ), launcher=BenchmarkLauncherConfig(visualizers=()), ) ) print(f"Collection FPS: {result.bundle.runtime.collection_fps.mean:,.0f}") print(f"Checkpoint: {result.bundle.checkpoint_path}") Pass the persisted ``checkpoint_path`` to a play request in a **new process**: .. code-block:: python from pathlib import Path from isaaclab.benchmark import ( BenchmarkLauncherConfig, BenchmarkOutputConfig, BenchmarkPlayRequest, run_play_benchmark, ) checkpoint = Path("checkpoint.txt").read_text().strip() result = run_play_benchmark( BenchmarkPlayRequest( backend="rsl_rl", task="Isaac-Cartpole-Direct", checkpoint=checkpoint, num_envs=4096, num_steps=1000, warmup_steps=50, seed=42, presets=("newton_mjwarp",), output=BenchmarkOutputConfig( path=Path("results/play"), formatters=("schema", "summary"), ), launcher=BenchmarkLauncherConfig(visualizers=()), ) ) print(f"Environment + inference FPS: {result.bundle.runtime.collection_fps.mean:,.0f}") Persist the training checkpoint path in an orchestration artifact, configuration store, or the training schema file before starting play. A simulator application owns process-level state and is not designed to be repeatedly launched and closed by several workflow calls in one Python process. Configure Requests ------------------ All requests share the task identifier, optional environment count and seed, task configuration, output configuration, and launcher configuration. .. list-table:: Shared request fields :header-rows: 1 :widths: 25 75 * - 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`` - :class:`~isaaclab.benchmark.BenchmarkOutputConfig` controlling the output directory and formatter set. * - ``launcher`` - :class:`~isaaclab.benchmark.BenchmarkLauncherConfig` controlling device, cameras, visualizers, Kit, livestream, and logging options. Workflow requests add the following measurement controls: .. list-table:: Workflow-specific fields :header-rows: 1 :widths: 22 26 52 * - 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. Prefer typed fields, presets, and ``hydra_args`` when they express the option; ``backend_args`` bypasses the common request contract. See the :mod:`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 :class:`~isaaclab.benchmark.BenchmarkOutputConfig`: .. list-table:: :header-rows: 1 :widths: 20 80 * - 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 while 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 Results ------------ Every workflow returns :class:`~isaaclab.benchmark.BenchmarkResult` with two members: * ``bundle`` is a :class:`~isaaclab.benchmark.RuntimeBundle`, :class:`~isaaclab.benchmark.StartupBundle`, :class:`~isaaclab.benchmark.TrainingBundle`, or :class:`~isaaclab.benchmark.PlayBundle`. * ``output_paths`` is the exact tuple of :class:`pathlib.Path` objects written by the selected formatters. Consume typed fields rather than scraping terminal text or depending on keys in ``bundle.extra``: .. code-block:: python # 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. See :ref:`testing_benchmarks` for the meaning of those scopes and the public API reference for every schema field. Handle Errors And Process Lifetime ---------------------------------- Typed dispatch converts a workflow parser rejection into ``ValueError``. A workflow that exits without returning a result raises ``RuntimeError``. Invalid output configuration, such as an empty formatter tuple or an unknown formatter, also fails rather than silently producing an incomplete result. Run each workflow in a separate process. This gives every benchmark a clean simulator lifecycle, prevents state from one workflow contaminating another, and matches the CLI execution model. Process startup is reported separately and is not included in steady-state throughput. .. 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. Extend The Framework -------------------- Use :class:`~isaaclab.benchmark.BaseIsaacLabBenchmark` when adding a producer whose lifecycle cannot be represented by a supported workflow. The framework provides phases, typed measurement and metadata records, system recorders, and formatters. It does not define the timing boundary for you. The main record types are: .. list-table:: :header-rows: 1 :widths: 30 70 * - Type - Use * - :class:`~isaaclab.benchmark.SingleMeasurement` - One scalar with a unit, such as total duration or completed operations. * - :class:`~isaaclab.benchmark.StatisticalMeasurement` - Mean, standard deviation, sample count, and unit for repeated samples. * - :class:`~isaaclab.benchmark.BooleanMeasurement` - A status or correctness outcome. * - :class:`~isaaclab.benchmark.DictMeasurement` - Structured measurement data that is not a scalar sample. * - :class:`~isaaclab.benchmark.ListMeasurement` - A retained sample or learning series. * - ``StringMetadata``, ``IntMetadata``, ``FloatMetadata``, ``DictMetadata`` - Configuration and identity needed to compare the measurement. Complete Custom Example ~~~~~~~~~~~~~~~~~~~~~~~ This executable example measures a CPU workload, records the sample distribution and configuration, updates system recorders, and always finalizes the benchmark: .. code-block:: python 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=False, 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), ) finally: output_paths = benchmark.finalize() for output_path in output_paths: print(output_path) Choose a meaningful phase name, state units explicitly, warm up before sampling, and report the sample count with the mean and standard deviation. For GPU work, define whether the measurement is submission latency or synchronized completion latency and 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, gathers recorder data, writes every selected formatter, and returns the paths written. System And Frametime Recorders ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 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 gracefully omits unavailable recorders. Frametime instrumentation is part of the workload; keep its setting fixed across comparisons. Isolate One Method Or Sensor ~~~~~~~~~~~~~~~~~~~~~~~~~~~~ :class:`~isaaclab.benchmark.MethodBenchmarkRunner` builds on the base class to repeat one method or property across input modes, warm-up steps, and instance counts. It is the common bridge used by the asset micro-benchmarks. Sensor benchmarks use live scenes, :func:`~isaaclab.benchmark.measure_latency` for paired timing boundaries, and :class:`~isaaclab.benchmark.LatencyBenchmarkRunner` for structured output. Do not infer end-to-end environment or training throughput from either kind of isolated result. The command matrix, backend prerequisites, mock-versus-live distinction, output examples, and extension protocol are documented in :ref:`testing_micro_benchmarks`. Troubleshooting --------------- Run scripts through ``./isaaclab.sh -p`` so the Isaac Lab and simulator Python environment is active. If output is missing, inspect the returned ``output_paths`` and make sure custom producers call ``finalize()``. Formatter names are lowercase and case-sensitive: ``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, continue with :ref:`testing_micro_benchmarks` before adding a new runner or timing convention.