Automates running image/video augmentation benchmarks for single or multiple libraries, validates outputs, generates comparison reports, and updates documentation. Use when running benchmarks, comparing library performance, or when the user mentions benchmark, benchmark.cli, pyperf, GCP benchmark runs, or performance testing.
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name
benchmark-runner
description
Automates running image/video augmentation benchmarks for single or multiple libraries, validates outputs, generates comparison reports, and updates documentation. Use when running benchmarks, comparing library performance, or when the user mentions benchmark, benchmark.cli, pyperf, GCP benchmark runs, or performance testing.
Benchmark Runner
Run augmentation benchmarks with standardized configurations and automatic result validation.
Running Image Benchmarks
Use the config-first CLI for paper, cloud, or repeatable runs. Legacy run_all.sh, run_single.sh, and flag-only
commands are stale for this repo; start from YAML and use CLI flags only as overrides.
Config-first runs
python -m benchmark.cli plan --config configs/examples/local_rgb_micro_cpu.yaml
python -m benchmark.cli run --config configs/examples/local_rgb_micro_cpu.yaml
python -m benchmark.cli run --config configs/paper/prod_g2_rgb_dataloader_gpu.yaml --gcp-dry-run
benchmark/parser.py owns parser construction and provided-flag tracking. benchmark/config/models.py defines
BenchmarkRunConfig. benchmark/config/resolve.py loads YAML, applies supported CLI overrides, writes
resolved_config.yaml, and shapes typed GCP payloads. benchmark/config/argv.py builds attached-mode CLI argv from
typed configs for cloud debug runs. benchmark/config/plan.py prints generated jobs and
expected files. benchmark/config/env.py embeds the resolved config in result metadata. benchmark/cloud/paths.py and
benchmark/output_naming.py keep dry-run plans aligned with real VM paths and result filenames.
Use checked-in configs under configs/examples/ for local smoke runs and configs/paper/ for paper/GCP runs. Prefer
--num-items, --num-runs, --device, --workers, --batch-size, and --output as overrides instead of editing many
flags by hand.
Use the unified CLI with a YAML config, for example
python -m benchmark.cli run --config configs/examples/local_video_micro_cpu.yaml --data-dir /path/to/videos.
Legacy run_video_*.sh scripts are not in-repo.
Google Cloud (detached)
Default --cloud gcp path: uploads repo + typed job.json to GCS, creates a VM with a startup script that downloads one dataset tarball from gs:// (for example val.tar or ucf101.tar), unpacks/stages media files on local disk, writes /root/benchmark-work/job_config.yaml, runs benchmark.cli run --resolved-config /root/benchmark-work/job_config.yaml, writes artifacts under gs://<results-base>/<run_id>/, then deletes the VM. benchmark/cloud/launch.py owns launch option resolution and typed job payload assembly; benchmark/cloud/gcp.py owns the lower-level GCP runner. See README Google Cloud (detached) and benchmark/cloud/paths.py. Use --gcp-attached for blocking SSH/debug runs.
Optimization Policies
Treat benchmark/matrix.py as the source of truth for built-in scenario/library/mode support, spec paths,
requirements, joined environment groups, paper transform sets, device support, pipeline scopes, and backend names.
Treat benchmark/policy.py as the source of truth for media defaults and slow-transform preflight thresholds. Do not
duplicate image/video defaults in individual runners.
Treat benchmark/config/models.py as the source of truth for run shape and validation. Add user-facing config fields
there first, then update benchmark/config/resolve.py, benchmark/config/plan.py, examples under configs/, and tests.
Use benchmark/output_naming.py for result filenames and benchmark/cloud/paths.py for detached GCP VM paths. Do not
duplicate filename or VM staging inference in CLI, planner, or cloud code.
Use benchmark/jobs.py for command construction and benchmark/orchestrator.py for backend dispatch. Do not add
backend-specific branches to benchmark/cli.py; DALI should remain a dali_pipeline job backend.
benchmark/runner.py is a compatibility/simple-timer runner. Production CLI micro runs use
benchmark/pyperf_micro_runner.py; production DataLoader runs use benchmark/pipeline_runner.py.
Stage datasets as one tarball in cloud runs; do not copy individual images/videos one by one for each VM. On macOS, create dataset tarballs with COPYFILE_DISABLE=1, tar --no-xattrs, and excludes for .DS_Store, AppleDouble ._*, and __MACOSX.
Keep benchmark/cloud/stage_dataset.py stdlib-only. It runs before the VM control venv exists, so importing Pydantic or
benchmark.config there breaks detached cloud runs.
Keep timed data local to the benchmark machine. Detached GCP runs unpack to local disk before running.
Micro benchmarks preload the requested number of media items once per library, in that library's native format. Video micro preloads fixed-length clips from --clip-length (16 frames for video-16f), not full source videos. Torchvision video clips stay uint8 tensors so torchvision.transforms.v2.JPEG can run; Kornia video clips use float16 on CUDA.
Micro specs measure only the named transform in native layout, then force returned outputs into contiguous memory before
timing stops. Never add , , axis conversion, or DataLoader collation work to .
Architecture And Tests
When changing benchmark orchestration, update the architecture docs and tests:
Docs: docs/benchmark_architecture.md, docs/benchmark_scope.md, and relevant README sections.
Config tests: tests/test_config_models.py, tests/test_config_plan.py, tests/test_cloud_paths.py, and
tests/test_output_naming.py.
See benchmark/transforms/albumentationsx_video_impl.py and .cursor/rules/video_custom_transforms_architecture.mdc.
Workflow
Prepare data: Ensure images/videos are in target directory
Run benchmark: Use appropriate script
Validate output: Check JSON exists and has expected structure
Generate reports: Create comparison tables and plots
Update docs: Run update_docs.sh if updating documentation
Normalize
ToTensor
*_impl.py
Pipeline specs (*_pipeline_impl.py) own recipe-level Normalize+ToTensor: AlbumentationsX uses ToTensorV2, Pillow
uses torchvision.transforms.PILToTensor before normalization, and torchvision/Kornia already operate on tensors. Video
pipeline specs are separate from video micro specs and use crop + transform + Normalize + ToTensor recipe semantics for
AlbumentationsX, torchvision, and Kornia. The pipeline runner should use default PyTorch collation and should not guess
or repair channel layouts.
TorchVision and Kornia image GPU DataLoader recipes are split inside their pipeline specs: DataLoader workers run
library-native CPU crop/pad shape preparation before collation, then the main process copies the fixed-shape batch to
GPU. Kornia runs batched augmentation with same_on_batch=False plus normalization. TorchVision runs the measured
augmentation in a per-sample GPU loop, then normalizes the batch, because TorchVision v2 lacks a same_on_batch=False
equivalent for batched image transforms.
TorchVision JpegCompression uses torchvision.transforms.v2.JPEG, which requires uint8 CPU input. Exclude it from
TorchVision GPU image rows; keep it in CPU TorchVision rows and other libraries that support it.
CUDA DataLoader runs record per-transform peak GPU memory in results.<transform>.gpu_memory. Pyperf micro runs do not
report peak memory because timing happens in pyperf worker processes.
Pyperf runs may use per-transform subprocesses, but those subprocesses must reuse the per-library media cache and must not decode images again.
Construct only the transform being measured in pyperf subprocesses. Avoid eager construction of all transforms because some libraries warn or do setup in constructors.
Use joined environments for compatible libraries (torch_stack for torchvision/Kornia/Pillow image runs, torch_video for torchvision/Kornia video runs).
Cache environments by resolved requirements, Python version, media type, and environment group; reuse the GCS venv cache for detached GCP unless deliberately rebuilding.
Requirement lock refresh is expected once per library or joined-environment launch when refresh is enabled. Do not add extra cross-library refresh orchestration unless it removes real work without changing dependency freshness semantics. Prefer --no-refresh-requirements for repeated local reruns with fixed locks.
Pipeline result filenames include key sweep parameters: library_scope_n{num_items|all}_r{num_runs}_w{workers}_b{batch_size}[_dev-{device}]_results.json.
Preflight slow transforms in both micro and pipeline modes, then record an early-stop payload instead of spending the full benchmark budget on transforms that exceed the slow threshold. Defaults: images skip at >=0.05 sec/image (<=20 img/s), videos skip at >=2.0 sec/video.
Keep the slow-transform guard enabled for paper/DataLoader sweeps. It prevents the benchmark from appearing stuck on transforms that are too slow for practical training use. Use --disable-slow-skip only when the user explicitly asks to measure slow transforms exhaustively.
Preserve single-thread internal execution for micro benchmarks. Main paper pipeline benchmarks should use
production-style workers/threading (thread_policy: pipeline-default) and must record those settings; reserve
pipeline-single-worker for controlled appendix/debug rows.
Watch for lazy or partially lazy outputs. Micro timing must force each library to finish its own transform work and return
contiguous outputs: NumPy arrays use np.ascontiguousarray, tensor-like outputs use .contiguous(), and Pillow
Image.Image outputs are converted to contiguous NumPy arrays. Do not add checksums or unrelated validation inside the
timed benchmark.
Only benchmark transforms a library supports directly. Do not build large benchmark-side helper implementations to imitate another library's API. For Pillow, keep direct Image / ImageOps / ImageFilter operations and skip Albumentations-style composites such as random crops, PadIfNeeded, SafeRotate, ShiftScaleRotate, LongestMaxSize, and SmallestMaxSize.
Kornia excludes benchmark/transforms/kornia_unstable.pyonly from video DataLoader/pipeline recipes. Kornia image
GPU jobs also exclude Shear in micro and DataLoader modes because the current CUDA shear path can fail while moving
the parameter generator to GPU. Kornia 9-channel image GPU jobs also exclude MedianBlur because the L4 9-channel GPU
micro row OOMed on Kornia's large temporary allocation. Keep MedianBlur in RGB GPU, CPU, and other-library rows.
Paper runs do not use every transform from benchmark/transforms/specs.py. Use --transform-set paper for the curated
lists in docs/paper_transform_sets/*.md; within each scenario's library set, keep transforms with at least two
implementations. Video DataLoader/pipeline support is additionally filtered by dedicated video recipe specs.
Keep benchmarks fair but fast. Avoid repeated decode, loader construction, conversion, synchronization, checksums, materialization, or dependency work unless it is explicitly part of the named measurement scope or needed to make lazy work complete.
Prefer --no-refresh-requirements for local reruns when dependency versions are intentionally fixed.
All long-running loops must expose visual progress with tqdm and a descriptive desc. Use labels such as scenario/mode for library loops, Load images (<library>, <channels>ch) for media loading, Micro transforms (<library>, <media>), Pyperf micro transforms (<library>, <media>), and Pipeline transforms (<library>, <scope>, w=<workers>, b=<batch_size>). Never add anonymous progress bars.
