PyTorch eager
RegNet · batch_size = 8 · image_width = 224 · image_height = 224 · input_channels = 3 · fp32 · run 01a03f0e-f840…
●passedReported evidence
Not re-observed recently.
Primary measurement
3.25ms±0.00 · mean of 100
Rank 2 in its comparison group · source-native comparison · observed 2026-03-05
max ms 3.28 · min ms 3.22 · std ms 0.0154 · mean ms 3.25
Identity
implementationPyTorch eager
projectPyTorch
revisionunknown
workloadbatch_size = 8 · image_width = 224 · image_height = 224 · input_channels = 3 · fp32
comparison keysha256:6bda1acf8de08c16…
sourceKernelBench baseline timings
external idH100_PCIe_LambdaLabs/torch/level3/27_RegNet.py
sha256:40f8ef74c96be54665d74de…
Correctness
Marked passed by the source; the correctness policy was not published.
Workload
batch_size8
image_width224
image_height224
input_channels3
input_1fp32 [8, 3, 224, 224]
definition comparatornot_asserted
Measurements
latency · max3.28 ms · n=100
latency · mean3.25 ms · n=100
latency · min3.22 ms · n=100
latency · std15.4 µs
Protocol
harnessKernelBench timing scripts
timercuda_events
primaryStatisticmean
comparabilityFamilykernelbench_baseline_timing
Environment
gpuNVIDIA H100 (sm_90)
Artifacts
No artifacts published with this run.
Replications and notes
No attestations yet.
Community attestations. They never change the evidence level; only a KernelIndex-controlled rerun does.
Add a reproduction or note
Canonical manifest
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{
"run": {
"kind": "BenchmarkRun",
"spec": {
"status": "passed",
"timing": {
"samples": 100,
"latencyNs": {
"mean": 3250000,
"maximum": 3280000,
"minimum": 3220000,
"confidence95": [
3246982,
3253018
]
},
"primaryStatistic": "mean"
},
"observedAt": "2026-03-05T08:38:15.000Z",
"measurements": [
{
"unit": "ns",
"value": 15400,
"metric": "latency",
"statistic": "std"
}
],
"sourceNative": {
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"metrics": {
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"min_ms": 3.22,
"std_ms": 0.0154,
"mean_ms": 3.25
},
"benchmark": "H100_PCIe_LambdaLabs/baseline_time_torch.json",
"externalId": "H100_PCIe_LambdaLabs/torch/level3/27_RegNet.py"
},
"protocolDigest": "sha256:547725b033a9911eae0c5ac362344a85aed824894ed4ab476af945b9bb491ea6",
"workloadDigest": "sha256:988fe9404aafac8f665168fcd6b58352d59729296f21bfcd8a5c6d2c07bbe14c",
"environmentDigest": "sha256:89f9b28b06758f22a1df331fea58bfde2c9cc0f3627d1829509743ea538f0011",
"implementationDigest": "sha256:c389162daac8a6a5da8bdd243e93ca5d2ea88e3632c01c83b7eaeb404a03aaf8"
},
"metadata": {
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"title": "RegNet · PyTorch eager · Lambda Labs"
},
"apiVersion": "kernelindex.dev/v1alpha1"
},
"protocol": {
"kind": "BenchmarkProtocol",
"spec": {
"harness": {
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},
"measurement": {
"timer": "cuda_events",
"primaryStatistic": "mean"
},
"comparability": {
"notes": "Mean of 100 timed forward passes (CUDA events, warm-up excluded) of the reference module on fixed inputs; the 95% interval is the mean's, from the reported standard deviation. The upstream JSON records no torch or CUDA version. Comparable only within one problem and one timing host.",
"family": "kernelbench_baseline_timing"
}
},
"metadata": {
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"environment": {
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},
"apiVersion": "kernelindex.dev/v1alpha1"
}
}Cite this record (permalink, digest, access date)
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Published 2026-08-26 · KernelBench baseline timings · MITAll results for RegNet →JSON