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PyTorch eager

DenseNet201 · width = 224 · height = 224 · batch_size = 10 · fp32 · run 01a03f0e-f6da…
●passedReported evidence

Not re-observed recently.

Primary measurement
10.4ms±0.05 · mean of 100

Rank 1 in its comparison group · source-native comparison · observed 2026-03-05

max ms 10.8 · min ms 9.83 · std ms 0.239 · mean ms 10.4

Identity
implementationPyTorch eager
projectPyTorch
revisionunknown
workloadwidth = 224 · height = 224 · batch_size = 10 · fp32
comparison keysha256:c37d1324f9e174f3…
sourceKernelBench baseline timings
external idH100_Modal/torch/level3/16_DenseNet201.py
sha256:0ccd923da57ee2acd84666b…

Correctness

Marked passed by the source; the correctness policy was not published.

Workload

width224
height224
batch_size10
input_1fp32 [10, 3, 224, 224]
definition comparatornot_asserted

Measurements

latency · max10.8 ms · n=100
latency · mean10.4 ms · n=100
latency · min9.83 ms · n=100
latency · std239.0 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

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Canonical manifest

Show manifest
{
  "run": {
    "kind": "BenchmarkRun",
    "spec": {
      "status": "passed",
      "timing": {
        "samples": 100,
        "latencyNs": {
          "mean": 10400000,
          "maximum": 10800000,
          "minimum": 9830000,
          "confidence95": [
            10353156,
            10446844
          ]
        },
        "primaryStatistic": "mean"
      },
      "observedAt": "2026-03-05T08:38:15.000Z",
      "measurements": [
        {
          "unit": "ns",
          "value": 239000,
          "metric": "latency",
          "statistic": "std"
        }
      ],
      "sourceNative": {
        "source": "kernelbench",
        "metrics": {
          "max_ms": 10.8,
          "min_ms": 9.83,
          "std_ms": 0.239,
          "mean_ms": 10.4
        },
        "benchmark": "H100_Modal/baseline_time_torch.json",
        "externalId": "H100_Modal/torch/level3/16_DenseNet201.py"
      },
      "protocolDigest": "sha256:547725b033a9911eae0c5ac362344a85aed824894ed4ab476af945b9bb491ea6",
      "workloadDigest": "sha256:df954a8644daccbd9dd0391719fd29c0b7d6a0e7c6c9ef7fffdb31d5a5f0eb1c",
      "environmentDigest": "sha256:2750ae381d6de582f1eb6b9008e0987463d19ede8af127d97d76c5ae09121393",
      "implementationDigest": "sha256:49977c3affb0b13e7fc2d75c9c9f8838f3d397a93bc97e01c12d703520d51d2f"
    },
    "metadata": {
      "name": "kernelbench-h100-modal-torch-l3-16-densenet201",
      "title": "DenseNet201 · PyTorch eager · Modal"
    },
    "apiVersion": "kernelindex.dev/v1alpha1"
  },
  "protocol": {
    "kind": "BenchmarkProtocol",
    "spec": {
      "harness": {
        "name": "KernelBench timing scripts",
        "repository": "https://github.com/ScalingIntelligence/KernelBench"
      },
      "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": {
      "name": "kernelbench-timing-v1",
      "title": "KernelBench baseline timing"
    },
    "apiVersion": "kernelindex.dev/v1alpha1"
  },
  "environment": {
    "kind": "ExecutionEnvironment",
    "spec": {
      "hardware": {
        "vendor": "nvidia",
        "product": "NVIDIA H100",
        "formFactor": "80GB HBM3",
        "architecture": "sm_90"
      },
      "software": {}
    },
    "metadata": {
      "name": "kernelbench-h100-modal",
      "title": "KernelBench host · H100 80GB HBM3 (Modal)"
    },
    "apiVersion": "kernelindex.dev/v1alpha1"
  }
}
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