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PyTorch eager

EfficientNetB2 · batch_size = 2 · fp32 · run 01a03f0e-f840…
●passedReported evidence

Not re-observed recently.

Primary measurement
1.27ms±0.01 · mean of 100

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

max ms 1.49 · min ms 1.24 · std ms 0.0331 · mean ms 1.27

Identity
implementationPyTorch eager
projectPyTorch
revisionunknown
workloadbatch_size = 2 · fp32
comparison keysha256:8aaf2eaf2c217473…
sourceKernelBench baseline timings
external idH100_PCIe_LambdaLabs/torch/level3/24_EfficientNetB2.py
sha256:ef5cc728cd46aa815eceefa…

Correctness

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

Workload

batch_size2
input_1fp32 [2, 3, 224, 224]
definition comparatornot_asserted

Measurements

latency · max1.49 ms · n=100
latency · mean1.27 ms · n=100
latency · min1.24 ms · n=100
latency · std33.1 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": 1270000,
          "maximum": 1490000,
          "minimum": 1240000,
          "confidence95": [
            1263512,
            1276488
          ]
        },
        "primaryStatistic": "mean"
      },
      "observedAt": "2026-03-05T08:38:15.000Z",
      "measurements": [
        {
          "unit": "ns",
          "value": 33100,
          "metric": "latency",
          "statistic": "std"
        }
      ],
      "sourceNative": {
        "source": "kernelbench",
        "metrics": {
          "max_ms": 1.49,
          "min_ms": 1.24,
          "std_ms": 0.0331,
          "mean_ms": 1.27
        },
        "benchmark": "H100_PCIe_LambdaLabs/baseline_time_torch.json",
        "externalId": "H100_PCIe_LambdaLabs/torch/level3/24_EfficientNetB2.py"
      },
      "protocolDigest": "sha256:547725b033a9911eae0c5ac362344a85aed824894ed4ab476af945b9bb491ea6",
      "workloadDigest": "sha256:0c9bb5b49e61d634994d80aa131845987c89ebcd4caf9475543a4e371b1c1d77",
      "environmentDigest": "sha256:89f9b28b06758f22a1df331fea58bfde2c9cc0f3627d1829509743ea538f0011",
      "implementationDigest": "sha256:736ee33c0e382ebe54dd57bd8f6c023d91f639f18e681b78bee737627b14a6fe"
    },
    "metadata": {
      "name": "kernelbench-h100-pcie-lambdalabs-torch-l3-24-efficientnetb2",
      "title": "EfficientNetB2 · PyTorch eager · Lambda Labs"
    },
    "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": "PCIe",
        "architecture": "sm_90"
      },
      "software": {}
    },
    "metadata": {
      "name": "kernelbench-h100-pcie-lambdalabs",
      "title": "KernelBench host · H100 PCIe (Lambda Labs)"
    },
    "apiVersion": "kernelindex.dev/v1alpha1"
  }
}
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