---
pid: 3245685
cwd: "/home/ubuntu/hpml_nyu"
command: "cd /home/ubuntu/hpml_nyu && source venv/bin/activate && PYTHONUNBUFFERED=1 python lab2_torchscript.py --cuda --epochs 1 2>&1"
started_at: 2026-03-05T10:59:01.395Z
running_for_seconds: 105   
---
Device: cuda

Training 1 epochs before scripting...
  Ep 1: loss=1.9109  acc=31.58%

C7: Scripted model saved -> resnet18_scripted.pt
C7: Save/load verification — max diff: 0.0e+00

============================================================
 C8: TorchScript Model Graph
============================================================
graph(%self.1 : __torch__.lab2.ResNet,
      %x.1 : Tensor):
  %42 : int = prim::Constant[value=-1]()
  %13 : Function = prim::Constant[name="relu"]()
  %12 : bool = prim::Constant[value=0]() # :0:0
  %41 : int = prim::Constant[value=1]() # /home/ubuntu/hpml_nyu/lab2.py:80:29
  %bn1.1 : __torch__.torch.nn.modules.batchnorm.BatchNorm2d = prim::GetAttr[name="bn1"](%self.1)
  %conv1.1 : __torch__.torch.nn.modules.conv.Conv2d = prim::GetAttr[name="conv1"](%self.1)
  %9 : Tensor = prim::CallMethod[name="forward"](%conv1.1, %x.1) # /home/ubuntu/hpml_nyu/lab2.py:74:28
  %10 : Tensor = prim::CallMethod[name="forward"](%bn1.1, %9) # /home/ubuntu/hpml_nyu/lab2.py:74:19
  %x0.1 : Tensor = prim::CallFunction(%13, %10, %12) # :0:0
  %layer1.1 : __torch__.torch.nn.modules.container.___torch_mangle_1.Sequential = prim::GetAttr[name="layer1"](%self.1)
  %x1.1 : Tensor = prim::CallMethod[name="forward"](%layer1.1, %x0.1) # /home/ubuntu/hpml_nyu/lab2.py:75:12
  %layer2.1 : __torch__.torch.nn.modules.container.___torch_mangle_9.Sequential = prim::GetAttr[name="layer2"](%self.1)
  %x2.1 : Tensor = prim::CallMethod[name="forward"](%layer2.1, %x1.1) # /home/ubuntu/hpml_nyu/lab2.py:76:12
  %layer3.1 : __torch__.torch.nn.modules.container.___torch_mangle_17.Sequential = prim::GetAttr[name="layer3"](%self.1)
  %x3.1 : Tensor = prim::CallMethod[name="forward"](%layer3.1, %x2.1) # /home/ubuntu/hpml_nyu/lab2.py:77:12
  %layer4.1 : __torch__.torch.nn.modules.container.___torch_mangle_25.Sequential = prim::GetAttr[name="layer4"](%self.1)
  %x4.1 : Tensor = prim::CallMethod[name="forward"](%layer4.1, %x3.1) # /home/ubuntu/hpml_nyu/lab2.py:78:12
  %avgpool.1 : __torch__.torch.nn.modules.pooling.AdaptiveAvgPool2d = prim::GetAttr[name="avgpool"](%self.1)
  %x5.1 : Tensor = prim::CallMethod[name="forward"](%avgpool.1, %x4.1) # /home/ubuntu/hpml_nyu/lab2.py:79:12
  %x6.1 : Tensor = aten::flatten(%x5.1, %41, %42) # /home/ubuntu/hpml_nyu/lab2.py:80:12
  %fc.1 : __torch__.torch.nn.modules.linear.Linear = prim::GetAttr[name="fc"](%self.1)
  %48 : Tensor = prim::CallMethod[name="forward"](%fc.1, %x6.1) # /home/ubuntu/hpml_nyu/lab2.py:81:15
  return (%48)


============================================================
 C9: Test Set Accuracy
============================================================
  PyTorch model:     39.17%
  TorchScript model: 39.17%
[W305 10:59:14.948402432 NNPACK.cpp:62] Could not initialize NNPACK! Reason: Unsupported hardware.

============================================================
 C10: Latency Comparison (single image, ms)
============================================================
                    CPU (ms)   GPU (ms)
  PyTorch             438.36       2.27
  TorchScript          15.19       1.46
  CPU speedup: 28.85x
  CUDA speedup: 1.56x

---
exit_code: 0
elapsed_ms: 108165
ended_at: 2026-03-05T11:00:49.560Z
---