content: """
  .. _dynamo_compile_resnet:
  
  Compiling ResNet using the Torch-TensorRT Dyanmo Frontend
  ==========================================================
  
  This interactive script is intended as a sample of the `torch_tensorrt.dynamo.compile` workflow on a ResNet model.
  """
  
  # %%
  # Imports and Model Definition
  # ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  
  import torch
  import torch_tensorrt
  import torchvision.models as models
  
  # %%
  
  # Initialize model with half precision and sample inputs
  model = models.resnet18(pretrained=True).half().eval().to("cuda")
  inputs = [torch.randn((1, 3, 224, 224)).to("cuda").half()]
  
  # %%
  # Optional Input Arguments to `torch_tensorrt.dynamo.compile`
  # ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  
  # Enabled precision for TensorRT optimization
  enabled_precisions = {torch.half}
  
  # Whether to print verbose logs
  debug = True
  
  # Workspace size for TensorRT
  workspace_size = 20 << 30
  
  # Maximum number of TRT Engines
  # (Lower value allows more graph segmentation)
  min_block_size = 3
  
  # Operations to Run in Torch, regardless of converter support
  torch_executed_ops = {}
  
  # %%
  # Compilation with `torch_tensorrt.dynamo.compile`
  # ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  
  # Build and compile the model with torch.compile, using Torch-TensorRT backend
  optimized_model = torch_tensorrt.dynamo.compile(
      model,
      inputs,
      enabled_precisions=enabled_precisions,
      debug=debug,
      workspace_size=workspace_size,
      min_block_size=min_block_size,
      torch_executed_ops=torch_executed_ops,
  )
  
  # %%
  # Equivalently, we could have run the above via the convenience frontend, as so:
  # `torch_tensorrt.compile(model, ir="dynamo_compile", inputs=inputs, ...)`
  
  # %%
  # Inference
  # ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  
  # Does not cause recompilation (same batch size as input)
  new_inputs = [torch.randn((1, 3, 224, 224)).half().to("cuda")]
  new_outputs = optimized_model(*new_inputs)
  
  # %%
  
  # Does cause recompilation (new batch size)
  new_batch_size_inputs = [torch.randn((8, 3, 224, 224)).half().to("cuda")]
  new_batch_size_outputs = optimized_model(*new_batch_size_inputs)
  
  # %%
  # Cleanup
  # ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  
  # Finally, we use Torch utilities to clean up the workspace
  torch._dynamo.reset()
  
  with torch.no_grad():
      torch.cuda.empty_cache()
  