message: Update README.md with new API & pointing to NGC container
diff: @@ -39,28 +39,26 @@ trt_mod.save("trt_torchscript_module.ts");
  import torch_tensorrt
  ...
  -compile_settings = {
  - "inputs": [torch_tensorrt.Input(
  +
  +trt_ts_module = torch_tensorrt.compile(torch_script_module,
  + inputs = [example_tensor, # Provide example tensor for input shape or...
  + torch_tensorrt.Input( # Specify input object with shape and dtype
  min_shape=[1, 3, 224, 224],
  opt_shape=[1, 3, 512, 512],
  max_shape=[1, 3, 1024, 1024],
  # For static size shape=[1, 3, 224, 224]
  - dtype=torch.half, # Datatype of input tensor. Allowed options torch.(float|half|int8|int32|bool)
  - )],
  - "enabled_precisions": {torch.half}, # Run with FP16
  -}
  -
  -trt_ts_module = torch_tensorrt.compile(torch_script_module, compile_settings)
  + dtype=torch.half) # Datatype of input tensor. Allowed options torch.(float|half|int8|int32|bool)
  + ],
  + enabled_precisions = {torch.half}, # Run with FP16)
  -input_data = input_data.half()
  -result = trt_ts_module(input_data)
  -torch.jit.save(trt_ts_module, "trt_torchscript_module.ts")
  +result = trt_ts_module(input_data) # run inference
  +torch.jit.save(trt_ts_module, "trt_torchscript_module.ts") # save the TRT embedded Torchscript
  ```
  > Notes on running in lower precisions:
  > - Enabled lower precisions with compile_spec.enabled_precisions
  > - The module should be left in FP32 before compilation (FP16 can support half tensor models)
  -> - In FP16 only input tensors by default should be FP16, other precisions use FP32. This can be overrided by setting Input::dtype
  +> - Provided input tensors dtype should be the same as module before compilation, regardless of `enabled_precisions`. This can be overrided by setting `Input::dtype`
  ## Platform Support
  @@ -72,6 +70,8 @@ torch.jit.save(trt_ts_module, "trt_torchscript_module.ts")
  | Windows / GPU | **Unofficial Support** |
  | Linux ppc64le / GPU | - |
  +Torch-TensorRT will be included in NVIDIA NGC containers (https://ngc.nvidia.com/catalog/containers/nvidia:pytorch) starting in 21.11.
  +
  > Note: Refer NVIDIA NGC container(https://ngc.nvidia.com/catalog/containers/nvidia:l4t-pytorch) for PyTorch libraries on JetPack.
  ### Dependencies
  