Update README.md with new API & pointing to NGC container

Author: ncomly-nvidia

README.md

@@ -39,28 +39,26 @@ trt_mod.save("trt_torchscript_module.ts");
 import torch_tensorrt
 
 ...
-compile_settings = {
-    "inputs": [torch_tensorrt.Input(
-        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)
-
-input_data = input_data.half()
-result = trt_ts_module(input_data)
-torch.jit.save(trt_ts_module, "trt_torchscript_module.ts")
+
+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)
+
+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