Various minor changes to public documentation

tripy/README.md

@@ -1,8 +1,8 @@
 
 # Tripy: A Python Programming Model For TensorRT
 
-[**Installation**](#installation)
-| [**Getting Started**](#getting-started)
+[**Quick Start**](#quick-start)
+| [**Installation**](#installation)
 | [**Examples**](https://github.com/NVIDIA/TensorRT-Incubator/tree/main/tripy/examples)
 | [**Notebooks**](https://github.com/NVIDIA/TensorRT-Incubator/tree/main/tripy/notebooks)
 | [**Contributing**](https://github.com/NVIDIA/TensorRT-Incubator/blob/main/tripy/CONTRIBUTING.md)
@@ -17,53 +17,63 @@ a deep learning inference compiler.
 
 What you can expect:
 
-- **High Performance:** Leveraging [TensorRT](https://developer.nvidia.com/tensorrt)'s optimization capabilties.
+- **High performance** by leveraging [TensorRT](https://developer.nvidia.com/tensorrt)'s optimization capabilties.
+- An **intuitive API** that follows conventions of the ecosystem.
+- **Debuggability** with features like **eager mode** to interactively debug mistakes.
+- **Excellent error messages** that are informative and actionable.
+- **Friendly documentation** that is comprehensive but concise, with code examples.
 
-- **Intuitive API:** A familiar API that follows conventions of the ecosystem.
 
-- **Debuggability:** **Eager mode** to interactively debug mistakes.
+## Quick Start
 
-- **Excellent Error Messages**: Informative and actionable.
+See the
+[Introduction To Tripy](https://nvidia.github.io/TensorRT-Incubator/pre0_user_guides/00-introduction-to-tripy.html)
+guide for details:
 
-- **Friendly Documentation**: Comprehensive but concise, with code examples.
+<!-- Tripy: DOC: NO_PRINT_LOCALS Start -->
+- **Defining** a model:
 
-Code is worth 1,000 words:
+    ```py
+    class Model(tp.Module):
+        def __init__(self):
+            self.conv = tp.Conv(in_channels=1, out_channels=1, kernel_dims=[3, 3])
 
-<!-- Tripy: DOC: NO_PRINT_LOCALS Start -->
-```py
-# Define our model:
-class Model(tp.Module):
-    def __init__(self):
-        self.conv = tp.Conv(in_channels=1, out_channels=1, kernel_dims=[3, 3])
-
-    def __call__(self, x):
-        x = self.conv(x)
-        x = tp.relu(x)
-        return x
-
-
-# Initialize the model and load weights:
-model = Model()
-model.load_state_dict(
-    {
-        "conv.weight": tp.ones((1, 1, 3, 3)),
-        "conv.bias": tp.ones((1,)),
-    }
-)
-
-inp = tp.ones((1, 1, 4, 4))
-
-# Eager mode:
-eager_out = model(inp)
-
-# Compiled mode:
-compiled_model = tp.compile(
-    model,
-    args=[tp.InputInfo(shape=(1, 1, 4, 4), dtype=tp.float32)],
-)
-
-compiled_out = compiled_model(inp)
-```
+        def __call__(self, x):
+            x = self.conv(x)
+            x = tp.relu(x)
+            return x
+    ```
+
+- **Initializing** it:
+
+    ```py
+    model = Model()
+    model.load_state_dict(
+        {
+            "conv.weight": tp.ones((1, 1, 3, 3)),
+            "conv.bias": tp.ones((1,)),
+        }
+    )
+
+    dummy_input = tp.ones((1, 1, 4, 4))
+    ```
+
+- Executing in **eager mode**:
+
+    ```py
+    eager_out = model(dummy_input)
+    ```
+
+- **Compiling** and executing:
+
+    ```py
+    compiled_model = tp.compile(
+        model,
+        args=[tp.InputInfo(shape=(1, 1, 4, 4), dtype=tp.float32)],
+    )
+
+    compiled_out = compiled_model(dummy_input)
+    ```
 <!-- Tripy: DOC: NO_PRINT_LOCALS End -->
 
 
@@ -106,14 +116,3 @@ For the latest changes, build Tripy wheels from source:
     python3 -c "import nvtripy as tp; x = tp.ones((5,), dtype=tp.int32); assert x.tolist() == [1] * 5"
     ```
 <!-- Tripy: DOC: OMIT End -->
-
-
-## Getting Started
-
-- **Start with**:
-    [Introduction To Tripy](https://nvidia.github.io/TensorRT-Incubator/pre0_user_guides/00-introduction-to-tripy.html)
-
-Other guides:
-
-- [Compiling For Better Performance](https://nvidia.github.io/TensorRT-Incubator/pre0_user_guides/02-compiler.html)
-- [Quantization](https://nvidia.github.io/TensorRT-Incubator/pre0_user_guides/01-quantization.html)

tripy/docs/pre0_user_guides/00-introduction-to-tripy.md

@@ -5,7 +5,7 @@ a deep learning inference compiler.
 
 ## API Semantics
 
-Unlike TensorRT's graph-based semantics, Tripy uses a functional style:
+Unlike TensorRT's graph-based semantics, Tripy uses a **functional** style:
 
 ```py
 # doc: no-print-locals

tripy/nvtripy/types.py

@@ -33,7 +33,7 @@
     module=sys.modules[__name__],
     symbol="TensorLike",
     doc="""
-        A Tripy :class:`Tensor` or a Python number that can be automatically converted into one.
+        A :class:`nvtripy.Tensor` or a Python number that can be automatically converted into one.
         """,
 )(Union["nvtripy.Tensor", numbers.Number])
 
@@ -53,6 +53,6 @@
     module=sys.modules[__name__],
     symbol="ShapeLike",
     doc="""
-        A shape of a :class:`Tensor` .
+        A shape of a :class:`nvtripy.Tensor` .
         """,
 )(Sequence[IntLike])