Fix the title in contribute pages. (#138)

Signed-off-by: YiYing He <yiying@secondstate.io>

Author: q82419

docs/contribute/source/_category_.json

@@ -3,6 +3,6 @@
   "position": 2,
   "link": {
     "type": "generated-index",
-    "description": "we will learn how to the WasmEdge Plugin System."
+    "description": "Here's the guides for building WasmEdge and its plug-ins from source on various platforms."
   }
 }

docs/contribute/source/build_from_src.md

@@ -2,7 +2,7 @@
 sidebar_position: 1
 ---
 
-# Build WasmEdge from source
+# Build Guide
 
 Please follow this guide to build and test WasmEdge from the source code.
 

docs/contribute/source/docker.md

@@ -12,10 +12,10 @@ Developers can use the `docker pull wasmedge/wasmedge:{tag_name}` command to pul
 
 | tag name | arch | based operating system | LLVM version | ENVs | compatibility | comments |
 | --- | --- | --- | --- | --- | --- | --- |
-| `latest` | x86_64 | Ubuntu 20.04 LTS | 12.0.0 | CC=clang, CXX=clang++ | Ubuntu 20.04+ | This is for CI, will always use the latest Ubuntu LTS release |
-| `ubuntu-build-gcc` | x86_64 | Ubuntu 20.04 LTS | 12.0.0 | CC=gcc, CXX=g++ | Ubuntu 20.04+ | This is for CI, will always use the latest Ubuntu LTS release |
-| `ubuntu-build-clang` | x86_64 | Ubuntu 20.04 LTS | 12.0.0 | CC=clang, CXX=clang++ | Ubuntu 20.04+ | This is for CI, will always use the latest Ubuntu LTS release |
+| `latest` | x86_64 | Ubuntu 22.04 LTS | 15.0.7 | CC=clang, CXX=clang++ | Ubuntu 22.04+ | This is for CI, will always use the latest Ubuntu LTS release |
+| `ubuntu-build-gcc` | x86_64 | Ubuntu 22.04 LTS | 15.0.7 | CC=gcc, CXX=g++ | Ubuntu 22.04+ | This is for CI, will always use the latest Ubuntu LTS release |
+| `ubuntu-build-clang` | x86_64 | Ubuntu 22.04 LTS | 15.0.7 | CC=clang, CXX=clang++ | Ubuntu 22.04+ | This is for CI, will always use the latest Ubuntu LTS release |
 | `ubuntu2004_x86_64` | x86_64 | Ubuntu 20.04 LTS | 10.0.0 | CC=gcc, CXX=g++ | Ubuntu 20.04+ | This is for developers who familiar with Ubuntu 20.04 LTS release |
 | `ubuntu2104_armv7l` | armhf | Ubuntu 21.04 | 12.0.0 | CC=gcc, CXX=g++ | Ubuntu 21.04+ | This is for armhf release |
-| `manylinux2014_x86_64` | x86_64 | CentOS 7, 7.9.2009 | 12.0.0 | CC=gcc, CXX=g++ | Ubuntu 16.04+, CentOS 7+ | This is for developers who familiar with CentOS on x86_64 architecture |
-| `manylinux2014_aarch64` | aarch64 | CentOS 7, 7.9.2009 | 12.0.0 | CC=gcc, CXX=g++ | Ubuntu 16.04+, CentOS 7+ | This is for developers who familiar with CentOS on aarch64 architecture |
+| `manylinux2014_x86_64` | x86_64 | CentOS 7, 7.9.2009 | 16.0.5 | CC=gcc, CXX=g++ | Ubuntu 16.04+, CentOS 7+ | This is for developers who familiar with CentOS on x86_64 architecture |
+| `manylinux2014_aarch64` | aarch64 | CentOS 7, 7.9.2009 | 16.0.5 | CC=gcc, CXX=g++ | Ubuntu 16.04+, CentOS 7+ | This is for developers who familiar with CentOS on aarch64 architecture |

docs/contribute/source/os/_category_.json

@@ -1,8 +1,8 @@
 {
-  "label": "Build WasmEdge",
+  "label": "Supported Platforms",
   "position": 3,
   "link": {
     "type": "generated-index",
-    "description": "we will learn how to build WasmEdge from source on different OSes."
+    "description": "We will learn how to build WasmEdge from source on different platfroms."
   }
 }

docs/contribute/source/plugin/_category_.json

@@ -1,8 +1,8 @@
 {
-  "label": "WasmEdge Plugin",
+  "label": "Build Plug-ins",
   "position": 4,
   "link": {
     "type": "generated-index",
-    "description": "In this chapter, we will learn how to build WasmEdge Plugins from source"
+    "description": "In this chapter, we will learn how to build WasmEdge plug-ins from source."
   }
 }

docs/develop/rust/command.md

@@ -17,7 +17,7 @@ Application developers for WasmEdge can use our Rust interface crate to access t
 
 ```toml
 [dependencies]
-rust_process_interface_library = "0.1.3"
+wasmedge_process_interface = "0.2.1"
 ```
 
 In the Rust application, you can now use the API methods to start a new process for the operating system command program, pass in arguments via the `arg()` method as well as via the `STDIN`, and receives the return values via the `STDOUT`.

docs/develop/rust/hello_world.md

@@ -4,7 +4,7 @@ sidebar_position: 2
 
 # Hello world
 
-Before we start, make sure [you have Rust and WasmEdge installed](setup).
+Before we start, make sure [you have Rust and WasmEdge installed](setup.md).
 
 ## A simple main app
 

docs/develop/rust/tensorflow.md

@@ -4,4 +4,133 @@ sidebar_position: 7
 
 # TensorFlow Interface
 
-WIP
+Developers can use [WASI-NN](/category/neural-networks-for-wasi) to inference the models. However, for the TensorFlow and TensorFlow-Lite users, the WASI-NN APIs are not so friendly to retrieve the input and output tensors. Therefore WasmEdge provides the TensorFlow related plug-in and rust SDK for inferencing models in WASM.
+
+## Prerequisite
+
+Please make sure that you [Rust and WasmEdge installed](setup.md).
+
+Developers will add the [`wasmedge_tensorflow_interface` crate](https://crates.io/crates/wasmedge_tensorflow_interface) as a dependency to their `Rust -> Wasm` applications. For example, add the following line to the application's `Cargo.toml` file.
+
+```toml
+[dependencies]
+wasmedge_tensorflow_interface = "0.3.0"
+```
+
+Developers will bring the functions of `wasmedge_tensorflow_interface` into scope within their `Rust -> Wasm` application's code. For example, adding the following code to the top of their `main.rs`
+
+```rust
+use wasmedge_tensorflow_interface;
+```
+
+## Image Loading And Conversion
+
+In this crate, we provide several functions to decode and convert images into tensors by using the `WasmEdge-Image` host functions.
+
+For using these funcions in WASM and executing in WasmEdge, users should [install WasmEdge with WasmEdge-Image plug-in](../../start/install.md#wasmedge-image-plug-in).
+
+For decoding the `JPEG` images, there are:
+
+```rust
+// Function to decode JPEG from buffer and resize to RGB8 format.
+pub fn load_jpg_image_to_rgb8(img_buf: &[u8], w: u32, h: u32) -> Vec<u8>
+// Function to decode JPEG from buffer and resize to BGR8 format.
+pub fn load_jpg_image_to_bgr8(img_buf: &[u8], w: u32, h: u32) -> Vec<u8>
+// Function to decode JPEG from buffer and resize to RGB32F format.
+pub fn load_jpg_image_to_rgb32f(img_buf: &[u8], w: u32, h: u32) -> Vec<f32>
+// Function to decode JPEG from buffer and resize to BGR32F format.
+pub fn load_jpg_image_to_rgb32f(img_buf: &[u8], w: u32, h: u32) -> Vec<f32>
+```
+
+For decoding the `PNG` images, there are:
+
+```rust
+// Function to decode PNG from buffer and resize to RGB8 format.
+pub fn load_png_image_to_rgb8(img_buf: &[u8], w: u32, h: u32) -> Vec<u8>
+// Function to decode PNG from buffer and resize to BGR8 format.
+pub fn load_png_image_to_bgr8(img_buf: &[u8], w: u32, h: u32) -> Vec<u8>
+// Function to decode PNG from buffer and resize to RGB32F format.
+pub fn load_png_image_to_rgb32f(img_buf: &[u8], w: u32, h: u32) -> Vec<f32>
+// Function to decode PNG from buffer and resize to BGR32F format.
+pub fn load_png_image_to_rgb32f(img_buf: &[u8], w: u32, h: u32) -> Vec<f32>
+```
+
+Developers can load, decode, and resize image as following:
+
+```rust
+let mut file_img = File::open("sample.jpg").unwrap();
+let mut img_buf = Vec::new();
+file_img.read_to_end(&mut img_buf).unwrap();
+let flat_img = wasmedge_tensorflow_interface::load_jpg_image_to_rgb32f(&img_buf, 224, 224);
+// The flat_img is a vec<f32> which contains normalized image in rgb32f format and resized to 224x224.
+```
+
+## Inferring TensorFlow And TensorFlow-Lite Models
+
+For using the `TFSession` struct to inference the TensorFlow models and executing in WasmEdge, users should install the [WasmEdge-TensorFlow plug-in with dependencies](../../start/install.md#wasmedge-tensorflow-plug-in).
+
+For using the `TFLiteSession` struct and to inference the TensorFlow-Lite models executing in WasmEdge, users should install the [WasmEdge-TensorFlowLite plug-in with dependencies](../../start/install.md#wasmedge-tensorflow-lite-plug-in).
+
+### Create Session
+
+First, developers should create a session to load the TensorFlow or TensorFlow-Lite model.
+
+```rust
+// The mod_buf is a vec<u8> which contains the model data.
+let mut session = wasmedge_tensorflow_interface::TFSession::new(&mod_buf);
+```
+
+The above function is create the session for TensorFlow frozen models. Developers can use the `new_from_saved_model` function to create from saved-models:
+
+```rust
+// The mod_path is a &str which is the path to saved-model directory.
+// The second argument is the list of tags.
+let mut session = wasmedge_tensorflow_interface::TFSession::new_from_saved_model(model_path, &["serve"]);
+```
+
+Or use the `TFLiteSession` to create a session for inferring the `tflite` models.
+
+```rust
+// The mod_buf is a vec<u8> which contains the model data.
+let mut session = wasmedge_tensorflow_interface::TFLiteSession::new(&mod_buf);
+```
+
+### Prepare Input Tensors
+
+```rust
+// The flat_img is a vec<f32> which contains normalized image in rgb32f format.
+session.add_input("input", &flat_img, &[1, 224, 224, 3])
+       .add_output("MobilenetV2/Predictions/Softmax");
+```
+
+### Run TensorFlow Models
+
+```rust
+session.run();
+```
+
+### Convert Output Tensors
+
+```rust
+let res_vec: Vec<f32> = session.get_output("MobilenetV2/Predictions/Softmax");
+```
+
+## Build And Execution
+
+After completing your code, you can follow the command to compile into WASM.
+
+```bash
+cargo build --target=wasm32-wasi
+```
+
+The output WASM file will be at `target/wasm32-wasi/debug/` or `target/wasm32-wasi/release`.
+
+Please refer to [WasmEdge CLI](../../start/build-and-run/cli.md) for WASM execution.
+
+## Examples
+
+Work in progress. Will update soon.
+
+## Old WasmEdge TensorFlow extension
+
+Work in progress. Will update soon.

docs/start/build-and-run/cli.md

@@ -4,7 +4,7 @@ sidebar_position: 1
 
 # The `wasmedge` CLI
 
-After intalling WasmEdge, you can use the `wasmedge` CLI to execute a WASM files. We will cover how to run WASM files with WasmEdge on your own machine and Docker images.
+After installing WasmEdge, you can use the `wasmedge` CLI to execute a WASM files. We will cover how to run WASM files with WasmEdge on your own machine and Docker images.
 
 The `wasmedge` binary is a command line interface (CLI) program that runs WebAssembly programs.
 
@@ -269,7 +269,7 @@ confidence: 0.8941176470588236
 
 ## Docker images for the CLI tools
 
-The Docker images in this section are mostly used for development purposes. They allow you to use WasmEdge tools in containerized Linux environments. If you want to containerize WASM apps, check out [this](../getting-started/quick_start_docker) section.
+The Docker images in this section are mostly used for development purposes. They allow you to use WasmEdge tools in containerized Linux environments. If you want to containerize WASM apps, check out [this](../getting-started/quick_start_docker.md) section.
 
 The `wasmedge/slim:{version}` Docker images provide a slim WasmEdge images built with [DockerSlim](https://dockersl.im) every releases.
 

i18n/zh/docusaurus-plugin-content-docs/current/contribute/source/_category_.json

@@ -3,6 +3,6 @@
   "position": 2,
   "link": {
     "type": "generated-index",
-    "description": "we will learn how to the WasmEdge Plugin System."
+    "description": "Here's the guides for building WasmEdge and its plug-ins from source on various platforms."
   }
 }