From 3e0a63109df98c48fe7d7c90d66732f1f683913c Mon Sep 17 00:00:00 2001 From: Adithya Krishna Date: Fri, 25 Aug 2023 12:50:08 +0530 Subject: [PATCH] chore: reordered contents Signed-off-by: Adithya Krishna --- .../start/usage}/serverless/_category_.json | 2 +- .../start/usage}/serverless/aws.md | 0 .../start/usage}/serverless/netlify.md | 0 .../start/usage}/serverless/tencent.md | 0 .../start/usage}/serverless/vercel.md | 0 docs/start/usage/use-cases.md | 4 +- .../start/usage}/wasm-smart-devices.md | 0 .../use-case => docs/start/usage}/web-app.md | 0 .../start/usage/serverless/_category_.json | 8 + .../current/start/usage/serverless/aws.md | 272 ++++++++++++++++++ .../current/start/usage/serverless/netlify.md | 189 ++++++++++++ .../current/start/usage/serverless/tencent.md | 11 + .../current/start/usage/serverless/vercel.md | 191 ++++++++++++ .../current/start/usage/use-cases.md | 4 +- .../current/start/usage/wasm-smart-devices.md | 14 + .../current/start/usage/web-app.md | 101 +++++++ 16 files changed, 791 insertions(+), 5 deletions(-) rename {i18n/zh/docusaurus-plugin-content-docs/current/embed/use-case => docs/start/usage}/serverless/_category_.json (85%) rename {i18n/zh/docusaurus-plugin-content-docs/current/embed/use-case => docs/start/usage}/serverless/aws.md (100%) rename {i18n/zh/docusaurus-plugin-content-docs/current/embed/use-case => docs/start/usage}/serverless/netlify.md (100%) rename {i18n/zh/docusaurus-plugin-content-docs/current/embed/use-case => docs/start/usage}/serverless/tencent.md (100%) rename {i18n/zh/docusaurus-plugin-content-docs/current/embed/use-case => docs/start/usage}/serverless/vercel.md (100%) rename {i18n/zh/docusaurus-plugin-content-docs/current/embed/use-case => docs/start/usage}/wasm-smart-devices.md (100%) rename {i18n/zh/docusaurus-plugin-content-docs/current/embed/use-case => docs/start/usage}/web-app.md (100%) create mode 100644 i18n/zh/docusaurus-plugin-content-docs/current/start/usage/serverless/_category_.json create mode 100644 i18n/zh/docusaurus-plugin-content-docs/current/start/usage/serverless/aws.md create mode 100644 i18n/zh/docusaurus-plugin-content-docs/current/start/usage/serverless/netlify.md create mode 100644 i18n/zh/docusaurus-plugin-content-docs/current/start/usage/serverless/tencent.md create mode 100644 i18n/zh/docusaurus-plugin-content-docs/current/start/usage/serverless/vercel.md create mode 100644 i18n/zh/docusaurus-plugin-content-docs/current/start/usage/wasm-smart-devices.md create mode 100644 i18n/zh/docusaurus-plugin-content-docs/current/start/usage/web-app.md diff --git a/i18n/zh/docusaurus-plugin-content-docs/current/embed/use-case/serverless/_category_.json b/docs/start/usage/serverless/_category_.json similarity index 85% rename from i18n/zh/docusaurus-plugin-content-docs/current/embed/use-case/serverless/_category_.json rename to docs/start/usage/serverless/_category_.json index 53e7dfdd..075ab1a1 100644 --- a/i18n/zh/docusaurus-plugin-content-docs/current/embed/use-case/serverless/_category_.json +++ b/docs/start/usage/serverless/_category_.json @@ -1,5 +1,5 @@ { - "label": "Serviceless Platforms", + "label": "Serverless Platforms", "position": 9, "link": { "type": "generated-index", diff --git a/i18n/zh/docusaurus-plugin-content-docs/current/embed/use-case/serverless/aws.md b/docs/start/usage/serverless/aws.md similarity index 100% rename from i18n/zh/docusaurus-plugin-content-docs/current/embed/use-case/serverless/aws.md rename to docs/start/usage/serverless/aws.md diff --git a/i18n/zh/docusaurus-plugin-content-docs/current/embed/use-case/serverless/netlify.md b/docs/start/usage/serverless/netlify.md similarity index 100% rename from i18n/zh/docusaurus-plugin-content-docs/current/embed/use-case/serverless/netlify.md rename to docs/start/usage/serverless/netlify.md diff --git a/i18n/zh/docusaurus-plugin-content-docs/current/embed/use-case/serverless/tencent.md b/docs/start/usage/serverless/tencent.md similarity index 100% rename from i18n/zh/docusaurus-plugin-content-docs/current/embed/use-case/serverless/tencent.md rename to docs/start/usage/serverless/tencent.md diff --git a/i18n/zh/docusaurus-plugin-content-docs/current/embed/use-case/serverless/vercel.md b/docs/start/usage/serverless/vercel.md similarity index 100% rename from i18n/zh/docusaurus-plugin-content-docs/current/embed/use-case/serverless/vercel.md rename to docs/start/usage/serverless/vercel.md diff --git a/docs/start/usage/use-cases.md b/docs/start/usage/use-cases.md index d9a6f355..3cd2b568 100644 --- a/docs/start/usage/use-cases.md +++ b/docs/start/usage/use-cases.md @@ -8,9 +8,9 @@ Featuring AOT compiler optimization, WasmEdge is one of the fastest WebAssembly - WasmEdge provides a lightweight, secure and high-performance runtime for microservices. It is fully compatible with application service frameworks such as Dapr, and service orchestrators like Kubernetes. WasmEdge microservices can run on edge servers, and have access to distributed cache, to support both stateless and stateful business logic functions for modern web apps. Also related: Serverless function-as-a-service in public clouds. -- [Serverless SaaS (Software-as-a-Service)](../../embed/use-case/serverless-saas.md) functions enables users to extend and customize their SaaS experience without operating their own API callback servers. The serverless functions can be embedded into the SaaS or reside on edge servers next to the SaaS servers. Developers simply upload functions to respond to SaaS events or to connect SaaS APIs. +- [Serverless SaaS (Software-as-a-Service)](./serverless/serverless-platforms) functions enables users to extend and customize their SaaS experience without operating their own API callback servers. The serverless functions can be embedded into the SaaS or reside on edge servers next to the SaaS servers. Developers simply upload functions to respond to SaaS events or to connect SaaS APIs. -- [Smart device apps](../../embed/use-case/wasm-smart-devices.md) could embed WasmEdge as a middleware runtime to render interactive content on the UI, connect to native device drivers, and access specialized hardware features (i.e, the GPU for AI inference). The benefits of the WasmEdge runtime over native-compiled machine code include security, safety, portability, manageability, and developer productivity. WasmEdge runs on Android, OpenHarmony, and seL4 RTOS devices. +- [Smart device apps](./wasm-smart-devices.md) could embed WasmEdge as a middleware runtime to render interactive content on the UI, connect to native device drivers, and access specialized hardware features (i.e, the GPU for AI inference). The benefits of the WasmEdge runtime over native-compiled machine code include security, safety, portability, manageability, and developer productivity. WasmEdge runs on Android, OpenHarmony, and seL4 RTOS devices. - WasmEdge could support high performance DSLs (Domain Specific Languages) or act as a cloud-native JavaScript runtime by embedding a JS execution engine or interpreter. diff --git a/i18n/zh/docusaurus-plugin-content-docs/current/embed/use-case/wasm-smart-devices.md b/docs/start/usage/wasm-smart-devices.md similarity index 100% rename from i18n/zh/docusaurus-plugin-content-docs/current/embed/use-case/wasm-smart-devices.md rename to docs/start/usage/wasm-smart-devices.md diff --git a/i18n/zh/docusaurus-plugin-content-docs/current/embed/use-case/web-app.md b/docs/start/usage/web-app.md similarity index 100% rename from i18n/zh/docusaurus-plugin-content-docs/current/embed/use-case/web-app.md rename to docs/start/usage/web-app.md diff --git a/i18n/zh/docusaurus-plugin-content-docs/current/start/usage/serverless/_category_.json b/i18n/zh/docusaurus-plugin-content-docs/current/start/usage/serverless/_category_.json new file mode 100644 index 00000000..075ab1a1 --- /dev/null +++ b/i18n/zh/docusaurus-plugin-content-docs/current/start/usage/serverless/_category_.json @@ -0,0 +1,8 @@ +{ + "label": "Serverless Platforms", + "position": 9, + "link": { + "type": "generated-index", + "description": "Run WebAssembly as an alternative lightweight runtime side-by-side with Docker and microVMs in cloud native infrastructure" + } +} diff --git a/i18n/zh/docusaurus-plugin-content-docs/current/start/usage/serverless/aws.md b/i18n/zh/docusaurus-plugin-content-docs/current/start/usage/serverless/aws.md new file mode 100644 index 00000000..c23c5610 --- /dev/null +++ b/i18n/zh/docusaurus-plugin-content-docs/current/start/usage/serverless/aws.md @@ -0,0 +1,272 @@ +--- +sidebar_position: 1 +--- + +# WebAssembly Serverless Functions in AWS Lambda + +In this article, we will show you two serverless functions in Rust and WasmEdge deployed on AWS Lambda. One is the image processing function, the other one is the TensorFlow inference function. + +> For the insight on why WasmEdge on AWS Lambda, please refer to the article [WebAssembly Serverless Functions in AWS Lambda](https://www.secondstate.io/articles/webassembly-serverless-functions-in-aws-lambda/) + +## Prerequisites + +Since our demo WebAssembly functions are written in Rust, you will need a [Rust compiler](https://www.rust-lang.org/tools/install). Make sure that you install the `wasm32-wasi` compiler target as follows, in order to generate WebAssembly bytecode. + +```bash +rustup target add wasm32-wasi +``` + +The demo application front end is written in [Next.js](https://nextjs.org/), and deployed on AWS Lambda. We will assume that you already have the basic knowledge of how to work with Next.js and Lambda. + +## Example 1: Image processing + +Our first demo application allows users to upload an image and then invoke a serverless function to turn it into black and white. A [live demo](https://second-state.github.io/aws-lambda-wasm-runtime/) deployed through GitHub Pages is available. + +Fork the [demo application’s GitHub repo](https://github.com/second-state/aws-lambda-wasm-runtime) to get started. To deploy the application on AWS Lambda, follow the guide in the repository [README](https://github.com/second-state/aws-lambda-wasm-runtime/blob/tensorflow/README.md). + +### Create the function + +This repo is a standard Next.js application. The backend serverless function is in the `api/functions/image_grayscale` folder. The `src/main.rs` file contains the Rust program’s source code. The Rust program reads image data from the `STDIN`, and then outputs the black-white image to the `STDOUT`. + +```rust +use hex; +use std::io::{self, Read}; +use image::{ImageOutputFormat, ImageFormat}; + +fn main() { + let mut buf = Vec::new(); + io::stdin().read_to_end(&mut buf).unwrap(); + + let image_format_detected: ImageFormat = image::guess_format(&buf).unwrap(); + let img = image::load_from_memory(&buf).unwrap(); + let filtered = img.grayscale(); + let mut buf = vec![]; + match image_format_detected { + ImageFormat::Gif => { + filtered.write_to(&mut buf, ImageOutputFormat::Gif).unwrap(); + }, + _ => { + filtered.write_to(&mut buf, ImageOutputFormat::Png).unwrap(); + }, + }; + io::stdout().write_all(&buf).unwrap(); + io::stdout().flush().unwrap(); +} +``` + +You can use Rust’s `cargo` tool to build the Rust program into WebAssembly bytecode or native code. + +```bash +cd api/functions/image-grayscale/ +cargo build --release --target wasm32-wasi +``` + +Copy the build artifacts to the `api` folder. + +```bash +cp target/wasm32-wasi/release/grayscale.wasm ../../ +``` + +> When we build the docker image, `api/pre.sh` is executed. `pre.sh` installs the WasmEdge runtime, and then compiles each WebAssembly bytecode program into a native `so` library for faster execution. + +### Create the service script to load the function + +The [`api/hello.js`](https://github.com/second-state/aws-lambda-wasm-runtime/blob/main/api/hello.js) script loads the WasmEdge runtime, starts the compiled WebAssembly program in WasmEdge, and passes the uploaded image data via `STDIN`. Notice that [`api/hello.js`](https://github.com/second-state/aws-lambda-wasm-runtime/blob/main/api/hello.js) runs the compiled `grayscale.so` file generated by [`api/pre.sh`](https://github.com/second-state/aws-lambda-wasm-runtime/blob/main/api/pre.sh) for better performance. + +```javascript +const { spawn } = require('child_process'); +const path = require('path'); + +function _runWasm(reqBody) { + return new Promise((resolve) => { + const wasmedge = spawn(path.join(__dirname, 'wasmedge'), [ + path.join(__dirname, 'grayscale.so'), + ]); + + let d = []; + wasmedge.stdout.on('data', (data) => { + d.push(data); + }); + + wasmedge.on('close', (code) => { + let buf = Buffer.concat(d); + resolve(buf); + }); + + wasmedge.stdin.write(reqBody); + wasmedge.stdin.end(''); + }); +} +``` + +The `exports.handler` part of `hello.js` exports an async function handler, used to handle different events every time the serverless function is called. In this example, we simply process the image by calling the function above and return the result, but more complicated event-handling behavior may be defined based on your need. We also need to return some `Access-Control-Allow` headers to avoid [Cross-Origin Resource Sharing (CORS)](https://developer.mozilla.org/en-US/docs/Web/HTTP/CORS) errors when calling the serverless function from a browser. You can read more about CORS errors [here](https://developer.mozilla.org/en-US/docs/Web/HTTP/CORS/Errors) if you encounter them when replicating our example. + +```javascript +exports.handler = async function (event, context) { + var typedArray = new Uint8Array( + event.body.match(/[\da-f]{2}/gi).map(function (h) { + return parseInt(h, 16); + }), + ); + let buf = await _runWasm(typedArray); + return { + statusCode: 200, + headers: { + 'Access-Control-Allow-Headers': + 'Content-Type,X-Amz-Date,Authorization,X-Api-Key,X-Amz-Security-Token', + 'Access-Control-Allow-Origin': '*', + 'Access-Control-Allow-Methods': + 'DELETE, GET, HEAD, OPTIONS, PATCH, POST, PUT', + }, + body: buf.toString('hex'), + }; +}; +``` + +### Build the Docker image for Lambda deployment + +Now we have the WebAssembly bytecode function and the script to load and connect to the web request. In order to deploy them as a function service on AWS Lambda, you still need to package the whole thing into a Docker image. + +We are not going to cover in detail about how to build the Docker image and deploy on AWS Lambda, as there are detailed steps in the [Deploy section of the repository README](https://github.com/second-state/aws-lambda-wasm-runtime/blob/tensorflow/README.md#deploy). However, we will highlight some lines in the [`Dockerfile`](https://github.com/second-state/aws-lambda-wasm-runtime/blob/tensorflow/api/Dockerfile) for you to avoid some pitfalls. + +```dockerfile +FROM public.ecr.aws/lambda/nodejs:14 + +# Change directory to /var/task +WORKDIR /var/task + +RUN yum update -y && yum install -y curl tar gzip + +# Bundle and pre-compile the wasm files +COPY *.wasm ./ +COPY pre.sh ./ +RUN chmod +x pre.sh +RUN ./pre.sh + +# Bundle the JS files +COPY *.js ./ + +CMD [ "hello.handler" ] +``` + +First, we are building the image from [AWS Lambda's Node.js base image](https://hub.docker.com/r/amazon/aws-lambda-nodejs). The advantage of using AWS Lambda's base image is that it includes the [Lambda Runtime Interface Client (RIC)](https://github.com/aws/aws-lambda-nodejs-runtime-interface-client), which we need to implement in our Docker image as it is required by AWS Lambda. The Amazon Linux uses `yum` as the package manager. + +> These base images contain the Amazon Linux Base operating system, the runtime for a given language, dependencies and the Lambda Runtime Interface Client (RIC), which implements the Lambda [Runtime API](https://docs.aws.amazon.com/lambda/latest/dg/runtimes-api.html). The Lambda Runtime Interface Client allows your runtime to receive requests from and send requests to the Lambda service. + +Second, we need to put our function and all its dependencies in the `/var/task` directory. Files in other folders will not be executed by AWS Lambda. + +Third, we need to define the default command when we start our container. `CMD [ "hello.handler" ]` means that we will call the `handler` function in `hello.js` whenever our serverless function is called. Recall that we have defined and exported the handler function in the previous steps through `exports.handler = ...` in `hello.js`. + +### Optional: test the Docker image locally + +Docker images built from AWS Lambda's base images can be tested locally following [this guide](https://docs.aws.amazon.com/lambda/latest/dg/images-test.html). Local testing requires [AWS Lambda Runtime Interface Emulator (RIE)](https://github.com/aws/aws-lambda-runtime-interface-emulator), which is already installed in all of AWS Lambda's base images. To test your image, first, start the Docker container by running: + +```bash +docker run -p 9000:8080 myfunction:latest +``` + +This command sets a function endpoint on your local machine at `http://localhost:9000/2015-03-31/functions/function/invocations`. + +Then, from a separate terminal window, run: + +```bash +curl -XPOST "http://localhost:9000/2015-03-31/functions/function/invocations" -d '{}' +``` + +And you should get your expected output in the terminal. + +If you don't want to use a base image from AWS Lambda, you can also use your own base image and install RIC and/or RIE while building your Docker image. Just follow **Create an image from an alternative base image** section from [this guide](https://docs.aws.amazon.com/lambda/latest/dg/images-create.html). + +That's it! After building your Docker image, you can deploy it to AWS Lambda following steps outlined in the repository [README](https://github.com/second-state/aws-lambda-wasm-runtime/blob/tensorflow/README.md#deploy). Now your serverless function is ready to rock! + +## Example 2: AI inference + +The [second demo](https://github.com/second-state/aws-lambda-wasm-runtime/tree/tensorflow) application allows users to upload an image and then invoke a serverless function to classify the main subject on the image. + +It is in [the same GitHub repo](https://github.com/second-state/aws-lambda-wasm-runtime/tree/tensorflow) as the previous example but in the `tensorflow` branch. The backend serverless function for image classification is in the `api/functions/image-classification` folder in the `tensorflow` branch. The `src/main.rs` file contains the Rust program’s source code. The Rust program reads image data from the `STDIN`, and then outputs the text output to the `STDOUT`. It utilizes the WasmEdge Tensorflow API to run the AI inference. + +```rust +pub fn main() { + // Step 1: Load the TFLite model + let model_data: &[u8] = include_bytes!("models/mobilenet_v1_1.0_224/mobilenet_v1_1.0_224_quant.tflite"); + let labels = include_str!("models/mobilenet_v1_1.0_224/labels_mobilenet_quant_v1_224.txt"); + + // Step 2: Read image from STDIN + let mut buf = Vec::new(); + io::stdin().read_to_end(&mut buf).unwrap(); + + // Step 3: Resize the input image for the tensorflow model + let flat_img = wasmedge_tensorflow_interface::load_jpg_image_to_rgb8(&buf, 224, 224); + + // Step 4: AI inference + let mut session = wasmedge_tensorflow_interface::Session::new(&model_data, wasmedge_tensorflow_interface::ModelType::TensorFlowLite); + session.add_input("input", &flat_img, &[1, 224, 224, 3]) + .run(); + let res_vec: Vec = session.get_output("MobilenetV1/Predictions/Reshape_1"); + + // Step 5: Find the food label that responds to the highest probability in res_vec + // ... ... + let mut label_lines = labels.lines(); + for _i in 0..max_index { + label_lines.next(); + } + + // Step 6: Generate the output text + let class_name = label_lines.next().unwrap().to_string(); + if max_value > 50 { + println!("It {} a {} in the picture", confidence.to_string(), class_name, class_name); + } else { + println!("It does not appears to be any food item in the picture."); + } +} +``` + +You can use the `cargo` tool to build the Rust program into WebAssembly bytecode or native code. + +```bash +cd api/functions/image-classification/ +cargo build --release --target wasm32-wasi +``` + +Copy the build artifacts to the `api` folder. + +```bash +cp target/wasm32-wasi/release/classify.wasm ../../ +``` + +Again, the `api/pre.sh` script installs WasmEdge runtime and its Tensorflow dependencies in this application. It also compiles the `classify.wasm` bytecode program to the `classify.so` native shared library at the time of deployment. + +The [`api/hello.js`](https://github.com/second-state/aws-lambda-wasm-runtime/blob/tensorflow/api/hello.js) script loads the WasmEdge runtime, starts the compiled WebAssembly program in WasmEdge, and passes the uploaded image data via `STDIN`. Notice [`api/hello.js`](https://github.com/second-state/aws-lambda-wasm-runtime/blob/tensorflow/api/hello.js) runs the compiled `classify.so` file generated by [`api/pre.sh`](https://github.com/second-state/aws-lambda-wasm-runtime/blob/tensorflow/api/pre.sh) for better performance. The handler function is similar to our previous example, and is omitted here. + +```javascript +const { spawn } = require('child_process'); +const path = require('path'); + +function _runWasm(reqBody) { + return new Promise(resolve => { + const wasmedge = spawn( + path.join(__dirname, 'wasmedge-tensorflow-lite'), + [path.join(__dirname, 'classify.so')], + {env: {'LD_LIBRARY_PATH': __dirname}} + ); + + let d = []; + wasmedge.stdout.on('data', (data) => { + d.push(data); + }); + + wasmedge.on('close', (code) => { + resolve(d.join('')); + }); + + wasmedge.stdin.write(reqBody); + wasmedge.stdin.end(''); + }); +} + +exports.handler = ... // _runWasm(reqBody) is called in the handler +``` + +You can build your Docker image and deploy the function in the same way as outlined in the previous example. Now you have created a web app for subject classification! + +Next, it's your turn to use the [aws-lambda-wasm-runtime repo](https://github.com/second-state/aws-lambda-wasm-runtime/tree/main) as a template to develop Rust serverless function on AWS Lambda. Looking forward to your great work. diff --git a/i18n/zh/docusaurus-plugin-content-docs/current/start/usage/serverless/netlify.md b/i18n/zh/docusaurus-plugin-content-docs/current/start/usage/serverless/netlify.md new file mode 100644 index 00000000..0f4b82db --- /dev/null +++ b/i18n/zh/docusaurus-plugin-content-docs/current/start/usage/serverless/netlify.md @@ -0,0 +1,189 @@ +--- +sidebar_position: 2 +--- + +# WebAssembly Serverless Functions in Netlify + +In this article we will show you two serverless functions in Rust and WasmEdge deployed on Netlify. One is the image processing function, the other one is the TensorFlow inference function. + +> For more insights on why WasmEdge on Netlify, please refer to the article [WebAssembly Serverless Functions in Netlify](https://www.secondstate.io/articles/netlify-wasmedge-webassembly-rust-serverless/). + +## Prerequisite + +Since our demo WebAssembly functions are written in Rust, you will need a [Rust compiler](https://www.rust-lang.org/tools/install). Make sure that you install the `wasm32-wasi` compiler target as follows, in order to generate WebAssembly bytecode. + +```bash +rustup target add wasm32-wasi +``` + +The demo application front end is written in [Next.js](https://nextjs.org/), and deployed on Netlify. We will assume that you already have the basic knowledge of how to work with Next.js and Netlify. + +## Example 1: Image processing + +Our first demo application allows users to upload an image and then invoke a serverless function to turn it into black and white. A [live demo](https://60fe22f9ff623f0007656040--reverent-hodgkin-dc1f51.netlify.app/) deployed on Netlify is available. + +Fork the [demo application’s GitHub repo](https://github.com/second-state/netlify-wasm-runtime) to get started. To deploy the application on Netlify, just [add your github repo to Netlify](https://www.netlify.com/blog/2016/09/29/a-step-by-step-guide-deploying-on-netlify/). + +This repo is a standard Next.js application for the Netlify platform. The backend serverless function is in the [`api/functions/image_grayscale`](https://github.com/second-state/netlify-wasm-runtime/tree/main/api/functions/image-grayscale) folder. The [`src/main.rs`](https://github.com/second-state/netlify-wasm-runtime/blob/main/api/functions/image-grayscale/src/main.rs) file contains the Rust program’s source code. The Rust program reads image data from the `STDIN`, and then outputs the black-white image to the `STDOUT`. + +```rust +use hex; +use std::io::{self, Read}; +use image::{ImageOutputFormat, ImageFormat}; + +fn main() { + let mut buf = Vec::new(); + io::stdin().read_to_end(&mut buf).unwrap(); + + let image_format_detected: ImageFormat = image::guess_format(&buf).unwrap(); + let img = image::load_from_memory(&buf).unwrap(); + let filtered = img.grayscale(); + let mut buf = vec![]; + match image_format_detected { + ImageFormat::Gif => { + filtered.write_to(&mut buf, ImageOutputFormat::Gif).unwrap(); + }, + _ => { + filtered.write_to(&mut buf, ImageOutputFormat::Png).unwrap(); + }, + }; + io::stdout().write_all(&buf).unwrap(); + io::stdout().flush().unwrap(); +} +``` + +You can use Rust’s `cargo` tool to build the Rust program into WebAssembly bytecode or native code. + +```bash +cd api/functions/image-grayscale/ +cargo build --release --target wasm32-wasi +``` + +Copy the build artifacts to the `api` folder. + +```bash +cp target/wasm32-wasi/release/grayscale.wasm ../../ +``` + +> The Netlify function runs [`api/pre.sh`](https://github.com/second-state/netlify-wasm-runtime/blob/main/api/pre.sh) upon setting up the serverless environment. It installs the WasmEdge runtime, and then compiles each WebAssembly bytecode program into a native `so` library for faster execution. + +The [`api/hello.js`](https://github.com/second-state/netlify-wasm-runtime/blob/main/api/hello.js) script loads the WasmEdge runtime, starts the compiled WebAssembly program in WasmEdge, and passes the uploaded image data via `STDIN`. Notice [`api/hello.js`](https://github.com/second-state/netlify-wasm-runtime/blob/main/api/hello.js) runs the compiled `grayscale.so` file generated by [`api/pre.sh`](https://github.com/second-state/netlify-wasm-runtime/blob/main/api/pre.sh) for better performance. + +```javascript +const fs = require('fs'); +const { spawn } = require('child_process'); +const path = require('path'); + +module.exports = (req, res) => { + const wasmedge = spawn(path.join(__dirname, 'wasmedge'), [ + path.join(__dirname, 'grayscale.so'), + ]); + + let d = []; + wasmedge.stdout.on('data', (data) => { + d.push(data); + }); + + wasmedge.on('close', (code) => { + let buf = Buffer.concat(d); + + res.setHeader('Content-Type', req.headers['image-type']); + res.send(buf); + }); + + wasmedge.stdin.write(req.body); + wasmedge.stdin.end(''); +}; +``` + +That's it. [Deploy the repo to Netlify](https://www.netlify.com/blog/2016/09/29/a-step-by-step-guide-deploying-on-netlify/) and you now have a Netlify Jamstack app with a high-performance Rust and WebAssembly based serverless backend. + +## Example 2: AI inference + +The [second demo](https://60ff7e2d10fe590008db70a9--reverent-hodgkin-dc1f51.netlify.app/) application allows users to upload an image and then invoke a serverless function to classify the main subject on the image. + +It is in [the same GitHub repo](https://github.com/second-state/netlify-wasm-runtime/tree/tensorflow) as the previous example but in the `tensorflow` branch. The backend serverless function for image classification is in the [`api/functions/image-classification`](https://github.com/second-state/netlify-wasm-runtime/tree/tensorflow/api/functions/image-classification) folder in the `tensorflow` branch. The [`src/main.rs`](https://github.com/second-state/netlify-wasm-runtime/blob/tensorflow/api/functions/image-classification/src/main.rs) file contains the Rust program’s source code. The Rust program reads image data from the `STDIN`, and then outputs the text output to the `STDOUT`. It utilizes the WasmEdge Tensorflow API to run the AI inference. + +```rust +pub fn main() { + // Step 1: Load the TFLite model + let model_data: &[u8] = include_bytes!("models/mobilenet_v1_1.0_224/mobilenet_v1_1.0_224_quant.tflite"); + let labels = include_str!("models/mobilenet_v1_1.0_224/labels_mobilenet_quant_v1_224.txt"); + + // Step 2: Read image from STDIN + let mut buf = Vec::new(); + io::stdin().read_to_end(&mut buf).unwrap(); + + // Step 3: Resize the input image for the tensorflow model + let flat_img = wasmedge_tensorflow_interface::load_jpg_image_to_rgb8(&buf, 224, 224); + + // Step 4: AI inference + let mut session = wasmedge_tensorflow_interface::Session::new(&model_data, wasmedge_tensorflow_interface::ModelType::TensorFlowLite); + session.add_input("input", &flat_img, &[1, 224, 224, 3]) + .run(); + let res_vec: Vec = session.get_output("MobilenetV1/Predictions/Reshape_1"); + + // Step 5: Find the food label that responds to the highest probability in res_vec + // ... ... + let mut label_lines = labels.lines(); + for _i in 0..max_index { + label_lines.next(); + } + + // Step 6: Generate the output text + let class_name = label_lines.next().unwrap().to_string(); + if max_value > 50 { + println!("It {} a {} in the picture", confidence.to_string(), class_name, class_name); + } else { + println!("It does not appears to be any food item in the picture."); + } +} +``` + +You can use the `cargo` tool to build the Rust program into WebAssembly bytecode or native code. + +```bash +cd api/functions/image-classification/ +cargo build --release --target wasm32-wasi +``` + +Copy the build artifacts to the `api` folder. + +```bash +cp target/wasm32-wasi/release/classify.wasm ../../ +``` + +Again, the [`api/pre.sh`](https://github.com/second-state/netlify-wasm-runtime/blob/tensorflow/api/pre.sh) script installs WasmEdge runtime and its Tensorflow dependencies in this application. It also compiles the `classify.wasm` bytecode program to the `classify.so` native shared library at the time of deployment. + +The [`api/hello.js`](https://github.com/second-state/netlify-wasm-runtime/blob/tensorflow/api/hello.js) script loads the WasmEdge runtime, starts the compiled WebAssembly program in WasmEdge, and passes the uploaded image data via `STDIN`. Notice [`api/hello.js`](https://github.com/second-state/netlify-wasm-runtime/blob/tensorflow/api/hello.js) runs the compiled `classify.so` file generated by [`api/pre.sh`](https://github.com/second-state/netlify-wasm-runtime/blob/tensorflow/api/pre.sh) for better performance. + +```javascript +const fs = require('fs'); +const { spawn } = require('child_process'); +const path = require('path'); + +module.exports = (req, res) => { + const wasmedge = spawn( + path.join(__dirname, 'wasmedge-tensorflow-lite'), + [path.join(__dirname, 'classify.so')], + { env: { LD_LIBRARY_PATH: __dirname } }, + ); + + let d = []; + wasmedge.stdout.on('data', (data) => { + d.push(data); + }); + + wasmedge.on('close', (code) => { + res.setHeader('Content-Type', `text/plain`); + res.send(d.join('')); + }); + + wasmedge.stdin.write(req.body); + wasmedge.stdin.end(''); +}; +``` + +You can now [deploy your forked repo to Netlify](https://www.netlify.com/blog/2016/09/29/a-step-by-step-guide-deploying-on-netlify/) and have a web app for subject classification. + +Next, it's your turn to develop Rust serverless functions in Netlify using the [netlify-wasm-runtime repo](https://github.com/second-state/netlify-wasm-runtime) as a template. Looking forward to your great work. diff --git a/i18n/zh/docusaurus-plugin-content-docs/current/start/usage/serverless/tencent.md b/i18n/zh/docusaurus-plugin-content-docs/current/start/usage/serverless/tencent.md new file mode 100644 index 00000000..9937f714 --- /dev/null +++ b/i18n/zh/docusaurus-plugin-content-docs/current/start/usage/serverless/tencent.md @@ -0,0 +1,11 @@ +--- +sidebar_position: 4 +--- + +# WebAssembly serverless functions on Tencent Cloud + +As the main users of Tencent Cloud are from China, so the tutorial is [written in Chinese](https://my.oschina.net/u/4532842/blog/5172639). + +We also provide a code template for deploying serverless WebAssembly functions on Tencent Cloud, please check out [the tencent-scf-wasm-runtime repo](https://github.com/second-state/tencent-scf-wasm-runtime). + +Fork the repo and start writing your own rust functions. diff --git a/i18n/zh/docusaurus-plugin-content-docs/current/start/usage/serverless/vercel.md b/i18n/zh/docusaurus-plugin-content-docs/current/start/usage/serverless/vercel.md new file mode 100644 index 00000000..3ef87bd5 --- /dev/null +++ b/i18n/zh/docusaurus-plugin-content-docs/current/start/usage/serverless/vercel.md @@ -0,0 +1,191 @@ +--- +sidebar_position: 5 +--- + +# Rust and WebAssembly Serverless functions in Vercel + +In this article, we will show you two serverless functions in Rust and WasmEdge deployed on Vercel. One is the image processing function, the other one is the TensorFlow inference function. + +> For more insights on why WasmEdge on Vercel, please refer to the article [Rust and WebAssembly Serverless Functions in Vercel](https://www.secondstate.io/articles/vercel-wasmedge-webassembly-rust/). + +## Prerequisite + +Since our demo WebAssembly functions are written in Rust, you will need a [Rust compiler](https://www.rust-lang.org/tools/install). Make sure that you install the `wasm32-wasi` compiler target as follows, in order to generate WebAssembly bytecode. + +```bash +rustup target add wasm32-wasi +``` + +The demo application front end is written in [Next.js](https://nextjs.org/), and deployed on Vercel. We will assume that you already have the basic knowledge of how to work with Vercel. + +## Example 1: Image processing + +Our first demo application allows users to upload an image and then invoke a serverless function to turn it into black and white. A [live demo](https://vercel-wasm-runtime.vercel.app/) deployed on Vercel is available. + +Fork the [demo application’s GitHub repo](https://github.com/second-state/vercel-wasm-runtime) to get started. To deploy the application on Vercel, just [import the Github repo](https://vercel.com/docs/git#deploying-a-git-repository) from [Vercel for Github](https://vercel.com/docs/git/vercel-for-github) web page. + +This repo is a standard Next.js application for the Vercel platform. The backend serverless function is in the [`api/functions/image_grayscale`](https://github.com/second-state/vercel-wasm-runtime/tree/main/api/functions/image-grayscale) folder. The [`src/main.rs`](https://github.com/second-state/vercel-wasm-runtime/blob/main/api/functions/image-grayscale/src/main.rs) file contains the Rust program’s source code. The Rust program reads image data from the `STDIN`, and then outputs the black-white image to the `STDOUT`. + +```rust +use hex; +use std::io::{self, Read}; +use image::{ImageOutputFormat, ImageFormat}; + +fn main() { + let mut buf = Vec::new(); + io::stdin().read_to_end(&mut buf).unwrap(); + + let image_format_detected: ImageFormat = image::guess_format(&buf).unwrap(); + let img = image::load_from_memory(&buf).unwrap(); + let filtered = img.grayscale(); + let mut buf = vec![]; + match image_format_detected { + ImageFormat::Gif => { + filtered.write_to(&mut buf, ImageOutputFormat::Gif).unwrap(); + }, + _ => { + filtered.write_to(&mut buf, ImageOutputFormat::Png).unwrap(); + }, + }; + io::stdout().write_all(&buf).unwrap(); + io::stdout().flush().unwrap(); +} +``` + +You can use Rust’s `cargo` tool to build the Rust program into WebAssembly bytecode or native code. + +```bash +cd api/functions/image-grayscale/ +cargo build --release --target wasm32-wasi +``` + +Copy the build artifacts to the `api` folder. + +```bash +cp target/wasm32-wasi/release/grayscale.wasm ../../ +``` + +> Vercel runs [`api/pre.sh`](https://github.com/second-state/vercel-wasm-runtime/blob/main/api/pre.sh) upon setting up the serverless environment. It installs the WasmEdge runtime, and then compiles each WebAssembly bytecode program into a native `so` library for faster execution. + +The [`api/hello.js`](https://github.com/second-state/vercel-wasm-runtime/blob/main/api/hello.js) file conforms Vercel serverless specification. It loads the WasmEdge runtime, starts the compiled WebAssembly program in WasmEdge, and passes the uploaded image data via `STDIN`. Notice [`api/hello.js`](https://github.com/second-state/vercel-wasm-runtime/blob/main/api/hello.js) runs the compiled `grayscale.so` file generated by [`api/pre.sh`](https://github.com/second-state/vercel-wasm-runtime/blob/main/api/pre.sh) for better performance. + +```javascript +const fs = require('fs'); +const { spawn } = require('child_process'); +const path = require('path'); + +module.exports = (req, res) => { + const wasmedge = spawn(path.join(__dirname, 'wasmedge'), [ + path.join(__dirname, 'grayscale.so'), + ]); + + let d = []; + wasmedge.stdout.on('data', (data) => { + d.push(data); + }); + + wasmedge.on('close', (code) => { + let buf = Buffer.concat(d); + + res.setHeader('Content-Type', req.headers['image-type']); + res.send(buf); + }); + + wasmedge.stdin.write(req.body); + wasmedge.stdin.end(''); +}; +``` + +That's it. [Deploy the repo to Vercel](https://vercel.com/docs/git#deploying-a-git-repository) and you now have a Vercel Jamstack app with a high-performance Rust and WebAssembly based serverless backend. + +## Example 2: AI inference + +The [second demo](https://vercel-wasm-runtime.vercel.app/) application allows users to upload an image and then invoke a serverless function to classify the main subject on the image. + +It is in [the same GitHub repo](https://github.com/second-state/vercel-wasm-runtime) as the previous example but in the `tensorflow` branch. Note: when you [import this GitHub repo](https://vercel.com/docs/git#deploying-a-git-repository) on the Vercel website, it will create a [preview URL](https://vercel.com/docs/platform/deployments#preview) for each branch. The `tensorflow` branch would have its own deployment URL. + +The backend serverless function for image classification is in the [`api/functions/image-classification`](https://github.com/second-state/vercel-wasm-runtime/tree/tensorflow/api/functions/image-classification) folder in the `tensorflow` branch. The [`src/main.rs`](https://github.com/second-state/vercel-wasm-runtime/blob/tensorflow/api/functions/image-classification/src/main.rs) file contains the Rust program’s source code. The Rust program reads image data from the `STDIN`, and then outputs the text output to the `STDOUT`. It utilizes the WasmEdge Tensorflow API to run the AI inference. + +```rust +pub fn main() { + // Step 1: Load the TFLite model + let model_data: &[u8] = include_bytes!("models/mobilenet_v1_1.0_224/mobilenet_v1_1.0_224_quant.tflite"); + let labels = include_str!("models/mobilenet_v1_1.0_224/labels_mobilenet_quant_v1_224.txt"); + + // Step 2: Read image from STDIN + let mut buf = Vec::new(); + io::stdin().read_to_end(&mut buf).unwrap(); + + // Step 3: Resize the input image for the tensorflow model + let flat_img = wasmedge_tensorflow_interface::load_jpg_image_to_rgb8(&buf, 224, 224); + + // Step 4: AI inference + let mut session = wasmedge_tensorflow_interface::Session::new(&model_data, wasmedge_tensorflow_interface::ModelType::TensorFlowLite); + session.add_input("input", &flat_img, &[1, 224, 224, 3]) + .run(); + let res_vec: Vec = session.get_output("MobilenetV1/Predictions/Reshape_1"); + + // Step 5: Find the food label that responds to the highest probability in res_vec + // ... ... + let mut label_lines = labels.lines(); + for _i in 0..max_index { + label_lines.next(); + } + + // Step 6: Generate the output text + let class_name = label_lines.next().unwrap().to_string(); + if max_value > 50 { + println!("It {} a {} in the picture", confidence.to_string(), class_name, class_name); + } else { + println!("It does not appears to be any food item in the picture."); + } +} +``` + +You can use the `cargo` tool to build the Rust program into WebAssembly bytecode or native code. + +```bash +cd api/functions/image-classification/ +cargo build --release --target wasm32-wasi +``` + +Copy the build artifacts to the `api` folder. + +```bash +cp target/wasm32-wasi/release/classify.wasm ../../ +``` + +Again, the [`api/pre.sh`](https://github.com/second-state/vercel-wasm-runtime/blob/tensorflow/api/pre.sh) script installs WasmEdge runtime and its Tensorflow dependencies in this application. It also compiles the `classify.wasm` bytecode program to the `classify.so` native shared library at the time of deployment. + +The [`api/hello.js`](https://github.com/second-state/vercel-wasm-runtime/blob/tensorflow/api/hello.js) file conforms Vercel serverless specification. It loads the WasmEdge runtime, starts the compiled WebAssembly program in WasmEdge, and passes the uploaded image data via `STDIN`. Notice [`api/hello.js`](https://github.com/second-state/vercel-wasm-runtime/blob/tensorflow/api/hello.js) runs the compiled `classify.so` file generated by [`api/pre.sh`](https://github.com/second-state/vercel-wasm-runtime/blob/tensorflow/api/pre.sh) for better performance. + +```javascript +const fs = require('fs'); +const { spawn } = require('child_process'); +const path = require('path'); + +module.exports = (req, res) => { + const wasmedge = spawn( + path.join(__dirname, 'wasmedge-tensorflow-lite'), + [path.join(__dirname, 'classify.so')], + { env: { LD_LIBRARY_PATH: __dirname } }, + ); + + let d = []; + wasmedge.stdout.on('data', (data) => { + d.push(data); + }); + + wasmedge.on('close', (code) => { + res.setHeader('Content-Type', `text/plain`); + res.send(d.join('')); + }); + + wasmedge.stdin.write(req.body); + wasmedge.stdin.end(''); +}; +``` + +You can now [deploy your forked repo to Vercel](https://vercel.com/docs/git#deploying-a-git-repository) and have a web app for subject classification. + +Next, it's your turn to use [the vercel-wasm-runtime repo](https://github.com/second-state/vercel-wasm-runtime) as a template to develop your own Rust serverless functions in Vercel. Looking forward to your great work. diff --git a/i18n/zh/docusaurus-plugin-content-docs/current/start/usage/use-cases.md b/i18n/zh/docusaurus-plugin-content-docs/current/start/usage/use-cases.md index d9a6f355..3cd2b568 100644 --- a/i18n/zh/docusaurus-plugin-content-docs/current/start/usage/use-cases.md +++ b/i18n/zh/docusaurus-plugin-content-docs/current/start/usage/use-cases.md @@ -8,9 +8,9 @@ Featuring AOT compiler optimization, WasmEdge is one of the fastest WebAssembly - WasmEdge provides a lightweight, secure and high-performance runtime for microservices. It is fully compatible with application service frameworks such as Dapr, and service orchestrators like Kubernetes. WasmEdge microservices can run on edge servers, and have access to distributed cache, to support both stateless and stateful business logic functions for modern web apps. Also related: Serverless function-as-a-service in public clouds. -- [Serverless SaaS (Software-as-a-Service)](../../embed/use-case/serverless-saas.md) functions enables users to extend and customize their SaaS experience without operating their own API callback servers. The serverless functions can be embedded into the SaaS or reside on edge servers next to the SaaS servers. Developers simply upload functions to respond to SaaS events or to connect SaaS APIs. +- [Serverless SaaS (Software-as-a-Service)](./serverless/serverless-platforms) functions enables users to extend and customize their SaaS experience without operating their own API callback servers. The serverless functions can be embedded into the SaaS or reside on edge servers next to the SaaS servers. Developers simply upload functions to respond to SaaS events or to connect SaaS APIs. -- [Smart device apps](../../embed/use-case/wasm-smart-devices.md) could embed WasmEdge as a middleware runtime to render interactive content on the UI, connect to native device drivers, and access specialized hardware features (i.e, the GPU for AI inference). The benefits of the WasmEdge runtime over native-compiled machine code include security, safety, portability, manageability, and developer productivity. WasmEdge runs on Android, OpenHarmony, and seL4 RTOS devices. +- [Smart device apps](./wasm-smart-devices.md) could embed WasmEdge as a middleware runtime to render interactive content on the UI, connect to native device drivers, and access specialized hardware features (i.e, the GPU for AI inference). The benefits of the WasmEdge runtime over native-compiled machine code include security, safety, portability, manageability, and developer productivity. WasmEdge runs on Android, OpenHarmony, and seL4 RTOS devices. - WasmEdge could support high performance DSLs (Domain Specific Languages) or act as a cloud-native JavaScript runtime by embedding a JS execution engine or interpreter. diff --git a/i18n/zh/docusaurus-plugin-content-docs/current/start/usage/wasm-smart-devices.md b/i18n/zh/docusaurus-plugin-content-docs/current/start/usage/wasm-smart-devices.md new file mode 100644 index 00000000..17cd9ad7 --- /dev/null +++ b/i18n/zh/docusaurus-plugin-content-docs/current/start/usage/wasm-smart-devices.md @@ -0,0 +1,14 @@ +--- +sidebar_position: 4 +--- + +# WasmEdge On Smart Devices + +Smart device apps could embed WasmEdge as a middleware runtime to render interactive content on the UI, connect to native device drivers, and access specialized hardware features (i.e., the GPU for AI inference). The benefits of the WasmEdge runtime over native-compiled machine code include security, safety, portability, manageability, OTA upgradability, and developer productivity. WasmEdge runs on the following device OSes. + +- [Android](/category/build-and-run-wasmedge-on-android) +- [OpenHarmony](../../contribute/source/os/openharmony.md) +- [Raspberry Pi](../../contribute/source/os/raspberrypi.md) +- [The seL4 RTOS](../../contribute/source/os/sel4.md) + +With WasmEdge on both the device and the edge server, we can support [isomorphic Server-Side Rendering (SSR)](../../develop/rust/ssr.md) and [microservices](../../start/build-and-run/docker_wasm.md#deploy-the-microservice-example) for rich-client mobile applications that are both portable and upgradeable. diff --git a/i18n/zh/docusaurus-plugin-content-docs/current/start/usage/web-app.md b/i18n/zh/docusaurus-plugin-content-docs/current/start/usage/web-app.md new file mode 100644 index 00000000..ba737d89 --- /dev/null +++ b/i18n/zh/docusaurus-plugin-content-docs/current/start/usage/web-app.md @@ -0,0 +1,101 @@ +--- +sidebar_position: 9 +--- + +# A simple WebAssembly example + +In this article, I will show you how to build a container image for a WebAssembly application. It can then be started and managed by Kubernetes ecosystem tools, such as CRI-O, Docker, crun, and Kubernetes. + +## Prerequisites + +> If you simply want a wasm bytecode file to test as a container image, you can skip the building process and just [download the wasm file here](https://github.com/second-state/wasm-learning/blob/master/cli/wasi/wasi_example_main.wasm). + +If you have not done so already, follow these simple instructions to [install Rust](https://www.rust-lang.org/tools/install). + +## Download example code + +```bash +git clone https://github.com/second-state/wasm-learning +cd wasm-learning/cli/wasi +``` + +## Build the WASM bytecode + +```bash +rustup target add wasm32-wasi +cargo build --target wasm32-wasi --release +``` + +The wasm bytecode application is in the `target/wasm32-wasi/release/wasi_example_main.wasm` file. You can now publish and use it as a container image. + +## Apply executable permission on the Wasm bytecode + +```bash +chmod +x target/wasm32-wasi/release/wasi_example_main.wasm +``` + +## Create Dockerfile + +Create a file called `Dockerfile` in the `target/wasm32-wasi/release/` folder with the following content: + +```dockerfile +FROM scratch +ADD wasi_example_main.wasm / +CMD ["/wasi_example_main.wasm"] +``` + +## Create container image with annotations + +> Please note that adding self-defined annotation is still a new feature in buildah. + +The `crun` container runtime can start the above WebAssembly-based container image. But it requires the `module.wasm.image/variant=compat-smart` annotation on the container image to indicate that it is a WebAssembly application without a guest OS. You can find the details in [Official crun repo](https://github.com/containers/crun/blob/main/docs/wasm-wasi-example.md). + +To add `module.wasm.image/variant=compat-smart` annotation in the container image, you will need the latest [buildah](https://buildah.io/). Currently, Docker does not support this feature. Please follow [the install instructions of buildah](https://github.com/containers/buildah/blob/main/install.md) to build the latest buildah binary. + +### Build and install the latest buildah on Ubuntu + +On Ubuntu zesty and xenial, use these commands to prepare for buildah. + +```bash +sudo apt-get -y install software-properties-common + +export OS="xUbuntu_20.04" +sudo bash -c "echo \"deb https://download.opensuse.org/repositories/devel:/kubic:/libcontainers:/stable/$OS/ /\" > /etc/apt/sources.list.d/devel:kubic:libcontainers:stable.list" +sudo bash -c "curl -L https://download.opensuse.org/repositories/devel:/kubic:/libcontainers:/stable/$OS/Release.key | apt-key add -" + +sudo add-apt-repository -y ppa:alexlarsson/flatpak +sudo apt-get -y -qq update +sudo apt-get -y install bats git libapparmor-dev libdevmapper-dev libglib2.0-dev libgpgme-dev libseccomp-dev libselinux1-dev skopeo-containers go-md2man containers-common +sudo apt-get -y install golang-1.16 make +``` + +Then, follow these steps to build and install buildah on Ubuntu. + +```bash +mkdir -p ~/buildah +cd ~/buildah +export GOPATH=`pwd` +git clone https://github.com/containers/buildah ./src/github.com/containers/buildah +cd ./src/github.com/containers/buildah +PATH=/usr/lib/go-1.16/bin:$PATH make +cp bin/buildah /usr/bin/buildah +buildah --help +``` + +### Create and publish a container image with buildah + +In the `target/wasm32-wasi/release/` folder, do the following. + +```bash +$ sudo buildah build --annotation "module.wasm.image/variant=compat-smart" -t wasm-wasi-example . +# make sure docker is install and running +# systemctl status docker +# to make sure regular user can use docker +# sudo usermod -aG docker $USER +# newgrp docker + +# You may need to use docker login to create the `~/.docker/config.json` for auth. +$ sudo buildah push --authfile ~/.docker/config.json wasm-wasi-example docker://docker.io/wasmedge/example-wasi:latest +``` + +That's it! Now you can try to run it in [CRI-O](../../develop/deploy/cri-runtime/crio-crun.md) or [Kubernetes](../../develop/deploy/kubernetes/kubernetes-cri-o.md)!