Adds PWD 1-click deploy

This commit is contained in:
Alicia Sykes
2021-06-11 13:38:55 +01:00
parent dbfbcf3284
commit 50bbce450f
2 changed files with 13 additions and 3 deletions

View File

@@ -16,7 +16,7 @@
The quickest way to get started on any system is with Docker, and Dashy is available though [Docker Hub](https://hub.docker.com/r/lissy93/dashy). You will need [Docker](https://docs.docker.com/get-docker/) installed on your system.
To test it out, just run: `docker run -p 8080:80 lissy93/dashy`, then open your browser and visit `http://localhost:8080`.
To test it out, just run: `docker run -p 8080:80 lissy93/dashy`, then open your browser and visit `http://localhost:8080`. Or to try it out in the cloud, you can deploy to PWD by [clicking here](https://labs.play-with-docker.com/?stack=https://raw.githubusercontent.com/Lissy93/dashy/master/docker-compose.yml).
To configure Dashy with your own services, and customize it to your liking, you will need to write a config file, and pass it to the Docker container as a volume.
@@ -64,11 +64,13 @@ Healthchecks are configured to periodically check that Dashy is up and running c
Now that you've got Dashy running, there are a few commands that you need to know.
The following commands are defined in the [`package.json`](https://github.com/Lissy93/dashy/blob/master/package.json#L5) file, and are run with `yarn`. If you prefer, you can use NPM, just replace instances of `yarn` with `npm run`. If you are using Docker, then you will need to precede each command with `docker exec -it [container-id]`, where container ID can be found by running `docker ps`. For example `docker exec -it 26c156c467b4 yarn build`
The following commands are defined in the [`package.json`](https://github.com/Lissy93/dashy/blob/master/package.json#L5) file, and are run with `yarn`. If you prefer, you can use NPM, just replace instances of `yarn` with `npm run`. If you are using Docker, then you will need to precede each command with `docker exec -it [container-id]`, where container ID can be found by running `docker ps`. For example `docker exec -it 26c156c467b4 yarn build`.
- **`yarn build`** - In the interest of speed, the application is pre-compiled, this means that the config file is read during build-time, and therefore the app needs to rebuilt for any new changes to take effect. Luckily this is very straight forward. Just run `yarn build` or `docker exec -it [container-id] yarn build`.
- **`yarn build`** - In the interest of speed, the application is pre-compiled, this means that the config file is read during build-time, and therefore the app needs to rebuilt for any new changes to take effect. Luckily this is very straight forward. Just run `yarn build` or `docker exec -it [container-id] yarn build`
- **`yarn validate-config`** - If you have quite a long configuration file, you may wish to check that it's all good to go, before deploying the app. This can be done with `yarn validate-config` or `docker exec -it [container-id] yarn validate-config`. Your config file needs to be in `/public/conf.yml` (or within your Docker container at `/app/public/conf.yml`). This will first check that your YAML is valid, and then validates it against Dashy's [schema](https://github.com/Lissy93/dashy/blob/master/src/utils/ConfigSchema.js).
- **`yarn health-check`** - Checks that the application is up and running on it's specified port, and outputs current status and response times. Useful for integrating into your monitoring service, if you need to maintain high system availability
- **`yarn build-watch`** - If you find yourself making frequent changes to your configuration, and do not want to have to keep manually rebuilding, then this option is for you. It will watch for changes to any files within the projects root, and then trigger a rebuild. Note that if you are developing new features, then `yarn dev` would be more appropriate, as it's significantly faster at recompiling (under 1 second), and has hot reloading, linting and testing integrated
- **`yarn build-and-start`** - Builds the app, runs checks and starts the production server. Commands are run in parallel, and so is faster than running them in independently
## Updating