We now support @jupyter.org notebooks! Deploy as a static site, run on a schedule, or invoke via REST API.
@ricochet.rsricochet 🐇
@ricochet.rs
Putting R, Julia, and Python in production with 🤍🐇 ricochet.rs #rstats #julialang #python
@ricochet.rs@ricochet.rsOr view all tasks and app workers server wide to keep track of memory hogs 🐷 and long running processes.
@ricochet.rsMonitor your Shiny app or FastAPI usage with live worker monitoring. RAM, CPU, and connections in the UI. All of which are exposed to an internal @prometheus.io 🔥 server.
@ricochet.rs📣 Jupyter notebooks 📓, CPU monitoring ⚙️, and UI improvements and more just shipped in v0.24 of ricochet!
🧵 Take a look below! 👇🏼
Full release notes: docs.ricochet.rs/releases/v0....
@josiah.rsReposted by @ricochet.rsreeeee feeling giddy about the next release of @ricochet.rs live monitoring of app (shiny in this case) processes—max ram, CPU, and connections.
@ricochet.rsThe source code for the example app 👇
github.com/ricochet-rs/...
@ricochet.rsv0.20 of ricochet supports the _server.yml standard which means you can now deploy {plumber2}, {mcptools}, {nanonext}, and {fiery}!
This app is built with {plumber2}:
@ricochet.rsEvery package has its own bespoke way to start an app: - plumber::pr_run() - plumber2::api_run() - shiny::runApp() The _server.yml standard defines a launch_server() function that is in each “engine” (a package 📦) with the same arguments: config, host, port. Keep the messy config to the yaml
@ricochet.rsApp deployment in R got a whole lot simpler thanks to {plumber2}🪠 and the _server.yml standard! 🧵1/4
@josiah.rsReposted by @ricochet.rs🌍 Scalable spatial viz📍 Or, why Lonboard from @developmentseed.org is so fast! 💨 And how R-spatial is embracing the pattern 🐇
TL;DR
- Forsake GeoJSON
- Embrace GeoArrow
✨The inaugural @ricochet.rs blog post!
#rspatial #gischat #databs #rstats
ricochet.rs/blog/high-pe...