postgres_exporter VS tinygrad

🌟 Support on GitHub Website: http://prometheus.io/community

An easy way forward would be to export it as a metric through something like https://github.com/prometheus-community/postgres_exporter and then set up an alert on the metric reporting >= 1.

We use postgres as RDBMS for our database needs and if you guys are already using postgres_exporter(https://github.com/prometheus-community/postgres_exporter) for exporting PostgreSQL server metrics to prometheus, then you can surely give coroot-pg-agent a try.

It’s not that simple…https://github.com/prometheus-community/postgres_exporter :)

New server and want a basic config for a production server? (I'm assuming brand new app here, no prior monitoring and knowledge on what to tune.)

Use this to get the values you need https://pgtune.leopard.in.ua/#/ .

This saved me a lot of headaches, and it just gets the server into a good enough state from which you can observe and optimise later.

I'd also add in monitoring early, add a Prometheus exporter https://github.com/prometheus-community/postgres_exporter and alerts https://awesome-prometheus-alerts.grep.to/rules#postgresql . There are a few Grafana dashboards available for the prometheus exporter, start with those.

I’m setting up some monitoring for a PostgreSQL instance and came across two seemingly mature projects for Prometheus monitoring, those being postgres_exporter and pg_exporter . From the outside it looks like postgres_exporter is linked from the Prometheus pages, but has fewer recent updates, and pg_exporter has a smaller user base but a more active development community.

PostgreSQL monitoring with Prometheus is an easy thing to do thanks to the PostgreSQL Exporter.

Thanks, that is probably a better fit. I was looking to use this part of the PostgreSQL exporter: https://github.com/prometheus-community/postgres_exporter/blob/master/queries.yaml and just not care about the metrics about Postgres itself.

The postgres_exporter provides the data about the state of replication. Then you have rules like these to validate the data.

Not sure if I completely understand what "Ryzen AI" does, but Tinygrad for example has some limited support for RDNA3[0]. It isn't quite there yet in matters of performance though, as you can read in the comments of that file.

There's also a small tutorial by AMD on how to use the WMMA intrinsic[1] using AMD's hipcc[2] compiler. Documentation is sparse kinda sparse, but the instruction set is not huge. The RDNA3 ISA guide[3] might also be helpful (and only a fraction of the pages are relevant.)

0. https://github.com/tinygrad/tinygrad/blob/master/extra/gemm/...

1. https://gpuopen.com/learn/wmma_on_rdna3/

2. https://github.com/ROCm/HIPCC

3. https://www.amd.com/content/dam/amd/en/documents/radeon-tech...

This post describes how I added automatic differentiation to Tensorken. Tensorken is my attempt to build a fully featured yet easy-to-understand and hackable implementation of a deep learning library in Rust. It takes inspiration from the likes of PyTorch, Tinygrad, and JAX.

what do you think about tinygrad? I think its a good example of growing and well written, (partially) well documented library with many close to reference implementations

The idea of model architecture making fast hardware design easier is what makes https://github.com/tinygrad/tinygrad so interesting.

🌟 Support on GitHub Website: https://tinygrad.org/

Before we begin, make sure you have TinyGrad and the required dependencies installed. You can find the installation instructions here.

Try to get something like tinygrad[1] running locally, that way you can tweak things a bit run it again and see how it performs. While doing this you'll pick up most of the concepts and get a feeling of how things work. Also, take a look at projects like llama.cpp[2], you don't have to fully understand what's going on here, tho.

You may need some intermediate knowledge of linear algebra and this thing called "data science" nowadays, which is pretty much knowing how to mangle data and visualize it.

Try creating a small model on your own, it doesn't have to be super fancy just make sure it does something you want it to do. And then ... you'll probably could go on your own then.

1: https://github.com/tinygrad/tinygrad

2: https://github.com/ggerganov/llama.cpp