ort VS librdkafka

To solve this, we built a native extension in Edge Runtime that enables using ONNX runtime via the Rust interface. This was made possible thanks to an excellent Rust wrapper called Ort:

hey hn, supabase ceo here

As the post points out, this comes in 2 parts:

1. Embeddings models for RAG workloads (specifically pgvector). Available today.

2. Large Language Models for GenAI workloads. This will be progressively rolled out as we get our hands on more GPUs.

We've always had a focus on architectures that can run anywhere (especially important for local dev and self-hosting). In that light, we've found that the Ollama[0] tooling is really unbeatable. I heard one of our engineers explain it like "docker for models" which I think is apt.

To support models that work best with GPUs, we're running them with Fly GPUs - pretty much this: https://fly.io/blog/scaling-llm-ollama (and then we stitch a native API around it). The plan is that you will be able to "BYO" model server and point the Edge Runtime towards it using simple env vars / config.

We've also made improvements for CPU models. We built a native extension in Edge Runtime that enables using ONNX runtime via the Rust interface. This was made possible thanks to an excellent Rust wrapper, Ort[1]. We have the models stored on disk, so there is no downloading, cold-boot, etc.

The thing I most like about this set up is that you can now use Edge Functions like background workers for your Postgres database, offloading heavy compute for generating embeddings. For example, you can trigger the worker when a user inserts some text, and then the worker will asynchronously create the embedding and store it back into your database.

I'll be around if there are any questions.

[0] ollama.com

[1] Ort: https://github.com/pykeio/ort

In the quest for more efficient solutions, the ONNX runtime emerged as a beacon of performance. The decision to transition from Typescript to Rust was an unconventional yet pivotal one. Driven by Rust's robust parallel processing capabilities using Rayon and seamless integration with ONNX through the ort crate, Repo-Query unlocked a realm of unparalleled efficiency. The result? A transformation from sluggish processing to, I have to say it, blazing-fast performance.

ort - ONNX runtime library.

Our main model in Rust is a deep neural network, using ONNX via the ort rust bindings. The application is some particular applications of process automation.

You could try ort https://github.com/pykeio/ort It looks like it's in active development and supports GPU inference

I would't try to distribute your ml models with the typical frameworks, especially not with python. Have you looked in to ONNX?For example: https://github.com/pykeio/ort

recent PR: https://github.com/confluentinc/librdkafka/pull/4275

# Kafka configuration # https://github.com/confluentinc/librdkafka/blob/master/CONFIGURATION.md bootstrap.servers= security.protocol=SASL_SSL sasl.mechanisms=PLAIN sasl.username= sasl.password= compression.type=gzip compression.level=9 statistics.interval.ms=1000

But what about Kafka producer clients in other languages? The excellent librdkafka project is a C/C++ implementation of Kafka clients and is widely used for non-JVM Kafka applications. Additionally, Kafka clients in other languages (Python, C#) build on top of it. The default partitioner for librdkafka uses the CRC32 hash function to get the correct partition for a key.

We could have made some changes at the librdkafka level (see this), but we didn’t really want to pursue this (at least not yet).

You want records with the same key to always land on the same partition, so you need all the clients to use the same hashing algorithm. The easiest way to do that is to make sure the librdkafka client uses the java compatible murmur2_random hash algorithm. See “Partitioner” section here: https://github.com/confluentinc/librdkafka/blob/master/CONFIGURATION.md

As my first "real world" (ish) project in Vlang, I'm trying to copy https://github.com/confluentinc/confluent-kafka-go, which is a Go wrapper for Kafka C client library, https://github.com/edenhill/librdkafka

If you're using Kafka in a Node.js app, it's likely that you'll need node-rdkafka. This is a library that wraps the librdkafka library and makes it available in Node.js. According to the project's README, "All the complexity of balancing writes across partitions and managing (possibly ever-changing) brokers should be encapsulated in the library."

# Parse the configuration. # See https://github.com/edenhill/librdkafka/blob/master/CONFIGURATION.md config_parser = ConfigParser() config_parser.read_file(args.config_file) config = dict(config_parser['default']) # Create Producer instance producer = Producer(config)

• git clone https://github.com/edenhill/librdkafka.git