towhee
proposals
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towhee | proposals | |
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26 | 60 | |
2,989 | 63 | |
1.8% | - | |
8.6 | 4.0 | |
3 months ago | 1 day ago | |
Python | ||
Apache License 2.0 | MIT License |
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towhee
- FLaNK Stack Weekly for 14 Aug 2023
- Welcome to generate your embeddings with Towhee
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Vector database built for scalable similarity search
As another commenter noted, Milvus is overkill and a "bit much" if you're learning/playing.
A good intro to the field with progression towards a full Milvus implementation could be starting with towhee[0] (which is also supported by Milvus).
towhee has an example to do exactly what you want with CLIP[1].
[0] - https://towhee.io/
[1] - https://github.com/towhee-io/examples/tree/main/image/text_i...
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What Is DocArray?
The description of this is kind of confusing but I think the easiest way to understand it is that it is a data processing pipeline of sorts. Take unstructured data and apply transformation and computation. A similar project to this is Towhee (https://github.com/towhee-io/towhee). This project tries to simplify unstructured data processing and provides pretrained models and pipelines from their hub.
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[P] My co-founder and I quit our engineering jobs at AWS to build “Tensor Search”. Here is why.
Milvus also has incredible flexibility when it comes to choosing an indexing strategy, and we also have a library specifically meant to help vectorize a variety of data called Towhee (https://github.com/towhee-io/towhee).
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Deep Dive into Real-World Image Search Engine with Python
Benchmarking the models with towhee is as simple as:
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A quick tip on DataFrame.apply
The project's homepage is https://github.com/towhee-io/towhee, and you can find more about towhee by going through the documents.
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Build an Image Search Engine in Minutes
I made a tutorial for building an image search engine with python. The code example is as simple as 10 lines of code, using Towhee and Milvus To put images into the search engine:
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Any good libraries for feature extraction?
Traditionally, I've done this through PyTorch by adding a hook, but this requires knowledge of the model itself (i.e. model arch and layer names). I found https://github.com/Hironsan/awesome-embedding-models but it didn't provide many CV-focused open-source projects. There's also https://github.com/towhee-io/towhee which is great but more targeted towards application development.
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A python framework for unstructured data processing
You can check the result from the tutorial.
proposals
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Is there an alternative for Airflow for running thousands of dynamic tasks?
Check out temporal.io open source project. It was built at Uber for large scale business-level processes. So any data pipelines are low-rate use cases by definition.
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KuFlow as a Temporal.io-based Workflow Orchestrator
With KuFlow it is also possible to work with serverless workflows apart from Temporal.io, we explain it in this blog entry, but in summary, almost as a no-code tool, the correct use It would be a rather low-code tool; in just a matter of minutes with our drag-and-drop tool, you can have a workflow that interacts with one or more users of the organization.
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How to handle background jobs in Rust?
Otherwise you may want to look into Kafka or Fluvio to ensure that task runs at least once. If you're doing something like batch operations as a background task, Temporal is another great option.
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No-code or Workflow as code? Better both
The runtime is developed using Temporal, which is one of the main tools that we are currently using at KuFlow. Thanks to, all the workflow executions are robust: your application will be durable, reliable, and scalable.
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Temporal Programming, a new name for an old paradigm
Hmmm I got confused by the name. I thought it's related to https://temporal.io/
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Possible innovations in Event Sourcing frameworks.
Have you looked at temporal.io open source platform? It uses event sourcing as an implementation detail. But it greatly simplifies the user experience compared to "raw event sourcing."
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After Airflow. Where next for DE?
Rewrite Airflow on top of temporal.io. This way, you get unlimited scalability and very high reliability out of the box and would be able to innovate on the features that matter for DE.
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Show HN: Retool Workflows – Cronjobs, but better
Hi all, founder @ Retool here. Over the past year, we’ve been working on Retool Workflows; a fast way for engineers to automate tasks with code. We started building the product because we ourselves (as developers) were looking for something in-between writing cron jobs (which involves a lot of boilerplate) and Zapier (which oftentimes isn’t customizable enough, since it doesn’t _really_ support writing code).
Workflows is a code-first automation tool: you’re _expected_ to write code, but we handle all the boilerplate for you. For example: out-of-the-box integration with 80+ resources (you probably don’t want to be trying to figure out OAuth 2.0 with Salesforce!), monitoring and observability (so you can see the output of every run in the past, and immediately be notified if something goes wrong), and permissions (e.g. some Okta groups can see the outputs of Workflows, but can’t change the code itself).
Right now, the product is cloud-only, but we’re hard at work at an on-prem, self-hosted version (in a Docker image). If you’re interested in that version, feel free to email us at [email protected]. We aim to get it out in the next few weeks. Self-hosted Retool is responsible for a large portion of our usage today, and we’re excited to be supporting Workflows too.
All Retool plans now include 1GB of Workflows throughput, which we think is quite generous (80% of active Workflows users are below 1GB). We don’t bill by run at all, so you’re welcome to run as many workflows as you want.
We use a bunch of interesting technology for Workflows; we are, for example, using Temporal (https://temporal.io/) under the hood. That’s something we’re going to be writing a blog post about later. (We’ve been hard at work on the launch, hah.)
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How KuFlow supports Temporal as a worfkows engine for our processes?
In such a diverse world, it would be boring to have a single way of doing things. That's why at KuFlow we support different ways to implement the logic of our processes and tasks. And in this post, we will talk about one of them, the orchestration through Temporal, which gives us a powerful way to manage our workflows.
- Library for manage tasks when make a workflow automation.
What are some alternatives?
qdrant - Qdrant - High-performance, massive-scale Vector Database for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/
conductor - Conductor is a microservices orchestration engine.
Milvus - A cloud-native vector database, storage for next generation AI applications
temporalite-archived - An experimental distribution of Temporal that runs as a single process
examples - Analyze the unstructured data with Towhee, such as reverse image search, reverse video search, audio classification, question and answer systems, molecular search, etc.
zenml - ZenML 🙏: Build portable, production-ready MLOps pipelines. https://zenml.io.
PySceneDetect - :movie_camera: Python and OpenCV-based scene cut/transition detection program & library.
seldon-core - An MLOps framework to package, deploy, monitor and manage thousands of production machine learning models
AI - Artificial Intelligence Projects
kubemq-community - KubeMQ is a Kubernetes native message queue broker
pgvector - Open-source vector similarity search for Postgres
nextjs-cron - Cron jobs with Github Actions for Next.js apps on Vercel▲