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Zappa | pydantic | |
---|---|---|
36 | 167 | |
3,040 | 18,521 | |
2.5% | 3.8% | |
7.5 | 9.8 | |
8 days ago | 1 day ago | |
Python | Python | |
MIT License | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Zappa
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Jets: The Ruby Serverless Framework
If people aren't familiar, there's a similar project for Python that's fantastic: https://github.com/zappa/Zappa
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Building serverless websites (lambdas written with python) - do I use FastAPI or plain old python?
Chalice was a consequence, a reaction from AWS to the release of (Zappa Framework)[https://github.com/zappa/Zappa] that provide a very good alternative to migrate very quickly a Django/Flask or any WSGI compliant solution in Python.
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Best way to host Django DRF on AWS? (so many competing options)
Use Zappa https://github.com/zappa/Zappa and host as a Lambda, simple setup and deployment, Lambda only costs when processing requests, no servers to mess around with
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How to deploy a project from git lab backend where I used django on backend and database
One of my favorite options that is probably the most cost-effective is to deploy using a 'severless' model on AWS Lambda using zappa which supports deploying Python webapps to AWS in this way. Zappa also makes it super easy to deploy in just a couple commands! The README includes instructions for everything you might need, including handling sensitive information like your database passwords, running django management commands, setting up DNS, etc.
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I’m a Brazilian salesforce developer and want to work with django stack. Any tips?
Deployment works nicely with Docker. I often use AWS AppRunner because it's really easy and just scales. Some people use AWS Lambda with Zappa but I don't recommend it unless you really want to spend less than $15 a month. You will probably need Django Storages to save uploads to an S3 bucket. At some stage you might want to put a CloudFront distribution in front of everything but the configuration of the caching behaviour might be a bit confusing when you do it the first time.
- lambda API deployment
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Why or why not use AWS Lambda instead of a web framework for your REST APIs? (Business projects)
It doesn't have to be an either-or! I have several apps in production that were developed on Django or Flask, and deployed to Lambda using Zappa.
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Backend Server with Django Rest API
If you need a relational DB, you can use AWS Aurora or RDS and use cloud functions ('lambda' in AWS) that you can invoke with HTTP to process the document first. Zappa will do a lot of the configuration for you if you go that route.
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Easiest/Best way to deploy django to AWS?
Lambda + API gateway, this library bundles a Django application into a lambda https://github.com/zappa/Zappa . 1 million free invokes from aws, scale to zero, plugs into your RDS
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We clone a running VM in 2 seconds
I use Zappa, it just schedules a frequent execution of the lambda: https://github.com/zappa/Zappa#keeping-the-server-warm
pydantic
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Advanced RAG with guided generation
First, note the method prefix_allowed_tokens_fn. This method applies a Pydantic model to constrain/guide how the LLM generates tokens. Next, see how that constrain can be applied to txtai's LLM pipeline.
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utype VS pydantic - a user suggested alternative
2 projects | 15 Feb 2024
utype is a concise alternative of pydantic with simplified parameters and usages, supporting both sync/async functions and generators parsing, and capable of using native logic operators to define logical types like AND/OR/NOT, also provides custom type parsing by register mechanism that supports libraries like pydantic, attrs and dataclasses
- Pydantic v2 ruined the elegance of Pydantic v1
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Ask HN: Pydantic has too much deprecation. Why is it popular?
I like some of the changes from v1 to v2. But then you have something like this [0] removed from the library without proper documentation or replacement, resulting in ugly workarounds in the link that wont' work properly.
- OpenAI uses Pydantic for their ChatCompletions API
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🍹GinAI - Cocktails mixed with generative AI
The easiest implementation I found was to use a PyDantic class for my target schema — and use that as a parameter for the method call to “ChatCompletion.create()”. Here’s a fragment of the GinAI Python classes used.
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FastStream: Python's framework for Efficient Message Queue Handling
Also, FastStream uses Pydantic to parse input JSON-encoded data into Python objects, making it easy to work with structured data in your applications, so you can serialize your input messages just using type annotations.
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Introducing FastStream: the easiest way to write microservices for Apache Kafka and RabbitMQ in Python
Pydantic Validation: Leverage Pydantic's validation capabilities to serialize and validate incoming messages
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Cannot get Langchain to work
Not sure if it is exactly related, but there is an open issue on Github for that exact message.
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FastAPI 0.100.0:Release Notes
Well the performance increase is so huge because pydantic1 is really really slow. And for using rust, I'd have expected more tbh…
I've been benchmarking pydantic v2 against typedload (which I write) and despite the rust, it still manages to be slower than pure python in some benchmarks.
The ones on the website are still about comparing to v1 because v2 was not out yet at the time of the last release.
pydantic's author will refuse to benchmark any library that is faster (https://github.com/pydantic/pydantic/pull/3264 https://github.com/pydantic/pydantic/pull/1525 https://github.com/pydantic/pydantic/pull/1810) and keep boasting about amazing performances.
On pypy, v2 beta was really really really slow.
What are some alternatives?
fastapi - FastAPI framework, high performance, easy to learn, fast to code, ready for production
Cerberus - Lightweight, extensible data validation library for Python
mangum - AWS Lambda support for ASGI applications
nexe - 🎉 create a single executable out of your node.js apps
chalice - Python Serverless Microframework for AWS
msgspec - A fast serialization and validation library, with builtin support for JSON, MessagePack, YAML, and TOML
Poetry - Python packaging and dependency management made easy
SQLAlchemy - The Database Toolkit for Python
aws-sqs-jobs-processer - Serverless jobs processor on AWS
sqlmodel - SQL databases in Python, designed for simplicity, compatibility, and robustness.
sample-django-docker - A sample of using Django with Docker and docker-compose
mypy - Optional static typing for Python