asgiref
NumPy
asgiref | NumPy | |
---|---|---|
17 | 272 | |
1,392 | 26,413 | |
0.9% | 1.1% | |
7.5 | 10.0 | |
about 1 month ago | 2 days ago | |
Python | Python | |
BSD 3-clause "New" or "Revised" License | GNU General Public License v3.0 or later |
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.
asgiref
-
Building Fast APIs with FastAPI: A Comprehensive Guide
uvicorn is an ASGI server that is recommended for running FastAPI applications.
-
You might want async in your project
I can't seem to be able to edit on mobile. OP either meant this, or its variation, such as async_to_sync and sync_to_async.
https://github.com/django/asgiref/blob/main/asgiref/sync.py
Ofc this is a python example. I have no idea how it works in different languages.
-
How to Dockerize and Deploy a Fast API Application to Kubernetes Cluster
FastAPI is a popular Python Web framework that developers use to create RESTful APIs. It is based on Pydantic and Python-type hints that assist in the serialization, deserialization, and validation of data. In this tutorial, we will use FastAPI to create a simple "Hello World" application. We test and run the application locally. FastAPI requires a ASGI server to run the application production such as Uvicorn.
- Quart is an async Python web microframework
-
Writing a chat application in Django 4.2 using async StreamingHttpResponse
Look at the intended semantics [1], and then read the implementation [2]. Can you figure out if the implementation is correct? Can you infer the possible limitations of the approach at glance? Can your async library actually handle being called with multiple event loops installed?
I have zero trust in this code and I have been bitten by fixes to this library that introduced deadlocks in my own code.
[1] https://github.com/django/asgiref#synchronous-code--threads.
[2] https://github.com/django/asgiref/blob/main/asgiref/sync.py#...
- Is it really advisable to try to run fastapi with predominantly sync routes in a real world application?
- Building GitHub with Ruby on Rails
-
Building a Realtime Chat App with Django Channels and WebSockets
Using WebSockets in Django utilizes asynchronous Python and Django channels, making the process straightforward. Using Django channels, you can create an ASGI server, and then create a group where users can send text messages to all the other users in the group in real time. This way, you are not communicating with a particular user, but with a group, multiple users can be added.
-
Starlite to drop Starlette
If you're interested in the architecture itself I recommend you start by making yourself familiar with [ASGI specification](https://asgi.readthedocs.io/en/latest/) .
-
Starlite Updates
We switched to using strong typing derived from the asgiref for typing ASGI types, which makes Starlite the strongest type framework of its kind.
NumPy
-
Dot vs Matrix vs Element-wise multiplication in PyTorch
In NumPy with @, dot() or matmul():
- NumPy 2.0.0 Beta1
-
Element-wise vs Matrix vs Dot multiplication
In NumPy with * or multiply(). ` or multiply()` can multiply 0D or more D arrays by element-wise multiplication.
- JSON dans les projets data science : Trucs & Astuces
-
JSON in data science projects: tips & tricks
Data science projects often use numpy. However, numpy objects are not JSON-serializable and therefore require conversion to standard python objects in order to be saved:
-
Introducing Flama for Robust Machine Learning APIs
numpy: A library for scientific computing in Python
- help with installing numpy, please
-
A Comprehensive Guide to NumPy Arrays
Python has become a preferred language for data analysis due to its simplicity and robust library ecosystem. Among these, NumPy stands out with its efficient handling of numerical data. Let’s say you’re working with numbers for large data sets—something Python’s native data structures may find challenging. That’s where NumPy arrays come into play, making numerical computations seamless and speedy.
-
Why do all the popular projects use relative imports in __init__ files if PEP 8 recommends absolute?
I was looking at all the big projects like numpy, pytorch, flask, etc.
-
NumPy 2.0 development status & announcements: major C-API and Python API cleanup
I wish the NumPy devs would more thoroughly consider adding full fluent API support, e.g. x.sqrt().ceil(). [Issue #24081]
What are some alternatives?
uvicorn - An ASGI web server, for Python. 🦄
SymPy - A computer algebra system written in pure Python
uvloop - Ultra fast asyncio event loop.
Pandas - Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
quart - An async Python micro framework for building web applications.
blaze - NumPy and Pandas interface to Big Data
mangum - AWS Lambda support for ASGI applications
SciPy - SciPy library main repository
quart - An async Python micro framework for building web applications.
Numba - NumPy aware dynamic Python compiler using LLVM
fastapi - FastAPI framework, high performance, easy to learn, fast to code, ready for production
Nim - Nim is a statically typed compiled systems programming language. It combines successful concepts from mature languages like Python, Ada and Modula. Its design focuses on efficiency, expressiveness, and elegance (in that order of priority).