CVPR Submission id changed D

When I logged into my Openreview CVPR author console, I found that my submission id has been changed from 9k+ to 42k+ . Interestingly, the openreview has applied some black colored mask on multiple pages of the pdf, probably to hide original id mentioned at the header in every page. Did anyone else notice that??

/r/MachineLearning
https://redd.it/1phygsa

How Pyrefly Works With Pydantic (Video)

https://www.youtube.com/watch?v=zXYpSQB57YI

Pyrefly now includes experimental support for Pydantic, a popular Python library for data validation and parsing. This feature aims to provide improved static type checking and IDE integration for Pydantic models. In this video we cover the basics of what is supported and how you can start using Pyrefly with your Pydantic code!

This is a reupload, the original one that went up last week had an error

/r/Python
https://redd.it/1piabju

messaging system within my project

What’s the best way to integrate simple messaging system within my project?
I found out there are a django-messages but not have any support for newer versions of django

Is there any legit easy to install and ready to operate messaging system without the need to instant messages?

/r/django
https://redd.it/1pic5fu

What is the marker of a project root for uv to create the .venv there?

By default uv will create a venv folder at the project root if none is present. During operation also uv is smart enough to find the correct venv if invoked in a sub folder.

Naively I thought that uv, when invoked, would check for a valid pyproject.toml, and the travnverse the tree path upward until it would find one.

Then I learned about uv workspace and discovered of being wrong:

- a workspace is composed by a parent pyproject.toml and many children pyproject.toml.
- the venv and lock file are created only at the parent folder (all the children share the same dependecies)
- the children pyproject.toml do not shows any information about being a member of the workspace
- only the parent pyproject.toml keeps a list of the child members of the workspace.

I tried to ask few AI, but their response is between too generic or wrong ish. I had a look at the source code, but I'm no familiar with rust at all, and there is a lot of it.

I ask because I kinda need the same functionality, find a specific env file at the root of a project, if present. I got it working, but mostly

/r/Python
https://redd.it/1pij98g

I built a local first tool that uses AST Parsing + Shannon Entropy to sanitize code for AI

I keep hearing about how people are uploading code with personal/confidential information.


So, I built ScrubDuck. It is a local first Python engine, that sanitizes your code before you send it to AI and then can restore the secrets when you paste AI's response back.

What My Project Does (Why it’s not just Regex):

I didn't want to rely solely on pattern matching, so I built a multi-layered detection engine:

1. AST Parsing (ast module): It parses the Python Abstract Syntax Tree to understand context. It knows that if a variable is named db_password, the string literal assigned to it is sensitive, even if the string itself ("correct-horse-battery") looks harmless.
2. Shannon Entropy: It calculates the mathematical randomness of string tokens. This catches API keys that don't match known formats (like generic random tokens) by flagging high-entropy strings.
3. Microsoft Presidio: I integrated Presidio’s NLP engine to catch PII like names and emails in comments.
4. Context-Aware Placeholders: It swaps secrets for tags like <AWS_KEY_1> or <SECRET_VAR_ASSIGNMENT_2>, so the LLM understands what the data is without seeing it.

How it works (Comparison):

1. Sanitize: You highlight code -> The Python noscript analyzes it locally -> Swaps secrets for placeholders -> Saves a map in memory.
2.

/r/Python
https://redd.it/1piick8

Template repo with uv, ruff, pyright, pytest (with TDD support) + CI and QoL Makefile

I've been using python from big monorepos to quick noscripts for a while now and landed on this (fairly opinionated) spec to deal with the common issues primarily around the loose type system.

Aims to not be too strict to facilitate quick iterations, but strict enough to enforce good patterns and check for common mistakes. TDD support with pytest-watch + uv for fast dependency management.


- Sensible defaults for ruff and pyright out of the box configured in pyproject.toml
- Basic uv directory structure, easy to use from quick hacks to published packages
- make watch <PATH> the main feature here - great for TDD, run in a background terminal and by the time you look over/tab tests have re-run for you.
- Makefile with standardised commands like make sync (dependencies) and other QoL.


Anyone looking for template uv repo structures, integrating ruff, pyright and pytest with CI.

Beginners looking for a "ready to go" base that enforces best-practices.

> Quite nice together with claude code or agentic workflows - make them run make check and make test after any changes and it tends to send them in a loop that cleans up common issues. Getting a lot more out of claude code this way.

---

Repo here


Same (outdated) concept

/r/Python
https://redd.it/1pi4rmg

Tasks Managements, Test Runner, Documentation Hub and Time Tracking VSCode/Cursor Extension

What My Project Does

Save any command once and run it forever – Eliminate the need to retype deployment noscripts or build commands.
Run tests without leaving your code – Benefit from automatic test discovery, inline test execution commands, and instant feedback.
Navigate documentation efficiently – Search across all markdown files and jump to specific sections seamlessly.
Track time effortlessly – Utilize automatic timers per Git branch, commit logging, and session management.

Target Audience
Developers that use vscode or cursor.

Comparison
We do have the built in test discovery but it way over complicated and hard to use, you can use the vscode tasks, but it not easy to run and configure, you can use a time tracking tool outside vscode, but now you can do everything without leaving the vscode window.

Free and open source, it is available now on the VS Code Marketplace and Open VSX Registry.
Search "Tasks, Tests & Doc Hub" in your VS Code extensions or access:

Vscode -> https://marketplace.visualstudio.com/items?itemName=LeonardoSouza.command-manager

Cursor -> https://open-vsx.org/extension/LeonardoSouza/command-manager

https://github.com/Leonardo8133/Leos-Shared-Commands

/r/Python
https://redd.it/1piiyzr

Finished My Agentic RAG Tutorial - Everything in Python, Fully Local

### 💡 What My Project Does

After 6 months of intensive study on RAG systems, I've completed a comprehensive educational repository for Agentic RAG. The entire system is in Python and runs fully locally, eliminating API costs!

This is a complete end-to-end example that demonstrates how all the pieces of an advanced agent architecture work together.

---

### 🎯 Target Audience

Anyone curious about how Agentic RAG actually works and wants to learn by building, rather than just reading theory.

### 🆚 The Comparison: Why This Is Different

Most RAG tutorials are scattered or skip the hard parts. This project provides a complete, working implementation that tackles the complexity head-on, offering:

✅ End-to-End Functionality: All components (chunking, vector store, agents) work together seamlessly.
🔒 Zero Dependency Cost: No API keys or expensive cloud services required.
🐍 Pure Python Stack: No JavaScript, just Python and your local machine.

### 🧠 What You'll Learn (Architectural Deep Dive)

This is a deep dive into the architecture, including:

PDF → Markdown conversion
Hierarchical chunking (parent/child)
Hybrid embeddings (dense + sparse)
Vector storage with Qdrant
Query rewriting & human-in-the-loop interaction
Context management with summarization
Multi-agent map-reduce – Parallel sub-queries for complex questions
Fully working agentic RAG with LangGraph
Pure Python UI with Gradio for

/r/Python
https://redd.it/1picui1

Django: what’s new in 6.0
https://adamj.eu/tech/2025/12/03/django-whats-new-6.0/

/r/django
https://redd.it/1piqho8

DTOs or classes with objects and methods

Which is preferred in Python?

DTOs or classes that encapsulate data and methods?

Wondering about this as I'm from a C# background where we rarely used classes that encapsulate data and methods. My current job (Python) goes way heavier on OOP than my previous.

/r/Python
https://redd.it/1piug7a

A Roadmap for AIML from scratch !!

*YT Channels:*

*Beginner Level (for python till classes are sufficient) :*

* Simplilearn
* Edureka
* edX

Advanced Level (for python till classes are sufficient):

* Patrick Loeber
* Sentdex

*Flow:*

coding => python => numpy , pandas , matplotlib, scikit-learn, tensorflow

Stats (till Chi-Square & ANOVA) → Basic Calculus → Basic Algebra

Check out *"stats"* and *"maths"* folder in below link

*Books:*

Check out the *“ML-DL-BROAD”* section on my GitHub: [Github | Books Repo](http://github.com/Rishabh-creator601/Books)

* Hands-On Machine Learning with Scikit-Learn & TensorFlow
* The Hundred-Page Machine Learning Book

>

Please let me How is it ? and if in case i missed any component

/r/Python
https://redd.it/1piwygn

Frist: Property base age, calendar windows and business calendar ages/windows using properties.

🐍 What Frist Does

Frist (a German word related to scheduling) is a package that allows for calculation of ages on different time scales, if dates fit into time/calendar windows (last 3 minutes, this week) and determine age and windows for business/working days.

At no time do you perform any "date math", interact with datetime or date fields or timespans or deltas. Ages are all directly accessed via time scale properties and time windows are accessed via method calls that work across all supported time scales (second, minute, hour, day, week, month, quarter, fiscal quarter, year, fiscal year). Objects in Frist are meant to be immutable.

Time windows are by default "half-open intervals" which are convenient for most cases but there is support for a generalized between that works like the Pandas implementation as well as a thru method that is inclusive of both end points.

All of the initializers allow wide data types. You can pass datetime, date, int/float time stamps and strings, which all are converted to datetimes. Ideally this sets you up to never write conversion code, beyond providing a non-ISO date format for "non-standard" string inputs.

The code is type annotated and fully doc-stringed for a good

/r/Python
https://redd.it/1pilirz

Should I generate images on the client or server side ?

In my django website I have a model called event , that has as attributes background image and overlay image .
My business flow is as follows :
The user uploads a picture
I open the background picture
I paste the user 's picture on it
I then paste the overlay picture

So I use pillow in the backend , but I feel this might be doing unnecessary and causing me too much memory and time . When I could just use the user 's browser and render the images .


After I changes my code to do this on the client side using canvas , I noticed my memory usage went down by 10 MB , due to me not opening the background picture in memory and then pasting imag3s in it.


Is this wise from performance pov ?

/r/django
https://redd.it/1piidtl

P Supertonic — Lightning Fast, On-Device TTS (66M Params.)

Hello!

I'd like to share Supertonic, a lightweight on-device TTS built for extreme speed and easy deployment across a wide range of environments (mobile, web browsers, desktops, etc).

It’s an open-weight model with 10 voice presets, and examples are available in 8+ programming languages (Python, C++, C#, Java, JavaScript, Rust, Go, and Swift).

For quick integration in Python, you can install it via pip install supertonic:

from supertonic import TTS

tts = TTS(autodownload=True)

# Choose a voice style
style = tts.getvoicestyle(voicename="M1")

# Generate speech
text = "The train delay was announced at 4:45 PM on Wed, Apr 3, 2024 due to track maintenance."
wav, duration = tts.synthesize(text, voicestyle=style)

# Save to file
tts.saveaudio(wav, "output.wav")

GitHub Repository

Web Demo

Python Docs

/r/MachineLearning
https://redd.it/1pj11sm

Introducing Serif: a zero-dependency, vector-first data library for Python

Since I began in Python, I wanted something simpler and more predictable. Something more "Pythonic" than existing data libraries. Something with vectors as first-class citizens. Something that's more forgiving if you need a for-loop, or you're not familiar with vector semantics. So I wrote Serif.

This is an early release (0.1.1), so don't expect perfection, but the core semantics are in place. I'm mainly looking for reactions to how the design feels, and for people to point out missing features or bugs.



What My Project Does

Serif is a lightweight vector and table library built around ergonomics and Python-native behavior. Vectors are first-class citizens, tables are simple collections of named columns, and you can use vectorized expressions or ordinary loops depending on what reads best. The goal is to keep the API small, predictable, and comfortable.

Serif makes a strategic choice: clarity and workflow ergonomics over raw speed.

pip install serif

Because it's zero dependency, in a fresh environment:

pip freeze
# serif==0.1.1



Sample Usage

Here’s a short example that shows the basics of working with Serif: clean column names, natural vector expressions, and a simple way to add derived columns:



/r/Python
https://redd.it/1pj5wdb

D Benchmark: Massive degradation in NVMe Random Read throughput on A100 vs H100 during Multi-GPU Model Loading

We recently conducted a series of benchmarks comparing A100 (PCIe Gen4) and H100 (PCIe Gen5) clusters to isolate bottlenecks during cold-start model loading (snapshot restoration).

We found a significant, non-linear degradation in disk throughput on A100 systems when scaling from single-GPU to multi-GPU loading, which does not appear on H100 systems.

The Setup:
We measured the throughput when loading large model snapshots (70GB - 500GB) from local NVMe RAIDs directly to VRAM.

The Results (Throughput in GiB/s):

| Configuration | A100 (Gen4) | H100 (Gen5) |
|:---|:---|:---|
| 1 GPU Load | ~1.71 GiB/s | ~1.57 GiB/s |
| 2 GPU Load | ~0.22 GiB/s | ~1.33 GiB/s |
| 4 GPU Load | ~0.21 GiB/s | ~2.20 GiB/s |
| 8 GPU Load | ~0.25 GiB/s | ~1.12 GiB/s |

Observations:
1. The "Cliff" on A100:On the A100 setup, as soon as we move to parallel loading for 2+ GPUs, throughput crashes by nearly 8x (from 1.7 to 0.2 GiB/s).

2. H100 Stability:The H100 setup maintains (and actually increases) aggregate throughput as we scale to 4 GPUs, likely due to the wider PCIe Gen5 bus handling the concurrent random read requests and interrupts much better.

Hypothesis:
The degradation on A100 seems to be caused by the saturation of the PCIe Gen4 lanes when handling

/r/MachineLearning
https://redd.it/1pj6c33

Giving Back to the Community - The Complete Backend Developer Roadmap

Hey everyone, I am a software developer with years of experience in server side development.

I’ve decided to open my course for free, I hope this helps people.

If you found this content helpful, please consider subscribing to the channel and liking the video so I can post new videos in the future.

Thank you, omri.
Backend Development Roadmap – SQL, Python, APIs, Docker, Kubernetres, Linux, Git & More

/r/Python
https://redd.it/1pj7k1u

Django-q problem

I am making a sort of reminder api with django it would take a message ,a time to start ,the reminder , an interval and the number of times the message should be sent, im using django-q to handle scheduling and I want to output a json payload that would contain the messages the with their timestamps for each time the message was called

/r/djangolearning
https://redd.it/1pia0xd

blank page on python flask project

Im working on a web application with python flask, html, css, bootstrap and sqlite db. I have created the base.html file (instead of index), base_guest.html (extends base) and the login_fron.html (extends base_guest) which is for the login. Even though everything seems to be fine everytime i try to run 127.0.0.1:5000/login nothing appears on my vs code terminal or the web page and when i press ctrl + u to see the source of the page nothing appears on the source. Does anyone have an idea what coulod be wrong. ( "* Serving Flask app 'app'

* Debug mode: on

WARNING: This is a development server. Do not use it in a production deployment. Use a production WSGI server instead.

* Running on http://127.0.0.1:5000

Press CTRL+C to quit

* Restarting with stat

* Debugger is active!

* Debugger PIN: 167-011-435" thats the only thing that appears on my vs code even when i run the web page)

base.html:

<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<noscript>Tasks {% block noscript %}{% endblock %}</noscript>
<link href="https://cdn.jsdelivr.net/npm/bootstrap@5.3.8/dist/css/bootstrap.min.css" rel="stylesheet" integrity="sha384-sRIl4kxILFvY47J16cr9ZwB07vP4J8+LH7qKQnuqkuIAvNWLzeN8tE5YBujZqJLB" crossorigin="anonymous">
</head>
<body>
<div class="container">
<nav class="navbar navbar-expand-lg bg-body-tertiary">
<div class="container-fluid">
<a

/r/flask
https://redd.it/1pj5fpr

django intertia might be the biggest game changer

Have been using it for a bit now when before I had a nextjs app and was using django as our api backend


We ended up trashing the nextjs app and brought it over to inertia so django can be the beautiful monolith it is and its been flawless


thats all. Had to share

/r/django
https://redd.it/1pjapxb

Howto: File Uploads with Django & DRF
https://django.wtf/blog/file-uploads-with-django-drf/

/r/django
https://redd.it/1pj99hk