cumulo-autumn / StreamDiffusion
StreamDiffusion: A Pipeline-Level Solution for Real-Time Interactive Generation
https://github.com/cumulo-autumn/StreamDiffusion
StreamDiffusion: A Pipeline-Level Solution for Real-Time Interactive Generation
https://github.com/cumulo-autumn/StreamDiffusion
GitHub
GitHub - cumulo-autumn/StreamDiffusion: StreamDiffusion: A Pipeline-Level Solution for Real-Time Interactive Generation
StreamDiffusion: A Pipeline-Level Solution for Real-Time Interactive Generation - cumulo-autumn/StreamDiffusion
UIs with Python – Use PyQt5 to Create a Music Player
Learn how to create UIs with Python by creating a Modern Music Player with the PyQt5 framework. Some of the features of the app are: Beautiful and modern user interface, Playlists and favorite songs functionalities, Custom context menus for different pages and Background slideshow for every song.
https://www.youtube.com/watch?v=DjutoyfCl2c
Learn how to create UIs with Python by creating a Modern Music Player with the PyQt5 framework. Some of the features of the app are: Beautiful and modern user interface, Playlists and favorite songs functionalities, Custom context menus for different pages and Background slideshow for every song.
https://www.youtube.com/watch?v=DjutoyfCl2c
YouTube
Python User Interface Project – Use PyQt5 to Code a Music Player
Learn how to create User Interfaces (UIs) with Python by creating a Modern Music Player with the PyQt5 framework. Some of the features of the app are:
- Beautiful and modern user interface
- Playlists and favourite songs functionalities
- Custom context menus…
- Beautiful and modern user interface
- Playlists and favourite songs functionalities
- Custom context menus…
Introduction to Polars
The article provides an overview of the Polars DataFrame library, highlighting its goal to offer a lightning-fast DataFrame library that optimizes queries, handles large datasets, and maintains a consistent and predictable API. It compares Polars to other solutions and introduces its design choices, making it a compelling option for high-performance data manipulation and analysis in Python.
https://pbpython.com/polars-intro.html
The article provides an overview of the Polars DataFrame library, highlighting its goal to offer a lightning-fast DataFrame library that optimizes queries, handles large datasets, and maintains a consistent and predictable API. It compares Polars to other solutions and introduces its design choices, making it a compelling option for high-performance data manipulation and analysis in Python.
https://pbpython.com/polars-intro.html
Pbpython
Introduction to Polars
Introduction to polars.
Synchronized in Python
In Java, you can make a variable thread safe by just adding the synchronized keyword. Is there anything that can achieve the same results in Python?
https://thiagowfx.github.io/2024/01/synchronized-in-python/
In Java, you can make a variable thread safe by just adding the synchronized keyword. Is there anything that can achieve the same results in Python?
https://thiagowfx.github.io/2024/01/synchronized-in-python/
thiagowfx.github.io
★ Synchronized in Python
In Java, you can make a variable thread safe by just adding the synchronized
keyword. Is there anything that can achieve the same results in
Python?
keyword. Is there anything that can achieve the same results in
Python?
Performance Analysis of Python's Dict() and {}
https://madebyme.today/blog/python-dict-vs-curly-brackets/
https://madebyme.today/blog/python-dict-vs-curly-brackets/
MadeByMe
Performance Analysis of Python's `dict()` and `{}`
Some time ago, during a code review, I had a discussion with a colleague of mine about preferring dict() over {} in new Python code. They argued that dict() is more readable — and expresses intent more clearly — therefore should be preferred. I wasn’t convinced…
7 Python Memory Optimization Tricks To Enhance Your Code’s Efficiency
https://medium.com/techtofreedom/7-python-memory-optimization-tricks-to-enhance-your-codes-efficiency-5ef65bf415e7?sk=df088bcfb5315fe1fca54d22dc57a1bb
https://medium.com/techtofreedom/7-python-memory-optimization-tricks-to-enhance-your-codes-efficiency-5ef65bf415e7?sk=df088bcfb5315fe1fca54d22dc57a1bb
Medium
7 Python Memory Optimization Tricks To Enhance Your Code’s Efficiency
Manage computing resources skillfully
The curious case of Pydantic and the 1970s timestamps
https://dev.arie.bovenberg.net/blog/pydantic-timestamps/
https://dev.arie.bovenberg.net/blog/pydantic-timestamps/
Arie Bovenberg
The curious case of Pydantic and the 1970s timestamps
When parsing Unix timestamps, Pydantic guesses whether to interpret them in seconds or milliseconds. While this is certainly convenient and works most of the time, it can drastically (and silently) distort timestamps from a few decades ago.
Running Python on air-gapped systems
How to reproducibly run python code on a system with no internet access.
https://iahmed.me/post/python-air-gapped/
How to reproducibly run python code on a system with no internet access.
https://iahmed.me/post/python-air-gapped/
iahmed.me
Ibrahim Ahmed: Running python on air-gapped systems
In this post, I talk about running python code on a “soft” air-gapped system. For the final script, scroll to the end.
For research with the Department of Defense (DoD) and the University of Tennessee, Knoxville (UTK), I worked with restricted data. The DoD…
For research with the Department of Defense (DoD) and the University of Tennessee, Knoxville (UTK), I worked with restricted data. The DoD…
friuns2 / Leaked-GPTs
Leaked GPTs Prompts Bypass the 25 message limit or to try out GPTs without a Plus subscription.
https://github.com/friuns2/Leaked-GPTs
Leaked GPTs Prompts Bypass the 25 message limit or to try out GPTs without a Plus subscription.
https://github.com/friuns2/Leaked-GPTs
GitHub
GitHub - friuns2/Leaked-GPTs: Leaked GPTs Prompts Bypass the 25 message limit or to try out GPTs without a Plus subscription.
Leaked GPTs Prompts Bypass the 25 message limit or to try out GPTs without a Plus subscription. - GitHub - friuns2/Leaked-GPTs: Leaked GPTs Prompts Bypass the 25 message limit or to try out GPTs w...
Python Packaging, One Year Later: A Look Back at 2023 in Python Packaging
https://chriswarrick.com/blog/2024/01/15/python-packaging-one-year-later/
https://chriswarrick.com/blog/2024/01/15/python-packaging-one-year-later/
Chris Warrick
Python Packaging, One Year Later: A Look Back at 2023 in Python Packag
Are there still fourteen tools, or are there even more? Has Python packaging improved in a year?
4 Tips for Building a Production-Ready FastAPI Backend
This video talks about 4 things that you typically won’t find in most FastAPI tutorials online. These tips are really useful, especially if you want to create a backend that’s used in a production setting.
https://www.youtube.com/watch?v=XlnmN4BfCxw
This video talks about 4 things that you typically won’t find in most FastAPI tutorials online. These tips are really useful, especially if you want to create a backend that’s used in a production setting.
https://www.youtube.com/watch?v=XlnmN4BfCxw
YouTube
4 Tips for Building a Production-Ready FastAPI Backend
💡 Learn how to design great software in 7 steps: https://arjan.codes/designguide.
In this video, I’ll talk about 4 things that you typically won’t find in most FastAPI tutorials online. These tips are really useful, especially if you want to create a backend…
In this video, I’ll talk about 4 things that you typically won’t find in most FastAPI tutorials online. These tips are really useful, especially if you want to create a backend…
How We Executed a Critical Supply Chain Attack on PyTorch
https://johnstawinski.com/2024/01/11/playing-with-fire-how-we-executed-a-critical-supply-chain-attack-on-pytorch/
https://johnstawinski.com/2024/01/11/playing-with-fire-how-we-executed-a-critical-supply-chain-attack-on-pytorch/
John Stawinski IV
Playing with Fire – How We Executed a Critical Supply Chain Attack on PyTorch
Security tends to lag behind adoption, and AI/ML is no exception. Four months ago, Adnan Khan and I exploited a critical CI/CD vulnerability in PyTorch, one of the world’s leading ML platform…
Python-Redlines
Docx tracked change redlines for the Python ecosystem.
https://github.com/JSv4/Python-Redlines
Docx tracked change redlines for the Python ecosystem.
https://github.com/JSv4/Python-Redlines
GitHub
GitHub - JSv4/Python-Redlines: Docx tracked change redlines for the Python ecosystem.
Docx tracked change redlines for the Python ecosystem. - GitHub - JSv4/Python-Redlines: Docx tracked change redlines for the Python ecosystem.
Python Hub @ Whatsapp
Join Python Hub Whatsapp Channel
https://whatsapp.com/channel/0029VaJrpbhKWEKiXQ2tOT0q
Join Python Hub Whatsapp Channel
https://whatsapp.com/channel/0029VaJrpbhKWEKiXQ2tOT0q
WhatsApp.com
Python Hub
WhatsApp Channel Invite
microsoft / LLMLingua
To speed up LLMs' inference and enhance LLM's perceive of key information, compress the prompt and KV-Cache, which achieves up to 20x compression with minimal performance loss.
https://github.com/microsoft/LLMLingua
To speed up LLMs' inference and enhance LLM's perceive of key information, compress the prompt and KV-Cache, which achieves up to 20x compression with minimal performance loss.
https://github.com/microsoft/LLMLingua
GitHub
GitHub - microsoft/LLMLingua: [EMNLP'23, ACL'24] To speed up LLMs' inference and enhance LLM's perceive of key information, compress…
[EMNLP'23, ACL'24] To speed up LLMs' inference and enhance LLM's perceive of key information, compress the prompt and KV-Cache, which achieves up to 20x compression ...