GitHub - webdataset/webdataset: A high-performance Python-based I/O system for large (and small) deep learning problems, with strong support for PyTorch.
WebDataset format files are tar files, with two conventions: within each tar file, files that belong together and make up a training sample share the same basename when stripped of all filename extensions the shards of a tar file are numbered like something-000000.tar to something-012345.tar, usually specified using brace notation something-{000000..012345}.tar WebDataset can read files from local
GitHub - wkentaro/gdown: Google Drive Public File Downloader when Curl/Wget Fails
$ gdown --help usage: gdown [-h] [-V] [-O OUTPUT] [-q] [--fuzzy] [--id] [--proxy PROXY] [--speed SPEED] [--no-cookies] [--no-check-certificate] [--continue] [--folder] [--remaining-ok] url_or_id ... $ # a large file (~500MB) $ gdown https://drive.google.com/uc?id=1l_5RK28JRL19wpT22B-DY9We3TVXnnQQ $ md5sum fcn8s_from_caffe.npz 256c2a8235c1c65e62e48d3284fbd384 $ # same as the above but with the file
GitHub - dmontagu/fastapi-utils: Reusable utilities for FastAPI
This package includes a number of utilities to help reduce boilerplate and reuse common functionality across projects: Class Based Views: Stop repeating the same dependencies over and over in the signature of related endpoints. Response-Model Inferring Router: Let FastAPI infer the response_model to use based on your return type annotation. Repeated Tasks: Easily trigger periodic tasks on server s
タスクランナーInvokeを使ってみよう - Qiita
Invoke について invoke は予め登録しているタスクを実行することができるタスクランナーです。 CLIアプリケーションでのオプション解析、サブコマンドの実行、タスクの編成（前処理/後処理、順次実行)といったことが簡単にできるように設計されています。 invoke のインストール invoke は拡張モジュールなのでインストールする必要があります。
GitHub - camelot-dev/camelot: A Python library to extract tabular data from PDFs
Camelot is a Python library that can help you extract tables from PDFs! Note: You can also check out Excalibur, the web interface to Camelot! Here's how you can extract tables from PDFs. You can check out the PDF used in this example here. >>> import camelot >>> tables = camelot.read_pdf('foo.pdf') >>> tables <TableList n=1> >>> tables.export('foo.csv', f='csv', compress=True) # json, excel, html,
SQLModel
SQLModel, SQL databases in Python, designed for simplicity, compatibility, and robustness. Documentation: https://sqlmodel.tiangolo.com Source Code: https://github.com/tiangolo/sqlmodel SQLModel is a library for interacting with SQL databases from Python code, with Python objects. It is designed to be intuitive, easy to use, highly compatible, and robust. SQLModel is based on Python type annotatio
Reliably download historical market data from with Python
Ever since Yahoo decommissioned their historical data API, Python developers looked for a reliable workaround. As a result, my library, yfinance, gained momentum and was downloaded over 100,000 enjoys 300k+ installs per month, acording to PyPi! Legal note: Yahoo!, Y!Finance, and Yahoo! finance are registered trademarks of Yahoo, Inc. yfinance is not affiliated, endorsed, or vetted by Yahoo, Inc. I
yfinanceを使って株価データや財務情報を取得する方法
こんにちは。カルークです。 前の記事では、pandas datareaderを用いた株価データの取得方法について紹介しました。 pandas datareaderを使って株価データを取得する方法こんにちは。カルークです。 今回はpandas datareaderを使って株価情報や財務情報などを取得する方法について纏めたいと思います。 pandas datareaderって何？ pandas datareaderはWeb上の色々な情...datatechlog.com2021.12.15 今回は、yfinanceというAPIを使って株価データや、企業の財務に関する情報を取得する方法について紹介したいと思います。 yfinanceについて yfinanceは名前から察する通り、Yahoo! Financeから情報を取得するためのAPIです。オープンソースとして公開されており、python環境が
HTTPX
>>> import httpx >>> r = httpx.get('https://www.example.org/') >>> r <Response [200 OK]> >>> r.status_code 200 >>> r.headers['content-type'] 'text/html; charset=UTF-8' >>> r.text '<!doctype html>\n<html>\n<head>\n<title>Example Domain</title>...' Which now allows us to use HTTPX directly from the command-line... Sending a request... Features HTTPX builds on the well-established usability of reques
llm, ttok and strip-tags—CLI tools for working with ChatGPT and other LLMs
llm, ttok and strip-tags—CLI tools for working with ChatGPT and other LLMs 18th May 2023 I’ve been building out a small suite of command-line tools for working with ChatGPT, GPT-4 and potentially other language models in the future. The three tools I’ve built so far are: llm—a command-line tool for sending prompts to the OpenAI APIs, outputting the response and logging the results to a SQLite data
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GitHub - simonw/paginate-json: Command-line tool for fetching JSON from paginated APIs
Profiling 101 for Python Developers: Existing Profilers 3/6 - Blackfire.io Le Blog
GitHub - ranaroussi/yfinance: Download market data from Yahoo! Finance's API
YFinance Library: The definitive guide! – QMR
python-semantic-release 9.4.1 documentation
pymoo: Multi-objective Optimization in Python
pymoode: Differential Evolution in Python
The modern portfolio theory: A multi-objective approach
GitHub - erdewit/ib_insync: Python sync/async framework for Interactive Brokers API
GitHub - zilliztech/GPTCache: Semantic cache for LLMs. Fully integrated with LangChain and llama_index.
