FastAPI入門 - モダンなPythonフレームワークの特性をチュートリアルで手軽に学ぶ｜ハイクラス転職・求人情報サイト アンビ（AMBI）
FastAPI入門 - モダンなPythonフレームワークの特性をチュートリアルで手軽に学ぶ PythonのWebフレームワークとしていま注目を集めるFastAPIは、シンプルにコードが書けるだけでなく、パフォーマンスが高いWebアプリケーションのバックエンドサーバーが構築可能です。同フレームワークの勘所をPythonスペシャリストの杜世橋さんが、初心者向けのハンズオン、そしてより実践的な画像への自動タグ付けサービス実装をとおして解説します。 FastAPIはいま非常に注目されているPythonのWebフレームワークの1つです。Flaskのようにシンプルに書ける一方でPythonのType Hintの機能をうまく活用し、HTTPのリクエスト/レスポンスをPythonの関数の引数/戻り値とシームレスにマッピングして非常に効率的に開発ができるのが最大の特徴です。非同期処理にも対応していてその名
TL;DR; We are changing std::sort in LLVM’s libcxx. That’s a long story of what it took us to get there and all possible consequences, bugs you might encounter with examples from open source. We provide some benchmarks, perspective, why we did this in the first place and what it cost us with exciting ideas from Hyrum’s Law to reinforcement learning. All changes went into open source and thus I can
Update 1.99.1: The update addresses these security issues. Update 1.99.2: The update addresses these issues. Update 1.99.3: The update addresses these issues. Downloads: Windows: x64 Arm64 | Mac: Universal Intel silicon | Linux: deb rpm tarball Arm snap Welcome to the March 2025 release of Visual Studio Code. There are many updates in this version that we hope you'll like, some of the key highligh
Golang Mini Reference 2022: A Quick Guide to the Modern Go Programming Language (REVIEW COPY)
Golang Mini Reference 2022 A Quick Guide to the Modern Go Programming Language (REVIEW COPY) Harry Yoon Version 0.9.0, 2022-08-24 REVIEW COPY This is review copy, not to be shared or distributed to others. Please forward any feedback or comments to the author. • feedback@codingbookspress.com The book is tentatively scheduled to be published on September 14th, 2022. We hope that when the release da
AST vs. Bytecode: Interpreters in the Age of Meta-Compilation
233 AST vs. Bytecode: Interpreters in the Age of Meta-Compilation OCTAVE LAROSE, University of Kent, UK SOPHIE KALEBA, University of Kent, UK HUMPHREY BURCHELL, University of Kent, UK STEFAN MARR, University of Kent, UK Thanks to partial evaluation and meta-tracing, it became practical to build language implementations that reach state-of-the-art peak performance by implementing only an interprete
How I developed a faster Ruby interpreter | Red Hat Developer
In this article, I will describe my efforts to implement a faster interpreter for CRuby, the Ruby language interpreter, using a dynamically specialized internal representation (IR). I believe this article will interest developers trying to improve the interpreter performance of dynamic programming languages (e.g., CPython developers). I will cover the following topics: Existing CRuby interpreter a
Update 1.80.1: The update addresses these issues. Update 1.80.2: The update addresses this security issue. Downloads: Windows: x64 Arm64 | Mac: Universal Intel silicon | Linux: deb rpm tarball Arm snap Welcome to the June 2023 release of Visual Studio Code. There are many updates in this version that we hope you'll like, some of the key highlights include: Accessibility improvements - Accessible V
How a simple Linux kernel memory corruption bug can lead to complete system compromise
In this case, reallocating the object as one of those three types didn't seem to me like a nice way forward (although it should be possible to exploit this somehow with some effort, e.g. by using count.counter to corrupt the buf field of seq_file). Also, some systems might be using the slab_nomerge kernel command line flag, which disables this merging behavior. Another approach that I didn't look
Tier 4 Support § Support for these targets is entirely experimental. If this target is provided by LLVM, LLVM may have the target as an experimental target, which means that you need to use Zig-provided binaries for the target to be available, or build LLVM from source with special configure flags. zig targets will display the target if it is available. This target may be considered deprecated by
今回は DeepSpeed-Chat による RLHF のご紹介です。正直、データセットや計算資源の都合もあり、とりあえず動かしてみました！的な話にはなりますが、RLHF の効果が実際に確認できるか見てみたいと思います。 1. はじめに 今回は DeepSpeed-Chat1 を使って RLHF を試してみたいと思います。RLHF は Reinforcement Learning from Human Feedback の略で文字通り「人からのフィードバックを用いた強化学習」ということですね。OpenAI が InstructGPT(ChatGPT の元になったモデル)2 で使ったことで注目された手法になります。 LLM がらみで何か記事にしたいと思いつつ、日々新たな LLM が発表されている昨今に、隔月＆内容が実時間から月単位で遅れ気味wの本連載です。 「どうしたもんかな。。。」と悩みに
Install Build and Install Usage Quick Start Guide Lazy Evaluation Unified Memory Indexing Arrays Saving and Loading Arrays Function Transforms Compilation Conversion to NumPy and Other Frameworks Distributed Communication Using Streams Exporting Functions Examples Linear Regression Multi-Layer Perceptron LLM inference Python API Reference Array mlx.core.array mlx.core.array.astype mlx.core.array.a
Type Parameters Proposal
Ian Lance Taylor Robert Griesemer August 20, 2021 StatusThis is the design for adding generic programming using type parameters to the Go language. This design has been proposed and accepted as a future language change. We currently expect that this change will be available in the Go 1.18 release in early 2022. AbstractWe suggest extending the Go language to add optional type parameters to type an
今回は Transformer 系のモデル、具体的には BERT, T5, GPT の推論を高速化してみます。高速化手法として FasterTransformer, Torch-TensorRT, AWS Neuron を用い、素 の transfomers に比べ、どの程度速くなるか（ならないか）、利点・欠点を確認してみましょう。 1. はじめに 今回は Transformer 系のモデル、具体的には BERT, T5, GPT の推論を様々な技術を使って高速化してみます。 高速化の元ネタは Hugging Face の transformers1 縛りとして、素の transformers で推論する場合に比べ、 どの程度速くなるか（ならないか）見てみましょう。 推論を高速化する技術としては FasterTransfomer2, Torch-TensorRT3, AWS Neuron(
The Alkyne GC · mcyoung
Alkyne is a scripting language I built a couple of years ago for generating configuration blobs. Its interpreter is a naive AST walker1 that uses ARC2 for memory management, so it’s pretty slow, and I’ve been gradually writing a new evaluation engine for it. This post isn’t about Alkyne itself, that’s for another day. For now, I’d like to write down some notes for the GC I wrote3 for it, and more
Large Text Compression Benchmark Matt Mahoney Last update: July 3, 2025. history This competition ranks lossless data compression programs by the compressed size (including the size of the decompression program) of the first 109 bytes of the XML text dump of the English version of Wikipedia on Mar. 3, 2006. About the test data. The goal of this benchmark is not to find the best overall compressi
Motivation Ever since TJ said “Personalized Development Environment,” the phrase latched onto me like a cobweb in a mineshaft. A Personalized Development Environment (PDE) describes an ideal setup that is tailored to your needs and preferences – it lies between a bare-bone text editor and a full-fledged IDE. It is a place where you can be productive, efficient, and comfortable. It is a place that
LispText.pdf
Lisp Common Lisp / Scheme 0.1 Copyright c � 2020, Katsunori Nakamura 2020 2 29 1 1 1.1 Common Lisp . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.3.1 Lisp . . . . . .
Pandas is a very popular tool for data analysis. It comes built-in with many useful features, it's battle tested and widely accepted. However, pandas is not always the best tool for the job. SQL databases have been around since the 1970s. Some of the smartest people in the world worked on making it easy to slice, dice, fetch and manipulate data quickly and efficiently. SQL databases have come such
Linux 5.19
linux-kernel.vger.kernel.org archive mirror help / color / mirror / Atom feed* Linux 5.19 @ 2022-07-31 21:43 Linus Torvalds 2022-08-01 12:47 ` Build regressions/improvements in v5.19 Geert Uytterhoeven ` (4 more replies) 0 siblings, 5 replies; 34+ messages in thread From: Linus Torvalds @ 2022-07-31 21:43 UTC (permalink / raw) To: Linux Kernel Mailing List So here we are, one week late, and 5.19 i
Reindex, Transform, and Aggregate datasets using pandas Library
Most of the time, the dataset we will get from the business will be dirty and cannot be used straight forward to train machine learning models. Therefore, we must treat the dataset and bring it to the desired form to input it into an algorithm. This tutorial discusses reindexing, transforming, and aggregating datasets in Pandas. What are Reindexing, Transforming, and Aggregating?Reindexing, transf
JupyterLab Changelog — JupyterLab 4.5.0a3 documentation
JupyterLab Changelog# v4.4# JupyterLab 4.4 includes a number of new features (described below), bug fixes, and enhancements. This release is compatible with extensions supporting JupyterLab 4.0. Extension authors are encouraged to consult the Extension Migration Guide which lists deprecations and changes to the public API. Code console improvements# The code console prompt can now be positioned on
文書データを数値表現に変換する手法の1つであるBag of Wordsを一からPythonで書いてみました。 Bag of Words(BoW)とは BoWの問題点 ｎグラムによるBoW sklearnのCountVectorizerのパラメータについて tokenizer preprocessor analyzer stop_words max_dfとmin_df BoWを自分で書いてみる 参考 Bag of Words(BoW)とは 単語が含まれているかどうかだけを考え、語順は考慮せずに文書をベクトル（数値表現）に変換する方法です。このベクトル化により、文書データを機械学習アルゴリズムで使用できるようになります。 「ベクトル化」と聞いたときは最初ちょっと身構えてしまいましたが、このやり方自体はそれほど難しいものではありませんでした。 そのやり方とはこのようなものです。 A = "副作用
Update 2022-12-15: New section “How will this proposal affect future JavaScript APIs?” In this blog post, we look at the ECMAScript proposal “Iterator helpers” by Gus Caplan, Michael Ficarra, Adam Vandolder, Jason Orendorff, Kevin Gibbons, and Yulia Startsev. It introduces utility methods for working with iterable data: .map(), .filter(), .take(), etc. The style of the proposed API clashes with th
GitHub - ComfyUI-Workflow/awesome-comfyui: A collection of awesome custom nodes for ComfyUI
ComfyUI-Gemini_Flash_2.0_Exp (⭐+172): A ComfyUI custom node that integrates Google's Gemini Flash 2.0 Experimental model, enabling multimodal analysis of text, images, video frames, and audio directly within ComfyUI workflows. ComfyUI-ACE_Plus (⭐+115): Custom nodes for various visual generation and editing tasks using ACE_Plus FFT Model. ComfyUI-Manager (⭐+113): ComfyUI-Manager itself is also a cu
Create an Azure OpenAI, LangChain, ChromaDB, and Chainlit chat app in AKS using Terraform | Microsoft Community Hub
Create an Azure OpenAI, LangChain, ChromaDB, and Chainlit chat app in AKS using Terraform In this sample, I demonstrate how to quickly build chat applications using Python and leveraging powerful technologies such as OpenAI ChatGPT models, Embedding models, LangChain framework, ChromaDB vector database, and Chainlit, an open-source Python package that is specifically designed to create user interf
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Changing std::sort at Google’s Scale and Beyond
March 2025 (version 1.99)
June 2023 (version 1.80)
0.10.0 Release Notes ⚡ The Zig Programming Language
はじめての自然言語処理 DeepSpeed-Chat による RLHF の紹介 | オブジェクトの広場
MLX — MLX 0.28.0 documentation
はじめての自然言語処理 Transformer 系モデルの推論高速化の検証 | オブジェクトの広場
Large Text Compression Benchmark
An Experienced (Neo)Vimmer's Workflow
Practical SQL for Data Analysis
Bag of WordsをPythonで書いてみる - 薬剤師のプログラミング学習日記
ECMAScript proposal: iterator helpers