Search Results for "can" - Page 8
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Showing 1120 open source projects for "can"
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PyScaffold
Python project template generator with batteries included
...Checkout out this demo project, which was set up using PyScaffold and if you are still not convinced yet, also have a look at these reasons to use PyScaffold. After cd-ing into your new project and creating (or activating) an isolated development environment (with virtualenv, conda or your preferred tool), you can do the usual editable install. All configuration can be done in setup.cfg like changing the description, URL, classifiers, installation requirements and so on as defined by setuptools. That means in most cases it is not necessary to tamper with setup.py. -
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Django OAuth Toolkit
OAuth2 goodies for the Djangonauts!
Django OAuth Toolkit can help you by providing, out of the box, all the endpoints, data, and logic needed to add OAuth2 capabilities to your Django projects. Django OAuth Toolkit makes extensive use of the excellent OAuthLib, so that everything is rfc-compliant. OAuth is an open standard for access delegation, commonly used as a way for Internet users to grant websites or applications access to their information on other websites but without giving them the passwords. -
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Petastorm
Petastorm library enables single machine or distributed training
...It can also be used from pure Python code. A dataset created using Petastorm is stored in Apache Parquet format. On top of a Parquet schema, petastorm also stores higher-level schema information that makes multidimensional arrays into a native part of a petastorm dataset. Petastorm supports extensible data codecs. These enable a user to use one of the standard data compressions (jpeg, png) or implement her own. -
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PennyLane
A cross-platform Python library for differentiable programming
...Train a quantum computer the same way as a neural network. Built-in automatic differentiation of quantum circuits, using the near-term quantum devices directly. You can combine multiple quantum devices with classical processing arbitrarily! Support for hybrid quantum and classical models, and compatible with existing machine learning libraries. Quantum circuits can be set up to interface with either NumPy, PyTorch, JAX, or TensorFlow, allowing hybrid CPU-GPU-QPU computations. The same quantum circuit model can be run on different devices. ... -
Host LLMs in Production With On-Demand GPUsDeploy your model, get an endpoint, pay only for compute time. No GPU provisioning or infrastructure management required.
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lightning AI
The most intuitive, flexible, way for researchers to build models
...Models are “easy”, the “glue” work is hard. Lightning Apps are community-built templates that stitch together your favorite ML lifecycle tools into cohesive ML workflows that can run on your laptop or any cluster. Find templates (Lightning Apps), modify them and publish your own. Lightning Apps can even be full standalone ML products! Run on your laptop for free! Download the code and type 'lightning run app'. Feel free to ssh into any machine and run from there as well. In research, we often have multiple separate scripts to train models, finetune them, collect results and more. -
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SageMaker Training Toolkit
Train machine learning models within Docker containers
Train machine learning models within a Docker container using Amazon SageMaker. Amazon SageMaker is a fully managed service for data science and machine learning (ML) workflows. You can use Amazon SageMaker to simplify the process of building, training, and deploying ML models. To train a model, you can include your training script and dependencies in a Docker container that runs your training code. A container provides an effectively isolated environment, ensuring a consistent runtime and reliable training process. ... -
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LINE Messaging API SDK for Python
LINE Messaging API SDK for Python
The LINE Messaging API SDK for Python makes it easy to develop bots using LINE Messaging API, and you can create a sample bot within minutes. You must upload a rich menu image and link the rich menu to a user for the rich menu to be displayed. You can create up to 1000 rich menus for one LINE Official Account with the Messaging API. The LINE Messaging API SDK for Python includes experimental asyncio support. (API may change without notice in a future version). -
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Azure SDK for Python
Active development of the Azure SDK for Python
...For consumers of the SDK we recommend visiting our public developer docs or our versioned developer docs. For your convenience, each service has a separate set of libraries that you can choose to use instead of one, large Azure package. To get started with a specific library, see the README.md (or README.rst) file located in the library's project folder. Last stable versions of packages that have been provided for usage with Azure and are production-ready. These libraries provide you with similar functionalities to the Preview ones as they allow you to use and consume existing resources and interact with them, for example: upload a blob. ... -
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Buildbot
Python-based continuous integration testing framework
...The masters monitor source-code repositories for changes, coordinate the activities of the workers, and report results to users and developers. Workers run on a variety of operating systems. You configure Buildbot by providing a Python configuration script to the master. This script can be very simple, configuring built-in components, but the full expressive power of Python is available. This allows dynamic generation of configuration, customized components, and anything else you can devise. The framework itself is implemented in Twisted Python, and compatible with all major operating systems. -
Build Agents and Models on One PlatformGemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
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PyTorch Lightning
The lightweight PyTorch wrapper for high-performance AI research
...With just a quick refactor you can run your code on any hardware, run distributed training, perform logging, metrics, visualization and so much more! -
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Impacket
A collection of Python classes for working with network protocols
...It features several protocols, including Ethernet, IP, TCP, UDP, ICMP, IGMP, ARP, NMB and SMB1, SMB2 and SMB3 and more. Impacket's object oriented API makes it easy to work with deep hierarchies of protocols. It can construct packets from scratch, as well as parse them from raw data. -
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MetricFlow
MetricFlow allows you to define, build, and maintain metrics in code
...Because metric definitions live centrally, you avoid duplication across teams and tools, reduce risk of inconsistent numbers, and make it easier to audit and evolve the logic over time. The project emphasizes explainability, performance and portability: you define metrics once and then they can be consumed in BI tools, notebooks, or even AI/agent-driven workflows. -
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aws-cli
Universal Command Line Interface for Amazon Web Services
The AWS CLI is the universal command-line interface for managing AWS services, automating tasks, and scripting cloud workflows. It exposes nearly every public API from EC2 and S3 to IAM, Lambda, and beyond, providing parity with the service SDKs in a tool you can run anywhere. Profiles, regions, single-sign-on, and credential helpers make it straightforward to switch contexts securely across accounts and environments. Its output controls and JMESPath querying let you slice, filter, and transform JSON responses directly in the shell, which is essential for automation. Waiters, paginators, and retries handle long-running or large list operations cleanly so scripts are resilient. ... -
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Click
Python composable command line interface toolkit
Click is a Python package for creating beautiful command line interfaces in a composable way with as little code as necessary. It’s the “Command Line Interface Creation Kit”. It’s highly configurable but comes with sensible defaults out of the box. It aims to make the process of writing command line tools quick and fun while also preventing any frustration caused by the inability to implement an intended CLI API. Click in three points, arbitrary nesting of commands, automatic help page... -
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Pythonic Data Structures and Algorithms
Minimal examples of data structures and algorithms in Python
...For students preparing for technical interviews, self-learners brushing up on fundamentals, or developers wanting to understand algorithm internals, this repository provides ready-to-run examples, and can serve as a sandbox to experiment, benchmark, or adapt code. Because it’s in pure Python, it’s easy to read and modify, making it accessible even to those with modest programming experience. The repo helps bridge the gap between theoretical algorithm descriptions and real-world code, giving concrete, working implementations that one can study, debug, or extend. -
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Imagen - Pytorch
Implementation of Imagen, Google's Text-to-Image Neural Network
...It also contains dynamic clipping for improved classifier-free guidance, noise level conditioning, and a memory-efficient unit design. It appears neither CLIP nor prior network is needed after all. And so research continues. For simpler training, you can directly supply text strings instead of precomputing text encodings. (Although for scaling purposes, you will definitely want to precompute the textual embeddings + mask) -
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Spyder notebook plugin
Jupyter notebook integration with Spyder
Spyder plugin to use Jupyter notebooks inside Spyder. Currently, it supports basic functionality such as creating new notebooks, opening any notebook in your filesystem and saving notebooks at any location. You can also use Spyder's file switcher to easily switch between notebooks and open an IPython console connected to the kernel of a notebook to inspect its variables in the Variable Explorer. -
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pangu.py
Paranoid text spacing in Python
...Mixed-script text often becomes cramped or ambiguous, and this library applies simple but effective typography rules to make it instantly more readable. It works both as a Python library and a command-line utility, so you can process strings in code, tidy files in bulk, or wire it into documentation and build pipelines. The transformation is idempotent: running it multiple times won’t keep adding spaces, which makes it safe in automated workflows. It’s designed to be pragmatic and lightweight, with sensible defaults that handle common edge cases found in websites, blogs, and multilingual technical docs. ... -
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Oh My Zsh (ohmyzsh)
A framework for managing your zsh configuration
...With each keystroke in your command prompt, you'll take advantage of the hundreds of powerful plugins and beautiful themes. It's a good idea to inspect the install script from projects you don't yet know. You can do that by downloading the install script first, looking through it so everything looks normal, then running it. -
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libtmux
Python API / wrapper for tmux
libtmux is a typed Python library that provides a wrapper for interacting programmatically with tmux, a terminal multiplexer. You can use it to manage tmux servers, sessions, windows, and panes. Additionally, libtmux powers tmuxp, a tmux workspace manager. libtmux builds upon tmux’s target and formats to create an object mapping to traverse, inspect and interact with live tmux sessions. -
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Translate Toolkit
Useful localization tools with Python API for building localization
The localization engineers' Swiss Army Knife. Use it to convert, count, manipulate, review and debug texts. Tools that you can expand, adapt, and grow. Convert between a number of localization, translation and software formats. Allowing you and your translators to work on industry-standard translation formats. Search for pattern matches. Run tests that adapt to languages and source projects. Extract terminology. A large toolset to allow you to increase localization quality. -
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Python LSP Server
Fork of the python-language-server project
...The base language server requires Jedi to provide Completions, Definitions, Hover, References, Signature Help, and Symbols. Like all language servers, the configuration can be passed from the client that talks to this server (i.e. your editor/IDE or another tool that has the same purpose). The details of how this is done depend on the editor or plugin that you are using to communicate with python-lsp-server. The configuration options are available at that level are documented in CONFIGURATION.md. Overall configuration is computed first from user configuration (in home directory), overridden by configuration passed in by the language client, and then overridden by configuration discovered in the workspace. -
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code-review-graph
Local-first code intelligence graph for MCP and CLI
...It parses source files with Tree-sitter and stores functions, classes, imports, calls, inheritance, and test relationships in a SQLite graph. When code changes, blast-radius analysis identifies the callers, dependents, and tests most likely to be affected. AI coding assistants can query this focused context through MCP instead of repeatedly scanning an entire codebase. Incremental indexing reparses only changed files and their relevant dependencies, making it practical for large repositories and monorepos. The command-line installer can detect and configure numerous supported coding tools automatically. It also offers watch mode, hooks, skills, a VS Code extension, and review-oriented risk analysis. -
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mimic
Intercept any app, then call it from Python like a library
...It records requests through mitmproxy, extracts stable authentication values, and uses an AI assistant to generate named Python methods for observed endpoints. Generated clients are ordinary source files built on mimic.App, so developers can inspect and modify them directly. The system can preserve request templates and model multi-step API flows in which one call supplies data for another. Its command-line workflow checks prerequisites, records traffic, lists captured hosts, and prepares reusable sessions. The current documentation focuses on routing iPhone traffic through a Mac-hosted proxy. ... -
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Think Bayes 2
Text and code for the second edition of Think Bayes, by Allen Downey
...It teaches Bayesian reasoning through computational methods instead of relying mainly on symbolic mathematics. Each chapter is presented as a Jupyter notebook where readers can study the text, run examples, and complete exercises. Separate solution materials help learners check their work and explore alternative approaches. The lessons cover probability distributions, Bayesian updating, estimation, prediction, comparison, and decision-making. Notebooks can run in Google Colab or be downloaded for local use. ...