Search Results for "validate"
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Showing 23 open source projects for "validate"
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Union Pandera
Light-weight, flexible, expressive statistical data testing library
...A simple, zero-configuration data testing framework for data scientists and ML engineers seeking correctness. Access a comprehensive suite of built-in tests, or easily create your own validation rules for your specific use cases. Validate the functions that produce your data by automatically generating test cases for them. Integrate seamlessly with the Python ecosystem. Overcome the initial hurdle of defining a schema by inferring one from clean data, then refine it over time. Identify the critical points in your data pipeline, and validate data going in and out of them. ... -
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Deepchecks
Test Suites for validating ML models & data
...Deepchecks accompany you through various validation and testing needs such as verifying your data’s integrity, inspecting its distributions, validating data splits, evaluating your model and comparing between different models. While you’re in the research phase, and want to validate your data, find potential methodological problems, and/or validate your model and evaluate it. To run a specific single check, all you need to do is import it and then to run it with the required (check-dependent) input parameters. More details about the existing checks and the parameters they can receive can be found in our API Reference. ... -
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Flask-WTF
Simple integration of Flask and WTForms, including CSRF
Simple integration of Flask and WTForms, including CSRF, file upload, and reCAPTCHA. Integration with WTForms. Secure Form with CSRF token. Global CSRF protection. reCAPTCHA support. File upload that works with Flask-Uploads. Internationalization using Flask-Babel. -
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pydantic
Data parsing and validation using Python type hints
Data validation and settings management using Python type hinting. Fast and extensible, pydantic plays nicely with your linters/IDE/brain. Define how data should be in pure, canonical Python 3.6+; validate it with pydantic. id is of type int; the annotation-only declaration tells pydantic that this field is required. Strings, bytes or floats will be coerced to ints if possible; otherwise an exception will be raised. name is inferred as a string from the provided default; because it has a default, it is not required. signup_ts is a datetime field which is not required (and takes the value None if it's not supplied). pydantic will process either a unix timestamp int (e.g. 1496498400) or a string representing the date & time. friends uses python's typing system, and requires a list of integers. ... -
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dynaconf
Configuration Management for Python
...Built-in support for Hashicorp Vault and Redis as settings and secrets storage. Built-in extensions for Django and Flask web frameworks. CLI for common operations such as init, list, write, validate, export. On your own code you import and use settings object imported from your config.py file. Dynaconf prioritizes the use of environment variables and you can optionally store settings in Settings Files using any of toml|yaml|json|ini|py extension. -
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Prompt Declaration Language
Prompt Declaration Language is a declarative prompt programming lang
...Whether LLMs are used as coding assistants or software components, reliability remains an important concern. LLMs have a textual interface and the structure of useful prompts is not captured formally. Programming frameworks do not enforce or validate such structures since they are not specified in a machine-consumable way. The purpose of the Prompt Declaration Language (PDL) is to allow developers to specify the structure of prompts and to enforce it, while providing a unified programming framework for composing LLMs with rule-based systems. -
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Airweave CLI
The Airweave CLI for developers and AI agents
...It emphasizes developer productivity by offering clear commands, modular configurations, and seamless integration with existing development workflows. Airweave CLI also supports local testing and remote deployment, allowing users to validate behavior before scaling to production environments. -
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ggshield
Detect and validate 500+ types of hardcoded secrets
GitGuardian’s ggshield is an open-source command-line interface (CLI) tool designed to help developers and security teams detect hardcoded secrets and sensitive credentials early in the development process, either locally or in CI/CD pipelines. It scans source code, configuration files, commit history, and other artifacts to automatically detect hundreds of different secret types — such as API keys, tokens, and passwords — helping prevent accidental leaks before they reach version control or... -
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GQL
A GraphQL client in Python
This is a GraphQL client for Python 3.7+. Plays nicely with graphene, graphql-core, graphql-js and any other GraphQL implementation compatible with the spec. GQL architecture is inspired by React-Relay and Apollo-Client. GQL 3 is a GraphQL Client for Python 3.6+ which plays nicely with other graphql implementations compatible with the spec. Under the hood, it uses GraphQL-core which is a Python port of GraphQL.js, the JavaScript reference implementation for GraphQL. GQL supports multiple... -
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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EKS Best Practices
A best practices guide for day 2 operations
The Amazon EKS Best Practices Guide is a public repository containing comprehensive documentation and guidance for operating production-grade Kubernetes clusters on AWS’s managed service, Amazon EKS. Rather than a code library, it serves as a reference catalogue of patterns, anti-patterns, checklists and architectures across domains such as security, reliability, scalability, networking, cost optimization and hybrid cloud deployments. The repository is maintained by AWS but open to... -
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Double Conversion
Efficient binary-decimal & decimal-binary conversion routines for IEEE
...It is optimized for both speed and correctness, making it ideal for numerical computation libraries, serialization systems, and scripting engines. The codebase includes detailed documentation and comprehensive unit tests to validate correctness across various platforms. With flexible build options using SCons, CMake, or Bazel, Double Conversion integrates seamlessly into modern C++ development workflows. -
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MetricFlow
MetricFlow allows you to define, build, and maintain metrics in code
MetricFlow is an open-source semantic layer engine designed to help organizations define, manage, and query business metrics in a consistent, governed way. It works alongside a data stack—typically built with dbt—and allows you to express metrics as YAML‐based definitions tied to semantic models and dimension tables, rather than embedding logic ad-hoc across many dashboards or scripts. When a user or tool requests a metric (e.g., “monthly revenue by region”), MetricFlow generates optimized,... -
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Super-Linter
Combination of multiple linters to install as a GitHub Action
This repository is for the GitHub Action to run a Super-Linter. It is a simple combination of various linters, written in bash, to help validate your source code. The super-linter finds issues and reports them to the console output. Fixes are suggested in the console output but not automatically fixed, and a status check will show up as failed on the pull request. The design of the Super-Linter is currently to allow linting to occur in GitHub Actions as a part of continuous integration occurring on pull requests as the commits get pushed. ... -
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Gluon CV Toolkit
Gluon CV Toolkit
GluonCV provides implementations of state-of-the-art (SOTA) deep learning algorithms in computer vision. It aims to help engineers, researchers, and students quickly prototype products, validate new ideas and learn computer vision. It features training scripts that reproduce SOTA results reported in latest papers, a large set of pre-trained models, carefully designed APIs and easy-to-understand implementations and community support. From fundamental image classification, object detection, semantic segmentation and pose estimation, to instance segmentation and video action recognition. ... -
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molten
A minimal, extensible, fast and productive framework
molten is a minimal, extensible, fast and productive framework for building HTTP APIs with Python. molten can automatically validate requests according to predefined schemas, ensuring that your handlers only ever run if given valid input. Schemas are PEP484-compatible, which means mypy and molten go hand-in-hand, making your code more easy to maintain. Schema instances are automatically serializable and you can pick and choose which fields to exclude from responses and requests. ... -
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NLP Architect
A model library for exploring state-of-the-art deep learning
...The library contains NLP/NLU-related models per task, different neural network topologies (which are used in models), procedures for simplifying workflows in the library, pre-defined data processors and dataset loaders and misc utilities. The library is designed to be a tool for model development: data pre-processing, build model, train, validate, infer, save or load a model. -
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Flasgger
Easy OpenAPI specs and Swagger UI for your Flask API
...Flasgger also comes with SwaggerUI embedded so you can access it and visualize and interact with your API resources. Flasgger also provides validation of the incoming data, using the same specification it can validate if the data received as a POST, PUT, PATCH is valid against the schema defined using YAML, Python dictionaries or Marshmallow Schemas. Flasgger can work with simple function views or MethodViews using docstring as specification, or using @swag_from decorator to get specification from YAML or dict and also provides SwaggerView which can use Marshmallow Schemas as specification. ... -
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interactive-coding-challenges
120+ interactive Python coding interview challenges
Interactive Coding Challenges is a collection of practice problems designed to strengthen data structures, algorithms, and problem-solving skills. The repository emphasizes a learn-by-doing approach: you read a prompt, attempt a solution, and verify behavior with tests, often within notebooks or scripts. Problems span arrays, strings, stacks, queues, linked lists, trees, graphs, dynamic programming, and more, mirroring common interview themes. Many challenges include hints and reference... -
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SkyWater PDK
Open source process design kit for usage with SkyWater Technology
The SkyWater PDK is the first broadly available open-source process design kit for a commercial-grade CMOS node, enabling researchers, startups, and students to design real ASICs without proprietary NDAs. It provides the essential artifacts for digital and analog flows: SPICE models, DRC/LVS rules, extraction decks, and technology files for open tools like Magic and KLayout. Standard-cell libraries and IO pads are included so digital designers can use open synthesis and place-and-route to... -
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Skater
Python library for model interpretation/explanations
...The concept of model interpretability in the field of machine learning is still new, largely subjective, and, at times, controversial. Model interpretation is the ability to explain and validate the decisions of a predictive model to enable fairness, accountability, and transparency in algorithmic decision-making. The library has embraced object-oriented and functional programming paradigms as deemed necessary to provide scalability and concurrency while keeping code brevity in mind. -
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Algorithms in Python
Data Structures and Algorithms in Python
Algorithms in Python is a collection of algorithm and data structure implementations (primarily in Python) meant to serve as both learning material and reference code for engineers. It includes code for graph algorithms, heap data structures, stacks, queues, and more — each implemented cleanly so learners can trace logic and adapt for their problems. The repository is particularly useful for people preparing for competitive programming, job interviews, or building a foundational... -
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Python XML Validator
Validates XML against xml schema. Based on Python lxml module.
Validates XML against xml schema. Based on Python lxml module. -
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FIDe is a software-based fault injector designed to validate Fault-Tolerant mechanisms and techniques used by applications. It was first used to measure database recovery coverage and costs, but can be used to test and validate ordinary applications.
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