experimental/cuda-ubi9/: pydantic-core-2.18.2 metadata and description

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Core functionality for Pydantic validation and serialization

author_email Samuel Colvin <s@muelcolvin.com>
  • Development Status :: 3 - Alpha
  • Programming Language :: Python
  • Programming Language :: Python :: 3
  • Programming Language :: Python :: 3 :: Only
  • Programming Language :: Python :: 3.8
  • Programming Language :: Python :: 3.9
  • Programming Language :: Python :: 3.10
  • Programming Language :: Python :: 3.11
  • Programming Language :: Python :: 3.12
  • Programming Language :: Rust
  • Framework :: Pydantic
  • Intended Audience :: Developers
  • Intended Audience :: Information Technology
  • License :: OSI Approved :: MIT License
  • Operating System :: POSIX :: Linux
  • Operating System :: Microsoft :: Windows
  • Operating System :: MacOS
  • Typing :: Typed
description_content_type text/markdown; charset=UTF-8; variant=GFM
license MIT
  • Homepage, https://github.com/pydantic/pydantic-core
  • Funding, https://github.com/sponsors/samuelcolvin
  • Source, https://github.com/pydantic/pydantic-core
  • typing-extensions >=4.6.0, !=4.7.0
requires_python >=3.8
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# pydantic-core


This package provides the core functionality for [pydantic](https://docs.pydantic.dev) validation and serialization.

Pydantic-core is currently around 17x faster than pydantic V1.
See [`tests/benchmarks/`](./tests/benchmarks/) for details.

## Example of direct usage

_NOTE: You should not need to use pydantic-core directly; instead, use pydantic, which in turn uses pydantic-core._

from pydantic_core import SchemaValidator, ValidationError

v = SchemaValidator(
'type': 'typed-dict',
'fields': {
'name': {
'type': 'typed-dict-field',
'schema': {
'type': 'str',
'age': {
'type': 'typed-dict-field',
'schema': {
'type': 'int',
'ge': 18,
'is_developer': {
'type': 'typed-dict-field',
'schema': {
'type': 'default',
'schema': {'type': 'bool'},
'default': True,

r1 = v.validate_python({'name': 'Samuel', 'age': 35})
assert r1 == {'name': 'Samuel', 'age': 35, 'is_developer': True}

# pydantic-core can also validate JSON directly
r2 = v.validate_json('{"name": "Samuel", "age": 35}')
assert r1 == r2

v.validate_python({'name': 'Samuel', 'age': 11})
except ValidationError as e:
1 validation error for model
Input should be greater than or equal to 18
[type=greater_than_equal, context={ge: 18}, input_value=11, input_type=int]

## Getting Started

You'll need rust stable [installed](https://rustup.rs/), or rust nightly if you want to generate accurate coverage.

With rust and python 3.8+ installed, compiling pydantic-core should be possible with roughly the following:

# clone this repo or your fork
git clone git@github.com:pydantic/pydantic-core.git
cd pydantic-core
# create a new virtual env
python3 -m venv env
source env/bin/activate
# install dependencies and install pydantic-core
make install

That should be it, the example shown above should now run.

You might find it useful to look at [`python/pydantic_core/_pydantic_core.pyi`](./python/pydantic_core/_pydantic_core.pyi) and
[`python/pydantic_core/core_schema.py`](./python/pydantic_core/core_schema.py) for more information on the python API,
beyond that, [`tests/`](./tests) provide a large number of examples of usage.

If you want to contribute to pydantic-core, you'll want to use some other make commands:
* `make build-dev` to build the package during development
* `make build-prod` to perform an optimised build for benchmarking
* `make test` to run the tests
* `make testcov` to run the tests and generate a coverage report
* `make lint` to run the linter
* `make format` to format python and rust code
* `make` to run `format build-dev lint test`

## Profiling

It's possible to profile the code using the [`flamegraph` utility from `flamegraph-rs`](https://github.com/flamegraph-rs/flamegraph). (Tested on Linux.) You can install this with `cargo install flamegraph`.

Run `make build-profiling` to install a release build with debugging symbols included (needed for profiling).

Once that is built, you can profile pytest benchmarks with (e.g.):

flamegraph -- pytest tests/benchmarks/test_micro_benchmarks.py -k test_list_of_ints_core_py --benchmark-enable
The `flamegraph` command will produce an interactive SVG at `flamegraph.svg`.

## Releasing

1. Bump package version locally. Do not just edit `Cargo.toml` on Github, you need both `Cargo.toml` and `Cargo.lock` to be updated.
2. Make a PR for the version bump and merge it.
3. Go to https://github.com/pydantic/pydantic-core/releases and click "Draft a new release"
4. In the "Choose a tag" dropdown enter the new tag `v<the.new.version>` and select "Create new tag on publish" when the option appears.
5. Enter the release title in the form "v<the.new.version> <YYYY-MM-DD>"
6. Click Generate release notes button
7. Click Publish release
8. Go to https://github.com/pydantic/pydantic-core/actions and ensure that all build for release are done successfully.
9. Go to https://pypi.org/project/pydantic-core/ and ensure that the latest release is published.
10. Done 🎉

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