aikernel-net 0.1.0

Python binding for AIKernel Core 0.1.0 functional primitives and managed assembly discovery.

AIKernel.Python

日本語

Python binding for AIKernel.Core functional primitives and managed assembly discovery.

The default package is CPU-only and is published as a universal py3-none-any wheel for Windows and Linux:

pip install aikernel-net

The PyPI package named aikernel belongs to another project. AIKernel.NET uses the distribution name aikernel-net to avoid that collision. Import the module as aikernel_net.

See RELEASE_NOTES.md for the 0.1.0 stable baseline notes.

Release Channels

Stable user releases are published to PyPI:

• distribution: aikernel-net
• versions: 0.1.0, then later stable releases
• policy: stable releases only

Development releases are reserved for CI/CD and developer validation through GitHub Packages:

• distribution: aikernel-net-dev
• versions: 0.1.0-dev.1 style prereleases
• policy: breaking changes are allowed

User documentation defaults to the PyPI stable package. Use development packages only for CI/CD or integration testing.

For source-based local validation, install directly from GitHub:

pip install git+https://github.com/AIKernel-NET/AIKernel.Core.git#subdirectory=python

Use a clean virtual environment or force a reinstall when validating a local checkout, especially if an older local aikernel-net package was installed previously. This is a development workflow, not the stable user install path:

pip install --force-reinstall \
  git+https://github.com/AIKernel-NET/AIKernel.Core.git#subdirectory=python

GPU integrations are opt-in Capability packages and are not installed by default. CUDA, ROCm, DirectML, and native model runtimes may publish their own platform-specific packages, but the base aikernel-net package stays universal.

Scope

AIKernel.Python is CUDA-free by default and does not include a native ABI bridge, CUDA Capability DLL, LibTorch binary, or GPU runtime. GPU integrations belong to external Capability repositories such as AIKernel.Cuda13.0.Libtorch2.12.win-x64.

The intended install paths are:

• C# applications on Windows or Linux: install the AIKernel.* NuGet packages.
• Python applications on Windows or Linux: install the aikernel-net pip package.
• GPU hosts: explicitly add a matching external Capability package.

The package provides:

• Result, Option, Either, and Try
• synchronous and asynchronous monad composition helpers
• do(...) and async_do(...) notation
• managed AIKernel assembly discovery helpers
• package/runtime layout discovery

The package does not provide:

• load_model
• forward
• unload_model
• libtorch_bridge
• Win32 / POSIX memory mapping implementations
• Kernel internals
• CUDA / LibTorch runtime code

Install GPU-specific Python or native bindings from the matching external Capability repository or package.

Managed Assemblies

By default, the package build runs dotnet publish and bundles the managed AIKernel assemblies under aikernel_net/managed:

• AIKernel.Abstractions.dll
• AIKernel.Common.dll
• AIKernel.Core.dll
• AIKernel.Kernel.dll
• AIKernel.Dtos.dll
• AIKernel.Enums.dll

The publish output also carries required transitive runtime DLLs such as Microsoft.Extensions.* and YamlDotNet.

Python exposes only discovery helpers for these assemblies. It does not reimplement Kernel internals, Win32MemoryMapper, PosixMemoryMapper, KernelContext, or OS memory APIs.

managed_assemblies() resolves bundled assemblies first, then paths from AIKERNEL_MANAGED_ASSEMBLY_PATH, then matching packages from the NuGet global-packages cache (NUGET_PACKAGES or ~/.nuget/packages).

For wrapper-only development or CI tests without a .NET SDK, disable managed assembly bundling:

pip install -e . \
  --config-settings=cmake.define.AIKERNEL_PYTHON_INCLUDE_MANAGED=OFF
pytest

For PyPI publication, build the wheel from the repository source tree so the managed assemblies can be bundled into aikernel_net/managed:

python -m build --wheel
python -m twine check dist/aikernel_net-0.1.0-py3-none-any.whl

API

import aikernel_net

assemblies = aikernel_net.managed_assemblies()
layout = aikernel_net.runtime_layout()

The wrapper surface is intentionally small:

• managed_assemblies() -> ManagedAssemblySet
• require_managed_assemblies() -> ManagedAssemblySet
• runtime_layout() -> RuntimeLayout

The package includes inline type hints and a py.typed marker for PEP 561 compatible type checkers.

Monad Syntax

AIKernel.Python includes lightweight Result, Option, Either, and Try helpers so Python user-land pipelines can mirror the C# AIKernel.Common monad style without copying Kernel or Capability internals.

Method-chain style:

from aikernel_net import Try

result = (
    Try.run(lambda: load_history())
    .bind(lambda history: Try.run(lambda: parse_json(history)))
    .bind(lambda document: Try.run(lambda: validate(document)))
)

Try(lambda: ...) is also supported as a shorthand for Try.run(lambda: ...).

LINQ-style aliases are available for Python method chains:

from aikernel_net import Right, Success, Try

route = (
    Try.run(lambda: load_context())
    .select(lambda context: context.strip())
    .where(lambda context: len(context) > 0)
    .select_many(
        lambda context: Success("cli" if context.startswith("aik") else "llm"),
        lambda context, provider: {"context": context, "provider": provider},
    )
    .tap(lambda decision: audit(decision))
)

capability = (
    Right({"provider": "cli", "operation": "observe"})
    .where(lambda decision: decision["provider"] == "cli", lambda: "not-cli")
    .select(lambda decision: decision["operation"])
)

The Python aliases map to the C# Common names:

• select(...) mirrors LINQ Select and delegates to map(...)
• select_many(...) mirrors LINQ SelectMany and is expressed as bind + map
• tap(...) observes success / Some / Right values without changing them
• where(...) filters in the same short-circuit style as the matching monad
• match(...) folds Result, Option, or Either into a plain value
• or_else(...) is available on Option

For users porting C# examples directly, PascalCase aliases are also available: Map, Bind, Tap, Where, Select, SelectMany, Match, and Option.OrElse.

Async pipelines are available through async_result, async_option, and async_either, matching the C# Task<Result<T>>, Task<Option<T>>, and Task<Either<L,R>> extension style:

from aikernel_net import Success, async_result

async def load_context_result():
    return Success(await load_context_async())

async def route_provider(context):
    return Success("cli" if context.startswith("aik") else "llm")

async def is_routable(context):
    return len(context) > 0

route = await (
    async_result(load_context_result())
    .Select(lambda context: context.strip())
    .Where(is_routable)
    .SelectMany(route_provider, lambda context, provider: {
        "context": context,
        "provider": provider,
    })
    .Tap(lambda decision: audit_async(decision))
)

Decorator-based do notation:

from aikernel_net import Result, Try, do

@do(Result)
def pipeline():
    context = yield Try.run(lambda: load_context())
    route = yield Try.run(lambda: route_context(context))
    return route

Async do notation is available when yielded steps may be awaitables, AsyncResult, AsyncOption, or AsyncEither instances:

from aikernel_net import Result, Success, async_do, async_result

async def load_context_result():
    return Success(await load_context_async())

@async_do(Result)
def async_pipeline():
    context = yield load_context_result()
    route = yield async_result(route_provider(context))
    return route

result = await async_pipeline()

Result captures exceptions as failures. Option is a pure short-circuit container and propagates exceptions normally. Either is also pure: Right(value) flows through bind / map, while Left(value) short-circuits without capturing exceptions. do(Result), do(Option), do(Either), async_do(Result), async_do(Option), and async_do(Either) are supported; only the Result forms convert exceptions into failures.

GPU Capability Packages

CUDA, ROCm, DirectML, Vulkan, and native model runtimes should be installed as external Capability packages. Those packages may provide their own Python API for model loading and inference while depending on this package for shared monad and managed assembly discovery behavior.