# nixl

**Repository Path**: xinzhige/nixl

## Basic Information

- **Project Name**: nixl
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: anants/manylinux-whl
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-04-29
- **Last Updated**: 2025-06-09

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

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# NVIDIA Inference Xfer Library (NIXL)

NVIDIA Inference Xfer Library (NIXL) is targeted for accelerating point to point communications in AI inference frameworks such as NVIDIA Dynamo, while providing an abstraction over various types of memory (e.g., CPU and GPU) and storage (e.g., file, block and object store) through a modular plug-in architecture.

[![License](https://img.shields.io/badge/License-Apache_2.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
[![GitHub Release](https://img.shields.io/github/v/release/ai-dynamo/nixl)](https://github.com/ai-dynamo/nixl/releases/latest)

## Prerequisites
### Ubuntu:

`$ sudo apt install build-essential cmake pkg-config`

### Fedora:

`$ sudo dnf install gcc-c++ cmake pkg-config`

### Python

`$ pip3 install meson ninja pybind11`

### UCX

NIXL was tested with UCX version 1.18.0.

```
$ wget https://github.com/openucx/ucx/releases/download/v1.18.0/ucx-1.18.0.tar.gz
$ tar xzf ucx-1.18.0.tar.gz
$ cd ucx-1.18.0
$ ./contrib/configure-release --prefix=<PATH_TO_INSTALL>/install
$ make -j8
$ make install
```

## Getting started
### Build & install

```
$ meson setup <name_of_build_dir>
$ cd <name_of_build_dir>
$ ninja
$ ninja-install
```

### pybind11 Python Interface
The pybind11 bindings for the public facing NIXL API are available in src/bindings/python. These bindings implement the headers in the src/api/cpp directory.

The preferred way is to build it through meson-python, which will just let it be installed with pip. This can be done from the root nixl directory:

` $ pip install .`

### Building Docker container
To build the docker container, first clone the current repository. Also make sure you are able to pull docker images to your machine before attempting to build the container.

Run the following from the root folder of the cloned NIXL repository:
```
$ ./contrib/build-container.sh
```

By default, the container is built with Ubuntu 24.04. To build a container for Ubuntu 22.04 use the --os option as follows:
```
$ ./contrib/build-container.sh --os 22.04
```

To see all the options supported by the container use:
```
$ ./contrib/build-container.sh -h
```

The container also includes a prebuilt python wheel in /workspace/dist if required for installing/distributing. Also, the wheel can be built with a separate script (see below).

### Building the python wheel
The contrib folder also includes a script to build the python wheel with the UCX dependencies. Note, that UCX and other NIXL dependencies are required to be installed.
```
$ ./contrib/build-wheel.sh
```

## Examples

* [C++ examples](https://github.com/ai-dynamo/nixl/tree/main/examples/cpp)

* [Python examples](https://github.com/ai-dynamo/nixl/tree/main/examples/python)