# ix-Container-Toolkit **Repository Path**: deepspark-bot/ix-container-toolkit ## Basic Information - **Project Name**: ix-Container-Toolkit - **Description**: ix-Container-Toolkit是一个能够帮助用户快速构建和运行天数加速卡容器的工具集，提供使能天数智芯加速卡的容器运行时。 - **Primary Language**: Unknown - **License**: Apache-2.0 - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 2 - **Created**: 2025-07-22 - **Last Updated**: 2025-07-22 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # IluvatarCorex Container Toolkit - [Introducton](#introduction) - [Building the IX Container Toolkit](#building-the-ix-container-toolkit) - [Configuring](#configuring) - [Configuring Docker](#configuring-docker) - [Configuring Containerd](#configuring-containerd) - [Configuring Crio](#configuring-crio) - [Running Samples](#running-samples) - [Running a Sample Workload with Docker](#running-a-sample-workload-with-docker) - [Running a Sample Workload with Containerd/Crio(for kubernetes 1.22+)](#running-a-sample-workload-with-containerd/crio(for-kubernetes-1.22+)) - [Running a Sample Workload with Podman](#running-a-sample-workload-with-podman) - [License](#license) ## Introduction The Iluvatar Container Toolkit allows users to build and run GPU accelerated containers. The toolkit includes a container runtime(ix-container-runtime) and utilities to automatically configure containers to leverage Iluvatar GPUs. ## Building the IX Container Toolkit ```shell make all ``` This will build the ix-container-runtime and ix-ctk binary, see logging for more details. ```shell sudo make install ``` This will install the ix-container-runtime and ix-ctk binary, see logging for more details. ## Configuring ### Configuring Docker 1. Run the ix-ctk ```shell sudo ix-ctk runtime configure --runtime docker --ix-set-as-default ``` Run the `runtime configure --runtime docker` command will automatically add the configuration of `ix-container-runtime` to `/etc/docker/daemon.json`, The default configuration is generated in the config.yaml `/etc/iluvatarcorex/ix-container-runtime/` file, and the docker service needs to be restarted for the configuration to take effect. 2. Restart docker service ```shell sudo systemctl daemon-reload sudo systemctl restart docker ``` ### Configuring Containerd (for Kubernetes) 1. Run the ix-ctk ```shell sudo ix-ctk runtime configure --runtime containerd --ix-set-as-default ``` Run the `runtime configure --runtime containerd` command will automatically add the configuration of `ix-container-runtime` to `/etc/containerd/config.toml`, The default configuration is generated in the config.yaml `/etc/iluvatarcorex/ix-container-runtime/` file, and the containerd service needs to be restarted for the configuration to take effect. 2. Restart containerd service ```shell sudo systemctl daemon-reload sudo systemctl restart containerd ``` ### Configuring Crio (for Kubernetes) 1. Run the ix-ctk ```shell sudo ix-ctk runtime configure --runtime crio --ix-set-as-default ``` Run the `runtime configure --runtime crio` command will automatically add the configuration of `ix-container-runtime` to `/etc/crio/crio.conf`, The default configuration is generated in the config.yaml `/etc/iluvatarcorex/ix-container-runtime/` file, and the containerd service needs to be restarted for the configuration to take effect. 2. Restart crio service ```shell sudo systemctl daemon-reload sudo systemctl restart crio ``` ## Running Samples ### Running a Sample Workload with Docker After you install and configure the toolkit and install Iluvatar GPU Driver and SDK, you can verify your installation by running a sample workload. ```shell sudo docker run -it --rm --runtime iluvatar -e IX_VISIBLE_DEVICES=0 corex:4.0.0 ixsmi ``` Your output should resemble the following output: ```shell +-----------------------------------------------------------------------------+ | IX-ML: 4.0.0 Driver Version: 4.1.0 CUDA Version: N/A | |-------------------------------+----------------------+----------------------| | GPU Name | Bus-Id | Clock-SM Clock-Mem | | Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. | |===============================+======================+======================| | 0 Iluvatar BI-V150S | 00000000:8A:00.0 | 500MHz 1600MHz | | 0% 33C P0 N/A / N/A | 114MiB / 32768MiB | 0% Default | +-------------------------------+----------------------+----------------------+ +-----------------------------------------------------------------------------+ | Processes: GPU Memory | | GPU PID Process name Usage(MiB) | |=============================================================================| | No running processes found | +-----------------------------------------------------------------------------+ ``` ### Running a Sample Workload with Containerd/Crio(for kubernetes 1.22+) After you install and configure the toolkit and install Iluvatar GPU Driver and SDK, you can verify your installation by running a sample workload with kubernetes. You can create a yaml file `corex-example.yaml` with the following content: ```shell apiVersion: node.k8s.io/v1 kind: RuntimeClass metadata: name: iluvatar handler: iluvatar --- apiVersion: v1 kind: Pod metadata: name: corex-example spec: runtimeClassName: iluvatar containers: - name: corex-example image: docker.io/library/corex:4.0.0 command: ["/usr/local/corex/bin/ixsmi"] args: ["-l"] env: - name: IX_VISIBLE_DEVICES value: "0" ``` apply the yaml file to kubernetes cluster and see the logs: ```shell kubectl apply -f corex-example.yaml kubectl logs corex-example ``` Your output should resemble the following output: ```shell +-----------------------------------------------------------------------------+ | IX-ML: 4.0.0 Driver Version: 4.1.0 CUDA Version: N/A | |-------------------------------+----------------------+----------------------| | GPU Name | Bus-Id | Clock-SM Clock-Mem | | Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. | |===============================+======================+======================| | 0 Iluvatar BI-V150S | 00000000:8A:00.0 | 500MHz 1600MHz | | 0% 33C P0 N/A / N/A | 114MiB / 32768MiB | 0% Default | +-------------------------------+----------------------+----------------------+ +-----------------------------------------------------------------------------+ | Processes: GPU Memory | | GPU PID Process name Usage(MiB) | |=============================================================================| | No running processes found | +-----------------------------------------------------------------------------+ ``` ### Running a Sample Workload with Podman After you install and configura the toolkit (including generating a CDI specification) and install Iluvatar GPU Driver and SDK, you can verify your installation by running a sample workload. 1. Generate a CDI specification ```shell $ sudo ix-ctk cdi generate --output=/etc/cdi/ix.yaml ``` - The CDI file path is usually `/etc/cdi` or `/var/run/cdi`. - Use `sudo` to ensure that the `/etc/cdi/ix.yaml` file can be created. - If the `--output` parameter is not used, the output will be output to `stdout` by default. `Example output:` ```shell 2024/11/07 03:11:56 Find GPU device count: 2 2024/11/07 03:11:56 Generated CDI spec with version 0.5.0 ``` 2. Test CDI with Podman(v4.3.0+) Podman can specify to use the GPU device defined in the CDI file with the `--device` parameter, the format is `iluvatar.com/gpu=device name`. Currently, the CDI file generated by ix-ctk contains three types of GPU device names: `index`, `UUID`, `all`. ```shell $ podman run --rm --device iluvatar.com/gpu=0 localhost/corex:4.0.0 ixsmi ``` Your output should resemble the following output: ```shell Timestamp Thu Nov 7 03:23:57 2024 +-----------------------------------------------------------------------------+ | IX-ML: 4.0.0 Driver Version: 4.2.0 CUDA Version: N/A | |-------------------------------+----------------------+----------------------| | GPU Name | Bus-Id | Clock-SM Clock-Mem | | Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. | |===============================+======================+======================| | 0 Iluvatar BI-V100 | 00000000:00:06.0 | 1500MHz 1200MHz | | 0% 35C P0 56W / 250W | 9MiB / 32768MiB | 0% Default | +-------------------------------+----------------------+----------------------+ +-----------------------------------------------------------------------------+ | Processes: GPU Memory | | GPU PID Process name Usage(MiB) | |=============================================================================| | No running processes found | +-----------------------------------------------------------------------------+ ``` ## License Copyright (c) 2024 Iluvatar CoreX. All rights reserved. This project has an Apache-2.0 license, as found in the [LICENSE](LICENSE) file.