From c9249c757d16c3c89c189fc6d56a26b009e84048 Mon Sep 17 00:00:00 2001 From: MacRimi <123239993+MacRimi@users.noreply.github.com> Date: Sun, 16 Feb 2025 17:36:18 +0100 Subject: [PATCH] Update page.tsx --- web/app/docs/hardware/coral-tpu-lxc/page.tsx | 75 ++++++++++++++++++-- 1 file changed, 71 insertions(+), 4 deletions(-) diff --git a/web/app/docs/hardware/coral-tpu-lxc/page.tsx b/web/app/docs/hardware/coral-tpu-lxc/page.tsx index c3b7c0e..b468c04 100644 --- a/web/app/docs/hardware/coral-tpu-lxc/page.tsx +++ b/web/app/docs/hardware/coral-tpu-lxc/page.tsx @@ -1,19 +1,86 @@ import type { Metadata } from "next" +import { Steps } from "@/components/ui/steps" export const metadata: Metadata = { title: "Coral TPU to an LXC | ProxMenux Documentation", - description: "Learn how to add a Coral TPU to an LXC container in Proxmox VE using ProxMenux.", + description: "Learn how to add Coral TPU support to an LXC container in Proxmox VE.", } export default function CoralTPULXC() { return (
- This guide will walk you through the process of adding a Coral TPU to an LXC container in Proxmox VE using - ProxMenux. + This script automates the process of adding Google Coral TPU (Tensor Processing Unit) support to LXC containers + in Proxmox VE. It configures containers to leverage the power of Coral TPU for AI and machine learning tasks, + significantly accelerating inference operations. +
+ +When executed, this script performs the following actions:
+You'll be prompted to select the LXC container you want to enable Coral TPU support for.
+The script modifies the container's configuration to allow Coral TPU and iGPU access. This includes:
+Inside the container, the script installs required packages:
++ If a Coral M.2 device is detected, you'll be prompted to choose between standard and maximum performance + drivers. +
++ This script simplifies the process of enabling Coral TPU and iGPU acceleration in your LXC containers without + the need for manual configuration file editing or running complex commands. This setup is ideal for AI and + machine learning workloads that can benefit from hardware acceleration.
- {/* Add more content here */}