From 0fcb6ba1c9bf974fa03b465d6f8ceb9caf8d64d0 Mon Sep 17 00:00:00 2001 From: revital Date: Tue, 16 Jul 2024 09:02:41 +0300 Subject: [PATCH] Update README --- README.md | 25 +++++++++++++++++++++---- 1 file changed, 21 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index 29f6642..ccd92df 100644 --- a/README.md +++ b/README.md @@ -10,12 +10,29 @@ ## 🔰 Introduction Sharing high-end GPUs or even prosumer & consumer GPUs between multiple users is the most cost-effective -way to accelerate AI development. Unfortunately until now the -only solution existed applied for MIG/Slicing high-end GPUs (A100+) and required Kubernetes,
+way to accelerate AI development. Unfortunately, until now the +only existing solution applied for MIG/Slicing high-end GPUs (A100+) and required Kubernetes,
+ 🔥 🎉 Welcome To Container Based Fractional GPU For Any Nvidia Card! 🎉 🔥
+ We present pre-packaged containers supporting CUDA 11.x & CUDA 12.x with pre-built hard memory limitation! -This means multiple containers can be launched on the same GPU ensuring one user cannot allocate the entire host GPU memory! -(no more greedy processes grabbing the entire GPU memory! finally we have a driver level hard limiting memory option) +This means multiple containers can be launched on the same GPU, ensuring one user cannot allocate the entire host GPU memory! +(No more greedy processes grabbing the entire GPU memory! Finally we have a driver level hard limiting memory option). + +## 🚀 Offerings + +ClearML offers several options to optimize GPU resource utilization by partitioning GPUs: +* **Container-based Memory Limits** (**this repository**): Use pre-packaged containers with built-in memory limits to +run multiple containers on the same GPU (available as part of the ClearML open source offering). +* **Kubernetes-based Static MIG Slicing**: Set up Kubernetes support for NVIDIA MIG (Multi-Instance GPU) to define GPU +fractions for specific workloads (available as part of the ClearML open source offering). +* **Dynamic GPU Slicing**: On-demand GPU slicing per task for both MIG and non-MIG devices, supporting both bare metal +and Kubernetes deployments (available under the ClearML Enterprise plan). + +With these options, ClearML enables running AI workloads with optimized hardware utilization and workload performance. +This repository covers container-based fractional GPUs. For more information on ClearML's fractional GPU offerings, see +the [ClearML documentation](https://clear.ml/docs/latest/docs/clearml_agent/clearml_agent_fractional_gpus). + ## ⚡ Installation