ClearML/clearml-fractional-gpu
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## 🔰 Introduction ## 🔰 Introduction
Sharing high-end GPUs or even prosumer & consumer GPUs between multiple users is the most cost-effective 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 way to accelerate AI development. Unfortunately, until now the
only solution existed applied for MIG/Slicing high-end GPUs (A100+) and required Kubernetes, <br> only existing solution applied for MIG/Slicing high-end GPUs (A100+) and required Kubernetes, <br>
🔥 🎉 Welcome To Container Based Fractional GPU For Any Nvidia Card! 🎉 🔥 <br> 🔥 🎉 Welcome To Container Based Fractional GPU For Any Nvidia Card! 🎉 🔥 <br>
We present pre-packaged containers supporting CUDA 11.x & CUDA 12.x with pre-built hard memory limitation! 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! 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) (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 ## ⚡ Installation