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.idea/clearml-fractional-gpu.iml
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<?xml version="1.0" encoding="UTF-8"?>
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<module type="PYTHON_MODULE" version="4">
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<component name="NewModuleRootManager">
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<content url="file://$MODULE_DIR$" />
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<orderEntry type="inheritedJdk" />
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</component>
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</module>
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@ -19,15 +19,20 @@ We present pre-packaged containers supporting CUDA 11.x & CUDA 12.x with pre-bui
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This means multiple containers can be launched on the same GPU, ensuring one user cannot allocate the entire host GPU memory!
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(No more greedy processes grabbing the entire GPU memory! Finally we have a driver level hard limiting memory option).
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![Fractional GPU diagram](docs/fractional_gpu_diagram.png)
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## 🚀 Offerings
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ClearML offers several options to optimize GPU resource utilization by partitioning GPUs:
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* [**Dynamic GPU Slicing**](https://clear.ml/docs/latest/docs/clearml_agent/clearml_agent_fractional_gpus#dynamic-gpu-fractions):
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On-demand GPU slicing per task for both MIG and non-MIG devices (available under the ClearML Enterprise plan):
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* [Bare Metal deployment](https://clear.ml/docs/latest/docs/clearml_agent/clearml_agent_fractional_gpus#bare-metal-deployment)
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* [Kubernetes deployment](https://clear.ml/docs/latest/docs/clearml_agent/clearml_agent_fractional_gpus#kubernetes-deploymen)
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* **Container-based Memory Limits** (**this repository**): Use pre-packaged containers with built-in memory limits to
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run multiple containers on the same GPU (available as part of the ClearML open source offering).
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* **Kubernetes-based Static MIG Slicing**: Set up Kubernetes support for NVIDIA MIG (Multi-Instance GPU) to define GPU
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* [**Kubernetes-based Static MIG Slicing**](https://clear.ml/docs/latest/docs/clearml_agent/clearml_agent_fractional_gpus#kubernetes-static-mig-fractions):
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Set up Kubernetes support for NVIDIA MIG (Multi-Instance GPU) to define GPU
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fractions for specific workloads (available as part of the ClearML open source offering).
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* **Dynamic GPU Slicing**: On-demand GPU slicing per task for both MIG and non-MIG devices, supporting both bare metal
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and Kubernetes deployments (available under the ClearML Enterprise plan).
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With these options, ClearML enables running AI workloads with optimized hardware utilization and workload performance.
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This repository covers container-based fractional GPUs. For more information on ClearML's fractional GPU offerings, see
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docs/fractional_gpu_diagram.png
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docs/fractional_gpu_diagram.png
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