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Reorder fractional GPU page (#875)

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@ -6,161 +6,15 @@ GPUs to them. In order to optimize your compute resource usage, you can partitio
device run multiple isolated workloads on separate slices that will not impact each other, and will only use the device run multiple isolated workloads on separate slices that will not impact each other, and will only use the
fraction of GPU memory allocated to them. fraction of GPU memory allocated to them.
ClearML provides several GPU slicing options to optimize compute resource utilization: ClearML provides several GPU slicing options to optimize compute resource utilization:
* [Dynamic GPU Slicing](#dynamic-gpu-fractions): On-demand GPU slicing per task for both MIG and non-MIG devices (**Available under the ClearML Enterprise plan**):
* [Bare Metal deployment](#bare-metal-deployment)
* [Kubernetes deployment](#kubernetes-deployment)
* [Container-based Memory Limits](#container-based-memory-limits): Use pre-packaged containers with built-in memory * [Container-based Memory Limits](#container-based-memory-limits): 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**) limits to run multiple containers on the same GPU (**Available as part of the ClearML open source offering**)
* [Kubernetes-based Static MIG Slicing](#kubernetes-static-mig-fractions): Set up Kubernetes support for NVIDIA MIG * [Kubernetes-based Static MIG Slicing](#kubernetes-static-mig-fractions): 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**) (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 (**Available under the ClearML Enterprise plan**):
* [Bare Metal deployment](#bare-metal-deployment)
* [Kubernetes deployment](#kubernetes-deployment)
## Container-based Memory Limits
Use [`clearml-fractional-gpu`](https://github.com/allegroai/clearml-fractional-gpu)'s pre-packaged containers with
built-in hard memory limitations. Workloads running in these containers will only be able to use up to the container's
memory limit. Multiple isolated workloads can run on the same GPU without impacting each other.
![Fractional GPU diagram](../img/fractional_gpu_diagram.png)
### Usage
#### Manual Execution
1. Choose the container with the appropriate memory limit. ClearML supports CUDA 11.x and CUDA 12.x with memory limits
ranging from 2 GB to 12 GB (see [clearml-fractional-gpu repository](https://github.com/allegroai/clearml-fractional-gpu/blob/main/README.md#-containers) for full list).
1. Launch the container:
```bash
docker run -it --gpus 0 --ipc=host --pid=host clearml/fractional-gpu:u22-cu12.3-8gb bash
```
This example runs the ClearML Ubuntu 22 with CUDA 12.3 container on GPU 0, which is limited to use up to 8GB of its memory.
:::note
--pid=host is required to allow the driver to differentiate between the container's processes and other host processes when limiting memory usage
:::
1. Run the following command inside the container to verify that the fractional gpu memory limit is working correctly:
```bash
nvidia-smi
```
Here is the expected output for the previous, 8GB limited, example on an A100:
```bash
+---------------------------------------------------------------------------------------+
| NVIDIA-SMI 545.23.08 Driver Version: 545.23.08 CUDA Version: 12.3 |
|-----------------------------------------+----------------------+----------------------+
| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|=========================================+======================+======================|
| 0 A100-PCIE-40GB Off | 00000000:01:00.0 Off | N/A |
| 32% 33C P0 66W / 250W | 0MiB / 8128MiB | 3% Default |
| | | Disabled |
+-----------------------------------------+----------------------+----------------------+
+---------------------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=======================================================================================|
+---------------------------------------------------------------------------------------+
```
#### Remote Execution
You can set a ClearML Agent to execute tasks in a fractional GPU container. Set an agent’s default container via its
command line. For example, all tasks pulled from the `default` queue by this agent will be executed in the Ubuntu 22
with CUDA 12.3 container, which is limited to use up to 8GB of its memory:
```bash
clearml-agent daemon --queue default --docker clearml/fractional-gpu:u22-cu12.3-8gb
```
The agent’s default container can be overridden via the UI:
1. Clone the task
1. Set the Docker in the cloned task's **Execution** tab > **Container** section
![Task container](../img/fractional_gpu_task_container.png)
1. Enqueue the cloned task
The task will be executed in the container specified in the UI.
For more information, see [Docker Mode](clearml_agent_execution_env.md#docker-mode).
#### Fractional GPU Containers on Kubernetes
Fractional GPU containers can be used to limit the memory consumption of your Kubernetes Job/Pod, and have multiple
containers share GPU devices without interfering with each other.
For example, the following configures a K8s pod to run using the `clearml/fractional-gpu:u22-cu12.3-8gb` container,
which limits the pod to 8 GB of the GPU's memory:
```
apiVersion: v1
kind: Pod
metadata:
name: train-pod
labels:
app: trainme
spec:
hostPID: true
containers:
- name: train-container
image: clearml/fractional-gpu:u22-cu12.3-8gb
command: ['python3', '-c', 'print(f"Free GPU Memory: (free, global) {torch.cuda.mem_get_info()}")']
```
:::note
`hostPID: true` is required to allow the driver to differentiate between the pod's processes and other host processes
when limiting memory usage.
:::
### Custom Container
Build your own custom fractional GPU container by inheriting from one of ClearML's containers: In your Dockerfile, make
sure to include `From <clearml_container_image>` so the container will inherit from the relevant container.
See example custom Dockerfiles in the [clearml-fractional-gpu repository](https://github.com/allegroai/clearml-fractional-gpu/tree/main/examples).
## Kubernetes Static MIG Fractions
Set up NVIDIA MIG (Multi-Instance GPU) support for Kubernetes to define GPU fraction profiles for specific workloads
through your NVIDIA device plugin.
The ClearML Agent Helm chart lets you specify a pod template for each queue which describes the resources that the pod
will use. The template should specify the requested GPU slices under `Containers.resources.limits` to have the pods use
the defined resources. For example, the following configures a K8s pod to run a `3g.20gb` MIG device:
```
# tf-benchmarks-mixed.yaml
apiVersion: v1
kind: Pod
metadata:
name: tf-benchmarks-mixed
spec:
restartPolicy: Never
Containers:
- name: tf-benchmarks-mixed
image: ""
command: []
args: []
resources:
limits:
nvidia.com/mig-3g.20gb: 1
nodeSelector: #optional
nvidia.com/gpu.product: A100-SXM4-40GB
```
When tasks are added to the relevant queue, the agent pulls the task and creates a pod to execute it, using the
specified GPU slice.
For example, the following configures tasks from the default queue to use `1g.5gb` MIG slices:
```
agentk8sglue:
queue: default
# …
basePodTemplate:
# …
resources:
limits:
nvidia.com/gpu: 1
nodeSelector:
nvidia.com/gpu.product: A100-SXM4-40GB-MIG-1g.5gb
```
## Dynamic GPU Fractions ## Dynamic GPU Fractions
:::important Enterprise Feature :::important Enterprise Feature
@ -175,6 +29,8 @@ simultaneously without worrying that one task will use all of the GPU's memory.
You can dynamically slice GPUs on [bare metal](#bare-metal-deployment) or on [Kubernetes](#kubernetes-deployment), for You can dynamically slice GPUs on [bare metal](#bare-metal-deployment) or on [Kubernetes](#kubernetes-deployment), for
both MIG-enabled and non-MIG devices. both MIG-enabled and non-MIG devices.
![Fractional GPU diagram](../img/fractional_gpu_diagram.png)
### Bare Metal Deployment ### Bare Metal Deployment
1. Install the required packages: 1. Install the required packages:
@ -211,7 +67,7 @@ the number of GPUs configured to the queue.
Let’s say that four tasks are enqueued, one task for each of the above queues (`dual_gpus`, `quarter_gpu`, `half_gpu`, Let’s say that four tasks are enqueued, one task for each of the above queues (`dual_gpus`, `quarter_gpu`, `half_gpu`,
`single_gpu`). The agent will first pull the task from the `dual_gpus` queue since it is listed first, and will run it `single_gpu`). The agent will first pull the task from the `dual_gpus` queue since it is listed first, and will run it
using 2 GPUs. It will next run the tasks from `quarter_gpu` and `half_gpu`--both will run on the remaining available using 2 GPUs. It will next run the tasks from `quarter_gpu` and `half_gpu`--both will run on the remaining available
GPU. This leaves the task in the `single_gpu` queue. Currently 2.75 GPUs out of the 3 are in use so the task will only GPU. This leaves the task in the `single_gpu` queue. Currently, 2.75 GPUs out of the 3 are in use so the task will only
be pulled and run when enough GPUs become available. be pulled and run when enough GPUs become available.
### Kubernetes Deployment ### Kubernetes Deployment
@ -356,3 +212,148 @@ Where `<gpu_fraction_value>` must be set to one of the following values:
* "0.625" * "0.625"
* "0.750" * "0.750"
* "0.875" * "0.875"
## Container-based Memory Limits
Use [`clearml-fractional-gpu`](https://github.com/allegroai/clearml-fractional-gpu)'s pre-packaged containers with
built-in hard memory limitations. Workloads running in these containers will only be able to use up to the container's
memory limit. Multiple isolated workloads can run on the same GPU without impacting each other.
### Usage
#### Manual Execution
1. Choose the container with the appropriate memory limit. ClearML supports CUDA 11.x and CUDA 12.x with memory limits
ranging from 2 GB to 12 GB (see [clearml-fractional-gpu repository](https://github.com/allegroai/clearml-fractional-gpu/blob/main/README.md#-containers) for full list).
1. Launch the container:
```bash
docker run -it --gpus 0 --ipc=host --pid=host clearml/fractional-gpu:u22-cu12.3-8gb bash
```
This example runs the ClearML Ubuntu 22 with CUDA 12.3 container on GPU 0, which is limited to use up to 8GB of its memory.
:::note
--pid=host is required to allow the driver to differentiate between the container's processes and other host processes when limiting memory usage
:::
1. Run the following command inside the container to verify that the fractional gpu memory limit is working correctly:
```bash
nvidia-smi
```
Here is the expected output for the previous, 8GB limited, example on an A100:
```bash
+---------------------------------------------------------------------------------------+
| NVIDIA-SMI 545.23.08 Driver Version: 545.23.08 CUDA Version: 12.3 |
|-----------------------------------------+----------------------+----------------------+
| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|=========================================+======================+======================|
| 0 A100-PCIE-40GB Off | 00000000:01:00.0 Off | N/A |
| 32% 33C P0 66W / 250W | 0MiB / 8128MiB | 3% Default |
| | | Disabled |
+-----------------------------------------+----------------------+----------------------+
+---------------------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=======================================================================================|
+---------------------------------------------------------------------------------------+
```
#### Remote Execution
You can set a ClearML Agent to execute tasks in a fractional GPU container. Set an agent’s default container via its
command line. For example, all tasks pulled from the `default` queue by this agent will be executed in the Ubuntu 22
with CUDA 12.3 container, which is limited to use up to 8GB of its memory:
```bash
clearml-agent daemon --queue default --docker clearml/fractional-gpu:u22-cu12.3-8gb
```
The agent’s default container can be overridden via the UI:
1. Clone the task
1. Set the Docker in the cloned task's **Execution** tab > **Container** section
![Task container](../img/fractional_gpu_task_container.png)
1. Enqueue the cloned task
The task will be executed in the container specified in the UI.
For more information, see [Docker Mode](clearml_agent_execution_env.md#docker-mode).
#### Fractional GPU Containers on Kubernetes
Fractional GPU containers can be used to limit the memory consumption of your Kubernetes Job/Pod, and have multiple
containers share GPU devices without interfering with each other.
For example, the following configures a K8s pod to run using the `clearml/fractional-gpu:u22-cu12.3-8gb` container,
which limits the pod to 8 GB of the GPU's memory:
```
apiVersion: v1
kind: Pod
metadata:
name: train-pod
labels:
app: trainme
spec:
hostPID: true
containers:
- name: train-container
image: clearml/fractional-gpu:u22-cu12.3-8gb
command: ['python3', '-c', 'print(f"Free GPU Memory: (free, global) {torch.cuda.mem_get_info()}")']
```
:::note
`hostPID: true` is required to allow the driver to differentiate between the pod's processes and other host processes
when limiting memory usage.
:::
### Custom Container
Build your own custom fractional GPU container by inheriting from one of ClearML's containers: In your Dockerfile, make
sure to include `From <clearml_container_image>` so the container will inherit from the relevant container.
See example custom Dockerfiles in the [clearml-fractional-gpu repository](https://github.com/allegroai/clearml-fractional-gpu/tree/main/examples).
## Kubernetes Static MIG Fractions
Set up NVIDIA MIG (Multi-Instance GPU) support for Kubernetes to define GPU fraction profiles for specific workloads
through your NVIDIA device plugin.
The ClearML Agent Helm chart lets you specify a pod template for each queue which describes the resources that the pod
will use. The template should specify the requested GPU slices under `Containers.resources.limits` to have the pods use
the defined resources. For example, the following configures a K8s pod to run a `3g.20gb` MIG device:
```
# tf-benchmarks-mixed.yaml
apiVersion: v1
kind: Pod
metadata:
name: tf-benchmarks-mixed
spec:
restartPolicy: Never
Containers:
- name: tf-benchmarks-mixed
image: ""
command: []
args: []
resources:
limits:
nvidia.com/mig-3g.20gb: 1
nodeSelector: #optional
nvidia.com/gpu.product: A100-SXM4-40GB
```
When tasks are added to the relevant queue, the agent pulls the task and creates a pod to execute it, using the
specified GPU slice.
For example, the following configures tasks from the default queue to use `1g.5gb` MIG slices:
```
agentk8sglue:
queue: default
# …
basePodTemplate:
# …
resources:
limits:
nvidia.com/gpu: 1
nodeSelector:
nvidia.com/gpu.product: A100-SXM4-40GB-MIG-1g.5gb
```