29.1c Requesting GPUs in Slurm: --gres=gpu:a100:8 and GRES configuration¶

🧠 Context Introduction¶

When you work with AI workloads on a cluster managed by Slurm, one of your most important tasks is telling the scheduler exactly which hardware resources your job needs. GPUs are the most critical resource for training and inference, and Slurm uses a system called Generic Resource Scheduling (GRES) to manage them. This topic explains how to request GPUs using the --gres flag, what the syntax --gres=gpu:a100:8 means, and how GRES configuration works behind the scenes.

⚙️ What is GRES?¶

GRES stands for Generic Resource Scheduling. It is Slurm's way of handling resources that are not standard CPU cores or memory — things like GPUs, FPGAs, or high-speed network interfaces.

• GRES allows Slurm to track and allocate these specialized resources.
• Each GPU type (e.g., A100, V100, H100) is defined as a separate GRES type.
• The system administrator configures GRES on each compute node so Slurm knows exactly what hardware is available.

🛠️ Understanding the --gres=gpu:a100:8 Syntax¶

When you submit a job, you use the --gres flag to specify which generic resources you need. The syntax follows a clear pattern:

Structure: --gres=<resource_type>:<type_name>:<count>

Breaking down --gres=gpu:a100:8:

• gpu — This is the resource type. It tells Slurm you want GPUs.
• a100 — This is the type name. It specifies the exact GPU model (NVIDIA A100).
• 8 — This is the count. It tells Slurm you need 8 of those GPUs.

Key points to remember:

• The type name (e.g., a100) must match exactly what the system administrator configured on the cluster.
• If you omit the type name (e.g., --gres=gpu:8), Slurm will allocate any available GPU type.
• The count must not exceed the number of GPUs available on a single node, unless you also request multiple nodes.

📊 Example: Requesting 8 A100 GPUs for a Training Job¶

When you submit a batch job that needs 8 NVIDIA A100 GPUs, your submission command would include the --gres flag. Here is how it looks in practice:

For reference:

sbatch --gres=gpu:a100:8 --ntasks=8 --cpus-per-task=4 --mem=256G my_training_script.sh

📤 Output: Job submitted with job ID 123456.

What this does: - Requests exactly 8 A100 GPUs. - Allocates 8 tasks (one per GPU). - Gives each task 4 CPU cores. - Reserves 256 GB of system memory.

🗺️ GRES Configuration on the Cluster (What Engineers Need to Know)¶

The GRES configuration is set by the cluster administrator in Slurm's configuration files. As a user, you don't change this, but understanding it helps you write correct job requests.

How GPUs are defined in gres.conf¶

Each compute node has a configuration file (typically /etc/slurm/gres.conf) that lists its GPUs. A typical entry looks like this:

For reference:

NodeName=compute-gpu-01 Name=gpu Type=a100 File=/dev/nvidia0
NodeName=compute-gpu-01 Name=gpu Type=a100 File=/dev/nvidia1
NodeName=compute-gpu-01 Name=gpu Type=a100 File=/dev/nvidia2
NodeName=compute-gpu-01 Name=gpu Type=a100 File=/dev/nvidia3
NodeName=compute-gpu-01 Name=gpu Type=a100 File=/dev/nvidia4
NodeName=compute-gpu-01 Name=gpu Type=a100 File=/dev/nvidia5
NodeName=compute-gpu-01 Name=gpu Type=a100 File=/dev/nvidia6
NodeName=compute-gpu-01 Name=gpu Type=a100 File=/dev/nvidia7

📤 Output: This node has 8 A100 GPUs, each mapped to a device file.

How slurm.conf references GRES¶

The main Slurm configuration file (slurm.conf) also includes GRES information for each node:

For reference:

NodeName=compute-gpu-01 Gres=gpu:a100:8 CPUs=64 RealMemory=512000

📤 Output: This line tells Slurm that compute-gpu-01 has 8 A100 GPUs, 64 CPU cores, and 512 GB of memory.

📊 Visual Representation: Slurm Generic Resource (GRES) GPU binding¶

This diagram displays how requesting a GPU via GRES tells Slurm to bind execution to matching cards.

🕵️ Checking Available GPUs with sinfo¶

Before submitting a job, you can check which nodes have the GPUs you need. Slurm provides the sinfo command with a special format option to display GRES information.

For reference:

sinfo -o "%n %G"

📤 Output:

compute-gpu-01 gpu:a100:8
compute-gpu-02 gpu:a100:8
compute-gpu-03 gpu:v100:4

This shows you which GPU types and counts are available on each node.

📋 Comparison Table: Common --gres Patterns¶

✅ Best Practices for Requesting GPUs¶

• Always specify the GPU type when your workload requires a specific architecture (e.g., A100 for mixed-precision training).
• Match GPU count to your model's parallelism strategy — if you use data parallelism, request GPUs in multiples of your batch size.
• Check node capacity first — use sinfo to ensure a node has enough GPUs before requesting 8 on a single node.
• Combine with CPU and memory requests — GPUs need data fed to them; ensure you have enough CPU cores and RAM to keep them busy.
• Use --gres-flags=allow-multi-node only when your application supports distributed training across nodes.

🧪 Troubleshooting Common GRES Issues¶

Issue: Job stays pending with reason "Resources" - This usually means no node has the exact GPU type and count you requested. - Solution: Check available GPUs with sinfo and adjust your request.

Issue: "Invalid generic resource (gres) specification" error - You typed the GPU type name incorrectly. - Solution: Verify the exact type name used on your cluster (e.g., a100 vs A100).

Issue: Job runs but GPUs are not visible inside the container - Slurm allocates the GPUs, but your application needs the NVIDIA_VISIBLE_DEVICES environment variable. - Solution: Ensure your container or script respects the CUDA_VISIBLE_DEVICES or NVIDIA_VISIBLE_DEVICES environment variable set by Slurm.

🔁 Summary¶

• GRES is Slurm's system for managing non-CPU resources like GPUs.
• The --gres=gpu:a100:8 flag requests 8 NVIDIA A100 GPUs for your job.
• The GPU type name must match the cluster's configuration exactly.
• Always check available resources with sinfo before submitting large GPU requests.
• Combine GPU requests with appropriate CPU and memory allocations for optimal performance.

By mastering the --gres flag and understanding GRES configuration, you can reliably request the exact GPU resources your AI workloads need.