Difference between revisions of "Submitting GPU Jobs"

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(Created page with "Start by reading Submitting CPU Jobs page. The GPU jobs are submitted to <code>gpu.q</code> queue. == Available GPU Cards == The GPU part of the cluster consists of the...")
 
 
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Start by reading [[Submitting CPU Jobs]] page.
 
Start by reading [[Submitting CPU Jobs]] page.
  
−
The GPU jobs are submitted to <code>gpu.q</code> queue.
+
The GPU jobs are submitted to <code>gpu</code> partition.
  
−
== Available GPU Cards ==
+
To ask for one GPU card, use <code>#SBATCH -G 1</code> directive or <code>-G 1</code> option on the command line. The submitted job has <code>CUDA_VISIBLE_DEVICES</code> set appropriately, so all CUDA applications should use only the allocated GPUs.
  
−
The GPU part of the cluster consists of the following nodes:
+
== Rules ==
 +
 
 +
* Always use GPUs via ''sbatch'' (or ''srun''), never via ''ssh''. You can ssh to any machine e.g. to run ''nvidia-smi'' or ''htop'', but not to start computing on GPU.
 +
* Don't forget to specify you RAM requirements with e.g. ''--mem=10G''.
 +
* Always specify the number of GPU cards (e.g. ''-G 1''). Thus e.g. <code>srun -p gpu --mem=64G -G 2 --pty bash</code>
 +
* For interactive jobs, you can use ''srun'', but make sure to end your job as soon as you don't need the GPU (so don't use srun for long training).
 +
* In general: don't reserve a GPU (as described above) without actually using it for longer time, e.g., try separating steps which need GPU and steps which do not and execute those separately on our GPU resp. CPU cluster.
 +
* If you know an approximate runtime of your job, please specify it with ''-t <time>''. Acceptable time formats include "minutes", "minutes:seconds", "hours:minutes:seconds", "days-hours", "days-hours:minutes" and "days-hours:minutes:seconds".
 +
 
 +
== CUDA and cuDNN ==
 +
 
 +
Available CUDA versions are in
 +
/lnet/aic/opt/cuda/
 +
and as of Apr 2023, available versions as 10.1, 10.2, 11.2, 11.7, 11.8.
 +
 
 +
The cuDNN library is also available in the subdirectory <code>cudnn/VERSION/lib64</code> of the respective CUDA directories.
  
−
{| class="wikitable"
+
Therefore, to use CUDA 11.2 with cuDNN 8.1.1, you should add the following to your <code>.profile</code>:
−
|-
+
  export PATH="/lnet/aic/opt/cuda/cuda-11.2/bin:$PATH"
−
! machine !! GPU type !! GPU driver version !! [[https://en.wikipedia.org/wiki/CUDA#GPUs_supported|cc]] !! GPU cnt !! GPU RAM (GB) !! machine RAM (GB) !! remarks
+
  export LD_LIBRARY_PATH="/lnet/aic/opt/cuda/cuda-11.2/lib64:/lnet/aic/opt/cuda/cuda-11.2/cudnn/8.1.1/lib64:/lnet/aic/opt/cuda/cuda-11.2/extras/CUPTI/lib64:$LD_LIBRARY_PATH"
−
|-
+
  export XLA_FLAGS=--xla_gpu_cuda_data_dir=/lnet/aic/opt/cuda/cuda-11.2 # XLA configuration if you are using TensorFlow
−
| gpu-node1 || GeForce GTX 1080 ||  418.39 ||  6.1 ||  2 ||  8.0 ||  64.0
 
−
|-
 
−
| gpu-node2 || GeForce GTX 1080 ||  418.39 ||  6.1 ||  2 ||  8.0 || 64.0
 
−
|-
 
−
| gpu-node3 || GeForce GTX 1080 ||  418.39 ||  6.1 ||  2 || 8.0 ||  64.0
 
−
|-
 
−
| gpu-node4 || GeForce GTX 1080 ||  418.39 ||  6.1 ||  2 ||  8.0 ||  64.0
 
−
|-
 
−
| gpu-node5 || GeForce GTX 1080 ||  418.39 ||  6.1 ||  2 ||  8.0 ||  64.0
 
−
|-
 
−
| gpu-node6 || GeForce GTX 1080 ||  418.39 ||  6.1 ||  2 || 8.0 ||  64.0
 
−
|-
 
−
| gpu-node7 || GeForce GTX 1080 ||  418.39 ||  6.1 ||  2 ||  8.0 ||  64.0  || only for group '''research'''
 
−
|-
 
−
| gpu-node8 || GeForce GTX 1080 ||  418.39 ||  6.1 ||  2 ||  8.0 ||  64.0  || only for group '''research'''
 
−
|}
 
  
−
== Rules ==
+
=== CUDA modules ===
 +
CUDA 11.2 and later can be also loaded as modules. This will set various environment variables for you so you should be able to use CUDA easily.
  
−
* Always use GPUs via ''qsub'' (or ''qrsh''), never via ''ssh''. You can ssh to any machine e.g. to run ''nvidia-smi'' or ''htop'', but not to start computing on GPU. Don't forget to specify you RAM requirements with e.g. ''-l mem_free=8G,act_mem_free=8G,h_data=12G''.
+
On a GPU node, you can do the following:
−
** Note that you need to use ''h_data'' instead of ''h_vmem'' for GPU jobs. CUDA driver allocates a lot of "unused" virtual memory (tens of GB per card), which is counted in ''h_vmem'', but not in ''h_data''. All usual allocations (''malloc'', ''new'', Python allocations) seem to be included in ''h_data''.
+
# list available modules with: <code>module avail</code>
−
* Always specify the number of GPU cards (e.g. ''gpu=1''). Thus e.g. <code>qsub -q 'gpu*' -l gpu=1</code>
+
# load the version you need (possibly specifying the version of CuDNN): <code>module load <modulename></code>
−
* If you need more than one GPU card (on a single machine), always require as many CPU cores (''-pe smp X'') as many GPU cards you need. E.g. <code>qsub -q 'gpu*' -l gpu=2 -pe smp 4</code>
+
# you can unload the module with: <code>module unload <modulename></code>
−
* For interactive jobs, you can use ''qrsh'', but make sure to end your job as soon as you don't need the GPU (so don't use qrsh for long training). '''Warning: <code>-pty yes bash -l</code> is necessary''', otherwise the variable ''$CUDA_VISIBLE_DEVICES'' will not be set correctly. E.g. <code>qrsh -q 'gpu*' -l gpu=1 -pty yes bash -l</code>
 
−
* In general: don't reserve a GPU (as described above) without actually using it for longer time, e.g., try separating steps which need GPU and steps which do not and execute those separately on our GPU resp. CPU cluster.
 
−
* If you know an approximate runtime of your job, please specify it with ''-l s_rt=hh:mm:ss'' - this is a soft constraint so your job won't be killed if it runs longer than specified.
 
  
−
== CUDA and CUDNN ==
+
As of Apr 2023, the available modules are
 +
cuda/11.2
 +
cuda/11.2-cudnn8.1
 +
cuda/11.7
 +
cuda/11.7-cudnn8.5
 +
cuda/11.8
 +
cuda/11.8-cudnn8.5
 +
cuda/11.8-cudnn8.6
 +
cuda/11.8-cudnn8.9
  
−
Default CUDA (currently 10.1 as of Nov 2019) is available in
+
=== List of installed GPUs ===
−
  /opt/cuda
+
root@gpu-node1:~# nvidia-smi -L
−
Specific version can be found in
+
GPU 0: NVIDIA GeForce RTX 3090 (UUID: GPU-ba293e60-32f9-6907-705b-e053d1bf453b)
−
  /lnet/aic/opt/cuda/cuda-{9.0,9.2,10.0,10.1,...}
+
GPU 1: NVIDIA GeForce GTX 1080 Ti (UUID: GPU-b29fe79f-6192-5ece-6f91-e59d97ab304e)
−
Depending on what version you need, you should add <code>LD_LIBRARY_PATH="/lnet/aic/opt/cuda/cuda-X.Y/lib64:$LD_LIBRARY_PATH"</code> to your configuration.
+
GPU 2: NVIDIA GeForce GTX 1080 Ti (UUID: GPU-fbcba5d0-61bd-cc4c-810e-c80cbd9cd563)
 +
GPU 3: NVIDIA GeForce GTX 1080 Ti (UUID: GPU-76ae3ae7-0a2d-ea68-3070-94c919f40169)
 +
GPU 4: NVIDIA GeForce GTX 1080 Ti (UUID: GPU-18174817-13c3-b930-1d68-37c47b41dc0b)
 +
GPU 5: NVIDIA GeForce GTX 1080 Ti (UUID: GPU-3af8a5c5-9e07-9468-e9dc-e1259f3e7890)
 +
GPU 6: NVIDIA GeForce GTX 1080 Ti (UUID: GPU-51e376db-189b-b11d-bd27-bbbb6470ff26)
  
−
CUDNN is available directly in ''lib64'' directory of the respective CUDA, so no need to configure it specifically.
+
root@gpu-node2:~# nvidia-smi -L
 +
GPU 0: NVIDIA GeForce RTX 2080 Ti (UUID: GPU-15b17780-d818-bcd2-566c-564aa1dfc38e)
 +
GPU 1: NVIDIA GeForce RTX 2080 Ti (UUID: GPU-e184b0d4-7147-af43-041b-caa7f597363a)
 +
GPU 2: NVIDIA GeForce RTX 2080 Ti (UUID: GPU-ac1a453e-1c30-3fe0-e246-dd07c7645066)
 +
GPU 3: NVIDIA GeForce RTX 2080 Ti (UUID: GPU-4d19d859-d044-fdc8-17e0-e84fef4a8a13)
 +
GPU 4: NVIDIA GeForce RTX 2080 Ti (UUID: GPU-8035e3f3-76c9-124f-c5ea-d1dd4369f2a8)
 +
GPU 5: NVIDIA GeForce RTX 2080 Ti (UUID: GPU-670d0788-a048-8eef-ad1b-1eb77b18980b)
 +
GPU 6: NVIDIA GeForce RTX 2080 Ti (UUID: GPU-18d030c6-5956-f45f-7d15-ab53cffa813e)
 +
GPU 7: NVIDIA GeForce RTX 2080 Ti (UUID: GPU-f7940219-84a7-8c9c-386f-14e4043c9884)

Latest revision as of 11:37, 7 December 2023

Start by reading Submitting CPU Jobs page.

The GPU jobs are submitted to gpu partition.

To ask for one GPU card, use #SBATCH -G 1 directive or -G 1 option on the command line. The submitted job has CUDA_VISIBLE_DEVICES set appropriately, so all CUDA applications should use only the allocated GPUs.

Rules

  • Always use GPUs via sbatch (or srun), never via ssh. You can ssh to any machine e.g. to run nvidia-smi or htop, but not to start computing on GPU.
  • Don't forget to specify you RAM requirements with e.g. --mem=10G.
  • Always specify the number of GPU cards (e.g. -G 1). Thus e.g. srun -p gpu --mem=64G -G 2 --pty bash
  • For interactive jobs, you can use srun, but make sure to end your job as soon as you don't need the GPU (so don't use srun for long training).
  • In general: don't reserve a GPU (as described above) without actually using it for longer time, e.g., try separating steps which need GPU and steps which do not and execute those separately on our GPU resp. CPU cluster.
  • If you know an approximate runtime of your job, please specify it with -t . Acceptable time formats include "minutes", "minutes:seconds", "hours:minutes:seconds", "days-hours", "days-hours:minutes" and "days-hours:minutes:seconds".

CUDA and cuDNN

Available CUDA versions are in 

/lnet/aic/opt/cuda/

and as of Apr 2023, available versions as 10.1, 10.2, 11.2, 11.7, 11.8.

The cuDNN library is also available in the subdirectory cudnn/VERSION/lib64 of the respective CUDA directories.

Therefore, to use CUDA 11.2 with cuDNN 8.1.1, you should add the following to your .profile: 

export PATH="/lnet/aic/opt/cuda/cuda-11.2/bin:$PATH"
export LD_LIBRARY_PATH="/lnet/aic/opt/cuda/cuda-11.2/lib64:/lnet/aic/opt/cuda/cuda-11.2/cudnn/8.1.1/lib64:/lnet/aic/opt/cuda/cuda-11.2/extras/CUPTI/lib64:$LD_LIBRARY_PATH"
export XLA_FLAGS=--xla_gpu_cuda_data_dir=/lnet/aic/opt/cuda/cuda-11.2 # XLA configuration if you are using TensorFlow

CUDA modules

CUDA 11.2 and later can be also loaded as modules. This will set various environment variables for you so you should be able to use CUDA easily.

On a GPU node, you can do the following:

  1. list available modules with: module avail
  2. load the version you need (possibly specifying the version of CuDNN): module load <modulename>
  3. you can unload the module with: module unload <modulename>

As of Apr 2023, the available modules are 

cuda/11.2
cuda/11.2-cudnn8.1
cuda/11.7
cuda/11.7-cudnn8.5
cuda/11.8
cuda/11.8-cudnn8.5
cuda/11.8-cudnn8.6
cuda/11.8-cudnn8.9

List of installed GPUs

root@gpu-node1:~# nvidia-smi -L
GPU 0: NVIDIA GeForce RTX 3090 (UUID: GPU-ba293e60-32f9-6907-705b-e053d1bf453b)
GPU 1: NVIDIA GeForce GTX 1080 Ti (UUID: GPU-b29fe79f-6192-5ece-6f91-e59d97ab304e)
GPU 2: NVIDIA GeForce GTX 1080 Ti (UUID: GPU-fbcba5d0-61bd-cc4c-810e-c80cbd9cd563)
GPU 3: NVIDIA GeForce GTX 1080 Ti (UUID: GPU-76ae3ae7-0a2d-ea68-3070-94c919f40169)
GPU 4: NVIDIA GeForce GTX 1080 Ti (UUID: GPU-18174817-13c3-b930-1d68-37c47b41dc0b)
GPU 5: NVIDIA GeForce GTX 1080 Ti (UUID: GPU-3af8a5c5-9e07-9468-e9dc-e1259f3e7890)
GPU 6: NVIDIA GeForce GTX 1080 Ti (UUID: GPU-51e376db-189b-b11d-bd27-bbbb6470ff26)

root@gpu-node2:~# nvidia-smi -L
GPU 0: NVIDIA GeForce RTX 2080 Ti (UUID: GPU-15b17780-d818-bcd2-566c-564aa1dfc38e)
GPU 1: NVIDIA GeForce RTX 2080 Ti (UUID: GPU-e184b0d4-7147-af43-041b-caa7f597363a)
GPU 2: NVIDIA GeForce RTX 2080 Ti (UUID: GPU-ac1a453e-1c30-3fe0-e246-dd07c7645066)
GPU 3: NVIDIA GeForce RTX 2080 Ti (UUID: GPU-4d19d859-d044-fdc8-17e0-e84fef4a8a13)
GPU 4: NVIDIA GeForce RTX 2080 Ti (UUID: GPU-8035e3f3-76c9-124f-c5ea-d1dd4369f2a8)
GPU 5: NVIDIA GeForce RTX 2080 Ti (UUID: GPU-670d0788-a048-8eef-ad1b-1eb77b18980b)
GPU 6: NVIDIA GeForce RTX 2080 Ti (UUID: GPU-18d030c6-5956-f45f-7d15-ab53cffa813e)
GPU 7: NVIDIA GeForce RTX 2080 Ti (UUID: GPU-f7940219-84a7-8c9c-386f-14e4043c9884)
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