Comments (6)
chulkilee
commented on June 23, 2024
1
ENV NVIDIA_VISIBLE_DEVICES all
ENV NVIDIA_DRIVER_CAPABILITIES compute,utility
Those were set. I'm using nvidia/cuda:11.8.0-base-ubuntu22.04 image - but still failing.
Update
declare -x CUDA_VERSION="11.8.0"
declare -x NVIDIA_REQUIRE_CUDA="cuda>=11.8 brand=tesla,driver>=450,driver<451 brand=tesla,driver>=470,driver<471 brand=unknown,driver>=470,driver<471 brand=nvidia,driver>=470,driver<471 brand=nvidiartx,driver>=470,driver<471 brand=geforce,driver>=470,driver<471 brand=geforcertx,driver>=470,driver<471 brand=quadro,driver>=470,driver<471 brand=quadrortx,driver>=470,driver<471 brand=titan,driver>=470,driver<471 brand=titanrtx,driver>=470,driver<471 brand=tesla,driver>=510,driver<511 brand=unknown,driver>=510,driver<511 brand=nvidia,driver>=510,driver<511 brand=nvidiartx,driver>=510,driver<511 brand=geforce,driver>=510,driver<511 brand=geforcertx,driver>=510,driver<511 brand=quadro,driver>=510,driver<511 brand=quadrortx,driver>=510,driver<511 brand=titan,driver>=510,driver<511 brand=titanrtx,driver>=510,driver<511 brand=tesla,driver>=515,driver<516 brand=unknown,driver>=515,driver<516 brand=nvidia,driver>=515,driver<516 brand=nvidiartx,driver>=515,driver<516 brand=geforce,driver>=515,driver<516 brand=geforcertx,driver>=515,driver<516 brand=quadro,driver>=515,driver<516 brand=quadrortx,driver>=515,driver<516 brand=titan,driver>=515,driver<516 brand=titanrtx,driver>=515,driver<516"
declare -x NV_CUDA_COMPAT_PACKAGE="cuda-compat-11-8"
declare -x NV_CUDA_CUDART_VERSION="11.8.89-1"
Even I unset NVIDIA_REQUIRE_CUDA - it still fails with the same error.
I also tested the same image with 1.19.4-4f0a078e and 1.19.5-64049ba8 AMI releases - both failed.
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chulkilee
commented on June 23, 2024
If it needs to be reported to https://github.com/awslabs/amazon-eks-ami/issues then please let me know.
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yeazelm
commented on June 23, 2024
Hello @chulkilee, thanks for cutting this issue! I don't believe this would be related to GSP on g4dn.xlarge instances but you could follow #3817 (comment) just to confirm that isn't the problem.
The difference in the output between Bottlerocket and Amazon Linux for the module config is:
Bottlerocket: ModifyDeviceFiles: 1
Amazon Linux: ModifyDeviceFiles: 0
Bottlerocket: EnableGpuFirmware: 18
Amazon Linux: EnableGpuFirmware: 0
EnableGpuFirmware is the GSP change and ModifyDeviceFiles will disable dynamic device file management when set to 0.
What is strange is that pytorch is reporting that CUDA is not available when it really should be since the other things you called out are there.
Can you also confirm what your podspec looks like just to make sure all the right settings are being passed from that perspective?
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yeazelm
commented on June 23, 2024
Hello @chulkilee, I just tried using an image from NVIDIA to confirm that pytorch can see the devices on a g4dn.xlarge node with latest bottlerocket and I don't get the same issue:
# python -c "import torch; print(torch.cuda.get_device_name(0))"
Tesla T4
Can you confirm which base container you are using and which CUDA version is included? I'm not able to replicate with the image I got.
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bryantbiggs
commented on June 23, 2024
@chulkilee do your container images contain the following environment variables?
ENV NVIDIA_VISIBLE_DEVICES all
ENV NVIDIA_DRIVER_CAPABILITIES compute,utility
If not, I would suggest adding them
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arnaldo2792
commented on June 23, 2024
@chulkilee , are you requesting GPUs in your pod specs? Or, do you need to oversubscribe your GPUs and thus you use NVIDIA_VISIBLE_DEVICES=all to get access to all the GPUs in the instance from your pod?
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