gpu24 and gpu25 added to the cluster

[Predrag Punosevac]

I just installed the RPM provided by you. However I am not sure that this
is the correct RPM. In the past I used to install something like



libcudnn8-8.1.1.33-1.cuda11.2.x86_64.rpm
libcudnn8-devel-8.1.1.33-1.cuda11.2.x86_64.rpm
libcudnn8-samples-8.1.1.33-1.cuda11.2.x86_64.rpm

On Fri, Dec 17, 2021 at 11:13 AM Viraj Mehta <virajm at cs.cmu.edu> wrote:

> Hi Predrag,
>
> This should be sitting in the scratch. Let me know if there are any issues.
>
> Cheers,
> Viraj
>
> On Fri, Dec 17, 2021 at 9:38 AM Predrag Punosevac <predragp at andrew.cmu.edu>
> wrote:
>
>> Yes. Please get me 5 RPS for RHEL 8.1 and put them in your scratch on
>> GPU24. Make sure they are for 64 bit AMD/Intel. They have them for ARM and
>> Power architecture.
>>
>> On Fri, Dec 17, 2021, 10:31 AM Viraj Mehta <virajm at cs.cmu.edu> wrote:
>>
>>> Hey Predrag,
>>>
>>> I can get it for you. Out of the options listed in the attached image,
>>> which one would make sense to install? I was thinking the RHEL x86  version
>>> would be most appropriate.
>>>
>>> Best,
>>> Viraj
>>>
>>>
>>>
>>> On Fri, Dec 17, 2021 at 9:10 AM Predrag Punosevac <
>>> predragp at andrew.cmu.edu> wrote:
>>>
>>>> It is not installed right now. It is proprietary software and I have to
>>>> locate my NVIDIA developer credentials to get RPS. If someone can download
>>>> it quickly for me I will install it.
>>>>
>>>> On Fri, Dec 17, 2021, 9:11 AM Ifigeneia Apostolopoulou <
>>>> iapostol at andrew.cmu.edu> wrote:
>>>>
>>>>> Hello Predrag,
>>>>>
>>>>> could you also please provide the cuDNN version? I couldn't find
>>>>> cudnn.h in /usr/include, /usr/local/cuda-11/include,
>>>>> /usr/local/cuda/include, /usr/local/cuda-11/include,
>>>>> /usr/local/cuda-11.5/include
>>>>>
>>>>> thanks!
>>>>>
>>>>>
>>>>> On Thu, Dec 16, 2021 at 1:27 PM Predrag Punosevac <
>>>>> predragp at andrew.cmu.edu> wrote:
>>>>>
>>>>>> Just to add to this info. The installed version of CUDA is
>>>>>>
>>>>>> cuda-11.5.1-1.x86_64
>>>>>>
>>>>>> We already have a bunch of servers using cuda 11.1 but perhaps
>>>>>> nothing newer than 11.3. Rolling back to EOL version CUDA 10 is the option
>>>>>> of the last resort.
>>>>>>
>>>>>> I installed /opt/miniconda-py39
>>>>>>
>>>>>> which is Python 3.9.5. Most older servers run Python 3.8 branch or
>>>>>> even 3.7 branch.
>>>>>>
>>>>>> I would like everyone to keep in mind that the OS packaging problem
>>>>>> is NP hard so rolling things back to some "sweet spot" might be a
>>>>>> prohibitively expensive approach.
>>>>>>
>>>>>> Predrag
>>>>>>
>>>>>> On Thu, Dec 16, 2021 at 12:40 PM Ifigeneia Apostolopoulou <
>>>>>> iapostol at andrew.cmu.edu> wrote:
>>>>>>
>>>>>>> Hi all,
>>>>>>>
>>>>>>> Has anyone tried to test the new servers?
>>>>>>>
>>>>>>> I have not managed to run neither pytorch nor tensorflow processes.
>>>>>>> I am getting the following errors:
>>>>>>>
>>>>>>> tensorflow: CUDA runtime implicit initialization on GPU:0 failed.
>>>>>>> Status: device kernel image is invalid
>>>>>>>
>>>>>>> pytorch: RuntimeError: CUDA error: no kernel image is available for
>>>>>>> execution on the device
>>>>>>>
>>>>>>> I am not sure whether this is a CUDA installation issue /
>>>>>>> incompatibility (however, I am facing a problem with both pytorch and
>>>>>>> tensorflow processes that can run on the rest of the servers).
>>>>>>>
>>>>>>> thanks!
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>> On Wed, Dec 15, 2021 at 10:26 PM Predrag Punosevac <
>>>>>>> predragp at andrew.cmu.edu> wrote:
>>>>>>>
>>>>>>>> Dear Autonians,
>>>>>>>>
>>>>>>>> I just finished provisioning two new GPU nodes. The purchase was
>>>>>>>> approved by Dr. Schneider in July but the order was not placed until late
>>>>>>>> August due to CMU internal issues just in time to be affected by supply
>>>>>>>> chain disruption. The servers were finally shipped on 11/24/2021
>>>>>>>> and received last Wednesday 12/8/2021. To add the final insult to
>>>>>>>> the injury the nodes were not tagged until Monday afternoon. I had
>>>>>>>> literally to hunt down people to do the work.
>>>>>>>> I spent half a day yesterday getting power cables and other misc
>>>>>>>> supplies. Thus they are only done today. However, I think they are
>>>>>>>> definitely worth the trouble.
>>>>>>>>
>>>>>>>> Each server comes with 8 NVIDIA RTX A6000 connected by high-speed
>>>>>>>> GPU interconnect NVIDIA links beside PCIe. Each server has 2 AMD EPYC 7502
>>>>>>>> 32-Core Processors for a total of 128 threads per server. These CPUs are
>>>>>>>> almost as fast as your desktop processors 3.5 GHz.
>>>>>>>> Each server has 512GB of RAM and 2TB of scratch. These servers have
>>>>>>>> 24 2'5" HDD bays so they could potentially be used as a storage space. I
>>>>>>>> don't have 2'5" HDDs in the lab right now to populate the bays.
>>>>>>>>
>>>>>>>> There is one thing which is for now done suboptimally. Namely the
>>>>>>>> servers were shipped with 1Gbs copper NIC and 10Gbs fiber optical NIC. I
>>>>>>>> could not locate long enough optical cables in our lab yesterday but I will
>>>>>>>> try to address this issue soon. I have exactly 2 optical connectors on the
>>>>>>>> switch so it is down to cabling.
>>>>>>>>
>>>>>>>> Have fun and sorry for a long delay.
>>>>>>>>
>>>>>>>> Predrag
>>>>>>>>
>>>>>>>
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