# GPU access from TF accessed through the C API on a Linux mainframe **URL:** \ **Category:** General Discussion\ **Tags:** help\_request\ **Created:** [December 6, 2024, 12:36pm UTC](https://discuss.ai.google.dev/t/gpu-access-from-tf-accessed-through-the-c-api-on-a-linux-mainframe/52998 "2024-12-06T12:36:14Z")\ **Posts on this page:** 2\ **Page:** 1
**Author:** ![Zsolt\_Szakaly](https://d1dlmcr85iqnpo.cloudfront.net/user_avatar/discuss.ai.google.dev/zsolt_szakaly/32/9255_2.png) [@Zsolt\_Szakaly](https://discuss.ai.google.dev/u/Zsolt_Szakaly)\ **Post date:** [December 6, 2024, 12:36pm UTC](https://discuss.ai.google.dev/t/gpu-access-from-tf-accessed-through-the-c-api-on-a-linux-mainframe/52998/1 "2024-12-06T12:36:14Z")
I have a program - actually written in Pascal - accessing Tensorflow through the C API. I have the program linked to libtensorflow.so and it works as it should on a computer without GPU. I tried the same on a high performance computer with GPU and something is different (it is actually a small partition with 1 GPU, but I tried on a large GPU partition and it is the same). Somehow it notices the GPU but complains that some libraries are missing, but I do not find what libraries. I read a lot on this forum and elsewhere, but most of the answears, examples are using Python examples, something I do not use. So, here are the details. The Linux: ```auto |LSB Version:|:core-4.1-amd64:core-4.1-noarch| |---|---| |Distributor ID:|RedHatEnterprise| |Description:|Red Hat Enterprise Linux release 8.6 (Ootpa)| |Release:|8.6| |Codename:|Ootpa| ``` The nvcc --version ```auto nvcc: NVIDIA (R) Cuda compiler driver Copyright (c) 2005-2023 NVIDIA Corporation Built on Wed_Nov_22_10:17:15_PST_2023 Cuda compilation tools, release 12.3, V12.3.107 Build cuda_12.3.r12.3/compiler.33567101_0 ``` The nvidia-smi ```auto +---------------------------------------------------------------------------------------+ | NVIDIA-SMI 530.30.02 Driver Version: 530.30.02 CUDA Version: 12.1 | |-----------------------------------------+----------------------+----------------------+ | 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 Quadro RTX 6000 Off| 00000000:A3:00.0 Off | Off | | 33% 26C P8 8W / 260W| 671MiB / 24576MiB | 0% Default | | | | N/A | +-----------------------------------------+----------------------+----------------------+ +---------------------------------------------------------------------------------------+ | Processes: | | GPU GI CI PID Type Process name GPU Memory | | ID ID Usage | |=======================================================================================| +---------------------------------------------------------------------------------------+ ``` The first error I get is I guess related to the fact that the TF version is optimized for GPU, but as no GPU is found, but the CPU could do more, it compains a bit, but as I understand it is OK. As I want to use the GPU, this should not be a problem. This error is only showed once when the program is started: ```auto 2024-12-06 13:14:57.084465: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations. To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags. WARNING: All log messages before absl::InitializeLog() is called are written to STDERR ``` Then every time a graph is created (or a session, I did not check), I get the following error. This is the one that bothers me, and what I would ask for help: ```auto W0000 00:00:1733487298.477811 174015 gpu_device.cc:2344] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform. Skipping registering GPU devices.. ``` I also checked internally from within the program with TF\_SessionListDevices and indeed it can only see the CPU: Device name, Device type, Device memory /job:localhost/replica:0/task:0/device:CPU:0 CPU 268435456 Needless to say, that I do not have root access, so I need a solution without it. Any help welcome. Thanks in advance, Zsolt ---
**Author:** ![Zsolt\_Szakaly](https://d1dlmcr85iqnpo.cloudfront.net/user_avatar/discuss.ai.google.dev/zsolt_szakaly/32/9255_2.png) [@Zsolt\_Szakaly](https://discuss.ai.google.dev/u/Zsolt_Szakaly)\ **Post date:** [December 7, 2024, 12:42pm UTC](https://discuss.ai.google.dev/t/gpu-access-from-tf-accessed-through-the-c-api-on-a-linux-mainframe/52998/3 "2024-12-07T12:42:27Z")
It seems I found this error. With the linux **strace** command I checked what libraries tensorflow is looking for and the missing one in my case was **libcudnn.so.9** , what I manually downloaded and put into a folder where it was accessed, and immediately the error is gone. Naturally - should somebody find this post once - others might have other missing files, but strace is a great tool for this. That I still do not understand though that if this error message is printed (in **gpu\_device.cc** line 2340) why the actually missing library is not printed. It could save hell a lot of time. Still I cannot use the GPU as I get an all devices are busy error. I read a lot about it and TF 2.18, so I still check, but that is another story.