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- Provision new Centos or Ubuntu instance.
- Select layout ending with
eumetsat-gpu and one of the plans listed above. Beside that, configure your instance as preferred and continue deployment process.
- Once VM is deployed, you can verify GPUs for example using
nvidia-smiprogram from command line (see below for confirming library installations and drivers).
Usage
Useful commands
You can see GPU information using nvidia-smi
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$ export PATH=$PATH:/usr/local/cuda-11.4/bin/ |
Libraries
CUDA version is currently 11.4 which need to be the same with drivers and thus can't be changed. Tensorflow library compatibility is available at: https://www.tensorflow.org/install/source#gpu. We have tested that TensorFlow > 2.6.1 work.
Using Conda
Update and conda installation
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$ nvidia-smi
Mon Jan 8 10:24:59 2024
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 470.161.03 Driver Version: 470.161.03 CUDA Version: 11.4 |
|-------------------------------+----------------------+----------------------+
| 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 NVIDIA RTXA6000... On | 00000000:00:05.0 Off | 0 |
| N/A N/A P8 N/A / N/A | 3712MiB / 48895MiB | 0% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=============================================================================|
| No running processes found |
+-----------------------------------------------------------------------------+
$ python3 --version
Python 3.8.18
$ nvcc --version
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2021 NVIDIA Corporation
Built on Mon_Oct_11_21:27:02_PDT_2021
Cuda compilation tools, release 11.4, V11.4.152
Build cuda_11.4.r11.4/compiler.30521435_0
$ whereis cuda
cuda: /usr/local/cuda
$ cat /home/<USERNAME>/miniforge3/envs/myenv/include/cudnn.h
.
.
.
/* cudnn : Neural Networks Library
*/
#if !defined(CUDNN_H_)
#define CUDNN_H_
#include <cuda_runtime.h>
#include <stdint.h>
#include "cudnn_version.h"
#include "cudnn_ops_infer.h"
#include "cudnn_ops_train.h"
#include "cudnn_adv_infer.h"
#include "cudnn_adv_train.h"
#include "cudnn_cnn_infer.h"
#include "cudnn_cnn_train.h"
#include "cudnn_backend.h"
#if defined(__cplusplus)
extern "C" {
#endif
#if defined(__cplusplus)
}
#endif
#endif /* CUDNN_H_ */
$ conda list | grep tensorflow
tensorflow 2.13.1 cuda118py38h409af0c_1 conda-forge
tensorflow-base 2.13.1 cuda118py38h52ca5c6_1 conda-forge
tensorflow-estimator 2.13.1 cuda118py38ha2f8a09_1 conda-forge
tensorflow-gpu 2.13.1 cuda118py38h0240f8b_1 conda-forge
$ conda list | grep keras
keras 2.13.1 pyhd8ed1ab_0 conda-forge
$ python
import tensorflow as tf
tf.test.is_built_with_cuda()
True
tf.config.list_physical_devices('GPU')
[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]
print(tf.__version__)
2.13.1 |
#Using Docker
If you want to use GPUs in docker, you need to take few extra steps after creating the VM.
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