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$ nvidia-smi
Mon Feb 5 13:14:45 2024
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 470.223.02 Driver Version: 470.223.02 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 RTXA6000-6C On | 00000000:00:05.0 Off | 0 |
| N/A N/A P8 N/A / N/A | 512MiB / 5976MiB | 0% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=============================================================================|
| No running processes found |
+-----------------------------------------------------------------------------+
|
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$ python3 --version
Python 3.8.18
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$ nvcc --version
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2022 NVIDIA Corporation
Built on Wed_Sep_21_10:33:58_PDT_2022
Cuda compilation tools, release 11.8, V11.8.89
Build cuda_11.8.r11.8/compiler.31833905_0
$ whereis cuda
cuda: /usr/local/cuda
$ |
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$ cat /home/<USERNAME>/miniforge3/envs/ML/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_ */ |
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$ 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
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$ conda list | grep keras
keras 2.13.1 pyhd8ed1ab_0 conda-forge |
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$ 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
# (OPTIONAL) Check pytorch
$ python
import torch
$ python
print(torch.__version__) # Print PyTorch version
2.2.0
$ python
print(torch.cuda.is_available()) # Check if CUDA is available
True
$ python
print(torch.version.cuda) # Print the CUDA version PyTorch is using
11.8
$ python
if torch.cuda.is_available():
# Create a tensor and move it to GPU
x = torch.tensor([1.0, 2.0]).cuda()
print(x) # Print the tensor to verify it's on the GPU
else:
print("CUDA is not available. Check your PyTorch installation.")
tensor([1., 2.], device='cuda:0')
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#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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