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- PC환경 정보
o Windows 11에서 conda 가상환경에서 GPU 인식하기
o Windows 환경정보 ( DXDIAG 명령어로 확인)
-Windows 11 Pro 64-bit (10.0, Build 22631) (22621.ni_release.220506-1250)
13th Gen Intel(R) Core(TM) i7-13620H (16 CPUs), ~2.4GHz
- NVIDIA GeForce RTX 4060 Laptop GPU\
o Windows 에 NVIDIA 설치 정보
- NVIDIA CUDA Version : 12.4
- NVIDIA Cunna Version : 8.9
- Windows Conda 가상환경에서 GPU를 사용하려면 다음과 같은 제약조건이 인터넷 검색결과로 나옴
o NVIDIA CUDA Version : 11.x 이하일것
o NVIDIA Cunna Version : 8.x 이하일것
o Python : 3.10.x 이하일것
o Tensorflow 2.10.x 이하일것
위의 제약조건에 맞게 Conda 가상환경을 만들어 보고 가상환경에서 GPU가 인식되는지 테스트 해봄
- Cuda-SDK 다운로드 사이트 https://developer.nvidia.com/cuda-toolkit-archive
- cuda cudnn archive 사이트 https://developer.nvidia.com/rdp/cudnn-archive
- NVIDIA 그래픽카드 지원 확인 사이트 https://en.wikipedia.org/wiki/CUDA#GPUs_supported
- NVIDIA Drive 확인 Nvidia-smi
# C:\Users\shim>nvidia-smi
Wed May 15 12:16:12 2024
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 552.44 Driver Version: 552.44 CUDA Version: 12.4 |
|-----------------------------------------+------------------------+----------------------+
| GPU Name TCC/WDDM | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|=========================================+========================+======================|
| 0 NVIDIA GeForce RTX 4060 ... WDDM | 00000000:01:00.0 Off | N/A |
| N/A 44C P8 1W / 80W | 0MiB / 8188MiB | 0% Default |
| | | N/A |
+-----------------------------------------+------------------------+----------------------+
+-----------------------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=========================================================================================|
| No running processes found |
+-----------------------------------------------------------------------------------------+
- NVIDIA Cuda 확인 nvcc -V
# C:\Users\shim>nvcc -V
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2024 NVIDIA Corporation
Built on Thu_Mar_28_02:30:10_Pacific_Daylight_Time_2024
Cuda compilation tools, release 12.4, V12.4.131
Build cuda_12.4.r12.4/compiler.34097967_0
conda를 통한 가상환경 생성 및 GPU 확인
- conda에서 가상환경 설치하기
# 설치요약
1. 가상환경 디렉토리를 cuda 로 설정
2. python 3.10 설치
3. cudatoolkit 11.2 설치
4. cudnn=8.1.0 설치
5. Tensorflow 2.10 설치
# (base) C:\Users\shim>conda create -n cuda python=3.10
Channels:
- defaults
Platform: win-64
Collecting package metadata (repodata.json): done
Solving environment: done
<< Package Plan >>
environment location: C:\Users\shim\anaconda3\envs\cuda
added / updated specs:
- python=3.10
The following packages will be downloaded:
package | build
---------------------------|-----------------
pip-24.0 | py310haa95532_0 2.9 MB
python-3.10.14 | he1021f5_1 15.9 MB
setuptools-69.5.1 | py310haa95532_0 1013 KB
wheel-0.43.0 | py310haa95532_0 136 KB
------------------------------------------------------------
Total: 20.0 MB
The following NEW packages will be INSTALLED:
bzip2 pkgs/main/win-64::bzip2-1.0.8-h2bbff1b_6
ca-certificates pkgs/main/win-64::ca-certificates-2024.3.11-haa95532_0
libffi pkgs/main/win-64::libffi-3.4.4-hd77b12b_1
openssl pkgs/main/win-64::openssl-3.0.13-h2bbff1b_1
pip pkgs/main/win-64::pip-24.0-py310haa95532_0
python pkgs/main/win-64::python-3.10.14-he1021f5_1
setuptools pkgs/main/win-64::setuptools-69.5.1-py310haa95532_0
sqlite pkgs/main/win-64::sqlite-3.45.3-h2bbff1b_0
tk pkgs/main/win-64::tk-8.6.14-h0416ee5_0
tzdata pkgs/main/noarch::tzdata-2024a-h04d1e81_0
vc pkgs/main/win-64::vc-14.2-h21ff451_1
vs2015_runtime pkgs/main/win-64::vs2015_runtime-14.27.29016-h5e58377_2
wheel pkgs/main/win-64::wheel-0.43.0-py310haa95532_0
xz pkgs/main/win-64::xz-5.4.6-h8cc25b3_1
zlib pkgs/main/win-64::zlib-1.2.13-h8cc25b3_1
Proceed ([y]/n)? y
Downloading and Extracting Packages:
Preparing transaction: done
Verifying transaction: done
Executing transaction: done
To activate this environment, use
$ conda activate cuda
To deactivate an active environment, use
$ conda deactivate
# (base) C:\Users\shim>conda env list
conda environments:
base * C:\Users\shim\anaconda3
cuda C:\Users\shim\anaconda3\envs\cuda
llm C:\Users\shim\anaconda3\envs\llm
textgen2 C:\Users\shim\anaconda3\envs\textgen2
# (base) C:\Users\shim>conda activate cuda
# (cuda) C:\Users\shim>conda install -c conda-forge cudatoolkit=11.2 cudnn=8.1.0
Channels:
- conda-forge
- defaults
Platform: win-64
Collecting package metadata (repodata.json): done
Solving environment: done
<< Package Plan >>
environment location: C:\Users\shim\anaconda3\envs\cuda
added / updated specs:
- cudatoolkit=11.2
- cudnn=8.1.0
The following packages will be downloaded:
package | build
---------------------------|-----------------
cudatoolkit-11.2.2 | h7d7167e_13 634.5 MB conda-forge
cudnn-8.1.0.77 | h3e0f4f4_0 610.8 MB conda-forge
"By downloading and using the cuDNN conda packages, you accept the terms and conditions of the NVID
done
# (cuda) C:\Users\shim>python -m pip install "tensorflow<2.11"
Collecting tensorflow<2.11
Downloading tensorflow-2.10.1-cp310-cp310-win_amd64.whl.metadata (3.1 kB)
Collecting absl-py>=1.0.0 (from tensorflow<2.11)
Using cached absl_py-2.1.0-py3-none-any.whl.metadata (2.3 kB)
... 중간 생략
Downloading pyasn1_modules-0.4.0-py3-none-any.whl (181 kB)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 181.2/181.2 kB 5.5 MB/s eta 0:00:00
Using cached urllib3-2.2.1-py3-none-any.whl (121 kB)
Downloading oauthlib-3.2.2-py3-none-any.whl (151 kB)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 151.7/151.7 kB 4.6 MB/s eta 0:00:00
Downloading pyasn1-0.6.0-py2.py3-none-any.whl (85 kB)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 85.3/85.3 kB ? eta 0:00:00
Installing collected packages: tensorboard-plugin-wit, libclang, keras, flatbuffers, wrapt, urllib3, typing-extensions, termcolor, tensorflow-io-gcs-filesystem, tensorflow-estimator, tensorboard-data-server, six, pyasn1, protobuf, packaging, oauthlib, numpy, MarkupSafe, markdown, idna, grpcio, gast, charset-normalizer, certifi, cachetools, absl-py, werkzeug, rsa, requests, pyasn1-modules, opt-einsum, keras-preprocessing, h5py, google-pasta, astunparse, requests-oauthlib, google-auth, google-auth-oauthlib, tensorboard, tensorflow
Successfully installed MarkupSafe-2.1.5 absl-py-2.1.0 astunparse-1.6.3 cachetools-5.3.3 certifi-2024.2.2 charset-normalizer-3.3.2 flatbuffers-24.3.25 gast-0.4.0 google-auth-2.29.0 google-auth-oauthlib-0.4.6 google-pasta-0.2.0 grpcio-1.63.0 h5py-3.11.0 idna-3.7 keras-2.10.0 keras-preprocessing-1.1.2 libclang-18.1.1 markdown-3.6 numpy-1.26.4 oauthlib-3.2.2 opt-einsum-3.3.0 packaging-24.0 protobuf-3.19.6 pyasn1-0.6.0 pyasn1-modules-0.4.0 requests-2.31.0 requests-oauthlib-2.0.0 rsa-4.9 six-1.16.0 tensorboard-2.10.1 tensorboard-data-server-0.6.1 tensorboard-plugin-wit-1.8.1 tensorflow-2.10.1 tensorflow-estimator-2.10.0 tensorflow-io-gcs-filesystem-0.31.0 termcolor-2.4.0 typing-extensions-4.11.0 urllib3-2.2.1 werkzeug-3.0.3 wrapt-1.16.0
- conda 가상환경 GPU 확인 실행 테스트 및 결과
# (cuda) C:\Users\shim>python -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))"
[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]
# (cuda) C:\Users\shim>python
Python 3.10.14 | packaged by Anaconda, Inc. | (main, May 6 2024, 19:44:50) [MSC v.1916 64 bit (AMD64)] on win32
Type "help", "copyright", "credits" or "license" for more information.
# >>> from tensorflow.python.client import device_lib
# >>> device_lib.list_local_devices()
2024-05-15 11:54:20.473703: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1616] Created device /device:GPU:0 with 5449 MB memory: -> device: 0, name: NVIDIA GeForce RTX 4060 Laptop GPU, pci bus id: 0000:01:00.0, compute capability: 8.9
[name: "/device:CPU:0"
device_type: "CPU"
memory_limit: 268435456
locality {
}
incarnation: 11119453684472034767
xla_global_id: -1
, name: "/device:GPU:0"
device_type: "GPU"
memory_limit: 5713690624
locality {
bus_id: 1
links {
}
}
incarnation: 4304763808021192206
physical_device_desc: "device: 0, name: NVIDIA GeForce RTX 4060 Laptop GPU, pci bus id: 0000:01:00.0, compute capability: 8.9"
xla_global_id: 416903419
]
# >>> gpus = tf.config.experimental.list_physical_devices('GPU')
# >>> for gpu in gpus:
... print(gpu)
...
PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')
# >>> import tensorflow as tf
# >>> print("Num GPUs Available: ", len(tf.config.experimental.list_physical_devices('GPU')))
Num GPUs Available: 1
- Vitual Studio Code 에서 실행결과
# 파이선 코드
import tensorflow as tf
from tensorflow.python.client import device_lib
print(device_lib.list_local_devices())
print("Num GPUs AVAILABLE: ", len(tf.config.list_physical_devices('GPU')))
# 실행결과
PS C:\Users\shim\anaconda3\envs\cuda> & c:/Users/shim/anaconda3/envs/cuda/python.exe c:/Users/shim/anaconda3/envs/cuda/cuda_gpu_test.py
2024-05-15 12:53:21.191667: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX AVX2
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-05-15 12:53:21.806525: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1616] Created device /device:GPU:0 with 5449 MB memory: -> device: 0, name: NVIDIA GeForce RTX 4060 Laptop GPU, pci bus id: 0000:01:00.0, compute capability: 8.9
[name: "/device:CPU:0"
device_type: "CPU"
memory_limit: 268435456
locality {
}
incarnation: 18426872582674291349
xla_global_id: -1
, name: "/device:GPU:0"
device_type: "GPU"
memory_limit: 5713690624
locality {
bus_id: 1
links {
}
}
incarnation: 3688180905390782346
physical_device_desc: "device: 0, name: NVIDIA GeForce RTX 4060 Laptop GPU, pci bus id: 0000:01:00.0, compute capability: 8.9"
xla_global_id: 416903419
]
Num GPUs AVAILABLE: 1
- conda 가상환경 패키지 설치 정보 확인
# conda 환경에 cudatoolkit 11.2.2, cudnn 8.1.0.77, python 3.10.14, tensorflow 2.10.1이
설치되어 있는것을 확인할수 있다.
#(cuda) C:\Users\shim\anaconda3\envs\cuda>conda list
packages in environment at C:\Users\shim\anaconda3\envs\cuda:
$ Name Version Build Channel
absl-py 2.1.0 pypi_0 pypi
astunparse 1.6.3 pypi_0 pypi
bzip2 1.0.8 h2bbff1b_6
ca-certificates 2024.3.11 haa95532_0
cachetools 5.3.3 pypi_0 pypi
certifi 2024.2.2 pypi_0 pypi
charset-normalizer 3.3.2 pypi_0 pypi
cudatoolkit 11.2.2 h7d7167e_13 conda-forge
cudnn 8.1.0.77 h3e0f4f4_0 conda-forge
flatbuffers 24.3.25 pypi_0 pypi
gast 0.4.0 pypi_0 pypi
google-auth 2.29.0 pypi_0 pypi
google-auth-oauthlib 0.4.6 pypi_0 pypi
google-pasta 0.2.0 pypi_0 pypi
grpcio 1.63.0 pypi_0 pypi
h5py 3.11.0 pypi_0 pypi
idna 3.7 pypi_0 pypi
keras 2.10.0 pypi_0 pypi
keras-preprocessing 1.1.2 pypi_0 pypi
libclang 18.1.1 pypi_0 pypi
libffi 3.4.4 hd77b12b_1
markdown 3.6 pypi_0 pypi
markupsafe 2.1.5 pypi_0 pypi
numpy 1.26.4 pypi_0 pypi
oauthlib 3.2.2 pypi_0 pypi
openssl 3.3.0 hcfcfb64_0 conda-forge
opt-einsum 3.3.0 pypi_0 pypi
packaging 24.0 pypi_0 pypi
pip 24.0 py310haa95532_0
protobuf 3.19.6 pypi_0 pypi
pyasn1 0.6.0 pypi_0 pypi
pyasn1-modules 0.4.0 pypi_0 pypi
python 3.10.14 he1021f5_1
requests 2.31.0 pypi_0 pypi
requests-oauthlib 2.0.0 pypi_0 pypi
rsa 4.9 pypi_0 pypi
setuptools 69.5.1 py310haa95532_0
six 1.16.0 pypi_0 pypi
sqlite 3.45.3 h2bbff1b_0
tensorboard 2.10.1 pypi_0 pypi
tensorboard-data-server 0.6.1 pypi_0 pypi
tensorboard-plugin-wit 1.8.1 pypi_0 pypi
tensorflow 2.10.1 pypi_0 pypi
tensorflow-estimator 2.10.0 pypi_0 pypi
tensorflow-io-gcs-filesystem 0.31.0 pypi_0 pypi
termcolor 2.4.0 pypi_0 pypi
tk 8.6.14 h0416ee5_0
typing-extensions 4.11.0 pypi_0 pypi
tzdata 2024a h04d1e81_0
ucrt 10.0.22621.0 h57928b3_0 conda-forge
urllib3 2.2.1 pypi_0 pypi
vc 14.2 h21ff451_1
vc14_runtime 14.38.33130 h82b7239_18 conda-forge
vs2015_runtime 14.38.33130 hcb4865c_18 conda-forge
werkzeug 3.0.3 pypi_0 pypi
wheel 0.43.0 py310haa95532_0
wrapt 1.16.0 pypi_0 pypi
xz 5.4.6 h8cc25b3_1
zlib 1.2.13 h8cc25b3_1
(cuda) C:\Users\shim\anaconda3\envs\cuda>
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