Anaconda Tensorflow GPU 버전 설치

◼ GPU: NVIDIA RTX A4000 (NVIDIA driver) 473.81-quadro-rtx-desktop-notebook-win10-win11-64bit-international-dch-whql.exe (CUDA) cuda_11.4.4_472.50_windows.exe (CUDNN) cudnn-11.4-windows-x64-v8.2.2.26.zip ◼ Anaconda 3 conda create -n tf python=3.10 ◼ Tensorflow 2.9.1 pip install tensorflow-gpu==2.9.1

1. Anaconda3 설치

 

2. NVIDIA driver / CUDAN / cuDNN 설치

- CUDA 11.4.4 / cuDNN 8.2.2 로 세팅

 

3. 환경변수 추가 (하단에 3개 환경변수 추가 완료)

 

4. Anaconda 환경 생성

conda create -n tf_test pip python==3.10
conda activate tf_test
pip install tensorflow-gpu==2.9.1

 

5. 예제 실행

import tensorflow as tf
from tensorflow import keras

import numpy as np
import matplotlib.pyplot as plt

print(tf.__version__)

fashion_mnist = keras.datasets.fashion_mnist
(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()

class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',
               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']

plt.figure()
plt.imshow(train_images[0])
plt.colorbar()
plt.grid(False)
plt.show()

train_images = train_images / 255.0
test_images = test_images / 255.0

plt.figure(figsize=(10,10))
for i in range(25):
    plt.subplot(5,5,i+1)
    plt.xticks([])
    plt.yticks([])
    plt.grid(False)
    plt.imshow(train_images[i], cmap=plt.cm.binary)
    plt.xlabel(class_names[train_labels[i]])
plt.show()

model = keras.Sequential([
    keras.layers.Flatten(input_shape=(28, 28)),
    keras.layers.Dense(128, activation='relu'),
    keras.layers.Dense(10, activation='softmax')
])

model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

model.fit(train_images, train_labels, epochs=5)

test_loss, test_acc = model.evaluate(test_images,  test_labels, verbose=2)

print('\n테스트 정확도:', test_acc)

 

---

참고사이트

https://junn.net/archives/2466

Anaconda Tensorflow GPU 버전 설치 (2023.01 수정) - junn.net

https://arxiv.org/pdf/2301.00407.pdf

https://chancoding.tistory.com/223