TensorFlow2、CUDA10、cuDNN7.6.5

浅浅的花香味﹌ 2023-07-02 03:27 19阅读 0赞

# ![20191009191333910.png][] #

# [日萌社][Link 1] #

[人工智能AI：Keras PyTorch MXNet TensorFlow PaddlePaddle 深度学习实战（不定时更新）][AI_Keras PyTorch MXNet TensorFlow PaddlePaddle]

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### **安装** ###

[TensorFlow2、CUDA10、cuDNN7.6.5][TensorFlow2_CUDA10_cuDNN7.6.5]

[Anaconda3 python 3.7、TensorFlow2、CUDA10、cuDNN7.6.5][Anaconda3 python 3.7_TensorFlow2_CUDA10_cuDNN7.6.5]

[TensorFlow 2.0 环境搭建][TensorFlow 2.0]

[window下安装 Keras、TensorFlow（先安装CUDA、cuDNN，再安装Keras、TensorFlow）][window_ Keras_TensorFlow_CUDA_cuDNN_Keras_TensorFlow]

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### 下载NVIDIA驱动：https://www.geforce.cn/drivers      ###

### TensorFlow2.0需要cuda10，所以应该装410.48以上版本驱动  ###

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3ppbWlhbzU1MjE0NzU3Mg_size_16_color_FFFFFF_t_70][]![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3ppbWlhbzU1MjE0NzU3Mg_size_16_color_FFFFFF_t_70 1][]

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### CUDA、cuDNN百度盘下载 ###

链接：https://pan.baidu.com/s/1oqYxOYob9MBuqHYsxkS-IA   
提取码：y8ub

链接：https://pan.baidu.com/s/1YqfX0ObJSSUIaHYW3OW3cQ   
提取码：21lu   
链接：https://pan.baidu.com/s/1yF7e6ntWpXpdPWFLN4gi2g   
提取码：f1zi

CUDA、tensorflow版本清单：https://tensorflow.google.cn/install/source#linux
    CUDA下载：https://developer.nvidia.com/cuda-toolkit-archive

### CUDA、cuDNN、tensorflow版本要求清单  ###

### **注意：使用cuDNN v7.4.2.24可能会报错（亲测），但使用cuDNN v7.6.5.32的话，并不会出现“cuDNN v7.4.2.24可能会出现的”报错，虽然官网推荐组合是cuDNN v7.4.2.24，但建议使用cuDNN v7.6.5.32** ###

### ![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3ppbWlhbzU1MjE0NzU3Mg_size_16_color_FFFFFF_t_70 2][] ###

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## 测试GPU是否能正常运行tensorflow  ##

>>> import tensorflow as tf
    >>> tf.test.is_gpu_available()

import tensorflow as tf
    import timeit
    
    with tf.device('/cpu:0'):
        cpu_a = tf.random.normal([10000, 1000])
        cpu_b = tf.random.normal([1000, 2000])
        print(cpu_a.device, cpu_b.device)
    
    with tf.device('/gpu:0'):
        gpu_a = tf.random.normal([10000, 1000])
        gpu_b = tf.random.normal([1000, 2000])
        print(gpu_a.device, gpu_b.device)
    
    
    def cpu_run():
        with tf.device('/cpu:0'):
            c = tf.matmul(cpu_a, cpu_b)
        return c
    
    
    def gpu_run():
        with tf.device('/gpu:0'):
            c = tf.matmul(gpu_a, gpu_b)
        return c
    
    
    # warm up
    cpu_time = timeit.timeit(cpu_run, number=10)
    gpu_time = timeit.timeit(gpu_run, number=10)
    print('warmup:', cpu_time, gpu_time)
    
    cpu_time = timeit.timeit(cpu_run, number=10)
    gpu_time = timeit.timeit(gpu_run, number=10)
    print('run time:', cpu_time, gpu_time)

## ![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9henVzYS5ibG9nLmNzZG4ubmV0_size_16_color_FFFFFF_t_70 20][] ##

##  ##

[20191009191333910.png]: https://img-blog.csdnimg.cn/20191009191333910.png
[Link 1]: http://www.rimengshe.com/
[AI_Keras PyTorch MXNet TensorFlow PaddlePaddle]: https://blog.csdn.net/zimiao552147572/article/details/88867161
[TensorFlow2_CUDA10_cuDNN7.6.5]: https://blog.csdn.net/zimiao552147572/article/details/104084170
[Anaconda3 python 3.7_TensorFlow2_CUDA10_cuDNN7.6.5]: https://blog.csdn.net/zimiao552147572/article/details/103658619
[TensorFlow 2.0]: https://blog.csdn.net/zimiao552147572/article/details/104222637
[window_ Keras_TensorFlow_CUDA_cuDNN_Keras_TensorFlow]: https://blog.csdn.net/zimiao552147572/article/details/88854746
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