jupyter notebook如何使用gpu
时间: 2023-10-03 10:06:40 浏览: 276
使用GPU加速在Jupyter Notebook中运行代码需要进行一些额外的设置。首先,您需要确保已正确安装了支持GPU的版本的TensorFlow(例如tensorflow-gpu)。然后,您可以按照以下步骤在Jupyter Notebook中使用GPU:
1. 导入必要的库和模块:
```python
import os
import tensorflow as tf
```
2. 设置CUDA可见设备:
```python
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
```
这里的"0"表示使用第一个可用的GPU设备。如果您有多个GPU设备,可以根据需要更改此值。
3. 创建一个会话并指定GPU设备:
```python
config = tf.compat.v1.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = 0.9
sess = tf.compat.v1.Session(config=config)
```
这里的`per_process_gpu_memory_fraction`参数指定了您想要分配给TensorFlow的GPU内存的比例。您可以根据需要进行调整。
4. 运行您的代码:
```python
# 在GPU上运行代码
with tf.device('/gpu:0'):
tf.compat.v1.disable_eager_execution() # 禁用eager execution
hello = tf.constant('Hello, TensorFlow')
print(sess.run(hello))
```
这样,您就可以在Jupyter Notebook中使用GPU加速运行代码了。
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