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# TensorFlow限制显存使用量

## 显式地设置显存使用比例

Tensorflow默认会完全占用满指定GPU的显存, 而线上部署后, 只需要进行推测, 是不需要占用完整块GPU显存的. 需要在代码中控制显存.

### 设置静态值

使用`per_process_gpu_memory_fraction`参数, 显式的指定每块GPU使用的内存比例, 设置的范围为0\~1, 代表占用上限为0\~100%.

```python
import tensorflow as tf
from keras.backend.tensorflow_backend import set_session
config = tf.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = 0.3
set_session(tf.Session(config=config))
```

keras需要使用`set_session`函数设置Session.

### 按需分配

```python
import tensorflow as tf
from keras.backend.tensorflow_backend import set_session
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
set_session(tf.Session(config=config))
```

使用`allow_growth`设置显存按需分配, 动态增长. 但注意一旦当前服务获取到了一定数量的显存, 后面是不会再还给系统的, 即使因为某些原因显存使用降低了. 总结来说, 这是一个**不可逆的过程**.

## 参考资料

* [限制TensorFlow显存使用量](https://zhuanlan.zhihu.com/p/57654552)


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