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@ -30,7 +30,95 @@ nvidia-sim简称NVSMI,提供监控GPU使用情况和更改GPU状态的功能 |
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更详细的介绍:https://blog.csdn.net/C_chuxin/article/details/82993350 |
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CMD中输入: |
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``` |
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```plant |
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nvidia-smi |
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``` |
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![图 2](../images/5bc51343e21394d4f82ce4a63916e4af7df2acf0be93a444928e300bd564a089.png) |
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- GPU:本机中的GPU编号(有多块显卡的时候,从0开始编号)图上GPU的编号是:0 |
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- Fan:风扇转速(0%-100%),N/A表示没有风扇 |
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- Name:GPU类型,图上GPU的类型是:Tesla T4 |
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- Temp:GPU的温度(GPU温度过高会导致GPU的频率下降) |
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- Perf:GPU的性能状态,从P0(最大性能)到P12(最小性能),图上是:P0 |
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- Persistence-M:持续模式的状态,持续模式虽然耗能大,但是在新的GPU应用启动时花费的时间更少,图上显示的是:off |
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- Pwr:Usager/Cap:能耗表示,Usage:用了多少,Cap总共多少 |
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- Bus-Id:GPU总线相关显示,domain:bus:device.function |
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- Disp.A:Display Active ,表示GPU的显示是否初始化 |
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- Memory-Usage:显存使用率 |
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- Volatile GPU-Util:GPU使用率 |
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- Uncorr. ECC:关于ECC的东西,是否开启错误检查和纠正技术,0/disabled,1/enabled |
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- Compute M:计算模式,0/DEFAULT,1/EXCLUSIVE_PROCESS,2/PROHIBITED |
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- Processes:显示每个进程占用的显存使用率、进程号、占用的哪个GPU |
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隔几秒刷新一下显存状态:nvidia-smi -l 秒数 |
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隔两秒刷新一下GPU的状态:nvidia-smi -l 2 |
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## tensorflow的显卡使用方式 |
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### 1、直接使用 |
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这种方式会把当前机器上所有的显卡的剩余显存基本都占用,注意是机器上所有显卡的剩余显存。 |
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```python |
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with tf.compat.v1.Session() as sess: |
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# 输入图片为256x256,2个分类 |
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shape, classes = (224, 224, 3), 20 |
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# 调用keras的ResNet50模型 |
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model = keras.applications.resnet50.ResNet50(input_shape = shape, weights=None, classes=classes) |
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model.compile(optimizer="adam", loss="sparse_categorical_crossentropy", metrics=["accuracy"]) |
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# 训练模型 categorical_crossentropy sparse_categorical_crossentropy |
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# training = model.fit(train_x, train_y, epochs=50, batch_size=10) |
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model.fit(train_x,train_y,validation_data=(test_x, test_y), epochs=20, batch_size=6,verbose=2) |
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# # 把训练好的模型保存到文件 |
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model.save('resnet_model_dog_n_face.h5') |
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``` |
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### 2、分配比例使用 |
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```python |
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from tensorflow.compat.v1 import ConfigProto# tf 2.x的写法 |
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config =ConfigProto() |
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config.gpu_options.per_process_gpu_memory_fraction=0.6 |
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with tf.compat.v1.Session(config=config) as sess: |
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model = keras.applications.resnet50.ResNet50(input_shape = shape, weights=None, classes=classes) |
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``` |
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### 3. 动态申请使用 |
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**这种方式是动态申请显存的,只会申请内存,不会释放内存。而且如果别人的程序把剩余显卡全部占了,就会报错。** |
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```python |
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config = tf.compat.v1.ConfigProto() |
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config.gpu_options.allow_growth = True |
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session = tf.compat.v1.InteractiveSession(config=config) |
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with tf.compat.v1.Session(config=config) as sess: |
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model |
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``` |
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### 4 指定GPU |
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在有多块GPU的服务器上运行tensorflow的时候,如果使用python编程,则可指定GPU,代码如下: |
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```python |
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import os |
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os.environ["CUDA_VISIBLE_DEVICES"] = "2" |
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``` |
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> 未完待续... |
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