深度学习的训练过程常常非常耗时，一个模型训练几个小时是家常便饭，训练几天也是常有的事情，有时候甚至要训练几十天。
训练过程的耗时主要来自于两个部分，一部分来自数据准备，另一部分来自参数迭代。
当数据准备过程还是模型训练时间的主要瓶颈时，我们可以使用更多进程来准备数据。
当参数迭代过程成为训练时间的主要瓶颈时，我们通常的方法是应用GPU或者Google的TPU来进行加速。
详见《用GPU加速Keras模型——Colab免费GPU使用攻略》
https://zhuanlan.zhihu.com/p/68509398
无论是内置fit方法，还是自定义训练循环，从CPU切换成单GPU训练模型都是非常方便的，无需更改任何代码。当存在可用的GPU时，如果不特意指定device，tensorflow会自动优先选择使用GPU来创建张量和执行张量计算。
但如果是在公司或者学校实验室的服务器环境，存在多个GPU和多个使用者时，为了不让单个同学的任务占用全部GPU资源导致其他同学无法使用（tensorflow默认获取全部GPU的全部内存资源权限，但实际上只使用一个GPU的部分资源），我们通常会在开头增加以下几行代码以控制每个任务使用的GPU编号和显存大小，以便其他同学也能够同时训练模型。
在Colab笔记本中：修改->笔记本设置->硬件加速器 中选择 GPU
注：以下代码只能在Colab 上才能正确执行。
可通过以下colab链接测试效果《tf_单GPU》：
https://colab.research.google.com/drive/1r5dLoeJq5z01sU72BX2M5UiNSkuxsEFe
%tensorflow_version 2.x import tensorflow as tf print(tf.__version__) from tensorflow.keras import * # 打印时间分割线 @tf.function def printbar(): ts = tf.timestamp() today_ts = ts%(24*60*60) hour = tf.cast(today_ts//3600+8,tf.int32)%tf.constant(24) minite = tf.cast((today_ts%3600)//60,tf.int32) second = tf.cast(tf.floor(today_ts%60),tf.int32) timeformat(m): if tf.strings.length(tf.strings.format("{}",m))==1: return(tf.strings.format(0{}"else tf.strings.join([timeformat(hour),timeformat(minite),timeformat(second)],separator = :) tf.print(=========="*8,end = ""print(timestring)
一，GPU设置
gpus = tf.config.list_physical_devices(GPU) if gpus: gpu0 = gpus[0] 如果有多个GPU，仅使用第0个GPU tf.config.experimental.set_memory_growth(gpu0,True) 设置GPU显存用量按需使用 或者也可以设置GPU显存为固定使用量(例如：4G) tf.config.experimental.set_virtual_device_configuration(gpu0, [tf.config.experimental.VirtualDeviceConfiguration(memory_limit=4096)]) tf.config.set_visible_devices([gpu0],) 比较GPU和CPU的计算速度 printbar() with tf.device(/gpu:0): tf.random.set_seed(0) a = tf.random.uniform((10000,100),minval = 0,maxval = 3.0) b = tf.random.uniform((100,100000),1)">) c = a@b tf.print(tf.reduce_sum(tf.reduce_sum(c,axis = 0),axis=0)) printbar() printbar() with tf.device(/cpu:00)) printbar()
================================================================================11:59:21 2.24953778e+11 ================================================================================11:59:23 ================================================================================11:59:23 2.24953795e+11 ================================================================================11:59:29
二，准备数据
MAX_LEN = 300 BATCH_SIZE = 32 (x_train,y_train),(x_test,y_test) = datasets.reuters.load_data() x_train = preprocessing.sequence.pad_sequences(x_train,maxlen=MAX_LEN) x_test = preprocessing.sequence.pad_sequences(x_test,1)">MAX_LEN) MAX_WORDS = x_train.max()+1 CAT_NUM = y_train.max()+1 ds_train = tf.data.Dataset.from_tensor_slices((x_train,y_train)) \ .shuffle(buffer_size = 1000).batch(BATCH_SIZE) \ .prefetch(tf.data.experimental.AUTOTUNE).cache() ds_test = tf.data.Dataset.from_tensor_slices((x_test,y_test)) \ .shuffle(buffer_size = 1000).batch(BATCH_SIZE) \ .prefetch(tf.data.experimental.AUTOTUNE).cache()
三，定义模型
tf.keras.backend.clear_session() create_model(): model = models.Sequential() model.add(layers.Embedding(MAX_WORDS,7,input_length=MAX_LEN)) model.add(layers.Conv1D(filters = 64,kernel_size = 5,activation = relu)) model.add(layers.MaxPool1D(2)) model.add(layers.Conv1D(filters = 32,kernel_size = 3,1)">)) model.add(layers.Flatten()) model.add(layers.Dense(CAT_NUM,activation = softmax)) return(model) model = create_model() model.summary()
Model: sequential" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= embedding (Embedding) (None,300,7) 216874 conv1d (Conv1D) (None,296,64) 2304 max_pooling1d (MaxPooling1D) (None,148,64) 0 conv1d_1 (Conv1D) (None,146,32) 6176 max_pooling1d_1 (MaxPooling1 (None,73,32) 0 flatten (Flatten) (None,2336) 0 dense (Dense) (None,46) 107502 ================================================================= Total params: 332,856 Trainable params: 332,1)"> Non-trainable params: 0 _________________________________________________________________
四，训练模型
optimizer = optimizers.Nadam() loss_func = losses.SparseCategoricalCrossentropy() train_loss = metrics.Mean(name='train_loss') train_metric = metrics.SparseCategoricalAccuracy(name=train_accuracy) valid_loss = metrics.Mean(name=valid_loss) valid_metric = metrics.SparseCategoricalAccuracy(name=valid_accuracy) @tf.function train_step(model,features,labels): with tf.GradientTape() as tape: predictions = model(features,training = True) loss = loss_func(labels,predictions) gradients = tape.gradient(loss,model.trainable_variables) optimizer.apply_gradients(zip(gradients,model.trainable_variables)) train_loss.update_state(loss) train_metric.update_state(labels,predictions) @tf.function valid_step(model,labels): predictions = model(features) batch_loss = train_model(model,ds_train,ds_valid,epochs): for epoch in tf.range(1,epochs+1): for features,labels in ds_train: train_step(model,labels) ds_valid: valid_step(model,labels) logs = Epoch={},Loss:{},Accuracy:{},Valid Loss:{},Valid Accuracy:{}' if epoch%1 ==0: printbar() tf.print(tf.strings.format(logs,(epoch,train_loss.result(),train_metric.result(),valid_loss.result(),valid_metric.result()))) tf.) train_loss.reset_states() valid_loss.reset_states() train_metric.reset_states() valid_metric.reset_states() train_model(model,ds_test,10)
================================================================================12:01:11 Epoch=1,Loss:2.00887108,Accuracy:0.470273882,Valid Loss:1.6704694,Valid Accuracy:0.566340148 ================================================================================12:01:13 Epoch=2,Loss:1.47044504,Accuracy:0.618681788,Valid Loss:1.51738906,Valid Accuracy:0.630454123 ================================================================================12:01:14 Epoch=3,Loss:1.1620506,Accuracy:0.700289488,Valid Loss:1.52190566,Valid Accuracy:0.641139805 ================================================================================12:01:16 Epoch=4,Loss:0.878907442,Accuracy:0.771654427,Valid Loss:1.67911685,Valid Accuracy:0.644256473 ================================================================================12:01:17 Epoch=5,Loss:0.647668123,Accuracy:0.836450696,Valid Loss:1.93839979,Valid Accuracy:0.642475486 ================================================================================12:01:19 Epoch=6,Loss:0.487838209,Accuracy:0.880538881,Valid Loss:2.20062685,Valid Accuracy:0.642030299 ================================================================================12:01:21 Epoch=7,Loss:0.390418053,Accuracy:0.90670228,Valid Loss:2.32795334,Valid Accuracy:0.646482646 ================================================================================12:01:22 Epoch=8,Loss:0.328294098,Accuracy:0.92351371,Valid Loss:2.44113493,Valid Accuracy:0.644701719 ================================================================================12:01:24 Epoch=9,Loss:0.286735713,Accuracy:0.931195736,Valid Loss:2.5071857,Valid Accuracy:0.642920732 ================================================================================12:01:25 Epoch=10,Loss:0.256434649,Accuracy:0.936428428,Valid Loss:2.60177088,Valid Accuracy:0.640249312
参考：
开源电子书地址：https://lyhue1991.github.io/eat_tensorflow2_in_30_days/
GitHub 项目地址：https://github.com/lyhue1991/eat_tensorflow2_in_30_days
版权声明：本文内容由互联网用户自发贡献，该文观点与技术仅代表作者本人。本站仅提供信息存储空间服务，不拥有所有权，不承担相关法律责任。如发现本站有涉嫌侵权/违法违规的内容， 请发送邮件至 dio@foxmail.com 举报，一经查实，本站将立刻删除。