如果使用多GPU训练模型，推荐使用内置fit方法，较为方便，仅需添加2行代码。
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。
https://colab.research.google.com/drive/1j2kp_t0S_cofExSN7IyJ4QtMscbVlXU-
MirroredStrategy过程简介：
- 训练开始前，该策略在所有 N 个计算设备上均各复制一份完整的模型；
- 每次训练传入一个批次的数据时，将数据分成 N 份，分别传入 N 个计算设备（即数据并行）；
- N 个计算设备使用本地变量（镜像变量）分别计算自己所获得的部分数据的梯度；
- 使用分布式计算的 All-reduce 操作，在计算设备间高效交换梯度数据并进行求和，使得最终每个设备都有了所有设备的梯度之和；
- 使用梯度求和的结果更新本地变量（镜像变量）；
- 当所有设备均更新本地变量后，进行下一轮训练（即该并行策略是同步的）。
tensorflow_version 2.x import tensorflow as tf print(tf.__version__) from tensorflow.keras import * # 此处在colab上使用1个GPU模拟出两个逻辑GPU进行多GPU训练 gpus = tf.config.experimental.list_physical_devices('GPU'if gpus: 设置两个逻辑GPU模拟多GPU训练 try: tf.config.experimental.set_virtual_device_configuration(gpus[0],[tf.config.experimental.VirtualDeviceConfiguration(memory_limit=1024),tf.config.experimental.VirtualDeviceConfiguration(memory_limit=1024)]) logical_gpus = tf.config.experimental.list_logical_devices() print(len(gpus),"Physical GPU,",len(logical_gpus),1)">Logical GPUs") except RuntimeError as e: print(e)
2.2.0-rc2
1 Physical GPU,2 Logical GPUs
一，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()
二，定义模型
tf.keras.backend.clear_session() def 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) compile_model(model): model.compile(optimizer=optimizers.Nadam(),loss=losses.SparseCategoricalCrossentropy(from_logits=True),metrics=[metrics.SparseCategoricalAccuracy(),metrics.SparseTopKCategoricalAccuracy(5)]) return(model)
三，训练模型
增加以下两行代码 strategy = tf.distribute.MirroredStrategy() with strategy.scope(): model = create_model() model.summary() model = compile_model(model) history = model.fit(ds_train,validation_data = ds_test,epochs = 10)
WARNING:tensorflow:NCCL is not supported when using virtual GPUs,fallingback to reduction to one device INFO:tensorflow:Using MirroredStrategy with devices (/job:localhost/replica:0/task:0/device:GPU:0',1)">/job:localhost/replica:0/task:0/device:GPU:1) 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 Epoch 1/10 INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:GPU:0 then broadcast to (). INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:GPU:0 then broadcast to (). INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:cpu:0 then broadcast to (/job:localhost/replica:0/task:0/device:cpu:0,). INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:cpu:0 then broadcast to (281/281 [==============================] - 4s 15ms/step - sparse_categorical_accuracy: 0.3546 - loss: 3.5168 - sparse_top_k_categorical_accuracy: 0.7163 - val_sparse_categorical_accuracy: 0.5000 - val_loss: 3.3722 - val_sparse_top_k_categorical_accuracy: 0.7066 Epoch 2/10 281/281 [==============================] - 5s 18ms/step - sparse_categorical_accuracy: 0.5279 - loss: 3.3386 - sparse_top_k_categorical_accuracy: 0.7267 - val_sparse_categorical_accuracy: 0.5387 - val_loss: 3.3299 - val_sparse_top_k_categorical_accuracy: 0.7173 Epoch 3/10 281/281 [==============================] - 5s 18ms/step - sparse_categorical_accuracy: 0.5583 - loss: 3.3094 - sparse_top_k_categorical_accuracy: 0.7238 - val_sparse_categorical_accuracy: 0.5490 - val_loss: 3.3169 - val_sparse_top_k_categorical_accuracy: 0.7217 Epoch 4/10 281/281 [==============================] - 5s 18ms/step - sparse_categorical_accuracy: 0.5856 - loss: 3.2818 - sparse_top_k_categorical_accuracy: 0.7244 - val_sparse_categorical_accuracy: 0.5574 - val_loss: 3.3077 - val_sparse_top_k_categorical_accuracy: 0.7217 Epoch 5/10 281/281 [==============================] - 5s 18ms/step - sparse_categorical_accuracy: 0.5967 - loss: 3.2693 - sparse_top_k_categorical_accuracy: 0.7242 - val_sparse_categorical_accuracy: 0.5659 - val_loss: 3.2993 - val_sparse_top_k_categorical_accuracy: 0.7248 Epoch 6/10 281/281 [==============================] - 5s 18ms/step - sparse_categorical_accuracy: 0.6030 - loss: 3.2626 - sparse_top_k_categorical_accuracy: 0.7262 - val_sparse_categorical_accuracy: 0.5690 - val_loss: 3.2974 - val_sparse_top_k_categorical_accuracy: 0.7244 Epoch 7/10 281/281 [==============================] - 5s 18ms/step - sparse_categorical_accuracy: 0.6054 - loss: 3.2600 - sparse_top_k_categorical_accuracy: 0.7266 - val_sparse_categorical_accuracy: 0.5677 - val_loss: 3.2980 - val_sparse_top_k_categorical_accuracy: 0.7262 Epoch 8/10 281/281 [==============================] - 5s 18ms/step - sparse_categorical_accuracy: 0.6065 - loss: 3.2581 - sparse_top_k_categorical_accuracy: 0.7273 - val_sparse_categorical_accuracy: 0.5708 - val_loss: 3.2990 - val_sparse_top_k_categorical_accuracy: 0.7262 Epoch 9/10 281/281 [==============================] - 5s 18ms/step - sparse_categorical_accuracy: 0.6091 - loss: 3.2558 - sparse_top_k_categorical_accuracy: 0.7283 - val_sparse_categorical_accuracy: 0.5726 - val_loss: 3.2952 - val_sparse_top_k_categorical_accuracy: 0.7253 Epoch 10/10 281/281 [==============================] - 5s 18ms/step - sparse_categorical_accuracy: 0.6093 - loss: 3.2551 - sparse_top_k_categorical_accuracy: 0.7288 - val_sparse_categorical_accuracy: 0.5726 - val_loss: 3.2908 - val_sparse_top_k_categorical_accuracy: 0.7244
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
原文链接：/tensorflow/991523.html