Tensorflow-keras 2.X multi gpu prediction-CodePudding

I have 4GPU(rtx 3090) in one pc.

I used only 1GPU for training and prediction, but now I'm going to use 4GPU.

During training, 4gpu activation was successful, but only 1GPU is active for prediction.

mirrored_strategy = tf.distribute.MirroredStrategy()
with mirrored_strategy.scope():
    model = MyModel()

model_checkpoint = ModelCheckpoint(model_path, monitor='loss', verbose=1, save_best_only=True)
history = model.fit(trainData, steps_per_epoch=num_images//batch_size, verbose=1)

It Use 4 GPU

model.predict(testData, verbose=1)
#for x in testData:
#   model.predict_on_batch(x)

It use only 1 GPU

How can I use my all GPU?

CodePudding user response：

I solved! changed my test dataset!

I'm using test dataset with generator.

def testGenerator(image_path):
for file in image_path:
    img = file
    img = img / 255
    img = np.reshape(img,img.shape (1,)) if (not flag_multi_class) else img 
    yield img

I wanted to load the dataset into GPU memory.

So I used "tf.data.Dataset.from_generator" and my GPU worked!

Load saved model

model = tf.keras.models.load_model(model_path)

Make tensorflow dataset

testGene = testGenerator(img_data)
test_dataset = tf.data.Dataset.from_generator(
    lambda: testGene,
    output_types = tf.float64,
    #output_shapes = tf.TensorShape([None]),
    output_shapes = tf.TensorShape([512, 512, 1])
    ).batch(global_batch_size)

result = model.predict(
    test_dataset,
    verbose=1,
    steps=math.ceil(max / global_batch_size),
    callbacks=[CustomCallback(root_file_list, int(max / len(root_file_list) / global_batch_size))])

It worked using my all GPU!