Tensorflow TPU 错误：Shuffle Buffer 已满？

如何解决Tensorflow TPU 错误：Shuffle Buffer 已满？

我一直在尝试使用 tensorflow 的 TPU 来训练计算机视觉模型，但是当我在 kaggle 的环境中提交 notebook 时一直出现错误。

这真的很奇怪，因为当我手动运行笔记本时，它可以正常工作并在

Kaggle Notebook

被以下消息困住了：

2021-01-08 00:28:59.042056：我 tensorflow/core/kernels/data/shuffle_dataset_op.cc:221] Shuffle Buffer Filled。

我的尝试

降低 buffer_size
改变加载函数的顺序
批量调整

如果有人知道发生了什么，请告诉我！

数据管道

AUTOTUNE = tf.data.experimental.AUTOTUNE
GCS_PATH = kaggleDatasets().get_gcs_path('cassava-leaf-disease-tfrecords-center-512x512')
BATCH_SIZE = 16 * strategy.num_replicas_in_sync
IMAGE_SIZE = [512,512]
TARGET_SIZE = 512
CLASSES = ['0','1','2','3','4']
EPOCHS = 20

def decode_image(image_data):
    image = tf.image.decode_jpeg(image_data,channels=3) #decoding jpeg-encoded img to uint8 tensor
    image = tf.cast(image,tf.float32) / 255.0 #cast int val to float so we can normalize it
    image = tf.image.resize(image,[*IMAGE_SIZE]) #added this back seeing if it does anything
    image = tf.reshape(image,[*IMAGE_SIZE,3]) #resizing to proper shape
    return image


def read_tfrecord(example,labeled=True):
    if labeled:
        TFREC_FORMAT = {
            'image': tf.io.FixedLenFeature([],tf.string),'target': tf.io.FixedLenFeature([],tf.int64),}
    else:
        TFREC_FORMAT = {
            'image': tf.io.FixedLenFeature([],'image_name': tf.io.FixedLenFeature([],}
    example = tf.io.parse_single_example(example,TFREC_FORMAT)
    image = decode_image(example['image'])
    if labeled:
        label_or_name = tf.cast(example['target'],tf.int32)
    else:
        label_or_name =  example['image_name']
    return image,label_or_name

def load_dataset(filenames,labeled=True,ordered=False):
    ignore_order = tf.data.Options()
    if not ordered:
        ignore_order.experimental_deterministic = False

    dataset = tf.data.TFRecordDataset(filenames,num_parallel_reads=AUTOTUNE)
    dataset = dataset.with_options(ignore_order)
    dataset = dataset.map(lambda x: read_tfrecord(x,labeled=labeled),num_parallel_calls=AUTOTUNE)
    return dataset

def get_training_dataset():
    dataset = load_dataset(TRAINING_FILENAMES,labeled=True)
    dataset = dataset.map(transform,num_parallel_calls=AUTOTUNE)
    dataset = dataset.repeat() # the training dataset must repeat for several epochs
    dataset = dataset.shuffle(2048) #set higher than input?
    dataset = dataset.batch(BATCH_SIZE)
    dataset = dataset.prefetch(AUTOTUNE) # prefetch next batch while training (autotune prefetch buffer size)
    return dataset

拟合模型：

history = model.fit(x=get_training_dataset(),epochs=EPOCHS,steps_per_epoch = STEPS_PER_EPOCH,validation_steps=VALID_STEPS,validation_data=get_validation_dataset(),callbacks = [lr_callback,model_save,my_early_stopper],verbose=1,)