Question

我使用Apache Beam预处理TensorFlow的数据。我想根据数据集中的示例数量选择TFRecord分片的数量。相关的代码部分是：

EXAMPLES_PER_SHARD = 5.0
num_tfexamples = tfexample_strs | "count tf examples" >> beam.combiners.Count.Globally()
num_shards = num_tfexamples | ("compute number of shards" >>
                               beam.Map(lambda num_examples: int(math.ceil(num_examples / EXAMPLES_PER_SHARD))))
_ = tfexample_strs | ("output to tfrecords" >>
                      beam.io.WriteToTFRecord(OUTPUT_DIR, num_shards=beam.pvalue.AsSingleton(num_shards)))

这与stacktrace失败了：

File "/usr/local/lib/python2.7/dist-packages/apache_beam/io/iobase.py", line 1011, in start_bundle
    self.counter = random.randint(0, self.count - 1)
TypeError: unsupported operand type(s) for -: 'AsSingleton' and 'int' [while running 'output VALIDATION to tfrecords/Write/WriteImpl/ParDo(_RoundRobinKeyFn)']

我在PTransform的类定义中看到了这一行

# By default, transforms don't have any side inputs.
side_inputs = ()

是否可以将侧输入传递给PTransforms？谢谢你的帮助

Answer 1

WriteToTFRecord不支持使用num_shards的旁边输入。理论上没有什么能阻止它这样做（并且在Java SDK中可能），它只是没有在Python SDK中实现。随意提交JIRA。

我可以将侧输入传递给Apache Beam PTransforms吗？

1 个答案: