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tensorflow

GPU memory usage

Sometimes Tensorflow exits with strange error like Could not create cudnn handle: CUDNN_STATUS_ALLOC_FAILED. That maybe happened because you wanted Tensorflow to run on a GPU, whoose memory is already in use by some other processes; in that case Tensorflow doesn't even give it a try with the remaining memory (StackOverflow question). To force Tensorflow into starting with less than all GPU memory set environment variable TF_FORCE_GPU_ALLOW_GROWTH = true. See e.g. here.

Datasets

Interleaving multiple Datasets

ds0 = tf.data.Dataset.range(0, 10, 2)
ds1 = tf.data.Dataset.range(1, 10, 2)

# Zip combines an element from each input into a single element, and flat_map
# enables you to map the combined element into two elements, then flattens the
# result.
dataset = tf.data.Dataset.zip((ds0, ds1)).flat_map(
    lambda x0, x1: tf.data.Dataset.from_tensors(x0).concatenate(
        tf.data.Dataset.from_tensors(x1)))

Source. https://stackoverflow.com/a/47344405