Julia ML：是否存在用于将数据加载到Flux，Knet，深度学习库的推荐数据格式

Question

I use Tensorflow for deep learning work, but I was interested in some of the features of Julia for ML. Now in Tensorflow, there is a clear standard that protocol buffers--meaning TFRecords format is the best way to load sizable datasets to the GPUs for model training. I have been reading the Flux, KNET, documentation as well as other forum posts looking to see if there is any particular recommendation on the most efficient data format. But I have not found one.

My question is, is there a recommended data format for the Julia ML libraries to facilitate training? In other words, are there any clear dataset formats that I should avoid because of bad performance?

Now, I know that there is a Protobuf.jl library so users can still use protocol buffers. I was planning to use protocol buffers for now, since I can then use the same data format for Tensorflow and Julia. However, I also found this interesting Reddit post about how the user is not using protocol buffers and just using straight Julia Vectors.

https://www.reddit.com/r/MachineLearning/comments/994dl7/d_hows_julia_language_mit_for_ml/

I get that the Julia ML libraries are likely data storage format agnostic. Meaning that no matter what format in which the data is stored, the data gets decoded to some sort of vector or matrix format anyway. So in that case I can use whatever format. But just wanted to make sure I did not miss anything in the documentation or such about problems or low performance due to using the wrong data storage format.

Answer 1

For in-memory use just use arrays and vectors. They're just big contiguous lumps of memory with some metadata. You can't really get any better than that.

For serializing to another Julia process, Julia will handle that for you and use the stdlib Serialization module.

For serializing to disk you should either Just use Serialization.serialize (possibly compressed) or, if you think you might need to read from another program or if you think you'll change Julia version before you're done with the data you can use BSON.jl or Feather.jl.

In the near future, JLSO.jl will be a good option for replacing Serialization.