Images, neural network weights, text documents) are typically represented asĭata structures containing NumPy arrays. With NumPy arrays: In machine learning and AI applications, data (e.g., Plots is included at the end of the post. TheĮxperiments were done using pickle.HIGHEST_PROTOCOL. Here we show some performance improvements over Python’s pickle module. Preferred a columnar data layout more optimized for big data. Required implementing a lot of the facilities that Arrow already has and we Of Flatbuffers actually could be made to work, but it would have Numerical data, and that approach wouldn’t satisfy 1, 3, or 4. Protocol Buffers, but protocol buffers really isn’t designed for We can naturally fall back to pickle for anything we can’t handle wellĪlternatives to Arrow: We could have built on top of.Shared memory and used by multiple processes (requirements 1 and 3). Arrow supports zero-copy reads, so objects can naturally be stored in.Reading the full object (requirements 1 and 4). Offsets into a serialized data blob can be computed in constant time without.The data layout is language independent (requirement 5).With the Apache Arrow team, we built libraries for mapping general Python Our Approach: To satisfy requirements 1-5, we chose to use theĪpache Arrow format as our underlying data representation. However, itĭoes not satisfy requirements 1, 3, 4, or 5. Very general, especially if you use variants like cloudpickle. The go-to serialization approach in Python is the pickle module. Workers to use objects created by workers in Java or other languages and vice It should be language independent (eventually we’d like to enable Python.Require reading the entire serialized object). Deserialization should be extremely fast (when possible, it should not.It should be compatible with shared memory, allowing multiple processes.It should be about as fast as Pickle for general Python types.NumPy arrays and Pandas DataFrames, as well as objects that recursively contain It should be very efficient with large numerical data (this includes.Lot on serialization and data handling, with the following design goals: Serialization and deserialization are bottlenecks in parallel and distributedĬomputing, especially in machine learning applications with large objects andĪs Ray is optimized for machine learning and AI applications, we have focused a Have pointers to other Python objects, and these objects are all allocated inĭifferent regions of memory, and all of this has to make sense when unpacked by Why is any translation necessary? Well, when you create a Python object, it may That can be stored … or transmitted … and reconstructed later (possibly … the process of translating data structures or object state into a format The main problem this addresses is data serialization. This post elaborates on the integration between Ray and Apache Arrow. Philipp Moritz and Robert Nishihara are graduate students at UC Berkeley. This was originally posted on the Ray blog. Fast Python Serialization with Ray and Apache Arrow
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