Pescador is a library for streaming (numerical) data, primarily for use in machine learning applications.
Pescador addresses the following use cases:
- Hierarchical sampling
- Out-of-core learning
- Parallel streaming
These use cases arise in the following common scenarios:
Say you have three data sources (A, B, C) that you want to sample. For example, each data source could contain all the examples of a particular category.
Pescador can dynamically interleave these sources to provide a randomized stream D <- (A, B, C). The distribution over (A, B, C) need not be uniform: you can specify any distribution you like!
Now, say you have 3000 data sources, each of which may contain a large number of samples. Maybe that’s too much data to fit in RAM at once.
Pescador makes it easy to interleave these sources while maintaining a small working set. Not all sources are simultaneously active, but Pescador manages the working set so you don’t have to. This way, you can process the full data set out of core, but using a bounded amount of memory.
If loading data incurs substantial latency (e.g., due to accessing storage access or pre-processing), this can be a problem.
Pescador can seamlessly move data generation into a background process, so that your main thread can continue working.
To make this all possible, Pescador provides the following utilities:
- Streamer objects encapsulate data generators for re-use, infinite sampling, and inter-process communication.
- Multiplexing objects allow flexible sampling from multiple streams
- Parallel streaming provides parallel processing with low communication overhead
- Transform or modify streams with Maps (see Processing Data Streams)
- Buffering of sampled data into fixed-size batches (see pescador.buffer_stream)
Pescador can be installed from PyPI through pip:
pip install pescador
or via conda using the conda-forge channel:
conda install -c conda-forge pescador