Abstract: Systems and methods for parallelization of machine learning computing code are described herein. In one aspect, embodiments of the present disclosure include a method of generating a plurality of instruction sets from machine learning computing code for parallel execution in a multi-processor environment, which may be implemented on a system, of, partitioning training data into two or more training data sets for performing machine learning, identifying a set of concurrently-executable tasks from the machine learning computing code, assigning the set of tasks to two or more of the computing elements in the multi-processor environment, and/or generating the plurality of instruction sets to be executed in the multi-processor environment to perform a set of processes represented by the machine learning computing code.