joblib

Why Joblib: Project Goals

Benefits of pipelines Pipeline processing systems can provide a set of useful features: Data-flow programming for performance On-demand computing: in pipeline systems such as labView or VTK, calculations are performed as needed by the outputs and only when inputs change. Transparent parallelization: a pipeline topology can be inspected to deduce which operations can be run in parallel …

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Data Persistence In Joblib

Use case joblib.dump() and joblib.load() provide a replacement for pickle to work efficiently on arbitrary Python objects containing large data, in particular large numpy arrays. A simple example First create a temporary directory: Then create an object to be persisted: which is saved into filename: The object can then be reloaded from the file: Persistence in file objects Instead …

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Module Reference for Joblibb

joblib.Memory class joblib.Memory(location=None, backend=’local’, cachedir=None, mmap_mode=None, compress=False, verbose=1, bytes_limit=None, backend_options={}) A context object for caching a function’s return value each time it is called with the same input arguments. All values are cached on the filesystem, in a deep directory structure. Parameter Name Description location: str or None The path of the base directory to use as a data store or None. If …

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Embarrassingly Parallel for Loops

Common usage Joblib provides a simple helper class to write parallel for loops using multiprocessing. The core idea is to write the code to be executed as a generator expression, and convert it to parallel computing: can be spread over 2 CPUs using the following: Thread-based parallelism vs process-based parallelism By default joblib.Parallel uses the ‘loky’ backend module to …

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