Joblib: running Python functions as pipeline jobs¶
Joblib is a set of tools to provide lightweight pipelining in Python. In particular:
transparent disk-caching of functions and lazy re-evaluation (memoize pattern)
easy simple parallel computing
Joblib is optimized to be fast and robust on large data in particular and has specific optimizations for numpy arrays. It is BSD-licensed.
The vision is to provide tools to easily achieve better performance and reproducibility when working with long running jobs.
Avoid computing the same thing twice: code is often rerun again and again, for instance when prototyping computational-heavy jobs (as in scientific development), but hand-crafted solutions to alleviate this issue are error-prone and often lead to unreproducible results.
Persist to disk transparently: efficiently persisting arbitrary objects containing large data is hard. Using joblib’s caching mechanism avoids hand-written persistence and implicitly links the file on disk to the execution context of the original Python object. As a result, joblib’s persistence is good for resuming an application status or computational job, eg after a crash.
Joblib addresses these problems while leaving your code and your flow control as unmodified as possible (no framework, no new paradigms).
Transparent and fast disk-caching of output value: a memoize or make-like functionality for Python functions that works well for arbitrary Python objects, including very large numpy arrays. Separate persistence and flow-execution logic from domain logic or algorithmic code by writing the operations as a set of steps with well-defined inputs and outputs: Python functions. Joblib can save their computation to disk and rerun it only if necessary:
>>> from joblib import Memory >>> cachedir = 'your_cache_dir_goes_here' >>> mem = Memory(cachedir) >>> import numpy as np >>> a = np.vander(np.arange(3)).astype(float) >>> square = mem.cache(np.square) >>> b = square(a) ________________________________________________________________________________ [Memory] Calling square... square(array([[0., 0., 1.], [1., 1., 1.], [4., 2., 1.]])) ___________________________________________________________square - 0...s, 0.0min >>> c = square(a) >>> # The above call did not trigger an evaluation
Embarrassingly parallel helper: to make it easy to write readable parallel code and debug it quickly:
>>> from joblib import Parallel, delayed >>> from math import sqrt >>> Parallel(n_jobs=1)(delayed(sqrt)(i**2) for i in range(10)) [0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0]
Fast compressed Persistence: a replacement for pickle to work efficiently on Python objects containing large data ( joblib.dump & joblib.load ).
- Why joblib: project goals
- Installing joblib
- On demand recomputing: the Memory class
- Embarrassingly parallel for loops
- Common usage
- Thread-based parallelism vs process-based parallelism
- Serialization & Processes
- Shared-memory semantics
- Reusing a pool of workers
- Working with numerical data in shared memory (memmapping)
- Avoiding over-subscription of CPU resources
- Custom backend API (experimental)
- Old multiprocessing backend
- Bad interaction of multiprocessing and third-party libraries
- Parallel reference documentation
A context object for caching a function's return value each time it is called with the same input arguments.
Helper class for readable parallel mapping.
Persist an arbitrary Python object into one file.
Reconstruct a Python object from a file persisted with joblib.dump.
Quick calculation of a hash to identify uniquely Python objects containing numpy arrays.
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