In this article, you will learn how to use Python Joblib to run code in parallel.
Python Joblib Library
You can use Python’s joblib library to run code in parallel by following these steps:
Import the necessary functions from the joblib library:
from joblib import Parallel, delayed
Define a function that you want to run in parallel:
def my_function(x): return x**2
Create a list of inputs to the function that you want to run in parallel:
inputs = [1, 2, 3, 4, 5]
Use the Parallel function from the joblib library to run the function in parallel:
results = Parallel(n_jobs=-1)(delayed(my_function)(i) for i in inputs)
In the above code, n_jobs=-1 tells joblib to use all available CPU cores to run the function in parallel. The delayed function is used to wrap the my_function function, so that it can be called with the inputs.
The results variable will contain the outputs of the function for each input. In this example, results will be [1, 4, 9, 16, 25], which are the results of calling my_function with the inputs 1, 2, 3, 4, and 5, respectively.
Here’s some more information about using Python’s joblib library for parallel processing:
- The joblib library provides easy-to-use tools for parallel computing in Python, including support for multi-core and distributed computing.
- The Parallel function is the main entry point for using joblib for parallel computing. It takes a list of computations (in the form of functions and inputs) and runs them in parallel.
- The n_jobs argument to the Parallel function specifies the number of parallel jobs to run. A value of -1 means to use all available cores. Other possible values include an integer for a fixed number of cores to use, or None for using a single core.
- The delayed function is used to create a “lazy” function call, which delays the actual execution of the function until it is needed. This allows the joblib library to parallelize the execution of the function across multiple cores or nodes.
- You can also use other functions from the joblib library, such as Parallel’s backend argument, to control the behavior of the parallel execution. For example, you can use the multiprocessing backend to run jobs in separate processes, or the loky backend to run jobs in separate threads.
- In addition to Parallel, joblib also provides other functions for parallel computing, such as Memory for caching intermediate results, and cpu_count for getting the number of available CPUs.
Overall, using joblib for parallel computing in Python can greatly speed up your code, especially for tasks that involve a lot of computation or data processing.