How AI developers optimize and reduce execution time for Python ML models?

4 min readApr 28, 2023

How an AI developer optimize and reduce execution time for python ML models? — Photo by Agê Barros on Unsplash

As a programmer you must know that Python is an interpreter programming language and these sorts of programming languages are slow in comparison to compiler programming languages like Java and C++. But there are some strategies that artificial intelligence(AI) developers can implement to optimize and decrease execution time for Python machine learning (ML) models, for instance:

Using binary formats for saving models

Saving machine learning models in binary formats like .pkl, .H5, or .pb can decrease execution time for Python.

Using JIT compilers

Just-In-Time (JIT) compilers can compile Python code on the fly, which can improve the performance of the code [1].

Vectorization

This technique involves performing operations on arrays and matrices instead of looping through each element. This can significantly enhance the performance of the code. AI developer can use NumPy or pandas libraries for vectorization. They can reduce the number of iterations required and speed up computations.

Parallelization

Parallel processing can be used to distribute the workload across multiple CPUs or GPUs. This can result in significant speedups for certain operations. Parallelization is a technique of executing multiple tasks simultaneously. You can use libraries like Joblib or Dask to parallelize the execution of machine learning models. It involves dividing a computation into smaller parts that can be run in parallel on multiple cores or CPUs [2].

Algorithm optimization

The choice of algorithm can have a significant impact on the performance of the code. AI developers can optimize the algorithms used in their machine learning models to make them more efficient. Some algorithms perform better than others on certain types of data. You can use libraries like scikit-learn or TensorFlow to try different algorithms and choose the best one for your data [3].

Hardware acceleration

Hardware acceleration techniques such as using GPUs or TPUs can remarkably speed up the training of machine learning models. You can use cloud computing services like AWS, Google Cloud Platform or Microsoft Azure to use hardware with better performance.

Using optimized libraries

Python has several libraries such as NumPy, SciPy, and Pandas that are optimized for scientific computing and machine learning. These libraries provide highly optimized algorithms for common operations and can significantly speed up the code[4].

Data preprocessing

Preprocess your data to remove any unwanted data and transform the data into a format that is easier for the machine learning model to use. This can help to speed up the execution.

Code optimization

AI developers can optimize their code by identifying bottlenecks and optimizing critical sections of the code. This can involve rewriting code in a lower-level language, reducing memory usage, or optimizing algorithms. AI developer can write optimized code that takes advantage of the strengths of Python and use libraries like Cython or Numba to optimize Python code.

Using pre-trained models

Pre-trained models can save time and computational resources by providing a pre-trained network that can be fine-tuned for a specific task.

Are binary formats faster?

Binary formats in ml models like .pkl, .H5, and .pb are file formats used to store data and machine learning models in a binary format, which is a compact, binary representation of the data that is faster to read and write compared to text-based formats like .py or .csv.

.pkl is the file format used by Python’s pickle library to serialize and deserialize Python objects, including machine learning models [5].
.H5 is the file format used by the Hierarchical Data Format (HDF) library to store large and complex datasets, including machine learning models.
.pb is the file format used by Google’s Protocol Buffers to serialize and deserialize structured data, including machine learning models.

Using binary formats to store machine learning models is often preferred over other formats because they are more compact and efficient to read and write, and can be easily loaded into memory for use in training or inference.

Summary

while Python may not be as fast as some other programming languages, there are several techniques that AI developers can use to optimize and decrease the executing time in their machine learning models. By means of these techniques, Python is still the most applied programming language for developing AI applications.