How to install a Machine Learning library?

Tiara Williamson
Tiara WilliamsonAnswered

A Machine Learning library or framework is a collection of procedures and functions developed in a specific computer language. Basically, they are interfaces, libraries, or tools that enable developers to construct machine learning models with ease and speed, going beyond the technical intricacies of the underlying algorithms. They let developers do complicated operations without having to modify several lines of code.


TensorFlow is an open-source and free software library designed for research and development by the Google Brain Team. It is a math library that facilitates the simple and effective construction of Machine Learning algorithms and is used for ML applications.

The advent of high-level APIs like Keras and Theano has made TensorFlow more successful in enhancing computers’ capacity to accurately anticipate answers.

Remember that TensorFlow provides robust Python and C APIs. It offers parallel processing, is easily trainable on CPU and GPU for distributed computing, and is supported by a large community. Additional benefits include improved computational graph visualizations, rapid notifications, common new releases with new features, good debugging techniques, and scalability.

So how do you install TensorFlow?

  • Install Miniconda.

Miniconda suggested for TensorFlow with GPU support. It establishes a distinct environment to prevent any installed program from being altered. This is also the simplest method for installing the necessary software, particularly for the GPU setup.

  • Create a conda environment.

Using this command:

conda create --name tf python = 3.9

Ensure it is enabled throughout the remainder of the installation.

  • Setup TensorFlow.

TensorFlow needs a current version of pip, so update your pip installation to ensure you are running the most recent version.

  • Install TensorFlow when using pip.

Conda cannot be used to install TensorFlow. It may not include the most recent stable version. Since TensorFlow is only officially distributed on PyPI, pip is suggested.

  • Verify, then install.

Verify the CPU configuration:

python3 -C " import tensorflow as tf ; print ( tf.reduce_sum ( tf.random.normal ( [ 1000 , 1000 ] ) ) ) "

If a tensor is received, TensorFlow was successfully deployed.

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