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Comet for Data Science

You're reading from   Comet for Data Science Enhance your ability to manage and optimize the life cycle of your data science project

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Product type Paperback
Published in Aug 2022
Publisher Packt
ISBN-13 9781801814430
Length 402 pages
Edition 1st Edition
Tools
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Author (1):
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Angelica Lo Duca Angelica Lo Duca
Author Profile Icon Angelica Lo Duca
Angelica Lo Duca
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Toc

Table of Contents (16) Chapters Close

Preface 1. Section 1 – Getting Started with Comet
2. Chapter 1: An Overview of Comet FREE CHAPTER 3. Chapter 2: Exploratory Data Analysis in Comet 4. Chapter 3: Model Evaluation in Comet 5. Section 2 – A Deep Dive into Comet
6. Chapter 4: Workspaces, Projects, Experiments, and Models 7. Chapter 5: Building a Narrative in Comet 8. Chapter 6: Integrating Comet into DevOps 9. Chapter 7: Extending the GitLab DevOps Platform with Comet 10. Section 3 – Examples and Use Cases
11. Chapter 8: Comet for Machine Learning 12. Chapter 9: Comet for Natural Language Processing 13. Chapter 10: Comet for Deep Learning 14. Chapter 11: Comet for Time Series Analysis 15. Other Books You May Enjoy

Technical requirements

The examples illustrated in this book use Python 3.8. You can download it from the official website at https://www.python.org/downloads/ and choose version 3.8.

The examples described in this chapter use the following Python packages:

  • comet-ml 3.23.0
  • matplotlib 3.4.3
  • numpy 1.19.5
  • pandas 1.3.4
  • scikit-learn 1.0

comet-ml

comet-ml is the main package to interact with Comet in Python. You can follow the official procedure to install the package, as explained at this link: https://www.comet.ml/docs/quick-start/.

Alternatively, you can install the package with pip in the command line, as follows:

pip install comet-ml==3.23.0

matplotlib

matplotlib is a very popular package for data visualization in Python. You can install it by following the official documentation, found at this link: https://matplotlib.org/stable/users/getting_started/index.html.

In pip, you can easily install matplotlib, as follows:

pip install matplotlib== 3.4.3

numpy

numpy is a package that provides useful functions on arrays and linear algebra. You can follow the official procedure, found at https://numpy.org/install/, to install numpy, or you can simply install it through pip, as follows:

pip install numpy==1.19.5

pandas

pandas is a very popular package for loading, cleaning, exploring, and managing datasets. You can install it by following the official procedure as explained at this link: https://pandas.pydata.org/getting_started.html.

Alternatively, you can install the pandas package through pip, as follows:

pip install pandas==1.3.4

scikit-learn

scikit-learn is a Python package for machine learning. It provides different machine learning algorithms, as well as functions and methods for data wrangling and model evaluation. You can install scikit-learn by following the official documentation, as explained at this link: https://scikit-learn.org/stable/install.html.

Alternatively, you can install scikit-learn through pip, as follows:

pip install scikit-learn==1.0

Now that we have installed all the required libraries, we can move on to how to get started with Comet, starting from the beginning. We will cover the motivation, purpose, and first access to the Comet platform.

You have been reading a chapter from
Comet for Data Science
Published in: Aug 2022
Publisher: Packt
ISBN-13: 9781801814430
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