Streamline Workflows: Sacred + Neptune.ai Integration

Table of Contents

1. Introduction
2. Installing Dependencies
3. Importing Libraries
4. Creating a Neptune Run
5. Passing in Neptune Observer into Sacred Experiments Observers list
6. Defining a Simple One-Layer Linear Model
7. Creating an Experiment Configuration
8. Defining a Run
9. Logging Loss and Metrics
10. Logging Model Architecture and Weights
11. Exploring the Data in Neptune

How to Use the Neptune Sacred Integration

Neptune AI is a powerful platform for building, training, and deploying machine learning models. It provides a simple and efficient way to log, Visualize, and analyze your experiments in real-time. In this article, we will demonstrate how to use the Neptune Sacred integration to track and monitor your sacred experiments in Neptune.

Introduction

In this tutorial, we will use the Neptune Sacred integration to track and monitor experiments run using Sacred. Neptune is a machine learning platform that provides a powerful and interactive way to log, visualize, and analyze your experiments in real-time. Sacred is a tool that helps you manage your machine learning experiments by providing a unified interface to interact with them.

Installing Dependencies

Before we can start using the Neptune Sacred integration, we need to install some dependencies. These dependencies are required for creating and running experiments using Sacred and Neptune.

Importing Libraries

Once the dependencies are installed, we need to import the necessary libraries into our code.

Creating a Neptune Run

The first important step is to Create a Neptune run. We can do this by using the Neptune init method and passing in the project name and the API token.

Passing in Neptune Observer into Sacred Experiments Observers list

The Second step is to pass in the Neptune observer into the Sacred experiments observers list. We can do this by creating a Sacred experiment and appending the Neptune observer to the list of observers in that experiment.

Defining a Simple One-Layer Linear Model

Next, we will define a simple one-layer linear model.

Creating an Experiment Configuration

Then, we will create an experiment configuration.

Defining a Run

After the configuration is set, we will define a run.

Logging Loss and Metrics

To log our loss and metrics, we will use experiment log scalar and pass in the base name space that we want to use.

Logging Model Architecture and Weights

We will log the weights of the model and the architecture of the model by saving the architecture into a text file and saving our torch model using torch.save.

Exploring the Data in Neptune

Once the model weights and architecture are saved, we can explore the data in Neptune. We can see how our run is progressing and track live metrics such as CPU usage, RAM usage, final accuracy, and final loss.

Highlights

• Neptune AI is a powerful platform for building, training, and deploying machine learning models.
• Sacred is a tool that helps You manage your machine learning experiments by providing a unified interface to Interact with them.
• Using the Neptune Sacred integration, you can track and monitor your sacred experiments in real-time.
• You can visualize your results, explore your data, and make informed decisions about your models.

FAQ

Q: What is Neptune AI?

A: Neptune AI is a powerful platform for building, training, and deploying machine learning models.

Q: What is Sacred?

A: Sacred is a tool that helps you manage your machine learning experiments by providing a unified interface to interact with them.

Q: What does the Neptune Sacred integration do?

A: The Neptune Sacred integration allows you to track and monitor your sacred experiments in real-time.

Q: What kind of experiments can I run with the Neptune Sacred integration?

A: You can run any Sacred experiment with the Neptune Sacred integration.

Q: Can I use Neptune without Sacred?

A: Yes, you can use Neptune without Sacred, but the two tools work best together.