Log Experimentation Metadata: Docker + neptune.ai Integration

Table of Contents:

1. Introduction
2. Setting Up Neptune and Docker
3. Creating a Neptune Run
4. Logging Model Configuration and Hyperparameters
5. Logging Metrics and Losses
6. Specifying Dependencies in a Requirements.txt File
7. Creating a Dockerfile
8. Building and Running the Docker Image
9. Monitoring Model Training Metrics Live
10. Creating a Custom Dashboard
11. Conclusion

Introduction

In this guide, we will explore how to use Neptune and Docker to log your experimentation metadata as a data scientist at Neptune AI. You can use Neptune in any Python environment to log and retrieve ML metadata, including containerized Python scripts or applications. We will focus on the Neptune-related parts of an image classification example and provide code examples on GitHub for further exploration.

Setting Up Neptune and Docker

Before we dive into the details, the first important step is to create a Neptune run. To do this, pass in the project name API token to the neptune.init() method. The API token can be passed as a hard-coded value, environment variable, or as a Docker secret. In this guide, we will use the option of passing it as an environment variable.

Creating a Neptune Run

Once the run is created, we can retrieve the run object that behaves like a dictionary. This allows us to create namespaces inside the run, which will be useful for logging different aspects of our experiment.

Logging Model Configuration and Hyperparameters

To track the model configuration and hyperparameters, we can assign variables with Relevant values, such as the path to the dataset and transformations, within a specific namespace. By using the log() method and passing it the assigned variables, we can log these important details.

Logging Metrics and Losses

In addition to configuration and hyperparameters, logging metrics and losses is crucial for tracking the performance of our model. We can use the log() method and pass it the respective metrics and losses within a namespace.

Specifying Dependencies in a Requirements.txt File

To ensure that the necessary dependencies are included in the Docker image, create a requirements.txt file where you can specify the required libraries. In this example, we include "neptune-client", "torch", and "torchvision" as dependencies. Make sure to specify any other libraries you will be using for your project, with "neptune-client" being particularly important.

Creating a Dockerfile

In order to containerize our code, we need to create a Dockerfile. In this file, we use a simple Python image, copy the requirements.txt file, install the dependencies in our Docker container, and then copy our training file. Finally, we specify the command to run our Python script.

Building and Running the Docker Image

With the Dockerfile in place, we can now build and run our image. The build command is used to create the Docker image, while the run command is used to run the Docker image. We pass the Neptune API token as an environment variable using the -e or --env flag.

Monitoring Model Training Metrics Live

After building and running the Docker image, we will receive a link. This link allows us to monitor our model's training metrics live. Initially, the link may appear empty, but as the training progresses, it will populate with the relevant metrics. We can Visualize the model's configuration, hyperparameters, metrics, accuracy, and loss.

Creating a Custom Dashboard

To gain a better overview of our experiment and how it is progressing, we can create a custom dashboard. This dashboard allows us to display all the relevant metrics in one place. By following the steps provided in the documentation, we can create charts, widgets, and visualize our model's accuracy and loss.

Conclusion

In conclusion, Neptune and Docker provide a powerful combination for logging experimentation metadata and monitoring model training metrics live. By following the steps outlined in this guide, you can effectively track model performance, hyperparameters, and dependencies. Creating a custom dashboard further enhances the visualization of your experiment. Start using Neptune and Docker today to streamline your machine learning workflow.

Highlights

• Use Neptune and Docker to log experimentation metadata and track model training metrics live
• Create a Neptune run and log model configuration, hyperparameters, metrics, and losses
• Specify dependencies in a requirements.txt file and create a Dockerfile to build and run the Docker image
• Monitor model training metrics live through Neptune's interactive interface
• Create a custom dashboard to visualize and analyze experiment results

FAQ

Q: Can I use Neptune in any Python environment? A: Yes, Neptune can be used in any Python environment to log and retrieve ML metadata.

Q: What is the role of the Neptune API token? A: The Neptune API token is necessary for authentication and allows you to access Neptune's features and functionality.

Q: How can I specify the dependencies for my Docker container? A: You can specify the dependencies in a requirements.txt file and include it in the Docker image.

Q: Can I track multiple experiments simultaneously with Neptune? A: Yes, Neptune allows you to create separate runs and namespaces to track multiple experiments concurrently.

Q: Is it possible to visualize other metrics apart from accuracy and loss? A: Yes, you can visualize any other metrics of interest by logging them with the appropriate namespaces and using Neptune's custom dashboard functionality.

Resources:

• Neptune AI
• Docker