# Efficient Tracking of PyTorch Experiments with Neptune.ai
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Chapter 1: Introduction to Neptune.ai
This guide continues the exploration of tools for managing machine learning (ML) experiments, following the earlier discussion on MLflow. Previously, I introduced MLflow, which allows users to save hyperparameters and metrics. However, I found its lack of an online dashboard somewhat limiting for performance visualization. Hence, I turned to Neptune.ai, a platform that not only saves trained models, hyperparameters, and metrics but also offers a visually appealing dashboard for easy result sharing among team members. Furthermore, it facilitates the comparison of multiple experiments seamlessly.
Neptune.ai serves as a comprehensive online platform for managing ML experiments, model registries, and monitoring. It goes beyond tracking a single model's performance by allowing users to oversee multiple models within a unified interface. Each time an experiment is executed, it populates a row in the Runs table, presenting all experiment runs for comparative analysis based on hyperparameters, metrics, and additional criteria.
Additionally, it supports the storage of various metadata for each experiment, including hyperparameters, metrics, learning curves, images, HTML objects, model files, and more. Users can also create a dashboard that integrates all stored metadata into one accessible screen. The platform promotes collaboration, enabling users to share project or experiment URLs with colleagues for quick access to model results. The basic features are available for free, while advanced functionalities require a subscription.
Section 1.1: Getting Started with Neptune.ai
Before integrating Neptune with PyTorch, there are four preliminary steps to follow:
- Ensure that Python version 3.6 or later is installed on your machine.
- Register as a user on the Neptune.ai website to access private projects.
- Copy the API token from your profile.
- Create a new project, which can be public or private based on your needs; a public project is ideal for sharing results with colleagues.
Once these steps are completed, you can install the Neptune client in your notebook:
pip install neptune-client
Next, initialize a Neptune Run by providing the API token and project path:
import neptune.new as neptune
from neptune.new.types import File
run = neptune.init(
project="/pytorchneptuneintegrat",
api_token=""
)
Executing this code will return a URL, confirming that the setup is complete, allowing you to focus on the exciting aspects of using Neptune.ai.
Section 1.2: Tracking Experiments with Neptune.ai
Let’s dive into the process of leveraging Neptune.ai for tracking experiments. This section consists of several key steps:
- Import necessary libraries and datasets.
- Save hyperparameters in Neptune.ai.
- Define your model and instantiate PyTorch objects.
- Log losses and save figures.
- Save model architecture and weights.
- Execute the script train_vae.py.
- Create a dashboard.
Subsection 1.2.1: Import Libraries and Datasets
The first task is to import the essential libraries, the Fashion MNIST dataset, and set up argument parsing for running the Python code train_vae.py. An ArgumentParser object is created to handle terminal arguments, such as the n_epochs variable, which defaults to 50.
Subsection 1.2.2: Save Hyperparameters in Neptune.ai
To save hyperparameters in Neptune.ai, simply create a folder called "hyperparameters" that will display all the specified values. After executing the file, you can view the saved information in a table format on the experiment link.
Subsection 1.2.3: Define Model and Instantiate PyTorch Objects
Here, you will define the Variational Autoencoder class, which includes two neural networks: Encoder and Decoder, along with their respective layers and activation functions. Additionally, methods for training and evaluating the model's performance are established.
Subsection 1.2.4: Log Losses and Save Figures
As you train and evaluate the Variational Autoencoder, there are two primary methods for saving information: the log method for tracking numerical data and the upload method for saving files, such as matplotlib figures, to a designated directory.
Subsection 1.2.5: Save Model Architecture and Weights
Neptune.ai allows for the storage of model architecture and learned parameters, which can be invaluable for comparing different experimental setups. When running the script, you’ll receive outputs that summarize the model’s architecture and weight files.
Subsection 1.2.6: Run the Script
Once the code is written, execute it using the following command line:
python train_vae.py
Experiment with various hyperparameter values by passing them via command line.
Subsection 1.2.7: Create a Dashboard Using Neptune.ai
The final step involves creating a dashboard to encapsulate your experiment results. This is done intuitively by selecting the + button in the menu, naming the dashboard, and adding visualizations through the +Add widget button.
The first video provides a comprehensive overview of tracking ML model training using the integration of PyTorch and Neptune.ai, showcasing efficient methodologies for managing your experiments.
The second video dives into smart ML experiment tracking and model registry functionalities offered by the Neptune.ai platform, illustrating advanced features that enhance model management.
Conclusion
I hope this tutorial has been insightful in helping you get started with Neptune.ai. This platform offers valuable capabilities for managing your ML experiments efficiently. Thank you for reading, and have a wonderful day!
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