What's included?
You can edit this quickstart to add helpful components. View the repository and open a pull request.
Why should you monitor PyTorch?
PyTorch is an open-source machine learning library widely employed to develop and train neural network-based deep learning models. New Relic's PyTorch quickstart performance monitoring provides out-of-the-box observability for PyTorch models. By using the PyTorch quickstart, you will be able to:
Identify performance bottlenecks:
Enhance the efficiency of your PyTorch models by pinpointing and optimizing performance-heavy areas.
Ensure model accuracy:
Monitor the output of your models in real-time to detect and correct discrepancies, thereby ensuring the desired model performance.
Error identification and resolution:
Active monitoring allows for quick detection and rectification of errors, ensuring the reliability and robustness of your PyTorch models.
Comprehensive monitoring quickstart for PyTorch models
With New Relic's PyTorch quickstart, you can actively oversee the performance of your PyTorch models, gaining insights to ensure they run effectively and efficiently, especially in real-world deep learning applications.
What’s included in the PyTorch quickstart?
New Relic PyTorch monitoring quickstart provides quality out-of-the-box reporting:
- Obtain insights into how your PyTorch models are performing in real-time
- Alerts on model performance: Set up proactive notifications for any unusual behavior or degradation in the performance of your models