Optimization Guide¶

Anya is designed to be a highly performant, scalable and efficient AI framework. However, achieving optimal performance requires careful tuning and configuration of various components. This guide provides a comprehensive overview of the various optimization techniques and best practices for optimizing Anya's performance.

Hardware Optimizations¶

Multi-Threading¶

Anya is designed to take advantage of multi-threading. By default, Anya will use the number of threads available on the system. However, you can control the number of threads used by setting the ANYA_NUM_THREADS environment variable.

GPU Acceleration¶

Anya supports GPU acceleration through the use of cuDNN and NCCL. To enable GPU acceleration, simply set the ANYA_USE_CUDA environment variable to 1. Anya will automatically detect and use the available NVIDIA GPUs.

Data Storage¶

Anya provides a variety of data storage options, including in-memory storage, disk-based storage and cloud-based storage. By default, Anya will use in-memory storage. However, if you need to store large amounts of data, you may want to consider using disk-based storage.

Software Optimizations¶

Model Optimizations¶

Anya provides a variety of model optimization techniques, including quantization, pruning and knowledge distillation. By applying these techniques, you can significantly reduce the size and complexity of your models, resulting in improved performance and efficiency.

Data Optimizations¶

Anya provides a variety of data optimization techniques, including data compression, data augmentation and data normalization. By applying these techniques, you can significantly reduce the size and complexity of your data, resulting in improved performance and efficiency.

Hyperparameter Tuning¶

Anya provides a variety of hyperparameter tuning techniques, including grid search, random search and Bayesian optimization. By applying these techniques, you can significantly improve the performance of your models by finding the optimal hyperparameters.

Best Practices¶

Use the Right Data Type¶

Using the right data type can significantly improve performance. For example, using f32 instead of f64 can result in a 2x performance improvement.

Use the Right Model¶

Using the right model can significantly improve performance. For example, using a convolutional neural network instead of a fully connected neural network can result in a 10x performance improvement.

Use the Right Optimizer¶

Using the right optimizer can significantly improve performance. For example, using the Adam optimizer instead of the Stochastic Gradient Descent optimizer can result in a 2x performance improvement.

Use the Right Loss Function¶

Using the right loss function can significantly improve performance. For example, using the cross-entropy loss function instead of the mean squared error loss function can result in a 2x performance improvement.

Monitor Performance¶

Monitoring performance is critical to achieving optimal performance. By monitoring performance, you can identify bottlenecks and optimize accordingly.

Profile Performance¶

Profiling performance is critical to achieving optimal performance. By profiling performance, you can identify bottlenecks and optimize accordingly.

Use the Right Hardware¶

Using the right hardware can significantly improve performance. For example, using a GPU instead of a CPU can result in a 10x performance improvement.

Last updated: 2025-06-02