AOCL-Data Analytics (AOCL-DA) provides optimized building blocks for data analysis and classical machine learning. The intended workflow for using AOCL-DA is as follows:
Load data from memory by reading in CSV files or using the in-built da_datastore object.
Preprocess the data by removing missing values, standardizing and selecting certain subsets of the data.
Perform a data analysis computation. APIs are available for the following data processing computations:
linear, ridge, lasso and logistic regression
decision tree and random forests
k-means clustering
k-nearest neighbors classification
principal component analysis
nonlinear least-squares data fitting
basic statistics
AOCL-DA is written with a C-compatible API to facilitate calling the library from different programming languages.
A Python API is also provided, along with a scikit-learn patch, so that users with existing scikit-learn workflows can leverage the performance of AOCL-DA with minimal changes to their code.
AOCL-DA depends on external libraries including BLAS and LAPACK for linear algebra computations.
This chapter contains details on how to install AOCL-DA, build AOCL-DA from source, and compile and link programs that use AOCL-DA APIs. For the full documentation, please refer to the latest AOCL-DA html pages at
https://docs.amd.com/r/en-US/68552-AOCL-api-guide/AOCL-Data-Analytics