End-to-end exampleΒΆ

Below you can find a typical workflow of using oneDAL algorithm on GPU. The example is provided for Principal Component Analysis algorithm (PCA).

The following steps depict how to:

  • Read the data from CSV file

  • Run the training and inference operations for PCA

  • Access intermediate results obtained at the training stage

  1. Include the following header that makes all oneDAL declarations available.

    #include "oneapi/dal.hpp"
    /* Standard library headers required by this example */
    #include <cassert>
    #include <iostream>
  2. Create a SYCL* queue with the desired device selector. In this case, GPU selector is used:

    const auto queue = sycl::queue{ sycl::gpu_selector{} };
  3. Since all oneDAL declarations are in the oneapi::dal namespace, import all declarations from the oneapi namespace to use dal instead of oneapi::dal for brevity:

    using namespace oneapi;
  4. Use CSV data source to read the data from the CSV file into a table:

    const auto data = dal::read<dal::table>(queue, dal::csv::data_source{"data.csv"});
  5. Create a PCA descriptor, configure its parameters, and run the training algorithm on the data loaded from CSV.

    const auto pca_desc = dal::pca::descriptor<float>
    const dal::pca::train_result train_res = dal::train(queue, pca_desc, data);
  6. Print the learned eigenvectors:

    const dal::table eigenvectors = train_res.get_eigenvectors();
    const auto acc = dal::row_accessor<const float>{eigenvectors};
    for (std::int64_t i = 0; i < eigenvectors.row_count(); i++) {
       /* Get i-th row from the table, the eigenvector stores pointer to USM */
       const dal::array<float> eigenvector = acc.pull(queue, {i, i + 1});
       assert(eigenvector.get_count() == eigenvectors.get_column_count());
       std::cout << i << "-th eigenvector: ";
       for (std::int64_t j = 0; j < eigenvector.get_count(); j++) {
          std::cout << eigenvector[j] << " ";
       std::cout << std::endl;
  7. Use the trained model for inference to reduce dimensionality of the data:

    const dal::pca::model model = train_res.get_model();
    const dal::table data_transformed =
       dal::infer(queue, pca_desc, data).get_transformed_data();
    assert(data_transformed.column_count() == 3);