Before You Begin

End-to-end Example

Read the data from CSV file
Run the training and inference operations for PCA
Access intermediate results obtained at the training stage

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>

Create a SYCL* queue with the desired device selector. In this case, GPU selector is used:

const auto queue = sycl::queue{ sycl::gpu_selector{} };

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;

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"});

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>
   .set_component_count(3)
   .set_deterministic(true);

const dal::pca::train_result train_res = dal::train(queue, pca_desc, data);

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;
}

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);

Build and Run Examples

Set up the required environment for oneDAL (variables such as
CPATH
,
LIBRARY_PATH
, and
LD_LIBRARY_PATH
):
On Linux, there are two possible ways to set up the required environment: via

vars.sh

script or via

modulefiles

.

Setting up oneDAL environment via

vars.sh

script

Run the following command:

source ./env/vars.sh

Setting up oneDAL environment via

modulefiles

Initialize

modules

:

source $MODULESHOME/init/bash

Refer to Environment Modules documentation for details.

Provide

modules

with a path to the

modulefiles

directory:

module use ./modulefiles

Run the module:

module load dal

On Windows, run the following command:

/env/vars.bat
Copy
./examples/oneapi/dpc
to a writable directory if necessary (since it creates temporary files):
cp –r ./examples/oneapi/dpc ${WRITABLE_DIR}
Set up the compiler environment for Intel® oneAPI DPC++/C++ Compiler. See Get Started with Intel® oneAPI DPC++/C++ Compiler for details.
Build and run the examples that show how to use oneDAL with SYCL support:
You need to have write permissions to the
examples
folder to build examples, and execute permissions to run them. Otherwise, you need to copy
examples/oneapi/dpc
and
examples/oneapi/data
folders to the directory with right permissions. These two folders must be retained in the same directory level relative to each other.
On Linux:

# Navigate to the directory containing examples and then build them:
cd /examples/oneapi/dpc
make so example=svm_two_class_thunder_dense_batch # This will compile and run Correlation example using Intel(R) oneAPI DPC++/C++ Compiler
make so mode=build                         # This compiles all examples in the current directory

On Windows:

# Navigate to the directory containing examples and then build them:
cd /examples/oneapi/dpc
nmake dll example=svm_two_class_thunder_dense_batch+ # This will compile and run Correlation example using Intel(R) oneAPI DPC++/C++ Compiler
nmake dll mode=build                         # This compiles all examples in the current directory
To see all available parameters of the build procedure, type
make
on Linux* or
nmake
on Windows*.
The resulting example binaries and log files are written into the
_results
directory.
You should run the examples from
examples/oneapi/dpc
folder, not from
_results
folder. Most examples require data to be stored in
examples/oneapi/data
folder and to have a relative link to it started from
examples/oneapi/dpc
folder.
You can build traditional C++ examples located in
examples/oneapi/cpp
folder in a similar way.

Compile and build applications with pkg-config

On Linux:
source ./env/vars.sh
On Windows:
/env/vars.bat

On Linux or macOS:

icc test.cpp pkg-config --cflags --libs dal-dynamic-threading-host

On Windows:

for /F "delims=," %i in ('pkg-config --cflags --libs dal-dynamic-threading-host) do icl test.cpp %i

On Linux or macOS:

icc -I source/ source/svm/svm_two_class_thunder_dense_batch.cpp icc test.cpp pkg-config --cflags --libs dal-dynamic-threading-host

On Windows:

for /F "delims=," %i in ('pkg-config --cflags --libs dal-dynamic-threading-host) do icl -I source/ icl svm_two_class_thunder_dense_batch.cpp %i

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Notices and Disclaimers