Examples with MLContext org.apache.sysml.api.mlcontext.MLContext used on opensource projects

Example 6 with MLContext

use of org.apache.sysml.api.mlcontext.MLContext in project incubator-systemml by apache.

the class GPUTests method runOnGPU.

/**
 * Runs a program on the GPU
 *
 * @param spark     a valid {@link SparkSession}
 * @param scriptStr the script to run (as a string)
 * @param inputs    map of input variables names in the scriptStr (of variable_name -> object)
 * @param outStrs   list of variable names needed as output from the scriptStr
 * @return list of output objects in order of outStrs
 */
protected List<Object> runOnGPU(SparkSession spark, String scriptStr, Map<String, Object> inputs, List<String> outStrs) {
    // and other side effects.
    synchronized (GPUTests.class) {
        MLContext gpuMLC = new MLContext(spark);
        gpuMLC.setConfigProperty("sysml.floating.point.precision", FLOATING_POINT_PRECISION);
        if (IGNORE_CLEAR_MEMORY_BUG)
            gpuMLC.setConfigProperty("sysml.gpu.eager.cudaFree", "true");
        gpuMLC.setGPU(true);
        gpuMLC.setForceGPU(true);
        gpuMLC.setStatistics(true);
        List<Object> outputs = new ArrayList<>();
        Script script = ScriptFactory.dmlFromString(scriptStr).in(inputs).out(outStrs);
        MLResults res = gpuMLC.execute(script);
        for (String outStr : outStrs) {
            Object output = res.get(outStr);
            outputs.add(output);
        }
        gpuMLC.close();
        return outputs;
    }
}

Example 7 with MLContext

use of org.apache.sysml.api.mlcontext.MLContext in project incubator-systemml by apache.

the class GPUTests method runOnCPU.

/**
 * Runs a program on the CPU
 *
 * @param spark     a valid {@link SparkSession}
 * @param scriptStr the script to run (as a string)
 * @param inputs    map of input variables names in the scriptStr (of variable_name -> object)
 * @param outStrs   list of variable names needed as output from the scriptStr
 * @return list of output objects in order of outStrs
 */
protected List<Object> runOnCPU(SparkSession spark, String scriptStr, Map<String, Object> inputs, List<String> outStrs) {
    MLContext cpuMLC = new MLContext(spark);
    List<Object> outputs = new ArrayList<>();
    Script script = ScriptFactory.dmlFromString(scriptStr).in(inputs).out(outStrs);
    MLResults res = cpuMLC.execute(script);
    for (String outStr : outStrs) {
        Object output = res.get(outStr);
        outputs.add(output);
    }
    cpuMLC.close();
    return outputs;
}

Example 8 with MLContext

use of org.apache.sysml.api.mlcontext.MLContext in project incubator-systemml by apache.

the class GPUTests method generateInputMatrix.

/**
 * Generates a random input matrix with a given size and sparsity
 *
 * @param spark    valid instance of {@link SparkSession}
 * @param m        number of rows
 * @param n        number of columns
 * @param sparsity sparsity (1 = completely dense, 0 = completely sparse)
 * @return a random matrix with given size and sparsity
 */
protected Matrix generateInputMatrix(SparkSession spark, int m, int n, double sparsity, int seed) {
    // Generate a random matrix of size m * n
    MLContext genMLC = new MLContext(spark);
    String scriptStr;
    if (sparsity == 0.0) {
        scriptStr = "in1 = matrix(0, rows=" + m + ", cols=" + n + ")";
    } else {
        scriptStr = "in1 = rand(rows=" + m + ", cols=" + n + ", sparsity = " + sparsity + ", seed= " + seed + ", min=-1.0, max=1.0)";
    }
    Script generateScript = ScriptFactory.dmlFromString(scriptStr).out("in1");
    Matrix in1 = genMLC.execute(generateScript).getMatrix("in1");
    genMLC.close();
    return in1;
}

Example 9 with MLContext

use of org.apache.sysml.api.mlcontext.MLContext in project incubator-systemml by apache.

the class GPUTests method generateIntegerSequenceMatrix.

/**
 * Generates an input matrix which is a sequence of integers
 * @param spark valid instance of {@link SparkSession}
 * @param m number of rows
 * @param n number of columns
 * @return a matrix with a sequence of integers
 */
protected Matrix generateIntegerSequenceMatrix(SparkSession spark, int m, int n) {
    MLContext genMLC = new MLContext(spark);
    String scriptStr;
    scriptStr = "temp = seq(1, " + (m * n) + ")" + "in1 = matrix(temp, rows=" + m + ", cols=" + n + ")";
    Script generateScript = ScriptFactory.dmlFromString(scriptStr).out("in1");
    Matrix in1 = genMLC.execute(generateScript).getMatrix("in1");
    genMLC.close();
    return in1;
}

Example 10 with MLContext

use of org.apache.sysml.api.mlcontext.MLContext in project incubator-systemml by apache.

the class GPUTests method generateInputMatrix.

/**
 * Generates a random input matrix with a given size and sparsity
 *
 * @param spark    valid instance of {@link SparkSession}
 * @param m        number of rows
 * @param n        number of columns
 * @param min      min for RNG
 * @param max      max for RNG
 * @param sparsity sparsity (1 = completely dense, 0 = completely sparse)
 * @param performRounding performs rounding after generation of random matrix
 * @return a random matrix with given size and sparsity
 */
protected Matrix generateInputMatrix(SparkSession spark, int m, int n, double min, double max, double sparsity, int seed, boolean performRounding) {
    // Generate a random matrix of size m * n
    MLContext genMLC = new MLContext(spark);
    String scriptStr;
    if (sparsity == 0.0) {
        scriptStr = "in1 = matrix(0, rows=" + m + ", cols=" + n + ")";
    } else {
        scriptStr = "in1 = rand(rows=" + m + ", cols=" + n + ", sparsity = " + sparsity + ", seed= " + seed + ", min=" + min + ", max=" + max + ")";
        if (performRounding)
            scriptStr += "; in1 = round(in1)";
    }
    Script generateScript = ScriptFactory.dmlFromString(scriptStr).out("in1");
    Matrix in1 = genMLC.execute(generateScript).getMatrix("in1");
    genMLC.close();
    return in1;
}