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import torch
import numpy as np

import time

elapsed = 0


for _ in range(10):
  a1 = np.random.random((8192, 8192)).astype(np.float32)

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xgb_params = {
  "booster": "gbtree",
  "objective": "binary:logistic",
  "eta": 0.1,
  "gamma": 0,
  "min_child_weight": 200,
  "max_depth": 6,
  "eval_train": 1,
  "tree_method": "gpu_hist",

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#include <vector>
#include <utility>
#include <cstdio>


std::pair<float, int> recursive_mean(
    const std::vector<float> &vec,
    const int first,
    const int last) {

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FROM centos:7

RUN yum makecache
RUN yum install -y centos-release-SCL which yum-utils centos-release-scl-rh epel-release
RUN yum install -y  python27  \
		python27-python-devel \
		python27-python-wheel \
                java-1.8.0-openjdk-devel \
		devtoolset-8-toolchain \
		devtoolset-6-toolchain \

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package com.twitter.sample.lib

import java.io.File
import org.apache.commons.io.{FileUtils, IOUtils}
import org.tensorflow.TensorFlow
import com.twitter.logging.Logger
import java.nio.file.{Files, Paths}

object TfLoader {

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import numpy as np
import tensorflow.compat.v2 as tf

class Sparse(tf.keras.layers.Dense):
  def call(self, inputs):
    outputs = tf.sparse.sparse_dense_matmul(inputs, self.kernel)
    if self.use_bias:
      outputs = tf.nn.bias_add(outputs, self.bias)
    return outputs