gpu_benchmark.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tested on Amazon standart g2.2xlarge instance with installed CUDA 7.5 and TensorFlow 0.9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import time\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/dist-packages/numpy/core/fromnumeric.py:2652: VisibleDeprecationWarning: `rank` is deprecated; use the `ndim` attribute or function instead. To find the rank of a matrix see `numpy.linalg.matrix_rank`.\n",
      "  VisibleDeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset shape: (13454, 784)\n",
      "Non-zeros rate: 0.163235158044\n",
      "Classes balance: 0.469228482236 / 0.530771517764\n"
     ]
    }
   ],
   "source": [
    "from sklearn.datasets import fetch_mldata\n",
    "from sklearn.preprocessing import scale\n",
    "from sklearn.cross_validation import train_test_split\n",
    "from sklearn.metrics import roc_auc_score, accuracy_score\n",
    "\n",
    "\n",
    "mnist = fetch_mldata('MNIST original', data_home='./tmp')\n",
    "\n",
    "# only binary classification supported\n",
    "mask = (mnist['target'] == 3) + (mnist['target'] == 5)\n",
    "\n",
    "X_all = scale(mnist['data'][mask].astype(float))\n",
    "y_all = (mnist['target'][mask]==3)*1\n",
    "\n",
    "# make it more sparse\n",
    "X_all = X_all * (np.random.uniform(0, 1, X_all.shape) > 0.8)\n",
    "\n",
    "print('Dataset shape: {}'.format(X_all.shape))\n",
    "print('Non-zeros rate: {}'.format(np.mean(X_all != 0)))\n",
    "print('Classes balance: {} / {}'.format(np.mean(y_all==0), np.mean(y_all==1)))\n",
    "\n",
    "X_tr, X_te, y_tr, y_te = train_test_split(X_all, y_all, random_state=42, test_size=0.3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Dense data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from tffm import TFFMClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU only / dense data\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:30<00:00,  3.38epoch/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 0.937081991578\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "model = TFFMClassifier(\n",
    "    order=3,\n",
    "    rank=10, \n",
    "    optimizer=tf.train.AdamOptimizer(learning_rate=0.01), \n",
    "    n_epochs=100, \n",
    "    batch_size=-1,\n",
    "    init_std=0.001,\n",
    "    reg=0.001,\n",
    "    input_type='dense',\n",
    "    session_config=tf.ConfigProto(log_device_placement=True, device_count = {'GPU': 0}),\n",
    "    log_dir='./tmp'\n",
    ")\n",
    "print('CPU only / dense data')\n",
    "model.fit(X_tr, y_tr, show_progress=True)\n",
    "predictions = model.predict(X_te)\n",
    "print('accuracy: {}'.format(accuracy_score(y_te, predictions)))\n",
    "model.destroy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "in stderr: mainBlock/order_3/Pow_1/y: /job:localhost/replica:0/task:0/cpu:0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GPU / dense data\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:09<00:00, 10.36epoch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 0.931384691603\n"
     ]
    }
   ],
   "source": [
    "model = TFFMClassifier(\n",
    "    order=3,\n",
    "    rank=10, \n",
    "    optimizer=tf.train.AdamOptimizer(learning_rate=0.01), \n",
    "    n_epochs=100, \n",
    "    batch_size=-1,\n",
    "    init_std=0.001,\n",
    "    reg=0.001,\n",
    "    input_type='dense',\n",
    "    session_config=tf.ConfigProto(log_device_placement=True),\n",
    "    log_dir='./tmp'\n",
    ")\n",
    "print('GPU / dense data')\n",
    "model.fit(X_tr, y_tr, show_progress=True)\n",
    "predictions = model.predict(X_te)\n",
    "print('accuracy: {}'.format(accuracy_score(y_te, predictions)))\n",
    "model.destroy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "in stderr: mainBlock/order_2/Pow_1/y: /job:localhost/replica:0/task:0/gpu:0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sparse example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import scipy.sparse as sp\n",
    "# only CRS format supported\n",
    "X_tr_sparse = sp.csr_matrix(X_tr)\n",
    "X_te_sparse = sp.csr_matrix(X_te)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU only / sparse data\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:37<00:00,  2.59epoch/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 0.935100322021\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "model = TFFMClassifier(\n",
    "    order=3,\n",
    "    rank=10, \n",
    "    optimizer=tf.train.AdamOptimizer(learning_rate=0.01), \n",
    "    n_epochs=100, \n",
    "    batch_size=-1,\n",
    "    init_std=0.001,\n",
    "    reg=0.001,\n",
    "    input_type='sparse',\n",
    "    session_config=tf.ConfigProto(log_device_placement=True, device_count = {'GPU': 0}),\n",
    "    log_dir='./tmp'\n",
    ")\n",
    "print('CPU only / sparse data')\n",
    "model.fit(X_tr_sparse, y_tr, show_progress=True)\n",
    "predictions = model.predict(X_te_sparse)\n",
    "print('accuracy: {}'.format(accuracy_score(y_te, predictions)))\n",
    "model.destroy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GPU / sparse data\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:18<00:00,  5.20epoch/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 0.931136982908\n"
     ]
    }
   ],
   "source": [
    "model = TFFMClassifier(\n",
    "    order=3,\n",
    "    rank=10, \n",
    "    optimizer=tf.train.AdamOptimizer(learning_rate=0.01), \n",
    "    n_epochs=100, \n",
    "    batch_size=-1,\n",
    "    init_std=0.001,\n",
    "    reg=0.001,\n",
    "    input_type='sparse',\n",
    "    session_config=tf.ConfigProto(log_device_placement=True),\n",
    "    log_dir='./tmp'\n",
    ")\n",
    "print('GPU / sparse data')\n",
    "model.fit(X_tr_sparse, y_tr, show_progress=True)\n",
    "predictions = model.predict(X_te_sparse)\n",
    "print('accuracy: {}'.format(accuracy_score(y_te, predictions)))\n",
    "model.destroy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# n_features/time complexity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 9/9 [03:34<00:00, 25.02s/it]\n"
     ]
    }
   ],
   "source": [
    "n_features = X_all.shape[1]\n",
    "used_features = range(100, 1000, 100)\n",
    "n_repeats = 5\n",
    "elapsed_mean = {'gpu':[], 'cpu':[]}\n",
    "elapsed_std = {'gpu':[], 'cpu':[]}\n",
    "model_title = ''\n",
    "\n",
    "for cur_n_feats in tqdm(used_features):\n",
    "    for mode, config in [('gpu', None), ('cpu', tf.ConfigProto(device_count = {'GPU': 0}))]:\n",
    "        \n",
    "        time_observation = []\n",
    "        for _ in range(n_repeats):\n",
    "            active_features = np.random.choice(range(n_features), size=cur_n_feats)\n",
    "            model = TFFMClassifier(\n",
    "                        order=5, \n",
    "                        rank=50,\n",
    "                        optimizer=tf.train.AdamOptimizer(learning_rate=0.001), \n",
    "                        n_epochs=1, \n",
    "                        batch_size=-1,\n",
    "                        init_std=0.01,\n",
    "                        session_config=config,\n",
    "                        input_type='dense'\n",
    "            )\n",
    "            model_title = model.__str__()\n",
    "            # manually initialize model without calling .fit()\n",
    "            model.core.set_num_features(cur_n_feats)\n",
    "            model.core.build_graph()\n",
    "            model.initialize_session()\n",
    "\n",
    "            start_time = time.time()\n",
    "            predictions = model.decision_function(X_all[:, active_features])\n",
    "            end_time = time.time()\n",
    "            model.destroy()\n",
    "            time_observation.append(end_time - start_time)\n",
    "        elapsed_mean[mode].append(np.mean(time_observation))\n",
    "        elapsed_std[mode].append(np.std(time_observation))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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QfQJ9gWxMHwXAOKCMEzu0t3iVoQVwGLgLM7W5N3VmB9hXX0FWFmRmwrPPQrNm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\nfdD4YyGaAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3c9c112d50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%pylab inline\n",
    "errorbar(used_features, elapsed_mean['cpu'], yerr=elapsed_std['cpu'], label='CPU')\n",
    "errorbar(used_features, elapsed_mean['gpu'], yerr=elapsed_std['gpu'], label='GPU')\n",
    "xlim(0, 1000)\n",
    "title('CPU/GPU times')\n",
    "xlabel('n_features')\n",
    "ylabel('test time')\n",
    "legend(loc=\"upper left\")\n",
    "grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}