dask_glm_Multi-GPU_demo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### In this notebook, we show an end-to-end demo of rapids dask-cudf + dask-glm `LogisticRegression` on mutliple GPUs. Experiments are done on a `DGX-1` with `8xGPUs` and `40-core CPUs`. With the HIGGS dataset, the GPU solution achieves `14x` speedup over CPU using the `lbfgs` solver. To get a more comprehensive speedup measurement, we vary the size of random synthetic data and the GPU solution achieves up to `27x` speedup over CPU."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Table of contents\n",
    "[1. Introduction](#bg)<br>\n",
    "[2. Install instructions](#install)<br>\n",
    "[3. Libraries and helper functions](#lib)<br>\n",
    "[4. Dask glm on GPU](#gpu)<br>\n",
    "[5. Dask glm on CPU](#cpu)<br>\n",
    "[6. Speedup and Conclusion](#speedup)<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction\n",
    "<a id=\"bg\"></a>\n",
    "**Multi-GPU support of dask-glm is enabled by recent efforts of allowing cupy dask arrays as inputs.** [dask/dask-glm#87](https://github.com/dask/dask-glm/pull/87) and [dask/dask-glm#89](https://github.com/dask/dask-glm/pull/89)\n",
    "\n",
    "dask-glm offers 3 estimators:\n",
    "- LinearRegression\n",
    "- LogisticRegression\n",
    "- PoissonRegression\n",
    "\n",
    "and 5 solvers:\n",
    "- admm,\n",
    "- gradient_descent,\n",
    "- newton,\n",
    "- lbfgs,\n",
    "- proximal_grad\n",
    "\n",
    "Currently, all 3 estimators and 5 algorithms work seamlessly with `dask-cudf` on multiple GPUs. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Install instructions:\n",
    "<a id=\"install\"></a>\n",
    "\n",
    "- conda create -n rapids-0.17 -c rapidsai-nightly -c nvidia -c conda-forge -c defaults rapids=0.17 python=3.7 cudatoolkit=10.2\n",
    "- conda activate rapids-0.17    \n",
    "- pip install git+https://github.com/dask/dask\n",
    "- pip install matplotlib\n",
    "- git clone https://github.com/dask/dask-glm\n",
    "- cd dask-glm\n",
    "- pip install -e ."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Libraries and helper functions\n",
    "<a id=\"lib\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "GPU_id = '0,1,2,3,4,5,6,7'\n",
    "os.environ['CUDA_VISIBLE_DEVICES'] = GPU_id\n",
    "os.environ[\"CUPY_EXPERIMENTAL_SLICE_COPY\"] = \"1\"\n",
    "num_gpus = len(GPU_id.split(','))\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dask 2.30.0+57.g3e264491\n",
      "dask_glm 0.2.1.dev52+g1daf4c5\n",
      "cupy 8.1.0\n",
      "dask_cudf 0.17.0a+265.g42fe2183f7\n"
     ]
    }
   ],
   "source": [
    "import dask.dataframe as dd\n",
    "from dask_glm.estimators import LogisticRegression\n",
    "from dask_glm.datasets import make_classification\n",
    "\n",
    "import time\n",
    "import dask_glm\n",
    "import dask\n",
    "import numpy as np\n",
    "import cupy as cp\n",
    "\n",
    "import dask_cudf\n",
    "from dask.distributed import Client, wait, LocalCluster\n",
    "from dask_cuda import LocalCUDACluster\n",
    "import dask.array as da\n",
    "\n",
    "print('dask', dask.__version__) # \n",
    "print('dask_glm', dask_glm.__version__) # \n",
    "print('cupy', cp.__version__) #\n",
    "print('dask_cudf', dask_cudf.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_run_time(solver,min_samples,max_samples,data_type='cupy'):\n",
    "    times = []\n",
    "    for n in range(min_samples,max_samples+1):\n",
    "        n_samples=10**n\n",
    "        n_features = 20\n",
    "        chunks = num_gpus if data_type=='cupy' else 80 # dgx-1 has 40 cores and 80 hyperthreads\n",
    "        \n",
    "        dx, dy = make_classification(n_samples=n_samples, n_features=n_features, n_informative=10, chunksize=n_samples//chunks)\n",
    "        #dx = dx.astype('float32')\n",
    "        if data_type=='cupy':\n",
    "            dx, dy = to_cupy(dx, dy)\n",
    "        dx, dy = dask.persist(dx, dy)\n",
    "        wait(dx)\n",
    "        wait(dy)\n",
    "        start = time.time()\n",
    "        lr = LogisticRegression(solver=solver, max_iter=2)\n",
    "        lr.fit(dx,dy)\n",
    "        duration = time.time()-start\n",
    "        del dx,dy\n",
    "        del lr\n",
    "        times.append(duration)\n",
    "        print('%s samples:10^%d time:%.2f'%(solver,n,duration))\n",
    "    return times\n",
    "\n",
    "def to_cupy(X, y):\n",
    "    X = X.map_blocks(lambda x: cp.asarray(x),\n",
    "                         dtype=X.dtype, meta=cp.asarray(X._meta))\n",
    "    y = y.map_blocks(lambda x: cp.asarray(x),\n",
    "                     dtype=y.dtype, meta=cp.asarray(y._meta))\n",
    "    return X,y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dask-glm on GPU\n",
    "<a id=\"gpu\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table style=\"border: 2px solid white;\">\n",
       "<tr>\n",
       "<td style=\"vertical-align: top; border: 0px solid white\">\n",
       "<h3 style=\"text-align: left;\">Client</h3>\n",
       "<ul style=\"text-align: left; list-style: none; margin: 0; padding: 0;\">\n",
       "  <li><b>Scheduler: </b>ucx://127.0.0.1:57555</li>\n",
       "  <li><b>Dashboard: </b><a href='http://127.0.0.1:8787/status' target='_blank'>http://127.0.0.1:8787/status</a></li>\n",
       "</ul>\n",
       "</td>\n",
       "<td style=\"vertical-align: top; border: 0px solid white\">\n",
       "<h3 style=\"text-align: left;\">Cluster</h3>\n",
       "<ul style=\"text-align: left; list-style:none; margin: 0; padding: 0;\">\n",
       "  <li><b>Workers: </b>8</li>\n",
       "  <li><b>Cores: </b>8</li>\n",
       "  <li><b>Memory: </b>1.08 TB</li>\n",
       "</ul>\n",
       "</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Client: 'ucx://127.0.0.1:57555' processes=8 threads=8, memory=1.08 TB>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster = LocalCUDACluster(protocol=\"ucx\", \n",
    "                           enable_tcp_over_ucx=True, enable_nvlink=True)\n",
    "client = Client(cluster)\n",
    "client"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There arer 8xV100 GPUs in dgx-1. `dask-cudf` is used to read data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1.06 s, sys: 463 ms, total: 1.53 s\n",
      "Wall time: 3.75 s\n"
     ]
    },
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>feature-01</th>\n",
       "      <th>feature-02</th>\n",
       "      <th>feature-03</th>\n",
       "      <th>feature-04</th>\n",
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       "      <th>feature-20</th>\n",
       "      <th>feature-21</th>\n",
       "      <th>feature-22</th>\n",
       "      <th>feature-23</th>\n",
       "      <th>feature-24</th>\n",
       "      <th>feature-25</th>\n",
       "      <th>feature-26</th>\n",
       "      <th>feature-27</th>\n",
       "      <th>feature-28</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>0.225690</td>\n",
       "      <td>0.327470</td>\n",
       "      <td>-0.689993</td>\n",
       "      <td>0.754202</td>\n",
       "      <td>-0.248573</td>\n",
       "      <td>-1.092064</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.010455</td>\n",
       "      <td>-0.045767</td>\n",
       "      <td>3.101961</td>\n",
       "      <td>1.353760</td>\n",
       "      <td>0.979563</td>\n",
       "      <td>0.978076</td>\n",
       "      <td>0.920005</td>\n",
       "      <td>0.721657</td>\n",
       "      <td>0.988751</td>\n",
       "      <td>0.876678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.907542</td>\n",
       "      <td>0.329147</td>\n",
       "      <td>0.359412</td>\n",
       "      <td>1.497970</td>\n",
       "      <td>-0.313010</td>\n",
       "      <td>1.095531</td>\n",
       "      <td>-0.557525</td>\n",
       "      <td>-1.588230</td>\n",
       "      <td>2.173076</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.138930</td>\n",
       "      <td>-0.000819</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.302220</td>\n",
       "      <td>0.833048</td>\n",
       "      <td>0.985700</td>\n",
       "      <td>0.978098</td>\n",
       "      <td>0.779732</td>\n",
       "      <td>0.992356</td>\n",
       "      <td>0.798343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.798835</td>\n",
       "      <td>1.470639</td>\n",
       "      <td>-1.635975</td>\n",
       "      <td>0.453773</td>\n",
       "      <td>0.425629</td>\n",
       "      <td>1.104875</td>\n",
       "      <td>1.282322</td>\n",
       "      <td>1.381664</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.128848</td>\n",
       "      <td>0.900461</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.909753</td>\n",
       "      <td>1.108330</td>\n",
       "      <td>0.985692</td>\n",
       "      <td>0.951331</td>\n",
       "      <td>0.803252</td>\n",
       "      <td>0.865924</td>\n",
       "      <td>0.780118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.344385</td>\n",
       "      <td>-0.876626</td>\n",
       "      <td>0.935913</td>\n",
       "      <td>1.992050</td>\n",
       "      <td>0.882454</td>\n",
       "      <td>1.786066</td>\n",
       "      <td>-1.646778</td>\n",
       "      <td>-0.942383</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.678379</td>\n",
       "      <td>-1.360356</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.946652</td>\n",
       "      <td>1.028704</td>\n",
       "      <td>0.998656</td>\n",
       "      <td>0.728281</td>\n",
       "      <td>0.869200</td>\n",
       "      <td>1.026736</td>\n",
       "      <td>0.957904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.105009</td>\n",
       "      <td>0.321356</td>\n",
       "      <td>1.522401</td>\n",
       "      <td>0.882808</td>\n",
       "      <td>-1.205349</td>\n",
       "      <td>0.681466</td>\n",
       "      <td>-1.070464</td>\n",
       "      <td>-0.921871</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.373566</td>\n",
       "      <td>0.113041</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.755856</td>\n",
       "      <td>1.361057</td>\n",
       "      <td>0.986610</td>\n",
       "      <td>0.838085</td>\n",
       "      <td>1.133295</td>\n",
       "      <td>0.872245</td>\n",
       "      <td>0.808487</td>\n",
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       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   label  feature-01  feature-02  feature-03  feature-04  feature-05  \\\n",
       "0    1.0    0.869293   -0.635082    0.225690    0.327470   -0.689993   \n",
       "1    1.0    0.907542    0.329147    0.359412    1.497970   -0.313010   \n",
       "2    1.0    0.798835    1.470639   -1.635975    0.453773    0.425629   \n",
       "3    0.0    1.344385   -0.876626    0.935913    1.992050    0.882454   \n",
       "4    1.0    1.105009    0.321356    1.522401    0.882808   -1.205349   \n",
       "\n",
       "   feature-06  feature-07  feature-08  feature-09  ...  feature-19  \\\n",
       "0    0.754202   -0.248573   -1.092064    0.000000  ...   -0.010455   \n",
       "1    1.095531   -0.557525   -1.588230    2.173076  ...   -1.138930   \n",
       "2    1.104875    1.282322    1.381664    0.000000  ...    1.128848   \n",
       "3    1.786066   -1.646778   -0.942383    0.000000  ...   -0.678379   \n",
       "4    0.681466   -1.070464   -0.921871    0.000000  ...   -0.373566   \n",
       "\n",
       "   feature-20  feature-21  feature-22  feature-23  feature-24  feature-25  \\\n",
       "0   -0.045767    3.101961    1.353760    0.979563    0.978076    0.920005   \n",
       "1   -0.000819    0.000000    0.302220    0.833048    0.985700    0.978098   \n",
       "2    0.900461    0.000000    0.909753    1.108330    0.985692    0.951331   \n",
       "3   -1.360356    0.000000    0.946652    1.028704    0.998656    0.728281   \n",
       "4    0.113041    0.000000    0.755856    1.361057    0.986610    0.838085   \n",
       "\n",
       "   feature-26  feature-27  feature-28  \n",
       "0    0.721657    0.988751    0.876678  \n",
       "1    0.779732    0.992356    0.798343  \n",
       "2    0.803252    0.865924    0.780118  \n",
       "3    0.869200    1.026736    0.957904  \n",
       "4    1.133295    0.872245    0.808487  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "# download data from:\n",
    "# https://archive.ics.uci.edu/ml/machine-learning-databases/00280/HIGGS.csv.gz\n",
    "\n",
    "fname = '/raid/data/ml/higgs/HIGGS.csv'\n",
    "colnames = ['label'] + ['feature-%02d' % i for i in range(1, 29)]\n",
    "\n",
    "dask_df = dask_cudf.read_csv(fname, header=None, names=colnames)\n",
    "\n",
    "ddf = dask_df.repartition(npartitions=num_gpus) # set it to the number of GPUs for best performance\n",
    "ddf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of samples: 11000000\n",
      "number of features: 29\n",
      "CPU times: user 302 ms, sys: 70.5 ms, total: 372 ms\n",
      "Wall time: 3.59 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "print('number of samples:', len(ddf))\n",
    "print('number of features:', ddf.shape[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean target:0.530\n",
      "CPU times: user 340 ms, sys: 54.3 ms, total: 394 ms\n",
      "Wall time: 2.35 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "print(f\"mean target:{ddf['label'].mean().compute():.3f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**So there are 11M samples and 28 numerical features**. The target column `label` is binary and it is pretty balanced."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11000000 8\n",
      "CPU times: user 260 ms, sys: 20.8 ms, total: 281 ms\n",
      "Wall time: 2.47 s\n"
     ]
    },
    {
     "data": {
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       "<div><strong>Dask DataFrame Structure:</strong></div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature-01</th>\n",
       "      <th>feature-02</th>\n",
       "      <th>feature-03</th>\n",
       "      <th>feature-04</th>\n",
       "      <th>feature-05</th>\n",
       "      <th>feature-06</th>\n",
       "      <th>feature-07</th>\n",
       "      <th>feature-08</th>\n",
       "      <th>feature-09</th>\n",
       "      <th>feature-10</th>\n",
       "      <th>feature-11</th>\n",
       "      <th>feature-12</th>\n",
       "      <th>feature-13</th>\n",
       "      <th>feature-14</th>\n",
       "      <th>feature-15</th>\n",
       "      <th>feature-16</th>\n",
       "      <th>feature-17</th>\n",
       "      <th>feature-18</th>\n",
       "      <th>feature-19</th>\n",
       "      <th>feature-20</th>\n",
       "      <th>feature-21</th>\n",
       "      <th>feature-22</th>\n",
       "      <th>feature-23</th>\n",
       "      <th>feature-24</th>\n",
       "      <th>feature-25</th>\n",
       "      <th>feature-26</th>\n",
       "      <th>feature-27</th>\n",
       "      <th>feature-28</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>npartitions=8</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
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       "    </tr>\n",
       "    <tr>\n",
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       "      <td>...</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "<div>Dask Name: getitem, 46 tasks</div>"
      ],
      "text/plain": [
       "<dask_cudf.DataFrame | 46 tasks | 8 npartitions>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "print(len(ddf), ddf.npartitions)\n",
    "y = ddf['label']\n",
    "X = ddf[ddf.columns.difference(['label'])]\n",
    "\n",
    "X"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Unfortunately `dask.dataframe` is not accepted by `dask-glm` at the moment. We have to convert them to arrays. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 8.79 ms, sys: 494 µs, total: 9.28 ms\n",
      "Wall time: 7.56 ms\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<tr>\n",
       "<td>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> unknown </td> <td> unknown </td></tr>\n",
       "    <tr><th> Shape </th><td> (nan, 28) </td> <td> (nan, 28) </td></tr>\n",
       "    <tr><th> Count </th><td> 54 Tasks </td><td> 8 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
       "\n",
       "</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "dask.array<values, shape=(nan, 28), dtype=float64, chunksize=(nan, 28), chunktype=numpy.ndarray>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "# convert dask dataframes to dask arrays\n",
    "Xa = X.values\n",
    "ya = y.values\n",
    "\n",
    "Xa"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We make two observations:\n",
    "- the chunk type is incorrect. it is supposed to be `cupy.ndarray`\n",
    "- the chunk shape is `nan`.\n",
    "\n",
    "Both of these lead to errors when we feed these data to `dask-glm`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Fix the chunk type**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 359 µs, sys: 0 ns, total: 359 µs\n",
      "Wall time: 263 µs\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Make the chunk type cupy\n",
    "Xa._meta = cp.asarray(Xa._meta)\n",
    "ya._meta = cp.asarray(ya._meta)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Compute the chunk shape**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 409 ms, sys: 129 ms, total: 538 ms\n",
      "Wall time: 4.77 s\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<td>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 2.46 GB </td> <td> 329.25 MB </td></tr>\n",
       "    <tr><th> Shape </th><td> (11000000, 28) </td> <td> (1469876, 28) </td></tr>\n",
       "    <tr><th> Count </th><td> 54 Tasks </td><td> 8 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float64 </td><td> cupy.ndarray </td></tr>\n",
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       "</table>\n",
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     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "Xa.compute_chunk_sizes()\n",
    "ya.compute_chunk_sizes()\n",
    "Xa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 226 ms, sys: 10.9 ms, total: 236 ms\n",
      "Wall time: 2.11 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "Xa, ya = dask.persist(Xa, ya)\n",
    "wait(Xa)\n",
    "_ = wait(ya)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy 0.641\n",
      "CPU times: user 2.55 s, sys: 341 ms, total: 2.89 s\n",
      "Wall time: 5.36 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "start = time.time()\n",
    "lr = LogisticRegression(solver='lbfgs', max_iter=10)\n",
    "lr.fit(Xa, ya)\n",
    "yp = lr.predict(Xa)\n",
    "acc = (yp==ya).mean().compute()\n",
    "print('Training Accuracy %.3f'%acc)\n",
    "gpu_higgs_time = time.time() - start"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**It took only `5` seconds to train and predict 11M samples!** As we will see below, it is **14x speedup** over the CPU solution."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**We also record the running time on random synthetic data with increasing sizes.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "admm samples:10^3 time:1.15\n",
      "admm samples:10^4 time:0.75\n",
      "admm samples:10^5 time:0.34\n",
      "admm samples:10^6 time:0.66\n",
      "admm samples:10^7 time:2.11\n",
      "admm samples:10^8 time:4.18\n",
      "lbfgs samples:10^3 time:0.61\n",
      "lbfgs samples:10^4 time:0.64\n",
      "lbfgs samples:10^5 time:0.64\n",
      "lbfgs samples:10^6 time:0.81\n",
      "lbfgs samples:10^7 time:1.02\n",
      "lbfgs samples:10^8 time:4.65\n",
      "newton samples:10^3 time:1.08\n",
      "newton samples:10^4 time:0.76\n",
      "newton samples:10^5 time:1.11\n",
      "newton samples:10^6 time:0.67\n",
      "newton samples:10^7 time:1.07\n",
      "newton samples:10^8 time:4.64\n",
      "proximal_grad samples:10^3 time:1.17\n",
      "proximal_grad samples:10^4 time:1.54\n",
      "proximal_grad samples:10^5 time:1.74\n",
      "proximal_grad samples:10^6 time:1.86\n",
      "proximal_grad samples:10^7 time:3.41\n",
      "proximal_grad samples:10^8 time:11.07\n",
      "gradient_descent samples:10^3 time:1.07\n",
      "gradient_descent samples:10^4 time:2.05\n",
      "gradient_descent samples:10^5 time:1.30\n",
      "gradient_descent samples:10^6 time:1.51\n",
      "gradient_descent samples:10^7 time:2.73\n",
      "gradient_descent samples:10^8 time:8.50\n",
      "CPU times: user 34.8 s, sys: 4.21 s, total: 39.1 s\n",
      "Wall time: 2min 21s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# record the running time on random synthetic data\n",
    "\n",
    "solvers = ['admm', 'lbfgs', 'newton', 'proximal_grad', 'gradient_descent']\n",
    "cupy_time = {}\n",
    "for s in solvers:\n",
    "    cupy_time[s] = get_run_time(s,3,8,data_type='cupy')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dask glm on CPU\n",
    "<a id=\"cpu\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table style=\"border: 2px solid white;\">\n",
       "<tr>\n",
       "<td style=\"vertical-align: top; border: 0px solid white\">\n",
       "<h3 style=\"text-align: left;\">Client</h3>\n",
       "<ul style=\"text-align: left; list-style: none; margin: 0; padding: 0;\">\n",
       "  <li><b>Scheduler: </b>tcp://127.0.0.1:44291</li>\n",
       "  <li><b>Dashboard: </b><a href='http://127.0.0.1:34975/status' target='_blank'>http://127.0.0.1:34975/status</a></li>\n",
       "</ul>\n",
       "</td>\n",
       "<td style=\"vertical-align: top; border: 0px solid white\">\n",
       "<h3 style=\"text-align: left;\">Cluster</h3>\n",
       "<ul style=\"text-align: left; list-style:none; margin: 0; padding: 0;\">\n",
       "  <li><b>Workers: </b>80</li>\n",
       "  <li><b>Cores: </b>80</li>\n",
       "  <li><b>Memory: </b>1.08 TB</li>\n",
       "</ul>\n",
       "</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Client: 'tcp://127.0.0.1:44291' processes=80 threads=80, memory=1.08 TB>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dask glm cpu baseline\n",
    "cluster = LocalCluster(n_workers=80)\n",
    "client = Client(cluster)\n",
    "client"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The CPU has 40 cores and 80 hyperthreads on `dgx-1`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11000000 80\n",
      "CPU times: user 10 s, sys: 5.08 s, total: 15.1 s\n",
      "Wall time: 17.3 s\n"
     ]
    },
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       "    <tr><th> Shape </th><td> (11000000, 28) </td> <td> (175225, 28) </td></tr>\n",
       "    <tr><th> Count </th><td> 80 Tasks </td><td> 80 Chunks </td></tr>\n",
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       "  <line x1=\"0\" y1=\"120\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 25.412616514582485,0.0 25.412616514582485,120.0 0.0,120.0\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"12.706308\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >28</text>\n",
       "  <text x=\"45.412617\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,45.412617,60.000000)\">11000000</text>\n",
       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "dask.array<values, shape=(11000000, 28), dtype=float64, chunksize=(175225, 28), chunktype=numpy.ndarray>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "dask_df = dd.read_csv(fname, header=None, names=colnames)\n",
    "dask_df = dask_df.repartition(npartitions=80) # one partition per thread\n",
    "\n",
    "print(len(dask_df), dask_df.npartitions)\n",
    "y = dask_df['label']\n",
    "X = dask_df[dask_df.columns.difference(['label'])]\n",
    "\n",
    "Xa = X.values\n",
    "ya = y.values\n",
    "\n",
    "Xa.compute_chunk_sizes()\n",
    "ya.compute_chunk_sizes()\n",
    "\n",
    "Xa, ya = dask.persist(Xa, ya)\n",
    "wait(Xa)\n",
    "wait(ya)\n",
    "\n",
    "Xa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy 0.641\n",
      "CPU times: user 35.1 s, sys: 10.9 s, total: 46 s\n",
      "Wall time: 1min 17s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "start = time.time()\n",
    "lr = LogisticRegression(solver='lbfgs', max_iter=10)\n",
    "lr.fit(Xa, ya)\n",
    "yp = lr.predict(Xa)\n",
    "acc = (yp==ya).mean().compute()\n",
    "print('Training Accuracy %.3f'%acc)\n",
    "cpu_higgs_time = time.time() - start"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see the same training accuracy but much longer running time. The GPU solution achieves **14x speedup** over the CPU solution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "admm samples:10^3 time:2.77\n",
      "admm samples:10^4 time:2.56\n",
      "admm samples:10^5 time:11.52\n",
      "admm samples:10^6 time:18.70\n",
      "admm samples:10^7 time:67.78\n",
      "admm samples:10^8 time:112.42\n",
      "lbfgs samples:10^3 time:5.53\n",
      "lbfgs samples:10^4 time:4.97\n",
      "lbfgs samples:10^5 time:10.91\n",
      "lbfgs samples:10^6 time:16.17\n",
      "lbfgs samples:10^7 time:17.40\n",
      "lbfgs samples:10^8 time:30.37\n",
      "newton samples:10^3 time:6.44\n",
      "newton samples:10^4 time:5.72\n",
      "newton samples:10^5 time:14.82\n",
      "newton samples:10^6 time:15.42\n",
      "newton samples:10^7 time:17.69\n",
      "newton samples:10^8 time:41.66\n",
      "proximal_grad samples:10^3 time:10.75\n",
      "proximal_grad samples:10^4 time:11.87\n",
      "proximal_grad samples:10^5 time:26.34\n",
      "proximal_grad samples:10^6 time:44.72\n",
      "proximal_grad samples:10^7 time:59.58\n",
      "proximal_grad samples:10^8 time:85.97\n",
      "gradient_descent samples:10^3 time:8.89\n",
      "gradient_descent samples:10^4 time:9.92\n",
      "gradient_descent samples:10^5 time:21.62\n",
      "gradient_descent samples:10^6 time:26.06\n",
      "gradient_descent samples:10^7 time:32.00\n",
      "gradient_descent samples:10^8 time:51.30\n",
      "CPU times: user 11min 18s, sys: 5min 20s, total: 16min 39s\n",
      "Wall time: 14min 26s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "solvers = ['admm', 'lbfgs', 'newton', 'proximal_grad', 'gradient_descent']\n",
    "numpy_time = {}\n",
    "for s in solvers:\n",
    "    numpy_time[s] = get_run_time(s,3,8,data_type='numpy')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Speedup and Conclusion\n",
    "<a id=\"speedup\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, axs = plt.subplots(1,5, figsize=(20, 5),sharex=True,sharey=True)\n",
    "solvers = ['admm', 'lbfgs', 'newton', 'proximal_grad', 'gradient_descent']\n",
    "speedup = [numpy_time[s][-1]/cupy_time[s][-1] for s in solvers]\n",
    "nsamples = [10**i for i in range(3,9)]\n",
    "axs[0].set_ylabel(\"Time:seconds\",fontsize=16)\n",
    "plt.suptitle(\"dask-glm DGX-1 8xGPU vs 40-core CPU\", fontsize=20)\n",
    "\n",
    "for c,s in enumerate(solvers):\n",
    "    axs[c].plot(nsamples,cupy_time[s],color='g',label='GPU')\n",
    "    axs[c].plot(nsamples,numpy_time[s],color='b',label='CPU')\n",
    "    axs[c].set_xscale(\"log\")\n",
    "    axs[c].set_yscale(\"log\")\n",
    "    axs[c].grid()\n",
    "    axs[c].set_title('%s speedup %.1f'%(s, speedup[c]),fontsize=16)\n",
    "    axs[c].set_xlabel(\"number of samples\",)\n",
    "    axs[c].legend(fontsize=14)\n",
    "\n",
    "#plt.savefig(\"dask_glm_cupy_vs_numpy.pdf\",format='pdf',quality=95)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook shows how `dask-cudf` works with `dask-glm` as the downstream classifiers. The key takeaway is to convert `dask cudf dataframes` to `dask arrays with cupy chunks`. A decent speedup is achieved with minimal code changes."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}