test notebook gist

test.ipynb

{
 "metadata": {
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4-final"
  },
  "orig_nbformat": 2,
  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3.7.4 64-bit",
   "metadata": {
    "interpreter": {
     "hash": "734c25f1dddaedb8b9bba2f649a8a093ffbd1259f7c9d6ab65bb2999aaac5917"
    }
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2,
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "tensor([[0.9672, 0.2479, 0.1008, 0.4450, 0.6202, 0.9538, 0.7583, 0.9702],\n",
       "        [0.0516, 0.1781, 0.9683, 0.8205, 0.6245, 0.6464, 0.9473, 0.1493],\n",
       "        [0.1046, 0.2402, 0.4190, 0.4587, 0.8884, 0.2851, 0.6582, 0.7962],\n",
       "        [0.1445, 0.4447, 0.2181, 0.5481, 0.5688, 0.7505, 0.0968, 0.3428],\n",
       "        [0.7179, 0.5586, 0.9876, 0.3671, 0.5281, 0.1759, 0.9024, 0.9538]])"
      ]
     },
     "metadata": {},
     "execution_count": 2
    }
   ],
   "source": [
    "x = torch.rand( 5 , 8 )\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "tensor([[1., 1., 1.],\n",
       "        [1., 1., 1.]])"
      ]
     },
     "metadata": {},
     "execution_count": 3
    }
   ],
   "source": [
    "y = torch.ones(2, 3)\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "cuda available:  True\n"
     ]
    }
   ],
   "source": [
    "print('cuda available: ' , torch.cuda.is_available() )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}