SQLAlchemy_setup_database.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2019-07-11 22:46:18,079 INFO sqlalchemy.engine.base.Engine SHOW VARIABLES LIKE 'sql_mode'\n",
      "2019-07-11 22:46:18,083 INFO sqlalchemy.engine.base.Engine ()\n",
      "2019-07-11 22:46:18,087 INFO sqlalchemy.engine.base.Engine SHOW VARIABLES LIKE 'lower_case_table_names'\n",
      "2019-07-11 22:46:18,088 INFO sqlalchemy.engine.base.Engine ()\n",
      "2019-07-11 22:46:18,091 INFO sqlalchemy.engine.base.Engine SELECT DATABASE()\n",
      "2019-07-11 22:46:18,093 INFO sqlalchemy.engine.base.Engine ()\n",
      "2019-07-11 22:46:18,094 INFO sqlalchemy.engine.base.Engine show collation where `Charset` = 'utf8mb4' and `Collation` = 'utf8mb4_bin'\n",
      "2019-07-11 22:46:18,095 INFO sqlalchemy.engine.base.Engine ()\n",
      "2019-07-11 22:46:18,099 INFO sqlalchemy.engine.base.Engine SELECT CAST('test plain returns' AS CHAR(60)) AS anon_1\n",
      "2019-07-11 22:46:18,101 INFO sqlalchemy.engine.base.Engine ()\n",
      "2019-07-11 22:46:18,102 INFO sqlalchemy.engine.base.Engine SELECT CAST('test unicode returns' AS CHAR(60)) AS anon_1\n",
      "2019-07-11 22:46:18,103 INFO sqlalchemy.engine.base.Engine ()\n",
      "2019-07-11 22:46:18,104 INFO sqlalchemy.engine.base.Engine SELECT CAST('test collated returns' AS CHAR CHARACTER SET utf8mb4) COLLATE utf8mb4_bin AS anon_1\n",
      "2019-07-11 22:46:18,105 INFO sqlalchemy.engine.base.Engine ()\n",
      "2019-07-11 22:46:18,107 INFO sqlalchemy.engine.base.Engine DESCRIBE `training_data`\n",
      "2019-07-11 22:46:18,112 INFO sqlalchemy.engine.base.Engine ()\n",
      "2019-07-11 22:46:18,115 INFO sqlalchemy.engine.base.Engine ROLLBACK\n",
      "2019-07-11 22:46:18,116 INFO sqlalchemy.engine.base.Engine DESCRIBE `parameters`\n",
      "2019-07-11 22:46:18,117 INFO sqlalchemy.engine.base.Engine ()\n",
      "2019-07-11 22:46:18,119 INFO sqlalchemy.engine.base.Engine ROLLBACK\n",
      "2019-07-11 22:46:18,120 INFO sqlalchemy.engine.base.Engine \n",
      "CREATE TABLE training_data (\n",
      "\tid INTEGER NOT NULL AUTO_INCREMENT, \n",
      "\tepoch INTEGER, \n",
      "\tdate DATETIME, \n",
      "\ttraining_loss FLOAT, \n",
      "\ttest_loss FLOAT, \n",
      "\ttraining_accuracy FLOAT, \n",
      "\ttest_accuracy FLOAT, \n",
      "\tPRIMARY KEY (id)\n",
      ")\n",
      "\n",
      "\n",
      "2019-07-11 22:46:18,121 INFO sqlalchemy.engine.base.Engine ()\n",
      "2019-07-11 22:46:18,163 INFO sqlalchemy.engine.base.Engine COMMIT\n",
      "2019-07-11 22:46:18,165 INFO sqlalchemy.engine.base.Engine \n",
      "CREATE TABLE parameters (\n",
      "\tid INTEGER NOT NULL AUTO_INCREMENT, \n",
      "\tparameters_id INTEGER, \n",
      "\tlayer VARCHAR(30), \n",
      "\tarr_param BLOB(4294967295), \n",
      "\tarr_grad BLOB(4294967295), \n",
      "\tPRIMARY KEY (id), \n",
      "\tFOREIGN KEY(parameters_id) REFERENCES training_data (id)\n",
      ")\n",
      "\n",
      "\n",
      "2019-07-11 22:46:18,167 INFO sqlalchemy.engine.base.Engine ()\n",
      "2019-07-11 22:46:18,212 INFO sqlalchemy.engine.base.Engine COMMIT\n"
     ]
    }
   ],
   "source": [
    "from sqlalchemy import create_engine, ForeignKey, Column, Integer, String, Float, DateTime, LargeBinary\n",
    "import datetime\n",
    "engine = create_engine(\"mysql://andreiliphd:123456789aA@localhost/carrot\",echo = True)\n",
    "from sqlalchemy.ext.declarative import declarative_base\n",
    "Base = declarative_base()\n",
    "from sqlalchemy.orm import relationship\n",
    "\n",
    "class TrainingData(Base):\n",
    "    __tablename__ = 'training_data'\n",
    "\n",
    "    id = Column(Integer, primary_key = True)\n",
    "    epoch = Column(Integer)\n",
    "    date = Column(DateTime, default=datetime.datetime.now())\n",
    "    training_loss = Column(Float)\n",
    "    test_loss = Column(Float)\n",
    "    training_accuracy = Column(Float)\n",
    "    test_accuracy = Column(Float)\n",
    "    \n",
    "\n",
    "class Parameters(Base):\n",
    "    __tablename__ = 'parameters'   \n",
    "   \n",
    "    id = Column(Integer, primary_key = True)\n",
    "    parameters_id = Column(Integer, ForeignKey('training_data.id'))\n",
    "    layer = Column(String(30))\n",
    "    arr_param = Column(LargeBinary(length=(2**32)-1))\n",
    "    arr_grad = Column(LargeBinary(length=(2**32)-1))\n",
    "    td = relationship(\"TrainingData\", back_populates = \"parameters\")\n",
    "TrainingData.parameters = relationship(\"Parameters\", order_by = Parameters.id, back_populates = \"training_data\")\n",
    "\n",
    "Base.metadata.create_all(engine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sqlalchemy import create_engine\n",
    "import datetime\n",
    "engine = create_engine(\"mysql://andreiliphd:123456789aA@localhost/carrot\",echo = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sqlalchemy import create_engine, ForeignKey, Column, Integer, String, Float, DateTime, LargeBinary\n",
    "import datetime\n",
    "from sqlalchemy.ext.declarative import declarative_base\n",
    "Base = declarative_base()\n",
    "from sqlalchemy.orm import relationship"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "class TrainingData(Base):\n",
    "    __tablename__ = 'training_data'\n",
    "    id = Column(Integer, primary_key = True)\n",
    "    epoch = Column(Integer)\n",
    "    date = Column(DateTime, default=datetime.datetime.now())\n",
    "    training_loss = Column(Float)\n",
    "    test_loss = Column(Float)\n",
    "    training_accuracy = Column(Float)\n",
    "    test_accuracy = Column(Float)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Parameters(Base):\n",
    "    __tablename__ = 'parameters'  \n",
    "   \n",
    "    id = Column(Integer, primary_key = True)\n",
    "    parameters_id = Column(Integer, ForeignKey('training_data.id'))\n",
    "    layer = Column(String(30))\n",
    "    arr_param = Column(BLOB)\n",
    "    arr_grad = Column(BLOB)\n",
    "    training_data = relationship(\"TrainingData\", back_populates = 'parameters')\n",
    "TrainingData.parameters = relationship(\"Parameters\", order_by = Parameters.id, back_populates = 'training_data')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2019-07-11 18:46:53,206 INFO sqlalchemy.engine.base.Engine DESCRIBE `training_data`\n",
      "2019-07-11 18:46:53,208 INFO sqlalchemy.engine.base.Engine ()\n",
      "2019-07-11 18:46:53,211 INFO sqlalchemy.engine.base.Engine DESCRIBE `parameters`\n",
      "2019-07-11 18:46:53,212 INFO sqlalchemy.engine.base.Engine ()\n"
     ]
    }
   ],
   "source": [
    "Base.metadata.create_all(engine)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class TrainingData(Base):\n",
    "    __tablename__ = 'training_data'\n",
    "    id = Column(Integer, primary_key = True)\n",
    "    epoch = Column(Integer)\n",
    "    date = Column(DateTime, default=datetime.datetime.now())\n",
    "    training_loss = Column(Float)\n",
    "    test_loss = Column(Float)\n",
    "    training_accuracy = Column(Float)\n",
    "    test_accuracy = Column(Float)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Parameters(Base):\n",
    "    __tablename__ = 'parameters'  \n",
    "   \n",
    "    id = Column(Integer, primary_key = True)\n",
    "    parameters_id = Column(Integer, ForeignKey('training_data.id'))\n",
    "    layer = Column(String(30))\n",
    "    arr_param = Column(BLOB)\n",
    "    arr_grad = Column(BLOB)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sqlalchemy.orm import sessionmaker\n",
    "Session = sessionmaker(bind = engine)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "session = Session()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = TrainingData(epoch=1)\n",
    "data1 = Parameters(layer='conv')\n",
    "session.add(data1)\n",
    "session.add(data)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2019-07-11 19:25:38,716 INFO sqlalchemy.engine.base.Engine BEGIN (implicit)\n",
      "2019-07-11 19:25:38,718 INFO sqlalchemy.engine.base.Engine INSERT INTO training_data (epoch, date, training_loss, test_loss, training_accuracy, test_accuracy) VALUES (%s, %s, %s, %s, %s, %s)\n",
      "2019-07-11 19:25:38,719 INFO sqlalchemy.engine.base.Engine (1, datetime.datetime(2019, 7, 11, 19, 22, 49, 957739), None, None, None, None)\n",
      "2019-07-11 19:25:38,721 INFO sqlalchemy.engine.base.Engine INSERT INTO parameters (parameters_id, layer, arr_param, arr_grad) VALUES (%s, %s, %s, %s)\n",
      "2019-07-11 19:25:38,722 INFO sqlalchemy.engine.base.Engine (None, 'conv', None, None)\n",
      "2019-07-11 19:25:38,724 INFO sqlalchemy.engine.base.Engine COMMIT\n"
     ]
    }
   ],
   "source": [
    "session.commit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}