wandb_issue_961.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fastai.vision import *\n",
    "import wandb\n",
    "from wandb.fastai import WandbCallback"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "MNIST_PATH = str(untar_data(URLs.MNIST_TINY))\n",
    "path = untar_data(MNIST_PATH)\n",
    "data = ImageDataBunch.from_folder(path, size=90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
       "                Project page: <a href=\"https://app.wandb.ai/borisd13/uncategorized\" target=\"_blank\">https://app.wandb.ai/borisd13/uncategorized</a><br/>\n",
       "                Run page: <a href=\"https://app.wandb.ai/borisd13/uncategorized/runs/3rxfppp1\" target=\"_blank\">https://app.wandb.ai/borisd13/uncategorized/runs/3rxfppp1</a><br/>\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "W&B Run: https://app.wandb.ai/borisd13/uncategorized/runs/3rxfppp1"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wandb.init()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn = cnn_learner(data, models.resnet18, metrics=accuracy, callback_fns=WandbCallback)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.278620</td>\n",
       "      <td>0.644932</td>\n",
       "      <td>0.693848</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.161620</td>\n",
       "      <td>1.431106</td>\n",
       "      <td>0.532189</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.114893</td>\n",
       "      <td>1.007536</td>\n",
       "      <td>0.619456</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Better model found at epoch 0 with valid_loss value: 0.6449324488639832.\n",
      "Loaded best saved model from /home/boris/Projects/fastai_bug/wandb/run-20200413_213315-3rxfppp1/bestmodel.pth\n"
     ]
    }
   ],
   "source": [
    "learn.fit(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.export('learner.pkl')"
   ]
  }
 ],
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