{
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   "source": [
    "## Saving a Cats v Dogs Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is a minimal example showing how to train a fastai model on Kaggle, and save it so you can use it in your app."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "# Make sure we've got the latest version of fastai:\n",
    "!pip install -Uqq fastai"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, import all the stuff we need from fastai:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-03T05:51:59.534478Z",
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    },
    "id": "44eb0ad3"
   },
   "outputs": [],
   "source": [
    "from fastai.vision.all import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Download and decompress our dataset, which is pictures of dogs and cats:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-03T05:52:02.180691Z",
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     "shell.execute_reply.started": "2022-05-03T05:52:02.180651Z"
    }
   },
   "outputs": [],
   "source": [
    "path = untar_data(URLs.PETS)/'images'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need a way to label our images as dogs or cats. In this dataset, pictures of cats are given a filename that starts with a capital letter:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-03T05:53:02.467572Z",
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     "shell.execute_reply.started": "2022-05-03T05:53:02.467536Z"
    },
    "id": "44eb0ad3"
   },
   "outputs": [],
   "source": [
    "def is_cat(x): return x[0].isupper() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can create our `DataLoaders`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-03T05:53:02.476084Z",
     "iopub.status.busy": "2022-05-03T05:53:02.475754Z",
     "iopub.status.idle": "2022-05-03T05:53:06.703777Z",
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     "shell.execute_reply.started": "2022-05-03T05:53:02.476052Z"
    },
    "id": "44eb0ad3"
   },
   "outputs": [],
   "source": [
    "dls = ImageDataLoaders.from_name_func('.',\n",
    "    get_image_files(path), valid_pct=0.2, seed=42,\n",
    "    label_func=is_cat,\n",
    "    item_tfms=Resize(192))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "... and train our model, a resnet18 (to keep it small and fast):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-03T05:53:28.093059Z",
     "iopub.status.busy": "2022-05-03T05:53:28.092381Z"
    },
    "id": "c107f724",
    "outputId": "fcc1de68-7c8b-43f5-b9eb-fcdb0773ef07"
   },
   "outputs": [],
   "source": [
    "learn = vision_learner(dls, resnet18, metrics=error_rate)\n",
    "learn.fine_tune(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can export our trained `Learner`. This contains all the information needed to run the model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "ae2bc6ac"
   },
   "outputs": [],
   "source": [
    "learn.export('model.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Q2HTrQKTf3BV"
   },
   "source": [
    "Finally, open the Kaggle sidebar on the right if it's not already, and find the section marked \"Output\". Open the `/kaggle/working` folder, and you'll see `model.pkl`. Click on it, then click on the menu on the right that appears, and choose \"Download\". After a few seconds, your model will be downloaded to your computer, where you can then create your app that uses the model."
   ]
  }
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