notebooks/training.ipynb

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   "source": [
    "# Train Various Models on MNIST using kubeflow and seldon-core\n",
    "\n",
    "Using:\n",
    "\n",
    " * kubeflow\n",
    " * seldon-core\n",
    " \n",
    "The example will be the MNIST handwriiten digit classification task.\n",
    "\n",
    "![MNIST](mnist.png \"MNIST Digits\")\n",
    "\n",
    "### Dependencies\n",
    "\n",
    "  * Argo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setup\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!kubectl config set-context $(kubectl config current-context) --namespace=kubeflow"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tensorflow Model\n",
    " A simple neural network in Tensorflow."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training\n",
    " * Create image from source\n",
    " * Run training\n",
    " \n",
    "\n",
    "Run with:\n",
    "  * ``` -p build-push-image=true``` to build image and push to repo, needed extra params:\n",
    "    * ``` -p version=<version>``` create ```<version>``` of model\n",
    "    * ``` -p github-user=<github-user>``` to download example-seldon source from ```<github-user>``` account\n",
    "    * ``` -p github-revision=<revision>``` to use the github branch ```<revision>```\n",
    "    * ``` -p docker-org=<docker-org>``` to use Docker repo ```<docker-org>``` to push image to. Needs docker credentials in secret as described in README."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pygmentize ../workflows/training-tf-mnist-workflow.yaml"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!argo submit ../workflows/training-tf-mnist-workflow.yaml -p tfjob-version-hack=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!argo list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Runtime Image\n",
    "\n",
    "Run with:\n",
    "  * ``` -p build-push-image=true``` to build image and push to repo, needed extra params:\n",
    "    * ``` -p version=<version>``` create ```<version>``` of model\n",
    "    * ``` -p github-user=<github-user>``` to download example-seldon source from ```<github-user>``` account\n",
    "    * ``` -p github-revision=<revision>``` to use the github branch ```<revision>```\n",
    "    * ``` -p docker-org=<docker-org>``` to use Docker user ```<docker-org>``` to push image to. Needs docker credentials in secret as described in README.\n",
    "  * ``` -p deploy-model=true``` to deploy model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pygmentize ../workflows/serving-tf-mnist-workflow.yaml"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!argo submit ../workflows/serving-tf-mnist-workflow.yaml"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!argo list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sklearn Model\n",
    "A Random forest in sklearn."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training\n",
    "\n",
    " * For options see above Tensorflow example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pygmentize ../workflows/training-sk-mnist-workflow.yaml"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!argo submit ../workflows/training-sk-mnist-workflow.yaml"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!argo list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Runtime Image\n",
    " * For options see above Tensorflow example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pygmentize ../workflows/serving-sk-mnist-workflow.yaml"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!argo submit ../workflows/serving-sk-mnist-workflow.yaml"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!argo list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# R Model\n",
    "A partial least squares model in R."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training\n",
    "\n",
    " * For options see above Tensorflow example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pygmentize ../workflows/training-r-mnist-workflow.yaml"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!argo submit ../workflows/training-r-mnist-workflow.yaml"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!argo list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Runtime Image\n",
    " * For options see above Tensorflow example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pygmentize ../workflows/serving-r-mnist-workflow.yaml"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!argo submit ../workflows/serving-r-mnist-workflow.yaml"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!argo list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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