04_Deploy_Model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Login to https://community.pipeline.ai"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Deploy Model to PipelineAI\n",
    "\n",
    "You will need to fill in the unique values for the following:\n",
    "* `<YOUR_USER_ID>`  - 8 character id that uniquely identifies the PipelineAI user.  You will see the UserId in the upper right hand corner of the Settings tab after you login to [PipelineAI Community Edition](https://community.cloud.pipeline.ai)\n",
    "* `<YOUR_API_TOKEN>`  - 8 character id that uniquely identifies the PipelineAI user.  You will see the UserId in the upper right hand corner of the Settings tab after you login to [PipelineAI Community Edition](https://community.cloud.pipeline.ai)\n",
    "\n",
    "![user-id](https://pipeline.ai/assets/img/user-id.png)\n",
    "\n",
    "* `<UNIQUE_MODEL_NAME>` - User-defined model/project name that uniquely identifies a model/project within your account.\n",
    "* `<UNIQUE_TAG_NAME>` - User-defined tag that uniquely identifies the model tag/version within a model/project\n",
    "```\n",
    "pipeline resource-upload --host community.cloud.pipeline.ai \\\n",
    "  --api-token <YOUR_API_TOKEN> \\\n",
    "  --user-id <YOUR_USER_ID> \\\n",
    "  --resource-type model \\\n",
    "  --resource-subtype tensorflow \\\n",
    "  --name <UNIQUE_MODEL_NAME> \\\n",
    "  --tag <UNIQUE_TAG_NAME> \\\n",
    "  --path ./mnist_model\n",
    "```\n",
    "\n",
    "Actions performed:\n",
    "* Compress resource source code into a tar archive.\n",
    "* Create required directories and generate deployment and service resource definitions.\n",
    "* Receive resource source code - or trained binary (ie. tensorflow SavedModel binary)\n",
    "  from client as a tar archive then uncompress and extract on the PipelineAI server.\n",
    "* Initialize training resource\n",
    "\n",
    "# Optimize and Deploy the Model\n",
    "You can optimize (select one or more chips and/or one or more runtimes) and deploy your model using the CLI or the [UI](https://community.cloud.pipeline.ai) (Choose either the CLI or UI).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%bash\n",
    "\n",
    "pipeline resource-upload --host community.cloud.pipeline.ai \\\n",
    "  --api-token <YOUR_API_TOKEN> \\\n",
    "  --user-id <YOUR_USER_ID> \\\n",
    "  --resource-type model \\\n",
    "  --resource-subtype tensorflow \\\n",
    "  --name mnist \\\n",
    "  --tag v1 \\\n",
    "  --path ./mnist_model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Navigate to https://community.pipeline.ai and deploy your model\n",
    "\n",
    "![](https://pipeline.ai/assets/img/pipelineai-100pct-deploy.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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