notebooks/3-processing/running-on-aws-lambda.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Running on AWS\n",
    "\n",
    "*Author: Creare* <br>\n",
    "*Date: April 01 2020* <br>\n",
    "\n",
    "**Keywords**: processing, cloud, AWS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Overview\n",
    "\n",
    "Runing PODPAC Pipeline on AWS Lambda."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prerequisites\n",
    "\n",
    "- Python 2.7 or above\n",
    "- [`podpac[datatype]`](https://podpac.org/install.html#install)\n",
    "- [`podpac[aws]`](https://podpac.org/install.html#install)\n",
    "- *Review the [README.md](../../README.md) and [jupyter-tutorial.ipynb](../jupyter-tutorial.ipynb) for additional info on using jupyter notebooks*\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### See Also\n",
    "\n",
    "- [AWS Lambda Tutorial](../4-advanced/aws-lambda.ipynb): Tutorial for **setting up the PODPAC Lambda function on AWS**\n",
    "- [AWS Budgeting Tutorial](../4-advanced/aws-budget.ipynb): Tutorial for budgeting with PODPAC and AWS\n",
    "- [SMAP Datalib](../5-datalib/smap/010-retrieving-SMAP-data.ipynb): SMAP Datalib tutorial\n",
    "- [100-analyzing-SMAP-data.ipynb](../5-datalib/smap/100-analyzing-SMAP-data.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# How to run analysis on AWS Lambda\n",
    "\n",
    "<i style='font-size:18pt'>PODPAC uses the AWS commandline tools to trigger the Lambda function execution. </i>\n",
    "\n",
    "<img src='../../images/PODPAC-AWS-Lambda.png' style='width:80%; margin-left:auto;margin-right:auto' />"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "We will:\n",
    "* Create a node\n",
    "* Execute the node on AWS Lambda\n",
    "\n",
    "**This example requires a pre-configured AWS Lambda node.** We will not cover how the AWS Lambda node was created. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "# Get the PODPAC logger\n",
    "import logging\n",
    "logger = logging.getLogger(\"podpac\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Setup PODPAC for AWS (1/2)\n",
    "\n",
    "* Configure AWS credentials\n",
    "* Can be specified at runtime, or in the podpac settings file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "import podpac\n",
    "from podpac import settings\n",
    "\n",
    "# need to allow unsafe evaluation to use `podpac.algorithm.Arithmetic` Node (below) \n",
    "settings.set_unsafe_eval(True)\n",
    "\n",
    "# # Credentials\n",
    "# settings[\"AWS_ACCESS_KEY_ID\"] = \"access key id\"\n",
    "# settings[\"AWS_SECRET_ACCESS_KEY\"] = \"secrect access key\"\n",
    "# settings[\"AWS_REGION_NAME\"] = \"region name\"\n",
    "\n",
    "# # General Settings\n",
    "# settings[\"AWS_TAGS\"] = {} # tags to assign to AWS resources created through PODPAC\n",
    "\n",
    "# # S3\n",
    "# settings[\"S3_BUCKET_NAME\"] = \"S3 bucket for Lambda functions or PODPAC cache\"\n",
    "\n",
    "# # Lambda\n",
    "# settings[\"FUNCTION_NAME\"] = \"name of lambda function to eval\"\n",
    "# settings[\"FUNCTION_ROLE_NAME\"] = \"role name for lambda function\"\n",
    "# settings[\"FUNCTION_DEPENDENCIES_KEY\"] = \"path on S3 bucket where function depedencies live\"\n",
    "# settings[\"FUNCTION_S3_INPUT\"] = \"path on S3 bucket for input pipelines. Objects put in this directory will trigger lambda function\",\n",
    "# settings[\"FUNCTION_S3_OUTPUT\"] = \"path on S3 bucket for pipeline outputs. Objects put in this directory will be returned to lambda function\",\n",
    "\n",
    "# # Paths - overwrite paths for Lambda caching\n",
    "# this will be fixed in future releases of PODPAC\n",
    "settings[\"ROOT_PATH\"] = \"/tmp/\"\n",
    "settings[\"LOG_FILE_PATH\"] = \"/tmp/\"\n",
    "settings[\"DISK_CACHE_DIR\"] = \"/tmp/cache\"\n",
    "settings['AUTOSAVE_SETTINGS'] = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Provide Earth Data Login Credentials\n",
    "If you do not have an earth data login, or have not activated OpenDAP access, follow the [instructions here](https://creare-com.github.io/podpac-docs/user/earthdata.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Username: mpuecker\n",
      "Password: ··················\n"
     ]
    }
   ],
   "source": [
    "import getpass\n",
    "username = password = None\n",
    "username = input(\"Username:\");   password = getpass.getpass('Password:')\n",
    "\n",
    "# EarthData Credentials need to get saved in `settings` in order to get passed\n",
    "# into the Lambda function on AWS. This will be automated in the future.\n",
    "settings[\"username@urs.earthdata.nasa.gov\"] = username\n",
    "settings[\"password@urs.earthdata.nasa.gov\"] = password"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Setup (2/2)\n",
    "\n",
    "* Create the PODPAC Pipeline\n",
    "* We'll use the same pipeline from the [100-analyzing-SMAP-data.ipynb](../5-datalib/smap/100-analyzing-SMAP-data.ipynb) notebook\n",
    "* This example computes the difference between the current soil moisture for a region, and that of the previous year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "import podpac.datalib\n",
    "\n",
    "# Create the Pipsmap_offset\n",
    "product = 'SPL4SMAU'\n",
    "smap = podpac.datalib.smap_egi.SMAP(product=product)\n",
    "smap_time1_offset = podpac.algorithm.ExpandCoordinates(source=smap, time=['-1,Y', '-1,Y', '1,Y'])\n",
    "smap_offset = podpac.algorithm.Mean(source=smap_time1_offset, dims=['time'])\n",
    "\n",
    "# This is the output Node of the Pipeline\n",
    "diff = podpac.algorithm.Arithmetic(eqn='B-A', A=smap, B=smap_offset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# Create PODPAC Coordinates \n",
    "* This specifies the region and date where the pipeline will be evaluated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "# Specify region of interest on a uniform grid\n",
    "lat = podpac.crange(  60,  10, -2.0)  # (start, stop, step)\n",
    "lon = podpac.crange(-130, -60,  2.0)  # (start, stop, step)\n",
    "\n",
    "# Specify date and time\n",
    "time = '2018-05-19T12:00:00'\n",
    "\n",
    "# Create the PODPAC Coordinates\n",
    "coords = podpac.Coordinates([lat, lon, time], dims=['lat', 'lon', 'time'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Evaluating node on AWS cloud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set up wrapper to run your node on AWS Lambda\n",
    "node = podpac.managers.aws.Lambda(source=diff, function_allow_unsafe_eval=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "node.delete_function()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:podpac.core.managers.aws:Lambda function will allow unsafe evaluation of Nodes with the current settings\n",
      "WARNING:podpac.core.managers.aws:Creating an AWS Budget with PODPAC is an experimental feature. Please continue to monitor AWS usage costs seperately.\n",
      "INFO:podpac.core.managers.aws:Successfully built AWS resources to support function podpac-scipy2020-demo-lambda\n"
     ]
    }
   ],
   "source": [
    "# if this is the first time your are using this node, build the AWS resources\n",
    "# if you have used this function before, skip this step\n",
    "node.build()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Lambda Node (built)\n",
      "    Function\n",
      "        Name: podpac-scipy2020-demo-lambda\n",
      "        Description: PODPAC Lambda Function (https://podpac.org)\n",
      "        ARN: arn:aws:lambda:us-east-1:120507435541:function:podpac-scipy2020-demo-lambda\n",
      "        Triggers: ['eval']\n",
      "        Handler: handler.handler\n",
      "        Environment Variables: {'PODPAC_UNSAFE_EVAL': 'b754a7523c574ec0b88c9c831a4e5ffb'}\n",
      "        Timeout: 600 seconds\n",
      "        Memory: 2048 MB\n",
      "        Tags: {'_podpac_resource': 'true', '_podpac_resource_hash': '03a8239f4bee9ccdb1356e3a92189b86', 'acct': '1010267.01.005', 'owner': 'mpu'}\n",
      "        Source Dist: 9DIAVAJaLlo5MKtPu+j4SweilV5bWe4G1XKhY4OijDM=\n",
      "        Source Dependencies: s3://podpac-scipy2020-demo/podpac_deps.zip\n",
      "        Last Modified: 2020-07-13T17:24:28.029+0000\n",
      "        Version: $LATEST\n",
      "        Restrict Evaluation: []\n",
      "\n",
      "    S3\n",
      "        Bucket: podpac-scipy2020-demo\n",
      "        Tags: {'owner': 'mpu', '_podpac_resource': 'true', '_podpac_resource_hash': 'c76dc430c8ce28d24658154781b1c521', 'acct': '1010267.01.005'}\n",
      "        Input Folder: input/\n",
      "        Output Folder: output/\n",
      "\n",
      "    Role\n",
      "        Name: podpac-scipy2020-demo-role\n",
      "        Description: PODPAC Lambda Role\n",
      "        ARN: arn:aws:iam::120507435541:role/podpac-scipy2020-demo-role\n",
      "        Policy Document: {'Version': '2012-10-17', 'Statement': [{'Effect': 'Allow', 'Action': ['s3:PutObject', 's3:GetObject', 's3:DeleteObject', 's3:ReplicateObject', 's3:ListBucket', 's3:ListMultipartUploadParts', 's3:ListBucketByTags', 's3:GetBucketTagging', 's3:ListBucketVersions', 's3:AbortMultipartUpload', 's3:GetObjectTagging', 's3:ListBucketMultipartUploads', 's3:GetBucketLocation', 's3:GetObjectVersion'], 'Resource': ['arn:aws:s3:::podpac-scipy2020-demo/*']}]}\n",
      "        Policy ARNs: ['arn:aws:iam::aws:policy/service-role/AWSLambdaBasicExecutionRole']\n",
      "        Assume Policy Document: {'Version': '2012-10-17', 'Statement': [{'Effect': 'Allow', 'Principal': {'Service': 'lambda.amazonaws.com'}, 'Action': 'sts:AssumeRole'}]}\n",
      "        Tags: {'owner': 'mpu', 'acct': '1010267.01.005', '_podpac_resource_hash': 'c76dc430c8ce28d24658154781b1c521', '_podpac_resource': 'true'}\n",
      "\n",
      "        \n",
      "\n",
      "        \n",
      "    Budget\n",
      "        Name: podpac-scipy2020-demo-lambda-budget\n",
      "        Amount: 10.0\n",
      "        Currency: USD\n",
      "        E-mail: test@podpac.org\n",
      "        Spent: 0.0 USD\n",
      "            \n",
      "        \n"
     ]
    }
   ],
   "source": [
    "# validate that the AWS resources are built\n",
    "node.validate()\n",
    "\n",
    "# review aws resources\n",
    "node.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Run the node (on AWS Lambda) and get the results back\n",
    "# (the first time this runs will be slower than subsequent calls)\n",
    "output = node.eval(coords)\n",
    "output.plot()\n",
    "pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:numexpr.utils:NumExpr defaulting to 8 threads.\n",
      "D:\\podpac-2.2.0\\miniconda\\envs\\podpac\\lib\\site-packages\\xarray\\core\\nanops.py:142: RuntimeWarning: Mean of empty slice\n",
      "  return np.nanmean(a, axis=axis, dtype=dtype)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "o = diff.eval(coords)\n",
    "o.plot()\n",
    "pass"
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Slideshow",
  "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": "3.7.7"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {},
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}