sagemaker-lf-credential-vending.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# SageMaker Processing Job & LakeFormation Integration - Vend Temporary Credentials to read data from S3 in a `Pandas` and `Spark` dataframe\n",
    "---\n",
    "\n",
    "This example shows how to use LakeFormation's `credential vending capability` from within a SageMaker Processing Job and based on that, query `LakeFormation` to see which `columns` does a `Role` being used have access to from the table being queried from Amazon S3.\n",
    "\n",
    "### Before running this notebook, read the following roles required, steps and permissions to be assigned given in the [README.md](/README.md) file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# This cell only needs to be run once\n",
    "# Setup - Run only once per Kernel App\n",
    "%conda install https://anaconda.org/conda-forge/openjdk/11.0.1/download/linux-64/openjdk-11.0.1-hacce0ff_1021.tar.bz2\n",
    "\n",
    "# install PySpark\n",
    "%pip install pyspark==3.4.0\n",
    "\n",
    "# restart kernel\n",
    "from IPython.core.display import HTML\n",
    "HTML(\"<script>Jupyter.notebook.kernel.restart()</script>\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# import libraries\n",
    "import os\n",
    "import sys\n",
    "import json\n",
    "import boto3\n",
    "import logging\n",
    "import pandas as pd\n",
    "from io import StringIO\n",
    "from typing import List, Dict\n",
    "from sagemaker import get_execution_role\n",
    "from botocore.exceptions import ClientError\n",
    "# import scripts as modules to grant fine-grained permissions\n",
    "# and read data from S3 in pandas and spark\n",
    "from lf_vend_credentials import get_lf_temp_credentials\n",
    "from read_data import read_pandas_lf_data, read_spark_lf_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# set a logger\n",
    "logging.basicConfig(format='[%(asctime)s] p%(process)s {%(filename)s:%(lineno)d} %(levelname)s - %(message)s', level=logging.INFO)\n",
    "logger = logging.getLogger(__name__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### If you have a table/database that already exists, replace the variable names below with your resource names, and skip the steps to create the Database and Table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "REGION = boto3.session.Session().region_name\n",
    "\n",
    "# initialize the sessions that will be used for creation of tables, databases, and calling the APIs\n",
    "lf_session = boto3.session.Session(region_name=REGION)\n",
    "lf_client = lf_session.client('lakeformation')\n",
    "glue_client = lf_session.client('glue')\n",
    "sts_client = lf_session.client('sts')\n",
    "\n",
    "# Initialize the account ID, caller ARN, table database name of your choice, and the bucket name (that you will create associated to the table and DB path)\n",
    "ACCOUNT_ID: str = sts_client.get_caller_identity()['Account'] \n",
    "CALLER_ARN: str = sts_client.get_caller_identity()['Arn']\n",
    "# Replace with your database and table name if this already exists\n",
    "DATABASE_NAME: str = f'cred_vending_test_db_{ACCOUNT_ID}'\n",
    "TABLE_NAME: str = f'cred_vending_test_table_{ACCOUNT_ID}'\n",
    "# replace with your bucket name here\n",
    "BUCKET_NAME: str = 'cred-vending-test-bucket'\n",
    "DUMMY_CSV_FILE: str = f'dummy_data_{ACCOUNT_ID}.csv'\n",
    "\n",
    "# Initialize the db and table location in s3  - register the s3 location with a relevant role and grant access for the sagemaker execution and admin role to the database and table\n",
    "DATABASE_S3_LOCATION: str = f's3:https://{BUCKET_NAME}/{DATABASE_NAME}'\n",
    "TABLE_S3_LOCATION: str = f'{DATABASE_S3_LOCATION}/{TABLE_NAME}'\n",
    "\n",
    "# Catalog id is the account id, set the name of the IAM role to be given fine-grained access to below\n",
    "ASSUMED_ROLE_NAME: str = 'lf_role_assumed'\n",
    "SAGEMAKER_EXECUTION_ROLE_NAME: str = get_execution_role()\n",
    "CATALOG_ID: str = ACCOUNT_ID # abstract the role name and account ID\n",
    "LF_ROLE_TO_ASSUME: str = f'arn:aws:iam::{ACCOUNT_ID}:role/{ASSUMED_ROLE_NAME}'\n",
    "# Set appropriate IAM and lakeformation permissions for this role in your aws account. Follow the README.md for detailed instructions\n",
    "SAGEMAKER_ROLE_ARN: str = f'arn:aws:iam::{ACCOUNT_ID}:role/service-role/{SAGEMAKER_EXECUTION_ROLE_NAME}'\n",
    "logger.info(f\"DB path location: {DATABASE_S3_LOCATION}, Table path location: {TABLE_S3_LOCATION}\")\n",
    "\n",
    "# Add your lakeformation session tag value below that you set on the console. For more instructions, \n",
    "# view: https://docs.aws.amazon.com/lake-formation/latest/dg/full-table-credential-vending.html\n",
    "lf_tag_value: str = 'sessionfortag'\n",
    "logger.info(f\"SageMaker execution role being used for this notebook: {SAGEMAKER_EXECUTION_ROLE_NAME}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a Database and Glue Table (Skip this following step if you already have a database and a Glue table in your account):\n",
    "\n",
    "Add the `sagemaker-execution-role` as an the administrator in the lakeformation console. Make sure to give this role full access to your specific `database` **AND** `table`.\n",
    "\n",
    "***Skip this step if you have a database and table already created. Make sure to use the table location for your Glue Data Catalogue to read from s3 later***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# create a database using the database name and database location\n",
    "try:\n",
    "    glue_client.create_database(\n",
    "        DatabaseInput = {\n",
    "            'Name': DATABASE_NAME,\n",
    "            'LocationUri' : DATABASE_S3_LOCATION\n",
    "        }\n",
    "    )\n",
    "except Exception as e:\n",
    "    logger.error(f\"the database could not be created: {e}\")\n",
    "\n",
    "# create a table using the table name, location, and table description\n",
    "try:\n",
    "    glue_client.create_table(\n",
    "        DatabaseName=DATABASE_NAME,\n",
    "        TableInput={\n",
    "            'Name': TABLE_NAME,\n",
    "            'StorageDescriptor': {\n",
    "                'Columns': [\n",
    "                    {'Name': 'customer_id','Type': 'bigint'},\n",
    "                    {'Name': 'prefix','Type': 'string'},\n",
    "                    {'Name': 'first_name','Type': 'string'},\n",
    "                    {'Name': 'middle_name','Type': 'string'},\n",
    "                    {'Name': 'last_name','Type': 'string'},\n",
    "                    {'Name': 'suffix','Type': 'string'},\n",
    "                    {'Name': 'gender','Type': 'string'},\n",
    "                    {'Name': 'dob','Type': 'string'},\n",
    "                    {'Name': 'phone','Type': 'string'},\n",
    "                    {'Name': 'building_number','Type': 'bigint'},\n",
    "                    {'Name': 'street_name','Type': 'string'},\n",
    "                    {'Name': 'city','Type': 'string'},\n",
    "                    {'Name': 'cust_zip','Type': 'bigint'},\n",
    "                    {'Name': 'country','Type': 'string' }\n",
    "                ],\n",
    "                'Location': TABLE_S3_LOCATION,\n",
    "                'InputFormat': 'org.apache.hadoop.mapred.TextInputFormat',\n",
    "                'OutputFormat': 'org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat',\n",
    "                'Compressed': False,\n",
    "                'NumberOfBuckets': 0,\n",
    "                'SerdeInfo': {\n",
    "                    'SerializationLibrary': 'org.apache.hadoop.hive.serde2.OpenCSVSerde',\n",
    "                    'Parameters': {\n",
    "                        'separatorChar': ','\n",
    "                    }\n",
    "                },\n",
    "                'SortColumns': [],\n",
    "                'StoredAsSubDirectories': False\n",
    "            },\n",
    "            'PartitionKeys': [],\n",
    "            'TableType': 'EXTERNAL_TABLE',\n",
    "            'Parameters': {\n",
    "                'classification': 'csv',\n",
    "                'lakeformation.aso.status': 'false'\n",
    "            }\n",
    "        }\n",
    "    )\n",
    "except Exception as e:\n",
    "    logger.error(f\"the table could not be created: {e}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inserting sample data in the Glue Table S3 Path\n",
    "---\n",
    "\n",
    "If you already have data in s3, then run the crawler and skip the step below. Replace the `DUMMY_CSV_FILE`, `DATABASE_S3_LOCATION`, and `TABLE_S3_LOCATION` with pre-configured resources if they already exist in your account. If not, follow the steps below to insert dummy data in the `S3_path` that corresponds to your Glue Data Catalog.\n",
    "\n",
    "***For this next cell, if you want to insert data in your S3 bucket, give put object access to the SageMaker Execution role for that `S3_path`, else insert it manually. You can choose not to run the step below***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Insert dummy data and send the table to the given s3 path for the table (this is an optional step, you will have to run the crawler\n",
    "# on your existing files in s3 to create a table). We are inserting this data to see the effect of granting fine grained access\n",
    "# to a role and getting only the metadata that that role has fine grained access to.\n",
    "\n",
    "# Sample data for insertion\n",
    "data = pd.DataFrame({\n",
    "    'customer_id': [1, 2],\n",
    "    'prefix': ['Mr.', 'Ms.'],\n",
    "    'first_name': ['Madhur', 'Jane'],\n",
    "    'middle_name': ['A', 'B'],\n",
    "    'last_name': ['Prashant', 'Doe'],\n",
    "    'suffix': ['Jr', 'Sr'],\n",
    "    'gender': ['Male', 'Female'],\n",
    "    'dob': ['2020-01-01', '1992-02-02'],\n",
    "    'phone': ['1234', '5678'],\n",
    "    'building_number': [123, 456],\n",
    "    'street_name': ['cunard St', 'cunard St'],\n",
    "    'city': ['city1', 'city2'],\n",
    "    'cust_zip': [1111, 2222],\n",
    "    'country': ['US', 'INDIA']\n",
    "})\n",
    "\n",
    "# Convert data to CSV\n",
    "csv_buffer = StringIO()\n",
    "data.to_csv(csv_buffer, index=False)\n",
    "s3_resource = boto3.resource('s3')\n",
    "\n",
    "try:\n",
    "    object_key = f\"{DATABASE_NAME}/{TABLE_NAME}/{DUMMY_CSV_FILE}\" \n",
    "    logger.info(f\"object key for the csv file: {object_key}\")\n",
    "    # add put object for this s3 bucket in the sagemaker execution role to run this cell\n",
    "    s3_resource.Object(BUCKET_NAME, object_key).put(Body=csv_buffer.getvalue())\n",
    "    logger.info(f\"sample data successfully uploaded to {TABLE_S3_LOCATION}\")\n",
    "except Exception as e:\n",
    "    logger.error(f\"Failed to upload data to {TABLE_S3_LOCATION}: {e}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# view the metadata of the entire data that is sent to Amazon S3\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Enable `AllowFullTableExternalDataAccess` in the `DataLakeSettings` using the `get_data_lake_settings` API to be able to get temporary credentials to get access to the glue table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "settings = lf_client.get_data_lake_settings()\n",
    "\n",
    "# Set the `AllowFullTableExternalDataAccess` in the settings for datalake to True for this role, in order to vend temporary glue table credentials\n",
    "if 'AllowFullTableExternalDataAccess' not in settings['DataLakeSettings'] or settings['DataLakeSettings']['AllowFullTableExternalDataAccess'] == False:\n",
    "    settings_raw = settings['DataLakeSettings']\n",
    "    settings_raw['AllowFullTableExternalDataAccess'] = True\n",
    "    lf_client.put_data_lake_settings(DataLakeSettings=settings_raw)\n",
    "\n",
    "logger.info(f\"Data lake settings: {json.dumps(settings, indent=2)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These are the credentials that are used by the role to get access to a database/table or fine grained access to certain columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# This is the format that is followed for the table arn. For more information, view: \n",
    "# https://repost.aws/questions/QU6S243zI_SQKYlezRl_Efqg/how-to-get-arn-for-lake-formation-table\n",
    "TableArn=f'arn:aws:glue:{REGION}:{ACCOUNT_ID}:table/{DATABASE_NAME}/{TABLE_NAME}'\n",
    "TableArn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# This works because the user has full table access, and the AllowFullTableExternalDataAccess is enabled\n",
    "resp = lf_client.get_temporary_glue_table_credentials(\n",
    "    TableArn=TableArn,\n",
    "    Permissions=[ 'ALL' ],\n",
    "    SupportedPermissionTypes=['COLUMN_PERMISSION'],\n",
    ")\n",
    "print(f\"Access Key: {resp['AccessKeyId']} Expiration: {resp['Expiration']}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Lakeformation Fine-Grained Access:\n",
    "---\n",
    "In this section, we call three functions: `get_lf_temp_credentials` to grant fine-grained access on the `LF_ROLE_TO_ASSUME` role and vend temporary glue table credentials, `read_pandas_lf_data` to read data from S3 in a pandas dataframe, and `read_spark_lf_data` to read data from S3 in a spark dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# This function call returns a dictionary that contains temporary glue table credentials, s3_path to the glue data catalog, file type, \n",
    "# to read metadata from and authorized columns\n",
    "table_info_and_credentials = get_lf_temp_credentials(role=LF_ROLE_TO_ASSUME,\n",
    "                                                        db_name=DATABASE_NAME,\n",
    "                                                        table_name=TABLE_NAME,\n",
    "                                                        columns=['street_name', 'city', 'cust_zip'],  \n",
    "                                                        lf_tag_value=lf_tag_value)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read Data from S3 using the Temporary credentials and s3_path\n",
    "---\n",
    "\n",
    "Read data in a `pandas dataframe`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# reading data using the temporary vended credentials into a pandas dataframe \n",
    "extracted_pandas_df = read_pandas_lf_data(table_info_and_credentials\n",
    "                                          =table_info_and_credentials)\n",
    "# View metadata on columns that the role has fine grained access on\n",
    "extracted_pandas_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read data in a `spark dataframe`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reading data using the temporary vended credentials into a pandas dataframe \n",
    "extracted_spark_df = read_spark_lf_data(table_info_and_credentials\n",
    "                                          =table_info_and_credentials, \n",
    "                                           use_s3a = True) # add the s3 bool\n",
    "extracted_spark_df"
   ]
  },
  {
   "attachments": {
    "fdfcf311-327c-4e26-b630-26fc524118f7.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Now, you can see that the second assumed role has been granted fine grained access to columns\n",
    "\n",
    "![Screenshot 2024-05-03 at 9.45.20 AM.png](attachment:fdfcf311-327c-4e26-b630-26fc524118f7.png)"
   ]
  }
 ],
 "metadata": {
  "availableInstances": [
   {
    "_defaultOrder": 0,
    "_isFastLaunch": true,
    "category": "General purpose",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 4,
    "name": "ml.t3.medium",
    "vcpuNum": 2
   },
   {
    "_defaultOrder": 1,
    "_isFastLaunch": false,
    "category": "General purpose",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 8,
    "name": "ml.t3.large",
    "vcpuNum": 2
   },
   {
    "_defaultOrder": 2,
    "_isFastLaunch": false,
    "category": "General purpose",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 16,
    "name": "ml.t3.xlarge",
    "vcpuNum": 4
   },
   {
    "_defaultOrder": 3,
    "_isFastLaunch": false,
    "category": "General purpose",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 32,
    "name": "ml.t3.2xlarge",
    "vcpuNum": 8
   },
   {
    "_defaultOrder": 4,
    "_isFastLaunch": true,
    "category": "General purpose",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 8,
    "name": "ml.m5.large",
    "vcpuNum": 2
   },
   {
    "_defaultOrder": 5,
    "_isFastLaunch": false,
    "category": "General purpose",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 16,
    "name": "ml.m5.xlarge",
    "vcpuNum": 4
   },
   {
    "_defaultOrder": 6,
    "_isFastLaunch": false,
    "category": "General purpose",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 32,
    "name": "ml.m5.2xlarge",
    "vcpuNum": 8
   },
   {
    "_defaultOrder": 7,
    "_isFastLaunch": false,
    "category": "General purpose",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 64,
    "name": "ml.m5.4xlarge",
    "vcpuNum": 16
   },
   {
    "_defaultOrder": 8,
    "_isFastLaunch": false,
    "category": "General purpose",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 128,
    "name": "ml.m5.8xlarge",
    "vcpuNum": 32
   },
   {
    "_defaultOrder": 9,
    "_isFastLaunch": false,
    "category": "General purpose",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 192,
    "name": "ml.m5.12xlarge",
    "vcpuNum": 48
   },
   {
    "_defaultOrder": 10,
    "_isFastLaunch": false,
    "category": "General purpose",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 256,
    "name": "ml.m5.16xlarge",
    "vcpuNum": 64
   },
   {
    "_defaultOrder": 11,
    "_isFastLaunch": false,
    "category": "General purpose",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 384,
    "name": "ml.m5.24xlarge",
    "vcpuNum": 96
   },
   {
    "_defaultOrder": 12,
    "_isFastLaunch": false,
    "category": "General purpose",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 8,
    "name": "ml.m5d.large",
    "vcpuNum": 2
   },
   {
    "_defaultOrder": 13,
    "_isFastLaunch": false,
    "category": "General purpose",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 16,
    "name": "ml.m5d.xlarge",
    "vcpuNum": 4
   },
   {
    "_defaultOrder": 14,
    "_isFastLaunch": false,
    "category": "General purpose",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 32,
    "name": "ml.m5d.2xlarge",
    "vcpuNum": 8
   },
   {
    "_defaultOrder": 15,
    "_isFastLaunch": false,
    "category": "General purpose",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 64,
    "name": "ml.m5d.4xlarge",
    "vcpuNum": 16
   },
   {
    "_defaultOrder": 16,
    "_isFastLaunch": false,
    "category": "General purpose",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 128,
    "name": "ml.m5d.8xlarge",
    "vcpuNum": 32
   },
   {
    "_defaultOrder": 17,
    "_isFastLaunch": false,
    "category": "General purpose",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 192,
    "name": "ml.m5d.12xlarge",
    "vcpuNum": 48
   },
   {
    "_defaultOrder": 18,
    "_isFastLaunch": false,
    "category": "General purpose",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 256,
    "name": "ml.m5d.16xlarge",
    "vcpuNum": 64
   },
   {
    "_defaultOrder": 19,
    "_isFastLaunch": false,
    "category": "General purpose",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 384,
    "name": "ml.m5d.24xlarge",
    "vcpuNum": 96
   },
   {
    "_defaultOrder": 20,
    "_isFastLaunch": false,
    "category": "General purpose",
    "gpuNum": 0,
    "hideHardwareSpecs": true,
    "memoryGiB": 0,
    "name": "ml.geospatial.interactive",
    "supportedImageNames": [
     "sagemaker-geospatial-v1-0"
    ],
    "vcpuNum": 0
   },
   {
    "_defaultOrder": 21,
    "_isFastLaunch": true,
    "category": "Compute optimized",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 4,
    "name": "ml.c5.large",
    "vcpuNum": 2
   },
   {
    "_defaultOrder": 22,
    "_isFastLaunch": false,
    "category": "Compute optimized",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 8,
    "name": "ml.c5.xlarge",
    "vcpuNum": 4
   },
   {
    "_defaultOrder": 23,
    "_isFastLaunch": false,
    "category": "Compute optimized",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 16,
    "name": "ml.c5.2xlarge",
    "vcpuNum": 8
   },
   {
    "_defaultOrder": 24,
    "_isFastLaunch": false,
    "category": "Compute optimized",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 32,
    "name": "ml.c5.4xlarge",
    "vcpuNum": 16
   },
   {
    "_defaultOrder": 25,
    "_isFastLaunch": false,
    "category": "Compute optimized",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 72,
    "name": "ml.c5.9xlarge",
    "vcpuNum": 36
   },
   {
    "_defaultOrder": 26,
    "_isFastLaunch": false,
    "category": "Compute optimized",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 96,
    "name": "ml.c5.12xlarge",
    "vcpuNum": 48
   },
   {
    "_defaultOrder": 27,
    "_isFastLaunch": false,
    "category": "Compute optimized",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 144,
    "name": "ml.c5.18xlarge",
    "vcpuNum": 72
   },
   {
    "_defaultOrder": 28,
    "_isFastLaunch": false,
    "category": "Compute optimized",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 192,
    "name": "ml.c5.24xlarge",
    "vcpuNum": 96
   },
   {
    "_defaultOrder": 29,
    "_isFastLaunch": true,
    "category": "Accelerated computing",
    "gpuNum": 1,
    "hideHardwareSpecs": false,
    "memoryGiB": 16,
    "name": "ml.g4dn.xlarge",
    "vcpuNum": 4
   },
   {
    "_defaultOrder": 30,
    "_isFastLaunch": false,
    "category": "Accelerated computing",
    "gpuNum": 1,
    "hideHardwareSpecs": false,
    "memoryGiB": 32,
    "name": "ml.g4dn.2xlarge",
    "vcpuNum": 8
   },
   {
    "_defaultOrder": 31,
    "_isFastLaunch": false,
    "category": "Accelerated computing",
    "gpuNum": 1,
    "hideHardwareSpecs": false,
    "memoryGiB": 64,
    "name": "ml.g4dn.4xlarge",
    "vcpuNum": 16
   },
   {
    "_defaultOrder": 32,
    "_isFastLaunch": false,
    "category": "Accelerated computing",
    "gpuNum": 1,
    "hideHardwareSpecs": false,
    "memoryGiB": 128,
    "name": "ml.g4dn.8xlarge",
    "vcpuNum": 32
   },
   {
    "_defaultOrder": 33,
    "_isFastLaunch": false,
    "category": "Accelerated computing",
    "gpuNum": 4,
    "hideHardwareSpecs": false,
    "memoryGiB": 192,
    "name": "ml.g4dn.12xlarge",
    "vcpuNum": 48
   },
   {
    "_defaultOrder": 34,
    "_isFastLaunch": false,
    "category": "Accelerated computing",
    "gpuNum": 1,
    "hideHardwareSpecs": false,
    "memoryGiB": 256,
    "name": "ml.g4dn.16xlarge",
    "vcpuNum": 64
   },
   {
    "_defaultOrder": 35,
    "_isFastLaunch": false,
    "category": "Accelerated computing",
    "gpuNum": 1,
    "hideHardwareSpecs": false,
    "memoryGiB": 61,
    "name": "ml.p3.2xlarge",
    "vcpuNum": 8
   },
   {
    "_defaultOrder": 36,
    "_isFastLaunch": false,
    "category": "Accelerated computing",
    "gpuNum": 4,
    "hideHardwareSpecs": false,
    "memoryGiB": 244,
    "name": "ml.p3.8xlarge",
    "vcpuNum": 32
   },
   {
    "_defaultOrder": 37,
    "_isFastLaunch": false,
    "category": "Accelerated computing",
    "gpuNum": 8,
    "hideHardwareSpecs": false,
    "memoryGiB": 488,
    "name": "ml.p3.16xlarge",
    "vcpuNum": 64
   },
   {
    "_defaultOrder": 38,
    "_isFastLaunch": false,
    "category": "Accelerated computing",
    "gpuNum": 8,
    "hideHardwareSpecs": false,
    "memoryGiB": 768,
    "name": "ml.p3dn.24xlarge",
    "vcpuNum": 96
   },
   {
    "_defaultOrder": 39,
    "_isFastLaunch": false,
    "category": "Memory Optimized",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 16,
    "name": "ml.r5.large",
    "vcpuNum": 2
   },
   {
    "_defaultOrder": 40,
    "_isFastLaunch": false,
    "category": "Memory Optimized",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 32,
    "name": "ml.r5.xlarge",
    "vcpuNum": 4
   },
   {
    "_defaultOrder": 41,
    "_isFastLaunch": false,
    "category": "Memory Optimized",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 64,
    "name": "ml.r5.2xlarge",
    "vcpuNum": 8
   },
   {
    "_defaultOrder": 42,
    "_isFastLaunch": false,
    "category": "Memory Optimized",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 128,
    "name": "ml.r5.4xlarge",
    "vcpuNum": 16
   },
   {
    "_defaultOrder": 43,
    "_isFastLaunch": false,
    "category": "Memory Optimized",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 256,
    "name": "ml.r5.8xlarge",
    "vcpuNum": 32
   },
   {
    "_defaultOrder": 44,
    "_isFastLaunch": false,
    "category": "Memory Optimized",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 384,
    "name": "ml.r5.12xlarge",
    "vcpuNum": 48
   },
   {
    "_defaultOrder": 45,
    "_isFastLaunch": false,
    "category": "Memory Optimized",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 512,
    "name": "ml.r5.16xlarge",
    "vcpuNum": 64
   },
   {
    "_defaultOrder": 46,
    "_isFastLaunch": false,
    "category": "Memory Optimized",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 768,
    "name": "ml.r5.24xlarge",
    "vcpuNum": 96
   },
   {
    "_defaultOrder": 47,
    "_isFastLaunch": false,
    "category": "Accelerated computing",
    "gpuNum": 1,
    "hideHardwareSpecs": false,
    "memoryGiB": 16,
    "name": "ml.g5.xlarge",
    "vcpuNum": 4
   },
   {
    "_defaultOrder": 48,
    "_isFastLaunch": false,
    "category": "Accelerated computing",
    "gpuNum": 1,
    "hideHardwareSpecs": false,
    "memoryGiB": 32,
    "name": "ml.g5.2xlarge",
    "vcpuNum": 8
   },
   {
    "_defaultOrder": 49,
    "_isFastLaunch": false,
    "category": "Accelerated computing",
    "gpuNum": 1,
    "hideHardwareSpecs": false,
    "memoryGiB": 64,
    "name": "ml.g5.4xlarge",
    "vcpuNum": 16
   },
   {
    "_defaultOrder": 50,
    "_isFastLaunch": false,
    "category": "Accelerated computing",
    "gpuNum": 1,
    "hideHardwareSpecs": false,
    "memoryGiB": 128,
    "name": "ml.g5.8xlarge",
    "vcpuNum": 32
   },
   {
    "_defaultOrder": 51,
    "_isFastLaunch": false,
    "category": "Accelerated computing",
    "gpuNum": 1,
    "hideHardwareSpecs": false,
    "memoryGiB": 256,
    "name": "ml.g5.16xlarge",
    "vcpuNum": 64
   },
   {
    "_defaultOrder": 52,
    "_isFastLaunch": false,
    "category": "Accelerated computing",
    "gpuNum": 4,
    "hideHardwareSpecs": false,
    "memoryGiB": 192,
    "name": "ml.g5.12xlarge",
    "vcpuNum": 48
   },
   {
    "_defaultOrder": 53,
    "_isFastLaunch": false,
    "category": "Accelerated computing",
    "gpuNum": 4,
    "hideHardwareSpecs": false,
    "memoryGiB": 384,
    "name": "ml.g5.24xlarge",
    "vcpuNum": 96
   },
   {
    "_defaultOrder": 54,
    "_isFastLaunch": false,
    "category": "Accelerated computing",
    "gpuNum": 8,
    "hideHardwareSpecs": false,
    "memoryGiB": 768,
    "name": "ml.g5.48xlarge",
    "vcpuNum": 192
   },
   {
    "_defaultOrder": 55,
    "_isFastLaunch": false,
    "category": "Accelerated computing",
    "gpuNum": 8,
    "hideHardwareSpecs": false,
    "memoryGiB": 1152,
    "name": "ml.p4d.24xlarge",
    "vcpuNum": 96
   },
   {
    "_defaultOrder": 56,
    "_isFastLaunch": false,
    "category": "Accelerated computing",
    "gpuNum": 8,
    "hideHardwareSpecs": false,
    "memoryGiB": 1152,
    "name": "ml.p4de.24xlarge",
    "vcpuNum": 96
   },
   {
    "_defaultOrder": 57,
    "_isFastLaunch": false,
    "category": "Accelerated computing",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 32,
    "name": "ml.trn1.2xlarge",
    "vcpuNum": 8
   },
   {
    "_defaultOrder": 58,
    "_isFastLaunch": false,
    "category": "Accelerated computing",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 512,
    "name": "ml.trn1.32xlarge",
    "vcpuNum": 128
   },
   {
    "_defaultOrder": 59,
    "_isFastLaunch": false,
    "category": "Accelerated computing",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 512,
    "name": "ml.trn1n.32xlarge",
    "vcpuNum": 128
   }
  ],
  "instance_type": "ml.t3.medium",
  "kernelspec": {
   "display_name": "Python 3 (Data Science 3.0)",
   "language": "python",
   "name": "python3__SAGEMAKER_INTERNAL__arn:aws:sagemaker:us-east-1:081325390199:image/sagemaker-data-science-310-v1"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}