 archived/image-generation-stable-diffusion.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "380b51db-e36e-4c72-9a10-d5a2f603c30f",
   "metadata": {},
   "source": [
    "# SageMaker JumpStart - Image Generation"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "---\n",
    "\n",
    "This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook.\n",
    "\n",
    "![This us-west-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-west-2/introduction_to_amazon_algorithms|jumpstart-foundation-models|image-generation-stable-diffusion.ipynb)\n",
    "\n",
    "---"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "id": "e740afe5",
   "metadata": {},
   "source": [
    "---\n",
    "Welcome to [Amazon SageMaker Jumpstart](https://docs.aws.amazon.com/sagemaker/latest/dg/studio-jumpstart.html)! You can use Amazon SageMaker Jumpstart to solve many Machine Learning tasks through one-click in SageMaker Studio, or through [SageMaker Python SDK](https://sagemaker.readthedocs.io/en/stable/overview.html#use-prebuilt-models-with-sagemaker-jumpstart).\n",
    "\n",
    "In this demo notebook, we demonstrate how to use the SageMaker Python SDK to deploy Stable Diffusion models for image generation task. The container being utilized here is a customized stabilityai container that has been optimized to excel in terms of both speed and quality. We demonstrate how to use SageMaker Python SDK for Text-to-Image and Image-to-Image generation. Text-to-Image is the task of generating realistic image given any text input. Here, we show how to use state-of-the-art pre-trained Stable Diffusion models for generating image from text and image.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2b17eca",
   "metadata": {},
   "source": [
    "1. [Set Up](#1.-Set-Up)\n",
    "2. [Select a model](#2.-Select-a-model)\n",
    "3. [Deploy an endpoint & Query endpoint](#3.-Deploy-an-Endpoint-&-Query-endpoint)\n",
    "4. [Clean Endpoint](#4.-Clean-Endpoint)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba8c73ef",
   "metadata": {},
   "source": [
    "### 1. Set Up"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe1848c2",
   "metadata": {},
   "source": [
    "---\n",
    "Before executing the notebook, there are some initial steps required for set up\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b4c9977e",
   "metadata": {
    "jumpStartAlterations": [
     "dropModelSelection"
    ],
    "tags": []
   },
   "outputs": [],
   "source": [
    "%pip install --upgrade sagemaker --quiet"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3903a7ce",
   "metadata": {},
   "source": [
    "## 2. Select a pre-trained model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9e91d71a",
   "metadata": {
    "jumpStartAlterations": [
     "dropModelSelection"
    ],
    "tags": []
   },
   "outputs": [],
   "source": [
    "model_id = \"model-imagegeneration-stabilityai-stable-diffusion-xl-base-1-0\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "adc4ba45",
   "metadata": {
    "jumpStartAlterations": [
     "dropModelSelection"
    ],
    "tags": []
   },
   "outputs": [],
   "source": [
    "import IPython\n",
    "from ipywidgets import Dropdown\n",
    "from sagemaker.jumpstart.notebook_utils import list_jumpstart_models\n",
    "from sagemaker.jumpstart.filters import And\n",
    "\n",
    "\n",
    "filter_value = And(\"task == imagegeneration\")\n",
    "ss_models = list_jumpstart_models(filter=filter_value)\n",
    "\n",
    "dropdown = Dropdown(\n",
    "    value=model_id,\n",
    "    options=ss_models,\n",
    "    description=\"Sagemaker Pre-Trained Image Generation Models:\",\n",
    "    style={\"description_width\": \"initial\"},\n",
    "    layout={\"width\": \"max-content\"},\n",
    ")\n",
    "display(IPython.display.Markdown(\"## Select a pre-trained model from the dropdown below\"))\n",
    "display(dropdown)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "328e4336",
   "metadata": {},
   "source": [
    "### 3. Deploy an Endpoint & Query Endpoint\n",
    "\n",
    "***\n",
    "\n",
    "Using SageMaker, we can perform inference on the pre-trained model.\n",
    "\n",
    "***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3af5b279",
   "metadata": {
    "jumpStartAlterations": [
     "dropModelSelection"
    ],
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Deploying the model\n",
    "from sagemaker.jumpstart.model import JumpStartModel\n",
    "from sagemaker.serializers import JSONSerializer\n",
    "import time\n",
    "\n",
    "# The model is deployed on the ml.g5.4xlarge instance. To see all the supported parameters by the JumpStartModel\n",
    "# class use this link - https://sagemaker.readthedocs.io/en/stable/api/inference/model.html#sagemaker.jumpstart.model.JumpStartModel\n",
    "my_model = JumpStartModel(model_id=dropdown.value)\n",
    "predictor = my_model.deploy()\n",
    "# We will wait for a few seconds so model is properly loaded.\n",
    "time.sleep(60)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36dd1358",
   "metadata": {},
   "source": [
    "Importing relevant packages for querying endpoint and image visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4c2c1462-f9a9-4fc7-8a6f-b7ff3b24c63b",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from PIL import Image\n",
    "import io\n",
    "import base64\n",
    "import json\n",
    "import boto3\n",
    "from typing import Union, Tuple\n",
    "import os"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab6fcceb-0ccc-43af-a204-da1612ca2b59",
   "metadata": {},
   "source": [
    "## Query Stable Diffusion endpoint\n",
    "\n",
    "### Supported features\n",
    "\n",
    "***\n",
    "This model supports many advanced parameters while performing inference. They include:\n",
    "\n",
    "* **text**: prompt to guide the image generation. Must be specified and should be string.\n",
    "* **width**: width of the hallucinated image. If specified, it must be a positive integer divisible by 8.\n",
    "* **height**: height of the hallucinated image. If specified, it must be a positive integer divisible by 8. Image size should be larger than 256x256.\n",
    "* **sampler**: Available samplers are EulerEDMSampler, HeunEDMSampler,EulerAncestralSampler, DPMPP2SAncestralSampler, DPMPP2MSampler, LinearMultistepSampler\n",
    "* **cfg_scale**: A higher cfg_scale results in image closely related to the prompt, at the expense of image quality. If specified, it must be a float. cfg_scale<=1 is ignored.\n",
    "* **steps**: number of denoising steps during image generation. More steps lead to higher quality image. If specified, it must a positive integer.\n",
    "* **seed**: fix the randomized state for reproducibility. If specified, it must be an integer.\n",
    "* **use_refiner**: Refiner is used by defauly with the SDXL model. You can disbale it by using this parameter\n",
    "* **init_image**: Image to be used as the starting point.\n",
    "* **image_strength**: Indicates extent to transform the reference image. Must be between 0 and 1.\n",
    "* **refiner_steps**: Number of denoising steps during image generation for the refiner. More steps lead to higher quality image. If specified, it must a positive integer.\n",
    "* **refiner_strength**: Indicates extent to transform the input image to the refiner.\n",
    "* **negative_prompt**: guide image generation against this prompt. If specified, it must be a string. It is specified in the text_prompts with a negative weight.\n",
    "\n",
    "\n",
    "***\n",
    "### Text to Image\n",
    "\n",
    "***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "00851369-9e37-4e14-b9ee-f440d538df5b",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "payload = {\n",
    "    \"text_prompts\": [{\"text\": \"jaguar in the Amazon rainforest\"}],\n",
    "    \"width\": 1024,\n",
    "    \"height\": 1024,\n",
    "    \"sampler\": \"DPMPP2MSampler\",\n",
    "    \"cfg_scale\": 7.0,\n",
    "    \"steps\": 50,\n",
    "    \"seed\": 133,\n",
    "    \"use_refiner\": True,\n",
    "    \"refiner_steps\": 40,\n",
    "    \"refiner_strength\": 0.2,\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c2f50de9",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "def decode_and_show(model_response) -> None:\n",
    "    \"\"\"\n",
    "    Decodes and displays an image from SDXL output\n",
    "\n",
    "    Args:\n",
    "        model_response (GenerationResponse): The response object from the deployed SDXL model.\n",
    "\n",
    "    Returns:\n",
    "        None\n",
    "    \"\"\"\n",
    "    image = Image.open(io.BytesIO(base64.b64decode(model_response)))\n",
    "    display(image)\n",
    "    image.close()\n",
    "\n",
    "\n",
    "response = predictor.predict(payload)\n",
    "# If you get time out error please check the endpoint logs in cloudWatch for model loading status\n",
    "# and invoke it again\n",
    "decode_and_show(response[\"generated_image\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77dc9b26",
   "metadata": {},
   "source": [
    "### Image to Image\n",
    "\n",
    "***\n",
    "To perform inference that takes an image as input, you must pass the image into init_image as a base64-encoded string.\n",
    "\n",
    "Below is a helper function for converting images to base64-encoded strings:\n",
    "\n",
    "***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9a19a41d",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "def encode_image(\n",
    "    image_path: str, resize: bool = True, size: Tuple[int, int] = (1024, 1024)\n",
    ") -> Union[str, None]:\n",
    "    \"\"\"\n",
    "    Encode an image as a base64 string, optionally resizing it to a supported resolution.\n",
    "\n",
    "    Args:\n",
    "        image_path (str): The path to the image file.\n",
    "        resize (bool, optional): Whether to resize the image. Defaults to True.\n",
    "\n",
    "    Returns:\n",
    "        Union[str, None]: The encoded image as a string, or None if encoding failed.\n",
    "    \"\"\"\n",
    "    assert os.path.exists(image_path)\n",
    "\n",
    "    if resize:\n",
    "        image = Image.open(image_path)\n",
    "        image = image.resize(size)\n",
    "        image.save(\"image_path_resized.png\")\n",
    "        image_path = \"image_path_resized.png\"\n",
    "    image = Image.open(image_path)\n",
    "    assert image.size == size\n",
    "    with open(image_path, \"rb\") as image_file:\n",
    "        img_byte_array = image_file.read()\n",
    "        # Encode the byte array as a Base64 string\n",
    "        try:\n",
    "            base64_str = base64.b64encode(img_byte_array).decode(\"utf-8\")\n",
    "            return base64_str\n",
    "        except Exception as e:\n",
    "            print(f\"Failed to encode image {image_path} as base64 string.\")\n",
    "            print(e)\n",
    "            return None\n",
    "    image.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e65fe817",
   "metadata": {},
   "source": [
    "Let's feed an image into the model as well as the prompt this time. We can set image_strength to weight the relative importance of the image and the prompt. For the demo, we'll use a picture of the dog."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2a4954c3",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "# Here is the original image:\n",
    "region = boto3.Session().region_name\n",
    "s3_bucket = f\"jumpstart-cache-prod-{region}\"\n",
    "key_prefix = \"model-metadata/assets\"\n",
    "input_img_file_name = \"dog_suit.jpg\"\n",
    "\n",
    "s3 = boto3.client(\"s3\")\n",
    "\n",
    "s3.download_file(s3_bucket, f\"{key_prefix}/{input_img_file_name}\", input_img_file_name)\n",
    "image = Image.open(input_img_file_name)\n",
    "display(image)\n",
    "image.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "780d4e8f",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "size = (512, 512)\n",
    "dog_data = encode_image(input_img_file_name, size=size)\n",
    "\n",
    "payload = {\n",
    "    \"text_prompts\": [{\"text\": \"dog in embroidery\"}],\n",
    "    \"init_image\": dog_data,\n",
    "    \"cfg_scale\": 9,\n",
    "    \"image_strength\": 0.8,\n",
    "    \"seed\": 42,\n",
    "}\n",
    "\n",
    "response = predictor.predict(payload)\n",
    "decode_and_show(response[\"generated_image\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 4. Clean Endpoint\n"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "# Delete the SageMaker endpoint\n",
    "predictor.delete_model()\n",
    "predictor.delete_endpoint()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Notebook CI Test Results\n",
    "\n",
    "This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
    "\n",
    "![This us-east-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-east-1/introduction_to_amazon_algorithms|jumpstart-foundation-models|image-generation-stable-diffusion.ipynb)\n",
    "\n",
    "![This us-east-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-east-2/introduction_to_amazon_algorithms|jumpstart-foundation-models|image-generation-stable-diffusion.ipynb)\n",
    "\n",
    "![This us-west-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-west-1/introduction_to_amazon_algorithms|jumpstart-foundation-models|image-generation-stable-diffusion.ipynb)\n",
    "\n",
    "![This ca-central-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ca-central-1/introduction_to_amazon_algorithms|jumpstart-foundation-models|image-generation-stable-diffusion.ipynb)\n",
    "\n",
    "![This sa-east-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/sa-east-1/introduction_to_amazon_algorithms|jumpstart-foundation-models|image-generation-stable-diffusion.ipynb)\n",
    "\n",
    "![This eu-west-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/eu-west-1/introduction_to_amazon_algorithms|jumpstart-foundation-models|image-generation-stable-diffusion.ipynb)\n",
    "\n",
    "![This eu-west-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/eu-west-2/introduction_to_amazon_algorithms|jumpstart-foundation-models|image-generation-stable-diffusion.ipynb)\n",
    "\n",
    "![This eu-west-3 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/eu-west-3/introduction_to_amazon_algorithms|jumpstart-foundation-models|image-generation-stable-diffusion.ipynb)\n",
    "\n",
    "![This eu-central-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/eu-central-1/introduction_to_amazon_algorithms|jumpstart-foundation-models|image-generation-stable-diffusion.ipynb)\n",
    "\n",
    "![This eu-north-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/eu-north-1/introduction_to_amazon_algorithms|jumpstart-foundation-models|image-generation-stable-diffusion.ipynb)\n",
    "\n",
    "![This ap-southeast-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-southeast-1/introduction_to_amazon_algorithms|jumpstart-foundation-models|image-generation-stable-diffusion.ipynb)\n",
    "\n",
    "![This ap-southeast-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-southeast-2/introduction_to_amazon_algorithms|jumpstart-foundation-models|image-generation-stable-diffusion.ipynb)\n",
    "\n",
    "![This ap-northeast-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-northeast-1/introduction_to_amazon_algorithms|jumpstart-foundation-models|image-generation-stable-diffusion.ipynb)\n",
    "\n",
    "![This ap-northeast-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-northeast-2/introduction_to_amazon_algorithms|jumpstart-foundation-models|image-generation-stable-diffusion.ipynb)\n",
    "\n",
    "![This ap-south-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-south-1/introduction_to_amazon_algorithms|jumpstart-foundation-models|image-generation-stable-diffusion.ipynb)"
   ],
   "metadata": {
    "collapsed": false
   }
  }
 ],
 "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
   }
  ],
  "instance_type": "ml.t3.medium",
  "kernelspec": {
   "display_name": "Python 3 (Data Science 2.0)",
   "language": "python",
   "name": "python3__SAGEMAKER_INTERNAL__arn:aws:sagemaker:us-west-2:236514542706:image/sagemaker-data-science-38"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}