musicgen-large/deploy-musicgen-large.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "88facfc2-7a17-4a44-8b34-29761d350b72",
   "metadata": {},
   "source": [
    "# MusicGen Async SageMaker Inference\n",
    "\n",
    "https://github.com/aws/amazon-sagemaker-examples/blob/main/async-inference/Transcription_on_SM_endpoint.ipynb\n",
    "https://huggingface.co/docs/transformers/model_doc/musicgen#generation"
   ]
  },
  {
   "attachments": {
    "3693baf3-1112-4a45-9440-82667f28f8b7.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "0ab0cd60-91e1-4c8e-b1f0-b3f35858b9a7",
   "metadata": {},
   "source": [
    "\n",
    "![image.png](attachment:3693baf3-1112-4a45-9440-82667f28f8b7.png)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca5dd1f7-755c-40e6-9628-e80b97c8130f",
   "metadata": {},
   "source": [
    "Install necessary packages to run this notebook on SageMaker Studio."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dda1f938-35f2-4eee-a319-ac2e199ff29e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "!pip install -Uq pip\n",
    "!pip install -Uq sagemaker"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4af52d69-a712-46d2-8d32-688d70b72fa6",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Prepare Inference Scripts\n",
    "\n",
    "We will create model directory that holds the code artefacts such as inference python script and the requirements.txt that holds all the relevant python packages that will be installed when the model is deployed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "104af8c1-e7ef-42e2-b419-28a899800f01",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "!mkdir model\n",
    "!mkdir model/code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bec2f848-b6fa-4320-8e2f-470e04696271",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "## requirements.txt https://github.com/facebookresearch/audiocraft/blob/main/README.md\n",
    "'''\n",
    "# Best to make sure you have torch installed first, in particular before installing xformers.\n",
    "# Don't run this if you already have PyTorch installed.\n",
    "pip install 'torch>=2.0'\n",
    "# Then proceed to one of the following\n",
    "pip install -U audiocraft  # stable release\n",
    "pip install -U git+https://git@github.com/facebookresearch/audiocraft#egg=audiocraft  # bleeding edge\n",
    "pip install -e .  # or if you cloned the repo locally (mandatory if you want to train).\n",
    "'''\n",
    "with open(\"model/code/requirements.txt\", \"w\") as f:\n",
    "    f.write(\"transformers==4.34.1\\n\")\n",
    "    f.write(\"boto3\\n\")\n",
    "    f.write(\"torch>=2.0\\n\")\n",
    "    f.write(\"scipy\\n\")\n",
    "    f.write(\"uuid\\n\")\n",
    "    f.write(\"audiocraft\\n\")\n",
    "    f.write(\"git+https://git@github.com/facebookresearch/audiocraft#egg=audiocraft\\n\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7449cccb-5535-4bcc-af1f-9b9ff057ca8b",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "%%writefile model/code/inference.py\n",
    "import uuid\n",
    "import os\n",
    "import logging\n",
    "import torch\n",
    "from transformers import AutoProcessor, MusicgenForConditionalGeneration\n",
    "import boto3\n",
    "import scipy\n",
    "\n",
    "\n",
    "# DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
    "# DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "\n",
    "logger = logging.getLogger(__name__)\n",
    "\n",
    "\n",
    "def model_fn(model_dir):\n",
    "    '''loads model'''\n",
    "    model = MusicgenForConditionalGeneration.from_pretrained(\"facebook/musicgen-large\")\n",
    "    #model.to(DEVICE)\n",
    "    return model\n",
    "\n",
    "\n",
    "def _process_input(data):\n",
    "    # defaults\n",
    "    default_generation_params = {\n",
    "        'guidance_scale': 3,\n",
    "        'max_new_tokens': 256,\n",
    "        'do_sample': True,\n",
    "        'temperature': 1 \n",
    "        }\n",
    "    default_texts = [\"Morning sunshine, beats, ukelele, happy swings\"]\n",
    "    # obtain input data\n",
    "    texts = data.get('texts', default_texts)\n",
    "    config = data.get('generation_params', default_generation_params)\n",
    "    guidance_scale = config.get('guidance_scale', default_generation_params.get('guidance_scale'))\n",
    "    max_new_tokens = config.get('max_new_tokens', default_generation_params.get('max_new_tokens'))\n",
    "    temperature = config.get('temperature', default_generation_params.get('temperature'))\n",
    "    do_sample = config.get('do_sample', default_generation_params.get('do_sample'))\n",
    "    generation_params = {\n",
    "        'guidance_scale': guidance_scale, \n",
    "        'do_sample': do_sample,\n",
    "        'max_new_tokens': max_new_tokens,\n",
    "        'temperature': temperature\n",
    "    }\n",
    "    return texts, generation_params\n",
    "\n",
    "\n",
    "def _upload_to_s3(wav_on_disk, bucket_name):\n",
    "    # https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/s3/object/upload_fileobj.html\n",
    "    target_file = wav_on_disk.split('/')[-1]\n",
    "    prefix = 'musicgen_large/output'\n",
    "    key = f'{prefix}/{target_file}'\n",
    "\n",
    "    s3 = boto3.resource('s3')\n",
    "    bucket = s3.Bucket(bucket_name)\n",
    "    obj = bucket.Object(key)\n",
    "\n",
    "    with open(wav_on_disk, 'rb') as data:\n",
    "        obj.upload_fileobj(data)\n",
    "\n",
    "    return f\"s3://{obj.bucket_name}/{obj.key}\"\n",
    "\n",
    "\n",
    "def _upload_wav_files(disk_wav_locations, bucket_name):\n",
    "    upload_paths = []\n",
    "    for loc in disk_wav_locations:\n",
    "        try:\n",
    "            uri = _upload_to_s3(loc, bucket_name)\n",
    "            upload_paths.append(uri)\n",
    "        except Exception as e:\n",
    "            logger.exception('error uploading %s: %s', loc, e)\n",
    "\n",
    "    return upload_paths\n",
    "\n",
    "\n",
    "def _write_wavs_to_disk(sampling_rate, audio_values):\n",
    "    r = len(audio_values)\n",
    "    prefix = str(uuid.uuid1())\n",
    "    disk_wav_locations = []\n",
    "    for i in range(r):\n",
    "        # Write wav to Disk\n",
    "        wav_file = f\"{prefix}_{i}_musicgen_large_out.wav\"\n",
    "        wav_on_disk = f'/tmp/{wav_file}'\n",
    "        scipy.io.wavfile.write(wav_on_disk,\n",
    "                                rate=sampling_rate,\n",
    "                                data=audio_values[i, 0].cpu().numpy()\n",
    "                            )\n",
    "        disk_wav_locations.append(wav_on_disk)\n",
    "    return disk_wav_locations\n",
    "\n",
    "\n",
    "def _delete_file_on_disk(filename):\n",
    "    if os.path.isfile(filename):\n",
    "        os.remove(filename)\n",
    "\n",
    "\n",
    "def predict_fn(data, model):\n",
    "    '''generates the music from the input data'''\n",
    "    result_dict = {\n",
    "            \"generated_outputs_s3\": []\n",
    "        }\n",
    "    try:\n",
    "        # https://sagemaker.readthedocs.io/en/stable/api/inference/predictors.html#sagemaker.predictor.Predictor.predict\n",
    "        bucket_name = data.pop('bucket_name', None)\n",
    "        if not bucket_name:\n",
    "            raise ValueError(\"bucket_name is required ex: sagemaker_default_bucket_007\")\n",
    "        texts, generation_params = _process_input(data)\n",
    "        processor = AutoProcessor.from_pretrained(\"facebook/musicgen-large\")\n",
    "        inputs = processor(\n",
    "            text = texts,\n",
    "            padding=True,\n",
    "            return_tensors=\"pt\",\n",
    "        )\n",
    "        logger.info('start generate music')\n",
    "        device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "        model.to(device)\n",
    "        audio_values = model.generate(**inputs.to(device),\n",
    "                                      **generation_params)\n",
    "\n",
    "        logger.info('end generate music')\n",
    "        # process generated output audio_values\n",
    "        sampling_rate = model.config.audio_encoder.sampling_rate\n",
    "\n",
    "        disk_wav_locations = _write_wavs_to_disk(sampling_rate, audio_values)\n",
    "\n",
    "        # Upload wavs to S3\n",
    "        result_dict[\"generated_outputs_s3\"] = _upload_wav_files(disk_wav_locations, bucket_name)\n",
    "        # Clean up disk\n",
    "        for wav_on_disk in disk_wav_locations:\n",
    "            _delete_file_on_disk(wav_on_disk)\n",
    "    except Exception as e:\n",
    "        logger.exception('error while prediction: %s', e)\n",
    "\n",
    "    logger.info('end predict_fn')\n",
    "    return result_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f5d35645-e796-40f3-bfe5-8dc8378da653",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "%cd model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "020c025c-9a98-442f-866a-7927a980494b",
   "metadata": {},
   "source": [
    "## Place the inference scripts archive on Amazon S3\n",
    "\n",
    "We will create the archive of the inference scripts and upload those to Amazon S3 bucket. The uploaded uri of this object on S3 will later be used to create the HuggingFace Model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dbe3098e-1194-4805-8cfd-f33880dd7211",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "!rm model.tar.gz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b8be2a9d-63a6-4938-bbfb-7ab687ad69ca",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "!rm -rf code/.ipynb_checkpoints*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "20a8ea5b-753f-4ae8-aeff-226fad09eabd",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "!tar zcvf model.tar.gz *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "de657b6f-7c22-4496-8c74-05d1661527c8",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import sagemaker\n",
    "import boto3\n",
    "\n",
    "sess = sagemaker.Session()\n",
    "\n",
    "sagemaker_session_bucket = None\n",
    "if sagemaker_session_bucket is None and sess is not None:\n",
    "    sagemaker_session_bucket = sess.default_bucket()\n",
    "\n",
    "try:\n",
    "    role = sagemaker.get_execution_role()\n",
    "except ValueError:\n",
    "    iam = boto3.client(\"iam\")\n",
    "    role = iam.get_role(RoleName=\"sagemaker_execution_role\")[\"Role\"][\"Arn\"]\n",
    "\n",
    "sess = sagemaker.Session(default_bucket=sagemaker_session_bucket)\n",
    "region = sess.boto_region_name\n",
    "print(f\"sagemaker role arn: {role}\")\n",
    "print(f\"sagemaker bucket: {sess.default_bucket()}\")\n",
    "print(f\"sagemaker session region: {region}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "89056fe5-98e8-44a6-b484-f01d83872293",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "musicgen_prefix = 'musicgen_large'\n",
    "s3_model_key = f'{musicgen_prefix}/model/model.tar.gz'\n",
    "s3_model_location = f\"s3://{sagemaker_session_bucket}/{s3_model_key}\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0c407864-cf11-4c09-9d59-645bfe187bf2",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "s3 = boto3.resource(\"s3\")\n",
    "s3.Bucket(sagemaker_session_bucket).upload_file(\"model.tar.gz\", s3_model_key)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "968baaea-6613-4f5e-9469-ccc55ea35ef7",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Deploy Asynchronous Inference Endpoint on Amazon SageMaker\n",
    "\n",
    "To create the Asynchronous Inference Endpoint, we will perform the following steps:\n",
    "\n",
    "1. Obtain the AWS DLC for Large Model Inference from [pre-built HuggingFace Inference Containers](https://github.com/aws/deep-learning-containers/blob/master/available_images.md#huggingface-inference-containers)\n",
    "2. Create a model of type HuggingFaceModel since we are using the musicgen large model from HuggingFace as model provider. \n",
    "3. We then create an asynchronous endpoint configuration whose notification configuration is associated to Amazon SNS for success and error topics. \n",
    "4. We will finally deploy the model to generate a SageMaker Endpoint for asynchronous inferencing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d6f33cfb",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "instance_type=\"ml.g5.2xlarge\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2dd1a102",
   "metadata": {},
   "source": [
    "### (Optional) Obtain AWS DLC for Large Model Inference\n",
    "\n",
    "In case you want to deploy to DLC, programmatically obtain AWS DLC for HF LMI from SageMaker. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "65bda6c3",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sagemaker import image_uris\n",
    "image_uri = image_uris.retrieve(\n",
    "                framework='huggingface',\n",
    "                region=region,\n",
    "                version='4.28.1',\n",
    "                image_scope='inference',\n",
    "                base_framework_version='pytorch2.0.0',\n",
    "                instance_type=instance_type)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3376633d-eb8c-46a4-bb55-110db4a72bfb",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Create a Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cb012660-5957-4ced-b2f3-e1527d99839a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sagemaker.huggingface.model import HuggingFaceModel\n",
    "from sagemaker.async_inference.async_inference_config import AsyncInferenceConfig\n",
    "from sagemaker.s3 import s3_path_join\n",
    "from sagemaker.utils import name_from_base\n",
    "\n",
    "async_endpoint_name = name_from_base(\"musicgen-large-v1-async\")\n",
    "\n",
    "# create Hugging Face Model Class\n",
    "# AudioCraft's models requires Python 3.9, PyTorch 2.0.0 or latest. (https://github.com/facebookresearch/audiocraft/blob/main/README.md)\n",
    "\n",
    "huggingface_model = HuggingFaceModel(\n",
    "    name=async_endpoint_name,\n",
    "    #image_uri=image_uri,\n",
    "    model_data=s3_model_location,  # path to your model and script\n",
    "    role=role,\n",
    "    env= { #https://docs.aws.amazon.com/sagemaker/latest/dg/async-inference-create-endpoint.html\n",
    "           'TS_MAX_REQUEST_SIZE': '100000000',\n",
    "           'TS_MAX_RESPONSE_SIZE': '100000000',\n",
    "           'TS_DEFAULT_RESPONSE_TIMEOUT': '3600'\n",
    "       },# iam role with permissions to create an Endpoint\n",
    "    transformers_version=\"4.28\",  # transformers version used\n",
    "    pytorch_version=\"2.0\",  # pytorch version used\n",
    "    py_version=\"py310\",  # python version used\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "064219c4-3796-4527-9643-71214a1ff7eb",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Asynchronous Inference Configuration"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81a716ed-cf50-41d5-8914-2cc3664bf339",
   "metadata": {},
   "source": [
    "#### Create Amazon SNS topics for Success and Failure Notification configuration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d6d7e75a-83d1-40d8-a553-b8d49a855222",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "%cd .."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3031370d-1e5b-4fa7-8a1b-742eba6726e3",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import sys, os\n",
    "# https://stackoverflow.com/a/8015152\n",
    "sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(\"__file__\"))))\n",
    "\n",
    "from utils.sns_client import SnsClient\n",
    "del sys.path[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "673834a2-92dc-4a9c-9654-97d821757904",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Create SNS topic\n",
    "import time\n",
    "#from utils.sns_client import SnsClient\n",
    "\n",
    "sns_client = SnsClient(boto3.client(\"sns\"))\n",
    "timestamp = time.time_ns()\n",
    "topic_names = [f\"musicgen-large-topic-SuccessTopic-{timestamp}\", f\"musicgen-large-topic-ErrorTopic-{timestamp}\"]\n",
    "\n",
    "topic_arns = []\n",
    "for topic_name in topic_names:\n",
    "    print(f\"Creating topic {topic_name}.\")\n",
    "    response = sns_client.create_topic(topic_name)\n",
    "    topic_arns.append(response.get('TopicArn'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "af98a18b-4227-41fb-8d64-2ba7993dfd0f",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "topic_arns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86874f07-8b7f-4140-8c76-41729a406f85",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Create Async Inference Configuration and associate it with SNS topics using notification configuration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d5545b7f-870f-4740-939d-c8ee3f225e20",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# create async endpoint configuration\n",
    "async_config = AsyncInferenceConfig(\n",
    "    output_path=s3_path_join(\n",
    "        \"s3://\", sagemaker_session_bucket, \"musicgen_large/async_inference/music_output\"\n",
    "    ),  # Where our results will be stored\n",
    "    # Add nofitication SNS if needed\n",
    "    notification_config={\n",
    "        \"SuccessTopic\": topic_arns[0],\n",
    "        \"ErrorTopic\": topic_arns[1],\n",
    "    },  #  Notification configuration\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71786423-5a25-4792-8122-465bb3a1840b",
   "metadata": {},
   "source": [
    "### Deploy the model to generate a SageMaker Endpoint for asynchronous inferencing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "979b625a-c478-4a01-9822-9fed453c8574",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# deploy the endpoint\n",
    "async_predictor = huggingface_model.deploy(\n",
    "    initial_instance_count=1,\n",
    "    instance_type=instance_type,\n",
    "    async_inference_config=async_config,\n",
    "    endpoint_name=async_endpoint_name,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "596e1fac-35de-45d9-bd22-6cb3410dbfd2",
   "metadata": {},
   "source": [
    "Let's save the variables that will be re-used in the infer notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7ea3be25-db70-4e9c-9054-e3978ecd62b5",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "endpoint_name=async_predictor.endpoint_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "626079df-5dec-4c67-be91-52698f52e567",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "%store \\\n",
    "endpoint_name \\\n",
    "sagemaker_session_bucket \\\n",
    "topic_arns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "214b74dd-b98d-4eda-80b5-1161eae18844",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "endpoint_name"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6dfa36b-ff09-430b-a534-1c78921d4f0d",
   "metadata": {},
   "source": [
    "Meanwhile the model gets deployed, you can refer to the following info about Amazon SageMaker and Facebook Musicgen.\n",
    "\n",
    "- https://huggingface.co/facebook/musicgen-large\n",
    "- https://huggingface.co/docs/transformers/model_doc/musicgen#generation\n",
    "- https://github.com/facebookresearch/audiocraft/blob/main/README.md\n",
    "- https://sagemaker.readthedocs.io/en/stable/frameworks/huggingface/sagemaker.huggingface.html#hugging-face-model\n",
    "- https://sagemaker.readthedocs.io/en/stable/api/inference/predictors.html#sagemaker.predictor.Predictor.predict\n",
    "- https://github.com/aws/amazon-sagemaker-examples/blob/main/async-inference/Transcription_on_SM_endpoint.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ccb05593-1d54-44d1-843a-0ba0b54e8221",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "329719a4-84f9-40b7-8dec-43b137ca13c6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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,
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