{ "cells": [ { "cell_type": "markdown", "id": "7060c891-cebd-4011-b350-b7d1e70b40b2", "metadata": { "tags": [] }, "source": [ "### 1. 安装HuggingFace 并下载模型到本地" ] }, { "cell_type": "code", "execution_count": null, "id": "9f413314-c410-43d3-bb3a-ba0aa18ec1be", "metadata": { "tags": [] }, "outputs": [], "source": [ "!pip install huggingface-hub -Uqq\n", "!pip install -Uqq sagemaker" ] }, { "cell_type": "markdown", "id": "df3b0a4b-f166-4f1a-a7cc-9c7277c68173", "metadata": {}, "source": [ "### 如果是外海region，执行下面cell通过huggingface_hub下载, 否则跳过" ] }, { "cell_type": "code", "execution_count": null, "id": "be112a00-cbef-4387-b0d7-80e5e7b7030d", "metadata": { "tags": [] }, "outputs": [], "source": [ "from huggingface_hub import snapshot_download\n", "from pathlib import Path\n", "\n", "local_model_path = Path(\"./bge-m3-model\")\n", "local_model_path.mkdir(exist_ok=True)\n", "model_name = \"BAAI/bge-m3\"\n", "commit_hash = \"4277867103fc67328e2033176de4387b85e9960f\"\n", "snapshot_download(repo_id=model_name, revision=commit_hash, cache_dir=local_model_path)" ] }, { "cell_type": "markdown", "id": "67712dfc-a411-433b-bc67-9734a1686480", "metadata": { "tags": [] }, "source": [ "### 如果是中国区，执行下面cell通过modelscope进行下载" ] }, { "cell_type": "code", "execution_count": null, "id": "7b3fa6b9-492d-477b-8a45-8b8e3bbfb6e8", "metadata": { "tags": [] }, "outputs": [], "source": [ "!pip install modelscope -i https://pypi.tuna.tsinghua.edu.cn/simple -Uqq" ] }, { "cell_type": "code", "execution_count": null, "id": "dda7d753-6faf-46fc-a8c1-da9a3f1cd1db", "metadata": { "tags": [] }, "outputs": [], "source": [ "from modelscope.hub.snapshot_download import snapshot_download\n", "from pathlib import Path\n", "\n", "local_model_path = Path(\"./bge-zh-model\")\n", "\n", "local_model_path.mkdir(exist_ok=True)\n", "model_name = \"Xorbits/bge-large-zh-v1.5\"\n", "commit_hash = \"v0.0.1\"\n", "\n", "snapshot_download(model_name, revision=commit_hash, cache_dir=local_model_path)" ] }, { "cell_type": "markdown", "id": "a6b61ad8-a8c2-48c2-8539-e7c1e2afe773", "metadata": {}, "source": [ "### 2. 把模型拷贝到S3为后续部署做准备" ] }, { "cell_type": "code", "execution_count": null, "id": "5e1873f4-1bfe-4146-8297-584e9ad76fc9", "metadata": { "tags": [] }, "outputs": [], "source": [ "import sagemaker\n", "from sagemaker import image_uris\n", "import boto3\n", "import os\n", "import time\n", "import json\n", "\n", "role = sagemaker.get_execution_role() # execution role for the endpoint\n", "sess = sagemaker.session.Session() # sagemaker session for interacting with different AWS APIs\n", "bucket = sess.default_bucket() # bucket to house artifacts\n", "\n", "region = sess._region_name\n", "account_id = sess.account_id()\n", "\n", "s3_client = boto3.client(\"s3\")\n", "sm_client = boto3.client(\"sagemaker\")\n", "smr_client = boto3.client(\"sagemaker-runtime\")" ] }, { "cell_type": "code", "execution_count": null, "id": "68394e44-4d51-48ae-adc1-d02f520a5d4d", "metadata": { "tags": [] }, "outputs": [], "source": [ "s3_model_prefix = \"LLM-RAG/workshop/bge-m3-model\" # folder where model checkpoint will go\n", "if region in ['cn-north-1', 'cn-northwest-1']:\n", " model_snapshot_path = f'{local_model_path}/{model_name}'\n", "else:\n", " model_snapshot_path = list(local_model_path.glob(\"**/snapshots/*\"))[0]\n", "s3_code_prefix = \"LLM-RAG/workshop/bge_m3_deploy_code\"\n", "print(f\"s3_code_prefix: {s3_code_prefix}\")\n", "print(f\"model_snapshot_path: {model_snapshot_path}\")" ] }, { "cell_type": "code", "execution_count": null, "id": "0b9e177a-886d-4838-891e-2e612a3cbc9d", "metadata": { "tags": [] }, "outputs": [], "source": [ "!aws s3 cp --recursive {model_snapshot_path} s3://{bucket}/{s3_model_prefix}" ] }, { "cell_type": "markdown", "id": "59f35a6f-5988-42ec-87b0-de36eaebe41b", "metadata": { "tags": [] }, "source": [ "### 3. 模型部署准备（entrypoint脚本，容器镜像，服务配置）" ] }, { "cell_type": "code", "execution_count": null, "id": "86daea77-a7ae-46b8-8800-212d07ce5605", "metadata": { "tags": [] }, "outputs": [], "source": [ "inference_image_uri = (\n", " f\"763104351884.dkr.ecr.{region}.amazonaws.com/djl-inference:0.23.0-deepspeed0.9.5-cu118\"\n", " \n", ")\n", "\n", "#中国区需要替换为下面的image_uri\n", "if region in ['cn-north-1', 'cn-northwest-1']:\n", " inference_image_uri = (\n", " f\"727897471807.dkr.ecr.{region}.amazonaws.com.cn/djl-inference:0.23.0-deepspeed0.9.5-cu118\"\n", " )\n", "\n", "print(f\"Image going to be used is ---- > {inference_image_uri}\")" ] }, { "cell_type": "code", "execution_count": null, "id": "49435172-e6c5-492a-8dcb-43e3fffb0f5c", "metadata": { "tags": [] }, "outputs": [], "source": [ "!mkdir -p bge_m3_deploy_code" ] }, { "cell_type": "code", "execution_count": null, "id": "70990dd3-431e-4dd0-a494-d26ceb454945", "metadata": { "tags": [] }, "outputs": [], "source": [ "%%writefile bge_m3_deploy_code/model.py\n", "from djl_python import Input, Output\n", "import torch\n", "import logging\n", "import math\n", "import os\n", "from FlagEmbedding import BGEM3FlagModel\n", "\n", "device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n", "print(f'--device={device}')\n", "\n", "def load_model(properties):\n", " tensor_parallel = properties[\"tensor_parallel_degree\"]\n", " model_location = properties['model_dir']\n", " if \"model_id\" in properties:\n", " model_location = properties['model_id']\n", " logging.info(f\"Loading model in {model_location}\")\n", "\n", " model = BGEM3FlagModel(model_location,use_fp16=True)\n", " \n", " return model\n", "\n", "model = None\n", "\n", "def handle(inputs: Input):\n", " global model\n", " if not model:\n", " model = load_model(inputs.get_properties())\n", "\n", " if inputs.is_empty():\n", " return None\n", " data = inputs.get_as_json()\n", " \n", " input_sentences = None\n", " inputs = data[\"inputs\"]\n", " if isinstance(inputs, list):\n", " input_sentences = inputs\n", " else:\n", " input_sentences = [inputs]\n", " \n", " is_query = data.get(\"is_query\")\n", " max_length = data.get(\"max_length\", 2048)\n", " instruction = data.get(\"instruction\",'')\n", " logging.info(f\"inputs: {input_sentences}\")\n", " logging.info(f\"is_query: {is_query}\")\n", " logging.info(f\"instruction: {instruction}\")\n", " \n", " if is_query and instruction:\n", " input_sentences = [ instruction + sent for sent in input_sentences ]\n", " \n", " sentence_embeddings = model.encode(input_sentences, max_length=max_length)\n", " \n", " result = {\"embeddings\": sentence_embeddings['dense_vecs']}\n", " return Output().add_as_json(result)" ] }, { "cell_type": "code", "execution_count": null, "id": "0f4c6d5f", "metadata": { "tags": [] }, "outputs": [], "source": [ "print(f\"option.s3url ==> s3://{bucket}/{s3_model_prefix}/\")" ] }, { "cell_type": "markdown", "id": "a1e1ecec-79cf-4ed4-bba1-95e2fe79daea", "metadata": {}, "source": [ "#### Note: option.s3url 为SageMaker部署时使用的模型S3路径" ] }, { "cell_type": "code", "execution_count": null, "id": "7b126565-66e2-4987-ac6b-e02f09070a65", "metadata": { "tags": [] }, "outputs": [], "source": [ "import os\n", "\n", "if not os.path.exists(\"bge_m3_deploy_code\"):\n", " os.mkdir(\"bge_m3_deploy_code\")\n", "\n", "with open('bge_m3_deploy_code/serving.properties', 'w') as f:\n", " f.write(\"engine=Python\")\n", " f.write(\"\\n\")\n", " f.write(\"option.tensor_parallel_degree=1\")\n", " f.write(\"\\n\")\n", " f.write(f\"option.s3url=s3://{bucket}/{s3_model_prefix}/\")" ] }, { "cell_type": "markdown", "id": "5e11b8f7-aebf-42a0-9a7f-31691059cc65", "metadata": { "tags": [] }, "source": [ "### 【注意】下面这两个Cell，根据region，仅挑选一个执行，否则会产生问题" ] }, { "cell_type": "markdown", "id": "e1434f9a-f114-4f83-a103-04fde82cb307", "metadata": {}, "source": [ "#### 如果是中国region，执行下面这个cell，在requirements.txt中添加国内的pip镜像" ] }, { "cell_type": "code", "execution_count": null, "id": "38bf548e-fb01-4951-b49f-15a91c61fb2e", "metadata": { "tags": [] }, "outputs": [], "source": [ "%%writefile bge_m3_deploy_code/requirements.txt\n", "-i https://pypi.tuna.tsinghua.edu.cn/simple\n", "FlagEmbedding" ] }, { "cell_type": "markdown", "id": "2a35bbdf-c246-4e9e-ab7f-aac5b389ddf2", "metadata": { "tags": [] }, "source": [ "#### 如果是海外region，执行下面这个cell，无需额外添加pip镜像" ] }, { "cell_type": "code", "execution_count": null, "id": "928a7806-afc4-4ae7-9253-1c9dfabfed99", "metadata": { "tags": [] }, "outputs": [], "source": [ "%%writefile bge_m3_deploy_code/requirements.txt\n", "FlagEmbedding" ] }, { "cell_type": "code", "execution_count": null, "id": "ffe41472-c2cf-4bb5-99aa-84df76c629b3", "metadata": { "tags": [] }, "outputs": [], "source": [ "!rm s2e_model.tar.gz\n", "!cd bge_m3_deploy_code && rm -rf \".ipynb_checkpoints\"\n", "!tar czvf s2e_model.tar.gz bge_m3_deploy_code" ] }, { "cell_type": "code", "execution_count": null, "id": "1fabd7ce-b855-4569-857c-ad872662800b", "metadata": { "tags": [] }, "outputs": [], "source": [ "s3_code_artifact = sess.upload_data(\"s2e_model.tar.gz\", bucket, s3_code_prefix)\n", "print(f\"S3 Code or Model tar ball uploaded to --- > {s3_code_artifact}\")" ] }, { "cell_type": "markdown", "id": "18fb01ed-6bd3-4880-a647-cfd71e692820", "metadata": { "tags": [] }, "source": [ "### 4. 创建模型 & 创建endpoint" ] }, { "cell_type": "code", "execution_count": null, "id": "e6209d24-8473-4256-93d3-02e4e144386b", "metadata": { "tags": [] }, "outputs": [], "source": [ "from sagemaker.utils import name_from_base\n", "import boto3\n", "\n", "model_name = name_from_base(\"bge-m3\") #Note: Need to specify model_name\n", "print(model_name)\n", "print(f\"Image going to be used is ---- > {inference_image_uri}\")\n", "\n", "create_model_response = sm_client.create_model(\n", " ModelName=model_name,\n", " ExecutionRoleArn=role,\n", " PrimaryContainer={\n", " \"Image\": inference_image_uri,\n", " \"ModelDataUrl\": s3_code_artifact\n", " },\n", " \n", ")\n", "model_arn = create_model_response[\"ModelArn\"]\n", "\n", "print(f\"Created Model: {model_arn}\")" ] }, { "cell_type": "code", "execution_count": null, "id": "686abae8-5db7-4ebd-9fbf-5bd54f36c0ab", "metadata": { "tags": [] }, "outputs": [], "source": [ "endpoint_config_name = f\"{model_name}-config\"\n", "endpoint_name = f\"{model_name}-endpoint\"\n", "\n", "endpoint_config_response = sm_client.create_endpoint_config(\n", " EndpointConfigName=endpoint_config_name,\n", " ProductionVariants=[\n", " {\n", " \"VariantName\": \"variant1\",\n", " \"ModelName\": model_name,\n", " \"InstanceType\": \"ml.g4dn.xlarge\",\n", " \"InitialInstanceCount\": 1,\n", " # \"VolumeSizeInGB\" : 400,\n", " # \"ModelDataDownloadTimeoutInSeconds\": 2400,\n", " \"ContainerStartupHealthCheckTimeoutInSeconds\": 5*60,\n", " },\n", " ],\n", ")\n", "endpoint_config_response" ] }, { "cell_type": "code", "execution_count": null, "id": "f4c1df06-ae4a-42e2-9695-da0afa9ad734", "metadata": { "tags": [] }, "outputs": [], "source": [ "create_endpoint_response = sm_client.create_endpoint(\n", " EndpointName=f\"{endpoint_name}\", EndpointConfigName=endpoint_config_name\n", ")\n", "print(f\"Created Endpoint: {create_endpoint_response['EndpointArn']}\")" ] }, { "cell_type": "code", "execution_count": null, "id": "d9c71240-6878-4fed-bf7d-2c1cf75f4ac5", "metadata": { "tags": [] }, "outputs": [], "source": [ "import time\n", "\n", "resp = sm_client.describe_endpoint(EndpointName=endpoint_name)\n", "status = resp[\"EndpointStatus\"]\n", "print(\"Status: \" + status)\n", "\n", "while status == \"Creating\":\n", " time.sleep(60)\n", " resp = sm_client.describe_endpoint(EndpointName=endpoint_name)\n", " status = resp[\"EndpointStatus\"]\n", " print(\"Status: \" + status)\n", "\n", "print(\"Arn: \" + resp[\"EndpointArn\"])\n", "print(\"Status: \" + status)" ] }, { "cell_type": "markdown", "id": "dddba20e-fc18-480d-9940-ae39695ac450", "metadata": {}, "source": [ "### 5. 模型测试" ] }, { "cell_type": "code", "execution_count": null, "id": "1f28db25-6996-440c-b004-14f96cfd982d", "metadata": { "tags": [] }, "outputs": [], "source": [ "def get_vector_by_sm_endpoint(questions, sm_client, endpoint_name):\n", " parameters = {\n", " }\n", "\n", " response_model = sm_client.invoke_endpoint(\n", " EndpointName=endpoint_name,\n", " Body=json.dumps(\n", " {\n", " \"inputs\": questions,\n", " # \"is_query\": True,\n", " \"instruction\" : \"Represent this sentence for searching relevant passages:\"\n", " }\n", " ),\n", " ContentType=\"application/json\",\n", " )\n", " # 中文instruction => 为这个句子生成表示以用于检索相关文章：\n", " json_str = response_model['Body'].read().decode('utf8')\n", " json_obj = json.loads(json_str)\n", " embeddings = json_obj['embeddings']\n", " return embeddings" ] }, { "cell_type": "code", "execution_count": null, "id": "52d4f56a-092e-4a6a-a920-48550ec9f20c", "metadata": { "tags": [] }, "outputs": [], "source": [ "prompts1 = [\"How to compete with OCI\",\"如何与OCI竞争\",\"如何與OCI競爭\"]\n", "\n", "emb = get_vector_by_sm_endpoint(prompts1, smr_client, endpoint_name)" ] }, { "cell_type": "code", "execution_count": null, "id": "2ea82819-1c9f-493c-8bda-5b1e16f056cf", "metadata": { "tags": [] }, "outputs": [], "source": [ "import numpy as np\n", "def cos_sim(vector1, vector2):\n", " dot_product = np.dot(vector1, vector2)\n", " norm_v1 = np.linalg.norm(vector1)\n", " norm_v2 = np.linalg.norm(vector2)\n", " cos_sim = dot_product / (norm_v1 * norm_v2)\n", " return cos_sim" ] }, { "cell_type": "code", "execution_count": null, "id": "2f59bd2e-5f8a-42ae-bfc0-6e7d05ae209b", "metadata": { "tags": [] }, "outputs": [], "source": [ "cos_sim(emb[1],emb[2])" ] } ], "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 }, { 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