comfyui-on-sagemaker-byoc.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "54c880c6",
   "metadata": {},
   "source": [
    "### 1. 升级boto3, sagemaker python sdk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f17e7cdf",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install --upgrade boto3 sagemaker"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "13486437",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sagemaker.config INFO - Not applying SDK defaults from location: /etc/xdg/sagemaker/config.yaml\n",
      "sagemaker.config INFO - Not applying SDK defaults from location: /home/ec2-user/.config/sagemaker/config.yaml\n",
      "sagemaker sdk version: 2.203.1\n",
      "role:  arn:aws:iam::687912291502:role/service-role/AmazonSageMaker-ExecutionRole-20211013T113123  \n",
      "bucket:  sagemaker-us-west-2-687912291502\n"
     ]
    }
   ],
   "source": [
    "#导入对应的库\n",
    "\n",
    "import re\n",
    "import os\n",
    "import json\n",
    "import uuid\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from time import gmtime, strftime\n",
    "\n",
    "\n",
    "import boto3\n",
    "import sagemaker\n",
    "\n",
    "from sagemaker import get_execution_role,session\n",
    "from sagemaker import Model, image_uris, serializers, deserializers\n",
    "\n",
    "role = get_execution_role()\n",
    "sage_session = session.Session()\n",
    "bucket = sage_session.default_bucket()\n",
    "aws_region = boto3.Session().region_name\n",
    "\n",
    "print(f'sagemaker sdk version: {sagemaker.__version__}\\nrole:  {role}  \\nbucket:  {bucket}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40088bc5",
   "metadata": {},
   "source": [
    "### 2. 编译docker image (comfyui-inference)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6e12c79e-b795-4b99-bab8-400f9be2625b",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "## You should change below region code to the region you used, here sample is use us-west-2\n",
    "!aws ecr get-login-password --region us-west-2 | docker login --username AWS --password-stdin 763104351884.dkr.ecr.us-west-2.amazonaws.com"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "47a1eac8",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "!./build_and_push.sh"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9083af1b",
   "metadata": {},
   "source": [
    "### 3. 部署Comfyui推理服务"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da982e13",
   "metadata": {},
   "source": [
    "#### 3.1 创建dummy model_data 文件(真正的模型使用code/infernece.py进行加载)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "efbc709d",
   "metadata": {},
   "outputs": [],
   "source": [
    "!touch dummy\n",
    "!tar czvf model.tar.gz dummy\n",
    "assets_dir = 's3://{0}/{1}/assets/'.format(bucket, 'stablediffusion')\n",
    "model_data = 's3://{0}/{1}/assets/model.tar.gz'.format(bucket, 'stablediffusion')\n",
    "!aws s3 cp model.tar.gz $assets_dir\n",
    "!rm -f dummy model.tar.gz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "35a1f1b2-25d8-4041-8fed-3afeb011e1da",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "!aws s3 ls s3://sagemaker-us-west-2-687912291502/stablediffusion/assets/model.tar.gz"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f53d36b8",
   "metadata": {},
   "source": [
    "#### 3.2 创建 model 配置"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "2d9c3289",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "boto3_session = boto3.session.Session()\n",
    "current_region=boto3_session.region_name\n",
    "\n",
    "client = boto3.client(\"sts\")\n",
    "account_id=client.get_caller_identity()[\"Account\"]\n",
    "\n",
    "client = boto3.client('sagemaker')\n",
    "\n",
    "#使用步骤2编译好的docker images\n",
    "#默认名字为: comfyui-inference-v2\n",
    "container = f'{account_id}.dkr.ecr.{current_region}.amazonaws.com/comfyui-inference:latest'\n",
    "\n",
    "model_data = f's3://{bucket}/stablediffusion/assets/model.tar.gz'\n",
    "\n",
    "model_name = 'AIGC-ComfyUI' +  strftime(\"%Y-%m-%d-%H-%M-%S\", gmtime())\n",
    "role = get_execution_role()\n",
    "\n",
    "primary_container = {\n",
    "    'Image': container,  \n",
    "    'ModelDataUrl': model_data,\n",
    "    'Environment':{\n",
    "        's3_bucket': bucket,\n",
    "        'MODEL_PATH': \"s3://sagemaker-us-west-2-687912291502/models/svd/\"\n",
    "    }\n",
    "}\n",
    "\n",
    "create_model_response = client.create_model(\n",
    "    ModelName = model_name,\n",
    "    ExecutionRoleArn = role,\n",
    "    PrimaryContainer = primary_container,\n",
    "\n",
    "\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "e8f2b4a2",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "_time_tag = strftime(\"%Y-%m-%d-%H-%M-%S\", gmtime())\n",
    "_variant_name =  'AIGC-ComfyUI-'+ _time_tag\n",
    "endpoint_config_name = 'AIGC-ComfyUI-' +  _time_tag\n",
    "\n",
    "response = client.create_endpoint_config(\n",
    "    EndpointConfigName=endpoint_config_name,\n",
    "    ProductionVariants=[\n",
    "        {\n",
    "            'VariantName': _variant_name,\n",
    "            'ModelName': model_name,\n",
    "            'InitialInstanceCount': 1,\n",
    "            #'VolumeSizeInGB': 300,\n",
    "            'InstanceType': 'ml.g5.4xlarge',\n",
    "            'InitialVariantWeight': 1,\n",
    "            \"ModelDataDownloadTimeoutInSeconds\": 800, # Specify the model download timeout in seconds.\n",
    "            \"ContainerStartupHealthCheckTimeoutInSeconds\": 800, # Specify the health checkup timeout in seconds\n",
    "            \n",
    "        },\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dbc23af4",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### 3.3 部署SageMaker endpoint (这里只需要运行一次!!!)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "f3d7ad93",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "终端节点:AIGC-ComfyUI-e3526a3a-9074-4edf-8248-078486a3d26b 正在创建中，首次启动中会加载模型，请耐心等待, 请在控制台上查看状态\n"
     ]
    }
   ],
   "source": [
    "endpoint_name = f'AIGC-ComfyUI-{str(uuid.uuid4())}'\n",
    "\n",
    "print(f'终端节点:{endpoint_name} 正在创建中，首次启动中会加载模型，请耐心等待, 请在控制台上查看状态')\n",
    "\n",
    "response = client.create_endpoint(\n",
    "    EndpointName=endpoint_name,\n",
    "    EndpointConfigName=endpoint_config_name,\n",
    ")\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ddc1b48",
   "metadata": {
    "tags": []
   },
   "source": [
    "* 检查endpoint 状态"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "d217375b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AIGC-ComfyUI-e3526a3a-9074-4edf-8248-078486a3d26b is ready, status: InService\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "def check_sendpoint_status(endpoint_name,timeout=600):\n",
    "    client = boto3.client('sagemaker')\n",
    "    current_time=0\n",
    "    while current_time<timeout:\n",
    "        client = boto3.client('sagemaker')\n",
    "        try:\n",
    "            response = client.describe_endpoint(\n",
    "            EndpointName=endpoint_name\n",
    "            )\n",
    "            if response['EndpointStatus'] !='InService':\n",
    "                raise Exception (f'{endpoint_name} not ready , please wait....')\n",
    "        except Exception as ex:\n",
    "            print(f'{endpoint_name} not ready , please wait....')\n",
    "            time.sleep(10)\n",
    "        else:\n",
    "            status = response['EndpointStatus']\n",
    "            print(f'{endpoint_name} is ready, status: {status}')\n",
    "            break\n",
    "\n",
    "check_sendpoint_status(endpoint_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e3557ab-3155-4e1e-ae6a-039ab72fcf00",
   "metadata": {
    "tags": []
   },
   "source": [
    "### alternative deploy api(ssh debug inference )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "41003ca7-fd5e-4218-b919-29e8e2b9b6e5",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "model = Model(image_uri=container, model_data=model_data, role=role,dependencies=[SSHModelWrapper.dependency_dir()] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8e0e61cd",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "instance_type = \"ml.g5.2xlarge\"\n",
    "from sagemaker_ssh_helper.wrapper import SSHModelWrapper\n",
    "\n",
    "endpoint_name = sagemaker.utils.name_from_base(\"comfyui-byoc\")\n",
    "\n",
    "model.deploy(initial_instance_count=1,\n",
    "             instance_type=instance_type,\n",
    "             endpoint_name=endpoint_name,\n",
    "             container_startup_health_check_timeout=800             \n",
    "            )\n",
    "ssh_wrapper = SSHModelWrapper.create(model, connection_wait_time_seconds=0)\n",
    "# our requests and responses will be in json format so we specify the serializer and the deserializer\n",
    "predictor = sagemaker.Predictor(\n",
    "    endpoint_name=endpoint_name,\n",
    "    sagemaker_session=sage_session,\n",
    "    serializer=serializers.JSONSerializer(),\n",
    ")\n",
    "print(f\"To connect over SSH run: sm-local-ssh-training connect {ssh_wrapper.training_job_name()}\")\n",
    "instance_ids = ssh_wrapper.get_instance_ids(timeout_in_sec=900)  # <--NEW-- \n",
    "print(f\"To connect over SSM run: aws ssm start-session --target {instance_ids[0]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab7868e7",
   "metadata": {
    "tags": []
   },
   "source": [
    "## 4.测试"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c73894c5-1667-4daf-9cc3-51d7fe983368",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### 4.1 创建测试辅助方法 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "938f36fc-ef9b-4eed-afa5-4f2b81967b6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "import datetime\n",
    "import uuid\n",
    "import io\n",
    "import traceback\n",
    "from PIL import Image\n",
    "import boto3\n",
    "import json\n",
    "\n",
    "\n",
    "s3_client = boto3.client('s3')\n",
    "\n",
    "def get_bucket_and_key(s3uri):\n",
    "    pos = s3uri.find('/', 5)\n",
    "    bucket = s3uri[5 : pos]\n",
    "    key = s3uri[pos + 1 : ]\n",
    "    return bucket, key\n",
    "\n",
    "\n",
    "def predict(endpoint_name,payload):\n",
    "    runtime_client = boto3.client('runtime.sagemaker')\n",
    "    response = runtime_client.invoke_endpoint(\n",
    "        EndpointName=endpoint_name,\n",
    "        ContentType='application/json',\n",
    "        Body=json.dumps(payload)\n",
    "    )\n",
    "    print(response)\n",
    "    result = json.loads(response['Body'].read().decode())\n",
    "    print(result)\n",
    "    return result\n",
    "\n",
    "\n",
    "def show_image(result):\n",
    "    try:\n",
    "        predictions = json.loads(result)\n",
    "        print(predictions['result'])\n",
    "        for image in predictions['result']:\n",
    "            bucket, key = get_bucket_and_key(image)\n",
    "            obj = s3_client.Object(bucket, key)\n",
    "            bytes = obj.get()['Body'].read()\n",
    "            image = Image.open(io.BytesIO(bytes))\n",
    "            #resize image to 50% size\n",
    "            half = 0.5\n",
    "            out_image = image.resize( [int(half * s) for s in image.size] )\n",
    "            out_image.show()\n",
    "    except Exception as e:\n",
    "        print(\"result is not completed, waiting...\")\n",
    "\n",
    "\n",
    "def show_gifs(result):\n",
    "    import base64\n",
    "    from IPython import display\n",
    "    try:\n",
    "        predictions = result['prediction']\n",
    "        s3_file_path = predictions[0]\n",
    "        print(\"s3 generated gifs path is {}\".format(s3_file_path))\n",
    "        bucket_name, key = get_bucket_and_key(s3_file_path)\n",
    "        timestamp = datetime.datetime.now().strftime('%Y%m%d%H%M%S')\n",
    "        local_file_path=\"./ComfyUI_\"+timestamp+\".gif\"\n",
    "        s3_client.download_file(bucket_name, key, local_file_path)\n",
    "        with open(local_file_path, 'rb') as fd:\n",
    "            b64 = base64.b64encode(fd.read()).decode('ascii')\n",
    "        return display.HTML(f'<img src=\"data:image/gif;base64,{b64}\" />')\n",
    "    except Exception as e:\n",
    "        print(e)\n",
    "        print(\"result is not completed, waiting...\")\n",
    "\n",
    "\n",
    "def check_sendpoint_status(endpoint_name,timeout=600):\n",
    "    client = boto3.client('sagemaker')\n",
    "    current_time=0\n",
    "    while current_time<timeout:\n",
    "        client = boto3.client('sagemaker')\n",
    "        try:\n",
    "            response = client.describe_endpoint(\n",
    "            EndpointName=endpoint_name\n",
    "            )\n",
    "            if response['EndpointStatus'] !='InService':\n",
    "                raise Exception (f'{endpoint_name} not ready , please wait....')\n",
    "        except Exception as ex:\n",
    "            print(f'{endpoint_name} not ready , please wait....')\n",
    "            time.sleep(10)\n",
    "        else:\n",
    "            status = response['EndpointStatus']\n",
    "            print(f'{endpoint_name} is ready, status: {status}')\n",
    "            break"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a21e515e",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### 4.2 测试ComfyUI 文生视频\n",
    "\n",
    "prompt从json文件加载"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f4de0b04-a4bc-4ff3-bb1d-5e64663c991a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "endpoint_name=\"AIGC-ComfyUI-e3526a3a-9074-4edf-8248-078486a3d26b\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c1a89797",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'ResponseMetadata': {'RequestId': 'c0010815-f3d7-4518-8338-f678d5eca24f', 'HTTPStatusCode': 200, 'HTTPHeaders': {'x-amzn-requestid': 'c0010815-f3d7-4518-8338-f678d5eca24f', 'x-amzn-invoked-production-variant': 'AIGC-ComfyUI-2024-01-23-07-50-37', 'date': 'Wed, 24 Jan 2024 12:56:29 GMT', 'content-type': 'application/json; charset=utf-8', 'content-length': '53', 'connection': 'keep-alive'}, 'RetryAttempts': 0}, 'ContentType': 'application/json; charset=utf-8', 'InvokedProductionVariant': 'AIGC-ComfyUI-2024-01-23-07-50-37', 'Body': <botocore.response.StreamingBody object at 0x7f79a4b85330>}\n",
      "{'prompt_id': '8257e432-10a5-4822-a46e-26d428152b2c'}\n"
     ]
    }
   ],
   "source": [
    "prompt_json_file=\"./workflow_api.json\"\n",
    "prompt_text=\"\"\n",
    "with open(prompt_json_file) as f:\n",
    "  prompt_text = json.load(f)\n",
    "\n",
    "client_id = str(uuid.uuid4())\n",
    "payload={\n",
    "     \"client_id\":client_id,\n",
    "     \"prompt\": prompt_text,\n",
    "     \"inference_type\":\"text2vid\",\n",
    "     \"method\":\"queue_prompt\"\n",
    "}\n",
    "prompt_id = predict(endpoint_name,payload)[\"prompt_id\"]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ff3dd800-c247-45d0-964a-04d5400a0897",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'ResponseMetadata': {'RequestId': '20930e62-bc51-4bc2-aaa0-0596cce3b18d', 'HTTPStatusCode': 200, 'HTTPHeaders': {'x-amzn-requestid': '20930e62-bc51-4bc2-aaa0-0596cce3b18d', 'x-amzn-invoked-production-variant': 'AIGC-ComfyUI-2024-01-23-07-50-37', 'date': 'Wed, 24 Jan 2024 12:56:31 GMT', 'content-type': 'application/json; charset=utf-8', 'content-length': '23', 'connection': 'keep-alive'}, 'RetryAttempts': 0}, 'ContentType': 'application/json; charset=utf-8', 'InvokedProductionVariant': 'AIGC-ComfyUI-2024-01-23-07-50-37', 'Body': <botocore.response.StreamingBody object at 0x7f79a7206260>}\n",
      "{'status': 'executing'}\n",
      "{'ResponseMetadata': {'RequestId': '2c55efed-e3f0-4b34-a4e2-b01492570502', 'HTTPStatusCode': 200, 'HTTPHeaders': {'x-amzn-requestid': '2c55efed-e3f0-4b34-a4e2-b01492570502', 'x-amzn-invoked-production-variant': 'AIGC-ComfyUI-2024-01-23-07-50-37', 'date': 'Wed, 24 Jan 2024 12:56:41 GMT', 'content-type': 'application/json; charset=utf-8', 'content-length': '23', 'connection': 'keep-alive'}, 'RetryAttempts': 0}, 'ContentType': 'application/json; charset=utf-8', 'InvokedProductionVariant': 'AIGC-ComfyUI-2024-01-23-07-50-37', 'Body': <botocore.response.StreamingBody object at 0x7f79a72041f0>}\n",
      "{'status': 'executing'}\n",
      "{'ResponseMetadata': {'RequestId': 'e183e093-a78b-432d-8d72-7d9b5136d200', 'HTTPStatusCode': 200, 'HTTPHeaders': {'x-amzn-requestid': 'e183e093-a78b-432d-8d72-7d9b5136d200', 'x-amzn-invoked-production-variant': 'AIGC-ComfyUI-2024-01-23-07-50-37', 'date': 'Wed, 24 Jan 2024 12:56:51 GMT', 'content-type': 'application/json; charset=utf-8', 'content-length': '23', 'connection': 'keep-alive'}, 'RetryAttempts': 0}, 'ContentType': 'application/json; charset=utf-8', 'InvokedProductionVariant': 'AIGC-ComfyUI-2024-01-23-07-50-37', 'Body': <botocore.response.StreamingBody object at 0x7f79a71b6c20>}\n",
      "{'status': 'executing'}\n",
      "{'ResponseMetadata': {'RequestId': '99e8f2cc-faf6-4f52-a926-182a8a261dac', 'HTTPStatusCode': 200, 'HTTPHeaders': {'x-amzn-requestid': '99e8f2cc-faf6-4f52-a926-182a8a261dac', 'x-amzn-invoked-production-variant': 'AIGC-ComfyUI-2024-01-23-07-50-37', 'date': 'Wed, 24 Jan 2024 12:57:01 GMT', 'content-type': 'application/json; charset=utf-8', 'content-length': '23', 'connection': 'keep-alive'}, 'RetryAttempts': 0}, 'ContentType': 'application/json; charset=utf-8', 'InvokedProductionVariant': 'AIGC-ComfyUI-2024-01-23-07-50-37', 'Body': <botocore.response.StreamingBody object at 0x7f79a4c18910>}\n",
      "{'status': 'executing'}\n",
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      "{'status': 'executing'}\n",
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      "{'status': 'success'}\n"
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   ],
   "source": [
    "payload={\n",
    "     \"client_id\":client_id,\n",
    "     \"prompt_id\":prompt_id,\n",
    "     \"inference_type\":\"text2vid\",\n",
    "     \"method\":\"get_status\"\n",
    "}\n",
    "while True:\n",
    "    status = predict(endpoint_name,payload)\n",
    "    time.sleep(10)\n",
    "    if status[\"status\"] == \"success\":\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "dc37620b-d941-4a16-b0ab-2b0f62b3116e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'ResponseMetadata': {'RequestId': 'b9aebf40-c101-4b35-86ec-9aa1534e69c4', 'HTTPStatusCode': 200, 'HTTPHeaders': {'x-amzn-requestid': 'b9aebf40-c101-4b35-86ec-9aa1534e69c4', 'x-amzn-invoked-production-variant': 'AIGC-ComfyUI-2024-01-23-07-50-37', 'date': 'Wed, 24 Jan 2024 13:00:43 GMT', 'content-type': 'application/json; charset=utf-8', 'content-length': '95', 'connection': 'keep-alive'}, 'RetryAttempts': 0}, 'ContentType': 'application/json; charset=utf-8', 'InvokedProductionVariant': 'AIGC-ComfyUI-2024-01-23-07-50-37', 'Body': <botocore.response.StreamingBody object at 0x7f79a7206080>}\n",
      "{'prediction': ['s3://sagemaker-us-west-2-687912291502/comfyui_output/images/Comfyui_110.gif']}\n"
     ]
    }
   ],
   "source": [
    "payload={\n",
    "     \"client_id\":client_id,\n",
    "     \"prompt_id\":prompt_id,\n",
    "     \"inference_type\":\"text2vid\",\n",
    "     \"method\":\"get_images\"\n",
    "}\n",
    "result = predict(endpoint_name,payload)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3dcf15c5-cc4b-4c8d-b0bb-28450647c2ad",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "s3 generated gifs path is s3://sagemaker-us-west-2-687912291502/comfyui_output/images/Comfyui_110.gif\n"
     ]
    }
   ],
   "source": [
    "show_gifs(result)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7ac47b8",
   "metadata": {
    "tags": []
   },
   "source": [
    "### 5 清除资源"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e29d998d",
   "metadata": {},
   "outputs": [],
   "source": [
    "response = client.delete_endpoint(\n",
    "    EndpointName=endpoint_name\n",
    "    \n",
    ")\n",
    "\n",
    "response = client.delete_endpoint_config(\n",
    "    EndpointConfigName=endpoint_config_name\n",
    ")\n",
    "\n",
    "\n",
    "print(f'终端节点:{endpoint_name} 已经被清除，请在控制台上查看状态')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "220bad7e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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