{ "cells": [ { "cell_type": "markdown", "id": "c261e5f4-17a8-40da-beb9-599f1717e0fe", "metadata": {}, "source": [ "### 1. 安装HuggingFace 并下载模型到本地" ] }, { "cell_type": "code", "execution_count": 1, "id": "02785614-9268-41c8-85a5-d579490edbbf", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Looking in indexes: https://pypi.org/simple, https://pip.repos.neuron.amazonaws.com\n", "Requirement already satisfied: sagemaker in /home/ec2-user/anaconda3/envs/pytorch_p39/lib/python3.9/site-packages (2.159.0)\n", "Collecting sagemaker\n", " Downloading sagemaker-2.168.0.tar.gz (844 kB)\n", "\u001b[2K \u001b[90mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m \u001b[32m844.7/844.7 kB\u001b[0m \u001b[31m19.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0ma \u001b[36m0:00:01\u001b[0m\n", "\u001b[?25h Preparing metadata (setup.py) ... \u001b[?25ldone\n", "\u001b[?25hRequirement already satisfied: attrs<24,>=23.1.0 in /home/ec2-user/anaconda3/envs/pytorch_p39/lib/python3.9/site-packages (from sagemaker) (23.1.0)\n", "Requirement already satisfied: boto3<2.0,>=1.26.131 in /home/ec2-user/anaconda3/envs/pytorch_p39/lib/python3.9/site-packages (from sagemaker) (1.26.140)\n", "Requirement already satisfied: cloudpickle==2.2.1 in /home/ec2-user/anaconda3/envs/pytorch_p39/lib/python3.9/site-packages (from sagemaker) (2.2.1)\n", "Requirement already satisfied: google-pasta in /home/ec2-user/anaconda3/envs/pytorch_p39/lib/python3.9/site-packages (from sagemaker) (0.2.0)\n", "Requirement already satisfied: numpy<2.0,>=1.9.0 in /home/ec2-user/anaconda3/envs/pytorch_p39/lib/python3.9/site-packages (from sagemaker) (1.23.5)\n", "Requirement already satisfied: protobuf<4.0,>=3.1 in /home/ec2-user/anaconda3/envs/pytorch_p39/lib/python3.9/site-packages (from sagemaker) (3.20.2)\n", "Requirement already satisfied: protobuf3-to-dict<1.0,>=0.1.5 in /home/ec2-user/anaconda3/envs/pytorch_p39/lib/python3.9/site-packages (from sagemaker) (0.1.5)\n", "Requirement already satisfied: smdebug_rulesconfig==1.0.1 in /home/ec2-user/anaconda3/envs/pytorch_p39/lib/python3.9/site-packages (from sagemaker) (1.0.1)\n", "Requirement already satisfied: importlib-metadata<5.0,>=1.4.0 in /home/ec2-user/anaconda3/envs/pytorch_p39/lib/python3.9/site-packages (from sagemaker) (4.13.0)\n", "Requirement already satisfied: packaging>=20.0 in /home/ec2-user/anaconda3/envs/pytorch_p39/lib/python3.9/site-packages (from sagemaker) (21.3)\n", "Requirement already satisfied: pandas in /home/ec2-user/anaconda3/envs/pytorch_p39/lib/python3.9/site-packages (from sagemaker) (1.4.4)\n", "Requirement already satisfied: pathos in /home/ec2-user/anaconda3/envs/pytorch_p39/lib/python3.9/site-packages (from sagemaker) (0.3.0)\n", "Requirement already satisfied: schema in /home/ec2-user/anaconda3/envs/pytorch_p39/lib/python3.9/site-packages (from sagemaker) (0.7.5)\n", "Requirement already satisfied: PyYAML==6.0 in /home/ec2-user/anaconda3/envs/pytorch_p39/lib/python3.9/site-packages (from sagemaker) (6.0)\n", "Requirement already satisfied: jsonschema in /home/ec2-user/anaconda3/envs/pytorch_p39/lib/python3.9/site-packages (from sagemaker) (3.2.0)\n", "Requirement already satisfied: platformdirs in /home/ec2-user/anaconda3/envs/pytorch_p39/lib/python3.9/site-packages (from sagemaker) (2.6.2)\n", "Requirement already satisfied: tblib==1.7.0 in /home/ec2-user/anaconda3/envs/pytorch_p39/lib/python3.9/site-packages (from sagemaker) (1.7.0)\n", "Requirement already satisfied: s3transfer<0.7.0,>=0.6.0 in /home/ec2-user/anaconda3/envs/pytorch_p39/lib/python3.9/site-packages (from boto3<2.0,>=1.26.131->sagemaker) (0.6.0)\n", "Requirement already satisfied: jmespath<2.0.0,>=0.7.1 in /home/ec2-user/anaconda3/envs/pytorch_p39/lib/python3.9/site-packages (from boto3<2.0,>=1.26.131->sagemaker) (1.0.1)\n", "Requirement already satisfied: botocore<1.30.0,>=1.29.140 in /home/ec2-user/anaconda3/envs/pytorch_p39/lib/python3.9/site-packages (from boto3<2.0,>=1.26.131->sagemaker) (1.29.140)\n", "Requirement already satisfied: zipp>=0.5 in /home/ec2-user/anaconda3/envs/pytorch_p39/lib/python3.9/site-packages (from importlib-metadata<5.0,>=1.4.0->sagemaker) (3.11.0)\n", "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /home/ec2-user/anaconda3/envs/pytorch_p39/lib/python3.9/site-packages (from packaging>=20.0->sagemaker) (3.0.9)\n", "Requirement already satisfied: six in /home/ec2-user/anaconda3/envs/pytorch_p39/lib/python3.9/site-packages (from protobuf3-to-dict<1.0,>=0.1.5->sagemaker) (1.16.0)\n", "Requirement already satisfied: pyrsistent>=0.14.0 in /home/ec2-user/anaconda3/envs/pytorch_p39/lib/python3.9/site-packages (from jsonschema->sagemaker) (0.19.3)\n", "Requirement already satisfied: setuptools in /home/ec2-user/anaconda3/envs/pytorch_p39/lib/python3.9/site-packages (from jsonschema->sagemaker) (65.6.3)\n", "Requirement already satisfied: python-dateutil>=2.8.1 in /home/ec2-user/anaconda3/envs/pytorch_p39/lib/python3.9/site-packages (from pandas->sagemaker) (2.8.2)\n", "Requirement already satisfied: pytz>=2020.1 in /home/ec2-user/anaconda3/envs/pytorch_p39/lib/python3.9/site-packages (from pandas->sagemaker) (2022.7)\n", "Requirement already satisfied: dill>=0.3.6 in /home/ec2-user/anaconda3/envs/pytorch_p39/lib/python3.9/site-packages (from pathos->sagemaker) (0.3.6)\n", "Requirement already satisfied: pox>=0.3.2 in /home/ec2-user/anaconda3/envs/pytorch_p39/lib/python3.9/site-packages (from pathos->sagemaker) (0.3.2)\n", "Requirement already satisfied: multiprocess>=0.70.14 in /home/ec2-user/anaconda3/envs/pytorch_p39/lib/python3.9/site-packages (from pathos->sagemaker) (0.70.14)\n", "Requirement already satisfied: ppft>=1.7.6.6 in /home/ec2-user/anaconda3/envs/pytorch_p39/lib/python3.9/site-packages (from pathos->sagemaker) (1.7.6.6)\n", "Requirement already satisfied: contextlib2>=0.5.5 in /home/ec2-user/anaconda3/envs/pytorch_p39/lib/python3.9/site-packages (from schema->sagemaker) (21.6.0)\n", "Requirement already satisfied: urllib3<1.27,>=1.25.4 in /home/ec2-user/anaconda3/envs/pytorch_p39/lib/python3.9/site-packages (from botocore<1.30.0,>=1.29.140->boto3<2.0,>=1.26.131->sagemaker) (1.26.8)\n", "Building wheels for collected packages: sagemaker\n", " Building wheel for sagemaker (setup.py) ... \u001b[?25ldone\n", "\u001b[?25h Created wheel for sagemaker: filename=sagemaker-2.168.0-py2.py3-none-any.whl size=1151019 sha256=b548c0d9f0519ac230e8495a9872543e532fa6c1a6d4363a099e6566a04ab61d\n", " Stored in directory: /home/ec2-user/.cache/pip/wheels/81/33/1d/c4fb556086a08d8e4ede95111dba93971c1caa14d095afdaa9\n", "Successfully built sagemaker\n", "Installing collected packages: sagemaker\n", " Attempting uninstall: sagemaker\n", " Found existing installation: sagemaker 2.159.0\n", " Uninstalling sagemaker-2.159.0:\n", " Successfully uninstalled sagemaker-2.159.0\n", "Successfully installed sagemaker-2.168.0\n" ] } ], "source": [ "!pip install huggingface-hub -Uqq\n", "!pip install -U sagemaker" ] }, { "cell_type": "code", "execution_count": 3, "id": "9e6bd7ee-16a3-4f5a-8857-8bbba83eb9e7", "metadata": { "tags": [] }, "outputs": [], "source": [ "from huggingface_hub import snapshot_download\n", "from pathlib import Path\n", "\n", "local_model_path = Path(\"./LLM_chatglm_model\")\n", "local_model_path.mkdir(exist_ok=True)\n", "model_name = \"THUDM/chatglm-6b\"\n", "commit_hash = \"f83182484538e663a03d3f73647f10f89878f438\"" ] }, { "cell_type": "code", "execution_count": 4, "id": "94e8abc5-a58e-40e2-b1e6-fbf48307c716", "metadata": { "tags": [] }, "outputs": [ { "data": { "application/json": { "ascii": false, "bar_format": null, "colour": null, "elapsed": 0.011066675186157227, "initial": 0, "n": 0, "ncols": null, "nrows": null, "postfix": null, "prefix": "Fetching 21 files", "rate": null, "total": 21, "unit": "it", "unit_divisor": 1000, "unit_scale": false }, "application/vnd.jupyter.widget-view+json": { "model_id": "1569328789014042b4769a6f55a0ae07", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Fetching 21 files: 0%| | 0/21 [00:00<?, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "'LLM_chatglm_model/models--THUDM--chatglm-6b/snapshots/f83182484538e663a03d3f73647f10f89878f438'" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "snapshot_download(repo_id=model_name, revision=commit_hash, cache_dir=local_model_path)" ] }, { "cell_type": "code", "execution_count": null, "id": "fa3e3d80-a121-402d-9ce3-86038ad9762f", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "id": "8d666c79-b039-4258-ac3b-46b19e63c3b8", "metadata": {}, "source": [ "### 2. 把模型拷è´åˆ°S3为åŽç»éƒ¨ç½²åšå‡†å¤‡" ] }, { "cell_type": "code", "execution_count": 4, "id": "bc40aa14-fbfa-48dc-9c47-46a88e561bac", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Note: you may need to restart the kernel to use updated packages.\n" ] } ], "source": [ "%pip install sagemaker pip --upgrade --quiet" ] }, { "cell_type": "code", "execution_count": 5, "id": "62ddd8de-238b-4c62-8f32-6f433048250d", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Looking in indexes: https://pypi.org/simple, https://pip.repos.neuron.amazonaws.com\n", "Processing ./botocore-1.29.157-py3-none-any.whl\n", "Processing ./boto3-1.26.157-py3-none-any.whl\n", "Collecting jmespath<2.0.0,>=0.7.1 (from botocore==1.29.157)\n", " Using cached jmespath-1.0.1-py3-none-any.whl (20 kB)\n", "Collecting python-dateutil<3.0.0,>=2.1 (from botocore==1.29.157)\n", " Using cached python_dateutil-2.8.2-py2.py3-none-any.whl (247 kB)\n", "Collecting urllib3<1.27,>=1.25.4 (from botocore==1.29.157)\n", " Downloading urllib3-1.26.16-py2.py3-none-any.whl (143 kB)\n", "\u001b[2K \u001b[90mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m \u001b[32m143.1/143.1 kB\u001b[0m \u001b[31m7.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25hCollecting s3transfer<0.7.0,>=0.6.0 (from boto3==1.26.157)\n", " Downloading s3transfer-0.6.1-py3-none-any.whl (79 kB)\n", "\u001b[2K \u001b[90mâ”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”â”\u001b[0m \u001b[32m79.8/79.8 kB\u001b[0m \u001b[31m18.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25hCollecting six>=1.5 (from python-dateutil<3.0.0,>=2.1->botocore==1.29.157)\n", " Using cached six-1.16.0-py2.py3-none-any.whl (11 kB)\n", "Installing collected packages: urllib3, six, jmespath, python-dateutil, botocore, s3transfer, boto3\n", " Attempting uninstall: urllib3\n", " Found existing installation: urllib3 1.26.8\n", " Uninstalling urllib3-1.26.8:\n", " Successfully uninstalled urllib3-1.26.8\n", " Attempting uninstall: six\n", " Found existing installation: six 1.16.0\n", " Uninstalling six-1.16.0:\n", " Successfully uninstalled six-1.16.0\n", " Attempting uninstall: jmespath\n", " Found existing installation: jmespath 1.0.1\n", " Uninstalling jmespath-1.0.1:\n", " Successfully uninstalled jmespath-1.0.1\n", " Attempting uninstall: python-dateutil\n", " Found existing installation: python-dateutil 2.8.2\n", " Uninstalling python-dateutil-2.8.2:\n", " Successfully uninstalled python-dateutil-2.8.2\n", " Attempting uninstall: botocore\n", " Found existing installation: botocore 1.29.140\n", " Uninstalling botocore-1.29.140:\n", " Successfully uninstalled botocore-1.29.140\n", " Attempting uninstall: s3transfer\n", " Found existing installation: s3transfer 0.6.0\n", " Uninstalling s3transfer-0.6.0:\n", " Successfully uninstalled s3transfer-0.6.0\n", " Attempting uninstall: boto3\n", " Found existing installation: boto3 1.26.140\n", " Uninstalling boto3-1.26.140:\n", " Successfully uninstalled boto3-1.26.140\n", "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", "awscli 1.27.114 requires botocore==1.29.114, but you have botocore 1.29.157 which is incompatible.\n", "awscli 1.27.114 requires PyYAML<5.5,>=3.10, but you have pyyaml 6.0 which is incompatible.\n", "distributed 2022.11.0 requires tornado<6.2,>=6.0.3, but you have tornado 6.2 which is incompatible.\n", "docker-compose 1.29.2 requires PyYAML<6,>=3.10, but you have pyyaml 6.0 which is incompatible.\n", "jupyter-events 0.6.0 requires jsonschema[format-nongpl]>=4.3.0, but you have jsonschema 3.2.0 which is incompatible.\n", "jupyterlab 3.3.4 requires jupyter-server~=1.4, but you have jupyter-server 2.0.6 which is incompatible.\n", "jupyterlab-server 2.18.0 requires jsonschema>=4.17.3, but you have jsonschema 3.2.0 which is incompatible.\u001b[0m\u001b[31m\n", "\u001b[0mSuccessfully installed boto3-1.26.157 botocore-1.29.157 jmespath-1.0.1 python-dateutil-2.8.2 s3transfer-0.6.1 six-1.16.0 urllib3-1.26.16\n", "Note: you may need to restart the kernel to use updated packages.\n" ] } ], "source": [ "# Note the following may error depending on which awscli is installed in your jupyter kernel, \n", "# but that is ok \n", "\n", "%pip install botocore-*-py3-none-any.whl boto3-*-py3-none-any.whl --force" ] }, { "cell_type": "code", "execution_count": 5, "id": "e9431deb-6359-442d-847b-1563f8dd3854", "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": 6, "id": "40dd8f16-ae7c-48bf-8e52-1a15425fa74d", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "s3_code_prefix: LLM-RAG/workshop/LLM_chatglm_deploy_code\n", "model_snapshot_path: LLM_chatglm_model/models--THUDM--chatglm-6b/snapshots/f83182484538e663a03d3f73647f10f89878f438\n" ] } ], "source": [ "s3_model_prefix = \"LLM-RAG/workshop/LLM_chatglm_model\" # folder where model checkpoint will go\n", "model_snapshot_path = list(local_model_path.glob(\"**/snapshots/*\"))[0]\n", "s3_code_prefix = \"LLM-RAG/workshop/LLM_chatglm_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": 7, "id": "067292c9-c066-4649-a61f-b460a24da584", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "upload: LLM_chatglm_model/models--THUDM--chatglm-6b/snapshots/f83182484538e663a03d3f73647f10f89878f438/config.json to s3://sagemaker-us-west-2-946277762357/LLM-RAG/workshop/LLM_chatglm_model/config.json\n", "upload: LLM_chatglm_model/models--THUDM--chatglm-6b/snapshots/f83182484538e663a03d3f73647f10f89878f438/.gitattributes to s3://sagemaker-us-west-2-946277762357/LLM-RAG/workshop/LLM_chatglm_model/.gitattributes\n", "upload: LLM_chatglm_model/models--THUDM--chatglm-6b/snapshots/f83182484538e663a03d3f73647f10f89878f438/LICENSE to s3://sagemaker-us-west-2-946277762357/LLM-RAG/workshop/LLM_chatglm_model/LICENSE\n", "upload: LLM_chatglm_model/models--THUDM--chatglm-6b/snapshots/f83182484538e663a03d3f73647f10f89878f438/README.md to s3://sagemaker-us-west-2-946277762357/LLM-RAG/workshop/LLM_chatglm_model/README.md\n", "upload: LLM_chatglm_model/models--THUDM--chatglm-6b/snapshots/f83182484538e663a03d3f73647f10f89878f438/configuration_chatglm.py to s3://sagemaker-us-west-2-946277762357/LLM-RAG/workshop/LLM_chatglm_model/configuration_chatglm.py\n", "upload: LLM_chatglm_model/models--THUDM--chatglm-6b/snapshots/f83182484538e663a03d3f73647f10f89878f438/MODEL_LICENSE to s3://sagemaker-us-west-2-946277762357/LLM-RAG/workshop/LLM_chatglm_model/MODEL_LICENSE\n", "upload: LLM_chatglm_model/models--THUDM--chatglm-6b/snapshots/f83182484538e663a03d3f73647f10f89878f438/modeling_chatglm.py to s3://sagemaker-us-west-2-946277762357/LLM-RAG/workshop/LLM_chatglm_model/modeling_chatglm.py\n", "upload: LLM_chatglm_model/models--THUDM--chatglm-6b/snapshots/f83182484538e663a03d3f73647f10f89878f438/ice_text.model to s3://sagemaker-us-west-2-946277762357/LLM-RAG/workshop/LLM_chatglm_model/ice_text.model\n", "upload: LLM_chatglm_model/models--THUDM--chatglm-6b/snapshots/f83182484538e663a03d3f73647f10f89878f438/pytorch_model-00002-of-00008.bin to s3://sagemaker-us-west-2-946277762357/LLM-RAG/workshop/LLM_chatglm_model/pytorch_model-00002-of-00008.bin\n", "upload: LLM_chatglm_model/models--THUDM--chatglm-6b/snapshots/f83182484538e663a03d3f73647f10f89878f438/pytorch_model-00004-of-00008.bin to s3://sagemaker-us-west-2-946277762357/LLM-RAG/workshop/LLM_chatglm_model/pytorch_model-00004-of-00008.bin\n", "upload: LLM_chatglm_model/models--THUDM--chatglm-6b/snapshots/f83182484538e663a03d3f73647f10f89878f438/pytorch_model-00001-of-00008.bin to s3://sagemaker-us-west-2-946277762357/LLM-RAG/workshop/LLM_chatglm_model/pytorch_model-00001-of-00008.bin\n", "upload: LLM_chatglm_model/models--THUDM--chatglm-6b/snapshots/f83182484538e663a03d3f73647f10f89878f438/pytorch_model.bin.index.json to s3://sagemaker-us-west-2-946277762357/LLM-RAG/workshop/LLM_chatglm_model/pytorch_model.bin.index.json\n", "upload: LLM_chatglm_model/models--THUDM--chatglm-6b/snapshots/f83182484538e663a03d3f73647f10f89878f438/pytorch_model-00005-of-00008.bin to s3://sagemaker-us-west-2-946277762357/LLM-RAG/workshop/LLM_chatglm_model/pytorch_model-00005-of-00008.bin\n", "upload: LLM_chatglm_model/models--THUDM--chatglm-6b/snapshots/f83182484538e663a03d3f73647f10f89878f438/quantization.py to s3://sagemaker-us-west-2-946277762357/LLM-RAG/workshop/LLM_chatglm_model/quantization.py\n", "upload: LLM_chatglm_model/models--THUDM--chatglm-6b/snapshots/f83182484538e663a03d3f73647f10f89878f438/test_modeling_chatglm.py to s3://sagemaker-us-west-2-946277762357/LLM-RAG/workshop/LLM_chatglm_model/test_modeling_chatglm.py\n", "upload: LLM_chatglm_model/models--THUDM--chatglm-6b/snapshots/f83182484538e663a03d3f73647f10f89878f438/tokenization_chatglm.py to s3://sagemaker-us-west-2-946277762357/LLM-RAG/workshop/LLM_chatglm_model/tokenization_chatglm.py\n", "upload: LLM_chatglm_model/models--THUDM--chatglm-6b/snapshots/f83182484538e663a03d3f73647f10f89878f438/tokenizer_config.json to s3://sagemaker-us-west-2-946277762357/LLM-RAG/workshop/LLM_chatglm_model/tokenizer_config.json\n", "upload: LLM_chatglm_model/models--THUDM--chatglm-6b/snapshots/f83182484538e663a03d3f73647f10f89878f438/pytorch_model-00003-of-00008.bin to s3://sagemaker-us-west-2-946277762357/LLM-RAG/workshop/LLM_chatglm_model/pytorch_model-00003-of-00008.bin\n", "upload: LLM_chatglm_model/models--THUDM--chatglm-6b/snapshots/f83182484538e663a03d3f73647f10f89878f438/pytorch_model-00007-of-00008.bin to s3://sagemaker-us-west-2-946277762357/LLM-RAG/workshop/LLM_chatglm_model/pytorch_model-00007-of-00008.bin\n", "upload: LLM_chatglm_model/models--THUDM--chatglm-6b/snapshots/f83182484538e663a03d3f73647f10f89878f438/pytorch_model-00008-of-00008.bin to s3://sagemaker-us-west-2-946277762357/LLM-RAG/workshop/LLM_chatglm_model/pytorch_model-00008-of-00008.bin\n", "upload: LLM_chatglm_model/models--THUDM--chatglm-6b/snapshots/f83182484538e663a03d3f73647f10f89878f438/pytorch_model-00006-of-00008.bin to s3://sagemaker-us-west-2-946277762357/LLM-RAG/workshop/LLM_chatglm_model/pytorch_model-00006-of-00008.bin\n" ] } ], "source": [ "!aws s3 cp --recursive {model_snapshot_path} s3://{bucket}/{s3_model_prefix}" ] }, { "cell_type": "markdown", "id": "696b70c3-90f1-4175-95bf-568bafbcd383", "metadata": {}, "source": [ "### 3. 模型部署准备(entrypoint脚本,容器镜åƒï¼ŒæœåŠ¡é…置)" ] }, { "cell_type": "code", "execution_count": 7, "id": "6f7c4277-4480-42c6-aee6-1fbcca94eb82", "metadata": { "tags": [] }, "outputs": [], "source": [ "# inference_image_uri = (\n", "# f\"763104351884.dkr.ecr.{region}.amazonaws.com/djl-inference:0.21.0-deepspeed0.8.3-cu117\"\n", "# )\n", "\n", "# print(f\"Image going to be used is ---- > {inference_image_uri}\")" ] }, { "cell_type": "code", "execution_count": 8, "id": "68d60730-528b-4707-9189-6bf6f5cad754", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Image going to be used is ---- > 763104351884.dkr.ecr.us-west-2.amazonaws.com/djl-inference:0.22.1-deepspeed0.8.3-cu118\n" ] } ], "source": [ "inference_image_uri = image_uris.retrieve(\n", " framework=\"djl-deepspeed\",\n", " region=sess.boto_session.region_name,\n", " version=\"0.22.1\"\n", ")\n", "print(f\"Image going to be used is ---- > {inference_image_uri}\")" ] }, { "cell_type": "code", "execution_count": 9, "id": "8d771bdb-11d2-45d2-9bef-face29221838", "metadata": { "tags": [] }, "outputs": [], "source": [ "!mkdir -p LLM_chatglm_deploy_code" ] }, { "cell_type": "code", "execution_count": 102, "id": "e5348ecb-43df-4094-97d8-a6723004862a", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Overwriting LLM_chatglm_deploy_code/model.py\n" ] } ], "source": [ "%%writefile LLM_chatglm_deploy_code/model.py\n", "from djl_python import Input, Output\n", "import torch\n", "import logging\n", "import math\n", "import os\n", "\n", "from transformers import pipeline, AutoModel, AutoTokenizer\n", "model = None\n", "tokenizer = None\n", "STOP_flag = \"[DONE]\"\n", "\n", "DEVICE = \"cuda\"\n", "DEVICE_ID = \"0\"\n", "CUDA_DEVICE = f\"{DEVICE}:{DEVICE_ID}\" if DEVICE_ID else DEVICE\n", "def torch_gc():\n", " if torch.cuda.is_available():\n", " with torch.cuda.device(CUDA_DEVICE):\n", " torch.cuda.empty_cache()\n", " torch.cuda.ipc_collect()\n", " \n", "def load_model(properties):\n", " global tokenizer,model\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", " tokenizer = AutoTokenizer.from_pretrained(model_location, trust_remote_code=True)\n", " \n", " model = AutoModel.from_pretrained(model_location, trust_remote_code=True).half().cuda()\n", " \n", " model.requires_grad_(False)\n", " model.eval()\n", " \n", " return model, tokenizer\n", "\n", "\n", "\n", "def stream_items(prompt, history, max_length, top_p, temperature):\n", " global model, tokenizer\n", " size = 0\n", " response = \"\"\n", " for response, history in model.stream_chat(tokenizer, prompt, history=history, max_length=max_length, top_p=top_p,\n", " temperature=temperature):\n", " this_response = response[size:]\n", " history = [list(h) for h in history]\n", " size = len(response)\n", " stream_buffer = { \"outputs\":this_response,\"finished\": False}\n", " yield stream_buffer\n", " ## stop\n", " yield {\"query\": prompt, \"outputs\": STOP_flag, \"response\": response, \"history\": history, \"finished\": True}\n", " \n", "\n", "\n", "def handle(inputs: Input):\n", " global model, tokenizer\n", " if not model:\n", " model, tokenizer = load_model(inputs.get_properties())\n", "\n", " if inputs.is_empty():\n", " return None\n", " data = inputs.get_as_json()\n", " \n", " input_sentences = data[\"inputs\"]\n", " params = data[\"parameters\"]\n", " history = data[\"history\"]\n", " print(f'input prompt:{input_sentences}') \n", " outputs = Output()\n", " outputs.add_property(\"content-type\", \"application/jsonlines\")\n", " outputs.add_stream_content(stream_items(input_sentences,history=history,**params))\n", " return outputs\n" ] }, { "cell_type": "markdown", "id": "a06d1e60-3914-4059-a08f-05ac26761165", "metadata": {}, "source": [ "#### Note: option.s3url 需è¦æŒ‰ç…§è‡ªå·±çš„è´¦å·è¿›è¡Œä¿®æ”¹" ] }, { "cell_type": "code", "execution_count": 103, "id": "8996fe44-8e70-468b-abc1-38187cb33f4f", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Overwriting LLM_chatglm_deploy_code/serving.properties\n" ] } ], "source": [ "%%writefile LLM_chatglm_deploy_code/serving.properties\n", "engine=Python\n", "option.tensor_parallel_degree=1\n", "option.enable_streaming=True\n", "option.predict_timeout=240\n", "option.s3url = s3://sagemaker-us-west-2-946277762357/LLM-RAG/workshop/LLM_chatglm_model/" ] }, { "cell_type": "markdown", "id": "feef22a2-27b9-4018-a46b-6a99b532512f", "metadata": {}, "source": [ "#### 注æ„: 必须把transformerså‡çº§åˆ°4.27.1以上,å¦åˆ™ä¼šå‡ºçŽ° [Issue344](https://github.com/THUDM/ChatGLM-6B/issues/344)" ] }, { "cell_type": "code", "execution_count": 104, "id": "7b7e76c6-6dbc-47fc-9f47-4765c526ab76", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Overwriting LLM_chatglm_deploy_code/requirements.txt\n" ] } ], "source": [ "%%writefile LLM_chatglm_deploy_code/requirements.txt\n", "transformers==4.28.1" ] }, { "cell_type": "code", "execution_count": 105, "id": "0ae6734a-aacd-410d-818d-0a962697c3c4", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "LLM_chatglm_deploy_code/\n", "LLM_chatglm_deploy_code/requirements.txt\n", "LLM_chatglm_deploy_code/serving.properties\n", "LLM_chatglm_deploy_code/model.py\n" ] } ], "source": [ "!rm model.tar.gz\n", "!cd LLM_chatglm_deploy_code && rm -rf \".ipynb_checkpoints\"\n", "!tar czvf model.tar.gz LLM_chatglm_deploy_code" ] }, { "cell_type": "code", "execution_count": 106, "id": "0f77dc76-6d8c-4665-ba88-f03e887c136c", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "S3 Code or Model tar ball uploaded to --- > s3://sagemaker-us-west-2-946277762357/LLM-RAG/workshop/LLM_chatglm_deploy_code/model.tar.gz\n" ] } ], "source": [ "s3_code_artifact = sess.upload_data(\"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": "a5853daa-b8a3-4485-8c0a-64bf83e93a18", "metadata": {}, "source": [ "### 4. 创建模型 & 创建endpoint" ] }, { "cell_type": "code", "execution_count": 107, "id": "ef974ca1-9638-45a8-9145-ea9d03b2b072", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "chatglm-stream-2023-06-25-07-34-15-297\n", "Image going to be used is ---- > 763104351884.dkr.ecr.us-west-2.amazonaws.com/djl-inference:0.22.1-deepspeed0.8.3-cu118\n", "Created Model: arn:aws:sagemaker:us-west-2:946277762357:model/chatglm-stream-2023-06-25-07-34-15-297\n" ] } ], "source": [ "from sagemaker.utils import name_from_base\n", "import boto3\n", "\n", "model_name = name_from_base(f\"chatglm-stream\") # Append a timestamp to the provided string\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": 108, "id": "233bb3a4-d737-41ad-8fcc-7082c6278e8c", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "{'EndpointConfigArn': 'arn:aws:sagemaker:us-west-2:946277762357:endpoint-config/chatglm-stream-2023-06-25-07-34-15-297-config',\n", " 'ResponseMetadata': {'RequestId': 'b523f4dd-34c1-4f4f-8c0d-b6ca7cd96372',\n", " 'HTTPStatusCode': 200,\n", " 'HTTPHeaders': {'x-amzn-requestid': 'b523f4dd-34c1-4f4f-8c0d-b6ca7cd96372',\n", " 'content-type': 'application/x-amz-json-1.1',\n", " 'content-length': '126',\n", " 'date': 'Sun, 25 Jun 2023 07:34:15 GMT'},\n", " 'RetryAttempts': 0}}" ] }, "execution_count": 108, "metadata": {}, "output_type": "execute_result" } ], "source": [ "endpoint_config_name = f\"{model_name}-config\"\n", "endpoint_name = f\"{model_name}-endpoint\"\n", "\n", "#Note: ml.g4dn.2xlarge 也å¯ä»¥é€‰æ‹©\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.g5.2xlarge\",\n", " \"InitialInstanceCount\": 1,\n", " # \"VolumeSizeInGB\" : 400,\n", " # \"ModelDataDownloadTimeoutInSeconds\": 2400,\n", " \"ContainerStartupHealthCheckTimeoutInSeconds\": 15*60,\n", " },\n", " ],\n", ")\n", "endpoint_config_response" ] }, { "cell_type": "code", "execution_count": 109, "id": "734a39b0-473e-4421-94c8-74d2b4105038", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Created Endpoint: arn:aws:sagemaker:us-west-2:946277762357:endpoint/chatglm-stream-2023-06-25-07-34-15-297-endpoint\n" ] } ], "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": "markdown", "id": "1262e826-a810-401d-a5a9-f62febb24e5f", "metadata": {}, "source": [ "#### æŒç»æ£€æµ‹æ¨¡åž‹éƒ¨ç½²è¿›åº¦" ] }, { "cell_type": "code", "execution_count": 110, "id": "08969928-6b9e-4d9c-a033-a31f5f77bdfb", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Status: Creating\n", "Status: Creating\n", "Status: Creating\n", "Status: Creating\n", "Status: Creating\n", "Status: Creating\n", "Status: Creating\n", "Status: InService\n", "Arn: arn:aws:sagemaker:us-west-2:946277762357:endpoint/chatglm-stream-2023-06-25-07-34-15-297-endpoint\n", "Status: InService\n" ] } ], "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": "d985b427-3959-46f7-9a50-5a2b45e2d513", "metadata": {}, "source": [ "### 5. 模型测试" ] }, { "cell_type": "code", "execution_count": 111, "id": "e56bfdaa-3469-4784-aa8a-e32177cde3f2", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 6.65 ms, sys: 0 ns, total: 6.65 ms\n", "Wall time: 13 ms\n" ] } ], "source": [ "%%time\n", "import json\n", "import boto3\n", "\n", "smr_client = boto3.client(\"sagemaker-runtime\")\n", "\n", "parameters = {\n", " \"max_length\": 2048,\n", " \"temperature\": 0.01,\n", " \"top_p\":1\n", "}" ] }, { "cell_type": "code", "execution_count": 112, "id": "3a2b6b7b-2e54-4f83-9258-cfbb30cfc829", "metadata": { "tags": [] }, "outputs": [], "source": [ "import io\n", "\n", "\n", "class StreamScanner:\n", " \"\"\"\n", " A helper class for parsing the InvokeEndpointWithResponseStream event stream. \n", " \n", " The output of the model will be in the following format:\n", " ```\n", " b'{\"outputs\": [\" a\"]}\\n'\n", " b'{\"outputs\": [\" challenging\"]}\\n'\n", " b'{\"outputs\": [\" problem\"]}\\n'\n", " ...\n", " ```\n", " \n", " While usually each PayloadPart event from the event stream will contain a byte array \n", " with a full json, this is not guaranteed and some of the json objects may be split across\n", " PayloadPart events. For example:\n", " ```\n", " {'PayloadPart': {'Bytes': b'{\"outputs\": '}}\n", " {'PayloadPart': {'Bytes': b'[\" problem\"]}\\n'}}\n", " ```\n", " \n", " This class accounts for this by concatenating bytes written via the 'write' function\n", " and then exposing a method which will return lines (ending with a '\\n' character) within\n", " the buffer via the 'readlines' function. It maintains the position of the last read \n", " position to ensure that previous bytes are not exposed again. \n", " \"\"\"\n", " \n", " def __init__(self):\n", " self.buff = io.BytesIO()\n", " self.read_pos = 0\n", " \n", " def write(self, content):\n", " self.buff.seek(0, io.SEEK_END)\n", " self.buff.write(content)\n", " \n", " def readlines(self):\n", " self.buff.seek(self.read_pos)\n", " for line in self.buff.readlines():\n", " if line[-1] != b'\\n':\n", " self.read_pos += len(line)\n", " yield line[:-1]\n", " \n", " def reset(self):\n", " self.read_pos = 0" ] }, { "cell_type": "code", "execution_count": null, "id": "e2c13afb-2c4a-41da-a58e-4d52ea13a6d9", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ä¸å›½çš„首都是北京,ä½äºŽä¸å›½åŒ—方平原地区的ä¸å¿ƒåœ°å¸¦ï¼Œæ˜¯ä¸å›½çš„政治ã€æ–‡åŒ–ã€ç»æµŽã€ç§‘技和交通ä¸å¿ƒã€‚\n", "\n", "北京是ä¸å›½çš„历å²æ–‡åŒ–å城,有ç€æ‚ 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\"parameters\": parameters,\n", " \"history\" : []\n", " }\n", " ),\n", " ContentType=\"application/json\",\n", " )\n", "\n", "event_stream = response_model['Body']\n", "scanner = StreamScanner()\n", "for event in event_stream:\n", " scanner.write(event['PayloadPart']['Bytes'])\n", " for line in scanner.readlines():\n", " try:\n", " resp = json.loads(line)\n", " print(resp.get(\"outputs\")['outputs'], end='')\n", " except Exception as e:\n", " # print(line)\n", " continue" ] }, { "cell_type": "code", "execution_count": 87, "id": "bd86caf8-01e9-41eb-9199-478ba471a02b", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "'{\"outputs\": {\"outputs\": \"AWS\", \"response\": \"AWS\", \"finished\": false}}\\n{\"outputs\": {\"outputs\": \" Clean\", \"response\": \"AWS Clean\", \"finished\": false}}\\n{\"outputs\": {\"outputs\": \" Room\", \"response\": \"AWS Clean Room\", \"finished\": false}}\\n{\"outputs\": {\"outputs\": \"s\", \"response\": \"AWS Clean Rooms\", 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Clean Rooms\\\\u662f\\\\u4e00\\\\u79cd\\\\u6570\\\\u636e\\\\u9690\\\\u79c1\\\\u4fdd\\\\u62a4\\\\u6280\\\\u672f\\\\uff0c\\\\u4f7f\\\\u7528\\\\u8be5\\\\u6280\\\\u672f\\\\uff0c\\\\u53ef\\\\u4ee5\\\\u5c06\\\\u654f\\\\u611f\\\\u6570\\\\u636e\\\\u8f6c\\\\u6362\\\\u4e3a\\\\u4e0d\\\\u53ef\\\\u89c1\\\\u7684\\\\u6570\\\\u636e\\\\u683c\\\\u5f0f\\\\uff0c\\\\u4ece\\\\u800c\\\\u4fdd\\\\u62a4\\\\u6570\\\\u636e\\\\u8d21\\\\u732e\\\\u8005\\\\u548c\\\\u63a5\\\\u6536\\\\u8005\\\\u7684\\\\u4e2a\\\\u4eba\\\\u9690\\\\u79c1\\\\u548c\\\\u5546\\\\u4e1a\\\\u673a\\\\u5bc6\\\\u3002\\\\u56e0\\\\u6b64\\\\uff0c\\\\u53ea\\\\u6709\\\\u63a5\\\\u6536\\\\u8005\\\\u4f1a\\\\u6536\\\\u5230\\\\u67e5\\\\u8be2\\\\u7ed3\\\\u679c\\\\uff0c\\\\u800c\\\\u53d1\\\\u8d77\\\\u8005\\\\u4e0d\\\\u4f1a\\\\u56e0\\\\u6b64\\\\u88ab\\\\u6536\\\\u8d39\\\\u3002\\\\n\\\\n\\\\u9700\\\\u8981\\\\u6ce8\\\\u610f\\\\u7684\\\\u662f\\\\uff0c\\\\u5982\\\\u679c\\\\u4f7f\\\\u7528\", \"finished\": false}}\\n{\"outputs\": {\"outputs\": \"AWS\", \"response\": \"AWS Clean Rooms\\\\u662f\\\\u4e00\\\\u79cd\\\\u6570\\\\u636e\\\\u9690\\\\u79c1\\\\u4fdd\\\\u62a4\\\\u6280\\\\u672f\\\\uff0c\\\\u4f7f\\\\u7528\\\\u8be5\\\\u6280\\\\u672f\\\\uff0c\\\\u53ef\\\\u4ee5\\\\u5c06\\\\u654f\\\\u611f\\\\u6570\\\\u636e\\\\u8f6c\\\\u6362\\\\u4e3a\\\\u4e0d\\\\u53ef\\\\u89c1\\\\u7684\\\\u6570\\\\u636e\\\\u683c\\\\u5f0f\\\\uff0c\\\\u4ece\\\\u800c\\\\u4fdd\\\\u62a4\\\\u6570\\\\u636e\\\\u8d21\\\\u732e\\\\u8005\\\\u548c\\\\u63a5\\\\u6536\\\\u8005\\\\u7684\\\\u4e2a\\\\u4eba\\\\u9690\\\\u79c1\\\\u548c\\\\u5546\\\\u4e1a\\\\u673a\\\\u5bc6\\\\u3002\\\\u56e0\\\\u6b64\\\\uff0c\\\\u53ea\\\\u6709\\\\u63a5\\\\u6536\\\\u8005\\\\u4f1a\\\\u6536\\\\u5230\\\\u67e5\\\\u8be2\\\\u7ed3\\\\u679c\\\\uff0c\\\\u800c\\\\u53d1\\\\u8d77\\\\u8005\\\\u4e0d\\\\u4f1a\\\\u56e0\\\\u6b64\\\\u88ab\\\\u6536\\\\u8d39\\\\u3002\\\\n\\\\n\\\\u9700\\\\u8981\\\\u6ce8\\\\u610f\\\\u7684\\\\u662f\\\\uff0c\\\\u5982\\\\u679c\\\\u4f7f\\\\u7528AWS\", \"finished\": false}}\\n{\"outputs\": {\"outputs\": \" Clean\", \"response\": \"AWS Clean Rooms\\\\u662f\\\\u4e00\\\\u79cd\\\\u6570\\\\u636e\\\\u9690\\\\u79c1\\\\u4fdd\\\\u62a4\\\\u6280\\\\u672f\\\\uff0c\\\\u4f7f\\\\u7528\\\\u8be5\\\\u6280\\\\u672f\\\\uff0c\\\\u53ef\\\\u4ee5\\\\u5c06\\\\u654f\\\\u611f\\\\u6570\\\\u636e\\\\u8f6c\\\\u6362\\\\u4e3a\\\\u4e0d\\\\u53ef\\\\u89c1\\\\u7684\\\\u6570\\\\u636e\\\\u683c\\\\u5f0f\\\\uff0c\\\\u4ece\\\\u800c\\\\u4fdd\\\\u62a4\\\\u6570\\\\u636e\\\\u8d21\\\\u732e\\\\u8005\\\\u548c\\\\u63a5\\\\u6536\\\\u8005\\\\u7684\\\\u4e2a\\\\u4eba\\\\u9690\\\\u79c1\\\\u548c\\\\u5546\\\\u4e1a\\\\u673a\\\\u5bc6\\\\u3002\\\\u56e0\\\\u6b64\\\\uff0c\\\\u53ea\\\\u6709\\\\u63a5\\\\u6536\\\\u8005\\\\u4f1a\\\\u6536\\\\u5230\\\\u67e5\\\\u8be2\\\\u7ed3\\\\u679c\\\\uff0c\\\\u800c\\\\u53d1\\\\u8d77\\\\u8005\\\\u4e0d\\\\u4f1a\\\\u56e0\\\\u6b64\\\\u88ab\\\\u6536\\\\u8d39\\\\u3002\\\\n\\\\n\\\\u9700\\\\u8981\\\\u6ce8\\\\u610f\\\\u7684\\\\u662f\\\\uff0c\\\\u5982\\\\u679c\\\\u4f7f\\\\u7528AWS Clean\", \"finished\": false}}\\n{\"outputs\": {\"outputs\": \" Room\", \"response\": \"AWS Clean Rooms\\\\u662f\\\\u4e00\\\\u79cd\\\\u6570\\\\u636e\\\\u9690\\\\u79c1\\\\u4fdd\\\\u62a4\\\\u6280\\\\u672f\\\\uff0c\\\\u4f7f\\\\u7528\\\\u8be5\\\\u6280\\\\u672f\\\\uff0c\\\\u53ef\\\\u4ee5\\\\u5c06\\\\u654f\\\\u611f\\\\u6570\\\\u636e\\\\u8f6c\\\\u6362\\\\u4e3a\\\\u4e0d\\\\u53ef\\\\u89c1\\\\u7684\\\\u6570\\\\u636e\\\\u683c\\\\u5f0f\\\\uff0c\\\\u4ece\\\\u800c\\\\u4fdd\\\\u62a4\\\\u6570\\\\u636e\\\\u8d21\\\\u732e\\\\u8005\\\\u548c\\\\u63a5\\\\u6536\\\\u8005\\\\u7684\\\\u4e2a\\\\u4eba\\\\u9690\\\\u79c1\\\\u548c\\\\u5546\\\\u4e1a\\\\u673a\\\\u5bc6\\\\u3002\\\\u56e0\\\\u6b64\\\\uff0c\\\\u53ea\\\\u6709\\\\u63a5\\\\u6536\\\\u8005\\\\u4f1a\\\\u6536\\\\u5230\\\\u67e5\\\\u8be2\\\\u7ed3\\\\u679c\\\\uff0c\\\\u800c\\\\u53d1\\\\u8d77\\\\u8005\\\\u4e0d\\\\u4f1a\\\\u56e0\\\\u6b64\\\\u88ab\\\\u6536\\\\u8d39\\\\u3002\\\\n\\\\n\\\\u9700\\\\u8981\\\\u6ce8\\\\u610f\\\\u7684\\\\u662f\\\\uff0c\\\\u5982\\\\u679c\\\\u4f7f\\\\u7528AWS Clean Room\", \"finished\": false}}\\n{\"outputs\": {\"outputs\": \"s\", \"response\": \"AWS Clean Rooms\\\\u662f\\\\u4e00\\\\u79cd\\\\u6570\\\\u636e\\\\u9690\\\\u79c1\\\\u4fdd\\\\u62a4\\\\u6280\\\\u672f\\\\uff0c\\\\u4f7f\\\\u7528\\\\u8be5\\\\u6280\\\\u672f\\\\uff0c\\\\u53ef\\\\u4ee5\\\\u5c06\\\\u654f\\\\u611f\\\\u6570\\\\u636e\\\\u8f6c\\\\u6362\\\\u4e3a\\\\u4e0d\\\\u53ef\\\\u89c1\\\\u7684\\\\u6570\\\\u636e\\\\u683c\\\\u5f0f\\\\uff0c\\\\u4ece\\\\u800c\\\\u4fdd\\\\u62a4\\\\u6570\\\\u636e\\\\u8d21\\\\u732e\\\\u8005\\\\u548c\\\\u63a5\\\\u6536\\\\u8005\\\\u7684\\\\u4e2a\\\\u4eba\\\\u9690\\\\u79c1\\\\u548c\\\\u5546\\\\u4e1a\\\\u673a\\\\u5bc6\\\\u3002\\\\u56e0\\\\u6b64\\\\uff0c\\\\u53ea\\\\u6709\\\\u63a5\\\\u6536\\\\u8005\\\\u4f1a\\\\u6536\\\\u5230\\\\u67e5\\\\u8be2\\\\u7ed3\\\\u679c\\\\uff0c\\\\u800c\\\\u53d1\\\\u8d77\\\\u8005\\\\u4e0d\\\\u4f1a\\\\u56e0\\\\u6b64\\\\u88ab\\\\u6536\\\\u8d39\\\\u3002\\\\n\\\\n\\\\u9700\\\\u8981\\\\u6ce8\\\\u610f\\\\u7684\\\\u662f\\\\uff0c\\\\u5982\\\\u679c\\\\u4f7f\\\\u7528AWS Clean 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{\"outputs\": \"\\\\u6570\\\\u636e\", \"response\": \"AWS Clean Rooms\\\\u662f\\\\u4e00\\\\u79cd\\\\u6570\\\\u636e\\\\u9690\\\\u79c1\\\\u4fdd\\\\u62a4\\\\u6280\\\\u672f\\\\uff0c\\\\u4f7f\\\\u7528\\\\u8be5\\\\u6280\\\\u672f\\\\uff0c\\\\u53ef\\\\u4ee5\\\\u5c06\\\\u654f\\\\u611f\\\\u6570\\\\u636e\\\\u8f6c\\\\u6362\\\\u4e3a\\\\u4e0d\\\\u53ef\\\\u89c1\\\\u7684\\\\u6570\\\\u636e\\\\u683c\\\\u5f0f\\\\uff0c\\\\u4ece\\\\u800c\\\\u4fdd\\\\u62a4\\\\u6570\\\\u636e\\\\u8d21\\\\u732e\\\\u8005\\\\u548c\\\\u63a5\\\\u6536\\\\u8005\\\\u7684\\\\u4e2a\\\\u4eba\\\\u9690\\\\u79c1\\\\u548c\\\\u5546\\\\u4e1a\\\\u673a\\\\u5bc6\\\\u3002\\\\u56e0\\\\u6b64\\\\uff0c\\\\u53ea\\\\u6709\\\\u63a5\\\\u6536\\\\u8005\\\\u4f1a\\\\u6536\\\\u5230\\\\u67e5\\\\u8be2\\\\u7ed3\\\\u679c\\\\uff0c\\\\u800c\\\\u53d1\\\\u8d77\\\\u8005\\\\u4e0d\\\\u4f1a\\\\u56e0\\\\u6b64\\\\u88ab\\\\u6536\\\\u8d39\\\\u3002\\\\n\\\\n\\\\u9700\\\\u8981\\\\u6ce8\\\\u610f\\\\u7684\\\\u662f\\\\uff0c\\\\u5982\\\\u679c\\\\u4f7f\\\\u7528AWS Clean 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\"finished\": false}}\\n{\"outputs\": {\"outputs\": \"\\\\u4fdd\\\\u62a4\", \"response\": \"AWS Clean 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Rooms\\\\u8fdb\\\\u884c\\\\u6570\\\\u636e\\\\u9690\\\\u79c1\\\\u4fdd\\\\u62a4\\\\uff0c\\\\u8bf7\\\\u786e\\\\u4fdd\\\\u6570\\\\u636e\\\\u8d21\\\\u732e\\\\u8005\\\\u548c\\\\u63a5\\\\u6536\\\\u8005\\\\u90fd\\\\u77e5\\\\u9053\\\\u8be5\\\\u6280\\\\u672f\\\\u7684\\\\u4f7f\\\\u7528\\\\u65b9\\\\u5f0f\\\\uff0c\\\\u4ee5\\\\u53ca\\\\u4efb\\\\u4f55\\\\u53ef\\\\u80fd\\\\u7684\\\\u98ce\\\\u9669\\\\u548c\\\\u9650\\\\u5236\\\\u3002\\\\u6b64\\\\u5916\\\\uff0cAWS Clean Rooms\\\\u4ec5\\\\u9002\\\\u7528\\\\u4e8e\\\\u533f\\\\u540d\\\\u67e5\\\\u8be2\\\\uff0c\\\\u4e0d\\\\u9002\\\\u7528\\\\u4e8e\\\\u5b9e\\\\u65f6\\\\u67e5\\\\u8be2\", \"finished\": false}}\\n{\"outputs\": {\"outputs\": \"\\\\u6216\", \"response\": \"AWS Clean Rooms\\\\u662f\\\\u4e00\\\\u79cd\\\\u6570\\\\u636e\\\\u9690\\\\u79c1\\\\u4fdd\\\\u62a4\\\\u6280\\\\u672f\\\\uff0c\\\\u4f7f\\\\u7528\\\\u8be5\\\\u6280\\\\u672f\\\\uff0c\\\\u53ef\\\\u4ee5\\\\u5c06\\\\u654f\\\\u611f\\\\u6570\\\\u636e\\\\u8f6c\\\\u6362\\\\u4e3a\\\\u4e0d\\\\u53ef\\\\u89c1\\\\u7684\\\\u6570\\\\u636e\\\\u683c\\\\u5f0f\\\\uff0c\\\\u4ece\\\\u800c\\\\u4fdd\\\\u62a4\\\\u6570\\\\u636e\\\\u8d21\\\\u732e\\\\u8005\\\\u548c\\\\u63a5\\\\u6536\\\\u8005\\\\u7684\\\\u4e2a\\\\u4eba\\\\u9690\\\\u79c1\\\\u548c\\\\u5546\\\\u4e1a\\\\u673a\\\\u5bc6\\\\u3002\\\\u56e0\\\\u6b64\\\\uff0c\\\\u53ea\\\\u6709\\\\u63a5\\\\u6536\\\\u8005\\\\u4f1a\\\\u6536\\\\u5230\\\\u67e5\\\\u8be2\\\\u7ed3\\\\u679c\\\\uff0c\\\\u800c\\\\u53d1\\\\u8d77\\\\u8005\\\\u4e0d\\\\u4f1a\\\\u56e0\\\\u6b64\\\\u88ab\\\\u6536\\\\u8d39\\\\u3002\\\\n\\\\n\\\\u9700\\\\u8981\\\\u6ce8\\\\u610f\\\\u7684\\\\u662f\\\\uff0c\\\\u5982\\\\u679c\\\\u4f7f\\\\u7528AWS Clean Rooms\\\\u8fdb\\\\u884c\\\\u6570\\\\u636e\\\\u9690\\\\u79c1\\\\u4fdd\\\\u62a4\\\\uff0c\\\\u8bf7\\\\u786e\\\\u4fdd\\\\u6570\\\\u636e\\\\u8d21\\\\u732e\\\\u8005\\\\u548c\\\\u63a5\\\\u6536\\\\u8005\\\\u90fd\\\\u77e5\\\\u9053\\\\u8be5\\\\u6280\\\\u672f\\\\u7684\\\\u4f7f\\\\u7528\\\\u65b9\\\\u5f0f\\\\uff0c\\\\u4ee5\\\\u53ca\\\\u4efb\\\\u4f55\\\\u53ef\\\\u80fd\\\\u7684\\\\u98ce\\\\u9669\\\\u548c\\\\u9650\\\\u5236\\\\u3002\\\\u6b64\\\\u5916\\\\uff0cAWS Clean Rooms\\\\u4ec5\\\\u9002\\\\u7528\\\\u4e8e\\\\u533f\\\\u540d\\\\u67e5\\\\u8be2\\\\uff0c\\\\u4e0d\\\\u9002\\\\u7528\\\\u4e8e\\\\u5b9e\\\\u65f6\\\\u67e5\\\\u8be2\\\\u6216\", \"finished\": false}}\\n{\"outputs\": {\"outputs\": \"\\\\u5e26\\\\u6709\", \"response\": \"AWS Clean Rooms\\\\u662f\\\\u4e00\\\\u79cd\\\\u6570\\\\u636e\\\\u9690\\\\u79c1\\\\u4fdd\\\\u62a4\\\\u6280\\\\u672f\\\\uff0c\\\\u4f7f\\\\u7528\\\\u8be5\\\\u6280\\\\u672f\\\\uff0c\\\\u53ef\\\\u4ee5\\\\u5c06\\\\u654f\\\\u611f\\\\u6570\\\\u636e\\\\u8f6c\\\\u6362\\\\u4e3a\\\\u4e0d\\\\u53ef\\\\u89c1\\\\u7684\\\\u6570\\\\u636e\\\\u683c\\\\u5f0f\\\\uff0c\\\\u4ece\\\\u800c\\\\u4fdd\\\\u62a4\\\\u6570\\\\u636e\\\\u8d21\\\\u732e\\\\u8005\\\\u548c\\\\u63a5\\\\u6536\\\\u8005\\\\u7684\\\\u4e2a\\\\u4eba\\\\u9690\\\\u79c1\\\\u548c\\\\u5546\\\\u4e1a\\\\u673a\\\\u5bc6\\\\u3002\\\\u56e0\\\\u6b64\\\\uff0c\\\\u53ea\\\\u6709\\\\u63a5\\\\u6536\\\\u8005\\\\u4f1a\\\\u6536\\\\u5230\\\\u67e5\\\\u8be2\\\\u7ed3\\\\u679c\\\\uff0c\\\\u800c\\\\u53d1\\\\u8d77\\\\u8005\\\\u4e0d\\\\u4f1a\\\\u56e0\\\\u6b64\\\\u88ab\\\\u6536\\\\u8d39\\\\u3002\\\\n\\\\n\\\\u9700\\\\u8981\\\\u6ce8\\\\u610f\\\\u7684\\\\u662f\\\\uff0c\\\\u5982\\\\u679c\\\\u4f7f\\\\u7528AWS Clean Rooms\\\\u8fdb\\\\u884c\\\\u6570\\\\u636e\\\\u9690\\\\u79c1\\\\u4fdd\\\\u62a4\\\\uff0c\\\\u8bf7\\\\u786e\\\\u4fdd\\\\u6570\\\\u636e\\\\u8d21\\\\u732e\\\\u8005\\\\u548c\\\\u63a5\\\\u6536\\\\u8005\\\\u90fd\\\\u77e5\\\\u9053\\\\u8be5\\\\u6280\\\\u672f\\\\u7684\\\\u4f7f\\\\u7528\\\\u65b9\\\\u5f0f\\\\uff0c\\\\u4ee5\\\\u53ca\\\\u4efb\\\\u4f55\\\\u53ef\\\\u80fd\\\\u7684\\\\u98ce\\\\u9669\\\\u548c\\\\u9650\\\\u5236\\\\u3002\\\\u6b64\\\\u5916\\\\uff0cAWS Clean Rooms\\\\u4ec5\\\\u9002\\\\u7528\\\\u4e8e\\\\u533f\\\\u540d\\\\u67e5\\\\u8be2\\\\uff0c\\\\u4e0d\\\\u9002\\\\u7528\\\\u4e8e\\\\u5b9e\\\\u65f6\\\\u67e5\\\\u8be2\\\\u6216\\\\u5e26\\\\u6709\", \"finished\": false}}\\n{\"outputs\": {\"outputs\": \"\\\\u4e2a\\\\u4eba\", \"response\": \"AWS Clean Rooms\\\\u662f\\\\u4e00\\\\u79cd\\\\u6570\\\\u636e\\\\u9690\\\\u79c1\\\\u4fdd\\\\u62a4\\\\u6280\\\\u672f\\\\uff0c\\\\u4f7f\\\\u7528\\\\u8be5\\\\u6280\\\\u672f\\\\uff0c\\\\u53ef\\\\u4ee5\\\\u5c06\\\\u654f\\\\u611f\\\\u6570\\\\u636e\\\\u8f6c\\\\u6362\\\\u4e3a\\\\u4e0d\\\\u53ef\\\\u89c1\\\\u7684\\\\u6570\\\\u636e\\\\u683c\\\\u5f0f\\\\uff0c\\\\u4ece\\\\u800c\\\\u4fdd\\\\u62a4\\\\u6570\\\\u636e\\\\u8d21\\\\u732e\\\\u8005\\\\u548c\\\\u63a5\\\\u6536\\\\u8005\\\\u7684\\\\u4e2a\\\\u4eba\\\\u9690\\\\u79c1\\\\u548c\\\\u5546\\\\u4e1a\\\\u673a\\\\u5bc6\\\\u3002\\\\u56e0\\\\u6b64\\\\uff0c\\\\u53ea\\\\u6709\\\\u63a5\\\\u6536\\\\u8005\\\\u4f1a\\\\u6536\\\\u5230\\\\u67e5\\\\u8be2\\\\u7ed3\\\\u679c\\\\uff0c\\\\u800c\\\\u53d1\\\\u8d77\\\\u8005\\\\u4e0d\\\\u4f1a\\\\u56e0\\\\u6b64\\\\u88ab\\\\u6536\\\\u8d39\\\\u3002\\\\n\\\\n\\\\u9700\\\\u8981\\\\u6ce8\\\\u610f\\\\u7684\\\\u662f\\\\uff0c\\\\u5982\\\\u679c\\\\u4f7f\\\\u7528AWS Clean Rooms\\\\u8fdb\\\\u884c\\\\u6570\\\\u636e\\\\u9690\\\\u79c1\\\\u4fdd\\\\u62a4\\\\uff0c\\\\u8bf7\\\\u786e\\\\u4fdd\\\\u6570\\\\u636e\\\\u8d21\\\\u732e\\\\u8005\\\\u548c\\\\u63a5\\\\u6536\\\\u8005\\\\u90fd\\\\u77e5\\\\u9053\\\\u8be5\\\\u6280\\\\u672f\\\\u7684\\\\u4f7f\\\\u7528\\\\u65b9\\\\u5f0f\\\\uff0c\\\\u4ee5\\\\u53ca\\\\u4efb\\\\u4f55\\\\u53ef\\\\u80fd\\\\u7684\\\\u98ce\\\\u9669\\\\u548c\\\\u9650\\\\u5236\\\\u3002\\\\u6b64\\\\u5916\\\\uff0cAWS Clean Rooms\\\\u4ec5\\\\u9002\\\\u7528\\\\u4e8e\\\\u533f\\\\u540d\\\\u67e5\\\\u8be2\\\\uff0c\\\\u4e0d\\\\u9002\\\\u7528\\\\u4e8e\\\\u5b9e\\\\u65f6\\\\u67e5\\\\u8be2\\\\u6216\\\\u5e26\\\\u6709\\\\u4e2a\\\\u4eba\", \"finished\": false}}\\n{\"outputs\": {\"outputs\": \"\\\\u8eab\\\\u4efd\\\\u4fe1\\\\u606f\", \"response\": \"AWS Clean Rooms\\\\u662f\\\\u4e00\\\\u79cd\\\\u6570\\\\u636e\\\\u9690\\\\u79c1\\\\u4fdd\\\\u62a4\\\\u6280\\\\u672f\\\\uff0c\\\\u4f7f\\\\u7528\\\\u8be5\\\\u6280\\\\u672f\\\\uff0c\\\\u53ef\\\\u4ee5\\\\u5c06\\\\u654f\\\\u611f\\\\u6570\\\\u636e\\\\u8f6c\\\\u6362\\\\u4e3a\\\\u4e0d\\\\u53ef\\\\u89c1\\\\u7684\\\\u6570\\\\u636e\\\\u683c\\\\u5f0f\\\\uff0c\\\\u4ece\\\\u800c\\\\u4fdd\\\\u62a4\\\\u6570\\\\u636e\\\\u8d21\\\\u732e\\\\u8005\\\\u548c\\\\u63a5\\\\u6536\\\\u8005\\\\u7684\\\\u4e2a\\\\u4eba\\\\u9690\\\\u79c1\\\\u548c\\\\u5546\\\\u4e1a\\\\u673a\\\\u5bc6\\\\u3002\\\\u56e0\\\\u6b64\\\\uff0c\\\\u53ea\\\\u6709\\\\u63a5\\\\u6536\\\\u8005\\\\u4f1a\\\\u6536\\\\u5230\\\\u67e5\\\\u8be2\\\\u7ed3\\\\u679c\\\\uff0c\\\\u800c\\\\u53d1\\\\u8d77\\\\u8005\\\\u4e0d\\\\u4f1a\\\\u56e0\\\\u6b64\\\\u88ab\\\\u6536\\\\u8d39\\\\u3002\\\\n\\\\n\\\\u9700\\\\u8981\\\\u6ce8\\\\u610f\\\\u7684\\\\u662f\\\\uff0c\\\\u5982\\\\u679c\\\\u4f7f\\\\u7528AWS Clean Rooms\\\\u8fdb\\\\u884c\\\\u6570\\\\u636e\\\\u9690\\\\u79c1\\\\u4fdd\\\\u62a4\\\\uff0c\\\\u8bf7\\\\u786e\\\\u4fdd\\\\u6570\\\\u636e\\\\u8d21\\\\u732e\\\\u8005\\\\u548c\\\\u63a5\\\\u6536\\\\u8005\\\\u90fd\\\\u77e5\\\\u9053\\\\u8be5\\\\u6280\\\\u672f\\\\u7684\\\\u4f7f\\\\u7528\\\\u65b9\\\\u5f0f\\\\uff0c\\\\u4ee5\\\\u53ca\\\\u4efb\\\\u4f55\\\\u53ef\\\\u80fd\\\\u7684\\\\u98ce\\\\u9669\\\\u548c\\\\u9650\\\\u5236\\\\u3002\\\\u6b64\\\\u5916\\\\uff0cAWS Clean Rooms\\\\u4ec5\\\\u9002\\\\u7528\\\\u4e8e\\\\u533f\\\\u540d\\\\u67e5\\\\u8be2\\\\uff0c\\\\u4e0d\\\\u9002\\\\u7528\\\\u4e8e\\\\u5b9e\\\\u65f6\\\\u67e5\\\\u8be2\\\\u6216\\\\u5e26\\\\u6709\\\\u4e2a\\\\u4eba\\\\u8eab\\\\u4efd\\\\u4fe1\\\\u606f\", \"finished\": false}}\\n{\"outputs\": {\"outputs\": \"\\\\u7684\", \"response\": \"AWS Clean Rooms\\\\u662f\\\\u4e00\\\\u79cd\\\\u6570\\\\u636e\\\\u9690\\\\u79c1\\\\u4fdd\\\\u62a4\\\\u6280\\\\u672f\\\\uff0c\\\\u4f7f\\\\u7528\\\\u8be5\\\\u6280\\\\u672f\\\\uff0c\\\\u53ef\\\\u4ee5\\\\u5c06\\\\u654f\\\\u611f\\\\u6570\\\\u636e\\\\u8f6c\\\\u6362\\\\u4e3a\\\\u4e0d\\\\u53ef\\\\u89c1\\\\u7684\\\\u6570\\\\u636e\\\\u683c\\\\u5f0f\\\\uff0c\\\\u4ece\\\\u800c\\\\u4fdd\\\\u62a4\\\\u6570\\\\u636e\\\\u8d21\\\\u732e\\\\u8005\\\\u548c\\\\u63a5\\\\u6536\\\\u8005\\\\u7684\\\\u4e2a\\\\u4eba\\\\u9690\\\\u79c1\\\\u548c\\\\u5546\\\\u4e1a\\\\u673a\\\\u5bc6\\\\u3002\\\\u56e0\\\\u6b64\\\\uff0c\\\\u53ea\\\\u6709\\\\u63a5\\\\u6536\\\\u8005\\\\u4f1a\\\\u6536\\\\u5230\\\\u67e5\\\\u8be2\\\\u7ed3\\\\u679c\\\\uff0c\\\\u800c\\\\u53d1\\\\u8d77\\\\u8005\\\\u4e0d\\\\u4f1a\\\\u56e0\\\\u6b64\\\\u88ab\\\\u6536\\\\u8d39\\\\u3002\\\\n\\\\n\\\\u9700\\\\u8981\\\\u6ce8\\\\u610f\\\\u7684\\\\u662f\\\\uff0c\\\\u5982\\\\u679c\\\\u4f7f\\\\u7528AWS Clean Rooms\\\\u8fdb\\\\u884c\\\\u6570\\\\u636e\\\\u9690\\\\u79c1\\\\u4fdd\\\\u62a4\\\\uff0c\\\\u8bf7\\\\u786e\\\\u4fdd\\\\u6570\\\\u636e\\\\u8d21\\\\u732e\\\\u8005\\\\u548c\\\\u63a5\\\\u6536\\\\u8005\\\\u90fd\\\\u77e5\\\\u9053\\\\u8be5\\\\u6280\\\\u672f\\\\u7684\\\\u4f7f\\\\u7528\\\\u65b9\\\\u5f0f\\\\uff0c\\\\u4ee5\\\\u53ca\\\\u4efb\\\\u4f55\\\\u53ef\\\\u80fd\\\\u7684\\\\u98ce\\\\u9669\\\\u548c\\\\u9650\\\\u5236\\\\u3002\\\\u6b64\\\\u5916\\\\uff0cAWS Clean Rooms\\\\u4ec5\\\\u9002\\\\u7528\\\\u4e8e\\\\u533f\\\\u540d\\\\u67e5\\\\u8be2\\\\uff0c\\\\u4e0d\\\\u9002\\\\u7528\\\\u4e8e\\\\u5b9e\\\\u65f6\\\\u67e5\\\\u8be2\\\\u6216\\\\u5e26\\\\u6709\\\\u4e2a\\\\u4eba\\\\u8eab\\\\u4efd\\\\u4fe1\\\\u606f\\\\u7684\", \"finished\": false}}\\n{\"outputs\": {\"outputs\": \"\\\\u67e5\\\\u8be2\", \"response\": \"AWS Clean Rooms\\\\u662f\\\\u4e00\\\\u79cd\\\\u6570\\\\u636e\\\\u9690\\\\u79c1\\\\u4fdd\\\\u62a4\\\\u6280\\\\u672f\\\\uff0c\\\\u4f7f\\\\u7528\\\\u8be5\\\\u6280\\\\u672f\\\\uff0c\\\\u53ef\\\\u4ee5\\\\u5c06\\\\u654f\\\\u611f\\\\u6570\\\\u636e\\\\u8f6c\\\\u6362\\\\u4e3a\\\\u4e0d\\\\u53ef\\\\u89c1\\\\u7684\\\\u6570\\\\u636e\\\\u683c\\\\u5f0f\\\\uff0c\\\\u4ece\\\\u800c\\\\u4fdd\\\\u62a4\\\\u6570\\\\u636e\\\\u8d21\\\\u732e\\\\u8005\\\\u548c\\\\u63a5\\\\u6536\\\\u8005\\\\u7684\\\\u4e2a\\\\u4eba\\\\u9690\\\\u79c1\\\\u548c\\\\u5546\\\\u4e1a\\\\u673a\\\\u5bc6\\\\u3002\\\\u56e0\\\\u6b64\\\\uff0c\\\\u53ea\\\\u6709\\\\u63a5\\\\u6536\\\\u8005\\\\u4f1a\\\\u6536\\\\u5230\\\\u67e5\\\\u8be2\\\\u7ed3\\\\u679c\\\\uff0c\\\\u800c\\\\u53d1\\\\u8d77\\\\u8005\\\\u4e0d\\\\u4f1a\\\\u56e0\\\\u6b64\\\\u88ab\\\\u6536\\\\u8d39\\\\u3002\\\\n\\\\n\\\\u9700\\\\u8981\\\\u6ce8\\\\u610f\\\\u7684\\\\u662f\\\\uff0c\\\\u5982\\\\u679c\\\\u4f7f\\\\u7528AWS Clean Rooms\\\\u8fdb\\\\u884c\\\\u6570\\\\u636e\\\\u9690\\\\u79c1\\\\u4fdd\\\\u62a4\\\\uff0c\\\\u8bf7\\\\u786e\\\\u4fdd\\\\u6570\\\\u636e\\\\u8d21\\\\u732e\\\\u8005\\\\u548c\\\\u63a5\\\\u6536\\\\u8005\\\\u90fd\\\\u77e5\\\\u9053\\\\u8be5\\\\u6280\\\\u672f\\\\u7684\\\\u4f7f\\\\u7528\\\\u65b9\\\\u5f0f\\\\uff0c\\\\u4ee5\\\\u53ca\\\\u4efb\\\\u4f55\\\\u53ef\\\\u80fd\\\\u7684\\\\u98ce\\\\u9669\\\\u548c\\\\u9650\\\\u5236\\\\u3002\\\\u6b64\\\\u5916\\\\uff0cAWS Clean Rooms\\\\u4ec5\\\\u9002\\\\u7528\\\\u4e8e\\\\u533f\\\\u540d\\\\u67e5\\\\u8be2\\\\uff0c\\\\u4e0d\\\\u9002\\\\u7528\\\\u4e8e\\\\u5b9e\\\\u65f6\\\\u67e5\\\\u8be2\\\\u6216\\\\u5e26\\\\u6709\\\\u4e2a\\\\u4eba\\\\u8eab\\\\u4efd\\\\u4fe1\\\\u606f\\\\u7684\\\\u67e5\\\\u8be2\", \"finished\": false}}\\n{\"outputs\": {\"outputs\": \"\\\\u3002\", \"response\": \"AWS Clean Rooms\\\\u662f\\\\u4e00\\\\u79cd\\\\u6570\\\\u636e\\\\u9690\\\\u79c1\\\\u4fdd\\\\u62a4\\\\u6280\\\\u672f\\\\uff0c\\\\u4f7f\\\\u7528\\\\u8be5\\\\u6280\\\\u672f\\\\uff0c\\\\u53ef\\\\u4ee5\\\\u5c06\\\\u654f\\\\u611f\\\\u6570\\\\u636e\\\\u8f6c\\\\u6362\\\\u4e3a\\\\u4e0d\\\\u53ef\\\\u89c1\\\\u7684\\\\u6570\\\\u636e\\\\u683c\\\\u5f0f\\\\uff0c\\\\u4ece\\\\u800c\\\\u4fdd\\\\u62a4\\\\u6570\\\\u636e\\\\u8d21\\\\u732e\\\\u8005\\\\u548c\\\\u63a5\\\\u6536\\\\u8005\\\\u7684\\\\u4e2a\\\\u4eba\\\\u9690\\\\u79c1\\\\u548c\\\\u5546\\\\u4e1a\\\\u673a\\\\u5bc6\\\\u3002\\\\u56e0\\\\u6b64\\\\uff0c\\\\u53ea\\\\u6709\\\\u63a5\\\\u6536\\\\u8005\\\\u4f1a\\\\u6536\\\\u5230\\\\u67e5\\\\u8be2\\\\u7ed3\\\\u679c\\\\uff0c\\\\u800c\\\\u53d1\\\\u8d77\\\\u8005\\\\u4e0d\\\\u4f1a\\\\u56e0\\\\u6b64\\\\u88ab\\\\u6536\\\\u8d39\\\\u3002\\\\n\\\\n\\\\u9700\\\\u8981\\\\u6ce8\\\\u610f\\\\u7684\\\\u662f\\\\uff0c\\\\u5982\\\\u679c\\\\u4f7f\\\\u7528AWS Clean Rooms\\\\u8fdb\\\\u884c\\\\u6570\\\\u636e\\\\u9690\\\\u79c1\\\\u4fdd\\\\u62a4\\\\uff0c\\\\u8bf7\\\\u786e\\\\u4fdd\\\\u6570\\\\u636e\\\\u8d21\\\\u732e\\\\u8005\\\\u548c\\\\u63a5\\\\u6536\\\\u8005\\\\u90fd\\\\u77e5\\\\u9053\\\\u8be5\\\\u6280\\\\u672f\\\\u7684\\\\u4f7f\\\\u7528\\\\u65b9\\\\u5f0f\\\\uff0c\\\\u4ee5\\\\u53ca\\\\u4efb\\\\u4f55\\\\u53ef\\\\u80fd\\\\u7684\\\\u98ce\\\\u9669\\\\u548c\\\\u9650\\\\u5236\\\\u3002\\\\u6b64\\\\u5916\\\\uff0cAWS Clean Rooms\\\\u4ec5\\\\u9002\\\\u7528\\\\u4e8e\\\\u533f\\\\u540d\\\\u67e5\\\\u8be2\\\\uff0c\\\\u4e0d\\\\u9002\\\\u7528\\\\u4e8e\\\\u5b9e\\\\u65f6\\\\u67e5\\\\u8be2\\\\u6216\\\\u5e26\\\\u6709\\\\u4e2a\\\\u4eba\\\\u8eab\\\\u4efd\\\\u4fe1\\\\u606f\\\\u7684\\\\u67e5\\\\u8be2\\\\u3002\", \"finished\": false}}\\n{\"outputs\": {\"query\": \"AWS Clean Rooms \\\\u7684FAQ\\\\u6587\\\\u6863\\\\u6709\\\\u63d0\\\\u5230 Q: \\\\u662f\\\\u5426\\\\u53d1\\\\u8d77\\\\u8005\\\\u548c\\\\u6570\\\\u636e\\\\u8d21\\\\u732e\\\\u8005\\\\u90fd\\\\u4f1a\\\\u88ab\\\\u6536\\\\u8d39\\\\uff1fA: \\\\u662f\\\\u5355\\\\u65b9\\\\u6536\\\\u8d39\\\\uff0c\\\\u53ea\\\\u6709\\\\u67e5\\\\u8be2\\\\u7684\\\\u63a5\\\\u6536\\\\u65b9\\\\u4f1a\\\\u6536\\\\u8d39\\\\u3002\\\\n\\\\u8bf7\\\\u95eeAWS Clean Rooms\\\\u662f\\\\u591a\\\\u65b9\\\\u90fd\\\\u4f1a\\\\u6536\\\\u8d39\\\\u5417\\\\uff1f\\\\n\", \"outputs\": \"[DONE]\", \"response\": \"AWS Clean Rooms\\\\u662f\\\\u4e00\\\\u79cd\\\\u6570\\\\u636e\\\\u9690\\\\u79c1\\\\u4fdd\\\\u62a4\\\\u6280\\\\u672f\\\\uff0c\\\\u4f7f\\\\u7528\\\\u8be5\\\\u6280\\\\u672f\\\\uff0c\\\\u53ef\\\\u4ee5\\\\u5c06\\\\u654f\\\\u611f\\\\u6570\\\\u636e\\\\u8f6c\\\\u6362\\\\u4e3a\\\\u4e0d\\\\u53ef\\\\u89c1\\\\u7684\\\\u6570\\\\u636e\\\\u683c\\\\u5f0f\\\\uff0c\\\\u4ece\\\\u800c\\\\u4fdd\\\\u62a4\\\\u6570\\\\u636e\\\\u8d21\\\\u732e\\\\u8005\\\\u548c\\\\u63a5\\\\u6536\\\\u8005\\\\u7684\\\\u4e2a\\\\u4eba\\\\u9690\\\\u79c1\\\\u548c\\\\u5546\\\\u4e1a\\\\u673a\\\\u5bc6\\\\u3002\\\\u56e0\\\\u6b64\\\\uff0c\\\\u53ea\\\\u6709\\\\u63a5\\\\u6536\\\\u8005\\\\u4f1a\\\\u6536\\\\u5230\\\\u67e5\\\\u8be2\\\\u7ed3\\\\u679c\\\\uff0c\\\\u800c\\\\u53d1\\\\u8d77\\\\u8005\\\\u4e0d\\\\u4f1a\\\\u56e0\\\\u6b64\\\\u88ab\\\\u6536\\\\u8d39\\\\u3002\\\\n\\\\n\\\\u9700\\\\u8981\\\\u6ce8\\\\u610f\\\\u7684\\\\u662f\\\\uff0c\\\\u5982\\\\u679c\\\\u4f7f\\\\u7528AWS Clean Rooms\\\\u8fdb\\\\u884c\\\\u6570\\\\u636e\\\\u9690\\\\u79c1\\\\u4fdd\\\\u62a4\\\\uff0c\\\\u8bf7\\\\u786e\\\\u4fdd\\\\u6570\\\\u636e\\\\u8d21\\\\u732e\\\\u8005\\\\u548c\\\\u63a5\\\\u6536\\\\u8005\\\\u90fd\\\\u77e5\\\\u9053\\\\u8be5\\\\u6280\\\\u672f\\\\u7684\\\\u4f7f\\\\u7528\\\\u65b9\\\\u5f0f\\\\uff0c\\\\u4ee5\\\\u53ca\\\\u4efb\\\\u4f55\\\\u53ef\\\\u80fd\\\\u7684\\\\u98ce\\\\u9669\\\\u548c\\\\u9650\\\\u5236\\\\u3002\\\\u6b64\\\\u5916\\\\uff0cAWS Clean Rooms\\\\u4ec5\\\\u9002\\\\u7528\\\\u4e8e\\\\u533f\\\\u540d\\\\u67e5\\\\u8be2\\\\uff0c\\\\u4e0d\\\\u9002\\\\u7528\\\\u4e8e\\\\u5b9e\\\\u65f6\\\\u67e5\\\\u8be2\\\\u6216\\\\u5e26\\\\u6709\\\\u4e2a\\\\u4eba\\\\u8eab\\\\u4efd\\\\u4fe1\\\\u606f\\\\u7684\\\\u67e5\\\\u8be2\\\\u3002\", \"history\": [[\"AWS Clean Rooms \\\\u7684FAQ\\\\u6587\\\\u6863\\\\u6709\\\\u63d0\\\\u5230 Q: \\\\u662f\\\\u5426\\\\u53d1\\\\u8d77\\\\u8005\\\\u548c\\\\u6570\\\\u636e\\\\u8d21\\\\u732e\\\\u8005\\\\u90fd\\\\u4f1a\\\\u88ab\\\\u6536\\\\u8d39\\\\uff1fA: \\\\u662f\\\\u5355\\\\u65b9\\\\u6536\\\\u8d39\\\\uff0c\\\\u53ea\\\\u6709\\\\u67e5\\\\u8be2\\\\u7684\\\\u63a5\\\\u6536\\\\u65b9\\\\u4f1a\\\\u6536\\\\u8d39\\\\u3002\\\\n\\\\u8bf7\\\\u95eeAWS Clean Rooms\\\\u662f\\\\u591a\\\\u65b9\\\\u90fd\\\\u4f1a\\\\u6536\\\\u8d39\\\\u5417\\\\uff1f\\\\n\", \"AWS Clean Rooms\\\\u662f\\\\u4e00\\\\u79cd\\\\u6570\\\\u636e\\\\u9690\\\\u79c1\\\\u4fdd\\\\u62a4\\\\u6280\\\\u672f\\\\uff0c\\\\u4f7f\\\\u7528\\\\u8be5\\\\u6280\\\\u672f\\\\uff0c\\\\u53ef\\\\u4ee5\\\\u5c06\\\\u654f\\\\u611f\\\\u6570\\\\u636e\\\\u8f6c\\\\u6362\\\\u4e3a\\\\u4e0d\\\\u53ef\\\\u89c1\\\\u7684\\\\u6570\\\\u636e\\\\u683c\\\\u5f0f\\\\uff0c\\\\u4ece\\\\u800c\\\\u4fdd\\\\u62a4\\\\u6570\\\\u636e\\\\u8d21\\\\u732e\\\\u8005\\\\u548c\\\\u63a5\\\\u6536\\\\u8005\\\\u7684\\\\u4e2a\\\\u4eba\\\\u9690\\\\u79c1\\\\u548c\\\\u5546\\\\u4e1a\\\\u673a\\\\u5bc6\\\\u3002\\\\u56e0\\\\u6b64\\\\uff0c\\\\u53ea\\\\u6709\\\\u63a5\\\\u6536\\\\u8005\\\\u4f1a\\\\u6536\\\\u5230\\\\u67e5\\\\u8be2\\\\u7ed3\\\\u679c\\\\uff0c\\\\u800c\\\\u53d1\\\\u8d77\\\\u8005\\\\u4e0d\\\\u4f1a\\\\u56e0\\\\u6b64\\\\u88ab\\\\u6536\\\\u8d39\\\\u3002\\\\n\\\\n\\\\u9700\\\\u8981\\\\u6ce8\\\\u610f\\\\u7684\\\\u662f\\\\uff0c\\\\u5982\\\\u679c\\\\u4f7f\\\\u7528AWS Clean Rooms\\\\u8fdb\\\\u884c\\\\u6570\\\\u636e\\\\u9690\\\\u79c1\\\\u4fdd\\\\u62a4\\\\uff0c\\\\u8bf7\\\\u786e\\\\u4fdd\\\\u6570\\\\u636e\\\\u8d21\\\\u732e\\\\u8005\\\\u548c\\\\u63a5\\\\u6536\\\\u8005\\\\u90fd\\\\u77e5\\\\u9053\\\\u8be5\\\\u6280\\\\u672f\\\\u7684\\\\u4f7f\\\\u7528\\\\u65b9\\\\u5f0f\\\\uff0c\\\\u4ee5\\\\u53ca\\\\u4efb\\\\u4f55\\\\u53ef\\\\u80fd\\\\u7684\\\\u98ce\\\\u9669\\\\u548c\\\\u9650\\\\u5236\\\\u3002\\\\u6b64\\\\u5916\\\\uff0cAWS Clean Rooms\\\\u4ec5\\\\u9002\\\\u7528\\\\u4e8e\\\\u533f\\\\u540d\\\\u67e5\\\\u8be2\\\\uff0c\\\\u4e0d\\\\u9002\\\\u7528\\\\u4e8e\\\\u5b9e\\\\u65f6\\\\u67e5\\\\u8be2\\\\u6216\\\\u5e26\\\\u6709\\\\u4e2a\\\\u4eba\\\\u8eab\\\\u4efd\\\\u4fe1\\\\u606f\\\\u7684\\\\u67e5\\\\u8be2\\\\u3002\"]], \"finished\": true}}\\n'" ] }, "execution_count": 87, "metadata": {}, "output_type": "execute_result" } ], "source": [ "prompts1 = \"\"\"AWS Clean Rooms çš„FAQ文档有æ到 Q: 是å¦å‘起者和数æ®è´¡çŒ®è€…都会被收费?A: 是å•æ–¹æ”¶è´¹ï¼Œåªæœ‰æŸ¥è¯¢çš„接收方会收费。\n", "请问AWS Clean Rooms是多方都会收费å—?\n", "\"\"\"\n", "response_model = smr_client.invoke_endpoint(\n", " EndpointName=endpoint_name,\n", " Body=json.dumps(\n", " {\n", " \"inputs\": prompts1,\n", " \"parameters\": parameters,\n", " \"history\" : []\n", " }\n", " ),\n", " ContentType=\"application/json\",\n", " )\n", "\n", "response_model['Body'].read().decode('utf8')" ] }, { "cell_type": "code", "execution_count": 48, "id": "2833f1c1-0c26-4c52-9e33-276ae48a9bce", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "'{\\n \"outputs\":\"æ ¹æ®AWS Clean Roomsçš„FAQ文档,æ¯ä¸ªå作AWS Clean Rooms最多支æŒäº”åå‚ä¸Žæ–¹ã€‚å› æ¤ï¼Œä¸€ä¸ªAWS Clean Roomså作ä¸å¯ä»¥æœ‰æœ€å¤šäº”åå‚与方。\",\\n \"history\":[\\n [\\n \"AWS Clean Rooms çš„FAQ文档有æ到:\\\\nQuestion: AWS Clean Rooms的一个å作ä¸å¯ä»¥æœ‰å¤šå°‘个å‚与方? \\\\nAnswer: æ¯ä¸ªå作AWS Clean Rooms最多支æŒäº”åå‚与方。\\\\n\\\\n\\\\n请问AWS Clean Rooms的一个å作ä¸å¯ä»¥æœ‰å¤šå°‘个å‚与方?\",\\n \"æ ¹æ®AWS Clean Roomsçš„FAQ文档,æ¯ä¸ªå作AWS Clean Rooms最多支æŒäº”åå‚ä¸Žæ–¹ã€‚å› æ¤ï¼Œä¸€ä¸ªAWS Clean Roomså作ä¸å¯ä»¥æœ‰æœ€å¤šäº”åå‚与方。\"\\n ]\\n ]\\n}'" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "prompts1 = \"\"\"AWS Clean Rooms çš„FAQ文档有æ到:\\nQuestion: AWS Clean Rooms的一个å作ä¸å¯ä»¥æœ‰å¤šå°‘个å‚与方? \\nAnswer: æ¯ä¸ªå作AWS Clean Rooms最多支æŒäº”åå‚与方。\\n\\n\n", "请问AWS Clean Rooms的一个å作ä¸å¯ä»¥æœ‰å¤šå°‘个å‚与方?\"\"\"\n", "\n", "response_model = smr_client.invoke_endpoint(\n", " EndpointName=endpoint_name,\n", " Body=json.dumps(\n", " {\n", " \"inputs\": prompts1,\n", " \"parameters\": parameters,\n", " \"history\" : []\n", " }\n", " ),\n", " ContentType=\"application/json\",\n", " )\n", "\n", "response_model['Body'].read().decode('utf8')" ] }, { "cell_type": "markdown", "id": "21c8b703-e312-4964-8be9-a754468e07cd", "metadata": {}, "source": [ "#### 清除模型Endpointå’Œconfig" ] }, { "cell_type": "code", "execution_count": null, "id": "f70d116f-4fb1-4f04-8732-3d6e4fb520de", "metadata": { "tags": [] }, "outputs": [], "source": [ "# !aws sagemaker delete-endpoint --endpoint-name bloomz-7b1-mt-2023-04-13-11-02-25-553-endpoint" ] }, { "cell_type": "code", "execution_count": null, "id": "184e4d1d-3d62-43df-9b17-5d64ece928bd", "metadata": { "tags": [] }, "outputs": [], "source": [ "# !aws sagemaker delete-endpoint-config --endpoint-config-name bloomz-7b1-mt-2023-04-13-11-02-25-553-config" ] } ], "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 } ], "instance_type": "ml.t3.medium", "kernelspec": { "display_name": "conda_pytorch_p39", "language": "python", "name": "conda_pytorch_p39" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.15" } }, "nbformat": 4, "nbformat_minor": 5 }