ReducingToxicity(PEFT+PPO).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "112288b1-a545-4339-b709-b5ec75a12aa0",
   "metadata": {},
   "source": [
    "# UTILIZING PEFT + LORA + PPO: Evaluating and Eradicating Toxicity from Model, Making it more human aligned and follow the 3H's (Helpful, Honest, Harmless)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "866345d2-bd89-45eb-bb29-831791aba398",
   "metadata": {},
   "source": [
    "#### First, let's go ahead and set up the kernel and install the dependencies that we need to get this started:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bb74343d-3905-4429-a8d0-3f6e549d515c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com\n",
      "Requirement already satisfied: pip in /opt/conda/lib/python3.10/site-packages (23.2.1)\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
      "\u001b[0mNote: you may need to restart the kernel to use updated packages.\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
      "\u001b[0mNote: you may need to restart the kernel to use updated packages.\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",
      "spyder 5.3.3 requires pyqt5<5.16, which is not installed.\n",
      "spyder 5.3.3 requires pyqtwebengine<5.16, which is not installed.\n",
      "pathos 0.3.1 requires dill>=0.3.7, but you have dill 0.3.6 which is incompatible.\n",
      "pathos 0.3.1 requires multiprocess>=0.70.15, but you have multiprocess 0.70.14 which is incompatible.\n",
      "spyder 5.3.3 requires ipython<8.0.0,>=7.31.1, but you have ipython 8.14.0 which is incompatible.\n",
      "spyder 5.3.3 requires pylint<3.0,>=2.5.0, but you have pylint 3.0.0a6 which is incompatible.\u001b[0m\u001b[31m\n",
      "\u001b[0m\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
      "\u001b[0mNote: you may need to restart the kernel to use updated packages.\n",
      "Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com\n",
      "Collecting git+https://github.com/lvwerra/trl.git@25fa1bd\n",
      "  Cloning https://github.com/lvwerra/trl.git (to revision 25fa1bd) to /tmp/pip-req-build-6j78_crn\n",
      "  Running command git clone --filter=blob:none --quiet https://github.com/lvwerra/trl.git /tmp/pip-req-build-6j78_crn\n",
      "\u001b[33m  WARNING: Did not find branch or tag '25fa1bd', assuming revision or ref.\u001b[0m\u001b[33m\n",
      "\u001b[0m  Running command git checkout -q 25fa1bd\n",
      "  Resolved https://github.com/lvwerra/trl.git to commit 25fa1bd\n",
      "  Preparing metadata (setup.py) ... \u001b[?25ldone\n",
      "\u001b[?25hRequirement already satisfied: torch>=1.4.0 in /opt/conda/lib/python3.10/site-packages (from trl==0.4.2.dev0) (1.13.1)\n",
      "Requirement already satisfied: transformers>=4.18.0 in /opt/conda/lib/python3.10/site-packages (from trl==0.4.2.dev0) (4.27.2)\n",
      "Requirement already satisfied: numpy>=1.18.2 in /opt/conda/lib/python3.10/site-packages (from trl==0.4.2.dev0) (1.25.1)\n",
      "Requirement already satisfied: accelerate in /opt/conda/lib/python3.10/site-packages (from trl==0.4.2.dev0) (0.22.0)\n",
      "Requirement already satisfied: datasets in /opt/conda/lib/python3.10/site-packages (from trl==0.4.2.dev0) (2.11.0)\n",
      "Requirement already satisfied: typing-extensions in /opt/conda/lib/python3.10/site-packages (from torch>=1.4.0->trl==0.4.2.dev0) (4.3.0)\n",
      "Requirement already satisfied: nvidia-cuda-runtime-cu11==11.7.99 in /opt/conda/lib/python3.10/site-packages (from torch>=1.4.0->trl==0.4.2.dev0) (11.7.99)\n",
      "Requirement already satisfied: nvidia-cudnn-cu11==8.5.0.96 in /opt/conda/lib/python3.10/site-packages (from torch>=1.4.0->trl==0.4.2.dev0) (8.5.0.96)\n",
      "Requirement already satisfied: nvidia-cublas-cu11==11.10.3.66 in /opt/conda/lib/python3.10/site-packages (from torch>=1.4.0->trl==0.4.2.dev0) (11.10.3.66)\n",
      "Requirement already satisfied: nvidia-cuda-nvrtc-cu11==11.7.99 in /opt/conda/lib/python3.10/site-packages (from torch>=1.4.0->trl==0.4.2.dev0) (11.7.99)\n",
      "Requirement already satisfied: setuptools in /opt/conda/lib/python3.10/site-packages (from nvidia-cublas-cu11==11.10.3.66->torch>=1.4.0->trl==0.4.2.dev0) (68.0.0)\n",
      "Requirement already satisfied: wheel in /opt/conda/lib/python3.10/site-packages (from nvidia-cublas-cu11==11.10.3.66->torch>=1.4.0->trl==0.4.2.dev0) (0.41.0)\n",
      "Requirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from transformers>=4.18.0->trl==0.4.2.dev0) (3.6.0)\n",
      "Requirement already satisfied: huggingface-hub<1.0,>=0.11.0 in /opt/conda/lib/python3.10/site-packages (from transformers>=4.18.0->trl==0.4.2.dev0) (0.16.4)\n",
      "Requirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.10/site-packages (from transformers>=4.18.0->trl==0.4.2.dev0) (21.3)\n",
      "Requirement already satisfied: pyyaml>=5.1 in /opt/conda/lib/python3.10/site-packages/PyYAML-6.0-py3.10-linux-x86_64.egg (from transformers>=4.18.0->trl==0.4.2.dev0) (6.0)\n",
      "Requirement already satisfied: regex!=2019.12.17 in /opt/conda/lib/python3.10/site-packages (from transformers>=4.18.0->trl==0.4.2.dev0) (2022.7.9)\n",
      "Requirement already satisfied: requests in /opt/conda/lib/python3.10/site-packages (from transformers>=4.18.0->trl==0.4.2.dev0) (2.31.0)\n",
      "Requirement already satisfied: tokenizers!=0.11.3,<0.14,>=0.11.1 in /opt/conda/lib/python3.10/site-packages (from transformers>=4.18.0->trl==0.4.2.dev0) (0.13.3)\n",
      "Requirement already satisfied: tqdm>=4.27 in /opt/conda/lib/python3.10/site-packages (from transformers>=4.18.0->trl==0.4.2.dev0) (4.64.1)\n",
      "Requirement already satisfied: psutil in /opt/conda/lib/python3.10/site-packages (from accelerate->trl==0.4.2.dev0) (5.9.0)\n",
      "Requirement already satisfied: pyarrow>=8.0.0 in /opt/conda/lib/python3.10/site-packages (from datasets->trl==0.4.2.dev0) (12.0.1)\n",
      "Requirement already satisfied: dill<0.3.7,>=0.3.0 in /opt/conda/lib/python3.10/site-packages (from datasets->trl==0.4.2.dev0) (0.3.6)\n",
      "Requirement already satisfied: pandas in /opt/conda/lib/python3.10/site-packages (from datasets->trl==0.4.2.dev0) (1.4.4)\n",
      "Requirement already satisfied: xxhash in /opt/conda/lib/python3.10/site-packages (from datasets->trl==0.4.2.dev0) (3.3.0)\n",
      "Requirement already satisfied: multiprocess in /opt/conda/lib/python3.10/site-packages (from datasets->trl==0.4.2.dev0) (0.70.14)\n",
      "Requirement already satisfied: fsspec[http]>=2021.11.1 in /opt/conda/lib/python3.10/site-packages (from datasets->trl==0.4.2.dev0) (2022.7.1)\n",
      "Requirement already satisfied: aiohttp in /opt/conda/lib/python3.10/site-packages (from datasets->trl==0.4.2.dev0) (3.8.5)\n",
      "Requirement already satisfied: responses<0.19 in /opt/conda/lib/python3.10/site-packages (from datasets->trl==0.4.2.dev0) (0.18.0)\n",
      "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /opt/conda/lib/python3.10/site-packages (from packaging>=20.0->transformers>=4.18.0->trl==0.4.2.dev0) (3.0.9)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in /opt/conda/lib/python3.10/site-packages (from requests->transformers>=4.18.0->trl==0.4.2.dev0) (2.0.4)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.10/site-packages (from requests->transformers>=4.18.0->trl==0.4.2.dev0) (3.3)\n",
      "Requirement already satisfied: urllib3<3,>=1.21.1 in /opt/conda/lib/python3.10/site-packages (from requests->transformers>=4.18.0->trl==0.4.2.dev0) (2.0.4)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.10/site-packages (from requests->transformers>=4.18.0->trl==0.4.2.dev0) (2023.7.22)\n",
      "Requirement already satisfied: attrs>=17.3.0 in /opt/conda/lib/python3.10/site-packages (from aiohttp->datasets->trl==0.4.2.dev0) (23.1.0)\n",
      "Requirement already satisfied: multidict<7.0,>=4.5 in /opt/conda/lib/python3.10/site-packages (from aiohttp->datasets->trl==0.4.2.dev0) (6.0.4)\n",
      "Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in /opt/conda/lib/python3.10/site-packages (from aiohttp->datasets->trl==0.4.2.dev0) (4.0.3)\n",
      "Requirement already satisfied: yarl<2.0,>=1.0 in /opt/conda/lib/python3.10/site-packages (from aiohttp->datasets->trl==0.4.2.dev0) (1.9.2)\n",
      "Requirement already satisfied: frozenlist>=1.1.1 in /opt/conda/lib/python3.10/site-packages (from aiohttp->datasets->trl==0.4.2.dev0) (1.4.0)\n",
      "Requirement already satisfied: aiosignal>=1.1.2 in /opt/conda/lib/python3.10/site-packages (from aiohttp->datasets->trl==0.4.2.dev0) (1.3.1)\n",
      "Requirement already satisfied: python-dateutil>=2.8.1 in /opt/conda/lib/python3.10/site-packages (from pandas->datasets->trl==0.4.2.dev0) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2020.1 in /opt/conda/lib/python3.10/site-packages (from pandas->datasets->trl==0.4.2.dev0) (2022.1)\n",
      "Requirement already satisfied: six>=1.5 in /opt/conda/lib/python3.10/site-packages (from python-dateutil>=2.8.1->pandas->datasets->trl==0.4.2.dev0) (1.16.0)\n",
      "Building wheels for collected packages: trl\n",
      "  Building wheel for trl (setup.py) ... \u001b[?25ldone\n",
      "\u001b[?25h  Created wheel for trl: filename=trl-0.4.2.dev0-py3-none-any.whl size=67534 sha256=361677fd80074393f2bfb816d7d507d78b9c812d425423922294079e1cf15fd4\n",
      "  Stored in directory: /tmp/pip-ephem-wheel-cache-rtgshodi/wheels/24/b4/20/2fa3a1e47c0411c39e198029315e3af2a2c1d59132913f136f\n",
      "Successfully built trl\n",
      "Installing collected packages: trl\n",
      "Successfully installed trl-0.4.2.dev0\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
      "\u001b[0mNote: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip install --upgrade pip\n",
    "%pip install --disable-pip-version-check \\\n",
    "    torch==1.13.1 \\\n",
    "    torchdata==0.5.1 --quiet\n",
    "\n",
    "%pip install \\\n",
    "    transformers==4.27.2 \\\n",
    "    datasets==2.11.0 \\\n",
    "    evaluate==0.4.0 \\\n",
    "    rouge_score==0.1.2 \\\n",
    "    peft==0.3.0 --quiet\n",
    "\n",
    "# Let's install the reinforcement learning library from github\n",
    "%pip install git+https://github.com/lvwerra/trl.git@25fa1bd    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2f15f6e-2492-48ac-8dff-0e0ab8ba0744",
   "metadata": {},
   "source": [
    "#### Now we will import some components that will be needed in loading our LlaMa 2 mode, prepare our reward model and get our toxicity evaluator in place:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fa67886b-ae15-447c-91e2-66b786394579",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n",
      "/opt/conda/lib/python3.10/site-packages/scipy/__init__.py:155: UserWarning: A NumPy version >=1.18.5 and <1.25.0 is required for this version of SciPy (detected version 1.25.1\n",
      "  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n"
     ]
    }
   ],
   "source": [
    "## Represents importing the necessary libraries from the transformers library\n",
    "from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification, AutoModelForSeq2SeqLM, GenerationConfig\n",
    "\n",
    "## Here, we will import load_dataset to load our dataset to fine tune our LlaMa-2 model\n",
    "from datasets import load_dataset\n",
    "\n",
    "## Here, we will import our PEFT and LoRA libraries to fine tune the model\n",
    "from peft import PeftModel, PeftConfig, LoraConfig, TaskType\n",
    "\n",
    "# trl: Transformer Reinforcement Learning library used to create the PPO libraries within our environment\n",
    "from trl import PPOTrainer, PPOConfig, AutoModelForSeq2SeqLMWithValueHead\n",
    "from trl import create_reference_model\n",
    "from trl.core import LengthSampler\n",
    "\n",
    "import torch\n",
    "import evaluate\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# tqdm library makes the loops show a smart progress meter.\n",
    "from tqdm import tqdm\n",
    "tqdm.pandas()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00e03732-f2db-464e-8893-f6aed46520fd",
   "metadata": {},
   "source": [
    "## Loading the FLAN-t5 Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f12b0645-112a-45c2-a4f0-8efef7cc24fe",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading readme: 100%|██████████| 4.56k/4.56k [00:00<00:00, 13.2MB/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading and preparing dataset csv/knkarthick--dialogsum to /root/.cache/huggingface/datasets/knkarthick___csv/knkarthick--dialogsum-c8fac5d84cd35861/0.0.0/6954658bab30a358235fa864b05cf819af0e179325c740e4bc853bcc7ec513e1...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading data files:   0%|          | 0/3 [00:00<?, ?it/s]\n",
      "Downloading data:   0%|          | 0.00/11.3M [00:00<?, ?B/s]\u001b[A\n",
      "Downloading data: 100%|██████████| 11.3M/11.3M [00:00<00:00, 69.2MB/s]\u001b[A\n",
      "Downloading data files:  33%|███▎      | 1/3 [00:00<00:00,  2.08it/s]\n",
      "Downloading data: 100%|██████████| 1.35M/1.35M [00:00<00:00, 54.0MB/s]\n",
      "Downloading data files:  67%|██████▋   | 2/3 [00:00<00:00,  3.08it/s]\n",
      "Downloading data: 100%|██████████| 442k/442k [00:00<00:00, 28.6MB/s]\n",
      "Downloading data files: 100%|██████████| 3/3 [00:00<00:00,  3.47it/s]\n",
      "Extracting data files: 100%|██████████| 3/3 [00:00<00:00, 54.59it/s]\n",
      "                                                                   \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset csv downloaded and prepared to /root/.cache/huggingface/datasets/knkarthick___csv/knkarthick--dialogsum-c8fac5d84cd35861/0.0.0/6954658bab30a358235fa864b05cf819af0e179325c740e4bc853bcc7ec513e1. Subsequent calls will reuse this data.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 3/3 [00:00<00:00, 50.38it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "DatasetDict({\n",
       "    train: Dataset({\n",
       "        features: ['id', 'dialogue', 'summary', 'topic'],\n",
       "        num_rows: 12460\n",
       "    })\n",
       "    test: Dataset({\n",
       "        features: ['id', 'dialogue', 'summary', 'topic'],\n",
       "        num_rows: 1500\n",
       "    })\n",
       "    validation: Dataset({\n",
       "        features: ['id', 'dialogue', 'summary', 'topic'],\n",
       "        num_rows: 500\n",
       "    })\n",
       "})"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_name=\"google/flan-t5-base\"\n",
    "huggingface_dataset_name = \"knkarthick/dialogsum\"\n",
    "\n",
    "dataset_original = load_dataset(huggingface_dataset_name)\n",
    "\n",
    "dataset_original"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3b47811-2268-4ef0-ab46-b1fb019c9292",
   "metadata": {},
   "source": [
    "### Preprocessing the DATASETS and getting the Prompts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "658d0f85-b8a2-4b75-a495-3da96b9b7997",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Found cached dataset csv (/root/.cache/huggingface/datasets/knkarthick___csv/knkarthick--dialogsum-c8fac5d84cd35861/0.0.0/6954658bab30a358235fa864b05cf819af0e179325c740e4bc853bcc7ec513e1)\n",
      "Downloading (…)okenizer_config.json: 100%|██████████| 2.54k/2.54k [00:00<00:00, 5.72MB/s]\n",
      "Downloading spiece.model: 100%|██████████| 792k/792k [00:00<00:00, 90.8MB/s]\n",
      "Downloading (…)/main/tokenizer.json: 100%|██████████| 2.42M/2.42M [00:00<00:00, 55.5MB/s]\n",
      "Downloading (…)cial_tokens_map.json: 100%|██████████| 2.20k/2.20k [00:00<00:00, 450kB/s]\n",
      "                                                                 "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DatasetDict({\n",
      "    train: Dataset({\n",
      "        features: ['id', 'dialogue', 'summary', 'topic', 'input_ids', 'query'],\n",
      "        num_rows: 8017\n",
      "    })\n",
      "    test: Dataset({\n",
      "        features: ['id', 'dialogue', 'summary', 'topic', 'input_ids', 'query'],\n",
      "        num_rows: 2005\n",
      "    })\n",
      "})\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "source": [
    "def build_dataset(model_name,\n",
    "                  dataset_name,\n",
    "                  input_min_text_length, \n",
    "                  input_max_text_length):\n",
    "\n",
    "    \"\"\"\n",
    "    Preprocess the dataset and split it into train and test parts.\n",
    "\n",
    "    Parameters:\n",
    "    - model_name (str): Tokenizer model name.\n",
    "    - dataset_name (str): Name of the dataset to load.\n",
    "    - input_min_text_length (int): Minimum length of the dialogues.\n",
    "    - input_max_text_length (int): Maximum length of the dialogues.\n",
    "        \n",
    "    Returns:\n",
    "    - dataset_splits (datasets.dataset_dict.DatasetDict): Preprocessed dataset containing train and test parts.\n",
    "    \"\"\"\n",
    "    \n",
    "    # load dataset (only \"train\" part will be enough for this lab).\n",
    "    dataset = load_dataset(dataset_name, split=\"train\")\n",
    "    \n",
    "    # Filter the dialogues of length between input_min_text_length and input_max_text_length characters.\n",
    "    dataset = dataset.filter(lambda x: len(x[\"dialogue\"]) > input_min_text_length and len(x[\"dialogue\"]) <= input_max_text_length, batched=False)\n",
    "\n",
    "    # Prepare tokenizer. Setting device_map=\"auto\" allows to switch between GPU and CPU automatically.\n",
    "    tokenizer = AutoTokenizer.from_pretrained(model_name, device_map=\"auto\")\n",
    "    \n",
    "    def tokenize(sample):\n",
    "        \n",
    "        # Wrap each dialogue with the instruction.\n",
    "        prompt = f\"\"\"\n",
    "Summarize the following conversation.\n",
    "\n",
    "{sample[\"dialogue\"]}\n",
    "\n",
    "Summary:\n",
    "\"\"\"\n",
    "        sample[\"input_ids\"] = tokenizer.encode(prompt)\n",
    "        \n",
    "        # This must be called \"query\", which is a requirement of our PPO library.\n",
    "        sample[\"query\"] = tokenizer.decode(sample[\"input_ids\"])\n",
    "        return sample\n",
    "\n",
    "    # Tokenize each dialogue.\n",
    "    dataset = dataset.map(tokenize, batched=False)\n",
    "    dataset.set_format(type=\"torch\")\n",
    "    \n",
    "    # Split the dataset into train and test parts.\n",
    "    dataset_splits = dataset.train_test_split(test_size=0.2, shuffle=False, seed=42)\n",
    "\n",
    "    return dataset_splits\n",
    "\n",
    "dataset = build_dataset(model_name=model_name,\n",
    "                        dataset_name=huggingface_dataset_name,\n",
    "                        input_min_text_length=200, \n",
    "                        input_max_text_length=1000)\n",
    "\n",
    "print(dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95e2abdd-d3c2-4e79-baa7-00112b600885",
   "metadata": {},
   "source": [
    "## Let's take a look at all of the parameters all model consumes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a374bbeb-f48e-4826-a63e-a06a928cb1b7",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def print_number_of_trainable_model_parameters(model):\n",
    "    trainable_model_params = 0\n",
    "    all_model_params = 0\n",
    "    for _, param in model.named_parameters():\n",
    "        all_model_params += param.numel()\n",
    "        if param.requires_grad:\n",
    "            trainable_model_params += param.numel()\n",
    "    print (f\"\\ntrainable model parameters: {trainable_model_params}\\nall model parameters: {all_model_params}\\npercentage of trainable model parameters: {100 * trainable_model_params / all_model_params:.2f}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0cc4b4d8-515a-42e7-948e-10e8741106c2",
   "metadata": {},
   "source": [
    "### LoRA:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "cd745a07-605f-4c35-8516-c136008e1405",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading (…)lve/main/config.json: 100%|██████████| 1.40k/1.40k [00:00<00:00, 4.72MB/s]\n",
      "Downloading model.safetensors: 100%|██████████| 990M/990M [00:04<00:00, 231MB/s] \n",
      "Downloading (…)neration_config.json: 100%|██████████| 147/147 [00:00<00:00, 609kB/s]\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "Can't find 'adapter_config.json' at '/kaggle/input/generative-ai-with-llms-lab-3/lab_3/peft-dialogue-summary-checkpoint-from-s3/'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mHFValidationError\u001b[0m                         Traceback (most recent call last)",
      "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/peft/utils/config.py:106\u001b[0m, in \u001b[0;36mPeftConfigMixin.from_pretrained\u001b[0;34m(cls, pretrained_model_name_or_path, subfolder, **kwargs)\u001b[0m\n\u001b[1;32m    105\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 106\u001b[0m     config_file \u001b[38;5;241m=\u001b[39m \u001b[43mhf_hub_download\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpretrained_model_name_or_path\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mCONFIG_NAME\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msubfolder\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msubfolder\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    107\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m:\n",
      "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py:110\u001b[0m, in \u001b[0;36mvalidate_hf_hub_args.<locals>._inner_fn\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    109\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m arg_name \u001b[38;5;129;01min\u001b[39;00m [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrepo_id\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfrom_id\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mto_id\u001b[39m\u001b[38;5;124m\"\u001b[39m]:\n\u001b[0;32m--> 110\u001b[0m     \u001b[43mvalidate_repo_id\u001b[49m\u001b[43m(\u001b[49m\u001b[43marg_value\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    112\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m arg_name \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtoken\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m arg_value \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py:158\u001b[0m, in \u001b[0;36mvalidate_repo_id\u001b[0;34m(repo_id)\u001b[0m\n\u001b[1;32m    157\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m repo_id\u001b[38;5;241m.\u001b[39mcount(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m/\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m--> 158\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m HFValidationError(\n\u001b[1;32m    159\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mRepo id must be in the form \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrepo_name\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m or \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnamespace/repo_name\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m:\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    160\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mrepo_id\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m. Use `repo_type` argument if needed.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    161\u001b[0m     )\n\u001b[1;32m    163\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m REPO_ID_REGEX\u001b[38;5;241m.\u001b[39mmatch(repo_id):\n",
      "\u001b[0;31mHFValidationError\u001b[0m: Repo id must be in the form 'repo_name' or 'namespace/repo_name': '/kaggle/input/generative-ai-with-llms-lab-3/lab_3/peft-dialogue-summary-checkpoint-from-s3/'. Use `repo_type` argument if needed.",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[22], line 13\u001b[0m\n\u001b[1;32m      1\u001b[0m lora_config \u001b[38;5;241m=\u001b[39m LoraConfig(\n\u001b[1;32m      2\u001b[0m     r\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m32\u001b[39m, \u001b[38;5;66;03m# Rank\u001b[39;00m\n\u001b[1;32m      3\u001b[0m     lora_alpha\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m32\u001b[39m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m      7\u001b[0m     task_type\u001b[38;5;241m=\u001b[39mTaskType\u001b[38;5;241m.\u001b[39mSEQ_2_SEQ_LM \u001b[38;5;66;03m# FLAN-T5\u001b[39;00m\n\u001b[1;32m      8\u001b[0m )\n\u001b[1;32m     10\u001b[0m model \u001b[38;5;241m=\u001b[39m AutoModelForSeq2SeqLM\u001b[38;5;241m.\u001b[39mfrom_pretrained(model_name, \n\u001b[1;32m     11\u001b[0m                                               torch_dtype\u001b[38;5;241m=\u001b[39mtorch\u001b[38;5;241m.\u001b[39mbfloat16)\n\u001b[0;32m---> 13\u001b[0m peft_model \u001b[38;5;241m=\u001b[39m \u001b[43mPeftModel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_pretrained\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[1;32m     14\u001b[0m \u001b[43m                                       \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m/kaggle/input/generative-ai-with-llms-lab-3/lab_3/peft-dialogue-summary-checkpoint-from-s3/\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[1;32m     15\u001b[0m \u001b[43m                                       \u001b[49m\u001b[43mlora_config\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlora_config\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     16\u001b[0m \u001b[43m                                       \u001b[49m\u001b[43mtorch_dtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbfloat16\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[1;32m     17\u001b[0m \u001b[43m                                       \u001b[49m\u001b[43mdevice_map\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mauto\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m                                       \u001b[49m\n\u001b[1;32m     18\u001b[0m \u001b[43m                                       \u001b[49m\u001b[43mis_trainable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m     20\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mPEFT model parameters to be updated:\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00mprint_number_of_trainable_model_parameters(peft_model)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m'\u001b[39m)\n",
      "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/peft/peft_model.py:164\u001b[0m, in \u001b[0;36mPeftModel.from_pretrained\u001b[0;34m(cls, model, model_id, adapter_name, is_trainable, **kwargs)\u001b[0m\n\u001b[1;32m    160\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmapping\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m MODEL_TYPE_TO_PEFT_MODEL_MAPPING, PEFT_TYPE_TO_CONFIG_MAPPING\n\u001b[1;32m    162\u001b[0m \u001b[38;5;66;03m# load the config\u001b[39;00m\n\u001b[1;32m    163\u001b[0m config \u001b[38;5;241m=\u001b[39m PEFT_TYPE_TO_CONFIG_MAPPING[\n\u001b[0;32m--> 164\u001b[0m     \u001b[43mPeftConfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_pretrained\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel_id\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msubfolder\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43msubfolder\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mpeft_type\n\u001b[1;32m    165\u001b[0m ]\u001b[38;5;241m.\u001b[39mfrom_pretrained(model_id, subfolder\u001b[38;5;241m=\u001b[39mkwargs\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msubfolder\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m))\n\u001b[1;32m    167\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\u001b[38;5;28mgetattr\u001b[39m(model, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhf_device_map\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m) \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(\n\u001b[1;32m    168\u001b[0m     \u001b[38;5;28mset\u001b[39m(model\u001b[38;5;241m.\u001b[39mhf_device_map\u001b[38;5;241m.\u001b[39mvalues())\u001b[38;5;241m.\u001b[39mintersection({\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcpu\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdisk\u001b[39m\u001b[38;5;124m\"\u001b[39m})\n\u001b[1;32m    169\u001b[0m ) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m    170\u001b[0m     remove_hook_from_submodules(model)\n",
      "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/peft/utils/config.py:108\u001b[0m, in \u001b[0;36mPeftConfigMixin.from_pretrained\u001b[0;34m(cls, pretrained_model_name_or_path, subfolder, **kwargs)\u001b[0m\n\u001b[1;32m    106\u001b[0m         config_file \u001b[38;5;241m=\u001b[39m hf_hub_download(pretrained_model_name_or_path, CONFIG_NAME, subfolder\u001b[38;5;241m=\u001b[39msubfolder)\n\u001b[1;32m    107\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m:\n\u001b[0;32m--> 108\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCan\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt find \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mCONFIG_NAME\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m at \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpretrained_model_name_or_path\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    110\u001b[0m loaded_attributes \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39mfrom_json_file(config_file)\n\u001b[1;32m    112\u001b[0m config \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mcls\u001b[39m(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "\u001b[0;31mValueError\u001b[0m: Can't find 'adapter_config.json' at '/kaggle/input/generative-ai-with-llms-lab-3/lab_3/peft-dialogue-summary-checkpoint-from-s3/'"
     ]
    }
   ],
   "source": [
    "lora_config = LoraConfig(\n",
    "    r=32, # Rank\n",
    "    lora_alpha=32,\n",
    "    target_modules=[\"q\", \"v\"],\n",
    "    lora_dropout=0.05,\n",
    "    bias=\"none\",\n",
    "    task_type=TaskType.SEQ_2_SEQ_LM # FLAN-T5\n",
    ")\n",
    "\n",
    "model = AutoModelForSeq2SeqLM.from_pretrained(model_name, \n",
    "                                              torch_dtype=torch.bfloat16)\n",
    "\n",
    "peft_model = PeftModel.from_pretrained(model, \n",
    "                                       '/kaggle/input/generative-ai-with-llms-lab-3/lab_3/peft-dialogue-summary-checkpoint-from-s3/', \n",
    "                                       lora_config=lora_config,\n",
    "                                       torch_dtype=torch.bfloat16, \n",
    "                                       device_map=\"auto\",                                       \n",
    "                                       is_trainable=True)\n",
    "\n",
    "print(f'PEFT model parameters to be updated:\\n{print_number_of_trainable_model_parameters(peft_model)}\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "20e46f01-80c1-48fc-9944-7bd53ca77a5d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "availableInstances": [
   {
    "_defaultOrder": 0,
    "_isFastLaunch": true,
    "category": "General purpose",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 4,
    "name": "ml.t3.medium",
    "vcpuNum": 2
   },
   {
    "_defaultOrder": 1,
    "_isFastLaunch": false,
    "category": "General purpose",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 8,
    "name": "ml.t3.large",
    "vcpuNum": 2
   },
   {
    "_defaultOrder": 2,
    "_isFastLaunch": false,
    "category": "General purpose",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 16,
    "name": "ml.t3.xlarge",
    "vcpuNum": 4
   },
   {
    "_defaultOrder": 3,
    "_isFastLaunch": false,
    "category": "General purpose",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 32,
    "name": "ml.t3.2xlarge",
    "vcpuNum": 8
   },
   {
    "_defaultOrder": 4,
    "_isFastLaunch": true,
    "category": "General purpose",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 8,
    "name": "ml.m5.large",
    "vcpuNum": 2
   },
   {
    "_defaultOrder": 5,
    "_isFastLaunch": false,
    "category": "General purpose",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 16,
    "name": "ml.m5.xlarge",
    "vcpuNum": 4
   },
   {
    "_defaultOrder": 6,
    "_isFastLaunch": false,
    "category": "General purpose",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 32,
    "name": "ml.m5.2xlarge",
    "vcpuNum": 8
   },
   {
    "_defaultOrder": 7,
    "_isFastLaunch": false,
    "category": "General purpose",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 64,
    "name": "ml.m5.4xlarge",
    "vcpuNum": 16
   },
   {
    "_defaultOrder": 8,
    "_isFastLaunch": false,
    "category": "General purpose",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 128,
    "name": "ml.m5.8xlarge",
    "vcpuNum": 32
   },
   {
    "_defaultOrder": 9,
    "_isFastLaunch": false,
    "category": "General purpose",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 192,
    "name": "ml.m5.12xlarge",
    "vcpuNum": 48
   },
   {
    "_defaultOrder": 10,
    "_isFastLaunch": false,
    "category": "General purpose",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 256,
    "name": "ml.m5.16xlarge",
    "vcpuNum": 64
   },
   {
    "_defaultOrder": 11,
    "_isFastLaunch": false,
    "category": "General purpose",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 384,
    "name": "ml.m5.24xlarge",
    "vcpuNum": 96
   },
   {
    "_defaultOrder": 12,
    "_isFastLaunch": false,
    "category": "General purpose",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 8,
    "name": "ml.m5d.large",
    "vcpuNum": 2
   },
   {
    "_defaultOrder": 13,
    "_isFastLaunch": false,
    "category": "General purpose",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 16,
    "name": "ml.m5d.xlarge",
    "vcpuNum": 4
   },
   {
    "_defaultOrder": 14,
    "_isFastLaunch": false,
    "category": "General purpose",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 32,
    "name": "ml.m5d.2xlarge",
    "vcpuNum": 8
   },
   {
    "_defaultOrder": 15,
    "_isFastLaunch": false,
    "category": "General purpose",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 64,
    "name": "ml.m5d.4xlarge",
    "vcpuNum": 16
   },
   {
    "_defaultOrder": 16,
    "_isFastLaunch": false,
    "category": "General purpose",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 128,
    "name": "ml.m5d.8xlarge",
    "vcpuNum": 32
   },
   {
    "_defaultOrder": 17,
    "_isFastLaunch": false,
    "category": "General purpose",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 192,
    "name": "ml.m5d.12xlarge",
    "vcpuNum": 48
   },
   {
    "_defaultOrder": 18,
    "_isFastLaunch": false,
    "category": "General purpose",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 256,
    "name": "ml.m5d.16xlarge",
    "vcpuNum": 64
   },
   {
    "_defaultOrder": 19,
    "_isFastLaunch": false,
    "category": "General purpose",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 384,
    "name": "ml.m5d.24xlarge",
    "vcpuNum": 96
   },
   {
    "_defaultOrder": 20,
    "_isFastLaunch": false,
    "category": "General purpose",
    "gpuNum": 0,
    "hideHardwareSpecs": true,
    "memoryGiB": 0,
    "name": "ml.geospatial.interactive",
    "supportedImageNames": [
     "sagemaker-geospatial-v1-0"
    ],
    "vcpuNum": 0
   },
   {
    "_defaultOrder": 21,
    "_isFastLaunch": true,
    "category": "Compute optimized",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 4,
    "name": "ml.c5.large",
    "vcpuNum": 2
   },
   {
    "_defaultOrder": 22,
    "_isFastLaunch": false,
    "category": "Compute optimized",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 8,
    "name": "ml.c5.xlarge",
    "vcpuNum": 4
   },
   {
    "_defaultOrder": 23,
    "_isFastLaunch": false,
    "category": "Compute optimized",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 16,
    "name": "ml.c5.2xlarge",
    "vcpuNum": 8
   },
   {
    "_defaultOrder": 24,
    "_isFastLaunch": false,
    "category": "Compute optimized",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 32,
    "name": "ml.c5.4xlarge",
    "vcpuNum": 16
   },
   {
    "_defaultOrder": 25,
    "_isFastLaunch": false,
    "category": "Compute optimized",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 72,
    "name": "ml.c5.9xlarge",
    "vcpuNum": 36
   },
   {
    "_defaultOrder": 26,
    "_isFastLaunch": false,
    "category": "Compute optimized",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 96,
    "name": "ml.c5.12xlarge",
    "vcpuNum": 48
   },
   {
    "_defaultOrder": 27,
    "_isFastLaunch": false,
    "category": "Compute optimized",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 144,
    "name": "ml.c5.18xlarge",
    "vcpuNum": 72
   },
   {
    "_defaultOrder": 28,
    "_isFastLaunch": false,
    "category": "Compute optimized",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 192,
    "name": "ml.c5.24xlarge",
    "vcpuNum": 96
   },
   {
    "_defaultOrder": 29,
    "_isFastLaunch": true,
    "category": "Accelerated computing",
    "gpuNum": 1,
    "hideHardwareSpecs": false,
    "memoryGiB": 16,
    "name": "ml.g4dn.xlarge",
    "vcpuNum": 4
   },
   {
    "_defaultOrder": 30,
    "_isFastLaunch": false,
    "category": "Accelerated computing",
    "gpuNum": 1,
    "hideHardwareSpecs": false,
    "memoryGiB": 32,
    "name": "ml.g4dn.2xlarge",
    "vcpuNum": 8
   },
   {
    "_defaultOrder": 31,
    "_isFastLaunch": false,
    "category": "Accelerated computing",
    "gpuNum": 1,
    "hideHardwareSpecs": false,
    "memoryGiB": 64,
    "name": "ml.g4dn.4xlarge",
    "vcpuNum": 16
   },
   {
    "_defaultOrder": 32,
    "_isFastLaunch": false,
    "category": "Accelerated computing",
    "gpuNum": 1,
    "hideHardwareSpecs": false,
    "memoryGiB": 128,
    "name": "ml.g4dn.8xlarge",
    "vcpuNum": 32
   },
   {
    "_defaultOrder": 33,
    "_isFastLaunch": false,
    "category": "Accelerated computing",
    "gpuNum": 4,
    "hideHardwareSpecs": false,
    "memoryGiB": 192,
    "name": "ml.g4dn.12xlarge",
    "vcpuNum": 48
   },
   {
    "_defaultOrder": 34,
    "_isFastLaunch": false,
    "category": "Accelerated computing",
    "gpuNum": 1,
    "hideHardwareSpecs": false,
    "memoryGiB": 256,
    "name": "ml.g4dn.16xlarge",
    "vcpuNum": 64
   },
   {
    "_defaultOrder": 35,
    "_isFastLaunch": false,
    "category": "Accelerated computing",
    "gpuNum": 1,
    "hideHardwareSpecs": false,
    "memoryGiB": 61,
    "name": "ml.p3.2xlarge",
    "vcpuNum": 8
   },
   {
    "_defaultOrder": 36,
    "_isFastLaunch": false,
    "category": "Accelerated computing",
    "gpuNum": 4,
    "hideHardwareSpecs": false,
    "memoryGiB": 244,
    "name": "ml.p3.8xlarge",
    "vcpuNum": 32
   },
   {
    "_defaultOrder": 37,
    "_isFastLaunch": false,
    "category": "Accelerated computing",
    "gpuNum": 8,
    "hideHardwareSpecs": false,
    "memoryGiB": 488,
    "name": "ml.p3.16xlarge",
    "vcpuNum": 64
   },
   {
    "_defaultOrder": 38,
    "_isFastLaunch": false,
    "category": "Accelerated computing",
    "gpuNum": 8,
    "hideHardwareSpecs": false,
    "memoryGiB": 768,
    "name": "ml.p3dn.24xlarge",
    "vcpuNum": 96
   },
   {
    "_defaultOrder": 39,
    "_isFastLaunch": false,
    "category": "Memory Optimized",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 16,
    "name": "ml.r5.large",
    "vcpuNum": 2
   },
   {
    "_defaultOrder": 40,
    "_isFastLaunch": false,
    "category": "Memory Optimized",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 32,
    "name": "ml.r5.xlarge",
    "vcpuNum": 4
   },
   {
    "_defaultOrder": 41,
    "_isFastLaunch": false,
    "category": "Memory Optimized",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 64,
    "name": "ml.r5.2xlarge",
    "vcpuNum": 8
   },
   {
    "_defaultOrder": 42,
    "_isFastLaunch": false,
    "category": "Memory Optimized",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 128,
    "name": "ml.r5.4xlarge",
    "vcpuNum": 16
   },
   {
    "_defaultOrder": 43,
    "_isFastLaunch": false,
    "category": "Memory Optimized",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 256,
    "name": "ml.r5.8xlarge",
    "vcpuNum": 32
   },
   {
    "_defaultOrder": 44,
    "_isFastLaunch": false,
    "category": "Memory Optimized",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 384,
    "name": "ml.r5.12xlarge",
    "vcpuNum": 48
   },
   {
    "_defaultOrder": 45,
    "_isFastLaunch": false,
    "category": "Memory Optimized",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 512,
    "name": "ml.r5.16xlarge",
    "vcpuNum": 64
   },
   {
    "_defaultOrder": 46,
    "_isFastLaunch": false,
    "category": "Memory Optimized",
    "gpuNum": 0,
    "hideHardwareSpecs": false,
    "memoryGiB": 768,
    "name": "ml.r5.24xlarge",
    "vcpuNum": 96
   },
   {
    "_defaultOrder": 47,
    "_isFastLaunch": false,
    "category": "Accelerated computing",
    "gpuNum": 1,
    "hideHardwareSpecs": false,
    "memoryGiB": 16,
    "name": "ml.g5.xlarge",
    "vcpuNum": 4
   },
   {
    "_defaultOrder": 48,
    "_isFastLaunch": false,
    "category": "Accelerated computing",
    "gpuNum": 1,
    "hideHardwareSpecs": false,
    "memoryGiB": 32,
    "name": "ml.g5.2xlarge",
    "vcpuNum": 8
   },
   {
    "_defaultOrder": 49,
    "_isFastLaunch": false,
    "category": "Accelerated computing",
    "gpuNum": 1,
    "hideHardwareSpecs": false,
    "memoryGiB": 64,
    "name": "ml.g5.4xlarge",
    "vcpuNum": 16
   },
   {
    "_defaultOrder": 50,
    "_isFastLaunch": false,
    "category": "Accelerated computing",
    "gpuNum": 1,
    "hideHardwareSpecs": false,
    "memoryGiB": 128,
    "name": "ml.g5.8xlarge",
    "vcpuNum": 32
   },
   {
    "_defaultOrder": 51,
    "_isFastLaunch": false,
    "category": "Accelerated computing",
    "gpuNum": 1,
    "hideHardwareSpecs": false,
    "memoryGiB": 256,
    "name": "ml.g5.16xlarge",
    "vcpuNum": 64
   },
   {
    "_defaultOrder": 52,
    "_isFastLaunch": false,
    "category": "Accelerated computing",
    "gpuNum": 4,
    "hideHardwareSpecs": false,
    "memoryGiB": 192,
    "name": "ml.g5.12xlarge",
    "vcpuNum": 48
   },
   {
    "_defaultOrder": 53,
    "_isFastLaunch": false,
    "category": "Accelerated computing",
    "gpuNum": 4,
    "hideHardwareSpecs": false,
    "memoryGiB": 384,
    "name": "ml.g5.24xlarge",
    "vcpuNum": 96
   },
   {
    "_defaultOrder": 54,
    "_isFastLaunch": false,
    "category": "Accelerated computing",
    "gpuNum": 8,
    "hideHardwareSpecs": false,
    "memoryGiB": 768,
    "name": "ml.g5.48xlarge",
    "vcpuNum": 192
   },
   {
    "_defaultOrder": 55,
    "_isFastLaunch": false,
    "category": "Accelerated computing",
    "gpuNum": 8,
    "hideHardwareSpecs": false,
    "memoryGiB": 1152,
    "name": "ml.p4d.24xlarge",
    "vcpuNum": 96
   },
   {
    "_defaultOrder": 56,
    "_isFastLaunch": false,
    "category": "Accelerated computing",
    "gpuNum": 8,
    "hideHardwareSpecs": false,
    "memoryGiB": 1152,
    "name": "ml.p4de.24xlarge",
    "vcpuNum": 96
   }
  ],
  "instance_type": "ml.t3.medium",
  "kernelspec": {
   "display_name": "Python 3 (Data Science 3.0)",
   "language": "python",
   "name": "python3__SAGEMAKER_INTERNAL__arn:aws:sagemaker:us-east-1:081325390199:image/sagemaker-data-science-310-v1"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}