{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "dc468afe",
   "metadata": {
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     "start_time": "2022-04-18T00:24:52.486821",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Hugging Face Sentiment Classification\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
    "\n",
    "![This us-west-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://h75twx4l60.execute-api.us-west-2.amazonaws.com/sagemaker-nb/us-west-2/introduction_to_applying_machine_learning|huggingface_sentiment_classification|huggingface_sentiment_outputs.ipynb)\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc468afe",
   "metadata": {
    "papermill": {
     "duration": 0.016531,
     "end_time": "2022-04-18T00:24:52.503352",
     "exception": false,
     "start_time": "2022-04-18T00:24:52.486821",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "__Binary Classification with Trainer and sst2 dataset__"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2452cc25",
   "metadata": {
    "papermill": {
     "duration": 0.015983,
     "end_time": "2022-04-18T00:24:52.535552",
     "exception": false,
     "start_time": "2022-04-18T00:24:52.519569",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Runtime\n",
    "\n",
    "This notebook takes approximately 45 minutes to run.\n",
    "\n",
    "## Contents\n",
    "\n",
    "1. [Introduction](#Introduction)  \n",
    "2. [Development environment and permissions](#Development-environment-and-permissions)\n",
    "    1. [Installation](#Installation)  \n",
    "    2. [Development environment](#Development-environment)  \n",
    "    3. [Permissions](#Permissions)\n",
    "3. [Pre-processing](#Pre-processing)   \n",
    "    1. [Tokenize sentences](#Tokenize-sentences)  \n",
    "    2. [Upload data to sagemaker_session_bucket](#Upload-data-to-sagemaker_session_bucket)  \n",
    "4. [Fine-tune the model and start a SageMaker training job](#Fine-tune-the-model-and-start-a-SageMaker-training-job)  \n",
    "    1. [Create an Estimator and start a training job](#Create-an-Estimator-and-start-a-training-job)  \n",
    "    2. [Estimator Parameters](#Estimator-Parameters)   \n",
    "    3. [Attach a previous training job to an estimator](#Attach-a-previous-training-job-to-an-estimator)  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "effb3477",
   "metadata": {
    "papermill": {
     "duration": 0.016278,
     "end_time": "2022-04-18T00:24:52.568387",
     "exception": false,
     "start_time": "2022-04-18T00:24:52.552109",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Introduction\n",
    "\n",
    "Welcome to our end-to-end binary text classification example. This notebook uses Hugging Face's `transformers` library with a custom Amazon sagemaker-sdk extension to fine-tune a pre-trained transformer on binary text classification. The pre-trained model is fine-tuned using the `sst2` dataset. To get started, we need to set up the environment with a few prerequisite steps for permissions, configurations, and so on. \n",
    "\n",
    "This notebook is adapted from Hugging Face's notebook [Huggingface Sagemaker-sdk - Getting Started Demo](https://github.com/huggingface/notebooks/blob/master/sagemaker/01_getting_started_pytorch/sagemaker-notebook.ipynb) and provided here courtesy of Hugging Face.\n",
    "\n",
    "<img src=\"text_classification.png\" width=\"700\"/>\n",
    "\n",
    "## Runtime\n",
    "\n",
    "This notebook takes approximately 40 minutes to run.\n",
    "\n",
    "<i>NOTE: You can run this notebook in SageMaker Studio, a SageMaker notebook instance, or your local machine. This notebook was tested in a notebook instance using the conda\\_pytorch\\_p36 kernel.</i>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e8f5307",
   "metadata": {
    "papermill": {
     "duration": 0.016313,
     "end_time": "2022-04-18T00:24:52.601194",
     "exception": false,
     "start_time": "2022-04-18T00:24:52.584881",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Development environment and permissions "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15b92a49",
   "metadata": {
    "papermill": {
     "duration": 0.016043,
     "end_time": "2022-04-18T00:24:52.633273",
     "exception": false,
     "start_time": "2022-04-18T00:24:52.617230",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Installation\n",
    "\n",
    "_*Note:* We install the required libraries from Hugging Face and AWS. You also need PyTorch, if you haven't installed it already._"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8533e87c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T00:24:52.670786Z",
     "iopub.status.busy": "2022-04-18T00:24:52.670186Z",
     "iopub.status.idle": "2022-04-18T00:26:26.077525Z",
     "shell.execute_reply": "2022-04-18T00:26:26.076694Z"
    },
    "papermill": {
     "duration": 93.428366,
     "end_time": "2022-04-18T00:26:26.077712",
     "exception": false,
     "start_time": "2022-04-18T00:24:52.649346",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: sagemaker in /opt/conda/lib/python3.6/site-packages (2.69.1.dev0)\n",
      "Collecting sagemaker\n",
      "  Downloading sagemaker-2.86.2.tar.gz (521 kB)\n",
      "\u001b[K     |\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 521 kB 6.6 MB/s eta 0:00:01\n",
      "...\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",
      "thinc 8.0.2 requires typing-extensions<4.0.0.0,>=3.7.4.1; python_version < \"3.8\", but you have typing-extensions 4.1.1 which is incompatible.\n",
      "spacy 3.0.5 requires typing-extensions<4.0.0.0,>=3.7.4; python_version < \"3.8\", but you have typing-extensions 4.1.1 which is incompatible.\n",
      "awscli 1.22.7 requires botocore==1.23.7, but you have botocore 1.23.24 which is incompatible.\u001b[0m\n",
      "Successfully installed aiobotocore-2.1.2 aiohttp-3.8.1 aioitertools-0.10.0 aiosignal-1.2.0 async-timeout-4.0.2 asynctest-0.13.0 boto3-1.20.24 botocore-1.23.24 charset-normalizer-2.0.12 datasets-2.1.0 filelock-3.4.1 frozenlist-1.2.0 fsspec-2022.1.0 huggingface-hub-0.4.0 idna-ssl-1.1.0 importlib-resources-5.4.0 multidict-5.2.0 regex-2022.3.15 responses-0.17.0 s3fs-2022.1.0 sacremoses-0.0.49 sagemaker-2.86.2 tokenizers-0.12.1 tqdm-4.64.0 transformers-4.18.0 typing-extensions-4.1.1 wrapt-1.14.0 xxhash-3.0.0 yarl-1.7.2\n"
     ]
    }
   ],
   "source": [
    "!pip install \"sagemaker\" \"transformers\" \"datasets[s3]\" \"s3fs\" --upgrade"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ba2da73",
   "metadata": {
    "papermill": {
     "duration": 0.52037,
     "end_time": "2022-04-18T00:26:27.084219",
     "exception": false,
     "start_time": "2022-04-18T00:26:26.563849",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Development environment "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0f92e4f1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T00:26:28.268992Z",
     "iopub.status.busy": "2022-04-18T00:26:28.266328Z",
     "iopub.status.idle": "2022-04-18T00:26:33.266343Z",
     "shell.execute_reply": "2022-04-18T00:26:33.265862Z"
    },
    "papermill": {
     "duration": 5.594308,
     "end_time": "2022-04-18T00:26:33.266483",
     "exception": false,
     "start_time": "2022-04-18T00:26:27.672175",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import sagemaker.huggingface"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "065e0cbe",
   "metadata": {
    "papermill": {
     "duration": 0.208248,
     "end_time": "2022-04-18T00:26:33.684347",
     "exception": false,
     "start_time": "2022-04-18T00:26:33.476099",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Permissions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52d6f89b",
   "metadata": {
    "papermill": {
     "duration": 0.217336,
     "end_time": "2022-04-18T00:26:34.182419",
     "exception": false,
     "start_time": "2022-04-18T00:26:33.965083",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "_If you are going to use SageMaker in a local environment, you need access to an IAM Role with the required permissions for SageMaker. You can read more at [SageMaker Roles](https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-roles.html)._"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4c655deb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T00:26:35.173041Z",
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     "iopub.status.idle": "2022-04-18T00:26:38.877002Z",
     "shell.execute_reply": "2022-04-18T00:26:38.878030Z"
    },
    "papermill": {
     "duration": 4.214375,
     "end_time": "2022-04-18T00:26:38.878222",
     "exception": false,
     "start_time": "2022-04-18T00:26:34.663847",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Role arn: arn:aws:iam::000000000000:role/ProdBuildSystemStack-ReleaseBuildRoleFB326D49-QK8LUA2UI1IC\n",
      "Bucket: sagemaker-us-west-2-521695447989\n",
      "Region: us-west-2\n"
     ]
    }
   ],
   "source": [
    "import sagemaker\n",
    "\n",
    "sess = sagemaker.Session()\n",
    "# The SageMaker session bucket is used for uploading data, models and logs\n",
    "# SageMaker will automatically create this bucket if it doesn't exist\n",
    "sagemaker_session_bucket = None\n",
    "if sagemaker_session_bucket is None and sess is not None:\n",
    "    # Set to default bucket if a bucket name is not given\n",
    "    sagemaker_session_bucket = sess.default_bucket()\n",
    "\n",
    "role = sagemaker.get_execution_role()\n",
    "sess = sagemaker.Session(default_bucket=sagemaker_session_bucket)\n",
    "\n",
    "print(f\"Role arn: {role}\")\n",
    "print(f\"Bucket: {sess.default_bucket()}\")\n",
    "print(f\"Region: {sess.boto_region_name}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09bc6925",
   "metadata": {
    "papermill": {
     "duration": 0.590734,
     "end_time": "2022-04-18T00:26:40.264636",
     "exception": false,
     "start_time": "2022-04-18T00:26:39.673902",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Pre-processing\n",
    "\n",
    "We use the `datasets` library to pre-process the `sst2` dataset (Stanford Sentiment Treebank). After pre-processing, the dataset is uploaded to the `sagemaker_session_bucket` for use within the training job. The [sst2](https://nlp.stanford.edu/sentiment/index.html) dataset consists of 67349 training samples and _ testing samples of highly polar movie reviews."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99ebe331",
   "metadata": {
    "papermill": {
     "duration": 0.600198,
     "end_time": "2022-04-18T00:26:41.375789",
     "exception": false,
     "start_time": "2022-04-18T00:26:40.775591",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Download the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "44647298",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T00:26:42.775808Z",
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    "papermill": {
     "duration": 18.999606,
     "end_time": "2022-04-18T00:27:01.075923",
     "exception": false,
     "start_time": "2022-04-18T00:26:42.076317",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
      "                                 Dload  Upload   Total   Spent    Left  Speed\n",
      "100  189k  100  189k    0     0  58903      0  0:00:03  0:00:03 --:--:-- 58917\n",
      "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
      "                                 Dload  Upload   Total   Spent    Left  Speed\n",
      "100 3716k  100 3716k    0     0  2194k      0  0:00:01  0:00:01 --:--:-- 2195k\n",
      "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
      "                                 Dload  Upload   Total   Spent    Left  Speed\n",
      "100 94916  100 94916    0     0  80806      0  0:00:01  0:00:01 --:--:-- 80848\n"
     ]
    }
   ],
   "source": [
    "from datasets import Dataset\n",
    "from transformers import AutoTokenizer\n",
    "import pandas as pd\n",
    "\n",
    "# Tokenizer used in pre-processing\n",
    "tokenizer_name = \"distilbert-base-uncased\"\n",
    "\n",
    "# S3 key prefix for the data\n",
    "s3_prefix = \"DEMO-samples/datasets/sst\"\n",
    "\n",
    "# Download the SST2 data from s3\n",
    "!curl https://sagemaker-sample-files.s3.amazonaws.com/datasets/text/SST2/sst2.test > ./sst2.test\n",
    "!curl https://sagemaker-sample-files.s3.amazonaws.com/datasets/text/SST2/sst2.train > ./sst2.train\n",
    "!curl https://sagemaker-sample-files.s3.amazonaws.com/datasets/text/SST2/sst2.val > ./sst2.val"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7e3efe3",
   "metadata": {
    "papermill": {
     "duration": 0.604992,
     "end_time": "2022-04-18T00:27:02.280819",
     "exception": false,
     "start_time": "2022-04-18T00:27:01.675827",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Tokenize sentences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b69a3cd7",
   "metadata": {
    "execution": {
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     "exception": false,
     "start_time": "2022-04-18T00:27:02.978876",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
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   "source": [
    "# Download tokenizer\n",
    "tokenizer = AutoTokenizer.from_pretrained(tokenizer_name)\n",
    "\n",
    "# Tokenizer helper function\n",
    "def tokenize(batch):\n",
    "    return tokenizer(batch[\"text\"], padding=\"max_length\", truncation=True)\n",
    "\n",
    "\n",
    "# Load dataset\n",
    "test_df = pd.read_csv(\"sst2.test\", sep=\"delimiter\", header=None, engine=\"python\", names=[\"line\"])\n",
    "train_df = pd.read_csv(\"sst2.train\", sep=\"delimiter\", header=None, engine=\"python\", names=[\"line\"])\n",
    "\n",
    "test_df[[\"label\", \"text\"]] = test_df[\"line\"].str.split(\" \", 1, expand=True)\n",
    "train_df[[\"label\", \"text\"]] = train_df[\"line\"].str.split(\" \", 1, expand=True)\n",
    "\n",
    "test_df.drop(\"line\", axis=1, inplace=True)\n",
    "train_df.drop(\"line\", axis=1, inplace=True)\n",
    "\n",
    "test_df[\"label\"] = pd.to_numeric(test_df[\"label\"], downcast=\"integer\")\n",
    "train_df[\"label\"] = pd.to_numeric(train_df[\"label\"], downcast=\"integer\")\n",
    "\n",
    "train_dataset = Dataset.from_pandas(train_df)\n",
    "test_dataset = Dataset.from_pandas(test_df)\n",
    "\n",
    "# Tokenize dataset\n",
    "train_dataset = train_dataset.map(tokenize, batched=True)\n",
    "test_dataset = test_dataset.map(tokenize, batched=True)\n",
    "\n",
    "# Set format for pytorch\n",
    "train_dataset = train_dataset.rename_column(\"label\", \"labels\")\n",
    "train_dataset.set_format(\"torch\", columns=[\"input_ids\", \"attention_mask\", \"labels\"])\n",
    "\n",
    "test_dataset = test_dataset.rename_column(\"label\", \"labels\")\n",
    "test_dataset.set_format(\"torch\", columns=[\"input_ids\", \"attention_mask\", \"labels\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b70101a6",
   "metadata": {
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     "duration": 0.290129,
     "end_time": "2022-04-18T00:28:02.670975",
     "exception": false,
     "start_time": "2022-04-18T00:28:02.380846",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Upload data to `sagemaker_session_bucket`\n",
    "\n",
    "After processing the `datasets`, we use the `FileSystem` [integration](https://huggingface.co/docs/datasets/filesystems.html) to upload the dataset to S3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "38ca822d",
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    "execution": {
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   "source": [
    "import botocore\n",
    "from datasets.filesystems import S3FileSystem\n",
    "\n",
    "s3 = S3FileSystem()\n",
    "\n",
    "# save train_dataset to s3\n",
    "training_input_path = f\"s3://{sess.default_bucket()}/{s3_prefix}/train\"\n",
    "train_dataset.save_to_disk(training_input_path, fs=s3)\n",
    "\n",
    "# save test_dataset to s3\n",
    "test_input_path = f\"s3://{sess.default_bucket()}/{s3_prefix}/test\"\n",
    "test_dataset.save_to_disk(test_input_path, fs=s3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "699717f6",
   "metadata": {
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     "duration": 0.07092,
     "end_time": "2022-04-18T00:28:09.507040",
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    "tags": []
   },
   "source": [
    "## Fine-tune the model and start a SageMaker training job\n",
    "\n",
    "In order to create a SageMaker training job, we need a `HuggingFace` Estimator. The Estimator handles end-to-end Amazon SageMaker training and deployment tasks. In an Estimator, we define which fine-tuning script should be used as `entry_point`, which `instance_type` should be used, which `hyperparameters` are passed in, etc:\n",
    "\n",
    "\n",
    "\n",
    "```python\n",
    "hf_estimator = HuggingFace(entry_point=\"train.py\",\n",
    "                            source_dir=\"./scripts\",\n",
    "                            base_job_name=\"huggingface-sdk-extension\",\n",
    "                            instance_type=\"ml.p3.2xlarge\",\n",
    "                            instance_count=1,\n",
    "                            transformers_version=\"4.4\",\n",
    "                            pytorch_version=\"1.6\",\n",
    "                            py_version=\"py36\",\n",
    "                            role=role,\n",
    "                            hyperparameters = {\"epochs\": 1,\n",
    "                                               \"train_batch_size\": 32,\n",
    "                                               \"model_name\":\"distilbert-base-uncased\"\n",
    "                                                })\n",
    "```\n",
    "\n",
    "When we create a SageMaker training job, SageMaker takes care of starting and managing all the required EC2 instances for us with the `huggingface` container, uploads the provided fine-tuning script `train.py`, and downloads the data from the `sagemaker_session_bucket` into the container at `/opt/ml/input/data`. Then, it starts the training job by running:\n",
    "\n",
    "```python\n",
    "/opt/conda/bin/python train.py --epochs 1 --model_name distilbert-base-uncased --train_batch_size 32\n",
    "```\n",
    "\n",
    "The `hyperparameters` defined in the `HuggingFace` estimator are passed in as named arguments. \n",
    "\n",
    "SageMaker provides useful properties about the training environment through various environment variables, including the following:\n",
    "\n",
    "* `SM_MODEL_DIR`: A string representing the path where the training job writes the model artifacts to. After training, artifacts in this directory are uploaded to S3 for model hosting.\n",
    "\n",
    "* `SM_NUM_GPUS`: An integer representing the number of GPUs available to the host.\n",
    "\n",
    "* `SM_CHANNEL_XXXX:` A string representing the path to the directory that contains the input data for the specified channel. For example, if you specify two input channels in the Hugging Face estimator's `fit()` call, named `train` and `test`, the environment variables `SM_CHANNEL_TRAIN` and `SM_CHANNEL_TEST` are set.\n",
    "\n",
    "\n",
    "To run the training job locally, you can define `instance_type=\"local\"` or `instance_type=\"local_gpu\"` for GPU usage.\n",
    "\n",
    "_Note: local mode is not supported in SageMaker Studio._\n"
   ]
  },
  {
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34mfrom\u001b[39;49;00m \u001b[04m\u001b[36mtransformers\u001b[39;49;00m \u001b[34mimport\u001b[39;49;00m AutoModelForSequenceClassification, Trainer, TrainingArguments, AutoTokenizer\n",
      "\u001b[34mfrom\u001b[39;49;00m \u001b[04m\u001b[36msklearn\u001b[39;49;00m\u001b[04m\u001b[36m.\u001b[39;49;00m\u001b[04m\u001b[36mmetrics\u001b[39;49;00m \u001b[34mimport\u001b[39;49;00m accuracy_score, precision_recall_fscore_support\n",
      "\u001b[34mfrom\u001b[39;49;00m \u001b[04m\u001b[36mdatasets\u001b[39;49;00m \u001b[34mimport\u001b[39;49;00m load_from_disk\n",
      "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36mrandom\u001b[39;49;00m\n",
      "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36mlogging\u001b[39;49;00m\n",
      "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36msys\u001b[39;49;00m\n",
      "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36margparse\u001b[39;49;00m\n",
      "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36mos\u001b[39;49;00m\n",
      "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36mtorch\u001b[39;49;00m\n",
      "\n",
      "\u001b[34mif\u001b[39;49;00m \u001b[31m__name__\u001b[39;49;00m == \u001b[33m\"\u001b[39;49;00m\u001b[33m__main__\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m:\n",
      "\n",
      "    parser = argparse.ArgumentParser()\n",
      "\n",
      "    \u001b[37m# Hyperparameters sent by the client are passed as command-line arguments to the script\u001b[39;49;00m\n",
      "    parser.add_argument(\u001b[33m\"\u001b[39;49;00m\u001b[33m--epochs\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m, \u001b[36mtype\u001b[39;49;00m=\u001b[36mint\u001b[39;49;00m, default=\u001b[34m3\u001b[39;49;00m)\n",
      "    parser.add_argument(\u001b[33m\"\u001b[39;49;00m\u001b[33m--train_batch_size\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m, \u001b[36mtype\u001b[39;49;00m=\u001b[36mint\u001b[39;49;00m, default=\u001b[34m32\u001b[39;49;00m)\n",
      "    parser.add_argument(\u001b[33m\"\u001b[39;49;00m\u001b[33m--eval_batch_size\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m, \u001b[36mtype\u001b[39;49;00m=\u001b[36mint\u001b[39;49;00m, default=\u001b[34m64\u001b[39;49;00m)\n",
      "    parser.add_argument(\u001b[33m\"\u001b[39;49;00m\u001b[33m--warmup_steps\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m, \u001b[36mtype\u001b[39;49;00m=\u001b[36mint\u001b[39;49;00m, default=\u001b[34m500\u001b[39;49;00m)\n",
      "    parser.add_argument(\u001b[33m\"\u001b[39;49;00m\u001b[33m--model_name\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m, \u001b[36mtype\u001b[39;49;00m=\u001b[36mstr\u001b[39;49;00m)\n",
      "    parser.add_argument(\u001b[33m\"\u001b[39;49;00m\u001b[33m--learning_rate\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m, \u001b[36mtype\u001b[39;49;00m=\u001b[36mstr\u001b[39;49;00m, default=\u001b[34m5e-5\u001b[39;49;00m)\n",
      "\n",
      "    \u001b[37m# Data, model, and output directories\u001b[39;49;00m\n",
      "    parser.add_argument(\u001b[33m\"\u001b[39;49;00m\u001b[33m--output_data_dir\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m, \u001b[36mtype\u001b[39;49;00m=\u001b[36mstr\u001b[39;49;00m, default=os.environ[\u001b[33m\"\u001b[39;49;00m\u001b[33mSM_OUTPUT_DATA_DIR\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m])\n",
      "    parser.add_argument(\u001b[33m\"\u001b[39;49;00m\u001b[33m--model_dir\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m, \u001b[36mtype\u001b[39;49;00m=\u001b[36mstr\u001b[39;49;00m, default=os.environ[\u001b[33m\"\u001b[39;49;00m\u001b[33mSM_MODEL_DIR\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m])\n",
      "    parser.add_argument(\u001b[33m\"\u001b[39;49;00m\u001b[33m--n_gpus\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m, \u001b[36mtype\u001b[39;49;00m=\u001b[36mstr\u001b[39;49;00m, default=os.environ[\u001b[33m\"\u001b[39;49;00m\u001b[33mSM_NUM_GPUS\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m])\n",
      "    parser.add_argument(\u001b[33m\"\u001b[39;49;00m\u001b[33m--training_dir\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m, \u001b[36mtype\u001b[39;49;00m=\u001b[36mstr\u001b[39;49;00m, default=os.environ[\u001b[33m\"\u001b[39;49;00m\u001b[33mSM_CHANNEL_TRAIN\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m])\n",
      "    parser.add_argument(\u001b[33m\"\u001b[39;49;00m\u001b[33m--test_dir\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m, \u001b[36mtype\u001b[39;49;00m=\u001b[36mstr\u001b[39;49;00m, default=os.environ[\u001b[33m\"\u001b[39;49;00m\u001b[33mSM_CHANNEL_TEST\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m])\n",
      "\n",
      "    args, _ = parser.parse_known_args()\n",
      "\n",
      "    \u001b[37m# Set up logging\u001b[39;49;00m\n",
      "    logger = logging.getLogger(\u001b[31m__name__\u001b[39;49;00m)\n",
      "\n",
      "    logging.basicConfig(\n",
      "        level=logging.getLevelName(\u001b[33m\"\u001b[39;49;00m\u001b[33mINFO\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m),\n",
      "        handlers=[logging.StreamHandler(sys.stdout)],\n",
      "        \u001b[36mformat\u001b[39;49;00m=\u001b[33m\"\u001b[39;49;00m\u001b[33m%(asctime)s\u001b[39;49;00m\u001b[33m - \u001b[39;49;00m\u001b[33m%(name)s\u001b[39;49;00m\u001b[33m - \u001b[39;49;00m\u001b[33m%(levelname)s\u001b[39;49;00m\u001b[33m - \u001b[39;49;00m\u001b[33m%(message)s\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m,\n",
      "    )\n",
      "\n",
      "    \u001b[37m# Load datasets\u001b[39;49;00m\n",
      "    train_dataset = load_from_disk(args.training_dir)\n",
      "    test_dataset = load_from_disk(args.test_dir)\n",
      "\n",
      "    logger.info(\u001b[33mf\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m\u001b[33mLoaded train_dataset length is: \u001b[39;49;00m\u001b[33m{\u001b[39;49;00m\u001b[36mlen\u001b[39;49;00m(train_dataset)\u001b[33m}\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m)\n",
      "    logger.info(\u001b[33mf\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m\u001b[33mLoaded test_dataset length is: \u001b[39;49;00m\u001b[33m{\u001b[39;49;00m\u001b[36mlen\u001b[39;49;00m(test_dataset)\u001b[33m}\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m)\n",
      "\n",
      "    \u001b[37m# Compute metrics function for binary classification\u001b[39;49;00m\n",
      "    \u001b[34mdef\u001b[39;49;00m \u001b[32mcompute_metrics\u001b[39;49;00m(pred):\n",
      "        labels = pred.label_ids\n",
      "        preds = pred.predictions.argmax(-\u001b[34m1\u001b[39;49;00m)\n",
      "        precision, recall, f1, _ = precision_recall_fscore_support(labels, preds, average=\u001b[33m\"\u001b[39;49;00m\u001b[33mbinary\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m)\n",
      "        acc = accuracy_score(labels, preds)\n",
      "        \u001b[34mreturn\u001b[39;49;00m {\u001b[33m\"\u001b[39;49;00m\u001b[33maccuracy\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m: acc, \u001b[33m\"\u001b[39;49;00m\u001b[33mf1\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m: f1, \u001b[33m\"\u001b[39;49;00m\u001b[33mprecision\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m: precision, \u001b[33m\"\u001b[39;49;00m\u001b[33mrecall\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m: recall}\n",
      "\n",
      "    \u001b[37m# Download model from model hub\u001b[39;49;00m\n",
      "    model = AutoModelForSequenceClassification.from_pretrained(args.model_name)\n",
      "    tokenizer = AutoTokenizer.from_pretrained(args.model_name)\n",
      "\n",
      "    \u001b[37m# Define training args\u001b[39;49;00m\n",
      "    training_args = TrainingArguments(\n",
      "        output_dir=args.model_dir,\n",
      "        num_train_epochs=args.epochs,\n",
      "        per_device_train_batch_size=args.train_batch_size,\n",
      "        per_device_eval_batch_size=args.eval_batch_size,\n",
      "        warmup_steps=args.warmup_steps,\n",
      "        evaluation_strategy=\u001b[33m\"\u001b[39;49;00m\u001b[33mepoch\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m,\n",
      "        logging_dir=\u001b[33mf\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m\u001b[33m{\u001b[39;49;00margs.output_data_dir\u001b[33m}\u001b[39;49;00m\u001b[33m/logs\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m,\n",
      "        learning_rate=\u001b[36mfloat\u001b[39;49;00m(args.learning_rate),\n",
      "    )\n",
      "\n",
      "    \u001b[37m# Create Trainer instance\u001b[39;49;00m\n",
      "    trainer = Trainer(\n",
      "        model=model,\n",
      "        args=training_args,\n",
      "        compute_metrics=compute_metrics,\n",
      "        train_dataset=train_dataset,\n",
      "        eval_dataset=test_dataset,\n",
      "        tokenizer=tokenizer,\n",
      "    )\n",
      "\n",
      "    \u001b[37m# Train model\u001b[39;49;00m\n",
      "    trainer.train()\n",
      "\n",
      "    \u001b[37m# Evaluate model\u001b[39;49;00m\n",
      "    eval_result = trainer.evaluate(eval_dataset=test_dataset)\n",
      "\n",
      "    \u001b[37m# Write eval result to file which can be accessed later in S3 ouput\u001b[39;49;00m\n",
      "    \u001b[34mwith\u001b[39;49;00m \u001b[36mopen\u001b[39;49;00m(os.path.join(args.output_data_dir, \u001b[33m\"\u001b[39;49;00m\u001b[33meval_results.txt\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m), \u001b[33m\"\u001b[39;49;00m\u001b[33mw\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m) \u001b[34mas\u001b[39;49;00m writer:\n",
      "        \u001b[36mprint\u001b[39;49;00m(\u001b[33mf\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m\u001b[33m***** Eval results *****\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m)\n",
      "        \u001b[34mfor\u001b[39;49;00m key, value \u001b[35min\u001b[39;49;00m \u001b[36msorted\u001b[39;49;00m(eval_result.items()):\n",
      "            writer.write(\u001b[33mf\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m\u001b[33m{\u001b[39;49;00mkey\u001b[33m}\u001b[39;49;00m\u001b[33m = \u001b[39;49;00m\u001b[33m{\u001b[39;49;00mvalue\u001b[33m}\u001b[39;49;00m\u001b[33m\\n\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m)\n",
      "\n",
      "    \u001b[37m# Save the model to s3\u001b[39;49;00m\n",
      "    trainer.save_model(args.model_dir)\n"
     ]
    }
   ],
   "source": [
    "!pygmentize ./scripts/train.py"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93076da5",
   "metadata": {
    "papermill": {
     "duration": 0.196903,
     "end_time": "2022-04-18T00:28:12.566594",
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     "start_time": "2022-04-18T00:28:12.369691",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Create an Estimator and start a training job"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e3d2d7f6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T00:28:12.978661Z",
     "iopub.status.busy": "2022-04-18T00:28:12.977505Z",
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     "shell.execute_reply": "2022-04-18T00:28:12.980289Z"
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sagemaker.huggingface import HuggingFace\n",
    "\n",
    "# Hyperparameters which are passed into the training job\n",
    "hyperparameters = {\"epochs\": 1, \"train_batch_size\": 32, \"model_name\": \"distilbert-base-uncased\"}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6b4cfda2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T00:28:13.465242Z",
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    },
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     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "hf_estimator = HuggingFace(\n",
    "    entry_point=\"train.py\",\n",
    "    source_dir=\"./scripts\",\n",
    "    instance_type=\"ml.p3.2xlarge\",\n",
    "    instance_count=1,\n",
    "    role=role,\n",
    "    transformers_version=\"4.12\",\n",
    "    pytorch_version=\"1.9\",\n",
    "    py_version=\"py38\",\n",
    "    hyperparameters=hyperparameters,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9f16af6d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T00:28:14.070049Z",
     "iopub.status.busy": "2022-04-18T00:28:14.068961Z",
     "iopub.status.idle": "2022-04-18T01:00:11.823306Z",
     "shell.execute_reply": "2022-04-18T01:00:11.823783Z"
    },
    "papermill": {
     "duration": 1917.9609,
     "end_time": "2022-04-18T01:00:11.823926",
     "exception": false,
     "start_time": "2022-04-18T00:28:13.863026",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-18 00:28:15 Starting - Starting the training job...ProfilerReport-1650241694: InProgress\n",
      "...\n",
      "2022-04-18 00:28:58 Starting - Preparing the instances for training......\n",
      "2022-04-18 00:30:07 Downloading - Downloading input data\n",
      "2022-04-18 00:30:07 Training - Downloading the training image................................\u001b[34mbash: cannot set terminal process group (-1): Inappropriate ioctl for device\u001b[0m\n",
      "\u001b[34mbash: no job control in this shell\u001b[0m\n",
      "\u001b[34m2022-04-18 00:35:22,153 sagemaker-training-toolkit INFO     Imported framework sagemaker_pytorch_container.training\u001b[0m\n",
      "\u001b[34m2022-04-18 00:35:22,181 sagemaker_pytorch_container.training INFO     Block until all host DNS lookups succeed.\u001b[0m\n",
      "\u001b[34m2022-04-18 00:35:22,187 sagemaker_pytorch_container.training INFO     Invoking user training script.\u001b[0m\n",
      "\u001b[34m2022-04-18 00:35:22,707 sagemaker-training-toolkit INFO     Invoking user script\u001b[0m\n",
      "...\n",
      "\u001b[34m***** Running Evaluation *****\u001b[0m\n",
      "\u001b[34m***** Running Evaluation *****\n",
      "  Num examples = 1821\u001b[0m\n",
      "\u001b[34mNum examples = 1821\n",
      "  Batch size = 64\u001b[0m\n",
      "\u001b[34mBatch size = 64\u001b[0m\n",
      "\u001b[34m0%|          | 0/29 [00:00<?, ?it/s]\u001b[0m\n",
      "\u001b[34m7%|\u258b         | 2/29 [00:00<00:04,  5.81it/s]\u001b[0m\n",
      "\u001b[34m10%|\u2588         | 3/29 [00:00<00:06,  4.14it/s]\u001b[0m\n",
      "\u001b[34m14%|\u2588\u258d        | 4/29 [00:01<00:06,  3.60it/s]\u001b[0m\n",
      "\u001b[34m17%|\u2588\u258b        | 5/29 [00:01<00:07,  3.35it/s]\u001b[0m\n",
      "\u001b[34m21%|\u2588\u2588        | 6/29 [00:01<00:07,  3.21it/s]\u001b[0m\n",
      "\u001b[34m24%|\u2588\u2588\u258d       | 7/29 [00:02<00:07,  3.13it/s]\u001b[0m\n",
      "\u001b[34m28%|\u2588\u2588\u258a       | 8/29 [00:02<00:06,  3.06it/s]\u001b[0m\n",
      "\u001b[34m31%|\u2588\u2588\u2588       | 9/29 [00:02<00:06,  3.02it/s]\u001b[0m\n",
      "\u001b[34m34%|\u2588\u2588\u2588\u258d      | 10/29 [00:03<00:06,  3.00it/s]\u001b[0m\n",
      "\u001b[34m38%|\u2588\u2588\u2588\u258a      | 11/29 [00:03<00:06,  2.99it/s]\u001b[0m\n",
      "\u001b[34m41%|\u2588\u2588\u2588\u2588\u258f     | 12/29 [00:03<00:05,  2.98it/s]\u001b[0m\n",
      "\u001b[34m45%|\u2588\u2588\u2588\u2588\u258d     | 13/29 [00:04<00:05,  2.96it/s]\u001b[0m\n",
      "\u001b[34m48%|\u2588\u2588\u2588\u2588\u258a     | 14/29 [00:04<00:05,  2.96it/s]\u001b[0m\n",
      "\u001b[34m52%|\u2588\u2588\u2588\u2588\u2588\u258f    | 15/29 [00:04<00:04,  2.96it/s]\u001b[0m\n",
      "\u001b[34m55%|\u2588\u2588\u2588\u2588\u2588\u258c    | 16/29 [00:05<00:04,  2.96it/s]\u001b[0m\n",
      "\u001b[34m59%|\u2588\u2588\u2588\u2588\u2588\u258a    | 17/29 [00:05<00:04,  2.96it/s]\u001b[0m\n",
      "\u001b[34m62%|\u2588\u2588\u2588\u2588\u2588\u2588\u258f   | 18/29 [00:05<00:03,  2.96it/s]\u001b[0m\n",
      "\u001b[34m66%|\u2588\u2588\u2588\u2588\u2588\u2588\u258c   | 19/29 [00:06<00:03,  2.96it/s]\u001b[0m\n",
      "\u001b[34m69%|\u2588\u2588\u2588\u2588\u2588\u2588\u2589   | 20/29 [00:06<00:03,  2.96it/s]\u001b[0m\n",
      "\u001b[34m72%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258f  | 21/29 [00:06<00:02,  2.96it/s]\u001b[0m\n",
      "\u001b[34m76%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258c  | 22/29 [00:07<00:02,  2.96it/s]\u001b[0m\n",
      "\u001b[34m79%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2589  | 23/29 [00:07<00:02,  2.96it/s]\u001b[0m\n",
      "\u001b[34m83%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258e | 24/29 [00:07<00:01,  2.96it/s]\u001b[0m\n",
      "\u001b[34m86%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258c | 25/29 [00:08<00:01,  2.95it/s]\u001b[0m\n",
      "\u001b[34m90%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2589 | 26/29 [00:08<00:01,  2.95it/s]\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m93%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258e| 27/29 [00:08<00:00,  2.93it/s]\u001b[0m\n",
      "\u001b[34m97%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258b| 28/29 [00:09<00:00,  2.91it/s]\u001b[0m\n",
      "\u001b[34m100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 29/29 [00:09<00:00,  3.46it/s]\u001b[0m\n",
      "\u001b[34m100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 29/29 [00:09<00:00,  3.11it/s]\u001b[0m\n",
      "\u001b[34m***** Eval results *****\u001b[0m\n",
      "\u001b[34mSaving model checkpoint to /opt/ml/model\u001b[0m\n",
      "\u001b[34mSaving model checkpoint to /opt/ml/model\u001b[0m\n",
      "\u001b[34mConfiguration saved in /opt/ml/model/config.json\u001b[0m\n",
      "\u001b[34mConfiguration saved in /opt/ml/model/config.json\u001b[0m\n",
      "\u001b[34mModel weights saved in /opt/ml/model/pytorch_model.bin\u001b[0m\n",
      "\u001b[34mModel weights saved in /opt/ml/model/pytorch_model.bin\u001b[0m\n",
      "\u001b[34mtokenizer config file saved in /opt/ml/model/tokenizer_config.json\u001b[0m\n",
      "\u001b[34mtokenizer config file saved in /opt/ml/model/tokenizer_config.json\u001b[0m\n",
      "\u001b[34mSpecial tokens file saved in /opt/ml/model/special_tokens_map.json\u001b[0m\n",
      "\u001b[34mSpecial tokens file saved in /opt/ml/model/special_tokens_map.json\u001b[0m\n",
      "\u001b[34m2022-04-18 00:53:15,602 sagemaker-training-toolkit INFO     Reporting training SUCCESS\u001b[0m\n",
      "\u001b[34m2022-04-18 00:53:15,602 asyncio      WARNING  Loop <_UnixSelectorEventLoop running=False closed=True debug=False> that handles pid 24 is closed\u001b[0m\n",
      "\n",
      "2022-04-18 00:53:44 Uploading - Uploading generated training model\n",
      "2022-04-18 00:59:55 Completed - Training job completed\n",
      "Training seconds: 1803\n",
      "Billable seconds: 1803\n"
     ]
    }
   ],
   "source": [
    "# Start the training job with the uploaded dataset as input\n",
    "hf_estimator.fit({\"train\": training_input_path, \"test\": test_input_path})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "460c2b73",
   "metadata": {
    "papermill": {
     "duration": 0.136538,
     "end_time": "2022-04-18T01:00:12.098781",
     "exception": false,
     "start_time": "2022-04-18T01:00:11.962243",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Deploy the endpoint\n",
    "\n",
    "To deploy the endpoint, call `deploy()` on the HuggingFace estimator object, passing in the desired number of instances and instance type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d7427005",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T01:00:12.323891Z",
     "iopub.status.busy": "2022-04-18T01:00:12.323314Z",
     "iopub.status.idle": "2022-04-18T01:07:14.245384Z",
     "shell.execute_reply": "2022-04-18T01:07:14.245772Z"
    },
    "papermill": {
     "duration": 422.038097,
     "end_time": "2022-04-18T01:07:14.245902",
     "exception": false,
     "start_time": "2022-04-18T01:00:12.207805",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------!"
     ]
    }
   ],
   "source": [
    "predictor = hf_estimator.deploy(1, \"ml.p3.2xlarge\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98e99279",
   "metadata": {
    "papermill": {
     "duration": 0.146386,
     "end_time": "2022-04-18T01:07:14.526862",
     "exception": false,
     "start_time": "2022-04-18T01:07:14.380476",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Then use the returned predictor object to perform inference."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "070e6933",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T01:07:14.838260Z",
     "iopub.status.busy": "2022-04-18T01:07:14.837582Z",
     "iopub.status.idle": "2022-04-18T01:07:15.483140Z",
     "shell.execute_reply": "2022-04-18T01:07:15.483539Z"
    },
    "papermill": {
     "duration": 0.805492,
     "end_time": "2022-04-18T01:07:15.483665",
     "exception": false,
     "start_time": "2022-04-18T01:07:14.678173",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'label': 'LABEL_1', 'score': 0.9633631706237793}]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sentiment_input = {\"inputs\": \"I love using the new Inference DLC.\"}\n",
    "\n",
    "predictor.predict(sentiment_input)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06d94c06",
   "metadata": {
    "papermill": {
     "duration": 0.11885,
     "end_time": "2022-04-18T01:07:15.716323",
     "exception": false,
     "start_time": "2022-04-18T01:07:15.597473",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "We see that the fine-tuned model classifies the test sentence \"I love using the new Inference DLC.\" as having positive sentiment with 98% probability!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "205cdad6",
   "metadata": {
    "papermill": {
     "duration": 0.113729,
     "end_time": "2022-04-18T01:07:15.944088",
     "exception": false,
     "start_time": "2022-04-18T01:07:15.830359",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Finally, delete the endpoint."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "413c2cfb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T01:07:16.180176Z",
     "iopub.status.busy": "2022-04-18T01:07:16.179596Z",
     "iopub.status.idle": "2022-04-18T01:07:16.423694Z",
     "shell.execute_reply": "2022-04-18T01:07:16.423217Z"
    },
    "papermill": {
     "duration": 0.361879,
     "end_time": "2022-04-18T01:07:16.423804",
     "exception": false,
     "start_time": "2022-04-18T01:07:16.061925",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "predictor.delete_endpoint()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd8b3616",
   "metadata": {
    "papermill": {
     "duration": 0.113825,
     "end_time": "2022-04-18T01:07:16.650693",
     "exception": false,
     "start_time": "2022-04-18T01:07:16.536868",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Extras"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4375513f",
   "metadata": {
    "papermill": {
     "duration": 0.113838,
     "end_time": "2022-04-18T01:07:16.883137",
     "exception": false,
     "start_time": "2022-04-18T01:07:16.769299",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Estimator Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7143e6aa",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T01:07:17.115826Z",
     "iopub.status.busy": "2022-04-18T01:07:17.115229Z",
     "iopub.status.idle": "2022-04-18T01:07:17.142404Z",
     "shell.execute_reply": "2022-04-18T01:07:17.141985Z"
    },
    "papermill": {
     "duration": 0.146461,
     "end_time": "2022-04-18T01:07:17.142524",
     "exception": false,
     "start_time": "2022-04-18T01:07:16.996063",
     "status": "completed"
    },
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Container image used for training job: \n",
      "None\n",
      "\n",
      "S3 URI where the trained model is located: \n",
      "s3://sagemaker-us-west-2-000000000000/huggingface-pytorch-training-2022-04-18-00-28-14-084/output/model.tar.gz\n",
      "\n",
      "Latest training job name for this estimator: \n",
      "huggingface-pytorch-training-2022-04-18-00-28-14-084\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(f\"Container image used for training job: \\n{hf_estimator.image_uri}\\n\")\n",
    "print(f\"S3 URI where the trained model is located: \\n{hf_estimator.model_data}\\n\")\n",
    "print(f\"Latest training job name for this estimator: \\n{hf_estimator.latest_training_job.name}\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "34a96aaa",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-18T01:07:17.383096Z",
     "iopub.status.busy": "2022-04-18T01:07:17.382339Z",
     "iopub.status.idle": "2022-04-18T01:07:17.875946Z",
     "shell.execute_reply": "2022-04-18T01:07:17.875488Z"
    },
    "papermill": {
     "duration": 0.614515,
     "end_time": "2022-04-18T01:07:17.876056",
     "exception": false,
     "start_time": "2022-04-18T01:07:17.261541",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-18 01:00:05 Starting - Preparing the instances for training\n",
      "2022-04-18 01:00:05 Downloading - Downloading input data\n",
      "2022-04-18 01:00:05 Training - Training image download completed. Training in progress.\n",
      "2022-04-18 01:00:05 Uploading - Uploading generated training model\n",
      "2022-04-18 01:00:05 Completed - Training job completed\u001b[34mbash: cannot set terminal process group (-1): Inappropriate ioctl for device\u001b[0m\n",
      "\u001b[34mbash: no job control in this shell\u001b[0m\n",
      "\u001b[34m2022-04-18 00:35:22,153 sagemaker-training-toolkit INFO     Imported framework sagemaker_pytorch_container.training\u001b[0m\n",
      "\u001b[34m2022-04-18 00:35:22,181 sagemaker_pytorch_container.training INFO     Block until all host DNS lookups succeed.\u001b[0m\n",
      "\u001b[34m2022-04-18 00:35:22,187 sagemaker_pytorch_container.training INFO     Invoking user training script.\u001b[0m\n",
      "\u001b[34m2022-04-18 00:35:22,707 sagemaker-training-toolkit INFO     Invoking user script\u001b[0m\n",
      "...\n",
      "\u001b[34m***** Running Evaluation *****\u001b[0m\n",
      "\u001b[34m***** Running Evaluation *****\n",
      "  Num examples = 1821\u001b[0m\n",
      "\u001b[34mNum examples = 1821\n",
      "  Batch size = 64\u001b[0m\n",
      "\u001b[34mBatch size = 64\u001b[0m\n",
      "\u001b[34m0%|          | 0/29 [00:00<?, ?it/s]\u001b[0m\n",
      "\u001b[34m7%|\u258b         | 2/29 [00:00<00:04,  5.81it/s]\u001b[0m\n",
      "\u001b[34m10%|\u2588         | 3/29 [00:00<00:06,  4.14it/s]\u001b[0m\n",
      "\u001b[34m14%|\u2588\u258d        | 4/29 [00:01<00:06,  3.60it/s]\u001b[0m\n",
      "\u001b[34m17%|\u2588\u258b        | 5/29 [00:01<00:07,  3.35it/s]\u001b[0m\n",
      "\u001b[34m21%|\u2588\u2588        | 6/29 [00:01<00:07,  3.21it/s]\u001b[0m\n",
      "\u001b[34m24%|\u2588\u2588\u258d       | 7/29 [00:02<00:07,  3.13it/s]\u001b[0m\n",
      "\u001b[34m28%|\u2588\u2588\u258a       | 8/29 [00:02<00:06,  3.06it/s]\u001b[0m\n",
      "\u001b[34m31%|\u2588\u2588\u2588       | 9/29 [00:02<00:06,  3.02it/s]\u001b[0m\n",
      "\u001b[34m34%|\u2588\u2588\u2588\u258d      | 10/29 [00:03<00:06,  3.00it/s]\u001b[0m\n",
      "\u001b[34m38%|\u2588\u2588\u2588\u258a      | 11/29 [00:03<00:06,  2.99it/s]\u001b[0m\n",
      "\u001b[34m41%|\u2588\u2588\u2588\u2588\u258f     | 12/29 [00:03<00:05,  2.98it/s]\u001b[0m\n",
      "\u001b[34m45%|\u2588\u2588\u2588\u2588\u258d     | 13/29 [00:04<00:05,  2.96it/s]\u001b[0m\n",
      "\u001b[34m48%|\u2588\u2588\u2588\u2588\u258a     | 14/29 [00:04<00:05,  2.96it/s]\u001b[0m\n",
      "\u001b[34m52%|\u2588\u2588\u2588\u2588\u2588\u258f    | 15/29 [00:04<00:04,  2.96it/s]\u001b[0m\n",
      "\u001b[34m55%|\u2588\u2588\u2588\u2588\u2588\u258c    | 16/29 [00:05<00:04,  2.96it/s]\u001b[0m\n",
      "\u001b[34m59%|\u2588\u2588\u2588\u2588\u2588\u258a    | 17/29 [00:05<00:04,  2.96it/s]\u001b[0m\n",
      "\u001b[34m62%|\u2588\u2588\u2588\u2588\u2588\u2588\u258f   | 18/29 [00:05<00:03,  2.96it/s]\u001b[0m\n",
      "\u001b[34m66%|\u2588\u2588\u2588\u2588\u2588\u2588\u258c   | 19/29 [00:06<00:03,  2.96it/s]\u001b[0m\n",
      "\u001b[34m69%|\u2588\u2588\u2588\u2588\u2588\u2588\u2589   | 20/29 [00:06<00:03,  2.96it/s]\u001b[0m\n",
      "\u001b[34m72%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258f  | 21/29 [00:06<00:02,  2.96it/s]\u001b[0m\n",
      "\u001b[34m76%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258c  | 22/29 [00:07<00:02,  2.96it/s]\u001b[0m\n",
      "\u001b[34m79%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2589  | 23/29 [00:07<00:02,  2.96it/s]\u001b[0m\n",
      "\u001b[34m83%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258e | 24/29 [00:07<00:01,  2.96it/s]\u001b[0m\n",
      "\u001b[34m86%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258c | 25/29 [00:08<00:01,  2.95it/s]\u001b[0m\n",
      "\u001b[34m90%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2589 | 26/29 [00:08<00:01,  2.95it/s]\u001b[0m\n",
      "\u001b[34m93%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258e| 27/29 [00:08<00:00,  2.93it/s]\u001b[0m\n",
      "\u001b[34m97%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258b| 28/29 [00:09<00:00,  2.91it/s]\u001b[0m\n",
      "\u001b[34m100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 29/29 [00:09<00:00,  3.46it/s]\u001b[0m\n",
      "\u001b[34m100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 29/29 [00:09<00:00,  3.11it/s]\u001b[0m\n",
      "\u001b[34m***** Eval results *****\u001b[0m\n",
      "\u001b[34mSaving model checkpoint to /opt/ml/model\u001b[0m\n",
      "\u001b[34mSaving model checkpoint to /opt/ml/model\u001b[0m\n",
      "\u001b[34mConfiguration saved in /opt/ml/model/config.json\u001b[0m\n",
      "\u001b[34mConfiguration saved in /opt/ml/model/config.json\u001b[0m\n",
      "\u001b[34mModel weights saved in /opt/ml/model/pytorch_model.bin\u001b[0m\n",
      "\u001b[34mModel weights saved in /opt/ml/model/pytorch_model.bin\u001b[0m\n",
      "\u001b[34mtokenizer config file saved in /opt/ml/model/tokenizer_config.json\u001b[0m\n",
      "\u001b[34mtokenizer config file saved in /opt/ml/model/tokenizer_config.json\u001b[0m\n",
      "\u001b[34mSpecial tokens file saved in /opt/ml/model/special_tokens_map.json\u001b[0m\n",
      "\u001b[34mSpecial tokens file saved in /opt/ml/model/special_tokens_map.json\u001b[0m\n",
      "\u001b[34m2022-04-18 00:53:15,602 sagemaker-training-toolkit INFO     Reporting training SUCCESS\u001b[0m\n",
      "\u001b[34m2022-04-18 00:53:15,602 asyncio      WARNING  Loop <_UnixSelectorEventLoop running=False closed=True debug=False> that handles pid 24 is closed\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "hf_estimator.sagemaker_session.logs_for_job(hf_estimator.latest_training_job.name)"
   ]
  },
  {
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    "tags": []
   },
   "source": [
    "### Attach a previous training job to an estimator\n",
    "\n",
    "In SageMaker, you can attach a previous training job to an estimator to continue training, get results, etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "32d33369",
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    "execution": {
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   "source": [
    "from sagemaker.estimator import Estimator\n",
    "\n",
    "# Uncomment the following lines and supply your training job name\n",
    "\n",
    "# old_training_job_name = \"<your-training-job-name>\"\n",
    "# hf_estimator_loaded = Estimator.attach(old_training_job_name)\n",
    "# hf_estimator_loaded.model_data"
   ]
  },
  {
   "attachments": {},
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   "metadata": {},
   "source": [
    "## Notebook CI Test Results\n",
    "\n",
    "This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
    "\n",
    "![This us-east-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://h75twx4l60.execute-api.us-west-2.amazonaws.com/sagemaker-nb/us-east-1/introduction_to_applying_machine_learning|huggingface_sentiment_classification|huggingface_sentiment_outputs.ipynb)\n",
    "\n",
    "![This us-east-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://h75twx4l60.execute-api.us-west-2.amazonaws.com/sagemaker-nb/us-east-2/introduction_to_applying_machine_learning|huggingface_sentiment_classification|huggingface_sentiment_outputs.ipynb)\n",
    "\n",
    "![This us-west-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://h75twx4l60.execute-api.us-west-2.amazonaws.com/sagemaker-nb/us-west-1/introduction_to_applying_machine_learning|huggingface_sentiment_classification|huggingface_sentiment_outputs.ipynb)\n",
    "\n",
    "![This ca-central-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://h75twx4l60.execute-api.us-west-2.amazonaws.com/sagemaker-nb/ca-central-1/introduction_to_applying_machine_learning|huggingface_sentiment_classification|huggingface_sentiment_outputs.ipynb)\n",
    "\n",
    "![This sa-east-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://h75twx4l60.execute-api.us-west-2.amazonaws.com/sagemaker-nb/sa-east-1/introduction_to_applying_machine_learning|huggingface_sentiment_classification|huggingface_sentiment_outputs.ipynb)\n",
    "\n",
    "![This eu-west-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://h75twx4l60.execute-api.us-west-2.amazonaws.com/sagemaker-nb/eu-west-1/introduction_to_applying_machine_learning|huggingface_sentiment_classification|huggingface_sentiment_outputs.ipynb)\n",
    "\n",
    "![This eu-west-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://h75twx4l60.execute-api.us-west-2.amazonaws.com/sagemaker-nb/eu-west-2/introduction_to_applying_machine_learning|huggingface_sentiment_classification|huggingface_sentiment_outputs.ipynb)\n",
    "\n",
    "![This eu-west-3 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://h75twx4l60.execute-api.us-west-2.amazonaws.com/sagemaker-nb/eu-west-3/introduction_to_applying_machine_learning|huggingface_sentiment_classification|huggingface_sentiment_outputs.ipynb)\n",
    "\n",
    "![This eu-central-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://h75twx4l60.execute-api.us-west-2.amazonaws.com/sagemaker-nb/eu-central-1/introduction_to_applying_machine_learning|huggingface_sentiment_classification|huggingface_sentiment_outputs.ipynb)\n",
    "\n",
    "![This eu-north-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://h75twx4l60.execute-api.us-west-2.amazonaws.com/sagemaker-nb/eu-north-1/introduction_to_applying_machine_learning|huggingface_sentiment_classification|huggingface_sentiment_outputs.ipynb)\n",
    "\n",
    "![This ap-southeast-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://h75twx4l60.execute-api.us-west-2.amazonaws.com/sagemaker-nb/ap-southeast-1/introduction_to_applying_machine_learning|huggingface_sentiment_classification|huggingface_sentiment_outputs.ipynb)\n",
    "\n",
    "![This ap-southeast-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://h75twx4l60.execute-api.us-west-2.amazonaws.com/sagemaker-nb/ap-southeast-2/introduction_to_applying_machine_learning|huggingface_sentiment_classification|huggingface_sentiment_outputs.ipynb)\n",
    "\n",
    "![This ap-northeast-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://h75twx4l60.execute-api.us-west-2.amazonaws.com/sagemaker-nb/ap-northeast-1/introduction_to_applying_machine_learning|huggingface_sentiment_classification|huggingface_sentiment_outputs.ipynb)\n",
    "\n",
    "![This ap-northeast-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://h75twx4l60.execute-api.us-west-2.amazonaws.com/sagemaker-nb/ap-northeast-2/introduction_to_applying_machine_learning|huggingface_sentiment_classification|huggingface_sentiment_outputs.ipynb)\n",
    "\n",
    "![This ap-south-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://h75twx4l60.execute-api.us-west-2.amazonaws.com/sagemaker-nb/ap-south-1/introduction_to_applying_machine_learning|huggingface_sentiment_classification|huggingface_sentiment_outputs.ipynb)\n"
   ]
  }
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