{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# HuggingFace Hub meets Amazon SageMaker\n",
    "### Fine-tune a Multi-Class Classification with `Trainer` and `emotion` dataset and push it to the [Hugging Face Hub](https://huggingface.co/models)"
   ]
  },
  {
   "attachments": {
    "emotion-widget.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction\n",
    "\n",
    "Welcome to our end-to-end multi-class Text-Classification example. In this demo, we will use the Hugging Faces `transformers` and `datasets` library together with a custom Amazon sagemaker-sdk extension to fine-tune a pre-trained transformer for multi-class text classification. In particular, the pre-trained model will be fine-tuned using the `emotion` dataset. To get started, we need to set up the environment with a few prerequisite steps, for permissions, configurations, and so on. \n",
    "\n",
    "![emotion-widget.png](./imgs/emotion-widget.png)\n",
    "\n",
    "_**NOTE: You can run this demo in Sagemaker Studio, your local machine or Sagemaker Notebook Instances**_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Development Environment and Permissions "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Installation\n",
    "\n",
    "_*Note:* we only install the required libraries from Hugging Face and AWS. You also need PyTorch or Tensorflow, if you haven´t it installed_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install \"sagemaker>=2.69.0\" \"transformers==4.12.3\" --upgrade\n",
    "# using older dataset due to incompatibility of sagemaker notebook & aws-cli with > s3fs and fsspec to >= 2021.10\n",
    "!pip install  \"datasets==1.13\" --upgrade"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sagemaker\n",
    "assert sagemaker.__version__ >= \"2.69.0\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sagemaker.huggingface"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Permissions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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 find [here](https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-roles.html) more about it._"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sagemaker\n",
    "\n",
    "sess = sagemaker.Session()\n",
    "# sagemaker session bucket -> used for uploading data, models and logs\n",
    "# sagemaker will automatically create this bucket if it not exists\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\"sagemaker role arn: {role}\")\n",
    "print(f\"sagemaker bucket: {sess.default_bucket()}\")\n",
    "print(f\"sagemaker session region: {sess.boto_region_name}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preprocessing\n",
    "\n",
    "We are using the `datasets` library to download and preprocess the `emotion` dataset. After preprocessing, the dataset will be uploaded to our `sagemaker_session_bucket` to be used within our training job. The [emotion](https://github.com/dair-ai/emotion_dataset) dataset consists of 16000 training examples, 2000 validation examples, and 2000 testing examples."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tokenization "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datasets import load_dataset\n",
    "from transformers import AutoTokenizer\n",
    "\n",
    "# tokenizer used in preprocessing\n",
    "tokenizer_name = 'distilbert-base-uncased'\n",
    "\n",
    "# dataset used\n",
    "dataset_name = 'emotion'\n",
    "\n",
    "# s3 key prefix for the data\n",
    "s3_prefix = 'samples/datasets/emotion'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading: 3.62kB [00:00, 629kB/s]                    \n",
      "Downloading: 3.28kB [00:00, 998kB/s]                    \n",
      "Using custom data configuration default\n",
      "Reusing dataset emotion (/Users/philipp/.cache/huggingface/datasets/emotion/default/0.0.0/348f63ca8e27b3713b6c04d723efe6d824a56fb3d1449794716c0f0296072705)\n",
      "100%|██████████| 2/2 [00:00<00:00, 99.32it/s]\n",
      "100%|██████████| 16/16 [00:02<00:00,  7.47ba/s]\n",
      "100%|██████████| 2/2 [00:00<00:00,  6.47ba/s]\n"
     ]
    }
   ],
   "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",
    "# load dataset\n",
    "train_dataset, test_dataset = load_dataset(dataset_name, split=['train', 'test'])\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",
    "test_dataset = test_dataset.rename_column(\"label\", \"labels\")\n",
    "test_dataset.set_format('torch', columns=['input_ids', 'attention_mask', 'labels'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Uploading data to `sagemaker_session_bucket`\n",
    "\n",
    "After we processed the `datasets` we are going to use the new `FileSystem` [integration](https://huggingface.co/docs/datasets/filesystems.html) to upload our dataset to S3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "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",
   "metadata": {},
   "source": [
    "## Creating an Estimator and start a training job"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "List of supported models: https://huggingface.co/models?library=pytorch,transformers&sort=downloads\n",
    "\n",
    "#### setting up `push_to_hub` for our model. \n",
    "\n",
    "The `train.py` scripts implements the `push_to_hub` using the `Trainer` and `TrainingArguments`. To push our model to the [Hub](https://huggingface.co/models) we need to define the `push_to_hub`. hyperparameter and set it to `True` and provide out [Hugging Face Token](https://hf.co/settings/token). Additionally, we can configure the repository name and saving strategy using the `hub_model_id`, `hub_strategy`.\n",
    "\n",
    "You can find documentation to those parameters [here](https://huggingface.co/transformers/main_classes/trainer.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are going to provide our HF Token securely with out exposing it to the public using `notebook_login` from the `huggingface_hub` SDK. \n",
    "But be careful your token will still be visible insight the logs of the training job. If you run `huggingface_estimator.fit(...,wait=True)` you will see the token in the logs.\n",
    "A better way of providing your `HF_TOKEN` to your training jobs would be using [AWS Secret Manager](https://docs.aws.amazon.com/secretsmanager/latest/userguide/intro.html)\n",
    "\n",
    "#### You can also directly find your token at [https://hf.co/settings/token](https://hf.co/settings/token).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "682aa21ca36447deb321e5fb37353477",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HTML(value=\"<center>\\n<img src=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from huggingface_hub import notebook_login\n",
    "\n",
    "notebook_login()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sagemaker.huggingface import HuggingFace\n",
    "from huggingface_hub import HfFolder\n",
    "import time\n",
    "\n",
    "# hyperparameters, which are passed into the training job\n",
    "hyperparameters={'epochs': 1,                                    # number of training epochs\n",
    "                 'train_batch_size': 32,                         # batch size for training\n",
    "                 'eval_batch_size': 64,                          # batch size for evaluation\n",
    "                 'learning_rate': 3e-5,                          # learning rate used during training\n",
    "                 'model_id':'distilbert-base-uncased',           # pre-trained model\n",
    "                 'fp16': True,                                   # Whether to use 16-bit (mixed) precision training\n",
    "                 'push_to_hub': True,                            # Defines if we want to push the model to the hub\n",
    "                 'hub_model_id': 'sagemaker-distilbert-emotion', # The model id of the model to push to the hub\n",
    "                 'hub_strategy': 'every_save',                   # The strategy to use when pushing the model to the hub\n",
    "                 'hub_token': HfFolder.get_token()               # HuggingFace token to have permission to push\n",
    "                }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define Training Job Name \n",
    "job_name = f'push-to-hub-sample-{time.strftime(\"%Y-%m-%d-%H-%M-%S\", time.localtime())}'\n",
    "\n",
    "# create the Estimator\n",
    "huggingface_estimator = HuggingFace(\n",
    "    entry_point          = 'train.py',        # fine-tuning script used in training jon\n",
    "    source_dir           = './scripts',       # directory where fine-tuning script is stored\n",
    "    instance_type        = 'ml.p3.2xlarge',   # instances type used for the training job\n",
    "    instance_count       = 1,                 # the number of instances used for training\n",
    "    base_job_name        = job_name,          # the name of the training job\n",
    "    role                 = role,              # Iam role used in training job to access AWS ressources, e.g. S3\n",
    "    transformers_version = '4.12',           # the transformers version used in the training job\n",
    "    pytorch_version      = '1.9',           # the pytorch_version version used in the training job\n",
    "    py_version           = 'py38',            # the python version used in the training job\n",
    "    hyperparameters      = hyperparameters,   # the hyperparameter used for running the training job\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# define a data input dictonary with our uploaded s3 uris\n",
    "data = {\n",
    "    'train': training_input_path,\n",
    "    'test': test_input_path\n",
    "}\n",
    "\n",
    "# starting the train job with our uploaded datasets as input\n",
    "# setting wait to False to not expose the HF Token\n",
    "huggingface_estimator.fit(data, wait=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# adding waiter to see when training is done\n",
    "waiter = huggingface_estimator.sagemaker_session.sagemaker_client.get_waiter('training_job_completed_or_stopped')\n",
    "waiter.wait(TrainingJobName=huggingface_estimator.latest_training_job.name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Accessing the model on hf.co/models\n",
    "\n",
    "we can access the model on [hf.co/models](https://hf.co/models) using the `hub_model_id` and our username."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "https://huggingface.co/philschmid/sagemaker-distilbert-emotion\n"
     ]
    }
   ],
   "source": [
    "from huggingface_hub import HfApi\n",
    "\n",
    "whoami = HfApi().whoami()\n",
    "username = whoami['name']\n",
    "\n",
    "print(f\"https://huggingface.co/{username}/{hyperparameters['hub_model_id']}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Deploying the model from Hugging Face to a SageMaker Endpoint\n",
    "\n",
    "To deploy our model to Amazon SageMaker we can create a `HuggingFaceModel` and provide the Hub configuration (`HF_MODEL_ID` & `HF_TASK`) to deploy it. Alternatively, we can use the the `hugginface_estimator` to deploy our model from S3 with `huggingface_estimator.deploy()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sagemaker.huggingface import HuggingFaceModel\n",
    "import sagemaker\n",
    "\n",
    "role = sagemaker.get_execution_role()\n",
    "# Hub Model configuration. https://huggingface.co/models\n",
    "hub = {\n",
    "\t'HF_MODEL_ID':f\"{username}/{hyperparameters['hub_model_id']}\",\n",
    "\t'HF_TASK':'text-classification'\n",
    "}\n",
    "\n",
    "# create Hugging Face Model Class\n",
    "huggingface_model = HuggingFaceModel(\n",
    "\ttransformers_version='4.12',\n",
    "\tpytorch_version='1.9',\n",
    "\tpy_version='py38',\n",
    "\tenv=hub,\n",
    "\trole=role, \n",
    ")\n",
    "\n",
    "# deploy model to SageMaker Inference\n",
    "predictor = huggingface_model.deploy(\n",
    "\tinitial_instance_count=1, # number of instances\n",
    "\tinstance_type='ml.m5.xlarge' # ec2 instance type\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, we use the returned predictor object to call the endpoint."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sentiment_input= {\"inputs\": \"Winter is coming and it will be dark soon.\"}\n",
    "\n",
    "predictor.predict(sentiment_input)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we delete the inference endpoint."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictor.delete_endpoint()"
   ]
  }
 ],
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