sagemaker/06_sagemaker_metrics/sagemaker-notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Huggingface Sagemaker-sdk - training with custom metrics\n",
    "### Binary Classification with `Trainer` and `imdb` dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this demo, we extend the basic classification demo by adding **metrics definition** to capture and visualize training metrics.\n",
    "\n",
    "The documentation of the SageMaker metrics capture feature can be seen at https://docs.aws.amazon.com/sagemaker/latest/dg/training-metrics.html\n",
    "\n",
    "We additionally use **SageMaker Checkpointing** to send intermediary checkpoint data to S3 uncompressed in parallel to the training happening https://docs.aws.amazon.com/sagemaker/latest/dg/model-checkpoints.html\n",
    "\n",
    "\n",
    "SageMaker Checkpointing is supported by HF Trainer after Transformers 4.4.0+"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import libraries and set environment\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": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "!pip install \"sagemaker>=2.140.0\" \"transformers==4.26.1\" \"datasets[s3]==2.10.1\" --upgrade"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Development environment "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import sagemaker.huggingface"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Permissions\n",
    "\n",
    "_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": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import sagemaker\n",
    "import boto3\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",
    "try:\n",
    "    role = sagemaker.get_execution_role()\n",
    "except ValueError:\n",
    "    iam = boto3.client('iam')\n",
    "    role = iam.get_role(RoleName='sagemaker_execution_role')['Role']['Arn']\n",
    "\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 `imdb` dataset. After preprocessing, the dataset will be uploaded to our `sagemaker_session_bucket` to be used within our training job. The [imdb](http://ai.stanford.edu/~amaas/data/sentiment/) dataset consists of 25000 training and 25000 testing highly polar movie reviews."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tokenization "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": []
   },
   "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 = 'imdb'\n",
    "\n",
    "# s3 key prefix for the data\n",
    "s3_prefix = 'samples/datasets/imdb'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# load dataset\n",
    "dataset = load_dataset(dataset_name)\n",
    "\n",
    "# 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('imdb', split=['train', 'test'])\n",
    "test_dataset = test_dataset.shuffle().select(range(10000)) # smaller the size for test dataset to 10k \n",
    "\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 Amazon S3\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": 6,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/json": {
       "ascii": false,
       "bar_format": null,
       "colour": null,
       "elapsed": 0.0074846744537353516,
       "initial": 0,
       "n": 0,
       "ncols": null,
       "nrows": null,
       "postfix": null,
       "prefix": "Saving the dataset (0/1 shards)",
       "rate": null,
       "total": 25000,
       "unit": " examples",
       "unit_divisor": 1000,
       "unit_scale": false
      },
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Saving the dataset (0/1 shards):   0%|          | 0/25000 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/json": {
       "ascii": false,
       "bar_format": null,
       "colour": null,
       "elapsed": 0.007180690765380859,
       "initial": 0,
       "n": 0,
       "ncols": null,
       "nrows": null,
       "postfix": null,
       "prefix": "Saving the dataset (0/1 shards)",
       "rate": null,
       "total": 10000,
       "unit": " examples",
       "unit_divisor": 1000,
       "unit_scale": false
      },
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Saving the dataset (0/1 shards):   0%|          | 0/10000 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 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)\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)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Launching a Training Job with custom metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sagemaker.huggingface import HuggingFace\n",
    "\n",
    "# hyperparameters, which will be passed into the training job\n",
    "hyperparameters={\n",
    "    'epochs': 3,\n",
    "    'train_batch_size': 32,\n",
    "    'checkpoints': '/opt/ml/checkpoints/',\n",
    "    'model_name':'distilbert-base-uncased'}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We create a metric_definition dictionary that contains regex-based definitions that will be used to parse the job logs and extract metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "metric_definitions=[\n",
    "    {'Name': 'loss', 'Regex': \"'loss': ([0-9]+(.|e\\-)[0-9]+),?\"},\n",
    "    {'Name': 'learning_rate', 'Regex': \"'learning_rate': ([0-9]+(.|e\\-)[0-9]+),?\"},\n",
    "    {'Name': 'eval_loss', 'Regex': \"'eval_loss': ([0-9]+(.|e\\-)[0-9]+),?\"},\n",
    "    {'Name': 'eval_accuracy', 'Regex': \"'eval_accuracy': ([0-9]+(.|e\\-)[0-9]+),?\"},\n",
    "    {'Name': 'eval_f1', 'Regex': \"'eval_f1': ([0-9]+(.|e\\-)[0-9]+),?\"},\n",
    "    {'Name': 'eval_precision', 'Regex': \"'eval_precision': ([0-9]+(.|e\\-)[0-9]+),?\"},\n",
    "    {'Name': 'eval_recall', 'Regex': \"'eval_recall': ([0-9]+(.|e\\-)[0-9]+),?\"},\n",
    "    {'Name': 'eval_runtime', 'Regex': \"'eval_runtime': ([0-9]+(.|e\\-)[0-9]+),?\"},\n",
    "    {'Name': 'eval_samples_per_second', 'Regex': \"'eval_samples_per_second': ([0-9]+(.|e\\-)[0-9]+),?\"},\n",
    "    {'Name': 'epoch', 'Regex': \"'epoch': ([0-9]+(.|e\\-)[0-9]+),?\"}]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "huggingface_estimator = HuggingFace(\n",
    "    entry_point='train.py',\n",
    "    source_dir='./scripts',\n",
    "    instance_type='ml.p3.2xlarge',\n",
    "    instance_count=1,\n",
    "    transformers_version='4.26',\n",
    "    pytorch_version='1.13',\n",
    "    py_version='py39',\n",
    "    role=role,\n",
    "    hyperparameters=hyperparameters,\n",
    "    metric_definitions=metric_definitions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# starting the train job with our uploaded datasets as input\n",
    "huggingface_estimator.fit({'train': training_input_path, 'test': test_input_path})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Accessing Training Metrics\n",
    "\n",
    "The training job doesn't emit metrics immediately. For example, it first needs to provision a training instance, download the training image, download the data. Additionally in this demo the first evaluation logs come after 500 steps (default in the Hugging Face trainer https://huggingface.co/transformers/main_classes/trainer.html#transformers.TrainingArguments).\n",
    "\n",
    "Hence, **run the below section 15 to 20 minutes after launching the training, otherwise it may not have available metrics yet and return an error**\n",
    "\n",
    "Note that you can also copy this code and run it from a different place (as long as connected to the cloud and authorized to use the API), by specifiying the exact training job name in the `TrainingJobAnalytics` API call.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:botocore.credentials:Found credentials from IAM Role: BaseNotebookInstanceEc2InstanceRole\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "      <th>metric_name</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>loss</td>\n",
       "      <td>0.347100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>300.0</td>\n",
       "      <td>loss</td>\n",
       "      <td>0.211800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>540.0</td>\n",
       "      <td>loss</td>\n",
       "      <td>0.150400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>780.0</td>\n",
       "      <td>loss</td>\n",
       "      <td>0.072300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>learning_rate</td>\n",
       "      <td>0.000050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>300.0</td>\n",
       "      <td>learning_rate</td>\n",
       "      <td>3.645720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>540.0</td>\n",
       "      <td>learning_rate</td>\n",
       "      <td>2.291441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>780.0</td>\n",
       "      <td>learning_rate</td>\n",
       "      <td>9.371614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.0</td>\n",
       "      <td>eval_loss</td>\n",
       "      <td>0.184432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>420.0</td>\n",
       "      <td>eval_loss</td>\n",
       "      <td>0.184467</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   timestamp    metric_name     value\n",
       "0        0.0           loss  0.347100\n",
       "1      300.0           loss  0.211800\n",
       "2      540.0           loss  0.150400\n",
       "3      780.0           loss  0.072300\n",
       "4        0.0  learning_rate  0.000050\n",
       "5      300.0  learning_rate  3.645720\n",
       "6      540.0  learning_rate  2.291441\n",
       "7      780.0  learning_rate  9.371614\n",
       "8        0.0      eval_loss  0.184432\n",
       "9      420.0      eval_loss  0.184467"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sagemaker import TrainingJobAnalytics\n",
    "\n",
    "# Captured metrics can be accessed as a Pandas dataframe\n",
    "df = TrainingJobAnalytics(training_job_name=huggingface_estimator.latest_training_job.name).dataframe()\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also plot some of the metrics collected\n",
    "\n",
    "*Note: the plot below were generated at the end of the training job, with metrics available for all training duration*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "!pip install seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "plt.rcParams['figure.figsize'] = [15,5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='timestamp', ylabel='value'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "evals = df[df.metric_name.isin(['eval_accuracy','eval_precision'])]\n",
    "losses = df[df.metric_name.isin(['loss', 'eval_loss'])]\n",
    "\n",
    "sns.lineplot(\n",
    "    x='timestamp', \n",
    "    y='value', \n",
    "    data=evals, \n",
    "    hue='metric_name', \n",
    "    palette=['blue', 'purple'])\n",
    "\n",
    "ax2 = plt.twinx()\n",
    "sns.lineplot(\n",
    "    x='timestamp', \n",
    "    y='value', \n",
    "    data=losses, \n",
    "    hue='metric_name', \n",
    "    palette=['orange', 'red'],\n",
    "    ax=ax2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Deploying the endpoint\n",
    "\n",
    "To deploy our endpoint, we call `deploy()` on our HuggingFace estimator object, passing in our desired number of instances and instance type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictor = huggingface_estimator.deploy(1,\"ml.g4dn.xlarge\")"
   ]
  },
  {
   "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\":\"I love using the new Inference DLC.\"}\n",
    "\n",
    "predictor.predict(sentiment_input)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we delete the endpoint again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictor.delete_model()\n",
    "predictor.delete_endpoint()"
   ]
  }
 ],
 "metadata": {
  "instance_type": "ml.m5.xlarge",
  "interpreter": {
   "hash": "c281c456f1b8161c8906f4af2c08ed2c40c50136979eaae69688b01f70e9f4a9"
  },
  "kernelspec": {
   "display_name": "conda_pytorch_p39",
   "language": "python",
   "name": "conda_pytorch_p39"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}