nsfw-training_built_in_aws.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## This Notebook contains code for tuning the hyper parameters and then training the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sagemaker\n",
    "from sagemaker import get_execution_role\n",
    "from sagemaker.amazon.amazon_estimator import get_image_uri\n",
    "\n",
    "role = get_execution_role()\n",
    "sess = sagemaker.Session()\n",
    "\n",
    "training_image = get_image_uri(sess.boto_region_name, 'image-classification', repo_version=\"latest\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## we need training and validation data in the form of record io format.\n",
    "Notice s3train_path and s3validation_path contains location of training data and validation data in record io format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "s3train_path = 's3://project-completion-udacity/nsfw_dataset/training'\n",
    "s3validation_path = 's3://project-completion-udacity/nsfw_dataset/validation'\n",
    "\n",
    "train_data = sagemaker.session.s3_input(\n",
    "    s3train_path, \n",
    "    distribution='FullyReplicated', \n",
    "    content_type='application/x-recordio', \n",
    "    s3_data_type='S3Prefix'\n",
    ")\n",
    "\n",
    "validation_data = sagemaker.session.s3_input(\n",
    "    s3validation_path, \n",
    "    distribution='FullyReplicated', \n",
    "    content_type='application/x-recordio', \n",
    "    s3_data_type='S3Prefix'\n",
    ")\n",
    "\n",
    "data_channels = {'train': train_data, 'validation': validation_data}\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Specifying the instance for training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "bucket = 'project-completion-udacity'\n",
    "dataset_name = 'nsfw_dataset'\n",
    "\n",
    "\n",
    "s3_output_location = 's3://{}/{}/output'.format(bucket, dataset_name)\n",
    "\n",
    "image_classifier = sagemaker.estimator.Estimator(\n",
    "    training_image,\n",
    "    role, \n",
    "    train_instance_count=1, \n",
    "    train_instance_type='ml.p2.xlarge',\n",
    "    output_path=s3_output_location,\n",
    "    sagemaker_session=sess\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "126254 5\n"
     ]
    }
   ],
   "source": [
    "num_classes=5\n",
    "\n",
    "num_training_samples=! cat nsfw_dataset_train.lst | wc -l\n",
    "num_training_samples = int(num_training_samples[0])\n",
    "print(num_training_samples , num_classes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Declaring base hyperparameters that we dont wish to tune. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# # Learn more about the Sagemaker built-in Image Classifier hyperparameters here: https://docs.aws.amazon.com/sagemaker/latest/dg/IC-Hyperparameter.html\n",
    "\n",
    "# # These hyperparameters we won't want to change, as they define things like\n",
    "# # the size of the images we'll be sending for input, the number of training classes we have, etc.\n",
    "base_hyperparameters=dict(\n",
    "    use_pretrained_model=1,\n",
    "    image_shape='3,224,224',\n",
    "    num_classes=num_classes,\n",
    "    num_training_samples=num_training_samples,\n",
    "    augmentation_type = 'crop_color_transform',\n",
    "    epochs = 1\n",
    "    \n",
    "    \n",
    ")\n",
    "\n",
    "# # These are hyperparameters we may want to tune, as they can affect the model training success:\n",
    "hyperparameters={\n",
    "    **base_hyperparameters, \n",
    "    **dict(\n",
    "        learning_rate=0.001,\n",
    "        mini_batch_size=5,\n",
    "    )\n",
    "}\n",
    "\n",
    "\n",
    "image_classifier.set_hyperparameters(**hyperparameters)\n",
    "\n",
    "# hyperparameters\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Declaring the hyperparameters we wish to tune.  And then creating hyper parameter tuning job."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sagemaker.tuner import HyperparameterTuner, IntegerParameter, CategoricalParameter, ContinuousParameter\n",
    "hyperparameter_ranges = {'optimizer': CategoricalParameter(['nag', 'adam']),\n",
    "                         'learning_rate': ContinuousParameter(0.0001, 0.01),\n",
    "                         'mini_batch_size': IntegerParameter(15, 32),\n",
    "                        }\n",
    "\n",
    "objective_metric_name = 'validation:accuracy'\n",
    "\n",
    "tuner = HyperparameterTuner(image_classifier,\n",
    "                            objective_metric_name,\n",
    "                            hyperparameter_ranges,\n",
    "                            max_jobs=5,\n",
    "                            max_parallel_jobs=1)\n",
    "\n",
    "tuner.fit(inputs=data_channels, logs=True, include_cls_metadata=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# best_image_classifier = sagemaker.estimator.Estimator.attach(tuner.best_training_job())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# %%time\n",
    "\n",
    "# import time\n",
    "# now = str(int(time.time()))\n",
    "# training_job_name = 'IC-' + dataset_name.replace('_', '-') + '-' + now\n",
    "\n",
    "# image_classifier.fit(inputs=data_channels, job_name=training_job_name, logs=True)\n",
    "\n",
    "# job = image_classifier.latest_training_job\n",
    "# model_path = f\"{base_dir}/{job.name}\"\n",
    "\n",
    "# print(f\"\\n\\n Finished training! The model is available for download at: {image_classifier.output_path}/{job.name}/output/model.tar.gz\")\n"
   ]
  }
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