04-save_model.ipynb

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   "source": [
    "# Save the Model\n",
    "\n",
    "To save this model so that you can use it from various locations, including other notebooks or the model server, upload it to s3-compatible storage."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Install the required packages and define a function for the upload"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install boto3 botocore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import boto3\n",
    "import botocore\n",
    "\n",
    "aws_access_key_id = os.environ.get('AWS_ACCESS_KEY_ID')\n",
    "aws_secret_access_key = os.environ.get('AWS_SECRET_ACCESS_KEY')\n",
    "endpoint_url = os.environ.get('AWS_S3_ENDPOINT')\n",
    "region_name = os.environ.get('AWS_DEFAULT_REGION')\n",
    "bucket_name = os.environ.get('AWS_S3_BUCKET')\n",
    "\n",
    "if not all([aws_access_key_id, aws_secret_access_key, endpoint_url, region_name, bucket_name]):\n",
    "    raise ValueError(\"One or data connection variables are empty.  \"\n",
    "                     \"Please check your data connection to an S3 bucket.\")\n",
    "\n",
    "session = boto3.session.Session(aws_access_key_id=aws_access_key_id,\n",
    "                                aws_secret_access_key=aws_secret_access_key)\n",
    "\n",
    "s3_resource = session.resource(\n",
    "    's3',\n",
    "    config=botocore.client.Config(signature_version='s3v4'),\n",
    "    endpoint_url=endpoint_url,\n",
    "    region_name=region_name)\n",
    "\n",
    "bucket = s3_resource.Bucket(bucket_name)\n",
    "\n",
    "def upload_file_to_s3(local_file, s3_prefix):\n",
    "    num_files = 0\n",
    "    filename = os.path.basename(local_file)\n",
    "    s3_key = os.path.join(s3_prefix, filename)\n",
    "    print(f\"{local_file} -> {s3_key}\")\n",
    "    bucket.upload_file(local_file, s3_key)\n",
    "    num_files += 1\n",
    "\n",
    "    return num_files\n",
    "\n",
    "def upload_directory_to_s3(local_directory, s3_prefix):\n",
    "    num_files = 0\n",
    "    for root, dirs, files in os.walk(local_directory):\n",
    "        for filename in files:\n",
    "            file_path = os.path.join(root, filename)\n",
    "            relative_path = os.path.relpath(file_path, local_directory)\n",
    "            s3_key = os.path.join(s3_prefix, relative_path)\n",
    "            print(f\"{file_path} -> {s3_key}\")\n",
    "            bucket.upload_file(file_path, s3_key)\n",
    "            num_files += 1\n",
    "    return num_files\n",
    "\n",
    "\n",
    "def list_objects(prefix):\n",
    "    filter = bucket.objects.filter(Prefix=prefix)\n",
    "    for obj in filter.all():\n",
    "        print(obj.key)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Verify the upload\n",
    "\n",
    "In your S3 bucket, upload the model file i.e. `yolov5n.onnx`, run the `list_object` command. As best practice, to avoid mixing up model files, keep only one model and its required files in a given prefix or directory. This practice allows you to download and serve a directory with all the files that a model requires. \n",
    "\n",
    "If this is the first time running the code, this cell will have no output.\n",
    "\n",
    "If you've already uploaded your model, you should see this output: `models/yolov5n.onnx`\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "list_objects(\"models\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Upload and check again"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the function to upload the `models` folder in a rescursive fashion:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "local_model_file = \"yolov5n.onnx\"\n",
    "\n",
    "if not os.path.isfile(local_model_file):\n",
    "    raise ValueError(f\"The file '{local_model_file}' does not exist.  \"\n",
    "                     \"Did you finish training the model in the previous notebook?\")\n",
    "\n",
    "num_files = upload_file_to_s3(local_model_file, \"models\")\n",
    "\n",
    "if num_files == 0:\n",
    "    raise ValueError(\"No files uploaded.  Did you finish training and \"\n",
    "                     \"saving the model to the \\\"models\\\" directory?  \"\n",
    "                     \"Check for \\\"models/fraud/1/model.onnx\\\"\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "To confirm this worked, run the `list_objects` function again:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "list_objects(\"models\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Next Step\n",
    "\n",
    "Now that you've saved the model to s3 storage, you can refer to the model by using the same data connection to serve the model as an API.\n"
   ]
  }
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