sagemaker-pipelines-deepfake-project.ipynb

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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "!pip install -U sagemaker --quiet # Ensure latest version of SageMaker is installed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import boto3\n",
    "import sagemaker\n",
    "import time\n",
    "\n",
    "region = boto3.Session().region_name\n",
    "role = sagemaker.get_execution_role()\n",
    "default_bucket = sagemaker.session.Session().default_bucket()\n",
    "s3_client = boto3.client(\"sagemaker\")\n",
    "\n",
    "model_package_group_name = \"deepfake-detection-models\"  \n",
    "pipeline_name = \"Deepfake-detection-pipeline\"  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Get the pipeline instance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from pipelines.pipeline import get_pipeline\n",
    "\n",
    "pipeline = get_pipeline(\n",
    "    region=region,\n",
    "    role=role,\n",
    "    default_bucket=default_bucket,\n",
    "    model_package_group_name=model_package_group_name,\n",
    "    pipeline_name=pipeline_name\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Submit the pipeline to SageMaker and start execution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "pipeline.upsert(role_arn=role)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "execution = pipeline.start()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pipeline Operations: examining and waiting for pipeline execution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "execution.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can list the execution steps to check out the status and artifacts:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "execution.list_steps()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clean up (optional)\n",
    "Delete the model registry and the pipeline to keep the studio environment tidy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def delete_model_package_group(sm_client, package_group_name):\n",
    "    try:\n",
    "        model_versions = sm_client.list_model_packages(ModelPackageGroupName=package_group_name)\n",
    "\n",
    "    except Exception as e:\n",
    "        print(\"{} \\n\".format(e))\n",
    "        return\n",
    "\n",
    "    for model_version in model_versions[\"ModelPackageSummaryList\"]:\n",
    "        try:\n",
    "            sm_client.delete_model_package(ModelPackageName=model_version[\"ModelPackageArn\"])\n",
    "        except Exception as e:\n",
    "            print(\"{} \\n\".format(e))\n",
    "        time.sleep(0.5)  # Ensure requests aren't throttled\n",
    "\n",
    "    try:\n",
    "        sm_client.delete_model_package_group(ModelPackageGroupName=package_group_name)\n",
    "        print(\"{} model package group deleted\".format(package_group_name))\n",
    "    except Exception as e:\n",
    "        print(\"{} \\n\".format(e))\n",
    "    return\n",
    "\n",
    "\n",
    "def delete_sagemaker_pipeline(sm_client, pipeline_name):\n",
    "    try:\n",
    "        sm_client.delete_pipeline(\n",
    "            PipelineName=pipeline_name,\n",
    "        )\n",
    "        print(\"{} pipeline deleted\".format(pipeline_name))\n",
    "    except Exception as e:\n",
    "        print(\"{} \\n\".format(e))\n",
    "        return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "delete_model_package_group(s3_client, model_package_group_name)\n",
    "delete_sagemaker_pipeline(s3_client, pipeline_name)"
   ]
  }
 ],
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