diff --git a/sagemaker_model_governance/AbaloneExample.ipynb b/sagemaker_model_governance/AbaloneExample.ipynb new file mode 100644 index 0000000000..4f4c3939c0 --- /dev/null +++ b/sagemaker_model_governance/AbaloneExample.ipynb @@ -0,0 +1,2399 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Amazon SageMaker Model Governance - unified model card and model registry\n", + "\n", + "This notebook walks you through the features of Amazon SageMaker Model Registry. For more information, see [Model Registry](https://docs.aws.amazon.com/sagemaker/latest/dg/model-registry-models.html) in the _Amazon SageMaker Developer Guide_.\n", + "\n", + "Amazon SageMaker integrates Model Cards into Model Registry, making it easier for customers to manage governance information for specific model versions directly in Model Registry in just a few clicks. Customers register ML models in Model Registry to manage their models. Additionally, they can register ML model versions early in the development lifecycle, including essential business details and technical metadata. This integration allows customers to seamlessly review and govern models across their lifecycle from a single place. Customers have greater visibility into the model lifecycle from experimentation and training to evaluation and deployment. This streamlined experience ensures that model governance is consistent and easily accessible throughout the development.\n", + "\n", + "In this example, you create a binary classification model along with persisting model card information into registry to manage unified governance information and view" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: sagemaker>=2 in /opt/conda/lib/python3.10/site-packages (2.232.1)\n", + "Requirement already satisfied: attrs<24,>=23.1.0 in /opt/conda/lib/python3.10/site-packages (from sagemaker>=2) (23.2.0)\n", + "Requirement already satisfied: boto3<2.0,>=1.34.142 in /opt/conda/lib/python3.10/site-packages (from sagemaker>=2) (1.35.23)\n", + "Requirement already satisfied: cloudpickle==2.2.1 in /opt/conda/lib/python3.10/site-packages (from sagemaker>=2) (2.2.1)\n", + "Requirement already satisfied: docker in 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Amazon DataZone is a data management service that makes it faster and easier for you to catalog, discover, share, and govern data stored across AWS, on-premises, and third-party sources. With Amazon DataZone, administrators who oversee organization’s data assets can manage and govern access to data using fine-grained controls. These controls help ensure access with the right level of privileges and context. Amazon DataZone makes it easy for engineers, data scientists, product managers, analysts, and business users to share and access data throughout an organization so they can discover, use, and collaborate to derive data-driven insights.\n", + "\n", + "First, [create a new datazone domain](https://us-east-1.console.aws.amazon.com/datazone/home?region=us-east-1#/) if it doesnt already exists.Then, [create a new DataZone Project](https://docs.aws.amazon.com/datazone/latest/userguide/create-new-project.html). Use the project id in the below check and run " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# Capture project id from DataZone project\n", + "project_id = \"5rn1teh0tv85rb\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data/Model Quality, Bias, and Model Explainability Checks in SageMaker Pipelines\n", + "\n", + "This notebook introduces two new step types in SageMaker Pipelines -\n", + "* `QualityCheckStep`\n", + "* `ClarifyCheckStep`\n", + "\n", + "With these two steps, the pipeline is able to perform baseline calculations that are needed as a standard against which data/model quality issues can be detected (including bias and explainability).\n", + "\n", + "These steps leverage SageMaker pre-built containers:\n", + "\n", + "* `QualityCheckStep` (for Data/Model Quality): [sagemaker-model-monitor-analyzer](https://docs.aws.amazon.com/sagemaker/latest/dg/model-monitor-pre-built-container.html)\n", + "* `ClarifyCheckStep` (for Data/Model Bias and Model Explainability): [sagemaker-clarify-processing](https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-configure-processing-jobs.html#clarify-processing-job-configure-container)\n", + "\n", + "The training dataset that you used to train the model is usually a good baseline dataset. The training dataset data schema and the inference dataset schema should exactly match (the number and order of the features). Note that the prediction/output columns are assumed to be the first columns in the training dataset. From the training dataset, you can ask SageMaker to suggest a set of baseline constraints and generate descriptive statistics to explore the data.\n", + "\n", + "These two new steps will always calculate new baselines using the dataset provided." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Drift Check Baselines in the Model Registry\n", + "\n", + "The `RegisterStep` has a new parameter called `drift_check_baselines`. This refers to the baseline files associated with the model. When deployed, these baseline files are used by Model Monitor for Model Quality/Data Quality checks. In addition, these baselines can be used in `QualityCheckStep` and `ClarifyCheckStep` to compare newly trained models against models that have already been registered in the Model Registry.\n", + "\n", + "### Step Properties\n", + "\n", + "The `QualityCheckStep` has the following properties -\n", + "\n", + "* `CalculatedBaselineStatistics` : The baseline statistics file calculated by the underlying Model Monitor container.\n", + "* `CalculatedBaselineConstraints` : The baseline constraints file calculated by the underlying Model Monitor container.\n", + "* `BaselineUsedForDriftCheckStatistics` and `BaselineUsedForDriftCheckConstraints` : These are the two properties used to set `drift_check_baseline` in the Model Registry. The values set in these properties vary depending on the parameters passed to the step. The different behaviors are described in the table below.\n", + "\n", + "The `ClarifyCheckStep` has the following properties -\n", + "\n", + "* `CalculatedBaselineConstraints` : The baseline constraints file calculated by the underlying Clarify container.\n", + "* `BaselineUsedForDriftCheckConstraints` : This property is used to set `drift_check_baseline` in the Model Registry. The values set in this property will vary depending on the parameters passed to the step. The different behaviors are described in the table below." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Notebook Overview\n", + "\n", + "This notebook should be run with `Python 3.9` using the SageMaker Studio `Python3 (Data Science)` kernel." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's start by installing the SageMaker Python SDK, boto, and AWS CLI." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: botocore in /opt/conda/lib/python3.10/site-packages (1.35.23)\n", + "Collecting botocore\n", + " Downloading botocore-1.35.25-py3-none-any.whl.metadata (5.6 kB)\n", + "Requirement already satisfied: boto3 in /opt/conda/lib/python3.10/site-packages (1.35.23)\n", + "Collecting boto3\n", + " Downloading boto3-1.35.25-py3-none-any.whl.metadata (6.6 kB)\n", + "Requirement already satisfied: awscli in /opt/conda/lib/python3.10/site-packages (1.34.23)\n", + "Collecting awscli\n", + " Downloading awscli-1.34.25-py3-none-any.whl.metadata (11 kB)\n", + "Requirement already satisfied: jmespath<2.0.0,>=0.7.1 in /opt/conda/lib/python3.10/site-packages (from botocore) (1.0.1)\n", + "Requirement already satisfied: python-dateutil<3.0.0,>=2.1 in /opt/conda/lib/python3.10/site-packages (from botocore) (2.9.0.post0)\n", + "Requirement already satisfied: urllib3!=2.2.0,<3,>=1.25.4 in /opt/conda/lib/python3.10/site-packages (from botocore) (2.2.3)\n", + "Requirement already satisfied: s3transfer<0.11.0,>=0.10.0 in /opt/conda/lib/python3.10/site-packages (from boto3) (0.10.2)\n", + "Requirement already satisfied: docutils<0.17,>=0.10 in /opt/conda/lib/python3.10/site-packages (from awscli) (0.16)\n", + "Requirement already satisfied: PyYAML<6.1,>=3.10 in /opt/conda/lib/python3.10/site-packages (from awscli) (6.0.2)\n", + "Requirement already satisfied: colorama<0.4.7,>=0.2.5 in /opt/conda/lib/python3.10/site-packages (from awscli) (0.4.6)\n", + "Requirement already satisfied: rsa<4.8,>=3.1.2 in /opt/conda/lib/python3.10/site-packages (from awscli) (4.7.2)\n", + "Requirement already satisfied: six>=1.5 in /opt/conda/lib/python3.10/site-packages (from python-dateutil<3.0.0,>=2.1->botocore) (1.16.0)\n", + "Requirement already satisfied: pyasn1>=0.1.3 in /opt/conda/lib/python3.10/site-packages (from rsa<4.8,>=3.1.2->awscli) (0.6.1)\n", + "Downloading botocore-1.35.25-py3-none-any.whl (12.6 MB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m12.6/12.6 MB\u001b[0m \u001b[31m68.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m0:01\u001b[0m\n", + "\u001b[?25hDownloading boto3-1.35.25-py3-none-any.whl (139 kB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m139.1/139.1 kB\u001b[0m \u001b[31m23.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hDownloading awscli-1.34.25-py3-none-any.whl (4.5 MB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m4.5/4.5 MB\u001b[0m \u001b[31m55.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m:00:01\u001b[0m\n", + "\u001b[?25h\u001b[33mWARNING: Error parsing requirements for pytz: [Errno 2] No such file or directory: '/opt/conda/lib/python3.10/site-packages/pytz-2023.3.dist-info/METADATA'\u001b[0m\u001b[33m\n", + "\u001b[0mInstalling collected packages: botocore, boto3, awscli\n", + " Attempting uninstall: botocore\n", + " Found existing installation: botocore 1.35.23\n", + " Uninstalling botocore-1.35.23:\n", + " Successfully uninstalled botocore-1.35.23\n", + " Attempting uninstall: boto3\n", + " Found existing installation: boto3 1.35.23\n", + " Uninstalling boto3-1.35.23:\n", + " Successfully uninstalled boto3-1.35.23\n", + " Attempting uninstall: awscli\n", + " Found existing installation: awscli 1.34.23\n", + " Uninstalling awscli-1.34.23:\n", + " Successfully uninstalled awscli-1.34.23\n", + "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", + "aiobotocore 2.7.0 requires botocore<1.31.65,>=1.31.16, but you have botocore 1.35.25 which is incompatible.\n", + "amazon-sagemaker-jupyter-scheduler 3.0.6 requires pydantic==1.*, but you have pydantic 2.5.1 which is incompatible.\n", + "autogluon-common 0.8.2 requires pandas<2.2.0,>=2.0.0, but you have pandas 2.2.2 which is incompatible.\n", + "autogluon-common 0.8.2 requires psutil<6,>=5.7.3, but you have psutil 6.0.0 which is incompatible.\n", + "autogluon-core 0.8.2 requires pandas<2.2.0,>=2.0.0, but you have pandas 2.2.2 which is incompatible.\n", + "autogluon-features 0.8.2 requires pandas<2.2.0,>=2.0.0, but you have pandas 2.2.2 which is incompatible.\n", + "autogluon-multimodal 0.8.2 requires jsonschema<4.18,>=4.14, but you have jsonschema 4.23.0 which is incompatible.\n", + "autogluon-multimodal 0.8.2 requires pandas<2.2.0,>=2.0.0, but you have pandas 2.2.2 which is incompatible.\n", + "autogluon-tabular 0.8.2 requires pandas<2.2.0,>=2.0.0, but you have pandas 2.2.2 which is incompatible.\n", + "autogluon-timeseries 0.8.2 requires pandas<2.2.0,>=2.0.0, but you have pandas 2.2.2 which is incompatible.\u001b[0m\u001b[31m\n", + "\u001b[0mSuccessfully installed awscli-1.34.25 boto3-1.35.25 botocore-1.35.25\n", + "Requirement already satisfied: sagemaker>=2.99.0 in /opt/conda/lib/python3.10/site-packages (2.232.1)\n", + "Requirement already satisfied: attrs<24,>=23.1.0 in /opt/conda/lib/python3.10/site-packages (from sagemaker>=2.99.0) (23.2.0)\n", + "Requirement already satisfied: boto3<2.0,>=1.34.142 in /opt/conda/lib/python3.10/site-packages (from sagemaker>=2.99.0) (1.35.25)\n", + "Requirement already satisfied: cloudpickle==2.2.1 in /opt/conda/lib/python3.10/site-packages (from sagemaker>=2.99.0) (2.2.1)\n", + "Requirement already satisfied: docker in /opt/conda/lib/python3.10/site-packages (from sagemaker>=2.99.0) (7.1.0)\n", + "Requirement already satisfied: 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pydantic<3.0.0,>=1.7.0->sagemaker-core<2.0.0,>=1.0.0->sagemaker>=2.99.0) (4.12.2)\n", + "\u001b[31mERROR: Could not install packages due to an OSError: [Errno 2] No such file or directory: '/opt/conda/lib/python3.10/site-packages/pytz-2023.3.dist-info/METADATA'\n", + "\u001b[0m\u001b[31m\n", + "\u001b[0m" + ] + } + ], + "source": [ + "! pip install botocore boto3 awscli --upgrade\n", + "! pip install \"sagemaker>=2.99.0\"" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "import os\n", + "import json\n", + "import boto3\n", + "import sagemaker\n", + "import sagemaker.session\n", + "\n", + "from sagemaker import utils\n", + "from sagemaker.estimator import Estimator\n", + "from sagemaker.inputs import TrainingInput, CreateModelInput, TransformInput\n", + "from sagemaker.model import Model\n", + "from sagemaker.transformer import Transformer\n", + "\n", + "from sagemaker.model_metrics import MetricsSource, ModelMetrics, FileSource\n", + "from sagemaker.drift_check_baselines import DriftCheckBaselines\n", + "from sagemaker.processing import (\n", + " ProcessingInput,\n", + " ProcessingOutput,\n", + " ScriptProcessor,\n", + ")\n", + "from sagemaker.sklearn.processing import SKLearnProcessor\n", + "from sagemaker.workflow.conditions import ConditionLessThanOrEqualTo\n", + "from sagemaker.workflow.condition_step import ConditionStep\n", + "from sagemaker.workflow.functions import JsonGet\n", + "\n", + "from sagemaker.workflow.parameters import (\n", + " ParameterBoolean,\n", + " ParameterInteger,\n", + " ParameterString,\n", + ")\n", + "from sagemaker.workflow.pipeline import Pipeline\n", + "from sagemaker.workflow.properties import PropertyFile\n", + "from sagemaker.workflow.steps import (\n", + " ProcessingStep,\n", + " TrainingStep,\n", + " CreateModelStep,\n", + " TransformStep,\n", + ")\n", + "from sagemaker.workflow.model_step import ModelStep\n", + "from sagemaker.workflow.pipeline_context import PipelineSession\n", + "\n", + "# Importing new steps and helper functions\n", + "\n", + "from sagemaker.workflow.check_job_config import CheckJobConfig\n", + "from sagemaker.workflow.clarify_check_step import (\n", + " DataBiasCheckConfig,\n", + " ClarifyCheckStep,\n", + " ModelBiasCheckConfig,\n", + " ModelPredictedLabelConfig,\n", + " ModelExplainabilityCheckConfig,\n", + " SHAPConfig,\n", + ")\n", + "from sagemaker.workflow.quality_check_step import (\n", + " DataQualityCheckConfig,\n", + " ModelQualityCheckConfig,\n", + " QualityCheckStep,\n", + ")\n", + "from sagemaker.workflow.execution_variables import ExecutionVariables\n", + "from sagemaker.workflow.functions import Join\n", + "from sagemaker.model_monitor import DatasetFormat, model_monitoring\n", + "from sagemaker.clarify import BiasConfig, DataConfig, ModelConfig" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create the SageMaker Session" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "region = sagemaker.Session().boto_region_name\n", + "sm_client = boto3.client(\"sagemaker\")\n", + "boto_session = boto3.Session(region_name=region)\n", + "sagemaker_session = sagemaker.session.Session(boto_session=boto_session, sagemaker_client=sm_client)\n", + "pipeline_session = PipelineSession()\n", + "prefix = \"model-monitor-clarify-step-pipeline\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Define variables and parameters needed for the Pipeline steps" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "role = sagemaker.get_execution_role()\n", + "default_bucket = sagemaker_session.default_bucket()\n", + "base_job_prefix = \"model-monitor-clarify\"\n", + "model_package_group_name = \"ExamplePackage\"\n", + "pipeline_name = \"examplepipeline\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Define pipeline parameters\n", + "\n", + "Both `QualityCheckStep` and `ClarifyCheckStep` use two boolean flags `skip_check` and `register_new_baseline` to control their behavior.\n", + "\n", + "* `skip_check` : This determines if a drift check is executed or not.\n", + "* `register_new_baseline` : This determines if the newly calculated baselines (in the step property `CalculatedBaselines`) should be set in the step property `BaselineUsedForDriftCheck`.\n", + "* `supplied_baseline_statistics` and `supplied_baseline_constraints` : If `skip_check` is set to False, baselines can be provided to this step through this parameter. If provided, the step will compare the newly calculated baselines (`CalculatedBaselines`) against those provided here instead of finding the latest baselines from the Model Registry. In the case of `ClarifyCheckStep`, only `supplied_baseline_constraints` is a valid parameter, for `QualityCheckStep`, both parameters are used.\n", + "* `model_package_group_name` : The step will use the `drift_check_baselines` from the latest approved model in the model package group for the drift check. If `supplied_baseline_*` is provided, this field will be ignored.\n", + "\n", + "The first time the pipeline is run, the `skip_check` value should be set to True using the pipeline execution parameters so that new baselines are registered and no drift check is executed." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Combining Pipeline parameters\n", + "\n", + "This table summarizes how the pipeline parameters work when combined.\n", + "\n", + "The parameter `drift_check_baselines` is used to supply baselines to the `RegisterStep` that will be used for all drift checks involving the model.\n", + "\n", + "Newly calculated baselines can be reference by the properties `CalculatedBaselineStatistics` and `CalculatedBaselineConstraints` on the `QualityCheckStep` and `CalculatedBaselineConstraints` on the `ClarifyCheckStep`.\n", + "\n", + "For example, `data_quality_check_step.properties.CalculatedBaselineStatistics` and `data_quality_check_step.properties.CalculatedBaselineConstraints`. This property refers to the baseline that is calculated when the data quality check step is executed." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "| `skip_check` / `register_new_baseline` | Does step do a drift check? | Value of step property `CalculatedBaseline` | Value of step property `BaselineUsedForDriftCheck` | Possible Circumstances for this parameter combination|\n", + "| -------------------------------------- | ---------------------------------------------------------|------------------------------------------------------------ |------------------------------------------------- | -----------------------------------------------------|\n", + "| F / F | Drift Check executed against existing baselines. | New baselines calculated by step execution | Baseline from latest approved model in Model Registry or baseline supplied as step parameter | Regular re-training with checks enabled to get a new model version, but carry over previous baselines as DriftCheckBaselines in Registry for new model version. |\n", + "| F / T | Drift Check executed against existing baselines. | New baselines calculated by step execution | Newly calculated baseline by step execution (value of property `CalculatedBaseline`) | Regular re-training with checks enabled to get a new model version, but refresh DriftCheckBaselines in Registry with newly calculated baselines for the new model version. |\n", + "| T / F | No Drift Check. | New baselines calculated by step execution | Baseline from latest approved model in Model Registry or baseline supplied as step parameter | Violation detected by the model monitor on endpoint for a particular type of check and the pipeline is triggered for retraining a new model. Skip the check against previous baselines, but carry over previous baselines as DriftCheckBaselines in Registry for new model version. |\n", + "| T / T | No Drift Check. | New baselines calculated by step execution | Newly calculated baseline by step execution (value of property `CalculatedBaseline`) | a. Initial run of the pipeline, building the first model version and generate initial baselines.
b. Violation detected by the model monitor on endpoint for a particular type of check and the pipeline is triggered for retraining a new model. Skip the check against previous baselines and refresh DriftCheckBaselines with newly calculated baselines in Registry directly. |" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "processing_instance_count = ParameterInteger(name=\"ProcessingInstanceCount\", default_value=1)\n", + "training_instance_type = ParameterString(name=\"TrainingInstanceType\", default_value=\"ml.m5.xlarge\")\n", + "model_approval_status = ParameterString(\n", + " name=\"ModelApprovalStatus\", default_value=\"PendingManualApproval\"\n", + ")\n", + "# The dataset used here is the open source Abalone dataset that can be found\n", + "# here - https://archive.ics.uci.edu/ml/datasets/abalone\n", + "input_data = ParameterString(\n", + " name=\"InputDataUrl\",\n", + " default_value=f\"s3://sagemaker-sample-files/datasets/tabular/uci_abalone/abalone.csv\",\n", + ")\n", + "\n", + "# for data quality check step\n", + "skip_check_data_quality = ParameterBoolean(name=\"SkipDataQualityCheck\", default_value=False)\n", + "register_new_baseline_data_quality = ParameterBoolean(\n", + " name=\"RegisterNewDataQualityBaseline\", default_value=False\n", + ")\n", + "supplied_baseline_statistics_data_quality = ParameterString(\n", + " name=\"DataQualitySuppliedStatistics\", default_value=\"\"\n", + ")\n", + "supplied_baseline_constraints_data_quality = ParameterString(\n", + " name=\"DataQualitySuppliedConstraints\", default_value=\"\"\n", + ")\n", + "\n", + "# for data bias check step\n", + "skip_check_data_bias = ParameterBoolean(name=\"SkipDataBiasCheck\", default_value=False)\n", + "register_new_baseline_data_bias = ParameterBoolean(\n", + " name=\"RegisterNewDataBiasBaseline\", default_value=False\n", + ")\n", + "supplied_baseline_constraints_data_bias = ParameterString(\n", + " name=\"DataBiasSuppliedBaselineConstraints\", default_value=\"\"\n", + ")\n", + "\n", + "# for model quality check step\n", + "skip_check_model_quality = ParameterBoolean(name=\"SkipModelQualityCheck\", default_value=False)\n", + "register_new_baseline_model_quality = ParameterBoolean(\n", + " name=\"RegisterNewModelQualityBaseline\", default_value=False\n", + ")\n", + "supplied_baseline_statistics_model_quality = ParameterString(\n", + " name=\"ModelQualitySuppliedStatistics\", default_value=\"\"\n", + ")\n", + "supplied_baseline_constraints_model_quality = ParameterString(\n", + " name=\"ModelQualitySuppliedConstraints\", default_value=\"\"\n", + ")\n", + "\n", + "# for model bias check step\n", + "skip_check_model_bias = ParameterBoolean(name=\"SkipModelBiasCheck\", default_value=False)\n", + "register_new_baseline_model_bias = ParameterBoolean(\n", + " name=\"RegisterNewModelBiasBaseline\", default_value=False\n", + ")\n", + "supplied_baseline_constraints_model_bias = ParameterString(\n", + " name=\"ModelBiasSuppliedBaselineConstraints\", default_value=\"\"\n", + ")\n", + "\n", + "# for model explainability check step\n", + "skip_check_model_explainability = ParameterBoolean(\n", + " name=\"SkipModelExplainabilityCheck\", default_value=False\n", + ")\n", + "register_new_baseline_model_explainability = ParameterBoolean(\n", + " name=\"RegisterNewModelExplainabilityBaseline\", default_value=False\n", + ")\n", + "supplied_baseline_constraints_model_explainability = ParameterString(\n", + " name=\"ModelExplainabilitySuppliedBaselineConstraints\", default_value=\"\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Processing step for feature engineering" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "!mkdir -p code" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Overwriting code/preprocess.py\n" + ] + } + ], + "source": [ + "%%writefile code/preprocess.py\n", + "\n", + "\"\"\"Feature engineers the abalone dataset.\"\"\"\n", + "import argparse\n", + "import logging\n", + "import os\n", + "import pathlib\n", + "import requests\n", + "import tempfile\n", + "\n", + "import boto3\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "from sklearn.compose import ColumnTransformer\n", + "from sklearn.impute import SimpleImputer\n", + "from sklearn.pipeline import Pipeline\n", + "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n", + "\n", + "logger = logging.getLogger()\n", + "logger.setLevel(logging.INFO)\n", + "logger.addHandler(logging.StreamHandler())\n", + "\n", + "\n", + "# Since we get a headerless CSV file we specify the column names here.\n", + "feature_columns_names = [\n", + " \"sex\",\n", + " \"length\",\n", + " \"diameter\",\n", + " \"height\",\n", + " \"whole_weight\",\n", + " \"shucked_weight\",\n", + " \"viscera_weight\",\n", + " \"shell_weight\",\n", + "]\n", + "label_column = \"rings\"\n", + "\n", + "feature_columns_dtype = {\n", + " \"sex\": str,\n", + " \"length\": np.float64,\n", + " \"diameter\": np.float64,\n", + " \"height\": np.float64,\n", + " \"whole_weight\": np.float64,\n", + " \"shucked_weight\": np.float64,\n", + " \"viscera_weight\": np.float64,\n", + " \"shell_weight\": np.float64,\n", + "}\n", + "label_column_dtype = {\"rings\": np.float64}\n", + "\n", + "\n", + "def merge_two_dicts(x, y):\n", + " \"\"\"Merges two dicts, returning a new copy.\"\"\"\n", + " z = x.copy()\n", + " z.update(y)\n", + " return z\n", + "\n", + "\n", + "if __name__ == \"__main__\":\n", + " logger.debug(\"Starting preprocessing.\")\n", + " parser = argparse.ArgumentParser()\n", + " parser.add_argument(\"--input-data\", type=str, required=True)\n", + " args = parser.parse_args()\n", + "\n", + " base_dir = \"/opt/ml/processing\"\n", + " pathlib.Path(f\"{base_dir}/data\").mkdir(parents=True, exist_ok=True)\n", + " input_data = args.input_data\n", + " bucket = input_data.split(\"/\")[2]\n", + " key = \"/\".join(input_data.split(\"/\")[3:])\n", + "\n", + " logger.info(\"Downloading data from bucket: %s, key: %s\", bucket, key)\n", + " fn = f\"{base_dir}/data/abalone-dataset.csv\"\n", + " s3 = boto3.resource(\"s3\")\n", + " s3.Bucket(bucket).download_file(key, fn)\n", + "\n", + " logger.debug(\"Reading downloaded data.\")\n", + " df = pd.read_csv(\n", + " fn,\n", + " header=None,\n", + " names=feature_columns_names + [label_column],\n", + " dtype=merge_two_dicts(feature_columns_dtype, label_column_dtype),\n", + " )\n", + " os.unlink(fn)\n", + "\n", + " logger.debug(\"Defining transformers.\")\n", + " numeric_features = list(feature_columns_names)\n", + " numeric_features.remove(\"sex\")\n", + " numeric_transformer = Pipeline(\n", + " steps=[\n", + " (\"imputer\", SimpleImputer(strategy=\"median\")),\n", + " (\"scaler\", StandardScaler()),\n", + " ]\n", + " )\n", + "\n", + " categorical_features = [\"sex\"]\n", + " categorical_transformer = Pipeline(\n", + " steps=[\n", + " (\"imputer\", SimpleImputer(strategy=\"constant\", fill_value=\"missing\")),\n", + " (\"onehot\", OneHotEncoder(handle_unknown=\"ignore\")),\n", + " ]\n", + " )\n", + "\n", + " preprocess = ColumnTransformer(\n", + " transformers=[\n", + " (\"num\", numeric_transformer, numeric_features),\n", + " (\"cat\", categorical_transformer, categorical_features),\n", + " ]\n", + " )\n", + "\n", + " logger.info(\"Applying transforms.\")\n", + " y = df.pop(\"rings\")\n", + " X_pre = preprocess.fit_transform(df)\n", + " y_pre = y.to_numpy().reshape(len(y), 1)\n", + "\n", + " X = np.concatenate((y_pre, X_pre), axis=1)\n", + "\n", + " logger.info(\"Splitting %d rows of data into train, validation, test datasets.\", len(X))\n", + " np.random.shuffle(X)\n", + " train, validation, test = np.split(X, [int(0.7 * len(X)), int(0.85 * len(X))])\n", + "\n", + " logger.info(\"Writing out datasets to %s.\", base_dir)\n", + " pd.DataFrame(train).to_csv(f\"{base_dir}/train/train.csv\", header=False, index=False)\n", + " pd.DataFrame(validation).to_csv(\n", + " f\"{base_dir}/validation/validation.csv\", header=False, index=False\n", + " )\n", + " pd.DataFrame(test).to_csv(f\"{base_dir}/test/test.csv\", header=False, index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:sagemaker.image_uris:Defaulting to only available Python version: py3\n", + "/opt/conda/lib/python3.10/site-packages/sagemaker/workflow/pipeline_context.py:332: UserWarning: Running within a PipelineSession, there will be No Wait, No Logs, and No Job being started.\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "sklearn_processor = SKLearnProcessor(\n", + " framework_version=\"0.23-1\",\n", + " instance_type=\"ml.m5.xlarge\",\n", + " instance_count=processing_instance_count,\n", + " base_job_name=f\"{base_job_prefix}/sklearn-abalone-preprocess\",\n", + " sagemaker_session=pipeline_session,\n", + " role=role,\n", + ")\n", + "processor_args = sklearn_processor.run(\n", + " outputs=[\n", + " ProcessingOutput(output_name=\"train\", source=\"/opt/ml/processing/train\"),\n", + " ProcessingOutput(output_name=\"validation\", source=\"/opt/ml/processing/validation\"),\n", + " ProcessingOutput(output_name=\"test\", source=\"/opt/ml/processing/test\"),\n", + " ],\n", + " code=\"code/preprocess.py\",\n", + " arguments=[\"--input-data\", input_data],\n", + ")\n", + "step_process = ProcessingStep(name=\"PreprocessAbaloneData\", step_args=processor_args)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Calculating the Data Quality\n", + "\n", + "`CheckJobConfig` is a helper function that's used to define the job configurations used by the `QualityCheckStep`. By separating the job configuration from the step parameters, the same `CheckJobConfig` can be used across multiple steps for quality checks.\n", + "\n", + "The `DataQualityCheckConfig` is used to define the Quality Check job by specifying the dataset used to calculate the baseline, in this case, the training dataset from the data processing step, the dataset format, in this case, a csv file with no headers, and the output path for the results of the data quality check." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:sagemaker.image_uris:Defaulting to the only supported framework/algorithm version: .\n", + "INFO:sagemaker.image_uris:Ignoring unnecessary instance type: None.\n" + ] + } + ], + "source": [ + "check_job_config = CheckJobConfig(\n", + " role=role,\n", + " instance_count=1,\n", + " instance_type=\"ml.c5.xlarge\",\n", + " volume_size_in_gb=120,\n", + " sagemaker_session=sagemaker_session,\n", + ")\n", + "\n", + "data_quality_check_config = DataQualityCheckConfig(\n", + " baseline_dataset=step_process.properties.ProcessingOutputConfig.Outputs[\"train\"].S3Output.S3Uri,\n", + " dataset_format=DatasetFormat.csv(header=False, output_columns_position=\"START\"),\n", + " output_s3_uri=Join(\n", + " on=\"/\",\n", + " values=[\n", + " \"s3:/\",\n", + " default_bucket,\n", + " base_job_prefix,\n", + " ExecutionVariables.PIPELINE_EXECUTION_ID,\n", + " \"dataqualitycheckstep\",\n", + " ],\n", + " ),\n", + ")\n", + "\n", + "data_quality_check_step = QualityCheckStep(\n", + " name=\"DataQualityCheckStep\",\n", + " skip_check=skip_check_data_quality,\n", + " register_new_baseline=register_new_baseline_data_quality,\n", + " quality_check_config=data_quality_check_config,\n", + " check_job_config=check_job_config,\n", + " supplied_baseline_statistics=supplied_baseline_statistics_data_quality,\n", + " supplied_baseline_constraints=supplied_baseline_constraints_data_quality,\n", + " model_package_group_name=model_package_group_name,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Calculating the Data Bias\n", + "\n", + "The job configuration from the previous step is used here and the `DataConfig` class is used to define how the `ClarifyCheckStep` should compute the data bias. The training dataset is used again for the bias evaluation, the column representing the label is specified through the `label` parameter, and a `BiasConfig` is provided.\n", + "\n", + "In the `BiasConfig`, we specify a facet name (the column that is the focal point of the bias calculation), the value of the facet that determines the range of values it can hold, and the threshold value for the label.\n", + "\n", + "More details on `BiasConfig` can be found [here](https://sagemaker.readthedocs.io/en/stable/api/training/processing.html#sagemaker.clarify.BiasConfig)." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:sagemaker.image_uris:Defaulting to the only supported framework/algorithm version: 1.0.\n", + "INFO:sagemaker.image_uris:Ignoring unnecessary instance type: None.\n", + "INFO:sagemaker.model_monitor.clarify_model_monitoring:Uploading analysis config to {s3_uri}.\n", + "INFO:sagemaker.image_uris:Defaulting to the only supported framework/algorithm version: 1.0.\n", + "INFO:sagemaker.image_uris:Ignoring unnecessary instance type: None.\n" + ] + } + ], + "source": [ + "data_bias_analysis_cfg_output_path = (\n", + " f\"s3://{default_bucket}/{base_job_prefix}/databiascheckstep/analysis_cfg\"\n", + ")\n", + "\n", + "data_bias_data_config = DataConfig(\n", + " s3_data_input_path=step_process.properties.ProcessingOutputConfig.Outputs[\n", + " \"train\"\n", + " ].S3Output.S3Uri,\n", + " s3_output_path=Join(\n", + " on=\"/\",\n", + " values=[\n", + " \"s3:/\",\n", + " default_bucket,\n", + " base_job_prefix,\n", + " ExecutionVariables.PIPELINE_EXECUTION_ID,\n", + " \"databiascheckstep\",\n", + " ],\n", + " ),\n", + " label=0,\n", + " dataset_type=\"text/csv\",\n", + " s3_analysis_config_output_path=data_bias_analysis_cfg_output_path,\n", + ")\n", + "\n", + "\n", + "data_bias_config = BiasConfig(\n", + " label_values_or_threshold=[15.0], facet_name=[8], facet_values_or_threshold=[[0.5]]\n", + ")\n", + "\n", + "data_bias_check_config = DataBiasCheckConfig(\n", + " data_config=data_bias_data_config,\n", + " data_bias_config=data_bias_config,\n", + ")\n", + "\n", + "data_bias_check_step = ClarifyCheckStep(\n", + " name=\"DataBiasCheckStep\",\n", + " clarify_check_config=data_bias_check_config,\n", + " check_job_config=check_job_config,\n", + " skip_check=skip_check_data_bias,\n", + " register_new_baseline=register_new_baseline_data_bias,\n", + " supplied_baseline_constraints=supplied_baseline_constraints_data_bias,\n", + " model_package_group_name=model_package_group_name,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Train an XGBoost Model" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "model_path = f\"s3://{sagemaker_session.default_bucket()}/{base_job_prefix}/AbaloneTrain\"\n", + "image_uri = sagemaker.image_uris.retrieve(\n", + " framework=\"xgboost\",\n", + " region=region,\n", + " version=\"1.0-1\",\n", + " py_version=\"py3\",\n", + " instance_type=\"ml.m5.xlarge\",\n", + ")\n", + "\n", + "xgb_train = Estimator(\n", + " image_uri=image_uri,\n", + " instance_type=training_instance_type,\n", + " instance_count=1,\n", + " output_path=model_path,\n", + " base_job_name=f\"{base_job_prefix}/abalone-train\",\n", + " sagemaker_session=pipeline_session,\n", + " role=role,\n", + ")\n", + "\n", + "xgb_train.set_hyperparameters(\n", + " objective=\"reg:linear\",\n", + " num_round=50,\n", + " max_depth=5,\n", + " eta=0.2,\n", + " gamma=4,\n", + " min_child_weight=6,\n", + " subsample=0.7,\n", + " silent=0,\n", + ")\n", + "\n", + "train_args = xgb_train.fit(\n", + " inputs={\n", + " \"train\": TrainingInput(\n", + " s3_data=step_process.properties.ProcessingOutputConfig.Outputs[\"train\"].S3Output.S3Uri,\n", + " content_type=\"text/csv\",\n", + " ),\n", + " \"validation\": TrainingInput(\n", + " s3_data=step_process.properties.ProcessingOutputConfig.Outputs[\n", + " \"validation\"\n", + " ].S3Output.S3Uri,\n", + " content_type=\"text/csv\",\n", + " ),\n", + " },\n", + ")\n", + "step_train = TrainingStep(\n", + " name=\"TrainAbaloneModel\",\n", + " step_args=train_args,\n", + " depends_on=[data_bias_check_step.name, data_quality_check_step.name],\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create the model\n", + "\n", + "The model is created so that a batch transform job can be used to get predictions from the model on a test dataset. These predictions are used when calculating model quality, model bias, and model explainability." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "model = Model(\n", + " image_uri=image_uri,\n", + " model_data=step_train.properties.ModelArtifacts.S3ModelArtifacts,\n", + " sagemaker_session=pipeline_session,\n", + " role=role,\n", + ")\n", + "\n", + "step_create_model = ModelStep(\n", + " name=\"AbaloneCreateModel\",\n", + " step_args=model.create(instance_type=\"ml.m5.large\", accelerator_type=\"ml.eia1.medium\"),\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Transform Output\n", + "\n", + "The output of the transform step combines the prediction and the input label. The output format is
\n", + "`prediction, original label`" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "transformer = Transformer(\n", + " model_name=step_create_model.properties.ModelName,\n", + " instance_type=\"ml.m5.xlarge\",\n", + " instance_count=1,\n", + " accept=\"text/csv\",\n", + " assemble_with=\"Line\",\n", + " output_path=f\"s3://{default_bucket}/AbaloneTransform\",\n", + ")\n", + "\n", + "step_transform = TransformStep(\n", + " name=\"AbaloneTransform\",\n", + " transformer=transformer,\n", + " inputs=TransformInput(\n", + " data=step_process.properties.ProcessingOutputConfig.Outputs[\"test\"].S3Output.S3Uri,\n", + " input_filter=\"$[1:]\",\n", + " join_source=\"Input\",\n", + " output_filter=\"$[0,-1]\",\n", + " content_type=\"text/csv\",\n", + " split_type=\"Line\",\n", + " ),\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Check the Model Quality\n", + "\n", + "In this `QualityCheckStep` we calculate the baselines for statistics and constraints using the predictions that the model generates from the test dataset (output from the TransformStep). We define the problem type as 'Regression' in the `ModelQualityCheckConfig` along with specifying the columns which represent the input and output. Since the dataset has no headers, `_c0`, `_c1` are auto-generated header names that should be used in the `ModelQualityCheckConfig`." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:sagemaker.image_uris:Defaulting to the only supported framework/algorithm version: .\n", + "INFO:sagemaker.image_uris:Ignoring unnecessary instance type: None.\n" + ] + } + ], + "source": [ + "model_quality_check_config = ModelQualityCheckConfig(\n", + " baseline_dataset=step_transform.properties.TransformOutput.S3OutputPath,\n", + " dataset_format=DatasetFormat.csv(header=False),\n", + " output_s3_uri=Join(\n", + " on=\"/\",\n", + " values=[\n", + " \"s3:/\",\n", + " default_bucket,\n", + " base_job_prefix,\n", + " ExecutionVariables.PIPELINE_EXECUTION_ID,\n", + " \"modelqualitycheckstep\",\n", + " ],\n", + " ),\n", + " problem_type=\"Regression\",\n", + " inference_attribute=\"_c0\", # use auto-populated headers since we don't have headers in the dataset\n", + " ground_truth_attribute=\"_c1\", # use auto-populated headers since we don't have headers in the dataset\n", + ")\n", + "\n", + "model_quality_check_step = QualityCheckStep(\n", + " name=\"ModelQualityCheckStep\",\n", + " skip_check=skip_check_model_quality,\n", + " register_new_baseline=register_new_baseline_model_quality,\n", + " quality_check_config=model_quality_check_config,\n", + " check_job_config=check_job_config,\n", + " supplied_baseline_statistics=supplied_baseline_statistics_model_quality,\n", + " supplied_baseline_constraints=supplied_baseline_constraints_model_quality,\n", + " model_package_group_name=model_package_group_name,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Check for Model Bias\n", + "\n", + "Similar to the Data Bias check step, a `BiasConfig` is defined and Clarify is used to calculate the model bias using the training dataset and the model." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:sagemaker.image_uris:Defaulting to the only supported framework/algorithm version: 1.0.\n", + "INFO:sagemaker.image_uris:Ignoring unnecessary instance type: None.\n", + "INFO:sagemaker.model_monitor.clarify_model_monitoring:Uploading analysis config to {s3_uri}.\n", + "INFO:sagemaker.image_uris:Defaulting to the only supported framework/algorithm version: 1.0.\n", + "INFO:sagemaker.image_uris:Ignoring unnecessary instance type: None.\n" + ] + } + ], + "source": [ + "model_bias_analysis_cfg_output_path = (\n", + " f\"s3://{default_bucket}/{base_job_prefix}/modelbiascheckstep/analysis_cfg\"\n", + ")\n", + "\n", + "model_bias_data_config = DataConfig(\n", + " s3_data_input_path=step_process.properties.ProcessingOutputConfig.Outputs[\n", + " \"train\"\n", + " ].S3Output.S3Uri,\n", + " s3_output_path=Join(\n", + " on=\"/\",\n", + " values=[\n", + " \"s3:/\",\n", + " default_bucket,\n", + " base_job_prefix,\n", + " ExecutionVariables.PIPELINE_EXECUTION_ID,\n", + " \"modelbiascheckstep\",\n", + " ],\n", + " ),\n", + " s3_analysis_config_output_path=model_bias_analysis_cfg_output_path,\n", + " label=0,\n", + " dataset_type=\"text/csv\",\n", + ")\n", + "\n", + "model_config = ModelConfig(\n", + " model_name=step_create_model.properties.ModelName,\n", + " instance_count=1,\n", + " instance_type=\"ml.m5.xlarge\",\n", + ")\n", + "\n", + "# We are using this bias config to configure Clarify to detect bias based on the first feature in the featurized vector for Sex\n", + "model_bias_config = BiasConfig(\n", + " label_values_or_threshold=[15.0], facet_name=[8], facet_values_or_threshold=[[0.5]]\n", + ")\n", + "\n", + "model_bias_check_config = ModelBiasCheckConfig(\n", + " data_config=model_bias_data_config,\n", + " data_bias_config=model_bias_config,\n", + " model_config=model_config,\n", + " model_predicted_label_config=ModelPredictedLabelConfig(),\n", + ")\n", + "\n", + "model_bias_check_step = ClarifyCheckStep(\n", + " name=\"ModelBiasCheckStep\",\n", + " clarify_check_config=model_bias_check_config,\n", + " check_job_config=check_job_config,\n", + " skip_check=skip_check_model_bias,\n", + " register_new_baseline=register_new_baseline_model_bias,\n", + " supplied_baseline_constraints=supplied_baseline_constraints_model_bias,\n", + " model_package_group_name=model_package_group_name,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Check Model Explainability\n", + "\n", + "SageMaker Clarify uses a model-agnostic feature attribution approach, which you can use to understand why a model made a prediction after training and to provide per-instance explanation during inference. The implementation includes a scalable and efficient implementation of SHAP, based on the concept of a Shapley value from the field of cooperative game theory that assigns each feature an importance value for a particular prediction.\n", + "\n", + "For Model Explainability, Clarify requires an explainability configuration to be provided. In this example, we use `SHAPConfig`. For more information of `explainability_config`, visit the [Clarify documentation](https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-model-explainability.html)." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:sagemaker.image_uris:Defaulting to the only supported framework/algorithm version: 1.0.\n", + "INFO:sagemaker.image_uris:Ignoring unnecessary instance type: None.\n", + "INFO:sagemaker.model_monitor.clarify_model_monitoring:Uploading analysis config to {s3_uri}.\n", + "INFO:sagemaker.image_uris:Defaulting to the only supported framework/algorithm version: 1.0.\n", + "INFO:sagemaker.image_uris:Ignoring unnecessary instance type: None.\n" + ] + } + ], + "source": [ + "model_explainability_analysis_cfg_output_path = \"s3://{}/{}/{}/{}\".format(\n", + " default_bucket, base_job_prefix, \"modelexplainabilitycheckstep\", \"analysis_cfg\"\n", + ")\n", + "\n", + "model_explainability_data_config = DataConfig(\n", + " s3_data_input_path=step_process.properties.ProcessingOutputConfig.Outputs[\n", + " \"train\"\n", + " ].S3Output.S3Uri,\n", + " s3_output_path=Join(\n", + " on=\"/\",\n", + " values=[\n", + " \"s3:/\",\n", + " default_bucket,\n", + " base_job_prefix,\n", + " ExecutionVariables.PIPELINE_EXECUTION_ID,\n", + " \"modelexplainabilitycheckstep\",\n", + " ],\n", + " ),\n", + " s3_analysis_config_output_path=model_explainability_analysis_cfg_output_path,\n", + " label=0,\n", + " dataset_type=\"text/csv\",\n", + ")\n", + "shap_config = SHAPConfig(seed=123, num_samples=10)\n", + "model_explainability_check_config = ModelExplainabilityCheckConfig(\n", + " data_config=model_explainability_data_config,\n", + " model_config=model_config,\n", + " explainability_config=shap_config,\n", + ")\n", + "model_explainability_check_step = ClarifyCheckStep(\n", + " name=\"ModelExplainabilityCheckStep\",\n", + " clarify_check_config=model_explainability_check_config,\n", + " check_job_config=check_job_config,\n", + " skip_check=skip_check_model_explainability,\n", + " register_new_baseline=register_new_baseline_model_explainability,\n", + " supplied_baseline_constraints=supplied_baseline_constraints_model_explainability,\n", + " model_package_group_name=model_package_group_name,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Evaluate the performance of the model\n", + "\n", + "Using a processing job, evaluate the performance of the model. The performance is used in the Condition Step to determine if the model should be registered or not." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Overwriting code/evaluate.py\n" + ] + } + ], + "source": [ + "%%writefile code/evaluate.py\n", + "\n", + "\"\"\"Evaluation script for measuring mean squared error.\"\"\"\n", + "import json\n", + "import logging\n", + "import pathlib\n", + "import pickle\n", + "import tarfile\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "import xgboost\n", + "\n", + "from sklearn.metrics import mean_squared_error\n", + "\n", + "logger = logging.getLogger()\n", + "logger.setLevel(logging.INFO)\n", + "logger.addHandler(logging.StreamHandler())\n", + "\n", + "\n", + "if __name__ == \"__main__\":\n", + " logger.debug(\"Starting evaluation.\")\n", + " model_path = \"/opt/ml/processing/model/model.tar.gz\"\n", + " with tarfile.open(model_path) as tar:\n", + " tar.extractall(path=\".\")\n", + "\n", + " logger.debug(\"Loading xgboost model.\")\n", + " model = pickle.load(open(\"xgboost-model\", \"rb\"))\n", + "\n", + " logger.debug(\"Reading test data.\")\n", + " test_path = \"/opt/ml/processing/test/test.csv\"\n", + " df = pd.read_csv(test_path, header=None)\n", + "\n", + " logger.debug(\"Reading test data.\")\n", + " y_test = df.iloc[:, 0].to_numpy()\n", + " df.drop(df.columns[0], axis=1, inplace=True)\n", + " X_test = xgboost.DMatrix(df.values)\n", + "\n", + " logger.info(\"Performing predictions against test data.\")\n", + " predictions = model.predict(X_test)\n", + "\n", + " logger.debug(\"Calculating mean squared error.\")\n", + " mse = mean_squared_error(y_test, predictions)\n", + " std = np.std(y_test - predictions)\n", + " report_dict = {\n", + " \"regression_metrics\": {\n", + " \"mse\": {\"value\": mse, \"standard_deviation\": std},\n", + " },\n", + " }\n", + "\n", + " output_dir = \"/opt/ml/processing/evaluation\"\n", + " pathlib.Path(output_dir).mkdir(parents=True, exist_ok=True)\n", + "\n", + " logger.info(\"Writing out evaluation report with mse: %f\", mse)\n", + " evaluation_path = f\"{output_dir}/evaluation.json\"\n", + " with open(evaluation_path, \"w\") as f:\n", + " f.write(json.dumps(report_dict))" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "script_eval = ScriptProcessor(\n", + " image_uri=image_uri,\n", + " command=[\"python3\"],\n", + " instance_type=\"ml.m5.xlarge\",\n", + " instance_count=1,\n", + " base_job_name=f\"{base_job_prefix}/script-abalone-eval\",\n", + " sagemaker_session=pipeline_session,\n", + " role=role,\n", + ")\n", + "evaluation_report = PropertyFile(\n", + " name=\"AbaloneEvaluationReport\",\n", + " output_name=\"evaluation\",\n", + " path=\"evaluation.json\",\n", + ")\n", + "\n", + "eval_args = script_eval.run(\n", + " inputs=[\n", + " ProcessingInput(\n", + " source=step_train.properties.ModelArtifacts.S3ModelArtifacts,\n", + " destination=\"/opt/ml/processing/model\",\n", + " ),\n", + " ProcessingInput(\n", + " source=step_process.properties.ProcessingOutputConfig.Outputs[\"test\"].S3Output.S3Uri,\n", + " destination=\"/opt/ml/processing/test\",\n", + " ),\n", + " ],\n", + " outputs=[\n", + " ProcessingOutput(output_name=\"evaluation\", source=\"/opt/ml/processing/evaluation\"),\n", + " ],\n", + " code=\"code/evaluate.py\",\n", + ")\n", + "step_eval = ProcessingStep(\n", + " name=\"EvaluateAbaloneModel\",\n", + " step_args=eval_args,\n", + " property_files=[evaluation_report],\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Define the metrics to be registered with the model in the Model Registry" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "model_metrics = ModelMetrics(\n", + " model_data_statistics=MetricsSource(\n", + " s3_uri=data_quality_check_step.properties.CalculatedBaselineStatistics,\n", + " content_type=\"application/json\",\n", + " ),\n", + " model_data_constraints=MetricsSource(\n", + " s3_uri=data_quality_check_step.properties.CalculatedBaselineConstraints,\n", + " content_type=\"application/json\",\n", + " ),\n", + " bias_pre_training=MetricsSource(\n", + " s3_uri=data_bias_check_step.properties.CalculatedBaselineConstraints,\n", + " content_type=\"application/json\",\n", + " ),\n", + " model_statistics=MetricsSource(\n", + " s3_uri=model_quality_check_step.properties.CalculatedBaselineStatistics,\n", + " content_type=\"application/json\",\n", + " ),\n", + " model_constraints=MetricsSource(\n", + " s3_uri=model_quality_check_step.properties.CalculatedBaselineConstraints,\n", + " content_type=\"application/json\",\n", + " ),\n", + " bias_post_training=MetricsSource(\n", + " s3_uri=model_bias_check_step.properties.CalculatedBaselineConstraints,\n", + " content_type=\"application/json\",\n", + " ),\n", + " explainability=MetricsSource(\n", + " s3_uri=model_explainability_check_step.properties.CalculatedBaselineConstraints,\n", + " content_type=\"application/json\",\n", + " ),\n", + ")\n", + "\n", + "drift_check_baselines = DriftCheckBaselines(\n", + " model_data_statistics=MetricsSource(\n", + " s3_uri=data_quality_check_step.properties.BaselineUsedForDriftCheckStatistics,\n", + " content_type=\"application/json\",\n", + " ),\n", + " model_data_constraints=MetricsSource(\n", + " s3_uri=data_quality_check_step.properties.BaselineUsedForDriftCheckConstraints,\n", + " content_type=\"application/json\",\n", + " ),\n", + " bias_pre_training_constraints=MetricsSource(\n", + " s3_uri=data_bias_check_step.properties.BaselineUsedForDriftCheckConstraints,\n", + " content_type=\"application/json\",\n", + " ),\n", + " bias_config_file=FileSource(\n", + " s3_uri=model_bias_check_config.monitoring_analysis_config_uri,\n", + " content_type=\"application/json\",\n", + " ),\n", + " model_statistics=MetricsSource(\n", + " s3_uri=model_quality_check_step.properties.BaselineUsedForDriftCheckStatistics,\n", + " content_type=\"application/json\",\n", + " ),\n", + " model_constraints=MetricsSource(\n", + " s3_uri=model_quality_check_step.properties.BaselineUsedForDriftCheckConstraints,\n", + " content_type=\"application/json\",\n", + " ),\n", + " bias_post_training_constraints=MetricsSource(\n", + " s3_uri=model_bias_check_step.properties.BaselineUsedForDriftCheckConstraints,\n", + " content_type=\"application/json\",\n", + " ),\n", + " explainability_constraints=MetricsSource(\n", + " s3_uri=model_explainability_check_step.properties.BaselineUsedForDriftCheckConstraints,\n", + " content_type=\"application/json\",\n", + " ),\n", + " explainability_config_file=FileSource(\n", + " s3_uri=model_explainability_check_config.monitoring_analysis_config_uri,\n", + " content_type=\"application/json\",\n", + " ),\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Register the model\n", + "\n", + "The two parameters in `RegisterModel` that hold the metrics calculated by the `ClarifyCheckStep` and `QualityCheckStep` are `model_metrics` and `drift_check_baselines`.\n", + "\n", + "`drift_check_baselines` - these are the baseline files that will be used for drift checks in `QualityCheckStep` or `ClarifyCheckStep` and model monitoring jobs that are set up on endpoints hosting this model.\n", + "\n", + "`model_metrics` - these should be the latest baselines calculated in the pipeline run. This can be set using the step property `CalculatedBaseline`\n", + "\n", + "The intention behind these parameters is to give users a way to configure the baselines associated with a model so they can be used in drift checks or model monitoring jobs. Each time a pipeline is executed, users can choose to update the `drift_check_baselines` with newly calculated baselines. The `model_metrics` can be used to register the newly calculated baselines or any other metrics associated with the model.\n", + "\n", + "Every time a baseline is calculated, it is not necessary that the baselines used for drift checks are updated to the newly calculated baselines. In some cases, users may retain an older version of the baseline file to be used for drift checks and not register new baselines that are calculated in the Pipeline run." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "import io\n", + "import os\n", + "import numpy as np\n", + "from six.moves.urllib.parse import urlparse\n", + "from pprint import pprint\n", + "import boto3\n", + "import sagemaker\n", + "from sagemaker.image_uris import retrieve\n", + "import sagemaker.amazon.common as smac\n", + "from sagemaker.model_card import (\n", + " ModelCard,\n", + " ModelOverview,\n", + " IntendedUses,\n", + " BusinessDetails,\n", + " AdditionalInformation,\n", + " ModelCardStatusEnum,\n", + " ObjectiveFunctionEnum,\n", + " RiskRatingEnum,\n", + " MetricTypeEnum,\n", + " TrainingDetails\n", + ")\n", + "from sagemaker.model import ModelPackage as MP" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "model_overview = ModelOverview(\n", + " #model_description=\"This is an example model used for a Python SDK demo of unified Amazon SageMaker Model Registry and Model Cards.\",\n", + " #problem_type=\"Binary Classification\",\n", + " #algorithm_type=\"Logistic Regression\",\n", + " model_creator=\"DEMO-Model-Registry-ModelCard-Unification\",\n", + " #model_owner=\"datascienceteam\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "intended_uses = IntendedUses(\n", + " purpose_of_model=\"Test model card.\",\n", + " intended_uses=\"Not used except this test.\",\n", + " factors_affecting_model_efficiency=\"No.\",\n", + " risk_rating=RiskRatingEnum.LOW,\n", + " explanations_for_risk_rating=\"Just an example.\",\n", + ")\n", + "business_details = BusinessDetails(\n", + " business_problem=\"The business problem that your model is used to solve.\",\n", + " business_stakeholders=\"The stakeholders who have the interest in the business that your model is used for.\",\n", + " line_of_business=\"Services that the business is offering.\",\n", + ")\n", + "additional_information = AdditionalInformation(\n", + " ethical_considerations=\"Your model ethical consideration.\",\n", + " caveats_and_recommendations=\"Your model's caveats and recommendations.\",\n", + " custom_details={\"Datazone projects\": project_id},\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "my_card = ModelCard(\n", + " name=\"MyModelCard\",\n", + " status=ModelCardStatusEnum.DRAFT,\n", + " model_overview=model_overview,\n", + " intended_uses=intended_uses,\n", + " business_details=business_details,\n", + " additional_information=additional_information,\n", + " sagemaker_session=pipeline_session,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "register_args = model.register(\n", + " content_types=[\"text/csv\"],\n", + " response_types=[\"text/csv\"],\n", + " inference_instances=[\"ml.t2.medium\", \"ml.m5.large\"],\n", + " transform_instances=[\"ml.m5.large\"],\n", + " model_package_group_name=model_package_group_name,\n", + " approval_status=model_approval_status,\n", + " model_metrics=model_metrics,\n", + " drift_check_baselines=drift_check_baselines,\n", + " model_card=my_card\n", + ")\n", + "\n", + "step_register = ModelStep(name=\"RegisterAbaloneModel\", step_args=register_args)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "# condition step for evaluating model quality and branching execution\n", + "cond_lte = ConditionLessThanOrEqualTo(\n", + " left=JsonGet(\n", + " step_name=step_eval.name,\n", + " property_file=evaluation_report,\n", + " json_path=\"regression_metrics.mse.value\",\n", + " ),\n", + " right=6.0,\n", + ")\n", + "step_cond = ConditionStep(\n", + " name=\"CheckMSEAbaloneEvaluation\",\n", + " conditions=[cond_lte],\n", + " if_steps=[step_register],\n", + " else_steps=[],\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create the Pipeline" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "# pipeline instance\n", + "pipeline = Pipeline(\n", + " name=pipeline_name,\n", + " parameters=[\n", + " processing_instance_count,\n", + " training_instance_type,\n", + " model_approval_status,\n", + " input_data,\n", + " skip_check_data_quality,\n", + " register_new_baseline_data_quality,\n", + " supplied_baseline_statistics_data_quality,\n", + " supplied_baseline_constraints_data_quality,\n", + " skip_check_data_bias,\n", + " register_new_baseline_data_bias,\n", + " supplied_baseline_constraints_data_bias,\n", + " skip_check_model_quality,\n", + " register_new_baseline_model_quality,\n", + " supplied_baseline_statistics_model_quality,\n", + " supplied_baseline_constraints_model_quality,\n", + " skip_check_model_bias,\n", + " register_new_baseline_model_bias,\n", + " supplied_baseline_constraints_model_bias,\n", + " skip_check_model_explainability,\n", + " register_new_baseline_model_explainability,\n", + " supplied_baseline_constraints_model_explainability,\n", + " ],\n", + " steps=[\n", + " step_process,\n", + " data_quality_check_step,\n", + " data_bias_check_step,\n", + " step_train,\n", + " step_create_model,\n", + " step_transform,\n", + " model_quality_check_step,\n", + " model_bias_check_step,\n", + " model_explainability_check_step,\n", + " step_eval,\n", + " step_cond,\n", + " ],\n", + " sagemaker_session=pipeline_session,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Get Pipeline definition" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING:sagemaker.workflow.utilities:Popping out 'ProcessingJobName' from the pipeline definition by default since it will be overridden at pipeline execution time. Please utilize the PipelineDefinitionConfig to persist this field in the pipeline definition if desired.\n", + "WARNING:sagemaker.workflow.utilities:Popping out 'ProcessingJobName' from the pipeline definition by default since it will be overridden at pipeline execution time. Please utilize the PipelineDefinitionConfig to persist this field in the pipeline definition if desired.\n", + "WARNING:sagemaker.workflow.utilities:Popping out 'ProcessingJobName' from the pipeline definition by default since it will be overridden at pipeline execution time. Please utilize the PipelineDefinitionConfig to persist this field in the pipeline definition if desired.\n", + "WARNING:sagemaker.workflow.utilities:Popping out 'TrainingJobName' from the pipeline definition by default since it will be overridden at pipeline execution time. Please utilize the PipelineDefinitionConfig to persist this field in the pipeline definition if desired.\n", + "WARNING:sagemaker.workflow.utilities:Popping out 'ModelName' from the pipeline definition by default since it will be overridden at pipeline execution time. Please utilize the PipelineDefinitionConfig to persist this field in the pipeline definition if desired.\n", + "WARNING:sagemaker.workflow.utilities:Popping out 'TransformJobName' from the pipeline definition by default since it will be overridden at pipeline execution time. Please utilize the PipelineDefinitionConfig to persist this field in the pipeline definition if desired.\n", + "WARNING:sagemaker.workflow.utilities:Popping out 'ProcessingJobName' from the pipeline definition by default since it will be overridden at pipeline execution time. Please utilize the PipelineDefinitionConfig to persist this field in the pipeline definition if desired.\n", + "WARNING:sagemaker.workflow.utilities:Popping out 'ProcessingJobName' from the pipeline definition by default since it will be overridden at pipeline execution time. Please utilize the PipelineDefinitionConfig to persist this field in the pipeline definition if desired.\n", + "WARNING:sagemaker.workflow.utilities:Popping out 'ProcessingJobName' from the pipeline definition by default since it will be overridden at pipeline execution time. Please utilize the PipelineDefinitionConfig to persist this field in the pipeline definition if desired.\n", + "WARNING:sagemaker.workflow.utilities:Popping out 'ProcessingJobName' from the pipeline definition by default since it will be overridden at pipeline execution time. Please utilize the PipelineDefinitionConfig to persist this field in the pipeline definition if desired.\n", + "WARNING:sagemaker.workflow._utils:Popping out 'CertifyForMarketplace' from the pipeline definition since it will be overridden in pipeline execution time.\n", + "WARNING:sagemaker.workflow.utilities:Popping out 'ModelPackageName' from the pipeline definition by default since it will be overridden at pipeline execution time. Please utilize the PipelineDefinitionConfig to persist this field in the pipeline definition if desired.\n" + ] + }, + { + "data": { + "text/plain": [ + "{'Version': '2020-12-01',\n", + " 'Metadata': {},\n", + " 'Parameters': [{'Name': 'ProcessingInstanceCount',\n", + " 'Type': 'Integer',\n", + " 'DefaultValue': 1},\n", + " {'Name': 'TrainingInstanceType',\n", + " 'Type': 'String',\n", + " 'DefaultValue': 'ml.m5.xlarge'},\n", + " {'Name': 'ModelApprovalStatus',\n", + " 'Type': 'String',\n", + " 'DefaultValue': 'PendingManualApproval'},\n", + " {'Name': 'InputDataUrl',\n", + " 'Type': 'String',\n", + " 'DefaultValue': 's3://sagemaker-sample-files/datasets/tabular/uci_abalone/abalone.csv'},\n", + " {'Name': 'SkipDataQualityCheck', 'Type': 'Boolean', 'DefaultValue': False},\n", + " {'Name': 'RegisterNewDataQualityBaseline',\n", + " 'Type': 'Boolean',\n", + " 'DefaultValue': False},\n", + " {'Name': 'DataQualitySuppliedStatistics',\n", + " 'Type': 'String',\n", + " 'DefaultValue': ''},\n", + " {'Name': 'DataQualitySuppliedConstraints',\n", + " 'Type': 'String',\n", + " 'DefaultValue': ''},\n", + " {'Name': 'SkipDataBiasCheck', 'Type': 'Boolean', 'DefaultValue': False},\n", + " {'Name': 'RegisterNewDataBiasBaseline',\n", + " 'Type': 'Boolean',\n", + " 'DefaultValue': False},\n", + " {'Name': 'DataBiasSuppliedBaselineConstraints',\n", + " 'Type': 'String',\n", + " 'DefaultValue': ''},\n", + " {'Name': 'SkipModelQualityCheck', 'Type': 'Boolean', 'DefaultValue': False},\n", + " {'Name': 'RegisterNewModelQualityBaseline',\n", + " 'Type': 'Boolean',\n", + " 'DefaultValue': False},\n", + " {'Name': 'ModelQualitySuppliedStatistics',\n", + " 'Type': 'String',\n", + " 'DefaultValue': ''},\n", + " {'Name': 'ModelQualitySuppliedConstraints',\n", + " 'Type': 'String',\n", + " 'DefaultValue': ''},\n", + " {'Name': 'SkipModelBiasCheck', 'Type': 'Boolean', 'DefaultValue': False},\n", + " {'Name': 'RegisterNewModelBiasBaseline',\n", + " 'Type': 'Boolean',\n", + " 'DefaultValue': False},\n", + " {'Name': 'ModelBiasSuppliedBaselineConstraints',\n", + " 'Type': 'String',\n", + " 'DefaultValue': ''},\n", + " {'Name': 'SkipModelExplainabilityCheck',\n", + " 'Type': 'Boolean',\n", + " 'DefaultValue': False},\n", + " {'Name': 'RegisterNewModelExplainabilityBaseline',\n", + " 'Type': 'Boolean',\n", + " 'DefaultValue': False},\n", + " {'Name': 'ModelExplainabilitySuppliedBaselineConstraints',\n", + " 'Type': 'String',\n", + " 'DefaultValue': ''}],\n", + " 'PipelineExperimentConfig': {'ExperimentName': {'Get': 'Execution.PipelineName'},\n", + " 'TrialName': {'Get': 'Execution.PipelineExecutionId'}},\n", + " 'Steps': [{'Name': 'PreprocessAbaloneData',\n", + " 'Type': 'Processing',\n", + " 'Arguments': {'ProcessingResources': {'ClusterConfig': {'InstanceType': 'ml.m5.xlarge',\n", + " 'InstanceCount': {'Get': 'Parameters.ProcessingInstanceCount'},\n", + " 'VolumeSizeInGB': 30}},\n", + " 'AppSpecification': {'ImageUri': '683313688378.dkr.ecr.us-east-1.amazonaws.com/sagemaker-scikit-learn:0.23-1-cpu-py3',\n", + " 'ContainerArguments': ['--input-data',\n", + " {'Get': 'Parameters.InputDataUrl'}],\n", + " 'ContainerEntrypoint': ['python3',\n", + " '/opt/ml/processing/input/code/preprocess.py']},\n", + " 'RoleArn': 'arn:aws:iam::340280328827:role/CFN-SM-IM-Lambda-Catalog-SageMakerExecutionRole-1TSLA2QB1DJKS',\n", + " 'ProcessingInputs': [{'InputName': 'code',\n", + " 'AppManaged': False,\n", + " 'S3Input': {'S3Uri': 's3://sagemaker-us-east-1-340280328827/examplepipeline/code/2d6df10ec89b5513bb118b8c4af0c648/preprocess.py',\n", + " 'LocalPath': '/opt/ml/processing/input/code',\n", + " 'S3DataType': 'S3Prefix',\n", + " 'S3InputMode': 'File',\n", + " 'S3DataDistributionType': 'FullyReplicated',\n", + " 'S3CompressionType': 'None'}}],\n", + " 'ProcessingOutputConfig': {'Outputs': [{'OutputName': 'train',\n", + " 'AppManaged': False,\n", + " 'S3Output': {'S3Uri': {'Std:Join': {'On': '/',\n", + " 'Values': ['s3:/',\n", + " 'sagemaker-us-east-1-340280328827',\n", + " 'examplepipeline',\n", + " {'Get': 'Execution.PipelineExecutionId'},\n", + " 'PreprocessAbaloneData',\n", + " 'output',\n", + " 'train']}},\n", + " 'LocalPath': '/opt/ml/processing/train',\n", + " 'S3UploadMode': 'EndOfJob'}},\n", + " {'OutputName': 'validation',\n", + " 'AppManaged': False,\n", + " 'S3Output': {'S3Uri': {'Std:Join': {'On': '/',\n", + " 'Values': ['s3:/',\n", + " 'sagemaker-us-east-1-340280328827',\n", + " 'examplepipeline',\n", + " {'Get': 'Execution.PipelineExecutionId'},\n", + " 'PreprocessAbaloneData',\n", + " 'output',\n", + " 'validation']}},\n", + " 'LocalPath': '/opt/ml/processing/validation',\n", + " 'S3UploadMode': 'EndOfJob'}},\n", + " {'OutputName': 'test',\n", + " 'AppManaged': False,\n", + " 'S3Output': {'S3Uri': {'Std:Join': {'On': '/',\n", + " 'Values': ['s3:/',\n", + " 'sagemaker-us-east-1-340280328827',\n", + " 'examplepipeline',\n", + " {'Get': 'Execution.PipelineExecutionId'},\n", + " 'PreprocessAbaloneData',\n", + " 'output',\n", + " 'test']}},\n", + " 'LocalPath': '/opt/ml/processing/test',\n", + " 'S3UploadMode': 'EndOfJob'}}]}}},\n", + " {'Name': 'DataQualityCheckStep',\n", + " 'Type': 'QualityCheck',\n", + " 'Arguments': {'ProcessingResources': {'ClusterConfig': {'InstanceType': 'ml.c5.xlarge',\n", + " 'InstanceCount': 1,\n", + " 'VolumeSizeInGB': 120}},\n", + " 'AppSpecification': {'ImageUri': '156813124566.dkr.ecr.us-east-1.amazonaws.com/sagemaker-model-monitor-analyzer'},\n", + " 'RoleArn': 'arn:aws:iam::340280328827:role/CFN-SM-IM-Lambda-Catalog-SageMakerExecutionRole-1TSLA2QB1DJKS',\n", + " 'ProcessingInputs': [{'InputName': 'baseline_dataset_input',\n", + " 'AppManaged': False,\n", + " 'S3Input': {'S3Uri': {'Get': \"Steps.PreprocessAbaloneData.ProcessingOutputConfig.Outputs['train'].S3Output.S3Uri\"},\n", + " 'LocalPath': '/opt/ml/processing/input/baseline_dataset_input',\n", + " 'S3DataType': 'S3Prefix',\n", + " 'S3InputMode': 'File',\n", + " 'S3DataDistributionType': 'FullyReplicated',\n", + " 'S3CompressionType': 'None'}}],\n", + " 'ProcessingOutputConfig': {'Outputs': [{'OutputName': 'quality_check_output',\n", + " 'AppManaged': False,\n", + " 'S3Output': {'S3Uri': {'Std:Join': {'On': '/',\n", + " 'Values': ['s3:/',\n", + " 'sagemaker-us-east-1-340280328827',\n", + " 'model-monitor-clarify',\n", + " {'Get': 'Execution.PipelineExecutionId'},\n", + " 'dataqualitycheckstep']}},\n", + " 'LocalPath': '/opt/ml/processing/output',\n", + " 'S3UploadMode': 'EndOfJob'}}]},\n", + " 'Environment': {'output_path': '/opt/ml/processing/output',\n", + " 'publish_cloudwatch_metrics': 'Disabled',\n", + " 'dataset_format': '{\"csv\": {\"header\": false, \"output_columns_position\": \"START\"}}',\n", + " 'dataset_source': '/opt/ml/processing/input/baseline_dataset_input'}},\n", + " 'CheckType': 'DATA_QUALITY',\n", + " 'ModelPackageGroupName': 'ExamplePackage',\n", + " 'SkipCheck': {'Get': 'Parameters.SkipDataQualityCheck'},\n", + " 'FailOnViolation': True,\n", + " 'RegisterNewBaseline': {'Get': 'Parameters.RegisterNewDataQualityBaseline'},\n", + " 'SuppliedBaselineStatistics': {'Get': 'Parameters.DataQualitySuppliedStatistics'},\n", + " 'SuppliedBaselineConstraints': {'Get': 'Parameters.DataQualitySuppliedConstraints'}},\n", + " {'Name': 'DataBiasCheckStep',\n", + " 'Type': 'ClarifyCheck',\n", + " 'Arguments': {'ProcessingResources': {'ClusterConfig': {'InstanceType': 'ml.c5.xlarge',\n", + " 'InstanceCount': 1,\n", + " 'VolumeSizeInGB': 120}},\n", + " 'AppSpecification': {'ImageUri': '205585389593.dkr.ecr.us-east-1.amazonaws.com/sagemaker-clarify-processing:1.0'},\n", + " 'RoleArn': 'arn:aws:iam::340280328827:role/CFN-SM-IM-Lambda-Catalog-SageMakerExecutionRole-1TSLA2QB1DJKS',\n", + " 'ProcessingInputs': [{'InputName': 'analysis_config',\n", + " 'AppManaged': False,\n", + " 'S3Input': {'S3Uri': 's3://sagemaker-us-east-1-340280328827/model-monitor-clarify/databiascheckstep/analysis_cfg/analysis_config.json',\n", + " 'LocalPath': '/opt/ml/processing/input/config',\n", + " 'S3DataType': 'S3Prefix',\n", + " 'S3InputMode': 'File',\n", + " 'S3DataDistributionType': 'FullyReplicated',\n", + " 'S3CompressionType': 'None'}},\n", + " {'InputName': 'dataset',\n", + " 'AppManaged': False,\n", + " 'S3Input': {'S3Uri': {'Get': \"Steps.PreprocessAbaloneData.ProcessingOutputConfig.Outputs['train'].S3Output.S3Uri\"},\n", + " 'LocalPath': '/opt/ml/processing/input/data',\n", + " 'S3DataType': 'S3Prefix',\n", + " 'S3InputMode': 'File',\n", + " 'S3DataDistributionType': 'FullyReplicated',\n", + " 'S3CompressionType': 'None'}}],\n", + " 'ProcessingOutputConfig': {'Outputs': [{'OutputName': 'analysis_result',\n", + " 'AppManaged': False,\n", + " 'S3Output': {'S3Uri': {'Std:Join': {'On': '/',\n", + " 'Values': ['s3:/',\n", + " 'sagemaker-us-east-1-340280328827',\n", + " 'model-monitor-clarify',\n", + " {'Get': 'Execution.PipelineExecutionId'},\n", + " 'databiascheckstep']}},\n", + " 'LocalPath': '/opt/ml/processing/output',\n", + " 'S3UploadMode': 'EndOfJob'}}]}},\n", + " 'CheckType': 'DATA_BIAS',\n", + " 'ModelPackageGroupName': 'ExamplePackage',\n", + " 'SkipCheck': {'Get': 'Parameters.SkipDataBiasCheck'},\n", + " 'FailOnViolation': True,\n", + " 'RegisterNewBaseline': {'Get': 'Parameters.RegisterNewDataBiasBaseline'},\n", + " 'SuppliedBaselineConstraints': {'Get': 'Parameters.DataBiasSuppliedBaselineConstraints'}},\n", + " {'Name': 'TrainAbaloneModel',\n", + " 'Type': 'Training',\n", + " 'Arguments': {'AlgorithmSpecification': {'TrainingInputMode': 'File',\n", + " 'TrainingImage': '683313688378.dkr.ecr.us-east-1.amazonaws.com/sagemaker-xgboost:1.0-1-cpu-py3'},\n", + " 'OutputDataConfig': {'S3OutputPath': 's3://sagemaker-us-east-1-340280328827/model-monitor-clarify/AbaloneTrain'},\n", + " 'StoppingCondition': {'MaxRuntimeInSeconds': 86400},\n", + " 'ResourceConfig': {'VolumeSizeInGB': 30,\n", + " 'InstanceCount': 1,\n", + " 'InstanceType': {'Get': 'Parameters.TrainingInstanceType'}},\n", + " 'RoleArn': 'arn:aws:iam::340280328827:role/CFN-SM-IM-Lambda-Catalog-SageMakerExecutionRole-1TSLA2QB1DJKS',\n", + " 'InputDataConfig': [{'DataSource': {'S3DataSource': {'S3DataType': 'S3Prefix',\n", + " 'S3Uri': {'Get': \"Steps.PreprocessAbaloneData.ProcessingOutputConfig.Outputs['train'].S3Output.S3Uri\"},\n", + " 'S3DataDistributionType': 'FullyReplicated'}},\n", + " 'ContentType': 'text/csv',\n", + " 'ChannelName': 'train'},\n", + " {'DataSource': {'S3DataSource': {'S3DataType': 'S3Prefix',\n", + " 'S3Uri': {'Get': \"Steps.PreprocessAbaloneData.ProcessingOutputConfig.Outputs['validation'].S3Output.S3Uri\"},\n", + " 'S3DataDistributionType': 'FullyReplicated'}},\n", + " 'ContentType': 'text/csv',\n", + " 'ChannelName': 'validation'}],\n", + " 'HyperParameters': {'objective': 'reg:linear',\n", + " 'num_round': '50',\n", + " 'max_depth': '5',\n", + " 'eta': '0.2',\n", + " 'gamma': '4',\n", + " 'min_child_weight': '6',\n", + " 'subsample': '0.7',\n", + " 'silent': '0'},\n", + " 'DebugHookConfig': {'S3OutputPath': 's3://sagemaker-us-east-1-340280328827/model-monitor-clarify/AbaloneTrain',\n", + " 'CollectionConfigurations': []},\n", + " 'ProfilerConfig': {'S3OutputPath': 's3://sagemaker-us-east-1-340280328827/model-monitor-clarify/AbaloneTrain',\n", + " 'DisableProfiler': False}},\n", + " 'DependsOn': ['DataBiasCheckStep', 'DataQualityCheckStep']},\n", + " {'Name': 'AbaloneCreateModel-CreateModel',\n", + " 'Type': 'Model',\n", + " 'Arguments': {'ExecutionRoleArn': 'arn:aws:iam::340280328827:role/CFN-SM-IM-Lambda-Catalog-SageMakerExecutionRole-1TSLA2QB1DJKS',\n", + " 'PrimaryContainer': {'Image': '683313688378.dkr.ecr.us-east-1.amazonaws.com/sagemaker-xgboost:1.0-1-cpu-py3',\n", + " 'Environment': {},\n", + " 'ModelDataUrl': {'Get': 'Steps.TrainAbaloneModel.ModelArtifacts.S3ModelArtifacts'}}}},\n", + " {'Name': 'AbaloneTransform',\n", + " 'Type': 'Transform',\n", + " 'Arguments': {'ModelName': {'Get': 'Steps.AbaloneCreateModel-CreateModel.ModelName'},\n", + " 'TransformInput': {'DataSource': {'S3DataSource': {'S3DataType': 'S3Prefix',\n", + " 'S3Uri': {'Get': \"Steps.PreprocessAbaloneData.ProcessingOutputConfig.Outputs['test'].S3Output.S3Uri\"}}},\n", + " 'ContentType': 'text/csv',\n", + " 'SplitType': 'Line'},\n", + " 'TransformOutput': {'S3OutputPath': 's3://sagemaker-us-east-1-340280328827/AbaloneTransform',\n", + " 'AssembleWith': 'Line',\n", + " 'Accept': 'text/csv'},\n", + " 'TransformResources': {'InstanceCount': 1, 'InstanceType': 'ml.m5.xlarge'},\n", + " 'DataProcessing': {'InputFilter': '$[1:]',\n", + " 'OutputFilter': '$[0,-1]',\n", + " 'JoinSource': 'Input'}}},\n", + " {'Name': 'ModelQualityCheckStep',\n", + " 'Type': 'QualityCheck',\n", + " 'Arguments': {'ProcessingResources': {'ClusterConfig': {'InstanceType': 'ml.c5.xlarge',\n", + " 'InstanceCount': 1,\n", + " 'VolumeSizeInGB': 120}},\n", + " 'AppSpecification': {'ImageUri': '156813124566.dkr.ecr.us-east-1.amazonaws.com/sagemaker-model-monitor-analyzer'},\n", + " 'RoleArn': 'arn:aws:iam::340280328827:role/CFN-SM-IM-Lambda-Catalog-SageMakerExecutionRole-1TSLA2QB1DJKS',\n", + " 'ProcessingInputs': [{'InputName': 'baseline_dataset_input',\n", + " 'AppManaged': False,\n", + " 'S3Input': {'S3Uri': {'Get': 'Steps.AbaloneTransform.TransformOutput.S3OutputPath'},\n", + " 'LocalPath': '/opt/ml/processing/input/baseline_dataset_input',\n", + " 'S3DataType': 'S3Prefix',\n", + " 'S3InputMode': 'File',\n", + " 'S3DataDistributionType': 'FullyReplicated',\n", + " 'S3CompressionType': 'None'}}],\n", + " 'ProcessingOutputConfig': {'Outputs': [{'OutputName': 'quality_check_output',\n", + " 'AppManaged': False,\n", + " 'S3Output': {'S3Uri': {'Std:Join': {'On': '/',\n", + " 'Values': ['s3:/',\n", + " 'sagemaker-us-east-1-340280328827',\n", + " 'model-monitor-clarify',\n", + " {'Get': 'Execution.PipelineExecutionId'},\n", + " 'modelqualitycheckstep']}},\n", + " 'LocalPath': '/opt/ml/processing/output',\n", + " 'S3UploadMode': 'EndOfJob'}}]},\n", + " 'Environment': {'output_path': '/opt/ml/processing/output',\n", + " 'publish_cloudwatch_metrics': 'Disabled',\n", + " 'dataset_format': '{\"csv\": {\"header\": false, \"output_columns_position\": \"START\"}}',\n", + " 'dataset_source': '/opt/ml/processing/input/baseline_dataset_input',\n", + " 'analysis_type': 'MODEL_QUALITY',\n", + " 'problem_type': 'Regression',\n", + " 'inference_attribute': '_c0',\n", + " 'ground_truth_attribute': '_c1'}},\n", + " 'CheckType': 'MODEL_QUALITY',\n", + " 'ModelPackageGroupName': 'ExamplePackage',\n", + " 'SkipCheck': {'Get': 'Parameters.SkipModelQualityCheck'},\n", + " 'FailOnViolation': True,\n", + " 'RegisterNewBaseline': {'Get': 'Parameters.RegisterNewModelQualityBaseline'},\n", + " 'SuppliedBaselineStatistics': {'Get': 'Parameters.ModelQualitySuppliedStatistics'},\n", + " 'SuppliedBaselineConstraints': {'Get': 'Parameters.ModelQualitySuppliedConstraints'}},\n", + " {'Name': 'ModelBiasCheckStep',\n", + " 'Type': 'ClarifyCheck',\n", + " 'Arguments': {'ProcessingResources': {'ClusterConfig': {'InstanceType': 'ml.c5.xlarge',\n", + " 'InstanceCount': 1,\n", + " 'VolumeSizeInGB': 120}},\n", + " 'AppSpecification': {'ImageUri': '205585389593.dkr.ecr.us-east-1.amazonaws.com/sagemaker-clarify-processing:1.0'},\n", + " 'RoleArn': 'arn:aws:iam::340280328827:role/CFN-SM-IM-Lambda-Catalog-SageMakerExecutionRole-1TSLA2QB1DJKS',\n", + " 'ProcessingInputs': [{'InputName': 'analysis_config',\n", + " 'AppManaged': False,\n", + " 'S3Input': {'S3Uri': 's3://sagemaker-us-east-1-340280328827/model-monitor-clarify/modelbiascheckstep/analysis_cfg/analysis_config.json',\n", + " 'LocalPath': '/opt/ml/processing/input/config',\n", + " 'S3DataType': 'S3Prefix',\n", + " 'S3InputMode': 'File',\n", + " 'S3DataDistributionType': 'FullyReplicated',\n", + " 'S3CompressionType': 'None'}},\n", + " {'InputName': 'dataset',\n", + " 'AppManaged': False,\n", + " 'S3Input': {'S3Uri': {'Get': \"Steps.PreprocessAbaloneData.ProcessingOutputConfig.Outputs['train'].S3Output.S3Uri\"},\n", + " 'LocalPath': '/opt/ml/processing/input/data',\n", + " 'S3DataType': 'S3Prefix',\n", + " 'S3InputMode': 'File',\n", + " 'S3DataDistributionType': 'FullyReplicated',\n", + " 'S3CompressionType': 'None'}}],\n", + " 'ProcessingOutputConfig': {'Outputs': [{'OutputName': 'analysis_result',\n", + " 'AppManaged': False,\n", + " 'S3Output': {'S3Uri': {'Std:Join': {'On': '/',\n", + " 'Values': ['s3:/',\n", + " 'sagemaker-us-east-1-340280328827',\n", + " 'model-monitor-clarify',\n", + " {'Get': 'Execution.PipelineExecutionId'},\n", + " 'modelbiascheckstep']}},\n", + " 'LocalPath': '/opt/ml/processing/output',\n", + " 'S3UploadMode': 'EndOfJob'}}]}},\n", + " 'CheckType': 'MODEL_BIAS',\n", + " 'ModelPackageGroupName': 'ExamplePackage',\n", + " 'SkipCheck': {'Get': 'Parameters.SkipModelBiasCheck'},\n", + " 'FailOnViolation': True,\n", + " 'RegisterNewBaseline': {'Get': 'Parameters.RegisterNewModelBiasBaseline'},\n", + " 'SuppliedBaselineConstraints': {'Get': 'Parameters.ModelBiasSuppliedBaselineConstraints'},\n", + " 'ModelName': {'Get': 'Steps.AbaloneCreateModel-CreateModel.ModelName'}},\n", + " {'Name': 'ModelExplainabilityCheckStep',\n", + " 'Type': 'ClarifyCheck',\n", + " 'Arguments': {'ProcessingResources': {'ClusterConfig': {'InstanceType': 'ml.c5.xlarge',\n", + " 'InstanceCount': 1,\n", + " 'VolumeSizeInGB': 120}},\n", + " 'AppSpecification': {'ImageUri': '205585389593.dkr.ecr.us-east-1.amazonaws.com/sagemaker-clarify-processing:1.0'},\n", + " 'RoleArn': 'arn:aws:iam::340280328827:role/CFN-SM-IM-Lambda-Catalog-SageMakerExecutionRole-1TSLA2QB1DJKS',\n", + " 'ProcessingInputs': [{'InputName': 'analysis_config',\n", + " 'AppManaged': False,\n", + " 'S3Input': {'S3Uri': 's3://sagemaker-us-east-1-340280328827/model-monitor-clarify/modelexplainabilitycheckstep/analysis_cfg/analysis_config.json',\n", + " 'LocalPath': '/opt/ml/processing/input/config',\n", + " 'S3DataType': 'S3Prefix',\n", + " 'S3InputMode': 'File',\n", + " 'S3DataDistributionType': 'FullyReplicated',\n", + " 'S3CompressionType': 'None'}},\n", + " {'InputName': 'dataset',\n", + " 'AppManaged': False,\n", + " 'S3Input': {'S3Uri': {'Get': \"Steps.PreprocessAbaloneData.ProcessingOutputConfig.Outputs['train'].S3Output.S3Uri\"},\n", + " 'LocalPath': '/opt/ml/processing/input/data',\n", + " 'S3DataType': 'S3Prefix',\n", + " 'S3InputMode': 'File',\n", + " 'S3DataDistributionType': 'FullyReplicated',\n", + " 'S3CompressionType': 'None'}}],\n", + " 'ProcessingOutputConfig': {'Outputs': [{'OutputName': 'analysis_result',\n", + " 'AppManaged': False,\n", + " 'S3Output': {'S3Uri': {'Std:Join': {'On': '/',\n", + " 'Values': ['s3:/',\n", + " 'sagemaker-us-east-1-340280328827',\n", + " 'model-monitor-clarify',\n", + " {'Get': 'Execution.PipelineExecutionId'},\n", + " 'modelexplainabilitycheckstep']}},\n", + " 'LocalPath': '/opt/ml/processing/output',\n", + " 'S3UploadMode': 'EndOfJob'}}]}},\n", + " 'CheckType': 'MODEL_EXPLAINABILITY',\n", + " 'ModelPackageGroupName': 'ExamplePackage',\n", + " 'SkipCheck': {'Get': 'Parameters.SkipModelExplainabilityCheck'},\n", + " 'FailOnViolation': True,\n", + " 'RegisterNewBaseline': {'Get': 'Parameters.RegisterNewModelExplainabilityBaseline'},\n", + " 'SuppliedBaselineConstraints': {'Get': 'Parameters.ModelExplainabilitySuppliedBaselineConstraints'},\n", + " 'ModelName': {'Get': 'Steps.AbaloneCreateModel-CreateModel.ModelName'}},\n", + " {'Name': 'EvaluateAbaloneModel',\n", + " 'Type': 'Processing',\n", + " 'Arguments': {'ProcessingResources': {'ClusterConfig': {'InstanceType': 'ml.m5.xlarge',\n", + " 'InstanceCount': 1,\n", + " 'VolumeSizeInGB': 30}},\n", + " 'AppSpecification': {'ImageUri': '683313688378.dkr.ecr.us-east-1.amazonaws.com/sagemaker-xgboost:1.0-1-cpu-py3',\n", + " 'ContainerEntrypoint': ['python3',\n", + " '/opt/ml/processing/input/code/evaluate.py']},\n", + " 'RoleArn': 'arn:aws:iam::340280328827:role/CFN-SM-IM-Lambda-Catalog-SageMakerExecutionRole-1TSLA2QB1DJKS',\n", + " 'ProcessingInputs': [{'InputName': 'input-1',\n", + " 'AppManaged': False,\n", + " 'S3Input': {'S3Uri': {'Get': 'Steps.TrainAbaloneModel.ModelArtifacts.S3ModelArtifacts'},\n", + " 'LocalPath': '/opt/ml/processing/model',\n", + " 'S3DataType': 'S3Prefix',\n", + " 'S3InputMode': 'File',\n", + " 'S3DataDistributionType': 'FullyReplicated',\n", + " 'S3CompressionType': 'None'}},\n", + " {'InputName': 'input-2',\n", + " 'AppManaged': False,\n", + " 'S3Input': {'S3Uri': {'Get': \"Steps.PreprocessAbaloneData.ProcessingOutputConfig.Outputs['test'].S3Output.S3Uri\"},\n", + " 'LocalPath': '/opt/ml/processing/test',\n", + " 'S3DataType': 'S3Prefix',\n", + " 'S3InputMode': 'File',\n", + " 'S3DataDistributionType': 'FullyReplicated',\n", + " 'S3CompressionType': 'None'}},\n", + " {'InputName': 'code',\n", + " 'AppManaged': False,\n", + " 'S3Input': {'S3Uri': 's3://sagemaker-us-east-1-340280328827/examplepipeline/code/3384adc475a3da31615b9ff745f950e3/evaluate.py',\n", + " 'LocalPath': '/opt/ml/processing/input/code',\n", + " 'S3DataType': 'S3Prefix',\n", + " 'S3InputMode': 'File',\n", + " 'S3DataDistributionType': 'FullyReplicated',\n", + " 'S3CompressionType': 'None'}}],\n", + " 'ProcessingOutputConfig': {'Outputs': [{'OutputName': 'evaluation',\n", + " 'AppManaged': False,\n", + " 'S3Output': {'S3Uri': {'Std:Join': {'On': '/',\n", + " 'Values': ['s3:/',\n", + " 'sagemaker-us-east-1-340280328827',\n", + " 'examplepipeline',\n", + " {'Get': 'Execution.PipelineExecutionId'},\n", + " 'EvaluateAbaloneModel',\n", + " 'output',\n", + " 'evaluation']}},\n", + " 'LocalPath': '/opt/ml/processing/evaluation',\n", + " 'S3UploadMode': 'EndOfJob'}}]}},\n", + " 'PropertyFiles': [{'PropertyFileName': 'AbaloneEvaluationReport',\n", + " 'OutputName': 'evaluation',\n", + " 'FilePath': 'evaluation.json'}]},\n", + " {'Name': 'CheckMSEAbaloneEvaluation',\n", + " 'Type': 'Condition',\n", + " 'Arguments': {'Conditions': [{'Type': 'LessThanOrEqualTo',\n", + " 'LeftValue': {'Std:JsonGet': {'PropertyFile': {'Get': 'Steps.EvaluateAbaloneModel.PropertyFiles.AbaloneEvaluationReport'},\n", + " 'Path': 'regression_metrics.mse.value'}},\n", + " 'RightValue': 6.0}],\n", + " 'IfSteps': [{'Name': 'RegisterAbaloneModel-RegisterModel',\n", + " 'Type': 'RegisterModel',\n", + " 'Arguments': {'ModelPackageGroupName': 'ExamplePackage',\n", + " 'ModelMetrics': {'ModelQuality': {'Statistics': {'ContentType': 'application/json',\n", + " 'S3Uri': {'Get': 'Steps.ModelQualityCheckStep.CalculatedBaselineStatistics'}},\n", + " 'Constraints': {'ContentType': 'application/json',\n", + " 'S3Uri': {'Get': 'Steps.ModelQualityCheckStep.CalculatedBaselineConstraints'}}},\n", + " 'ModelDataQuality': {'Statistics': {'ContentType': 'application/json',\n", + " 'S3Uri': {'Get': 'Steps.DataQualityCheckStep.CalculatedBaselineStatistics'}},\n", + " 'Constraints': {'ContentType': 'application/json',\n", + " 'S3Uri': {'Get': 'Steps.DataQualityCheckStep.CalculatedBaselineConstraints'}}},\n", + " 'Bias': {'PreTrainingReport': {'ContentType': 'application/json',\n", + " 'S3Uri': {'Get': 'Steps.DataBiasCheckStep.CalculatedBaselineConstraints'}},\n", + " 'PostTrainingReport': {'ContentType': 'application/json',\n", + " 'S3Uri': {'Get': 'Steps.ModelBiasCheckStep.CalculatedBaselineConstraints'}}},\n", + " 'Explainability': {'Report': {'ContentType': 'application/json',\n", + " 'S3Uri': {'Get': 'Steps.ModelExplainabilityCheckStep.CalculatedBaselineConstraints'}}}},\n", + " 'DriftCheckBaselines': {'ModelQuality': {'Statistics': {'ContentType': 'application/json',\n", + " 'S3Uri': {'Get': 'Steps.ModelQualityCheckStep.BaselineUsedForDriftCheckStatistics'}},\n", + " 'Constraints': {'ContentType': 'application/json',\n", + " 'S3Uri': {'Get': 'Steps.ModelQualityCheckStep.BaselineUsedForDriftCheckConstraints'}}},\n", + " 'ModelDataQuality': {'Statistics': {'ContentType': 'application/json',\n", + " 'S3Uri': {'Get': 'Steps.DataQualityCheckStep.BaselineUsedForDriftCheckStatistics'}},\n", + " 'Constraints': {'ContentType': 'application/json',\n", + " 'S3Uri': {'Get': 'Steps.DataQualityCheckStep.BaselineUsedForDriftCheckConstraints'}}},\n", + " 'Bias': {'ConfigFile': {'S3Uri': 's3://sagemaker-us-east-1-340280328827/model-monitor-clarify/modelbiascheckstep/analysis_cfg/bias-monitoring-configuration/bias-monitoring-config-2024-09-23-21-05-03-909/c568d056-6fa3-4a63-acbf-2781cfc96700/analysis_config.json',\n", + " 'ContentType': 'application/json'},\n", + " 'PreTrainingConstraints': {'ContentType': 'application/json',\n", + " 'S3Uri': {'Get': 'Steps.DataBiasCheckStep.BaselineUsedForDriftCheckConstraints'}},\n", + " 'PostTrainingConstraints': {'ContentType': 'application/json',\n", + " 'S3Uri': {'Get': 'Steps.ModelBiasCheckStep.BaselineUsedForDriftCheckConstraints'}}},\n", + " 'Explainability': {'Constraints': {'ContentType': 'application/json',\n", + " 'S3Uri': {'Get': 'Steps.ModelExplainabilityCheckStep.BaselineUsedForDriftCheckConstraints'}},\n", + " 'ConfigFile': {'S3Uri': 's3://sagemaker-us-east-1-340280328827/model-monitor-clarify/modelexplainabilitycheckstep/analysis_cfg/model-explainability-monitoring-configuration/model-explainability-monitoring-config-2024-09-23-21-05-04-268/c66b6d48-6718-4d38-a2ce-9eb904a92442/analysis_config.json',\n", + " 'ContentType': 'application/json'}}},\n", + " 'InferenceSpecification': {'Containers': [{'Image': '683313688378.dkr.ecr.us-east-1.amazonaws.com/sagemaker-xgboost:1.0-1-cpu-py3',\n", + " 'Environment': {},\n", + " 'ModelDataUrl': {'Get': 'Steps.TrainAbaloneModel.ModelArtifacts.S3ModelArtifacts'}}],\n", + " 'SupportedContentTypes': ['text/csv'],\n", + " 'SupportedResponseMIMETypes': ['text/csv'],\n", + " 'SupportedRealtimeInferenceInstanceTypes': ['ml.t2.medium',\n", + " 'ml.m5.large'],\n", + " 'SupportedTransformInstanceTypes': ['ml.m5.large']},\n", + " 'ModelApprovalStatus': {'Get': 'Parameters.ModelApprovalStatus'},\n", + " 'SkipModelValidation': 'None',\n", + " 'ModelCard': {'ModelCardStatus': 'Draft',\n", + " 'ModelCardContent': '{\"model_overview\": {\"model_creator\": \"DEMO-Model-Registry-ModelCard-Unification\", \"model_artifact\": []}, \"intended_uses\": {\"purpose_of_model\": \"Test model card.\", \"intended_uses\": \"Not used except this test.\", \"factors_affecting_model_efficiency\": \"No.\", \"risk_rating\": \"Low\", \"explanations_for_risk_rating\": \"Just an example.\"}, \"business_details\": {\"business_problem\": \"The business problem that your model is used to solve.\", \"business_stakeholders\": \"The stakeholders who have the interest in the business that your model is used for.\", \"line_of_business\": \"Services that the business is offering.\"}, \"additional_information\": {\"ethical_considerations\": \"Your model ethical consideration.\", \"caveats_and_recommendations\": \"Your model\\'s caveats and recommendations.\", \"custom_details\": {\"Datazone projects\": \"5rn1teh0tv85rb\"}}}'}}}],\n", + " 'ElseSteps': []}}]}" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import json\n", + "\n", + "definition = json.loads(pipeline.definition())\n", + "definition" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING:sagemaker.workflow.utilities:Popping out 'ProcessingJobName' from the pipeline definition by default since it will be overridden at pipeline execution time. Please utilize the PipelineDefinitionConfig to persist this field in the pipeline definition if desired.\n", + "WARNING:sagemaker.workflow.utilities:Popping out 'ProcessingJobName' from the pipeline definition by default since it will be overridden at pipeline execution time. Please utilize the PipelineDefinitionConfig to persist this field in the pipeline definition if desired.\n", + "WARNING:sagemaker.workflow.utilities:Popping out 'ProcessingJobName' from the pipeline definition by default since it will be overridden at pipeline execution time. Please utilize the PipelineDefinitionConfig to persist this field in the pipeline definition if desired.\n", + "WARNING:sagemaker.workflow.utilities:Popping out 'TrainingJobName' from the pipeline definition by default since it will be overridden at pipeline execution time. Please utilize the PipelineDefinitionConfig to persist this field in the pipeline definition if desired.\n", + "WARNING:sagemaker.workflow.utilities:Popping out 'ModelName' from the pipeline definition by default since it will be overridden at pipeline execution time. Please utilize the PipelineDefinitionConfig to persist this field in the pipeline definition if desired.\n", + "WARNING:sagemaker.workflow.utilities:Popping out 'TransformJobName' from the pipeline definition by default since it will be overridden at pipeline execution time. Please utilize the PipelineDefinitionConfig to persist this field in the pipeline definition if desired.\n", + "WARNING:sagemaker.workflow.utilities:Popping out 'ProcessingJobName' from the pipeline definition by default since it will be overridden at pipeline execution time. Please utilize the PipelineDefinitionConfig to persist this field in the pipeline definition if desired.\n", + "WARNING:sagemaker.workflow.utilities:Popping out 'ProcessingJobName' from the pipeline definition by default since it will be overridden at pipeline execution time. Please utilize the PipelineDefinitionConfig to persist this field in the pipeline definition if desired.\n", + "WARNING:sagemaker.workflow.utilities:Popping out 'ProcessingJobName' from the pipeline definition by default since it will be overridden at pipeline execution time. Please utilize the PipelineDefinitionConfig to persist this field in the pipeline definition if desired.\n", + "WARNING:sagemaker.workflow.utilities:Popping out 'ProcessingJobName' from the pipeline definition by default since it will be overridden at pipeline execution time. Please utilize the PipelineDefinitionConfig to persist this field in the pipeline definition if desired.\n", + "WARNING:sagemaker.workflow.utilities:Popping out 'ModelPackageName' from the pipeline definition by default since it will be overridden at pipeline execution time. Please utilize the PipelineDefinitionConfig to persist this field in the pipeline definition if desired.\n", + "WARNING:sagemaker.workflow.utilities:Popping out 'ProcessingJobName' from the pipeline definition by default since it will be overridden at pipeline execution time. Please utilize the PipelineDefinitionConfig to persist this field in the pipeline definition if desired.\n", + "WARNING:sagemaker.workflow.utilities:Popping out 'ProcessingJobName' from the pipeline definition by default since it will be overridden at pipeline execution time. Please utilize the PipelineDefinitionConfig to persist this field in the pipeline definition if desired.\n", + "WARNING:sagemaker.workflow.utilities:Popping out 'ProcessingJobName' from the pipeline definition by default since it will be overridden at pipeline execution time. Please utilize the PipelineDefinitionConfig to persist this field in the pipeline definition if desired.\n", + "WARNING:sagemaker.workflow.utilities:Popping out 'TrainingJobName' from the pipeline definition by default since it will be overridden at pipeline execution time. Please utilize the PipelineDefinitionConfig to persist this field in the pipeline definition if desired.\n", + "WARNING:sagemaker.workflow.utilities:Popping out 'ModelName' from the pipeline definition by default since it will be overridden at pipeline execution time. Please utilize the PipelineDefinitionConfig to persist this field in the pipeline definition if desired.\n", + "WARNING:sagemaker.workflow.utilities:Popping out 'TransformJobName' from the pipeline definition by default since it will be overridden at pipeline execution time. Please utilize the PipelineDefinitionConfig to persist this field in the pipeline definition if desired.\n", + "WARNING:sagemaker.workflow.utilities:Popping out 'ProcessingJobName' from the pipeline definition by default since it will be overridden at pipeline execution time. Please utilize the PipelineDefinitionConfig to persist this field in the pipeline definition if desired.\n", + "WARNING:sagemaker.workflow.utilities:Popping out 'ProcessingJobName' from the pipeline definition by default since it will be overridden at pipeline execution time. Please utilize the PipelineDefinitionConfig to persist this field in the pipeline definition if desired.\n", + "WARNING:sagemaker.workflow.utilities:Popping out 'ProcessingJobName' from the pipeline definition by default since it will be overridden at pipeline execution time. Please utilize the PipelineDefinitionConfig to persist this field in the pipeline definition if desired.\n", + "WARNING:sagemaker.workflow.utilities:Popping out 'ProcessingJobName' from the pipeline definition by default since it will be overridden at pipeline execution time. Please utilize the PipelineDefinitionConfig to persist this field in the pipeline definition if desired.\n", + "WARNING:sagemaker.workflow.utilities:Popping out 'ModelPackageName' from the pipeline definition by default since it will be overridden at pipeline execution time. Please utilize the PipelineDefinitionConfig to persist this field in the pipeline definition if desired.\n" + ] + }, + { + "data": { + "text/plain": [ + "{'PipelineArn': 'arn:aws:sagemaker:us-east-1:340280328827:pipeline/ExamplePipeline',\n", + " 'ResponseMetadata': {'RequestId': '68d144f7-c4d3-45e1-8d5c-b2c44ca535e5',\n", + " 'HTTPStatusCode': 200,\n", + " 'HTTPHeaders': {'x-amzn-requestid': '68d144f7-c4d3-45e1-8d5c-b2c44ca535e5',\n", + " 'content-type': 'application/x-amz-json-1.1',\n", + " 'content-length': '83',\n", + " 'date': 'Mon, 23 Sep 2024 21:05:06 GMT'},\n", + " 'RetryAttempts': 0}}" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pipeline.upsert(role_arn=role)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### First time executing\n", + "\n", + "The first time the pipeline is run the parameters need to be overridden so that the checks are skipped and newly calculated baselines are registered" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "execution = pipeline.start(\n", + " parameters=dict(\n", + " SkipDataQualityCheck=True,\n", + " RegisterNewDataQualityBaseline=True,\n", + " SkipDataBiasCheck=True,\n", + " RegisterNewDataBiasBaseline=True,\n", + " SkipModelQualityCheck=True,\n", + " RegisterNewModelQualityBaseline=True,\n", + " SkipModelBiasCheck=True,\n", + " RegisterNewModelBiasBaseline=True,\n", + " SkipModelExplainabilityCheck=True,\n", + " RegisterNewModelExplainabilityBaseline=True,\n", + " )\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Wait for the pipeline execution to complete" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "execution.wait()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Cleaning up resources\n", + "\n", + "Users are responsible for cleaning up resources created when running this notebook. Specify the ModelName, ModelPackageName, and ModelPackageGroupName that need to be deleted. The model names are generated by the CreateModel step of the Pipeline and the property values are available only in the Pipeline context. To delete the models created by this pipeline, navigate to the Model Registry and Console to find the models to delete.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "# Create a SageMaker client\n", + "# sm_client = boto3.client(\"sagemaker\")\n", + "\n", + "# # Delete SageMaker Models\n", + "# sm_client.delete_model(ModelName=\"...\")\n", + "\n", + "# # Delete Model Packages\n", + "# sm_client.delete_model_package(ModelPackageName=\"...\")\n", + "\n", + "# # Delete the Model Package Group\n", + "# sm_client.delete_model_package_group(ModelPackageGroupName=\"model-monitor-clarify-group\")\n", + "\n", + "# # Delete the Pipeline\n", + "# sm_client.delete_pipeline(PipelineName=\"model-monitor-clarify-pipeline\")" + ] + } + ], + "metadata": { + "instance_type": "ml.t3.medium", + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/sagemaker_model_governance/Directmarketing.ipynb b/sagemaker_model_governance/Directmarketing.ipynb new file mode 100644 index 0000000000..1dc3419bde --- /dev/null +++ b/sagemaker_model_governance/Directmarketing.ipynb @@ -0,0 +1,1980 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Amazon SageMaker Model Governance - unified model card and model registry\n", + "\n", + "This notebook walks you through the features of Amazon SageMaker Model Registry. For more information, see [Model Registry](https://docs.aws.amazon.com/sagemaker/latest/dg/model-registry-models.html) in the _Amazon SageMaker Developer Guide_.\n", + "\n", + "Amazon SageMaker integrates Model Cards into Model Registry, making it easier for customers to manage governance information for specific model versions directly in Model Registry in just a few clicks. Customers register ML models in Model Registry to manage their models. Additionally, they can register ML model versions early in the development lifecycle, including essential business details and technical metadata. This integration allows customers to seamlessly review and govern models across their lifecycle from a single place. Customers have greater visibility into the model lifecycle from experimentation and training to evaluation and deployment. This streamlined experience ensures that model governance is consistent and easily accessible throughout the development.\n", + "\n", + "In this example, you create a binary classification model along with persisting model card information into registry to manage unified governance information and view" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Notebook Overview\n", + "\n", + "This notebook shows how to:\n", + "\n", + "* Define a set of Pipeline parameters that can be used to parametrize a SageMaker Pipeline.\n", + "\n", + "* Define a Processing step that performs cleaning, feature engineering, and splitting the input data into train and test data sets.\n", + "\n", + "* Define a Training step that trains a model on the preprocessed train data set.\n", + "\n", + "* Define a Processing step that evaluates the trained model's performance on the test dataset.\n", + "\n", + "* Define a Register Model step that creates a model package from the estimator and model \n", + "artifacts used to train the model.\n", + "\n", + "* Define a Conditional step that measures a condition based on output from prior steps and conditionally executes other steps." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Update the Sagemaker Python SDK to latest" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: sagemaker>=2 in /opt/conda/lib/python3.10/site-packages (2.232.1)\n", + "Requirement already satisfied: attrs<24,>=23.1.0 in /opt/conda/lib/python3.10/site-packages (from sagemaker>=2) (23.2.0)\n", + "Requirement already satisfied: boto3<2.0,>=1.34.142 in /opt/conda/lib/python3.10/site-packages (from sagemaker>=2) (1.35.23)\n", + "Requirement already satisfied: cloudpickle==2.2.1 in /opt/conda/lib/python3.10/site-packages (from sagemaker>=2) (2.2.1)\n", + "Requirement already satisfied: 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Amazon DataZone is a data management service that makes it faster and easier for you to catalog, discover, share, and govern data stored across AWS, on-premises, and third-party sources. With Amazon DataZone, administrators who oversee organization’s data assets can manage and govern access to data using fine-grained controls. These controls help ensure access with the right level of privileges and context. Amazon DataZone makes it easy for engineers, data scientists, product managers, analysts, and business users to share and access data throughout an organization so they can discover, use, and collaborate to derive data-driven insights.\n", + "\n", + "First, [create a new datazone domain](https://us-east-1.console.aws.amazon.com/datazone/home?region=us-east-1#/) if it doesnt already exists.Then, [create a new DataZone Project](https://docs.aws.amazon.com/datazone/latest/userguide/create-new-project.html). Use the project id in the below check and run " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Capture project id from DataZone project\n", + "project_id = \"5rn1teh0tv85rb\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Usecase and Dataset\n", + "\n", + "In this tutorial, you will assume the role of a machine learning developer working at a bank. You have been asked to develop a machine learning model to predict whether a customer will enroll for a certificate of deposit (CD). The model will be trained on the marketing dataset that contains information on customer demographics, responses to marketing events, and external factors.\n", + "\n", + "The data has been labeled for your convenience and a column in the dataset identifies whether the customer is enrolled for a product offered by the bank. A version of this dataset is publicly available from the ML repository curated by the University of California, Irvine. This tutorial implements a supervised machine learning model, since the data is labeled. (Unsupervised learning occurs when the datasets are not labeled.)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Pipelines Instance\n", + "A SageMaker Pipeline instance is composed of three components:\n", + " 1. Name\n", + " 2. Parameters\n", + " 3. Steps" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Initial set up" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "sagemaker.config INFO - Not applying SDK defaults from location: /etc/xdg/sagemaker/config.yaml\n", + "sagemaker.config INFO - Not applying SDK defaults from location: /home/sagemaker-user/.config/sagemaker/config.yaml\n" + ] + } + ], + "source": [ + "# Setup\n", + "import boto3\n", + "import sagemaker\n", + "\n", + "region = boto3.Session().region_name\n", + "sagemaker_session = sagemaker.session.Session()\n", + "role = sagemaker.get_execution_role()\n", + "default_bucket = sagemaker_session.default_bucket()\n", + "model_package_group_name = f\"DirectMarketingExamplePackageGroupName\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Upload the data into the default bucket. You can select your own data set for the `input_data_uri` as is appropriate." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "s3://sagemaker-us-east-1-340280328827/marketing/bank-additional-full.csv\n" + ] + } + ], + "source": [ + "local_path = \"bank-additional-full.csv\"\n", + "\n", + "base_uri = f\"s3://{default_bucket}/marketing\"\n", + "input_data_uri = sagemaker.s3.S3Uploader.upload(\n", + " local_path=local_path, \n", + " desired_s3_uri=base_uri,\n", + ")\n", + "print(input_data_uri)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Define Parameters to Parametrize Pipeline Execution\n", + "\n", + "Define Pipeline parameters that you can use to parametrize the pipeline. Parameters enable custom pipeline executions and schedules without having to modify the Pipeline definition.\n", + "\n", + "The supported parameter types include:\n", + "\n", + "* `ParameterString` - represents a `str` Python type\n", + "* `ParameterInteger` - represents an `int` Python type\n", + "* `ParameterFloat` - represents a `float` Python type\n", + "\n", + "These parameters support providing a default value, which can be overridden on pipeline execution. The default value specified should be an instance of the type of the parameter.\n", + "\n", + "The parameters defined in this workflow include:\n", + "\n", + "* `processing_instance_type` - The `ml.*` instance type of the processing job.\n", + "* `processing_instance_count` - The instance count of the processing job.\n", + "* `training_instance_type` - The `ml.*` instance type of the training job.\n", + "* `model_approval_status` - What approval status to register the trained model with for CI/CD purposes ( \"PendingManualApproval\" is the default).\n", + "* `input_data` - The S3 bucket URI location of the input data" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# Define pipeline parameters\n", + "from sagemaker.workflow.parameters import (\n", + " ParameterInteger,\n", + " ParameterString,\n", + ")\n", + "\n", + "processing_instance_count = ParameterInteger(\n", + " name=\"ProcessingInstanceCount\",\n", + " default_value=1\n", + ")\n", + "processing_instance_type = ParameterString(\n", + " name=\"ProcessingInstanceType\",\n", + " default_value=\"ml.m5.xlarge\"\n", + ")\n", + "training_instance_type = ParameterString(\n", + " name=\"TrainingInstanceType\",\n", + " default_value=\"ml.m5.xlarge\"\n", + ")\n", + "model_approval_status = ParameterString(\n", + " name=\"ModelApprovalStatus\",\n", + " default_value=\"PendingManualApproval\"\n", + ")\n", + "input_data = ParameterString(\n", + " name=\"InputData\",\n", + " default_value=input_data_uri,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Define a Processing Step for Feature Engineering\n", + "\n", + "First, develop a preprocessing script that is specified in the Processing step.\n", + "\n", + "This notebook cell writes a file `preprocessing.py`, which contains the preprocessing script. You can update the script, and rerun this cell to overwrite. The preprocessing script uses `scikit-learn` to do the following:\n", + "\n", + "* It creates new variables: no_previous_contact, not_working\n", + "* It drops columns that are not necessary\n", + "* It one-hot encodes categorical variables\n", + "\n", + "The Processing step executes the script on the input data. The Training step uses the preprocessed training features and labels to train a model. The Evaluation step uses the trained model and preprocessed test features and labels to evaluate the model." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "!mkdir -p marketing" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Overwriting marketing/preprocessing.py\n" + ] + } + ], + "source": [ + "%%writefile marketing/preprocessing.py\n", + "import argparse\n", + "import os\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "# Helper method\n", + "def process(df):\n", + " # Add two new indicators\n", + " df[\"no_previous_contact\"] = (df[\"pdays\"] == 999).astype(int)\n", + " df[\"not_working\"] = df[\"job\"].isin([\"student\", \"retired\", \"unemployed\"]).astype(int)\n", + " columns = list(df.columns)\n", + " \n", + " toremove = [\"emp.var.rate\", \"cons.price.idx\", \"cons.conf.idx\", \"euribor3m\", \"nr.employed\"]\n", + " columns = [x for x in columns if x not in toremove]\n", + " \n", + " # Keeping only columns that we need\n", + " df = df[columns]\n", + " \n", + " # One hot encode\n", + " df=pd.get_dummies(df)\n", + " df = pd.concat([df['y_yes'], df.drop(['y_no', 'y_yes'], axis=1)], axis=1)\n", + " df = df.sample(frac=1).reset_index(drop=True)\n", + " return df\n", + "\n", + "if __name__ == \"__main__\":\n", + " parser = argparse.ArgumentParser()\n", + " parser.add_argument(\"--input-path\", type=str, default=\"/opt/ml/processing\")\n", + " args, _ = parser.parse_known_args()\n", + " \n", + " base_dir = args.input_path\n", + "\n", + " df = pd.read_csv(\n", + " f\"{base_dir}/input/bank-additional-full.csv\",\n", + " header=0\n", + " )\n", + " \n", + " # Call the helper method\n", + " df = process(df)\n", + " \n", + " train, validation, test = np.split(df, [int(.7*len(df)), int(.85*len(df))])\n", + "\n", + " train.to_csv(f\"{base_dir}/train/train.csv\", header=False, index=False)\n", + " validation.to_csv(f\"{base_dir}/validation/validation.csv\", header=False, index=False)\n", + " test.to_csv(f\"{base_dir}/test/test.csv\", header=False, index=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, create an instance of an `SKLearnProcessor` processor and use that in our `ProcessingStep`.\n", + "\n", + "You also specify the `framework_version` to use throughout this notebook.\n", + "\n", + "Note the `processing_instance_type` and `processing_instance_count` parameters used by the processor instance." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "The input argument instance_type of function (sagemaker.image_uris.retrieve) is a pipeline variable (), which is interpreted in pipeline execution time only. As the function needs to evaluate the argument value in SDK compile time, the default_value of this Parameter object will be used to override it. Please make sure the default_value is valid.\n" + ] + } + ], + "source": [ + "from sagemaker.sklearn.processing import SKLearnProcessor\n", + "\n", + "\n", + "framework_version = \"0.23-1\"\n", + "\n", + "sklearn_processor = SKLearnProcessor(\n", + " framework_version=framework_version,\n", + " instance_type=processing_instance_type,\n", + " instance_count=processing_instance_count,\n", + " base_job_name=\"sklearn-marketing-process\",\n", + " role=role,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, use the processor instance to construct a `ProcessingStep`, along with the input and output channels, and the code that will be executed when the pipeline invokes pipeline execution. This is similar to a processor instance's `run` method in the Python SDK.\n", + "\n", + "Note the `input_data` parameters passed into `ProcessingStep` is the input data used in the step. This input data is used by the processor instance when it is run.\n", + "\n", + "Also, note the `\"train_data\"` and `\"test_data\"` named channels specified in the output configuration for the processing job. Step `Properties` can be used in subsequent steps and resolve to their runtime values at execution. Specifically, this usage is called out when you define the training step." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "from sagemaker.processing import ProcessingInput, ProcessingOutput\n", + "from sagemaker.workflow.steps import ProcessingStep\n", + " \n", + "\n", + "step_process = ProcessingStep(\n", + " name=\"MarketingProcess\",\n", + " processor=sklearn_processor,\n", + " inputs=[\n", + " ProcessingInput(source=input_data, destination=\"/opt/ml/processing/input\"), \n", + " ],\n", + " outputs=[\n", + " ProcessingOutput(output_name=\"train\", source=\"/opt/ml/processing/train\"),\n", + " ProcessingOutput(output_name=\"validation\", source=\"/opt/ml/processing/validation\"),\n", + " ProcessingOutput(output_name=\"test\", source=\"/opt/ml/processing/test\")\n", + " ],\n", + " code=\"marketing/preprocessing.py\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Define a Training Step to Train a Model\n", + "\n", + "In this section, use Amazon SageMaker's [XGBoost Algorithm](https://docs.aws.amazon.com/sagemaker/latest/dg/xgboost.html) to train on this dataset. Configure an Estimator for the XGBoost algorithm and the input dataset. A typical training script loads data from the input channels, configures training with hyperparameters, trains a model, and saves a model to `model_dir` so that it can be hosted later.\n", + "\n", + "The model path where the models from training will be saved is also specified.\n", + "\n", + "Note the `training_instance_type` parameter may be used in multiple places in the pipeline. In this case, the `training_instance_type` is passed into the estimator." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "The input argument instance_type of function (sagemaker.image_uris.retrieve) is a pipeline variable (), which is interpreted in pipeline execution time only. As the function needs to evaluate the argument value in SDK compile time, the default_value of this Parameter object will be used to override it. Please make sure the default_value is valid.\n" + ] + } + ], + "source": [ + "from sagemaker.estimator import Estimator\n", + "\n", + "model_path = f\"s3://{default_bucket}/MarketingTrain\"\n", + "image_uri = sagemaker.image_uris.retrieve(\n", + " framework=\"xgboost\",\n", + " region=region,\n", + " version=\"1.0-1\",\n", + " py_version=\"py3\",\n", + " instance_type=training_instance_type,\n", + ")\n", + "xgb_train = Estimator(\n", + " image_uri=image_uri,\n", + " instance_type=training_instance_type,\n", + " instance_count=1,\n", + " output_path=model_path,\n", + " role=role,\n", + ")\n", + "xgb_train.set_hyperparameters(\n", + " objective=\"binary:logistic\",\n", + " num_round=50,\n", + " max_depth=5,\n", + " eta=0.2,\n", + " gamma=4,\n", + " min_child_weight=6,\n", + " subsample=0.7,\n", + " silent=0\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, use the estimator instance to construct a `TrainingStep` as well as the `properties` of the prior `ProcessingStep` used as input in the `TrainingStep` inputs and the code that's executed when the pipeline invokes the pipeline execution. This is similar to an estimator's `fit` method in the Python SDK.\n", + "\n", + "Pass in the `S3Uri` of the `\"train_data\"` output channel to the `TrainingStep`. Also, use the other `\"test_data\"` output channel for model evaluation in the pipeline. The `properties` attribute of a Pipeline step matches the object model of the corresponding response of a describe call. These properties can be referenced as placeholder values and are resolved at runtime. For example, the `ProcessingStep` `properties` attribute matches the object model of the [DescribeProcessingJob](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_DescribeProcessingJob.html) response object." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "from sagemaker.inputs import TrainingInput\n", + "from sagemaker.workflow.steps import TrainingStep\n", + "\n", + "\n", + "step_train = TrainingStep(\n", + " name=\"MarketingTrain\",\n", + " estimator=xgb_train,\n", + " inputs={\n", + " \"train\": TrainingInput(\n", + " s3_data=step_process.properties.ProcessingOutputConfig.Outputs[\n", + " \"train\"\n", + " ].S3Output.S3Uri,\n", + " content_type=\"text/csv\"\n", + " ),\n", + " \"validation\": TrainingInput(\n", + " s3_data=step_process.properties.ProcessingOutputConfig.Outputs[\n", + " \"validation\"\n", + " ].S3Output.S3Uri,\n", + " content_type=\"text/csv\"\n", + " )\n", + " },\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Define a Model Evaluation Step to Evaluate the Trained Model\n", + "\n", + "First, develop an evaluation script that is specified in a Processing step that performs the model evaluation.\n", + "\n", + "After pipeline execution, you can examine the resulting `evaluation.json` for analysis.\n", + "\n", + "The evaluation script uses `xgboost` to do the following:\n", + "\n", + "* Load the model.\n", + "* Read the test data.\n", + "* Issue predictions against the test data.\n", + "* Build a classification report, including accuracy and ROC curve.\n", + "* Save the evaluation report to the evaluation directory." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Overwriting marketing/evaluation.py\n" + ] + } + ], + "source": [ + "%%writefile marketing/evaluation.py\n", + "import os\n", + "import json\n", + "import pathlib\n", + "import pickle\n", + "import tarfile\n", + "\n", + "import pandas as pd\n", + "import xgboost\n", + "\n", + "import joblib\n", + "import numpy as np\n", + "import pandas as pd\n", + "import xgboost\n", + "\n", + "from sklearn.metrics import accuracy_score, classification_report, roc_auc_score\n", + "\n", + "if __name__ == \"__main__\":\n", + " model_path = f\"/opt/ml/processing/model/model.tar.gz\"\n", + " with tarfile.open(model_path) as tar:\n", + " tar.extractall(path=\".\")\n", + " \n", + " model = pickle.load(open(\"xgboost-model\", \"rb\"))\n", + "\n", + " print(\"Loading test input data\")\n", + " test_path = \"/opt/ml/processing/test/test.csv\"\n", + " df = pd.read_csv(test_path, header=None)\n", + " \n", + " y_test = df.iloc[:, 0].to_numpy()\n", + " df.drop(df.columns[0], axis=1, inplace=True)\n", + " \n", + " X_test = xgboost.DMatrix(df.values)\n", + " \n", + " predictions = model.predict(X_test)\n", + "\n", + " print(\"Creating classification evaluation report\")\n", + " acc = accuracy_score(y_test, predictions.round())\n", + " auc = roc_auc_score(y_test, predictions.round())\n", + " \n", + " # The metrics reported can change based on the model used, \n", + " # but it must be a specific name per \n", + " # (https://docs.aws.amazon.com/sagemaker/latest/dg/model-monitor-model-quality-metrics.html)\n", + " report_dict = {\n", + " \"binary_classification_metrics\": {\n", + " \"accuracy\": {\n", + " \"value\": acc,\n", + " \"standard_deviation\": \"NaN\",\n", + " },\n", + " \"auc\": {\"value\": auc, \"standard_deviation\": \"NaN\"},\n", + " },\n", + " }\n", + " \n", + " print(\"Classification report:\\n{}\".format(report_dict))\n", + "\n", + " evaluation_output_path = os.path.join(\"/opt/ml/processing/evaluation\", \"evaluation.json\")\n", + " print(\"Saving classification report to {}\".format(evaluation_output_path))\n", + "\n", + " with open(evaluation_output_path, \"w\") as f:\n", + " f.write(json.dumps(report_dict))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, create an instance of a `ScriptProcessor` processor and use it in the `ProcessingStep`.\n", + "\n", + "Note the `processing_instance_type` parameter passed into the processor." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "from sagemaker.processing import ScriptProcessor\n", + "\n", + "\n", + "script_eval = ScriptProcessor(\n", + " image_uri=image_uri,\n", + " command=[\"python3\"],\n", + " instance_type=processing_instance_type,\n", + " instance_count=1,\n", + " base_job_name=\"script-marketing-eval\",\n", + " role=role,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Use the processor instance to construct a `ProcessingStep`, along with the input and output channels and the code that will be executed when the pipeline invokes pipeline execution. This is similar to a processor instance's `run` method in the Python SDK.\n", + "\n", + "Specifically, the `S3ModelArtifacts` from the `step_train` `properties` and the `S3Uri` of the `\"test_data\"` output channel of the `step_process` `properties` are passed into the inputs. The `TrainingStep` and `ProcessingStep` `properties` attribute matches the object model of the [DescribeTrainingJob](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_DescribeTrainingJob.html) and [DescribeProcessingJob](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_DescribeProcessingJob.html) response objects, respectively." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "from sagemaker.workflow.properties import PropertyFile\n", + "\n", + "\n", + "evaluation_report = PropertyFile(\n", + " name=\"EvaluationReport\",\n", + " output_name=\"evaluation\",\n", + " path=\"evaluation.json\"\n", + ")\n", + "step_eval = ProcessingStep(\n", + " name=\"MarketingEval\",\n", + " processor=script_eval,\n", + " inputs=[\n", + " ProcessingInput(\n", + " source=step_train.properties.ModelArtifacts.S3ModelArtifacts,\n", + " destination=\"/opt/ml/processing/model\"\n", + " ),\n", + " ProcessingInput(\n", + " source=step_process.properties.ProcessingOutputConfig.Outputs[\n", + " \"test\"\n", + " ].S3Output.S3Uri,\n", + " destination=\"/opt/ml/processing/test\"\n", + " )\n", + " ],\n", + " outputs=[\n", + " ProcessingOutput(output_name=\"evaluation\", source=\"/opt/ml/processing/evaluation\"),\n", + " ],\n", + " code=\"marketing/evaluation.py\",\n", + " property_files=[evaluation_report],\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Define a Register Model Step to Create a Model Package\n", + "\n", + "Use the estimator instance specified in the training step to construct an instance of `RegisterModel`. The result of executing `RegisterModel` in a pipeline is a model package. A model package is a reusable model artifacts abstraction that packages all ingredients required for inference. Primarily, it consists of an inference specification that defines the inference image to use along with an optional model weights location.\n", + "\n", + "A model package group is a collection of model packages. A model package group can be created for a specific ML business problem, and new versions of the model packages can be added to it. Typically, customers are expected to create a ModelPackageGroup for a SageMaker pipeline so that model package versions can be added to the group for every SageMaker Pipeline run.\n", + "\n", + "The construction of `RegisterModel` is similar to an estimator instance's `register` method in the Python SDK.\n", + "\n", + "Specifically, pass in the `S3ModelArtifacts` from the `TrainingStep`, `step_train` properties. The `TrainingStep` `properties` attribute matches the object model of the [DescribeTrainingJob](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_DescribeTrainingJob.html) response object.\n", + "\n", + "Note that the specific model package group name provided in this notebook can be used in the model registry and CI/CD work with SageMaker Projects." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "import io\n", + "import os\n", + "import numpy as np\n", + "from six.moves.urllib.parse import urlparse\n", + "from pprint import pprint\n", + "import boto3\n", + "import sagemaker\n", + "from sagemaker.image_uris import retrieve\n", + "import sagemaker.amazon.common as smac\n", + "from sagemaker.model_card import (\n", + " ModelCard,\n", + " ModelOverview,\n", + " IntendedUses,\n", + " BusinessDetails,\n", + " AdditionalInformation,\n", + " ModelCardStatusEnum,\n", + " ObjectiveFunctionEnum,\n", + " RiskRatingEnum,\n", + " MetricTypeEnum,\n", + " TrainingDetails\n", + ")\n", + "from sagemaker.model import ModelPackage as MP" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "model_overview = ModelOverview(\n", + " #model_description=\"This is an example model used for a Python SDK demo of unified Amazon SageMaker Model Registry and Model Cards.\",\n", + " #problem_type=\"Binary Classification\",\n", + " #algorithm_type=\"Logistic Regression\",\n", + " model_creator=\"DEMO-Model-Registry-ModelCard-Unification\",\n", + " #model_owner=\"datascienceteam\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "intended_uses = IntendedUses(\n", + " purpose_of_model=\"Test model card.\",\n", + " intended_uses=\"Not used except this test.\",\n", + " factors_affecting_model_efficiency=\"No.\",\n", + " risk_rating=RiskRatingEnum.LOW,\n", + " explanations_for_risk_rating=\"Just an example.\",\n", + ")\n", + "business_details = BusinessDetails(\n", + " business_problem=\"The business problem that your model is used to solve.\",\n", + " business_stakeholders=\"The stakeholders who have the interest in the business that your model is used for.\",\n", + " line_of_business=\"Services that the business is offering.\",\n", + ")\n", + "additional_information = AdditionalInformation(\n", + " ethical_considerations=\"Your model ethical consideration.\",\n", + " caveats_and_recommendations=\"Your model's caveats and recommendations.\",\n", + " custom_details={\"Datazone projects\": project_id},\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "my_card = ModelCard(\n", + " name=\"MyModelCard\",\n", + " status=ModelCardStatusEnum.DRAFT,\n", + " model_overview=model_overview,\n", + " intended_uses=intended_uses,\n", + " business_details=business_details,\n", + " additional_information=additional_information,\n", + " sagemaker_session=sagemaker_session,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Popping out 'ProcessingJobName' from the pipeline definition by default since it will be overridden at pipeline execution time. Please utilize the PipelineDefinitionConfig to persist this field in the pipeline definition if desired.\n" + ] + } + ], + "source": [ + "from sagemaker.model_metrics import MetricsSource, ModelMetrics \n", + "from sagemaker.workflow.step_collections import RegisterModel\n", + "\n", + "\n", + "model_metrics = ModelMetrics(\n", + " model_statistics=MetricsSource(\n", + " s3_uri=\"{}/evaluation.json\".format(\n", + " step_eval.arguments[\"ProcessingOutputConfig\"][\"Outputs\"][0][\"S3Output\"][\"S3Uri\"]\n", + " ),\n", + " content_type=\"application/json\"\n", + " )\n", + ")\n", + "step_register = RegisterModel(\n", + " name=\"MarketingRegisterModel\",\n", + " estimator=xgb_train,\n", + " model_data=step_train.properties.ModelArtifacts.S3ModelArtifacts,\n", + " content_types=[\"text/csv\"],\n", + " response_types=[\"text/csv\"],\n", + " inference_instances=[\"ml.t2.medium\", \"ml.m5.xlarge\"],\n", + " transform_instances=[\"ml.m5.xlarge\"],\n", + " model_package_group_name=model_package_group_name,\n", + " approval_status=model_approval_status,\n", + " model_metrics=model_metrics,\n", + " model_card=my_card\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Define a Condition Step to Check Accuracy and Conditionally Register a Model in the Model Registry\n", + "\n", + "In this step, the model is registered only if the accuracy of the model, as determined by the evaluation step `step_eval`, exceeded a specified value. A `ConditionStep` enables pipelines to support conditional execution in the pipeline DAG based on the conditions of the step properties. \n", + "\n", + "In the following section, you:\n", + "\n", + "* Define a `ConditionLessThanOrEqualTo` on the accuracy value found in the output of the evaluation step, `step_eval`.\n", + "* Use the condition in the list of conditions in a `ConditionStep`.\n", + "* Pass the `RegisterModel` step collection into the `if_steps` of the `ConditionStep`, which are only executed, if the condition evaluates to `True`." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "The class JsonGet has been renamed in sagemaker>=2.\n", + "See: https://sagemaker.readthedocs.io/en/stable/v2.html for details.\n" + ] + } + ], + "source": [ + "from sagemaker.workflow.conditions import ConditionGreaterThanOrEqualTo\n", + "from sagemaker.workflow.condition_step import (\n", + " ConditionStep,\n", + " JsonGet,\n", + ")\n", + "\n", + "cond_lte = ConditionGreaterThanOrEqualTo( # You can change the condition here\n", + " left=JsonGet(\n", + " step=step_eval,\n", + " property_file=evaluation_report,\n", + " json_path=\"binary_classification_metrics.accuracy.value\", # This should follow the structure of your report_dict defined in the evaluate.py file.\n", + " ),\n", + " right=0.8, # You can change the threshold here\n", + ")\n", + "\n", + "step_cond = ConditionStep(\n", + " name=\"MarketingAccuracyCond\",\n", + " conditions=[cond_lte],\n", + " if_steps=[step_register],\n", + " else_steps=[]\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Define a Pipeline of Parameters, Steps, and Conditions\n", + "\n", + "In this section, combine the steps into a Pipeline so it can be executed.\n", + "\n", + "A pipeline requires a `name`, `parameters`, and `steps`. Names must be unique within an `(account, region)` pair.\n", + "\n", + "Note:\n", + "\n", + "* All of the parameters used in the definitions must be present.\n", + "* Steps passed into the pipeline do not have to be listed in the order of execution. The SageMaker Pipeline service resolves the _data dependency_ DAG as steps for the execution to complete.\n", + "* Steps must be unique to across the pipeline step list and all condition step if/else lists." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "from sagemaker.workflow.pipeline import Pipeline\n", + "\n", + "\n", + "pipeline_name = f\"ExamplePipeline\"\n", + "pipeline = Pipeline(\n", + " name=pipeline_name,\n", + " parameters=[\n", + " processing_instance_type, \n", + " processing_instance_count,\n", + " training_instance_type,\n", + " model_approval_status,\n", + " input_data,\n", + " ],\n", + " steps=[step_process, step_train, step_eval, step_cond],\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### (Optional) Examining the pipeline definition\n", + "\n", + "The JSON of the pipeline definition can be examined to confirm the pipeline is well-defined and the parameters and step properties resolve correctly." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING:sagemaker.workflow.utilities:Popping out 'ProcessingJobName' from the pipeline definition by default since it will be overridden at pipeline execution time. Please utilize the PipelineDefinitionConfig to persist this field in the pipeline definition if desired.\n", + "WARNING:sagemaker.workflow.utilities:Popping out 'TrainingJobName' from the pipeline definition by default since it will be overridden at pipeline execution time. Please utilize the PipelineDefinitionConfig to persist this field in the pipeline definition if desired.\n", + "WARNING:sagemaker.workflow.utilities:Popping out 'ProcessingJobName' from the pipeline definition by default since it will be overridden at pipeline execution time. Please utilize the PipelineDefinitionConfig to persist this field in the pipeline definition if desired.\n", + "WARNING:sagemaker.estimator:No finished training job found associated with this estimator. Please make sure this estimator is only used for building workflow config\n", + "WARNING:sagemaker.workflow._utils:Popping out 'CertifyForMarketplace' from the pipeline definition since it will be overridden in pipeline execution time.\n", + "WARNING:sagemaker.workflow.utilities:Popping out 'ModelPackageName' from the pipeline definition by default since it will be overridden at pipeline execution time. Please utilize the PipelineDefinitionConfig to persist this field in the pipeline definition if desired.\n" + ] + }, + { + "data": { + "text/plain": [ + "{'Version': '2020-12-01',\n", + " 'Metadata': {},\n", + " 'Parameters': [{'Name': 'ProcessingInstanceType',\n", + " 'Type': 'String',\n", + " 'DefaultValue': 'ml.m5.xlarge'},\n", + " {'Name': 'ProcessingInstanceCount', 'Type': 'Integer', 'DefaultValue': 1},\n", + " {'Name': 'TrainingInstanceType',\n", + " 'Type': 'String',\n", + " 'DefaultValue': 'ml.m5.xlarge'},\n", + " {'Name': 'ModelApprovalStatus',\n", + " 'Type': 'String',\n", + " 'DefaultValue': 'PendingManualApproval'},\n", + " {'Name': 'InputData',\n", + " 'Type': 'String',\n", + " 'DefaultValue': 's3://sagemaker-us-east-1-340280328827/marketing/bank-additional-full.csv'}],\n", + " 'PipelineExperimentConfig': {'ExperimentName': {'Get': 'Execution.PipelineName'},\n", + " 'TrialName': {'Get': 'Execution.PipelineExecutionId'}},\n", + " 'Steps': [{'Name': 'MarketingProcess',\n", + " 'Type': 'Processing',\n", + " 'Arguments': {'ProcessingResources': {'ClusterConfig': {'InstanceType': {'Get': 'Parameters.ProcessingInstanceType'},\n", + " 'InstanceCount': {'Get': 'Parameters.ProcessingInstanceCount'},\n", + " 'VolumeSizeInGB': 30}},\n", + " 'AppSpecification': {'ImageUri': '683313688378.dkr.ecr.us-east-1.amazonaws.com/sagemaker-scikit-learn:0.23-1-cpu-py3',\n", + " 'ContainerEntrypoint': ['python3',\n", + " '/opt/ml/processing/input/code/preprocessing.py']},\n", + " 'RoleArn': 'arn:aws:iam::340280328827:role/CFN-SM-IM-Lambda-Catalog-SageMakerExecutionRole-1TSLA2QB1DJKS',\n", + " 'ProcessingInputs': [{'InputName': 'input-1',\n", + " 'AppManaged': False,\n", + " 'S3Input': {'S3Uri': {'Get': 'Parameters.InputData'},\n", + " 'LocalPath': '/opt/ml/processing/input',\n", + " 'S3DataType': 'S3Prefix',\n", + " 'S3InputMode': 'File',\n", + " 'S3DataDistributionType': 'FullyReplicated',\n", + " 'S3CompressionType': 'None'}},\n", + " {'InputName': 'code',\n", + " 'AppManaged': False,\n", + " 'S3Input': {'S3Uri': 's3://sagemaker-us-east-1-340280328827/MarketingProcess-7ece76b7a927727349ed8e8eb15a8a46/input/code/preprocessing.py',\n", + " 'LocalPath': '/opt/ml/processing/input/code',\n", + " 'S3DataType': 'S3Prefix',\n", + " 'S3InputMode': 'File',\n", + " 'S3DataDistributionType': 'FullyReplicated',\n", + " 'S3CompressionType': 'None'}}],\n", + " 'ProcessingOutputConfig': {'Outputs': [{'OutputName': 'train',\n", + " 'AppManaged': False,\n", + " 'S3Output': {'S3Uri': {'Std:Join': {'On': '/',\n", + " 'Values': ['s3:/',\n", + " 'sagemaker-us-east-1-340280328827',\n", + " 'ExamplePipeline',\n", + " {'Get': 'Execution.PipelineExecutionId'},\n", + " 'MarketingProcess',\n", + " 'output',\n", + " 'train']}},\n", + " 'LocalPath': '/opt/ml/processing/train',\n", + " 'S3UploadMode': 'EndOfJob'}},\n", + " {'OutputName': 'validation',\n", + " 'AppManaged': False,\n", + " 'S3Output': {'S3Uri': {'Std:Join': {'On': '/',\n", + " 'Values': ['s3:/',\n", + " 'sagemaker-us-east-1-340280328827',\n", + " 'ExamplePipeline',\n", + " {'Get': 'Execution.PipelineExecutionId'},\n", + " 'MarketingProcess',\n", + " 'output',\n", + " 'validation']}},\n", + " 'LocalPath': '/opt/ml/processing/validation',\n", + " 'S3UploadMode': 'EndOfJob'}},\n", + " {'OutputName': 'test',\n", + " 'AppManaged': False,\n", + " 'S3Output': {'S3Uri': {'Std:Join': {'On': '/',\n", + " 'Values': ['s3:/',\n", + " 'sagemaker-us-east-1-340280328827',\n", + " 'ExamplePipeline',\n", + " {'Get': 'Execution.PipelineExecutionId'},\n", + " 'MarketingProcess',\n", + " 'output',\n", + " 'test']}},\n", + " 'LocalPath': '/opt/ml/processing/test',\n", + " 'S3UploadMode': 'EndOfJob'}}]}}},\n", + " {'Name': 'MarketingTrain',\n", + " 'Type': 'Training',\n", + " 'Arguments': {'AlgorithmSpecification': {'TrainingInputMode': 'File',\n", + " 'TrainingImage': '683313688378.dkr.ecr.us-east-1.amazonaws.com/sagemaker-xgboost:1.0-1-cpu-py3'},\n", + " 'OutputDataConfig': {'S3OutputPath': 's3://sagemaker-us-east-1-340280328827/MarketingTrain'},\n", + " 'StoppingCondition': {'MaxRuntimeInSeconds': 86400},\n", + " 'ResourceConfig': {'VolumeSizeInGB': 30,\n", + " 'InstanceCount': 1,\n", + " 'InstanceType': {'Get': 'Parameters.TrainingInstanceType'}},\n", + " 'RoleArn': 'arn:aws:iam::340280328827:role/CFN-SM-IM-Lambda-Catalog-SageMakerExecutionRole-1TSLA2QB1DJKS',\n", + " 'InputDataConfig': [{'DataSource': {'S3DataSource': {'S3DataType': 'S3Prefix',\n", + " 'S3Uri': {'Get': \"Steps.MarketingProcess.ProcessingOutputConfig.Outputs['train'].S3Output.S3Uri\"},\n", + " 'S3DataDistributionType': 'FullyReplicated'}},\n", + " 'ContentType': 'text/csv',\n", + " 'ChannelName': 'train'},\n", + " {'DataSource': {'S3DataSource': {'S3DataType': 'S3Prefix',\n", + " 'S3Uri': {'Get': \"Steps.MarketingProcess.ProcessingOutputConfig.Outputs['validation'].S3Output.S3Uri\"},\n", + " 'S3DataDistributionType': 'FullyReplicated'}},\n", + " 'ContentType': 'text/csv',\n", + " 'ChannelName': 'validation'}],\n", + " 'HyperParameters': {'objective': 'binary:logistic',\n", + " 'num_round': '50',\n", + " 'max_depth': '5',\n", + " 'eta': '0.2',\n", + " 'gamma': '4',\n", + " 'min_child_weight': '6',\n", + " 'subsample': '0.7',\n", + " 'silent': '0'},\n", + " 'DebugHookConfig': {'S3OutputPath': 's3://sagemaker-us-east-1-340280328827/MarketingTrain',\n", + " 'CollectionConfigurations': []},\n", + " 'ProfilerConfig': {'S3OutputPath': 's3://sagemaker-us-east-1-340280328827/MarketingTrain',\n", + " 'DisableProfiler': False}}},\n", + " {'Name': 'MarketingEval',\n", + " 'Type': 'Processing',\n", + " 'Arguments': {'ProcessingResources': {'ClusterConfig': {'InstanceType': {'Get': 'Parameters.ProcessingInstanceType'},\n", + " 'InstanceCount': 1,\n", + " 'VolumeSizeInGB': 30}},\n", + " 'AppSpecification': {'ImageUri': '683313688378.dkr.ecr.us-east-1.amazonaws.com/sagemaker-xgboost:1.0-1-cpu-py3',\n", + " 'ContainerEntrypoint': ['python3',\n", + " '/opt/ml/processing/input/code/evaluation.py']},\n", + " 'RoleArn': 'arn:aws:iam::340280328827:role/CFN-SM-IM-Lambda-Catalog-SageMakerExecutionRole-1TSLA2QB1DJKS',\n", + " 'ProcessingInputs': [{'InputName': 'input-1',\n", + " 'AppManaged': False,\n", + " 'S3Input': {'S3Uri': {'Get': 'Steps.MarketingTrain.ModelArtifacts.S3ModelArtifacts'},\n", + " 'LocalPath': '/opt/ml/processing/model',\n", + " 'S3DataType': 'S3Prefix',\n", + " 'S3InputMode': 'File',\n", + " 'S3DataDistributionType': 'FullyReplicated',\n", + " 'S3CompressionType': 'None'}},\n", + " {'InputName': 'input-2',\n", + " 'AppManaged': False,\n", + " 'S3Input': {'S3Uri': {'Get': \"Steps.MarketingProcess.ProcessingOutputConfig.Outputs['test'].S3Output.S3Uri\"},\n", + " 'LocalPath': '/opt/ml/processing/test',\n", + " 'S3DataType': 'S3Prefix',\n", + " 'S3InputMode': 'File',\n", + " 'S3DataDistributionType': 'FullyReplicated',\n", + " 'S3CompressionType': 'None'}},\n", + " {'InputName': 'code',\n", + " 'AppManaged': False,\n", + " 'S3Input': {'S3Uri': 's3://sagemaker-us-east-1-340280328827/MarketingEval-31b0d4fc6c62a4b00c655e1c275ac100/input/code/evaluation.py',\n", + " 'LocalPath': '/opt/ml/processing/input/code',\n", + " 'S3DataType': 'S3Prefix',\n", + " 'S3InputMode': 'File',\n", + " 'S3DataDistributionType': 'FullyReplicated',\n", + " 'S3CompressionType': 'None'}}],\n", + " 'ProcessingOutputConfig': {'Outputs': [{'OutputName': 'evaluation',\n", + " 'AppManaged': False,\n", + " 'S3Output': {'S3Uri': 's3://sagemaker-us-east-1-340280328827/MarketingEval-31b0d4fc6c62a4b00c655e1c275ac100/output/evaluation',\n", + " 'LocalPath': '/opt/ml/processing/evaluation',\n", + " 'S3UploadMode': 'EndOfJob'}}]}},\n", + " 'PropertyFiles': [{'PropertyFileName': 'EvaluationReport',\n", + " 'OutputName': 'evaluation',\n", + " 'FilePath': 'evaluation.json'}]},\n", + " {'Name': 'MarketingAccuracyCond',\n", + " 'Type': 'Condition',\n", + " 'Arguments': {'Conditions': [{'Type': 'GreaterThanOrEqualTo',\n", + " 'LeftValue': {'Std:JsonGet': {'PropertyFile': {'Get': 'Steps.MarketingEval.PropertyFiles.EvaluationReport'},\n", + " 'Path': 'binary_classification_metrics.accuracy.value'}},\n", + " 'RightValue': 0.8}],\n", + " 'IfSteps': [{'Name': 'MarketingRegisterModel-RegisterModel',\n", + " 'Type': 'RegisterModel',\n", + " 'Arguments': {'ModelPackageGroupName': 'DirectMarketingExamplePackageGroupName',\n", + " 'ModelMetrics': {'ModelQuality': {'Statistics': {'ContentType': 'application/json',\n", + " 'S3Uri': 's3://sagemaker-us-east-1-340280328827/MarketingEval-31b0d4fc6c62a4b00c655e1c275ac100/output/evaluation/evaluation.json'}},\n", + " 'Bias': {},\n", + " 'Explainability': {}},\n", + " 'InferenceSpecification': {'Containers': [{'Image': '683313688378.dkr.ecr.us-east-1.amazonaws.com/sagemaker-xgboost:1.0-1-cpu-py3',\n", + " 'ModelDataUrl': {'Get': 'Steps.MarketingTrain.ModelArtifacts.S3ModelArtifacts'}}],\n", + " 'SupportedContentTypes': ['text/csv'],\n", + " 'SupportedResponseMIMETypes': ['text/csv'],\n", + " 'SupportedRealtimeInferenceInstanceTypes': ['ml.t2.medium',\n", + " 'ml.m5.xlarge'],\n", + " 'SupportedTransformInstanceTypes': ['ml.m5.xlarge']},\n", + " 'ModelApprovalStatus': {'Get': 'Parameters.ModelApprovalStatus'},\n", + " 'SkipModelValidation': 'None',\n", + " 'ModelCard': {'ModelCardStatus': 'Draft',\n", + " 'ModelCardContent': '{\"model_overview\": {\"model_creator\": \"DEMO-Model-Registry-ModelCard-Unification\", \"model_artifact\": []}, \"intended_uses\": {\"purpose_of_model\": \"Test model card.\", \"intended_uses\": \"Not used except this test.\", \"factors_affecting_model_efficiency\": \"No.\", \"risk_rating\": \"Low\", \"explanations_for_risk_rating\": \"Just an example.\"}, \"business_details\": {\"business_problem\": \"The business problem that your model is used to solve.\", \"business_stakeholders\": \"The stakeholders who have the interest in the business that your model is used for.\", \"line_of_business\": \"Services that the business is offering.\"}, \"additional_information\": {\"ethical_considerations\": \"Your model ethical consideration.\", \"caveats_and_recommendations\": \"Your model\\'s caveats and recommendations.\", \"custom_details\": {\"Datazone projects\": \"5rn1teh0tv85rb\"}}}'}}}],\n", + " 'ElseSteps': []}}]}" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import json\n", + "\n", + "definition = json.loads(pipeline.definition())\n", + "definition" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Submit the pipeline to SageMaker and start execution\n", + "\n", + "Submit the pipeline definition to the Pipeline service. The role passed in will be used by the Pipeline service to create all the jobs defined in the steps." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING:sagemaker.workflow.utilities:Popping out 'ProcessingJobName' from the pipeline definition by default since it will be overridden at pipeline execution time. Please utilize the PipelineDefinitionConfig to persist this field in the pipeline definition if desired.\n", + "WARNING:sagemaker.workflow.utilities:Popping out 'TrainingJobName' from the pipeline definition by default since it will be overridden at pipeline execution time. Please utilize the PipelineDefinitionConfig to persist this field in the pipeline definition if desired.\n", + "WARNING:sagemaker.workflow.utilities:Popping out 'ProcessingJobName' from the pipeline definition by default since it will be overridden at pipeline execution time. Please utilize the PipelineDefinitionConfig to persist this field in the pipeline definition if desired.\n", + "WARNING:sagemaker.estimator:No finished training job found associated with this estimator. Please make sure this estimator is only used for building workflow config\n", + "WARNING:sagemaker.workflow._utils:Popping out 'CertifyForMarketplace' from the pipeline definition since it will be overridden in pipeline execution time.\n", + "WARNING:sagemaker.workflow.utilities:Popping out 'ModelPackageName' from the pipeline definition by default since it will be overridden at pipeline execution time. Please utilize the PipelineDefinitionConfig to persist this field in the pipeline definition if desired.\n", + "WARNING:sagemaker.workflow.utilities:Popping out 'ProcessingJobName' from the pipeline definition by default since it will be overridden at pipeline execution time. Please utilize the PipelineDefinitionConfig to persist this field in the pipeline definition if desired.\n", + "WARNING:sagemaker.workflow.utilities:Popping out 'TrainingJobName' from the pipeline definition by default since it will be overridden at pipeline execution time. Please utilize the PipelineDefinitionConfig to persist this field in the pipeline definition if desired.\n", + "WARNING:sagemaker.workflow.utilities:Popping out 'ProcessingJobName' from the pipeline definition by default since it will be overridden at pipeline execution time. Please utilize the PipelineDefinitionConfig to persist this field in the pipeline definition if desired.\n", + "WARNING:sagemaker.estimator:No finished training job found associated with this estimator. Please make sure this estimator is only used for building workflow config\n", + "WARNING:sagemaker.workflow._utils:Popping out 'CertifyForMarketplace' from the pipeline definition since it will be overridden in pipeline execution time.\n", + "WARNING:sagemaker.workflow.utilities:Popping out 'ModelPackageName' from the pipeline definition by default since it will be overridden at pipeline execution time. Please utilize the PipelineDefinitionConfig to persist this field in the pipeline definition if desired.\n" + ] + }, + { + "data": { + "text/plain": [ + "{'PipelineArn': 'arn:aws:sagemaker:us-east-1:340280328827:pipeline/ExamplePipeline',\n", + " 'ResponseMetadata': {'RequestId': '966bebd5-7bb6-4aac-9e58-7c97544700ae',\n", + " 'HTTPStatusCode': 200,\n", + " 'HTTPHeaders': {'x-amzn-requestid': '966bebd5-7bb6-4aac-9e58-7c97544700ae',\n", + " 'content-type': 'application/x-amz-json-1.1',\n", + " 'content-length': '83',\n", + " 'date': 'Mon, 23 Sep 2024 17:31:05 GMT'},\n", + " 'RetryAttempts': 0}}" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pipeline.upsert(role_arn=role)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Start the pipeline and accept all of the default parameters." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "execution = pipeline.start()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Pipeline Operations: Examining and Waiting for Pipeline Execution\n", + "\n", + "Describe the pipeline execution." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'PipelineArn': 'arn:aws:sagemaker:us-east-1:340280328827:pipeline/ExamplePipeline',\n", + " 'PipelineExecutionArn': 'arn:aws:sagemaker:us-east-1:340280328827:pipeline/ExamplePipeline/execution/qs4nfbqgsscf',\n", + " 'PipelineExecutionDisplayName': 'execution-1727112665835',\n", + " 'PipelineExecutionStatus': 'Executing',\n", + " 'CreationTime': datetime.datetime(2024, 9, 23, 17, 31, 5, 733000, tzinfo=tzlocal()),\n", + " 'LastModifiedTime': datetime.datetime(2024, 9, 23, 17, 31, 5, 733000, tzinfo=tzlocal()),\n", + " 'CreatedBy': {'UserProfileArn': 'arn:aws:sagemaker:us-east-1:340280328827:user-profile/d-ykydvorozt8h/default-1710454128270',\n", + " 'UserProfileName': 'default-1710454128270',\n", + " 'DomainId': 'd-ykydvorozt8h',\n", + " 'IamIdentity': {'Arn': 'arn:aws:sts::340280328827:assumed-role/CFN-SM-IM-Lambda-Catalog-SageMakerExecutionRole-1TSLA2QB1DJKS/SageMaker',\n", + " 'PrincipalId': 'AROAU6OSJYJ5VWYPHAFI6:SageMaker'}},\n", + " 'LastModifiedBy': {'UserProfileArn': 'arn:aws:sagemaker:us-east-1:340280328827:user-profile/d-ykydvorozt8h/default-1710454128270',\n", + " 'UserProfileName': 'default-1710454128270',\n", + " 'DomainId': 'd-ykydvorozt8h',\n", + " 'IamIdentity': {'Arn': 'arn:aws:sts::340280328827:assumed-role/CFN-SM-IM-Lambda-Catalog-SageMakerExecutionRole-1TSLA2QB1DJKS/SageMaker',\n", + " 'PrincipalId': 'AROAU6OSJYJ5VWYPHAFI6:SageMaker'}},\n", + " 'ResponseMetadata': {'RequestId': 'b98dc7f2-9a9a-4212-adf4-a7269adfa630',\n", + " 'HTTPStatusCode': 200,\n", + " 'HTTPHeaders': {'x-amzn-requestid': 'b98dc7f2-9a9a-4212-adf4-a7269adfa630',\n", + " 'content-type': 'application/x-amz-json-1.1',\n", + " 'content-length': '1119',\n", + " 'date': 'Mon, 23 Sep 2024 17:31:05 GMT'},\n", + " 'RetryAttempts': 0}}" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "execution.describe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Wait for the execution to complete." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "execution.wait()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "List the steps in the execution. These are the steps in the pipeline that have been resolved by the step executor service." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'StepName': 'MarketingRegisterModel-RegisterModel',\n", + " 'StartTime': datetime.datetime(2024, 9, 23, 17, 39, 8, 150000, tzinfo=tzlocal()),\n", + " 'EndTime': datetime.datetime(2024, 9, 23, 17, 39, 10, 333000, tzinfo=tzlocal()),\n", + " 'StepStatus': 'Succeeded',\n", + " 'Metadata': {'RegisterModel': {'Arn': 'arn:aws:sagemaker:us-east-1:340280328827:model-package/DirectMarketingExamplePackageGroupName/1'}},\n", + " 'AttemptCount': 1},\n", + " {'StepName': 'MarketingAccuracyCond',\n", + " 'StartTime': datetime.datetime(2024, 9, 23, 17, 39, 7, 310000, tzinfo=tzlocal()),\n", + " 'EndTime': datetime.datetime(2024, 9, 23, 17, 39, 7, 755000, tzinfo=tzlocal()),\n", + " 'StepStatus': 'Succeeded',\n", + " 'Metadata': {'Condition': {'Outcome': 'True'}},\n", + " 'AttemptCount': 1},\n", + " {'StepName': 'MarketingEval',\n", + " 'StartTime': datetime.datetime(2024, 9, 23, 17, 36, 30, 513000, tzinfo=tzlocal()),\n", + " 'EndTime': datetime.datetime(2024, 9, 23, 17, 39, 6, 100000, tzinfo=tzlocal()),\n", + " 'StepStatus': 'Succeeded',\n", + " 'Metadata': {'ProcessingJob': {'Arn': 'arn:aws:sagemaker:us-east-1:340280328827:processing-job/pipelines-qs4nfbqgsscf-MarketingEval-oqyhKFL7Zk'}},\n", + " 'AttemptCount': 1},\n", + " {'StepName': 'MarketingTrain',\n", + " 'StartTime': datetime.datetime(2024, 9, 23, 17, 34, 6, 272000, tzinfo=tzlocal()),\n", + " 'EndTime': datetime.datetime(2024, 9, 23, 17, 36, 29, 693000, tzinfo=tzlocal()),\n", + " 'StepStatus': 'Succeeded',\n", + " 'Metadata': {'TrainingJob': {'Arn': 'arn:aws:sagemaker:us-east-1:340280328827:training-job/pipelines-qs4nfbqgsscf-MarketingTrain-Y0wnnyFc4z'}},\n", + " 'AttemptCount': 1},\n", + " {'StepName': 'MarketingProcess',\n", + " 'StartTime': datetime.datetime(2024, 9, 23, 17, 31, 7, 486000, tzinfo=tzlocal()),\n", + " 'EndTime': datetime.datetime(2024, 9, 23, 17, 34, 4, 807000, tzinfo=tzlocal()),\n", + " 'StepStatus': 'Succeeded',\n", + " 'Metadata': {'ProcessingJob': {'Arn': 'arn:aws:sagemaker:us-east-1:340280328827:processing-job/pipelines-qs4nfbqgsscf-MarketingProcess-Ktj0pD3TmZ'}},\n", + " 'AttemptCount': 1}]" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "execution.list_steps()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Examining the Evalution\n", + "\n", + "Examine the resulting model evaluation after the pipeline completes. Download the resulting `evaluation.json` file from S3 and print the report." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING:sagemaker.workflow.utilities:Popping out 'ProcessingJobName' from the pipeline definition by default since it will be overridden at pipeline execution time. Please utilize the PipelineDefinitionConfig to persist this field in the pipeline definition if desired.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'binary_classification_metrics': {'accuracy': {'standard_deviation': 'NaN',\n", + " 'value': 0.9101796407185628},\n", + " 'auc': {'standard_deviation': 'NaN',\n", + " 'value': 0.7215769660516066}}}\n" + ] + } + ], + "source": [ + "from pprint import pprint\n", + "\n", + "\n", + "evaluation_json = sagemaker.s3.S3Downloader.read_file(\"{}/evaluation.json\".format(\n", + " step_eval.arguments[\"ProcessingOutputConfig\"][\"Outputs\"][0][\"S3Output\"][\"S3Uri\"]\n", + "))\n", + "pprint(json.loads(evaluation_json))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Lineage\n", + "\n", + "Review the lineage of the artifacts generated by the pipeline." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'StepName': 'MarketingProcess', 'StartTime': datetime.datetime(2024, 9, 23, 17, 31, 7, 486000, tzinfo=tzlocal()), 'EndTime': datetime.datetime(2024, 9, 23, 17, 34, 4, 807000, tzinfo=tzlocal()), 'StepStatus': 'Succeeded', 'Metadata': {'ProcessingJob': {'Arn': 'arn:aws:sagemaker:us-east-1:340280328827:processing-job/pipelines-qs4nfbqgsscf-MarketingProcess-Ktj0pD3TmZ'}}, 'AttemptCount': 1}\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Name/Source Direction Type \\\n", + "0 s3://...c655e1c275ac100/input/code/evaluation.py Input DataSet \n", + "1 s3://...s4nfbqgsscf/MarketingProcess/output/test Input DataSet \n", + "2 s3://...tingTrain-Y0wnnyFc4z/output/model.tar.gz Input Model \n", + "3 68331...naws.com/sagemaker-xgboost:1.0-1-cpu-py3 Input Image \n", + "4 s3://...62a4b00c655e1c275ac100/output/evaluation Output DataSet \n", + "\n", + " Association Type Lineage Type \n", + "0 ContributedTo artifact \n", + "1 ContributedTo artifact \n", + "2 ContributedTo artifact \n", + "3 ContributedTo artifact \n", + "4 Produced artifact " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'StepName': 'MarketingAccuracyCond', 'StartTime': datetime.datetime(2024, 9, 23, 17, 39, 7, 310000, tzinfo=tzlocal()), 'EndTime': datetime.datetime(2024, 9, 23, 17, 39, 7, 755000, tzinfo=tzlocal()), 'StepStatus': 'Succeeded', 'Metadata': {'Condition': {'Outcome': 'True'}}, 'AttemptCount': 1}\n" + ] + }, + { + "data": { + "text/plain": [ + "None" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'StepName': 'MarketingRegisterModel-RegisterModel', 'StartTime': datetime.datetime(2024, 9, 23, 17, 39, 8, 150000, tzinfo=tzlocal()), 'EndTime': datetime.datetime(2024, 9, 23, 17, 39, 10, 333000, tzinfo=tzlocal()), 'StepStatus': 'Succeeded', 'Metadata': {'RegisterModel': {'Arn': 'arn:aws:sagemaker:us-east-1:340280328827:model-package/DirectMarketingExamplePackageGroupName/1'}}, 'AttemptCount': 1}\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Name/Source Direction Type \\\n", + "0 s3://...tingTrain-Y0wnnyFc4z/output/model.tar.gz Input Model \n", + "1 68331...naws.com/sagemaker-xgboost:1.0-1-cpu-py3 Input Image \n", + "2 DirectMarketingExamplePackageGroupName-1-Pendi... Input Approval \n", + "3 DirectMarketingExamplePackageGroupName-1727113... Output ModelGroup \n", + "\n", + " Association Type Lineage Type \n", + "0 ContributedTo artifact \n", + "1 ContributedTo artifact \n", + "2 ContributedTo action \n", + "3 AssociatedWith context " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import time\n", + "from sagemaker.lineage.visualizer import LineageTableVisualizer\n", + "\n", + "\n", + "viz = LineageTableVisualizer(sagemaker.session.Session())\n", + "for execution_step in reversed(execution.list_steps()):\n", + " print(execution_step)\n", + " display(viz.show(pipeline_execution_step=execution_step))\n", + " time.sleep(5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Parametrized Executions\n", + "\n", + "You can run additional executions of the pipeline and specify different pipeline parameters. The parameters argument is a dictionary containing parameter names, and where the values are used to override the defaults values.\n", + "\n", + "Based on the performance of the model, you might want to kick off another pipeline execution on a compute-optimized instance type and set the model approval status to \"Approved\" automatically. This means that the model package version generated by the `RegisterModel` step is automatically ready for deployment through CI/CD pipelines, such as with SageMaker Projects." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "execution = pipeline.start(\n", + " parameters=dict(\n", + " ProcessingInstanceType=\"ml.c5.xlarge\",\n", + " ModelApprovalStatus=\"Approved\",\n", + " )\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "execution.wait()" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'StepName': 'MarketingRegisterModel-RegisterModel',\n", + " 'StartTime': datetime.datetime(2024, 9, 23, 17, 47, 32, 254000, tzinfo=tzlocal()),\n", + " 'EndTime': datetime.datetime(2024, 9, 23, 17, 47, 34, 441000, tzinfo=tzlocal()),\n", + " 'StepStatus': 'Succeeded',\n", + " 'Metadata': {'RegisterModel': {'Arn': 'arn:aws:sagemaker:us-east-1:340280328827:model-package/DirectMarketingExamplePackageGroupName/2'}},\n", + " 'AttemptCount': 1},\n", + " {'StepName': 'MarketingAccuracyCond',\n", + " 'StartTime': datetime.datetime(2024, 9, 23, 17, 47, 31, 146000, tzinfo=tzlocal()),\n", + " 'EndTime': datetime.datetime(2024, 9, 23, 17, 47, 31, 791000, tzinfo=tzlocal()),\n", + " 'StepStatus': 'Succeeded',\n", + " 'Metadata': {'Condition': {'Outcome': 'True'}},\n", + " 'AttemptCount': 1},\n", + " {'StepName': 'MarketingEval',\n", + " 'StartTime': datetime.datetime(2024, 9, 23, 17, 44, 56, 945000, tzinfo=tzlocal()),\n", + " 'EndTime': datetime.datetime(2024, 9, 23, 17, 47, 30, 570000, tzinfo=tzlocal()),\n", + " 'StepStatus': 'Succeeded',\n", + " 'Metadata': {'ProcessingJob': {'Arn': 'arn:aws:sagemaker:us-east-1:340280328827:processing-job/pipelines-gcjrvw70vqlk-MarketingEval-iVsFdUI5oB'}},\n", + " 'AttemptCount': 1},\n", + " {'StepName': 'MarketingTrain',\n", + " 'StartTime': datetime.datetime(2024, 9, 23, 17, 42, 41, 707000, tzinfo=tzlocal()),\n", + " 'EndTime': datetime.datetime(2024, 9, 23, 17, 44, 56, 253000, tzinfo=tzlocal()),\n", + " 'StepStatus': 'Succeeded',\n", + " 'Metadata': {'TrainingJob': {'Arn': 'arn:aws:sagemaker:us-east-1:340280328827:training-job/pipelines-gcjrvw70vqlk-MarketingTrain-ztut8DyeXC'}},\n", + " 'AttemptCount': 1},\n", + " {'StepName': 'MarketingProcess',\n", + " 'StartTime': datetime.datetime(2024, 9, 23, 17, 40, 6, 420000, tzinfo=tzlocal()),\n", + " 'EndTime': datetime.datetime(2024, 9, 23, 17, 42, 41, 89000, tzinfo=tzlocal()),\n", + " 'StepStatus': 'Succeeded',\n", + " 'Metadata': {'ProcessingJob': {'Arn': 'arn:aws:sagemaker:us-east-1:340280328827:processing-job/pipelines-gcjrvw70vqlk-MarketingProcess-ACorRAfKyH'}},\n", + " 'AttemptCount': 1}]" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "execution.list_steps()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "instance_type": "ml.t3.medium", + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/sagemaker_model_governance/bank-additional-full.csv b/sagemaker_model_governance/bank-additional-full.csv new file mode 100644 index 0000000000..0743b8a6fa --- /dev/null +++ b/sagemaker_model_governance/bank-additional-full.csv @@ -0,0 +1,41189 @@ +age,job,marital,education,default,housing,loan,contact,month,day_of_week,duration,campaign,pdays,previous,poutcome,emp.var.rate,cons.price.idx,cons.conf.idx,euribor3m,nr.employed,y 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+38,technician,married,university.degree,no,yes,no,cellular,jun,wed,171,1,999,0,nonexistent,-2.9,92.963,-40.8,1.26,5076.2,no +36,admin.,married,university.degree,no,no,no,cellular,jun,wed,334,1,999,0,nonexistent,-2.9,92.963,-40.8,1.26,5076.2,no +37,self-employed,married,professional.course,no,no,no,cellular,jun,wed,321,1,999,0,nonexistent,-2.9,92.963,-40.8,1.26,5076.2,yes +28,student,single,high.school,no,yes,no,cellular,jun,wed,296,4,999,0,nonexistent,-2.9,92.963,-40.8,1.26,5076.2,yes +58,management,married,basic.4y,no,yes,no,cellular,jun,wed,86,1,999,0,nonexistent,-2.9,92.963,-40.8,1.26,5076.2,no +20,student,single,unknown,no,yes,no,cellular,jun,wed,213,1,999,0,nonexistent,-2.9,92.963,-40.8,1.26,5076.2,yes +29,admin.,single,university.degree,no,yes,yes,telephone,jun,wed,252,2,999,0,nonexistent,-2.9,92.963,-40.8,1.26,5076.2,no +28,technician,single,professional.course,no,no,no,cellular,jun,wed,411,1,999,0,nonexistent,-2.9,92.963,-40.8,1.26,5076.2,yes 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+28,admin.,single,high.school,no,yes,yes,cellular,jun,thu,296,2,999,0,nonexistent,-2.9,92.963,-40.8,1.26,5076.2,yes +53,housemaid,married,high.school,no,no,yes,cellular,jun,thu,147,1,999,2,failure,-2.9,92.963,-40.8,1.26,5076.2,yes +35,admin.,married,university.degree,no,no,no,cellular,jun,thu,121,4,999,0,nonexistent,-2.9,92.963,-40.8,1.26,5076.2,yes +32,admin.,single,university.degree,no,yes,no,cellular,jun,thu,89,2,999,0,nonexistent,-2.9,92.963,-40.8,1.26,5076.2,yes +26,blue-collar,single,professional.course,no,yes,no,cellular,jun,thu,77,2,999,0,nonexistent,-2.9,92.963,-40.8,1.26,5076.2,no +53,unknown,married,professional.course,no,no,no,cellular,jun,thu,217,2,3,1,success,-2.9,92.963,-40.8,1.26,5076.2,no +30,technician,single,professional.course,no,no,no,cellular,jun,thu,137,1,999,0,nonexistent,-2.9,92.963,-40.8,1.26,5076.2,yes +26,student,single,university.degree,no,yes,no,cellular,jun,thu,112,1,999,0,nonexistent,-2.9,92.963,-40.8,1.26,5076.2,no 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us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n", + "\n", + "![This us-west-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-west-2/sagemaker_model_governance|model_card.ipynb)\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This notebook walks you through the features of Amazon SageMaker Model Cards. For more information, see [Model Cards](https://docs.aws.amazon.com/sagemaker/latest/dg/model-cards.html) in the _Amazon SageMaker Developer Guide_.\n", + "\n", + "Amazon SageMaker integrates Model Cards into Model Registry, making it easier for customers to manage governance information for specific model versions directly in Model Registry in just a few clicks. Customers register ML models in Model Registry to manage their models. Additionally, they can register ML model versions early in the development lifecycle, including essential business details and technical metadata. This integration allows customers to seamlessly review and govern models across their lifecycle from a single place. Customers have greater visibility into the model lifecycle from experimentation and training to evaluation and deployment. This streamlined experience ensures that model governance is consistent and easily accessible throughout the development.\n", + "\n", + "In this example, you create a binary classification model along with a model card and registry to manage unified governance information and view\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "! pip install --upgrade sagemaker\n", + "! pip install --force-reinstall -v \"awscli==1.33.1\"\n", + "! aws --version " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import boto3\n", + "from sagemaker.session import Session\n", + "from sagemaker import get_execution_role\n", + "\n", + "role = get_execution_role()\n", + "\n", + "sagemaker_session = Session()\n", + "\n", + "bucket = sagemaker_session.default_bucket()\n", + "prefix = \"model-card-sample-notebook\"\n", + "\n", + "region = sagemaker_session.boto_region_name\n", + "s3 = boto3.client(\"s3\", region_name=region)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, import the necessary Python libraries." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import io\n", + "import os\n", + "import numpy as np\n", + "from six.moves.urllib.parse import urlparse\n", + "from pprint import pprint\n", + "import boto3\n", + "import sagemaker\n", + "from sagemaker.image_uris import retrieve\n", + "import sagemaker.amazon.common as smac\n", + "from sagemaker.model_card import (\n", + " ModelCard,\n", + " ModelOverview,\n", + " ObjectiveFunction,\n", + " Function,\n", + " TrainingDetails,\n", + " IntendedUses,\n", + " BusinessDetails,\n", + " EvaluationJob,\n", + " AdditionalInformation,\n", + " Metric,\n", + " MetricGroup,\n", + " ModelCardStatusEnum,\n", + " ObjectiveFunctionEnum,\n", + " FacetEnum,\n", + " RiskRatingEnum,\n", + " MetricTypeEnum,\n", + " EvaluationMetricTypeEnum,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Prepare a Model\n", + "The following code creates an example binary classification model trained on a synthetic dataset. The target variable (0 or 1) is the second variable in the tuple.\n", + "\n", + "### 1. Prepare the training data\n", + "The code will upload example data to your S3 bucket." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# synthetic data\n", + "raw_data = (\n", + " (0.5, 0),\n", + " (0.75, 0),\n", + " (1.0, 0),\n", + " (1.25, 0),\n", + " (1.50, 0),\n", + " (1.75, 0),\n", + " (2.0, 0),\n", + " (2.25, 1),\n", + " (2.5, 0),\n", + " (2.75, 1),\n", + " (3.0, 0),\n", + " (3.25, 1),\n", + " (3.5, 0),\n", + " (4.0, 1),\n", + " (4.25, 1),\n", + " (4.5, 1),\n", + " (4.75, 1),\n", + " (5.0, 1),\n", + " (5.5, 1),\n", + ")\n", + "training_data = np.array(raw_data).astype(\"float32\")\n", + "labels = training_data[:, 1]\n", + "\n", + "# upload data to S3 bucket\n", + "buf = io.BytesIO()\n", + "smac.write_numpy_to_dense_tensor(buf, training_data, labels)\n", + "buf.seek(0)\n", + "boto3.resource(\"s3\").Bucket(bucket).Object(os.path.join(prefix, \"train\")).upload_fileobj(buf)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2. Train a model\n", + "Train a binary classification model with the training data from the previous step." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true, + "tags": [] + }, + "outputs": [], + "source": [ + "s3_train_data = f\"s3://{bucket}/{prefix}/train\"\n", + "output_location = f\"s3://{bucket}/{prefix}/output\"\n", + "container = retrieve(\"linear-learner\", sagemaker_session.boto_session.region_name)\n", + "estimator = sagemaker.estimator.Estimator(\n", + " container,\n", + " role=role,\n", + " instance_count=1,\n", + " instance_type=\"ml.m4.xlarge\",\n", + " output_path=output_location,\n", + " sagemaker_session=sagemaker_session,\n", + ")\n", + "estimator.set_hyperparameters(feature_dim=2, mini_batch_size=10, predictor_type=\"binary_classifier\")\n", + "estimator.fit({\"train\": s3_train_data})\n", + "print(f\"Training job name: {estimator.latest_training_job.name}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3. Create a model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "model_name = \"model-card-test-model\"\n", + "model = estimator.create_model(name=model_name)\n", + "container_def = model.prepare_container_def()\n", + "sagemaker_session.create_model(model_name, role, container_def)\n", + "print(f\"Model name: {model_name}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Define Model Card\n", + "Document your binary classification model details in an Amazon SageMaker Model Card using the SageMaker Python SDK.\n", + "\n", + "### 1. Auto-collect model details\n", + "Automatically collect basic model information like model ID, training environment, and the model output S3 URI. Add additional model information such as a description, problem type, algorithm type, model creator, and model owner." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "model_overview = ModelOverview.from_model_name(\n", + " model_name=model_name,\n", + " sagemaker_session=sagemaker_session,\n", + " model_description=\"This is an example binary classification model used for a Python SDK demo of Amazon SageMaker Model Cards.\",\n", + " problem_type=\"Binary Classification\",\n", + " algorithm_type=\"Logistic Regression\",\n", + " model_creator=\"DEMO-ModelCard\",\n", + " model_owner=\"DEMO-ModelCard\",\n", + ")\n", + "print(f\"Model id: {model_overview.model_id}\")\n", + "print(f\"Model training images: {model_overview.inference_environment.container_image}\")\n", + "print(f\"Model: {model_overview.model_artifact}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2. Auto-collect training details\n", + "Automatically collect basic training information like training ID, training environment, and training metrics. Add additional training information such as objective function details and training observations." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "objective_function = ObjectiveFunction(\n", + " function=Function(\n", + " function=ObjectiveFunctionEnum.MINIMIZE,\n", + " facet=FacetEnum.LOSS,\n", + " ),\n", + " notes=\"This is an example objective function.\",\n", + ")\n", + "training_details = TrainingDetails.from_model_overview(\n", + " model_overview=model_overview,\n", + " sagemaker_session=sagemaker_session,\n", + " objective_function=objective_function,\n", + " training_observations=\"Add model training observations here.\",\n", + ")\n", + "print(f\"Training job id: {training_details.training_job_details.training_arn}\")\n", + "print(\n", + " f\"Training image: {training_details.training_job_details.training_environment.container_image}\"\n", + ")\n", + "print(\"Training Metrics: \")\n", + "pprint(\n", + " [\n", + " {\"name\": i.name, \"value\": i.value}\n", + " for i in training_details.training_job_details.training_metrics\n", + " ]\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3. Collect evaluation details\n", + "Add evaluation observations, datasets, and metrics." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "manual_metric_group = MetricGroup(\n", + " name=\"binary classification metrics\",\n", + " metric_data=[Metric(name=\"accuracy\", type=MetricTypeEnum.NUMBER, value=0.5)],\n", + ")\n", + "example_evaluation_job = EvaluationJob(\n", + " name=\"Example evaluation job\",\n", + " evaluation_observation=\"Evaluation observations.\",\n", + " datasets=[\"s3://path/to/evaluation/data\"],\n", + " metric_groups=[manual_metric_group],\n", + ")\n", + "evaluation_details = [example_evaluation_job]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### (Optional) 3.1 Parse metrics from existing evaluation report\n", + "If you have existing evaluation reports generated by [SageMaker Clarify](https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-processing-job-run.html) or [SageMaker Model Monitor](https://docs.aws.amazon.com/sagemaker/latest/dg/model-monitor-model-quality.html), upload them to S3 and provide an S3 URI to automatically parse evaluation metrics. To add your own generic model card evaluation report, provide a report in the [evaluation results JSON format](https://docs.aws.amazon.com/sagemaker/latest/dg/model-cards-json-schema.html). See the example JSON files in the `./example_metrics` folder for reference.\n", + "##### Collect metrics from a JSON format evaluation report" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "report_type = \"clarify_bias.json\"\n", + "example_evaluation_job.add_metric_group_from_json(\n", + " f\"example_metrics/{report_type}\", EvaluationMetricTypeEnum.CLARIFY_BIAS\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Collect metrics from S3" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# upload metric file to s3\n", + "with open(f\"example_metrics/{report_type}\", \"rb\") as metrics:\n", + " s3.upload_fileobj(\n", + " metrics,\n", + " Bucket=bucket,\n", + " Key=f\"{prefix}/{report_type}\",\n", + " ExtraArgs={\"ContentType\": \"application/json\"},\n", + " )\n", + "\n", + "metric_s3_url = f\"s3://{bucket}/{prefix}/{report_type}\"\n", + "example_evaluation_job.add_metric_group_from_s3(\n", + " session=sagemaker_session.boto_session,\n", + " s3_url=metric_s3_url,\n", + " metric_type=EvaluationMetricTypeEnum.CLARIFY_BIAS,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4. Collect additional details\n", + "Add the intended uses of your model and business details and any additional information that you want to include in your model card. For more information on intended uses and business details, see [Model Cards](https://docs.aws.amazon.com/sagemaker/latest/dg/model-cards.html) in the `Amazon SageMaker Developer Guide`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "intended_uses = IntendedUses(\n", + " purpose_of_model=\"Test model card.\",\n", + " intended_uses=\"Not used except this test.\",\n", + " factors_affecting_model_efficiency=\"No.\",\n", + " risk_rating=RiskRatingEnum.LOW,\n", + " explanations_for_risk_rating=\"Just an example.\",\n", + ")\n", + "business_details = BusinessDetails(\n", + " business_problem=\"The business problem that your model is used to solve.\",\n", + " business_stakeholders=\"The stakeholders who have the interest in the business that your model is used for.\",\n", + " line_of_business=\"Services that the business is offering.\",\n", + ")\n", + "additional_information = AdditionalInformation(\n", + " ethical_considerations=\"Your model ethical consideration.\",\n", + " caveats_and_recommendations=\"Your model's caveats and recommendations.\",\n", + " custom_details={\"custom details1\": \"details value\"},\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 5. Define model card\n", + "Define model card with the information collected in the previous steps. Amazon SageMaker integrates Model Cards into Model Registry. A model package registered in the Model Registry includes a simplified Model Card as a component of the model package" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "model_card_name = \"sample-notebook-model-card\"\n", + "my_card = ModelCard(\n", + " name=model_card_name,\n", + " status=ModelCardStatusEnum.DRAFT,\n", + " model_overview=model_overview,\n", + " training_details=training_details,\n", + " intended_uses=intended_uses,\n", + " business_details=business_details,\n", + " evaluation_details=evaluation_details,\n", + " additional_information=additional_information,\n", + " sagemaker_session=sagemaker_session,\n", + ")\n", + "my_card_req = my_card._create_request_args()\n", + "#my_card.create()\n", + "#print(f\"Model card {my_card.name} is successfully created with id {my_card.arn}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Create Model Package with Model Card Content\n", + "We will use Model Card object to create request to model package with model card details" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# create a model package group\n", + "model_package_group_name = \"test-notebook-model-package-group-example\"\n", + "sagemaker_session.sagemaker_client.create_model_package_group(\n", + " ModelPackageGroupName=model_package_group_name\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "from sagemaker.model_card import (\n", + " ModelCard,\n", + " ModelPackage,\n", + " IntendedUses,\n", + " ModelCardStatusEnum,\n", + ")\n", + "from sagemaker.model_card.helpers import (\n", + " _JSONEncoder\n", + ")\n", + "from sagemaker.model_card.model_card import ModelApprovalStatusEnum\n", + "import json\n", + "\n", + "# describe training job to get model_data_url and image\n", + "training_job_name = estimator.latest_training_job.name\n", + "training_job = sagemaker_session.sagemaker_client.describe_training_job(\n", + " TrainingJobName=training_job_name\n", + ")\n", + "\n", + "model_data_url = training_job[\"ModelArtifacts\"][\"S3ModelArtifacts\"]\n", + "image = training_job[\"AlgorithmSpecification\"][\"TrainingImage\"]\n", + "\n", + "\n", + "# model card details\n", + "model_card_content_dict = {\n", + " \"model_overview\": {\n", + " \"model_creator\": my_card.model_overview.model_creator,\n", + " \"model_artifact\": my_card.model_overview.model_artifact\n", + " },\n", + " \"intended_uses\": my_card.intended_uses,\n", + " \"business_details\": my_card.business_details,\n", + " \"training_details\": my_card.training_details,\n", + " \"evaluation_details\": my_card.evaluation_details,\n", + " \"additional_information\": my_card.additional_information \n", + "}\n", + "\n", + "model_card_details_input_dict = { \n", + " \"ModelCardContent\": json.dumps(model_card_content_dict, cls=_JSONEncoder),\n", + " \"ModelCardStatus\": my_card.status\n", + "}\n", + "\n", + "# model package request input object\n", + "create_model_package_input_dict = {\n", + " \"ModelPackageGroupName\": model_package_group_name,\n", + " \"ModelPackageDescription\": \"Test model package registered for integ test\",\n", + " \"ModelApprovalStatus\": ModelApprovalStatusEnum.PENDING_MANUAL_APPROVAL,\n", + " \"InferenceSpecification\": {\n", + " \"Containers\": [{\"Image\": image, \"ModelDataUrl\": model_data_url}],\n", + " \"SupportedContentTypes\": [\"text/csv\"],\n", + " \"SupportedResponseMIMETypes\": [\"text/csv\"],\n", + " },\n", + " \"ModelCard\": model_card_details_input_dict\n", + "}\n", + "\n", + "\n", + "model_pkg = sagemaker_session.sagemaker_client.create_model_package(\n", + " **create_model_package_input_dict\n", + ")\n", + "print(\"Model package ARN:\", model_pkg[\"ModelPackageArn\"])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "updated_model_card_details_input_dict = { \n", + " \"ModelCardStatus\": ModelApprovalStatusEnum.APPROVED\n", + "}\n", + "update_model_package_input_dict = {\n", + " \"ModelPackageArn\": model_pkg[\"ModelPackageArn\"],\n", + " \"ModelCard\": updated_model_card_details_input_dict\n", + "}\n", + "model_pkg = sagemaker_session.sagemaker_client.update_model_package(\n", + " **update_model_package_input_dict\n", + ")\n", + "print(\"Model package ARN:\", model_pkg[\"ModelPackageArn\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will View and Update the Details of a Model Version. The response is a list of model package summaries. You can get the Amazon Resource Name (ARN) of the model versions from this list." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# View and Update the Details of a Model Version\n", + "\n", + "sagemaker_session.sagemaker_client.list_model_packages(ModelPackageGroupName=\"test-notebook-model-package-group-example\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Call describe_model_package to see the details of the model version. You pass in the ARN of a model version that you got in the output of the call to list_model_packages.The output of this call is a JSON object with the model version details." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# model package request input object\n", + "\n", + "print(sagemaker_session.sagemaker_client.describe_model_package(ModelPackageName=model_pkg[\"ModelPackageArn\"]))\n", + "response=sagemaker_session.sagemaker_client.describe_model_package(ModelPackageName=model_pkg[\"ModelPackageArn\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can get the model card details from the model registry package itself" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Parse the JSON response\n", + "import json\n", + "model_card_content = response.get('ModelCard')\n", + "print(model_card_content)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "## Cleanup\n", + "Delete the following resources:\n", + " The example binary classification model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "\n", + "\n", + "sagemaker_session.delete_model(model_name)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Notebook CI Test Results\n", + "\n", + "This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n", + "\n", + "![This us-east-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-east-1/sagemaker_model_governance|model_card.ipynb)\n", + "\n", + "![This us-east-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-east-2/sagemaker_model_governance|model_card.ipynb)\n", + "\n", + "![This us-west-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-west-1/sagemaker_model_governance|model_card.ipynb)\n", + "\n", + "![This ca-central-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ca-central-1/sagemaker_model_governance|model_card.ipynb)\n", + "\n", + "![This sa-east-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/sa-east-1/sagemaker_model_governance|model_card.ipynb)\n", + "\n", + "![This eu-west-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/eu-west-1/sagemaker_model_governance|model_card.ipynb)\n", + "\n", + "![This eu-west-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/eu-west-2/sagemaker_model_governance|model_card.ipynb)\n", + "\n", + "![This eu-west-3 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/eu-west-3/sagemaker_model_governance|model_card.ipynb)\n", + "\n", + "![This eu-central-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/eu-central-1/sagemaker_model_governance|model_card.ipynb)\n", + "\n", + "![This eu-north-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/eu-north-1/sagemaker_model_governance|model_card.ipynb)\n", + "\n", + "![This ap-southeast-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-southeast-1/sagemaker_model_governance|model_card.ipynb)\n", + "\n", + "![This ap-southeast-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-southeast-2/sagemaker_model_governance|model_card.ipynb)\n", + "\n", + "![This ap-northeast-1 badge failed to load. 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Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-south-1/sagemaker_model_governance|model_card.ipynb)\n" + ] + } + ], + "metadata": { + "instance_type": "ml.t3.medium", + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.14" + }, + "vscode": { + "interpreter": { + "hash": "1571133684af95a48d70cfb4aef2840ed1e20d7c2d2a63af1685000148425678" + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +}