A wrapper around SageMaker ML Lineage Tracking extending ML Lineage to end-to-end ML lifecycles, including additional capabilities around Feature Store groups, queries, and other relevant artifacts.

Overview

ML Lineage Helper

This library is a wrapper around the SageMaker SDK to support ease of lineage tracking across the ML lifecycle. Lineage artifacts include data, code, feature groups, features in a feature group, feature group queries, training jobs, and models.

Install

pip install git+https://github.com/aws-samples/ml-lineage-helper

Usage

Import ml_lineage_helper.

from ml_lineage_helper import *
from ml_lineage_helper.query_lineage import QueryLineage

Creating and Displaying ML Lineage

Lineage tracking can tie together a SageMaker Processing job, the raw data being processed, the processing code, the query you used against the Feature Store to fetch your training and test sets, the training and test data in S3, and the training code into a lineage represented as a DAG.

ml_lineage = MLLineageHelper()
lineage = ml_lineage.create_ml_lineage(estimator_or_training_job_name, model_name=model_name,
                                       query=query, sagemaker_processing_job_description=preprocessing_job_description,
                                       feature_group_names=['customers', 'claims'])
lineage

If you cloned your code from a version control hosting platform like GitHub or GitLab, ml_lineage_tracking can associate the URLs of the code with the artifacts that will be created. See below:

# Get repo links to processing and training code
processing_code_repo_url = get_repo_link(os.getcwd(), 'processing.py')
training_code_repo_url = get_repo_link(os.getcwd(), 'pytorch-model/train_deploy.py', processing_code=False)
repo_links = [processing_code_repo_url, training_code_repo_url]

# Create lineage
ml_lineage = MLLineageHelper()
lineage = ml_lineage.create_ml_lineage(estimator, model_name=model_name,
                                       query=query, sagemaker_processing_job_description=preprocessing_job_description,
                                       feature_group_names=['customers', 'claims'],
                                       repo_links=repo_links)
lineage
Name/Source Association Name/Destination Artifact Source ARN Artifact Destination ARN Source URI Base64 Feature Store Query String Git URL
pytorch-hosted-model-2021-08-26-15-55-22-071-aws-training-job Produced Model arn:aws:sagemaker:us-west-2:000000000000:experiment-trial-component/pytorch-hosted-model-2021-08-26-15-55-22-071-aws-training-job arn:aws:sagemaker:us-west-2:000000000000:artifact/013fa1be4ec1d192dac21abaf94ddded None None None
TrainingCode ContributedTo pytorch-hosted-model-2021-08-26-15-55-22-071-aws-training-job arn:aws:sagemaker:us-west-2:000000000000:artifact/902d23ff64ef6d85dc27d841a967cd7d arn:aws:sagemaker:us-west-2:000000000000:experiment-trial-component/pytorch-hosted-model-2021-08-26-15-55-22-071-aws-training-job s3://sagemaker-us-west-2-000000000000/pytorch-hosted-model-2021-08-26-15-55-22-071/source/sourcedir.tar.gz None https://gitlab.com/bwlind/ml-lineage-tracking/blob/main/ml-lineage-tracking/pytorch-model/train_deploy.py
TestingData ContributedTo pytorch-hosted-model-2021-08-26-15-55-22-071-aws-training-job arn:aws:sagemaker:us-west-2:000000000000:artifact/1ae9dfab7a3817cbf14708d932d9142d arn:aws:sagemaker:us-west-2:000000000000:experiment-trial-component/pytorch-hosted-model-2021-08-26-15-55-22-071-aws-training-job s3://sagemaker-us-west-2-000000000000/ml-lineage-tracking-v1/test.npy None None
TrainingData ContributedTo pytorch-hosted-model-2021-08-26-15-55-22-071-aws-training-job arn:aws:sagemaker:us-west-2:000000000000:artifact/a0fd47c730f883b8e5228577fc5d5ef4 arn:aws:sagemaker:us-west-2:000000000000:experiment-trial-component/pytorch-hosted-model-2021-08-26-15-55-22-071-aws-training-job s3://sagemaker-us-west-2-000000000000/ml-lineage-tracking-v1/train.npy CnNlbGVjdCAqCmZyb20gImJvc3Rvbi1ob3VzaW5nLXY1LTE2Mjk3MzEyNjkiCg== None
fg-boston-housing-v5 ContributedTo TestingData arn:aws:sagemaker:us-west-2:000000000000:artifact/1969cb21bf48405e0f2bb2d33f48b7b2 arn:aws:sagemaker:us-west-2:000000000000:artifact/1ae9dfab7a3817cbf14708d932d9142d arn:aws:sagemaker:us-west-2:000000000000:feature-group/boston-housing-v5 None None
fg-boston-housing ContributedTo TestingData arn:aws:sagemaker:us-west-2:000000000000:artifact/d1b82165341cd78b93995d492b5adf7f arn:aws:sagemaker:us-west-2:000000000000:artifact/1ae9dfab7a3817cbf14708d932d9142d arn:aws:sagemaker:us-west-2:000000000000:feature-group/boston-housing None None
ProcessingJob ContributedTo fg-boston-housing-v5 arn:aws:sagemaker:us-west-2:000000000000:artifact/0a665c42c57f3b561e18a51a327d0a2f arn:aws:sagemaker:us-west-2:000000000000:artifact/1969cb21bf48405e0f2bb2d33f48b7b2 arn:aws:sagemaker:us-west-2:000000000000:processing-job/pytorch-workflow-preprocessing-26-15-41-18 None None
ProcessingInputData ContributedTo ProcessingJob arn:aws:sagemaker:us-west-2:000000000000:artifact/2204290e557c4c9feaaa4ef7e4d88f0c arn:aws:sagemaker:us-west-2:000000000000:artifact/0a665c42c57f3b561e18a51a327d0a2f s3://sagemaker-us-west-2-000000000000/ml-lineage-tracking-v1/data/raw None None
ProcessingCode ContributedTo ProcessingJob arn:aws:sagemaker:us-west-2:000000000000:artifact/69de4723ab0643c6ca8257bc6fbcfb4f arn:aws:sagemaker:us-west-2:000000000000:artifact/0a665c42c57f3b561e18a51a327d0a2f s3://sagemaker-us-west-2-000000000000/pytorch-workflow-preprocessing-26-15-41-18/input/code/preprocessing.py None https://gitlab.com/bwlind/ml-lineage-tracking/blob/main/ml-lineage-tracking/processing.py
ProcessingJob ContributedTo fg-boston-housing arn:aws:sagemaker:us-west-2:000000000000:artifact/0a665c42c57f3b561e18a51a327d0a2f arn:aws:sagemaker:us-west-2:000000000000:artifact/d1b82165341cd78b93995d492b5adf7f arn:aws:sagemaker:us-west-2:000000000000:processing-job/pytorch-workflow-preprocessing-26-15-41-18 None None
fg-boston-housing-v5 ContributedTo TrainingData arn:aws:sagemaker:us-west-2:000000000000:artifact/1969cb21bf48405e0f2bb2d33f48b7b2 arn:aws:sagemaker:us-west-2:000000000000:artifact/a0fd47c730f883b8e5228577fc5d5ef4 arn:aws:sagemaker:us-west-2:000000000000:feature-group/boston-housing-v5 None None
fg-boston-housing ContributedTo TrainingData arn:aws:sagemaker:us-west-2:000000000000:artifact/d1b82165341cd78b93995d492b5adf7f arn:aws:sagemaker:us-west-2:000000000000:artifact/a0fd47c730f883b8e5228577fc5d5ef4 arn:aws:sagemaker:us-west-2:000000000000:feature-group/boston-housing None None

You can optionally see the lineage represented as a graph instead of a Pandas DataFrame:

ml_lineage.graph()

If you're jumping in a notebook fresh and already have a model whose ML Lineage has been tracked, you can get this MLLineage object by using the following line of code:

ml_lineage = MLLineageHelper(sagemaker_model_name_or_model_s3_uri='my-sagemaker-model-name')
ml_lineage.df

Querying ML Lineage

If you have a data source, you can find associated Feature Groups by providing the data source's S3 URI or Artifact ARN:

query_lineage = QueryLineage()
query_lineage.get_feature_groups_from_data_source(artifact_arn_or_s3_uri)

You can also start with a Feature Group, and find associated data sources:

query_lineage = QueryLineage()
query_lineage.get_data_sources_from_feature_group(artifact_or_fg_arn, max_depth=3)

Given a Feature Group, you can also find associated models:

query_lineage = QueryLineage()
query_lineage.get_models_from_feature_group(artifact_or_fg_arn)

Given a SageMaker model name or artifact ARN, you can find associated Feature Groups.

query_lineage = QueryLineage()
query_lineage.get_feature_groups_from_model(artifact_arn_or_model_name)

Security

See CONTRIBUTING for more information.

License

This project is licensed under the Apache-2.0 License.

Owner
AWS Samples
AWS Samples
NEATEST: Evolving Neural Networks Through Augmenting Topologies with Evolution Strategy Training

NEATEST: Evolving Neural Networks Through Augmenting Topologies with Evolution Strategy Training

Göktuğ Karakaşlı 16 Dec 05, 2022
A PyTorch Implementation of Single Shot MultiBox Detector

SSD: Single Shot MultiBox Object Detector, in PyTorch A PyTorch implementation of Single Shot MultiBox Detector from the 2016 paper by Wei Liu, Dragom

Max deGroot 4.8k Jan 07, 2023
Official code base for the poster "On the use of Cortical Magnification and Saccades as Biological Proxies for Data Augmentation" published in NeurIPS 2021 Workshop (SVRHM)

Self-Supervised Learning (SimCLR) with Biological Plausible Image Augmentations Official code base for the poster "On the use of Cortical Magnificatio

Binxu 8 Aug 17, 2022
Code related to the manuscript "Averting A Crisis In Simulation-Based Inference"

Abstract We present extensive empirical evidence showing that current Bayesian simulation-based inference algorithms are inadequate for the falsificat

Montefiore Artificial Intelligence Research 3 Nov 14, 2022
NeuralTalk is a Python+numpy project for learning Multimodal Recurrent Neural Networks that describe images with sentences.

#NeuralTalk Warning: Deprecated. Hi there, this code is now quite old and inefficient, and now deprecated. I am leaving it on Github for educational p

Andrej 5.3k Jan 07, 2023
LeafSnap replicated using deep neural networks to test accuracy compared to traditional computer vision methods.

Deep-Leafsnap Convolutional Neural Networks have become largely popular in image tasks such as image classification recently largely due to to Krizhev

Sujith Vishwajith 48 Nov 27, 2022
Code artifacts for the submission "Mind the Gap! A Study on the Transferability of Virtual vs Physical-world Testing of Autonomous Driving Systems"

Code Artifacts Code artifacts for the submission "Mind the Gap! A Study on the Transferability of Virtual vs Physical-world Testing of Autonomous Driv

Andrea Stocco 2 Aug 24, 2022
Official codebase for "B-Pref: Benchmarking Preference-BasedReinforcement Learning" contains scripts to reproduce experiments.

B-Pref Official codebase for B-Pref: Benchmarking Preference-BasedReinforcement Learning contains scripts to reproduce experiments. Install conda env

48 Dec 20, 2022
PyExplainer: A Local Rule-Based Model-Agnostic Technique (Explainable AI)

PyExplainer PyExplainer is a local rule-based model-agnostic technique for generating explanations (i.e., why a commit is predicted as defective) of J

AI Wizards for Software Management (AWSM) Research Group 14 Nov 13, 2022
Code for the paper Relation Prediction as an Auxiliary Training Objective for Improving Multi-Relational Graph Representations (AKBC 2021).

Relation Prediction as an Auxiliary Training Objective for Knowledge Base Completion This repo provides the code for the paper Relation Prediction as

Facebook Research 85 Jan 02, 2023
Contra is a lightweight, production ready Tensorflow alternative for solving time series prediction challenges with AI

Contra AI Engine A lightweight, production ready Tensorflow alternative developed by Styvio styvio.com » How to Use · Report Bug · Request Feature Tab

styvio 14 May 25, 2022
Source Code for DialogBERT: Discourse-Aware Response Generation via Learning to Recover and Rank Utterances (https://arxiv.org/pdf/2012.01775.pdf)

DialogBERT This is a PyTorch implementation of the DialogBERT model described in DialogBERT: Neural Response Generation via Hierarchical BERT with Dis

Xiaodong Gu 67 Jan 06, 2023
Learning Open-World Object Proposals without Learning to Classify

Learning Open-World Object Proposals without Learning to Classify Pytorch implementation for "Learning Open-World Object Proposals without Learning to

Dahun Kim 149 Dec 22, 2022
The codebase for Data-driven general-purpose voice activity detection.

Data driven GPVAD Repository for the work in TASLP 2021 Voice activity detection in the wild: A data-driven approach using teacher-student training. S

Heinrich Dinkel 75 Nov 27, 2022
On Evaluation Metrics for Graph Generative Models

On Evaluation Metrics for Graph Generative Models Authors: Rylee Thompson, Boris Knyazev, Elahe Ghalebi, Jungtaek Kim, Graham Taylor This is the offic

13 Jan 07, 2023
[CVPR 2022] Thin-Plate Spline Motion Model for Image Animation.

[CVPR2022] Thin-Plate Spline Motion Model for Image Animation Source code of the CVPR'2022 paper "Thin-Plate Spline Motion Model for Image Animation"

yoyo-nb 1.4k Dec 30, 2022
EMNLP 2021: Single-dataset Experts for Multi-dataset Question-Answering

MADE (Multi-Adapter Dataset Experts) This repository contains the implementation of MADE (Multi-adapter dataset experts), which is described in the pa

Princeton Natural Language Processing 68 Jul 18, 2022
a simple, efficient, and intuitive text editor

Oxygen beta a simple, efficient, and intuitive text editor Overview oxygen is a simple, efficient, and intuitive text editor designed as more featured

Aarush Gupta 1 Feb 23, 2022
Performance Analysis of Multi-user NOMA Wireless-Powered mMTC Networks: A Stochastic Geometry Approach

Performance Analysis of Multi-user NOMA Wireless-Powered mMTC Networks: A Stochastic Geometry Approach Thanh Luan Nguyen, Tri Nhu Do, Georges Kaddoum

Thanh Luan Nguyen 2 Oct 10, 2022
Reduce end to end training time from days to hours (or hours to minutes), and energy requirements/costs by an order of magnitude using coresets and data selection.

COResets and Data Subset selection Reduce end to end training time from days to hours (or hours to minutes), and energy requirements/costs by an order

decile-team 244 Jan 09, 2023