This library provides an abstraction to perform Model Versioning using Weight & Biases.

Last update: Jan 28, 2022

Overview

Description

This library provides an abstraction to perform Model Versioning using Weight & Biases.

Features

Version a new trained model
Promote a model to another stage (e.g production)

Example of usage

The following code snippet shows how to promote a newly trained model to the best_model on the validation set. First, it creates the model providing the checkpoint path, the artifact name and type, a description, and the metadata, where the metrics or any desired information is stored.

The comparision_type indicates how to compare the metrics between the two models. E.g if smaller, the smaller the metric from the new model the better

run = wandb.init(...)

# Create the desired artifact
artifact = versioner.create_artifact(
                            checkpoint='model.ckpt',
                            artifact_name='prueba',
                            artifact_type='model',
                            description='Prueba Wandb-MV',
                            metadata={
                                'val_metric': 78.0,
                                'test_metric': 0.0
                            })

# Promote the desired artifact to the 'best_model' tag
versioner.promote_model(new_model=artifact,
                        artifact_name='prueba',
                        artifact_type='model',
                        comparision_metric='val_metric',
                        promotion_alias='best_model',
                        comparision_type='smaller'
                       )

This code snippet shows how to promote a trained model to production after being validated on the test set. The already_deployed parameter indicates that the model is already logged, so it only needs to be updated.

versioner = Versioner(run)
versioner.promote_model(new_model=artifact,
                        artifact_name='prueba',
                        artifact_type='model',
                        comparision_metric='test_metric',
                        promotion_alias='production',
                        comparision_type='smaller',
                        already_deployed=True
                        )

Next features

Allow providing a custom comparision function to promote a model
...

This library provides an abstraction to perform Model Versioning using Weight & Biases.

Related tags

Overview

Description

Features

Example of usage

Next features

Owner

Hector Lopez Almazan

A Real-Time-Strategy game for Deep Learning research

DeepConsensus uses gap-aware sequence transformers to correct errors in Pacific Biosciences (PacBio) Circular Consensus Sequencing (CCS) data.

Learning Features with Parameter-Free Layers (ICLR 2022)

Regularizing Nighttime Weirdness: Efficient Self-supervised Monocular Depth Estimation in the Dark (ICCV 2021)

Official implementation of the paper Chunked Autoregressive GAN for Conditional Waveform Synthesis

Ensemble Visual-Inertial Odometry (EnVIO)

RAANet: Range-Aware Attention Network for LiDAR-based 3D Object Detection with Auxiliary Density Level Estimation

Gluon CV Toolkit

SiT: Self-supervised vIsion Transformer

Code for paper "ASAP-Net: Attention and Structure Aware Point Cloud Sequence Segmentation"

On Evaluation Metrics for Graph Generative Models

HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis

Doosan robotic arm, simulation, control, visualization in Gazebo and ROS2 for Reinforcement Learning.

Official implementation of Unfolded Deep Kernel Estimation for Blind Image Super-resolution.

VISSL is FAIR's library of extensible, modular and scalable components for SOTA Self-Supervised Learning with images.

QuakeLabeler is a Python package to create and manage your seismic training data, processes, and visualization in a single place — so you can focus on building the next big thing.

DABO: Data Augmentation with Bilevel Optimization

CLEAR algorithm for multi-view data association

The Face Mask recognition system uses AI technology to detect the person with or without a mask.

rliable is an open-source Python library for reliable evaluation, even with a handful of runs, on reinforcement learning and machine learnings benchmarks.