Hyperparameter Tuning with W&B Sweeps

Показать описание

A sample of what you'll learn while getting your MLOps certification from the *free* Weights & Biases course.

*About this sample:*
Hyperparameter tuning is an essential part of the machine learning process, as it can significantly impact the performance of your model. It can be a tedious and manual process, requiring the testing of various combinations and the tracking of results.

In this video, we demonstrate how to use Weights & Biases Sweeps to automate the hyperparameter tuning process.

With Weights & Biases Sweeps, you can define the hyperparameters you want to test and the range of values for each parameter. The platform will then automatically run a series of experiments, tracking the results in real-time and providing insights into the best performing combinations. This can save time and help you quickly find the optimal hyperparameters for your model. If you want to streamline your hyperparameter tuning process, be sure to watch this video.

Рекомендации по теме

Комментарии

Where is the next video? I want to learn more about using the data of different sweeps and how to manipulate and plot insightful graphs :(

SnipeSniperNEW

What if I want to just log my results to Weights & Biases. Do I still use sweeps? I usually use Optuna for my hyperparameter search. Can it work together?

mareks.

so multipul GPUs run different sweeps instead of many GPUs run a sweep together? and in that way it reduces time. am i correcet?

XiqqMa

Hyperparameter Tuning with W&B Sweeps

Hyperparameter Tuning with W&B Sweeps

🧹 Tune Hyperparameters Easily with W&B Sweeps

Hyperparameter Tuning and Visualization in Deep Learning - Lukas Biewald, Weights & Biases

MLOps20: Explore/Exploit - Hyper-parameter Tuning in Deep Learning

Tensor Programs V: Tuning Large Neural Networks via Zero-Shot Hyperparameter Transfer (μTransfer)

Log Your First Run With W&B

Tuning Large Neural Networks via Zero-Shot Hyperparameter Transfer

Track Your Keras Machine Learning Experiments with Weights & Biases

Hyperparameter Optimization for Reinforcement Learning using Meta’s Ax | DigiKey

Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters (Paper)

11-785 Deep Learning Recitation 0H: WandB

F23 Recitation 0P: WandB (Part 1)

Tech Talk: Model Tracking with Weights & Biases

Weights & Biases End-to-End Demo

#TWIMLfest: Deep Learning for Time Series in Industry

Ray + Weights & Biases: Build and deploy real-world ML models

W&B Models LLM fine-tuning end-to-end demo

Tuning GPT-3 on a Single GPU via Zero-Shot Hyperparameter Transfer

Topic 8a -- Introduction to Optimization

Alihan Zihna: A Review of Hyperparameter Optimization Frameworks on Python

Better tracking for your deep learning training - Wandb.ai (Weights & Biases)

MLOps Hands-on Workshop - Monitoring Model with Weights and Biases

DLRL Summer School 2021 - Online Hyperparameter Tuning - Kyunghyun Cho

ML #15 - Multiclass Machine Learning in healthcare.ai Using XGBoost