Organizing ML projects — W&B walkthrough (2020)

Weights & Biases · Intermediate ·📐 ML Fundamentals ·6y ago
Skills: ML Pipelines80%

Key Takeaways

The video demonstrates how to organize machine learning projects using Weights & Biases, showcasing features such as auto-generated visualizations, customizable plots, and project page workspace management. It covers creating sections, editing charts, and using custom visualizations like parallel coordinates charts.

Full Transcript

here's a quick overview of how to use the project page workspace we auto-populate line charts for all the metrics you're logging and you can edit settings on each of those charts individually or you can use the settings up here to edit them all at once if you put prefixes in your metric names will automatically create sections for you I'm creating a new section and renaming it top graphs I can drag graphs to that section or going to the next page if I want to move accuracy I can either drag or click the move button and select the destination now I have a couple of charts that I'd like to resize and if I click the lower corner and drag both charts will resize at once the second one got sent to the second page here so I can resize the section or I can switch to a custom layout that lets me resize each graph individually and organize it exactly how I want it now I'd like to save this section as a report that gives me a snapshot of the graphs and the run sets so I can get back to it later going back to my workspace I'll show you how to delete an undelete charts I'm clicking delete in the upper right corner or I can delete the whole section at once to get it back I go to hidden panels and I can drag any of these graphs into the sections above to restore them I can also clear my workspace in the lower left-hand corner to reset to my original layout we also have a variety of custom visualizations here's one of my favorites the parallel coordinates chart each line here is a different run and I can see how hyper parameters affect the output metric flipping the axes solo losses at the top I'm gonna change the gradient to different color and click OK to save now that looks a little small in this section I'll create a new section and drag it in so I can resize it separately I'll switch this section to custom layout so I can have panels of different sizes and I'll add a run compare this lets me see a table of all of my runs and parameters last all out a parameter importance panel this panel lets me see how different hyper parameters affect my output metrics I'll switch to accuracy and it looks like learning rate is pretty important learn about more cool features at Docs WMV com

Original Description

Follow along with a free account: https://wandb.ai Learn how to organize visualizations in your project page workspace. Manage machine learning projects easily with beautiful auto-generated visualizations and customizable plots. Learn more about Weights & Biases: - Create a free account: https://app.wandb.ai/ - View documentation: https://docs.wandb.com/ - Quickstart PyTorch example: http://wandb.me/pytorch-intro Weights & Biases is building a set of developer tools for machine learning. Use the Dashboard to track experiments, compare, and visualize results in a central place where your whole team can collaborate.
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Playlist

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1 0. What is machine learning?
0. What is machine learning?
Weights & Biases
 2 1. Build Your First Machine Learning Model
1. Build Your First Machine Learning Model
Weights & Biases
 3 Intro to ML: Course Overview
Intro to ML: Course Overview
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 4 2. Multi-Layer Perceptrons
2. Multi-Layer Perceptrons
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 5 3. Convolutional Neural Networks
3. Convolutional Neural Networks
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 6 Weights & Biases at OpenAI
Weights & Biases at OpenAI
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 7 Why Experiment Tracking is Crucial to OpenAI
Why Experiment Tracking is Crucial to OpenAI
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 8 4. Autoencoders
4. Autoencoders
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 9 5. Sentiment Analysis
5. Sentiment Analysis
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 10 6. Recurrent Neural Networks [RNNs]
6. Recurrent Neural Networks [RNNs]
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 11 7. Text Generation using LSTMs and GRUs
7. Text Generation using LSTMs and GRUs
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 12 8. Text Classification Using Convolutional Neural Networks
8. Text Classification Using Convolutional Neural Networks
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 13 9. Hybrid LSTMs [Long Short-Term Memory]
9. Hybrid LSTMs [Long Short-Term Memory]
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 14 Toyota Research Institute on Experiment Tracking with Weights & Biases
Toyota Research Institute on Experiment Tracking with Weights & Biases
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 15 Weights and Biases - Developer Tools for Deep Learning
Weights and Biases - Developer Tools for Deep Learning
Weights & Biases
 16 Introducing Weights & Biases
Introducing Weights & Biases
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 17 10. Seq2Seq Models
10. Seq2Seq Models
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 18 11. Transfer Learning for Domain-Specific Image Classification with Small Datasets
11. Transfer Learning for Domain-Specific Image Classification with Small Datasets
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 19 12. One-shot learning for teaching neural networks to classify objects never seen before
12. One-shot learning for teaching neural networks to classify objects never seen before
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 20 13. Speech Recognition with Convolutional Neural Networks in Keras/TensorFlow
13. Speech Recognition with Convolutional Neural Networks in Keras/TensorFlow
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 21 14. Data Augmentation | Keras
14. Data Augmentation | Keras
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 22 15. Batch Size and Learning Rate in CNNs
15. Batch Size and Learning Rate in CNNs
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 23 Applied Deep Learning Fellowship Overview and Project Selection with Josh Tobin (2019)
Applied Deep Learning Fellowship Overview and Project Selection with Josh Tobin (2019)
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 24 Grading Rubric for AI Applications with Sergey Karayev (2019)
Grading Rubric for AI Applications with Sergey Karayev (2019)
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 25 16. Video Frame Prediction using CNNs and LSTMs (2019)
16. Video Frame Prediction using CNNs and LSTMs (2019)
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 26 Image to LaTeX - Applied Deep Learning Fellowship (2019)
Image to LaTeX - Applied Deep Learning Fellowship (2019)
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 27 17. Build and Deploy an Emotion Classifier (2019)
17. Build and Deploy an Emotion Classifier (2019)
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 28 Applied Deep Learning - Data Management with Josh Tobin (2019)
Applied Deep Learning - Data Management with Josh Tobin (2019)
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 29 Snorkel: Programming Training Data with Paroma Varma of Stanford University (2019)
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 30 Applied Deep Learning - Troubleshooting and Debugging with Josh Tobin (2019)
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 31 Troubleshooting and Iterating ML Models with Lee Redden (2019)
Troubleshooting and Iterating ML Models with Lee Redden (2019)
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 32 Designing a Machine Learning Project with Neal Khosla (2019)
Designing a Machine Learning Project with Neal Khosla (2019)
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 33 Lukas Beiwald on ML Tools and Experiment Management (2019)
Lukas Beiwald on ML Tools and Experiment Management (2019)
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 34 Building Machine Learning Teams with Josh Tobin (2019)
Building Machine Learning Teams with Josh Tobin (2019)
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 35 Pieter Abeel on Potential Deep Learning Research Directions (2019)
Pieter Abeel on Potential Deep Learning Research Directions (2019)
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 36 Testing and Deployment of Deep Learning Models with Josh Tobin (2019)
Testing and Deployment of Deep Learning Models with Josh Tobin (2019)
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 37 Five Lessons for Team-Oriented Research with Peter Welder (2019)
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 38 Applied Deep Learning - Rosanne Liu on AI Research (2019)
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 39 Making the Mid-career Leap from Urban Design to Deep Learning/Data Science
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 ▶ Organizing ML projects — W&B walkthrough (2020)
Organizing ML projects — W&B walkthrough (2020)
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 41 Brandon Rohrer — Machine Learning in Production for Robots
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 42 Nicolas Koumchatzky — Machine Learning in Production for Self-Driving Cars
Nicolas Koumchatzky — Machine Learning in Production for Self-Driving Cars
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 43 My experiments with Reinforcement Learning with Jariullah Safi
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 44 Applications of Machine Learning to COVID-19 Research with Isaac Godfried
Applications of Machine Learning to COVID-19 Research with Isaac Godfried
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 45 Testing Machine Learning Models with Eric Schles
Testing Machine Learning Models with Eric Schles
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 46 How Linear Algebra is not like Algebra with Charles Frye
How Linear Algebra is not like Algebra with Charles Frye
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 47 Predicting Protein Structures using Deep Learning with Jonathan King
Predicting Protein Structures using Deep Learning with Jonathan King
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 48 Rachael Tatman — Conversational AI and Linguistics
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 49 Reformer by Han Lee
Reformer by Han Lee
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 50 Sequence Models with Pujaa Rajan
Sequence Models with Pujaa Rajan
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 51 GitHub Actions & Machine Learning Workflows with Hamel Husain
GitHub Actions & Machine Learning Workflows with Hamel Husain
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 52 Look Mom, No Indices! Vector Calculus with the Fréchet Derivative by Charles Frye
Look Mom, No Indices! Vector Calculus with the Fréchet Derivative by Charles Frye
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 53 Jack Clark — Building Trustworthy AI Systems
Jack Clark — Building Trustworthy AI Systems
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 54 Surprising Utility of Surprise: Why ML Uses Negative Log Probabilities - Charles Frye
Surprising Utility of Surprise: Why ML Uses Negative Log Probabilities - Charles Frye
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 55 Track your machine learning experiments locally, with W&B Local - Chris Van Pelt
Track your machine learning experiments locally, with W&B Local - Chris Van Pelt
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 56 Antipatterns in open source research code with Jariullah Safi
Antipatterns in open source research code with Jariullah Safi
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 57 Attention for time series forecasting & COVID predictions - Isaac Godfried
Attention for time series forecasting & COVID predictions - Isaac Godfried
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 58 Made with ML - Goku Mohandas
Made with ML - Goku Mohandas
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 59 Angela & Danielle — Designing ML Models for Millions of Consumer Robots
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 60 Deep Learning Salon by Weights & Biases
Deep Learning Salon by Weights & Biases
Weights & Biases

Learn how to organize machine learning projects using Weights & Biases, including creating sections, editing charts, and using custom visualizations. Discover how to manage hyperparameters and analyze output metrics.

Key Takeaways
  1. Create a new section in the project page workspace
  2. Edit chart settings individually or in bulk
  3. Use prefixes in metric names to auto-create sections
  4. Drag and drop charts to organize the workspace
  5. Resize charts and sections as needed
  6. Save a section as a report for later reference
  7. Delete and undelete charts as needed
  8. Use custom visualizations like parallel coordinates charts
💡 Using a tool like Weights & Biases can significantly simplify the organization and analysis of machine learning projects, allowing for more efficient hyperparameter tuning and model comparison.

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