Accelerating ML workflows: IBM x Weights & Biases

Weights & Biases · Advanced ·🛠️ AI Tools & Apps ·10mo ago

Key Takeaways

The video discusses how IBM partnered with Weights & Biases to accelerate their ML workflows, specifically in training state-of-the-art foundation models like IBM Granite, by utilizing Weights & Biases for parameter tuning and job restarting.

Full Transcript

[Music] We have very ambitious goals in IBM for for AI. It's going to change the way that we do our jobs. It's going to change the way that teams work together. Everybody's got an opportunity to participate in this change and to imagine how the future could be. Granite is IBM's series of models that are open source and that are optimized for enterprise use. We use weights and biases a lot for tuning the models before we set off on big runs. So we will have for for some of the larger models, we'll have runs that will run for weeks or even months. And there's a period of time when you're tuning the parameters where weights and bias is really invaluable for doing that work. One of the problems that you have a lot with training models is the job stop and then you want to be able to restart them. And it's actually it's actually a core component of our restart routine which means that the job can stop in the middle of the night the researcher can carry on sleeping and we'll restart the job automatically for them. And we actually use weights and biases to make sure we pick up in the right place for that. So I think we were surprised by weights and biases how much we could use it for actually controlling the jobs as well as just monitoring them. [Music]

Original Description

Training a state-of-the-art foundation model like IBM Granite is no small feat, and requires an intricate coordination of AI researchers working throughout a complex workflow. In this video, IBM leaders share how partnering with Weights & Biases enabled their teams to accelerate their ML workflows and improve the quality of their models -- all while improving their own quality of lives and work
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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
Weights & Biases
 4 2. Multi-Layer Perceptrons
2. Multi-Layer Perceptrons
Weights & Biases
 5 3. Convolutional Neural Networks
3. Convolutional Neural Networks
Weights & Biases
 6 Weights & Biases at OpenAI
Weights & Biases at OpenAI
Weights & Biases
 7 Why Experiment Tracking is Crucial to OpenAI
Why Experiment Tracking is Crucial to OpenAI
Weights & Biases
 8 4. Autoencoders
4. Autoencoders
Weights & Biases
 9 5. Sentiment Analysis
5. Sentiment Analysis
Weights & Biases
 10 6. Recurrent Neural Networks [RNNs]
6. Recurrent Neural Networks [RNNs]
Weights & Biases
 11 7. Text Generation using LSTMs and GRUs
7. Text Generation using LSTMs and GRUs
Weights & Biases
 12 8. Text Classification Using Convolutional Neural Networks
8. Text Classification Using Convolutional Neural Networks
Weights & Biases
 13 9. Hybrid LSTMs [Long Short-Term Memory]
9. Hybrid LSTMs [Long Short-Term Memory]
Weights & Biases
 14 Toyota Research Institute on Experiment Tracking with Weights & Biases
Toyota Research Institute on Experiment Tracking with Weights & Biases
Weights & Biases
 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
Weights & Biases
 17 10. Seq2Seq Models
10. Seq2Seq Models
Weights & Biases
 18 11. Transfer Learning for Domain-Specific Image Classification with Small Datasets
11. Transfer Learning for Domain-Specific Image Classification with Small Datasets
Weights & Biases
 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
Weights & Biases
 20 13. Speech Recognition with Convolutional Neural Networks in Keras/TensorFlow
13. Speech Recognition with Convolutional Neural Networks in Keras/TensorFlow
Weights & Biases
 21 14. Data Augmentation | Keras
14. Data Augmentation | Keras
Weights & Biases
 22 15. Batch Size and Learning Rate in CNNs
15. Batch Size and Learning Rate in CNNs
Weights & Biases
 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)
Weights & Biases
 24 Grading Rubric for AI Applications with Sergey Karayev (2019)
Grading Rubric for AI Applications with Sergey Karayev (2019)
Weights & Biases
 25 16. Video Frame Prediction using CNNs and LSTMs (2019)
16. Video Frame Prediction using CNNs and LSTMs (2019)
Weights & Biases
 26 Image to LaTeX - Applied Deep Learning Fellowship (2019)
Image to LaTeX - Applied Deep Learning Fellowship (2019)
Weights & Biases
 27 17. Build and Deploy an Emotion Classifier (2019)
17. Build and Deploy an Emotion Classifier (2019)
Weights & Biases
 28 Applied Deep Learning - Data Management with Josh Tobin (2019)
Applied Deep Learning - Data Management with Josh Tobin (2019)
Weights & Biases
 29 Snorkel: Programming Training Data with Paroma Varma of Stanford University (2019)
Snorkel: Programming Training Data with Paroma Varma of Stanford University (2019)
Weights & Biases
 30 Applied Deep Learning - Troubleshooting and Debugging with Josh Tobin (2019)
Applied Deep Learning - Troubleshooting and Debugging with Josh Tobin (2019)
Weights & Biases
 31 Troubleshooting and Iterating ML Models with Lee Redden (2019)
Troubleshooting and Iterating ML Models with Lee Redden (2019)
Weights & Biases
 32 Designing a Machine Learning Project with Neal Khosla (2019)
Designing a Machine Learning Project with Neal Khosla (2019)
Weights & Biases
 33 Lukas Beiwald on ML Tools and Experiment Management (2019)
Lukas Beiwald on ML Tools and Experiment Management (2019)
Weights & Biases
 34 Building Machine Learning Teams with Josh Tobin (2019)
Building Machine Learning Teams with Josh Tobin (2019)
Weights & Biases
 35 Pieter Abeel on Potential Deep Learning Research Directions (2019)
Pieter Abeel on Potential Deep Learning Research Directions (2019)
Weights & Biases
 36 Testing and Deployment of Deep Learning Models with Josh Tobin (2019)
Testing and Deployment of Deep Learning Models with Josh Tobin (2019)
Weights & Biases
 37 Five Lessons for Team-Oriented Research with Peter Welder (2019)
Five Lessons for Team-Oriented Research with Peter Welder (2019)
Weights & Biases
 38 Applied Deep Learning - Rosanne Liu on AI Research (2019)
Applied Deep Learning - Rosanne Liu on AI Research (2019)
Weights & Biases
 39 Making the Mid-career Leap from Urban Design to Deep Learning/Data Science
Making the Mid-career Leap from Urban Design to Deep Learning/Data Science
Weights & Biases
 40 Organizing ML projects — W&B walkthrough (2020)
Organizing ML projects — W&B walkthrough (2020)
Weights & Biases
 41 Brandon Rohrer — Machine Learning in Production for Robots
Brandon Rohrer — Machine Learning in Production for Robots
Weights & Biases
 42 Nicolas Koumchatzky — Machine Learning in Production for Self-Driving Cars
Nicolas Koumchatzky — Machine Learning in Production for Self-Driving Cars
Weights & Biases
 43 My experiments with Reinforcement Learning with Jariullah Safi
My experiments with Reinforcement Learning with Jariullah Safi
Weights & Biases
 44 Applications of Machine Learning to COVID-19 Research with Isaac Godfried
Applications of Machine Learning to COVID-19 Research with Isaac Godfried
Weights & Biases
 45 Testing Machine Learning Models with Eric Schles
Testing Machine Learning Models with Eric Schles
Weights & Biases
 46 How Linear Algebra is not like Algebra with Charles Frye
How Linear Algebra is not like Algebra with Charles Frye
Weights & Biases
 47 Predicting Protein Structures using Deep Learning with Jonathan King
Predicting Protein Structures using Deep Learning with Jonathan King
Weights & Biases
 48 Rachael Tatman — Conversational AI and Linguistics
Rachael Tatman — Conversational AI and Linguistics
Weights & Biases
 49 Reformer by Han Lee
Reformer by Han Lee
Weights & Biases
 50 Sequence Models with Pujaa Rajan
Sequence Models with Pujaa Rajan
Weights & Biases
 51 GitHub Actions & Machine Learning Workflows with Hamel Husain
GitHub Actions & Machine Learning Workflows with Hamel Husain
Weights & Biases
 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
Weights & Biases
 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
Weights & Biases
 56 Antipatterns in open source research code with Jariullah Safi
Antipatterns in open source research code with Jariullah Safi
Weights & Biases
 57 Attention for time series forecasting & COVID predictions - Isaac Godfried
Attention for time series forecasting & COVID predictions - Isaac Godfried
Weights & Biases
 58 Made with ML - Goku Mohandas
Made with ML - Goku Mohandas
Weights & Biases
 59 Angela & Danielle — Designing ML Models for Millions of Consumer Robots
Angela & Danielle — Designing ML Models for Millions of Consumer Robots
Weights & Biases
 60 Deep Learning Salon by Weights & Biases
Deep Learning Salon by Weights & Biases
Weights & Biases

The video showcases how IBM leveraged Weights & Biases to accelerate their ML workflows, enabling researchers to focus on high-level tasks while automating routine operations like job restarting. By utilizing Weights & Biases, IBM was able to optimize model training and streamline their workflow. This partnership demonstrates the potential for AI tools to transform the way teams work together and drive innovation.

Key Takeaways
  1. Identify areas for workflow optimization
  2. Implement Weights & Biases for parameter tuning
  3. Configure job restarting with Weights & Biases
  4. Monitor and analyze model training
  5. Integrate Weights & Biases with existing workflow
💡 Weights & Biases can be used not only for monitoring but also for controlling jobs, enabling automated restarting and optimizing model training

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