Inside TensorFlow: MLIR for TF developers

TensorFlow · Intermediate ·📐 ML Fundamentals ·6y ago

Skills: ML Pipelines80%LLM Engineering60%

Key Takeaways

The TensorFlow team discusses MLIR, a multi-level intermediate representation compiler infrastructure, and its applications for TensorFlow developers, covering technical deep dives and internal training sessions.

Original Description

Take an inside look into the TensorFlow team’s own internal training sessions--technical deep dives into TensorFlow by the very people who are building it. On this episode of Inside TensorFlow, Software Engineer Jacques Pienaar discusses MLIR: multi-level intermediate representation compiler infrastructure for TensorFlow developers. Let us know what you think about this presentation in the comments below! Links: MLIR on GitHub → https://goo.gle/2NM2AcV MLIR SIG → https://goo.gle/2OfQZBU TF-Lite Converter → https://goo.gle/2CLyekx Watch more from Inside TensorFlow Playlist → https://goo.gle/31Ge5GF Subscribe to the TensorFlow channel → https://goo.gle/TensorFlow

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The TensorFlow YouTube Channel is Here!

The TensorFlow YouTube Channel is Here!

Answering Your TF Questions #AskTensorFlow

Answering Your TF Questions #AskTensorFlow

Chatting With the TensorFlow Community (TensorFlow Meets)

Chatting With the TensorFlow Community (TensorFlow Meets)

All About TensorFlow Code (Coding TensorFlow)

All About TensorFlow Code (Coding TensorFlow)

TensorFlow: an ML platform for solving impactful and challenging problems

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Keynote (TensorFlow Dev Summit 2018)

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tf.data: Fast, flexible, and easy-to-use input pipelines (TensorFlow Dev Summit 2018)

tf.data: Fast, flexible, and easy-to-use input pipelines (TensorFlow Dev Summit 2018)

Eager Execution (TensorFlow Dev Summit 2018)

Eager Execution (TensorFlow Dev Summit 2018)

Machine Learning in JavaScript (TensorFlow Dev Summit 2018)

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Training Performance: A user’s guide to converge faster (TensorFlow Dev Summit 2018)

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The Practitioner's Guide with TF High Level APIs (TensorFlow Dev Summit 2018)

The Practitioner's Guide with TF High Level APIs (TensorFlow Dev Summit 2018)

Distributed TensorFlow (TensorFlow Dev Summit 2018)

Distributed TensorFlow (TensorFlow Dev Summit 2018)

Debugging TensorFlow with TensorBoard plugins (TensorFlow Dev Summit 2018)

Debugging TensorFlow with TensorBoard plugins (TensorFlow Dev Summit 2018)

TensorFlow Lite (TensorFlow Dev Summit 2018)

TensorFlow Lite (TensorFlow Dev Summit 2018)

Searching Over Ideas (TensorFlow Dev Summit 2018)

Searching Over Ideas (TensorFlow Dev Summit 2018)

Reconstructing Fusion Plasmas (TensorFlow Dev Summit 2018)

Reconstructing Fusion Plasmas (TensorFlow Dev Summit 2018)

Nucleus: TensorFlow toolkit for Genomics (TensorFlow Dev Summit 2018)

Nucleus: TensorFlow toolkit for Genomics (TensorFlow Dev Summit 2018)

Open Source Collaboration (TensorFlow Dev Summit 2018)

Open Source Collaboration (TensorFlow Dev Summit 2018)

Swift for TensorFlow - TFiwS (TensorFlow Dev Summit 2018)

Swift for TensorFlow - TFiwS (TensorFlow Dev Summit 2018)

TensorFlow Hub (TensorFlow Dev Summit 2018)

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Applied AI at The Coca-Cola Company (TensorFlow Dev Summit 2018)

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Real-World Robot Learning (TensorFlow Dev Summit 2018)

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TensorFlow Extended (TFX) (TensorFlow Dev Summit 2018)

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Project Magenta (TensorFlow Dev Summit 2018)

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TensorFlow Dev Summit 2018 - Livestream

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Introducing TensorFlow Lite (Coding TensorFlow)

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TensorFlow Dev Summit 2018 Highlights

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Jeff Dean, Head of AI at Google discusses the impact of ML (TensorFlow Meets)

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TensorFlow Mobile vs. TF Lite and More! #AskTensorFlow

TensorFlow Mobile vs. TF Lite and More! #AskTensorFlow

Using TensorFlow to enable research & production across many fields (TensorFlow Meets)

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Teaching TensorFlow for Deep Learning at Stanford University (TensorFlow Meets)

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TensorFlow Lite for Android (Coding TensorFlow)

TensorFlow Lite for Android (Coding TensorFlow)

Using the tf.data API to build input pipelines (TensorFlow Meets)

Using the tf.data API to build input pipelines (TensorFlow Meets)

Training Models in the Cloud & the Benefits of AI Toolkits #AskTensorFlow

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Execute operations immediately with TensorFlow's Eager Execution (TensorFlow Meets)

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TensorFlow Lite for iOS (Coding TensorFlow)

TensorFlow Lite for iOS (Coding TensorFlow)

Get started with TensorFlow's High-Level APIs (Google I/O '18)

Get started with TensorFlow's High-Level APIs (Google I/O '18)

TensorFlow for JavaScript (Google I/O '18)

TensorFlow for JavaScript (Google I/O '18)

TensorFlow in production: TF Extended, TF Hub, and TF Serving (Google I/O '18)

TensorFlow in production: TF Extended, TF Hub, and TF Serving (Google I/O '18)

Get started with TensorFlow's High-Level APIs in 5 mins | Google I/O 2018

Get started with TensorFlow's High-Level APIs in 5 mins | Google I/O 2018

TensorFlow and deep reinforcement learning, without a PhD (Google I/O '18)

TensorFlow and deep reinforcement learning, without a PhD (Google I/O '18)

TensorFlow Lite for mobile developers (Google I/O '18)

TensorFlow Lite for mobile developers (Google I/O '18)

Advances in machine learning and TensorFlow (Google I/O '18)

Advances in machine learning and TensorFlow (Google I/O '18)

Distributed TensorFlow training (Google I/O '18)

Distributed TensorFlow training (Google I/O '18)

Classification using neural networks & ML regression models #AskTensorFlow

Classification using neural networks & ML regression models #AskTensorFlow

TensorFlow and Keras in R - Josh Gordon meets with J.J. Allaire (TensorFlow Meets)

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Focus on your experiment with TensorFlow Estimators (TensorFlow Meets)

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How to get started with AI/ML, retraining models, & more! #AskTensorFlow

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TensorFlow - the deep learning solution for mobile platforms (TensorFlow Meets)

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MiniGo: TensorFlow Meets Andrew Jackson (TensorFlow Meets)

MiniGo: TensorFlow Meets Andrew Jackson (TensorFlow Meets)

The growth of TensorFlow with added support for JS & Swift (TensorFlow Meets)

The growth of TensorFlow with added support for JS & Swift (TensorFlow Meets)

At the intersection of TensorFlow & nuclear physics (TensorFlow Meets)

At the intersection of TensorFlow & nuclear physics (TensorFlow Meets)

NVidia TensorRT: high-performance deep learning inference accelerator (TensorFlow Meets)

NVidia TensorRT: high-performance deep learning inference accelerator (TensorFlow Meets)

Try TensorFlow.js in your browser (Coding TensorFlow)

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TensorFlow Hub: reusing machine learning modules (TensorFlow Meets)

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How to use TensorFlow in PyCharm (TensorFlow Tip of the Week)

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Training models faster with TensorFlow Hub (TensorFlow Meets)

Training models faster with TensorFlow Hub (TensorFlow Meets)

Prepare your dataset for machine learning (Coding TensorFlow)

Prepare your dataset for machine learning (Coding TensorFlow)

Using ML to predict insulin use for Type 1 Diabetes (TensorFlow Meets)

Using ML to predict insulin use for Type 1 Diabetes (TensorFlow Meets)

TFX: an end-to-end machine learning platform for TensorFlow (TensorFlow Meets)

TFX: an end-to-end machine learning platform for TensorFlow (TensorFlow Meets)

This video provides an in-depth look at MLIR, a compiler infrastructure for TensorFlow, and its applications for developers. The TensorFlow team shares their expertise and experience with MLIR, covering its benefits and use cases.

Key Takeaways

Explore MLIR on GitHub
Join the MLIR SIG
Use the TF-Lite Converter
Watch more Inside TensorFlow episodes
Subscribe to the TensorFlow channel

💡 MLIR is a powerful tool for optimizing and improving the performance of TensorFlow models, and its applications extend to LLM engineering and development.

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