Tensorflow Ecosystem
•Download as PPTX, PDF•
0 likes•179 views
Presented at Tensorflow Everywhere Tamil Event
Recommended
Recommended
More Related Content
What's hot
What's hot (20)
Similar to Tensorflow Ecosystem
Similar to Tensorflow Ecosystem (20)
More from Vivek Raja P S
More from Vivek Raja P S (8)
Recently uploaded
Recently uploaded (20)
Tensorflow Ecosystem
- 1. TensorFlow Everywhere Tamil (in Tamil language) Presents TensorFlow Ecosystem Vivek Raja P S, Data Scientist @VivekRaja00 Vivek0712
- 3. 200M+ 52M+ 8.5M+ 9.7M 800K Downloads Tutorial and guide views Blog articles read & views on YouTube Learners on open online courses
- 4. Data Design tf.data TF Datasets Model Design Keras Estimators Training Distribution Strategy Tooling TensorBoard: Analysis XProf: Performance TFMA: Explainability TFI: Fairness Serialization SavedModel Model Repository TensorFlow Hub Cloud, On-prem TensorFlow Serving Android, iOS, Raspberry Pi TensorFlow Lite Browser and Node TensorFlow.js Other Language Bindings C, Java, Go, C#, Rust, R, ... Training Deployment CP U GP U TPU
- 5. TF 2.0 TF 2.1 TF 2.2 TensorFlow Ecosystem is committed to 2.x TF 2.3 ● Eager execution by default ● Tight integration with Keras ● TPU training support ● Performance improvements across both graph (tf.function) and eager execution ● New TF Profiler for CPU/GPU/TPU ● Experimental tf.data as a service ● Experimental Keras Preprocessing for data preprocessing ● Improved TF Profiler, stable TPUStrategy
- 6. Improvements to Distributed Training: ● Robust Multi-Worker Mirrored Strategy ● Experimental support for Parameter Server training TF now runs with CUDA 11 and CuDNN8 and uses TF-32 by default. Adds experimental support for a subset of numpy APIs `tf.experimental.numpy`. Lands major internal refactor of the Keras Functional API improving reliability, stability and performance. Mirrored variables performance improvements, enabled by default for TPUs, improving the startup time. 2.4
- 7. Empowering Responsible AI From research To production Deployed everywhere
- 8. Empowering Responsible AI From research To production Deployed everywhere
- 9. Real-time sign language detection model Google Research: Real-Time Sign Language Detection using Human Pose Estimation a Unified Text-to-Text Transformer Massively Scaling RL with SEED RL architecture Arxiv.org: SEED RL: Scalable and Efficient Deep-RL with Accelerated Central Inference Cutting edge ML research with TensorFlow 2.x RL with Quantum Variational Circuits Arxiv.org: Reinforcement Learning with Quantum Variational Circuits
- 10. Add-ons and extensions in the TF ecosystem ● TF Probability ● TF Graphics ● Mesh TensorFlow ● TF Model Garden ● TF Agents ● TF Text ● Swift for TensorFlow ● Sonnet ● Neural Structured Learning ● TF Quantum ● ...and more on tensorflow.org!
- 11. Research models migrating to TF 2.x Object Detection API TF Blog: TF 2 meets the OD API TF Recommenders (TFRS) TF Blog: Introducing TF Recommenders
- 12. TensorFlow Recommenders End-to-end recommender systems ● Built on TensorFlow 2.0 and Keras. ● Flexible, multi-objective retrieval and ranking models. ● Easy path to production via TensorFlow Serving and approximate nearest neighbour retrieval. 12 http://tensorflow.org/recommenders
- 13. Empowering Responsible AI Powered by the community From research To production Deployed everywhere
- 14. TensorFlow Cloud From Local Flexibility to Cloud Scalability Python package with APIs to go from local debugging to distributed Cloud training Minimal Setup - No changes to your model import tensorflow_cloud as tfc tfc.run()
- 15. TensorFlow 2.x supports TPUs +
- 16. Empowering Responsible AI From research To production Deployed everywhere
- 17. ●Lower latency ●Network connectivity ●Privacy preserving
- 18. TensorFlow.js : Train + Deploy ML Models to the Browser
- 19. Latest on-device ML with TensorFlow Lite Hold For Me on new Pixel 5/4a OCR translation in Google Translate HDR+ on Pixel 4a
- 20. Improvements to TensorFlow Lite Conversion Performance Model Optimization High-level APIs Improve conversion - LSTM/RNN MLIR based converter Reduced binary size when using TF Ops in TFLite Faster CPU inference with XNN pack Faster GPU inference with OpenCL Memory optimization - shared buffers Core ML Delegate for iOS Keras quantization aware training Post training Dynamic Range Quant LSTM Quantization Weight Clustering TF Lite task library - easier inference Model Maker - model customization Newer SOTA models and ref apps Codegen - studio model import
- 21. Empowering Responsible AI From research To production Deployed everywhere
- 22. What is responsible AI?
- 23. Define Problem Construct and Prepare Data Build and Train Model Deploy Iterate How is my model performing? Am I using a representative dataset? Who is my ML system for? What can I do to improve the model? Where do I get relevant features in a privacy preserving way? How does my data affect model performance? Should I deploy my model? Are there any privacy considerations? Is there real-world / human bias in my data? Are test users diverse? Are there complex feedback loops?
- 24. TF Privacy: Introducing new Attacks and Partnerships
- 26. Responsible AI with TensorFlow Toolkit tensorflow.org/resources/responsible-ai
- 27. From research Empowering Responsible AI Powered by the community To production Deployed everywhere
- 28. Collaborative ML with TensorBoard.dev
- 29. Collaborative ML with TensorBoard.dev https://twitter.com/zacharynado/status/1276252206967160834
- 30. Jupyter notebook community translation support (currently 13 languages) Translation of guides, tutorials and other documentation into Japanese, Korean, and Simplified Chinese (with more to come!) Translation and Localization
- 31. TensorFlow User Groups Get Involved 80 TFUGs around the globe ● Grassroot communities ● For example, TFUG India Summit & Tensorflow Everywhere India was a huge success! Interested in creating one? Email tfug- help@tensorflow.org
- 32. Google Developer Experts Get Involved 165 ML GDES Globally ● In 2020, they gave 961 techtalk/workshops and 620 videos/articles ● Great collaboration projects like Background Stylizer and AI vs COVID,
- 33. Topics: ● Basic ML programming concepts in TensorFlow ● Text Classification ● Computer Vision ● Sequences and Prediction *Stipends available TensorFlow.org/certificate Education Certificate in TensorFlow Development Congrats to our 1K developers from 50+ countries who have passed!
- 34. நன்றி
Editor's Notes
- Hi everyone. Welcome to the 2020 TensorFlow Everywhere event. I’m [Name], [Title]. Thanks for tuning in. TensorFlow Everywhere is a series of global events led by TensorFlow and machine learning communities around the world. We are dropping into these events around the world over the next few weeks, and while we can’t meet in person, we’re hoping these virtual events are more accessible than before. Since the TFDS in March, the last time our global community came together virtually, a lot has happened... A global pandemic A resurfaced fight for racial justice is happening around the world Many of us are experiencing changing personal circumstances And a different way of working.
- In times of uncertainty, it’s core to who we are to turn to technology, like machine learning, to help. We have found comfort in knowing we are part of an important infrastructure for the world that has the ability to help others. For example, within Google during these times, we’ve seen how TensorFlow played a timely role in supporting Google Meet, our video conferencing software - you can see it being used as an education platform in the bottom middle photo. TensorFlow helps the software adapt to changing bandwidth and enhances picture quality. Since making Meet's advanced features free for all G Suite and G Suite for Education users in March, we've seen daily usage grow by 30x, with Meet hosting 3 billion minutes of video meetings daily (blog post with more details here). This is just one example of how TensorFlow is helping us adapt and support new forms of communication and collaboration.
- For TensorFlow, we are seeing unprecedented scale and growth. TensorFlow crossed 100 million downloads with over 10 million in May (or June?) alone. 45M tutorial and guide views More than 8M articles read on the TF blog and 8.1M views on YT And 500K learnings on Coursera and Udacity This is a testament to the community, so we want to say thank you for continuing to support TensorFlow. We have some exciting new updates, but first we want to recap what we launched the last time we got together at the TensorFlow Dev Summit.
- TensorFlow evolved from a single library to a full ecosystem of tools: Everything you need to build a model: Data Design, Model Design, Distribution Strategy, Tooling A model store And everything you need to deploy a model, anywhere
- At the heart of our Ecosystem, TF 2.x is our new API, which we built following your feedback. You needed a simpler, easier way to build your ML systems. We launched TF2.x a year ago, and we’ve been hard at work getting it to cover all the features you need 2.0 was about eager execution, and offering a simpler high-level API with Keras The 2.1 brought TPU training with same code, 2.2 brought significant performance wins, and 2.3 focused on better data and performance tooling.
- And Just last week, we released version 2.4, which builds on this momentum. The major focus in this release is on distributed training... It will make multi-worker synchronous training with mirrored strategy more robust against deadlocks. There will also be experimental support for parameter server training in tf.keras. Plus there are more fixes and improved documentation for better usability. TensorFlow 2.4 will also add support for CUDA11, allowing support for NVIDIA’s latest GPUs. And lastly there is tf.experimental.numpy: which will add support for accelerating some numpy ops using TensorFlow runtime, and allow you to use TensorFlow APIs in your numpy code.
- One of the advantages of the TensorFlow ecosystem is that it’s flexible enough to meet different needs. If you’re a researcher, it gives you the control and flexibility for experimentation, For applied ML engineers or data scientists, you get tools that help your models have real-world impact. Finally, there are libraries in the ecosystem that can help create better AI experiences no matter where they are running. All of this is underscored by our common goal of building AI responsibly. I want to now pass it to Sarah who’s going to show you how the TF ecosystem in 2.x is touching upon these areas.
- Thanks Kemal, I’m Sarah and I am an eng. lead on TensorFlow Like Kemal mentioned earlier, the TF ecosystem serves the needs of diverse users and I’d like to spend a few minutes talking about each of them today. Let’s start with researchers, a segment that we care deeply about. NEXT_SLIDE
- TensorFlow’s beginnings were as a research-focused framework. And it continues to support cutting edge research today. You can see here some of the latest publications that were published using 2.x - including a new real-time sign language detection model and reinforcement learning using Quantum circuits. NEXT_SLIDE
- The TF ecosystem is a rich collection of components, add-ons and extensions and it is no different for research. We support several of these libraries to support the specialized needs of research. Many of these were developed by researchers, for researchers. NEXT_SLIDE SKIP Libraries like TF Probability and TF Agents work with 2.x and we’re working across multiple teams to make sure the rest of the extensions are compatible with 2.x.
- With 2.x, the experience has gotten better, and we have gotten feedback that it address a lot of the usability needs of researchers. And we continue to focus on compatibility across the ecosystem - including APIs, libraries and models. We’re seeing that about 57% of XManager Python jobs now support TF 2.x, which we’re excited about. In 2021, making the migration process easier is something that we will focus on. NEXT_SLIDE
- TensorFlow Recommenders (TFRS), an open-source TensorFlow package that makes building, evaluating, and serving sophisticated recommender models easy. Built with TensorFlow 2.x, TFRS makes it possible to: Build and evaluate flexible candidate nomination models; Freely incorporate item, user, and context information into recommendation models; Train multi-task models that jointly optimize multiple recommendation objectives; Efficiently serve the resulting models using TensorFlow Serving.
- Moving on production users. TF remains the mainstay of production deployment across Google and is powering the production ML needs of all our PAs and developers. NEXT_SLIDE
- TensorFlow Cloud is a Python package that provides APIs for a seamless transition from local debugging to distributed training in Google Cloud. It simplifies the process of training TensorFlow models on the cloud into a single, simple function call, requiring minimal setup and no changes to your model. TensorFlow Cloud handles cloud-specific tasks such as creating VM instances and distribution strategies for your models automatically. This guide will demonstrate how to interface with Google Cloud through TensorFlow Cloud, and the wide range of functionality provided within TensorFlow Cloud. We'll start with the simplest use-case. Not going to work with every model for every time.
- In terms of hardwarre TPUs are pushing the frontier of what is possible in terms of ML compute. and users at Google are able to realize their benefits via TensorFlow. We continue to focus on making TPUs easier to use as well as more scalable to meet the demands of our products and users. NEXT_SLIDE
- Now, Cross-platform readiness and capabilities are a core part of TF. and tf makes it possible and easy to deploy your production model to edge devices or browser and these solutions are already running on billions of devices. NEXT_SLIDE
- However, running on different platforms and devices requires operating in unique constraints, like low latency environments, working with poor network connectivity, all while trying to preserve privacy. Libraries like TensorFlow.js and TensorFlow Lite are built to keep these constraints in mind. NEXT_SLIDE
- TFJS is a library used for training and deploying models in the browser. -- Be sure to mention electron NEXT_SLIDE
- Next, let’s talk about on-device. TensorFlow Lite is powering machine learrning on mobile and embedded devices across Google. It’s used in pretty much every on-device ML feature in large products across Google and is the industry leader across several performance dimensions. Just the other week, we announced the new Pixel 4a with new AI features like “Hold For Me” and HDR+ which run w/TFLite. NEXT_SLIDE
- Later today, you’ll hear from Arun about improvements we’re making to TF Lite, for better usability, performance and model optimization gains, all based off of internal and external feedback. Also, we have a full day of talks for on-device ML at Google set up for tomorrow, please check those out if you are interested. NEXT_SLIDE
- Whether you’re a researcher or an applied ML engineer, one thing that we all have in common is the responsibility that comes with building a machine learning system. I’d like to quickly talk about how the TensorFlow ecosystem is helping to empower all users to build AI responsibly and the tools and resources that are available. NEXT_SLIDE
- Responsible AI is a set of principles and practices that help guide developers in building AI for everyone. It touches upon areas like fairness, interpretability, privacy, and security, and making sure we keep these in mind when we build AI system.
- A typical ML workflow will have several stages. We think Responsible AI can be incorporated into each one of them. To do this, you can ask key questions to ensure your AI system is inclusive and secure. For example, asking ‘Who is my system for? Or are there any privacy considerations when collecting my data?’ are important because fairness and privacy concerns may be experienced differently in different applications.
- And tackling these issues is not a one-size-fits-all task. There are several tools and resources to help. For example, TF Privacy’s new functionality allows you to assess your model’s privacy. Training with privacy is not always possible. The new functionality allows you to measure a model’s privacy without having to retrain it.
- Another tool example is the Model Card Toolkit, which can automate transparency reporting for models. Model cards are a framework for sharing the essential facts of a machine learning model in a structured, accessible way. Building a Model Card is time intensive & requires specific expertise -- the Model Card Toolkit facilitates this process by auto-generating key Model Card components using existing pipeline artifacts and displays them in a standardized UI.
- To help make things easier, we launched the Responsible AI with TensorFlow Toolkit in June. This toolkit packages all of the Responsible AI principles, tools, and resources in the TF ecosystem into a single destination on tensorflow.org.
- I want to end on talking about the most important part of what we are building, and that’s the machine learning community. And I want to begin by thanking all of YOU - your feedback, your contributions, what you build, this is what makes all of this possible.
- We understand that ML is often a collaborative experience. We built TensorBoard.dev to enable anyone to easily upload their TensorBoard logs and get a permalink that can be shared in blog posts, papers, and on social media. Since launch last year, we have added more of TensorBoard’s dashboards, the ability to download metrics data as Pandas DataFrames, adding experiment metadata like title and description, and more.
- We’re still at an early stage, but we’re excited to see how researchers are using TensorBoard.dev, like showing the 550 curves that don’t fit in a paper, or provide access to the raw metrics so that others can reproduce the charts in your paper and explore them further.
- To support these product updates and make TensorFlow more accessible for everyone, we’re excited to an update on translation. Through a new partnership with GitLocalize, we have improved the current process/contribute experience for community translators by adding Jupyter notebook support to our tooling. Now it's easier than ever for the community to contribute translations for our runnable documentation. Currently 13 languages have community contributed docs: Arabic, Greek, Spanish, French, Indonesian, Italian, Japanese, Korean, Portugese, Russian, Turkish, Vietnamese, and Simplified Chinese. And we are also supplementing the number of translations. We completed the translation of our guides, tutorials and other documentation into Japanese, Korean, and Simplified Chinese with more to follow! We hope this improves the product experience for our global practitioners.
- One great way to connect is to join a TensorFlow User Group. These grassroots communities started organically, and we now have 80 of them globally. Even with pandemic situation in 2020, our communities remained strong. For example the Indian TFUGs hosted a 4-day, 20- session event covering wide range of areas including mobile, web, production and research. I’m sure this map can have a lot more dots! If you want to start a user group, please reach out and we’ll help you get started!
- We love our 165 GDEs. They give techtalks, run workshops, shoot videos and write articles for developers worldwide. In 2020 in particular, the GDEs expanded their effort toward development project that the Background Stylizer won TF community spotlight award and the AI vs COVID project released very large COVID related dataset.
- Speaking of the certificate program, we launched this at the Dev Summit in March and have had over 1,000 people spanning 53 countries pass the exam! This exam created by the TensorFlow team, covering topics such as: Text classification: using NLP to build spam filters Computer vision: using CNNs to do image recognition Sequences and Prediction By passing this foundational certification, you’ll be able to share your expertise with the world, and display your certificate badge on Linkedin, GitHub, or the TensorFlow Certificate Network. And to widen access to people of diverse backgrounds and experiences, we're excited to offer a limited number of stipends to help cover the certificate costs.