PaLM API & MakerSuite: an approachable way to start prototyping and building generative AI applications

Posted by Scott Huffman, Vice President, Engineering and Josh Woodward, Senior Director, Product Management

We’re seeing a new wave of generative AI applications that are transforming the way people interact with technology – from games and dialog agents to creative brainstorming and coding tools. At Google, we want to continue making AI accessible by empowering all developers to start building the next generation of applications with generative AI by providing easy-to-use APIs and tools.

Earlier today, we announced the PaLM API, a new developer offering that makes it easy and safe to experiment with Google’s large language models. Alongside the API, we’re releasing MakerSuite, a tool that lets developers start prototyping quickly and easily. We’ll be making these tools available to select developers through a Private Preview, and stay tuned for our waitlist soon.

Access Google’s large language models using the PaLM API

The PaLM API is a simple entry point for Google’s large language models, which can be used for a variety of applications. It will provide developers access to models that are optimized for multi-turn use cases, such as content generation and chat, and general purpose models that are optimized for use cases such as summarization, classification, and more. Starting today, we’re making an efficient model available in terms of size and capabilities, and we’ll add other models and sizes soon.

Start building quickly

We’ve spent the last several years building and deploying large language models—from bringing MUM to Search to exploring applications with LaMDA in the AI Test Kitchen. We learned a lot about generative AI development workflows and how fragmented they can be. Developers have to use different tools to accomplish tasks like crafting and iterating on a prompt, generating synthetic data, and tuning a custom model.

That’s why we’re releasing MakerSuite, a tool that simplifies this workflow. With MakerSuite, you’ll be able to iterate on prompts, augment your dataset with synthetic data, and easily tune custom models. When you’re ready to move to code, MakerSuite will let you export your prompt as code in your favorite languages and frameworks, like Python and Node.js.

Tune a model

Generative models offer developers powerful out-of-the-box functionality. But for specialized tasks, tuning leads to better results. Our tooling will enable developers to leverage parameter-efficient tuning techniques to create models customized to their use case. And with MakerSuite, you’ll be able to quickly test and iterate on your tuned model right in the browser.

Augment your dataset with synthetic data

High-quality data is crucial when developing with AI, and developers are often limited by the data they have. Our tooling will allow you to generate additional data based on a few examples, and then you’ll be able to manage and manipulate the data from there. This synthetic data can be used in various scenarios, such as tuning or evaluations.

Generate state of the art embeddings

We’ve been excited by the range of applications developers have found for embeddings, from semantic search to recommendations and classification. With embeddings generated through the PaLM API, developers will be able to build applications with their own data or on top of external data sources. Embeddings can also be used in downstream applications built with TensorFlow, Keras, JAX, and other open-source libraries.

Build responsibly and safely

We built our models according to Google’s AI Principles to give developers a responsible AI foundation to start from. We know that control is necessary so developers can define and enforce responsibility and safety in the context of their own applications. Our tools will give developers an easy way to test and adjust safety dimensions to best suit each unique application and use case.

Scale your generative AI application

These developer tools will make it easy to start prototyping and building generative AI applications, but when you need scale, we want to make sure you have the support you need. Google’s infrastructure supports the PaLM API and MakerSuite, so you don’t have to worry about hosting or serving. For developers who want to scale their ideas and get enterprise-grade support, security and compliance, and service level agreement (SLA), they can go to Google Cloud Vertex AI and access the same models, along with a host of advanced capabilities such as enterprise search and conversation AI.

It’s an exciting time in AI for developers and we want to continue to make sure we build AI tools that help make your lives easier. We plan to onboard new developers, roll out new features, and make this technology available to the broader developer community soon. During this time, we’ll listen to feedback, learn, and improve these tools to meet developers where they are.

To stay updated on our progress, subscribe to the Google Developers newsletter.

Machine Learning Communities: Q4 ‘22 highlights and achievements

Posted by Nari Yoon, Hee Jung, DevRel Community Manager / Soonson Kwon, DevRel Program Manager

Let’s explore highlights and accomplishments of vast Google Machine Learning communities over the last quarter of 2022. We are enthusiastic and grateful about all the activities by the global network of ML communities. Here are the highlights!

ML at DevFest 2022

A group of ML Developers attending DevFest 2022
A large number of members of ML GDE, TFUG, and 3P ML communities participated in DevFests 2022 worldwide covering various ML topics with Google products. Machine Learning with Jax: Zero to Hero (DevFest Conakry) by ML GDE Yannick Serge Obam Akou (Cameroon) and Easy ML on Google Cloud (DevFest Med) by ML GDE Nathaly Alarcon Torrico (Bolivia) hosted great sessions.

ML Community Summit 2022

A group of ML Developers attending ML Community Summit
ML Community Summit 2022 was hosted on Oct 22-23, 2022, in Bangkok, Thailand. Twenty-five most active community members (ML GDE or TFUG organizer) were invited and shared their past activities and thoughts on Google’s ML products. A video sketch from ML Developer Programs team and a blog posting by ML GDE Margaret Maynard-Reid (United States) help us revisit the moments.

TensorFlow

MAXIM in TensorFlow by ML GDE Sayak Paul (India) shows his implementation of the MAXIM family of models in TensorFlow.

Diagram of gMLP block

gMLP: What it is and how to use it in practice with Tensorflow and Keras? by ML GDE Radostin Cholakov (Bulgaria) demonstrates the state-of-the-art results on NLP and computer vision tasks using a lot less trainable parameters than corresponding Transformer models. He also wrote Differentiable discrete sampling in TensorFlow.

Building Computer Vision Model using TensorFlow: Part 2 by TFUG Pune for the developers who want to deep dive into training an object detection model on Google Colab, inspecting the TF Lite model, and deploying the model on an Android application. ML GDE Nitin Tiwari (India) covered detailed aspects for end-to-end training and deployment of object model detection.

Advent of Code 2022 in pure TensorFlow (days 1-5) by ML GDE Paolo Galeone (Italy) solving the Advent of Code (AoC) puzzles using only TensorFlow. The articles contain a description of the solutions of the Advent of Code puzzles 1-5, in pure TensorFlow.

tf.keras.metrics / tf.keras.optimizers by TFUG Taipei helped people learn the TF libraries. They shared basic concepts and how to use them using Colab.
Screen shot of TensorFlow Lite on Android Project Practical Course
A hands-on course on TensorFlow Lite projects on Android by ML GDE Xiaoxing Wang (China) is the book mainly introducing the application of TensorFlow Lite in Android development. The content focuses on applying three typical ML applications in Android development.

Build tensorflow-lite-select-tf-ops.aar and tensorflow-lite.aar files with Colab by ML GDE George Soloupis (Greece) guides how you can shrink the final size of your Android application’s .apk by building tensorflow-lite-select-tf-ops.aar and tensorflow-lite.aar files without the need of Docker or personal PC environment.

TensorFlow Lite and MediaPipe Application by ML GDE XuHua Hu (China) explains how to use TFLite to deploy an ML model into an application on devices. He shared experiences with developing a motion sensing game with MediaPipe, and how to solve problems that we may meet usually.

Train and Deploy TensorFlow models in Go by ML GDE Paolo Galeone (Italy) delivered the basics of the TensorFlow Go bindings, the limitations, and how the tfgo library simplifies their usage.

Keras

Diagram of feature maps concatenated together and flattened
Complete Guide on Deep Learning Architectures, Chapter 1 on ConvNets by ML GDE Merve Noyan (France) brings you into the theory of ConvNets and shows how it works with Keras.
Hazy Image Restoration Using Keras by ML GDE Soumik Rakshit (India) provides an introduction to building an image restoration model using TensorFlow, Keras, and Weights & Biases. He also shared an article Improving Generative Images with Instructions: Prompt-to-Prompt Image Editing with Cross Attention Control.
Mixed precision in Keras based Stable Diffusion
Let’s Generate Images with Keras based Stable Diffusion by ML GDE Chansung Park (Korea) delivered how to generate images with given text and what stable diffusion is. He also talked about Keras-based stable diffusion, basic building blocks, and the advantages of using Keras-based stable diffusion.
A Deep Dive into Transformers with TensorFlow and Keras: Part 1, Part 2, Part3 by ML GDE Aritra Roy Gosthipaty (India) covered the journey from the intuition of attention to formulating the multi-head self-attention. And TensorFlow port of GroupViT in 🤗 transformers library was his contribution to Hugging Face transformers library.

TFX

Digits + TFX banner
How startups can benefit from TFX by ML GDE Hannes Hapke (United States) explains how the San Francisco-based FinTech startup Digits has benefitted from applying TFX early, how TFX helps Digits grow, and how other startups can benefit from TFX too.

Usha Rengaraju (India) shared TensorFlow Extended (TFX) Tutorials (Part 1, Part 2, Part 3) and the following TF projects: TensorFlow Decision Forests Tutorial and FT Transformer TensorFlow Implementation.

Hyperparameter Tuning and ML Pipeline by ML GDE Chansung Park (Korea) explained hyperparam tuning, why it is important; Introduction to KerasTuner, basic usage; how to visualize hyperparam tuning results with TensorBoard; and integration within ML pipeline with TFX.

JAX/Flax

JAX High-performance ML Research by TFUG Taipei and ML GDE Jerry Wu (Taiwan) introduced JAX and how to start using JAX to solve machine learning problems.

[TensorFlow + TPU] GatedTabTransformer[W&B] and its JAX/Flax counterpart GatedTabTransformer-FLAX[W&B] by Usha Rengaraju (India) are tutorial series containing the implementation of GatedTabTransformer paper in both TensorFlow (TPU) and FLAX.
Putting NeRF on a diet: Semantically consistent Few-Shot View Synthesis Implementation
JAX implementation of Diet NeRf by ML GDE Wan Hong Lau (Singapore) implemented the paper “Putting NeRF on a Diet (DietNeRF)” in JAX/Flax. And he also implemented a JAX-and-Flax training pipeline with the ResNet model in his Kaggle notebook, 🐳HappyWhale🔥Flax/JAX⚡TPU&GPU – ResNet Baseline.
Introduction to JAX with Flax (slides) by ML GDE Phillip Lippe (Netherlands) reviewed from the basics of the requirements we have on a DL framework to what JAX has to offer. Further, he focused on the powerful function-oriented view JAX offers and how Flax allows you to use them in training neural networks.
Screen grab of ML GDE David Cardozo and Cristian Garcia during a live coding session of a review of new features, specifically Shared Arrays, in the recent release of JAX
JAX Streams: Exploring JAX 0.4 by ML GDE David Cardozo (Canada) and Cristian Garcia (Colombia) showed a review of new features (specifically Shared Arrays) in the recent release of JAX and demonstrated live coding.
[LiveCoding] Train ResNet/MNIST with JAX/Flax by ML GDE Qinghua Duan (China) demonstrated how to train ResNet using JAX by writing code online.

Kaggle

Low-light Image Enhancement using MirNetv2 by ML GDE Soumik Rakshit (India) demonstrated the task of Low-light Image Enhancement.

Heart disease Prediction and Diabetes Prediction Competition hosted by TFUG Chandigarh were to familiarize participants with ML problems and find solutions using classification techniques.
TensorFlow User Group Bangalore Sentiment Analysis Kaggle Competition 1
TFUG Bangalore Kaggle Competition – Sentiment Analysis hosted by TFUG Bangalore was to find the best sentiment analysis algorithm. Participants were given a set of training data and asked to submit an ML/DL algorithm that could predict the sentiment of a text. The group also hosted Kaggle Challenge Finale + Vertex AI Session to support the participants and guide them in learning how to use Vertex AI in a workflow.

Cloud AI

Better Hardware Provisioning for ML Experiments on GCP by ML GDE Sayak Paul (India) discussed the pain points of provisioning hardware (especially for ML experiments) and how we can get better provision hardware with code using Vertex AI Workbench instances and Terraform.

Jayesh Sharma, Platform Engineer, Zen ML; MLOps workshop with TensorFlow and Vertex AI November 12, 2022|TensorFlow User Group Chennai
MLOps workshop with TensorFlow and Vertex AI by TFUG Chennai targeted beginners and intermediate-level practitioners to give hands-on experience on the E2E MLOps pipeline with GCP. In the workshop, they shared the various stages of an ML pipeline, the top tools to build a solution, and how to design a workflow using an open-source framework like ZenML.
10 Predictions on the Future of Cloud Computing by 2025: Insights from Google Next Conference by ML GDE Victor Dibia (United States) includes a recap of his notes reflecting on the top 10 cloud technology predictions discussed at the Google Cloud Next 2022 keynote.
Workflow of Google Virtual Career Center
O uso do Vertex AI Matching Engine no Virtual Career Center (VCC) do Google Cloud by ML GDE Rubens Zimbres (Brazil) approaches the use of Vertex AI Matching Engine as part of the Google Cloud Virtual Career Center solution.

More practical time-series model with BQML by ML GDE JeongMin Kwon (Korea) introduced BQML and time-series modeling and showed some practical applications with BQML ARIMA+ and Python implementations.

Vertex AI Forecast – Demand Forecasting with AutoML by ML GDE Rio Kurihara (Japan) presented a time series forecast overview, time series fusion transformers, and the benefits and desired features of AutoML.

Research & Ecosystem

AI in Healthcare by ML GDE Sara EL-ATEIF (Morocco) introduced AI applications in healthcare and the challenges facing AI in its adoption into the health system.

Women in AI APAC finished their journey at ML Paper Reading Club. During 10 weeks, participants gained knowledge on outstanding machine learning research, learned the latest techniques, and understood the notion of “ML research” among ML engineers. See their session here.

A Natural Language Understanding Model LaMDA for Dialogue Applications by ML GDE Jerry Wu (Taiwan) introduced the natural language understanding (NLU) concept and shared the operation mode of LaMDA, model fine-tuning, and measurement indicators.

Python library for Arabic NLP preprocessing (Ruqia) by ML GDE Ruqiya Bin (Saudi Arabia) is her first python library to serve Arabic NLP.

Screengrab of ML GDEs Margaret Maynard-Reid and Akash Nain during Chat with ML GDE Akash
Chat with ML GDE Vikram & Chat with ML GDE Aakash by ML GDE Margaret Maynard-Reid (United States) shared the stories of ML GDEs’ including how they became ML GDE and how they proceeded with their ML projects.

Anatomy of Capstone ML Projects 🫀by ML GDE Sayak Paul (India) discussed working on capstone ML projects that will stay with you throughout your career. He covered various topics ranging from problem selection to tightening up the technical gotchas to presentation. And in Improving as an ML Practitioner he shared his learning from experience in the field working on several aspects.

Screen grab of statement of objectives in MLOps Development Environment by ML GDE Vinicius Carida
MLOps Development Environment by ML GDE Vinicius Caridá (Brazil) aims to build a full development environment where you can write your own pipelines connecting MLFLow, Airflow, GCP and Streamlit, and build amazing MLOps pipelines to practice your skills.

Transcending Scaling Laws with 0.1% Extra Compute by ML GDE Grigory Sapunov (UK) reviewed a recent Google article on UL2R. And his posting Discovering faster matrix multiplication algorithms with reinforcement learning explained how AlphaTensor works and why it is important.

Back in Person – Prompting, Instructions and the Future of Large Language Models by TFUG Singapore and ML GDE Sam Witteveen (Singapore) and Martin Andrews (Singapore). This event covered recent advances in the field of large language models (LLMs).

ML for Production: The art of MLOps in TensorFlow Ecosystem with GDG Casablanca by TFUG Agadir discussed the motivation behind using MLOps and how it can help organizations automate a lot of pain points in the ML production process. It also covered the tools used in the TensorFlow ecosystem.

Migrating from App Engine Users to Cloud Identity Platform (Module 21)

Posted by Wesley Chun (@wescpy), Developer Advocate, Google Cloud

The Serverless Migration Station series is aimed at helping developers modernize their apps running one of Google Cloud’s serverless platforms. The preceding (Migration Module 20) video demonstrates how to add use of App Engine’s Users service to a Python 2 App Engine sample app. Today’s Module 21 video picks up from where that leaves off, migrating that usage to Cloud Identity Platform.
How to migrate the App Engine Users to Cloud Identity Platform
Moving away from proprietary App Engine bundled services like Users makes apps more portable, giving them enough flexibility to:

    Understanding the overall migration

    Overall, Module 21 features major changes to the Module 20 sample app, implementing a move from App Engine bundled services (NDB & Users) to standalone Cloud services (Cloud Datastore & Identity Platform). Identity Platform doesn’t know anything about App Engine admins, so that must be built, requiring the use of the Cloud Resource Manager API. Apps dependent on Python 2 have additional required updates. Let’s discuss in a bit more detail.

    Migration “parts”

    The following changes to the sample app are required:

    • Migrate from App Engine Users (server-side) to Cloud Identity Platform (client-side)
    • Migrate from App Engine NDB, the other bundled service used in Module 20, to Cloud NDB (requires use of the Cloud Datastore API)
    • Use the Cloud Resource Manager* (via its API) to fetch the Cloud project’s IAM allow policy to collate the set of App Engine admin users for the app.
    • Use the Firebase Admin SDK to validate whether the user is an App Engine admin
    • Migrate from Python 2 to 3 (and possibly back to Python 2 [more on this below])
     

    *At the time of this writing, the Resource Manager documentation only features setup instructions for accessing the API from the lower-level Google APIs client library rather than the Resource Manager client library. To learn how to set up the latter, go to the Resource Manager client library documentation directly. The lower-level client library should only be used in circumstances when a Cloud client library doesn’t exist or doesn’t have the features your app needs. One such use case is Python 2, and we’ll be covering that shortly.

     

      Move from App Engine bundled services to standalone Cloud services

      The NDB to Cloud NDB migration is identical to the Module 2 migration content, so it’s not covered in-depth here in Module 21. The primary focus is on switching to Identity Platform to continue supporting user logins as well as implementing use of the Resource Manager and Firebase Admin SDK to build a proxy for recognizing App Engine admin users as provided by the Users service. Below is pseudocode implementing the key changes to the main application where new or updated lines of code are bolded:

      Table showing changes in code 'Before'(Module 20) and 'After'(Module 21)
      Migrating from App Engine Users to Cloud Identity Platform(click to enlarge)

      The key differences to note:

      1. The server-side Users service code vanishes from the main application, moving into the (client-side) web template (not shown here).
      2. Practically all of the new code in the Module 21 app above is for recognizing App Engine admin users. There are no changes to app operations or data models other than Cloud NDB requiring use of Python context managers to wrap all Datastore code (using Python with blocks).

      Complete versions of the app before and after the updates can be found in the Module 20 (Python 2) and Module 21 (Python 3) repo folders, respectively. In addition to the video, be sure to check out the Identity Platform documentation as well as the Module 21 codelab which leads you step-by-step through the migrations discussed.

      Aside from the necessary coding changes as well as moving from server-side to client-side, note that the Users service usage is covered by App Engine’s pricing model while Identity Platform is an independent Cloud service billed by MAUs (monthly active users), so costs should be taken into account if migrating. More information can be found in the Identity Platform pricing documentation.

      Python 2 considerations

      With the sunset of Python 2, Java 8, PHP 5, and Go 1.11, by their respective communities, Google Cloud has assured users by expressing continued long-term support of these legacy App Engine runtimes, including maintaining the Python 2 runtime. So while there is no current requirement for users to migrate, developers themselves are expressing interest in updating their applications to the latest language releases.
      The primary Module 21 migration automatically includes a port from Python 2 to 3 as that’s where most developers are headed. For those with dependencies requiring remaining on Python 2, some additional effort is required:

        The codelab covers this backport in-depth, so check out the specific section for Python 2 users if you’re in this situation. If you don’t want to think about it, just head to the repo for a working Python 2 version of the Module 21 app.

        Wrap-up

        Module 21 features migrations of App Engine bundled services to appropriate standalone Cloud services. While we recommend users modernize their App Engine apps by moving to the latest offerings from Google Cloud, these migrations are not required. In Fall 2021, the App Engine team extended support of many of the bundled services to 2nd generation runtimes (that have a 1st generation runtime), meaning you don’t have to migrate to standalone services before porting your app to Python 3. You can continue using App Engine NDB and Users in Python 3 so long as you retrofit your code to access bundled services from next-generation runtimes. Then should you opt to migrate, you can do so on your own timeline.

        If you’re using other App Engine legacy services be sure to check out the other Migration Modules in this series. All Serverless Migration Station content (codelabs, videos, source code [when available]) can be accessed at its open source repo. While our content initially focuses on Python users, the Cloud team is working on covering other language runtimes, so stay tuned. For additional video content, check out our broader Serverless Expeditions series.

        How to use the App Engine Users service (Module 20)

        Posted by Wesley Chun (@wescpy), Developer Advocate, Google Cloud

        Introduction and background

        The Serverless Migration Station video series and corresponding codelabs aim to help App Engine developers modernize their apps, whether it’s upgrading language runtimes like from Python 2 to 3 and Java 8 to 17, or to move laterally to sister serverless platforms like Cloud Functions or Cloud Run. For developers who want more control, like being able to SSH into instances, Compute Engine VMs or GKE, our managed Kubernetes service, are also viable options.

        In order to consider moving App Engine apps to other compute services, developers must move their apps away from its original APIs (now referred to as legacy bundled services), either to Cloud standalone replacement or alternative 3rd-party services. Once no longer dependent on these proprietary services, apps become much more portable. Apps can stay on App Engine while upgrading to its 2nd-generation platform, or move to other compute platforms as listed above.

        Today’s Migration Module 20 content focuses on helping developers refamiliarize themselves with App Engine’s Users service, a user authentication system serving as a lightweight wrapper around Google Sign-In (now called Google Identity Services). The video and its corresponding codelab (self-paced, hands-on tutorial) demonstrate how to add use of the Users service to the sample baseline app from Module 1. After adding the Users service in Module 20, Module 21 follows, showing developers how to migrate that usage to Cloud Identity Platform.

        How to use the App Engine Users service

        Adding use of Users service

        The sample app’s basic functionality consists of registering each page visit in Datastore and displaying the most recent visits. The Users service helps apps support user logins, App Engine administrative (“admin'”) users. It also provides convenient functions for generating login/logout links and retrieving basic user information for logged-in users. Below is a screenshot of the modified app which now supports user logins via the user interface (UI):
        Sample app now supports user logins and App Engine admin users (click to enlarge) 
        Below is the pseudocode reflecting the changes made to support user logins for the sample app, including integrating the Users service and updating what shows up in the UI:
        • If the user is logged in, show their “nickname” (display name or email address) and display a Logout button. If the logged-in user is an App Engine app admin, also display an “admin” badge (between nickname and Logout button).
        • If the user is not logged in, display the username generically as “user”, remove any admin badge, and display a Login button.
        Because the Users service is primarily a user-facing endeavor, the most significant changes take place in the UI, whereas the data model and core functionality of registering visits remain unchanged. The new support for user management primarily results in additional context to be rendered in the web template. New or altered code is bolded to highlight the updates.
        Table showing code 'Before'(Module 1) on left, and 'After' (Module 20) on the right
         Adding App Engine Users service usage to sample app (click to enlarge)

        Wrap-up

        Today’s “migration” consists of adding usage of the App Engine Users service to support user management and recognize App Engine admin users, starting with the Module 1 baseline app and finishing with the Module 20 app. To get hands-on experience doing it yourself, try the codelab and follow along with the video. Then you’ll be ready to upgrade to Identity Platform should you choose to do so.

        In Fall 2021, the App Engine team extended support of many of the bundled services to 2nd generation runtimes (that have a 1st generation runtime), meaning you are no longer required to migrate from the Users service to Identity Platform when porting your app to Python 3. You can continue using the Users service in your Python 3 app so long as you retrofit the code to access bundled services from next-generation runtimes.

        If you do want to move to Identity Platform, see the Module 21 content, including its codelab. All Serverless Migration Station content (codelabs, videos, and source code [when available]) are available at its open source repo. While we’re initially focusing on Python users, the Cloud team is covering other runtimes soon, so stay tuned. Also check out other videos in the broader Serverless Expeditions series.

        Migrating from App Engine pull tasks to Cloud Pub/Sub (Module 19)

        Posted by Wesley Chun (@wescpy), Developer Advocate, Google Cloud

        Introduction and background

        The Serverless Migration Station series is aimed at helping developers modernize their apps running one of Google Cloud’s serverless platforms. The preceding (Migration Module 18) video demonstrates how to add use of App Engine’s Task Queue pull tasks service to a Python 2 App Engine sample app. Today’s Module 19 video picks up from where that leaves off, migrating that pull task usage to Cloud Pub/Sub.

        Moving away from proprietary App Engine services like Task Queue makes apps more portable, giving them enough flexibility to:

         

          Understanding the migrations

          Module 19 consists of implementing three different migrations on the Module 18 sample app:

          • Migrate from App Engine NDB to Cloud NDB
          • Migrate from App Engine Task Queue pull tasks to Cloud Pub/Sub
          • Migrate from Python 2 to Python (2 and) 3

          The NDB to Cloud NDB migration is identical to the Module 2 migration content, so it’s not covered in-depth in Module 19. The original app was designed to be Python 2 and 3 compatible, so there’s no work there either. Module 19 boils down to three key updates:

          • Setup: Enable APIs and create Pub/Sub Topic & Subscription
          • How work is created: Publish Pub/Sub messages instead of adding pull tasks
          • How work is processed: Pull messages instead of leasing tasks
          Aside from these physical changes, a key hurdle to overcome is understanding the differences in terminology between pull tasks and Pub/Sub. The following chart attempts to demystify this so developers can more easily grasp how they differ:
          Table of terminology with related GAE Pull Tasks and Cloud Pub/Sub
          Terminology differences between App Engine pull tasks and Cloud Pub/Sub

          Reflecting the chart, these differences can be summarized like this:

          1. With Pull Queues, work is created in pull queues while work is sent to Pub/Sub topics
          2. Task Queue pull tasks are called messages in Pub/Sub
          3. With Task Queues, workers access pull tasks; with Pub/Sub, subscribers receive messages
          4. Leasing a pull task is the same as pulling a message from a Pub/Sub topic via a subscription
          5. Deleting a task from a pull queue when you’re done is analogous to successfully acknowledging a Pub/Sub message
          The video walks developers through the terminology as well as the code changes described above. Below is pseudocode implementing the key changes to the main application (new or updated lines of code bolded):
          Table showing changes in code Before (Module 18) on the left, and After (Module 19) on the right
          Migration from App Engine Task Queue pull tasks to Cloud Pub/Sub

          Observe how most of the code, especially app operations and data models are left relatively unchanged. The only visible changes are switching from App Engine NDB and Task Queue to Cloud NDB and Pub/Sub. Complete versions of the app before and after making the changes can be found in the Module 18 and Module 19 repo folders, respectively. In addition to the video, be sure to check out the Module 19 codelab which leads you step-by-step through the migrations discussed.

          Wrap-up

          Module 19 features a migration of App Engine pull tasks to Cloud Pub/Sub, but developers should note that Pub/Sub itself is not based on pull tasks. It is a fully-featured asynchronous, scalable messaging service that has many more features than the pull functionality provided by Task Queue. For example, Pub/Sub has other features like streaming to BigQuery and push functionality. Pub/Sub push operates differently than Task Queue push tasks, hence why we recommend push tasks be migrated to Cloud Tasks instead (see Module 8). For more information on all of its features, see the Pub/Sub documentation. Because Cloud Tasks doesn’t support pull functionality, we turn to Pub/Sub instead for pull task users.

          While we recommend users move to the latest offerings from Google Cloud, neither of those migrations are required, and should you opt to do so, can do them on your own timeline. In Fall 2021, the App Engine team extended support of many of the bundled services to 2nd generation runtimes (that have a 1st generation runtime), meaning you don’t have to migrate to standalone Cloud services before porting your app to Python 3. You can continue using Task Queue in Python 3 so long as you retrofit your code to access bundled services from next-generation runtimes.

          If you’re using other App Engine legacy services be sure to check out the other Migration Modules in this series. All Serverless Migration Station content (codelabs, videos, source code [when available]) can be accessed at its open source repo. While our content initially focuses on Python users, the Cloud team is working on covering other language runtimes, so stay tuned. For additional video content, check out our broader Serverless Expeditions series.

          Lynn Langit: Turning a passion for learning into online courses viewed by millions

          Posted by Kevin Hernandez, Developer Relations Community Manager

          Lynn Langit is not only a Cloud GDE – she’s one of the first ever GDEs to join the program. Despite joining the GDE program early after its establishment, she got a relatively late start with development. Lynn is a self-taught developer that started coding when she was 38 years old – before we had the advent of online educational resources that we do today. To teach herself how to code, she relied on certifications and books and went to her local electronics store to buy equipment to build her own server. Through this process, she found that she was a talented developer and became inspired to try her hand at teaching. She started out with teaching basic topics such as user applications. Today she has 28 Cloud courses on LinkedIn and has an audience of 5 million students. With this immense reach, Lynn runs into her students at various conferences around the world and has even had students recognize her from her voice. She mentions,“Before the pandemic, I used to travel and work globally and it was so gratifying to meet all my students because they would want to come and talk to me. It was incredible to meet students from all over the world.”

          Getting into teaching

          When Lynn left her corporate job, she started her own consultancy in 2011 with two ideas: technical teaching and building. She started out in a classroom with these two ideas but as traditional learning started to usher in a new era of online learning, Lynn followed suit and started to put her lessons on YouTube. This caught the attention of Lynda.com (now LinkedIn Learning) where she was asked to become an author.

          Teaching has proven to be rewarding in several ways. It allows Lynn to have an impact on learners interested in Cloud and dive deeper into topics she’s interested in, all while getting paid for her academic pursuits and instructing. She states, “I can’t say I’m an expert in all the services, but I know a lot of the services across all the Clouds. So while I’m learning, I might as well teach and get paid for it.”

          Choosing lessons

          Lynn is in the constant pursuit for knowledge and in the ever-changing world of Cloud, there is always something new to learn or teach and in Lynn’s case, both. “Oftentimes I’ll create a course in something that I am genuinely interested in that doesn’t have an existing course. It’s so that I can focus my energies, learn it, and then teach it,” she adds. A recent example is with a book club she led last year in quantum computing. “I’m just really taking baby steps into it and as part of that, I started exploring the vendor Cloud quantum offerings. Then I decided to share that as a course,” she says.

          She also mentions that there is a preconceived notion that online content has to be super polished. She believes it’s important to put your lessons out there and more importantly, to learn together. “We’re one community and we need to share when we discover something,” she observed.

          Teaching style

          Every instructor has their own teaching style and for Lynn, her brand is a conversational style of instruction. Very much like our interview, her lessons feel as if she’s talking to the audience one-on-one. This is in part by design – Lynn doesn’t write a script and she imagines someone sitting across from her. She can also sprinkle in some useful case studies from her consultancy work and can draw from some real-world examples.

          When asked about effective educators, Lynn says, “Don’t be a jerk. The point is not to show how smart you are. The point is to communicate information that you have found useful, that you think other people will find useful and in a way they can understand.”

          Advice for educating online

          Lynn has met a lot of educators in her career and has had the fortune of being able to see published and unpublished content. One thing she noticed is that the problem with a lot of content is that it just simply doesn’t see the light of the day. Some content creators feel as if there is a missing piece or their content needs to be ultra polished but Lynn’s advice is to just click “publish”. She also notes that this can be attributed to imposter syndrome, which shows emotional intelligence, but as a counterpoint she advises, “There’s value in the learning, not just the result. That is probably the biggest insight I’ve gained over my years because I always thought you just had to show polished content.” Lynn believes that your audience wants to go along with you on your journey and since people are busy, they think of you as a curator of knowledge.

          She also advises to start small. She is particularly fond of “snack-sized” pieces of content such as the short-form articles on Dev.to. These “snacks” are easier to produce and in reality, it’s easier on the audience. She says, “It’s funny because people want to make a course but this is not a Hollywood movie, I am sorry to break it to you, but people are not going to be rapturously glued to your screen for two hours no matter who you are. So just make little snacks.” If you find something interesting, just put it out there. Over time, as you get practice, you can start to produce longer-form content.

          Advice for GDEs

          Lynn offers valuable advice to any present or future GDE. She encourages, “Really get to know the GDEs. We’re all kind of doing the same thing and just jump right in. The bar is high to become a GDE and it’s a great community that I’ve learned a lot from.” There is a wealth of knowledge offered by your community. Maybe you’ll learn how to create an Android app, build a ML model, or build an online course with the guidance of Lynn. Just jump right in.

          You can check out Lynn’s LinkedIn courses or find her on LinkedIn or Substack.

          The Google Developer Experts (GDE) program is a global network of highly experienced technology experts, influencers, and thought leaders who actively support developers, companies, and tech communities by speaking at events and publishing content.

          How to use App Engine pull tasks (Module 18)

          Posted by Wesley Chun (@wescpy), Developer Advocate, Google Cloud

          Introduction and background

          The Serverless Migration Station mini-series helps App Engine developers modernize their apps to the latest language runtimes, such as from Python 2 to 3 or Java 8 to 17, or to sister serverless platforms Cloud Functions and Cloud Run. Another goal of this series is to demonstrate how to move away from App Engine’s original APIs (now referred to as legacy bundled services) to Cloud standalone replacement services. Once no longer dependent on these proprietary services, apps become much more portable, making them flexible enough to:

          App Engine’s Task Queue service provides infrastructure for executing tasks outside of the standard request-response workflow. Tasks may consist of workloads exceeding request timeouts or periodic tangential work. The Task Queue service provides two different queue types, push and pull, for developers to perform auxiliary work.

          Push queues are covered in Migration Modules 7-9, demonstrating how to add use of push tasks to an existing baseline app followed by steps to migrate that functionality to Cloud Tasks, the standalone successor to the Task Queues push service. We turn to pull queues in today’s video where Module 18 demonstrates how to add use of pull tasks to the same baseline sample app. Module 19 follows, showing how to migrate that usage to Cloud Pub/Sub.

          Adding use of pull queues

          In addition to registering page visits, the sample app needs to be modified to track visitors. Visits are comprised of a timestamp and visitor information such as the IP address and user agent. We’ll modify the app to use the IP address and track how many visits come from each address seen. The home page is modified to show the top visitors in addition to the most recent visits:

          Screen grab of the sample app's updated home page tracking visits and visitors
          The sample app’s updated home page tracking visits and visitors

          When visits are registered, pull tasks are created to track the visitors. The pull tasks sit patiently in the queue until they are processed in aggregate periodically. Until that happens, the top visitors table stays static. These tasks can be processed in a number of ways: periodically by a cron or Cloud Scheduler job, a separate App Engine backend service, explicitly by a user (via browser or command-line HTTP request), event-triggered Cloud Function, etc. In the tutorial, we issue a curl request to the app’s endpoint to process the enqueued tasks. When all tasks have completed, the table then reflects any changes to the current top visitors and their visit counts:

          Screen grab of processed pull tasks updated in the top visitors table
          Processed pull tasks update the top visitors table

          Below is some pseudocode representing the core part of the app that was altered to add Task Queue pull task usage, namely a new data model class, VisitorCount, to track visitor counts, enqueuing a (pull) task to update visitor counts when registering individual visits in store_visit(), and most importantly, a new function fetch_counts(), accessible via /log, to process enqueued tasks and update overall visitor counts. The bolded lines represent the new or altered code.

          Adding App Engine Task Queue pull task usage to sample app showing 'Before'[Module 1] on the left and 'After' [Module 18] with altered code on the right
          Adding App Engine Task Queue pull task usage to sample app

          Wrap-up

          This “migration” is comprised of adding Task Queue pull task usage to support tracking visitor counts to the Module 1 baseline app and arrives at the finish line with the Module 18 app. To get hands-on experience doing it yourself, do the codelab by hand and follow along with the video. Then you’ll be ready to upgrade to Cloud Pub/Sub should you choose to do so.

          In Fall 2021, the App Engine team extended support of many of the bundled services to 2nd generation runtimes (that have a 1st generation runtime), meaning you are no longer required to migrate pull tasks to Pub/Sub when porting your app to Python 3. You can continue using Task Queue in your Python 3 app so long as you retrofit the code to access bundled services from next-generation runtimes.

          If you do want to move to Pub/Sub, see Module 19, including its codelab. All Serverless Migration Station content (codelabs, videos, and source code) are available at its open source repo. While we’re initially focusing on Python users, the Cloud team is covering other runtimes soon, so stay tuned. Also check out other videos in the broader Serverless Expeditions series.

          Machine Learning Communities: Q3 ‘22 highlights and achievements

          Posted by Nari Yoon, Hee Jung, DevRel Community Manager / Soonson Kwon, DevRel Program Manager

          Let’s explore highlights and accomplishments of vast Google Machine Learning communities over the third quarter of the year! We are enthusiastic and grateful about all the activities by the global network of ML communities. Here are the highlights!

          TensorFlow/Keras

          Load-testing TensorFlow Serving’s REST Interface

          Load-testing TensorFlow Serving’s REST Interface by ML GDE Sayak Paul (India) and Chansung Park (Korea) shares the lessons and findings they learned from conducting load tests for an image classification model across numerous deployment configurations.

          TFUG Taipei hosted events (Python + Hugging Face-Translation+ tf.keras.losses, Python + Object detection, Python+Hugging Face-Token Classification+tf.keras.initializers) in September and helped community members learn how to use TF and Hugging face to implement machine learning model to solve problems.

          Neural Machine Translation with Bahdanau’s Attention Using TensorFlow and Keras and the related video by ML GDE Aritra Roy Gosthipaty (India) explains the mathematical intuition behind neural machine translation.

          Serving a TensorFlow image classification model as RESTful and gRPC based services with TFServing, Docker, and Kubernetes

          Automated Deployment of TensorFlow Models with TensorFlow Serving and GitHub Actions by ML GDE Chansung Park (Korea) and Sayak Paul (India) explains how to automate TensorFlow model serving on Kubernetes with TensorFlow Serving and GitHub Action.

          Deploying 🤗 ViT on Kubernetes with TF Serving by ML GDE Sayak Paul (India) and Chansung Park (Korea) shows how to scale the deployment of a ViT model from 🤗 Transformers using Docker and Kubernetes.

          Screenshot of the TensorFlow Forum in the Chinese Language run by the tf.wiki team

          Long-term TensorFlow Guidance on tf.wiki Forum by ML GDE Xihan Li (China) provides TensorFlow guidance by answering the questions from Chinese developers on the forum.

          photo of a phone with the Hindi letter 'Ohm' drawn on the top half of the screen. Hinidi Character recognition shows the letter Ohm as the Predicted Result below.

          Hindi Character Recognition on Android using TensorFlow Lite by ML GDE Nitin Tiwari (India) shares an end-to-end tutorial on training a custom computer vision model to recognize Hindi characters. In TFUG Pune event, he also gave a presentation titled Building Computer Vision Model using TensorFlow: Part 1.

          Using TFlite Model Maker to Complete a Custom Audio Classification App by ML GDE Xiaoxing Wang (China) shows how to use TFLite Model Maker to build a custom audio classification model based on YAMNet and how to import and use the YAMNet-based custom models in Android projects.

          SoTA semantic segmentation in TF with 🤗 by ML GDE Sayak Paul (India) and Chansung Park (Korea). The SegFormer model was not available on TensorFlow.

          Text Augmentation in Keras NLP by ML GDE Xiaoquan Kong (China) explains what text augmentation is and how the text augmentation feature in Keras NLP is designed.

          The largest vision model checkpoint (public) in TF (10 Billion params) through 🤗 transformers by ML GDE Sayak Paul (India) and Aritra Roy Gosthipaty (India). The underlying model is RegNet, known for its ability to scale.

          A simple TensorFlow implementation of a DCGAN to generate CryptoPunks

          CryptoGANs open-source repository by ML GDE Dimitre Oliveira (Brazil) shows simple model implementations following TensorFlow best practices that can be extended to more complex use-cases. It connects the usage of TensorFlow with other relevant frameworks, like HuggingFace, Gradio, and Streamlit, building an end-to-end solution.

          TFX

          TFX Machine Learning Pipeline from data injection in TFRecord to pushing out Vertex AI

          MLOps for Vision Models from 🤗 with TFX by ML GDE Chansung Park (Korea) and Sayak Paul (India) shows how to build a machine learning pipeline for a vision model (TensorFlow) from 🤗 Transformers using the TF ecosystem.

          First release of TFX Addons Package by ML GDE Hannes Hapke (United States). The package has been downloaded a few thousand times (source). Google and other developers maintain it through bi-weekly meetings. Google’s Open Source Peer Award has recognized the work.

          TFUG São Paulo hosted TFX T1 | E4 & TFX T1 | E5. And ML GDE Vinicius Caridá (Brazil) shared how to train a model in a TFX pipeline. The fifth episode talks about Pusher: publishing your models with TFX.

          Semantic Segmentation model within ML pipeline by ML GDE Chansung Park (Korea) and Sayak Paul (India) shows how to build a machine learning pipeline for semantic segmentation task with TFX and various GCP products such as Vertex Pipeline, Training, and Endpoints.

          JAX/Flax

          Screen shot of Tutorial 2 (JAX): Introduction to JAX+Flax with GitHub Repo and Codelab via university of Amseterdam

          JAX Tutorial by ML GDE Phillip Lippe (Netherlands) is meant to briefly introduce JAX, including writing and training neural networks with Flax.

          TFUG Malaysia hosted Introduction to JAX for Machine Learning (video) and Leong Lai Fong gave a talk. The attendees learned what JAX is and its fundamental yet unique features, which make it efficient to use when executing deep learning workloads. After that, they started training their first JAX-powered deep learning model.

          TFUG Taipei hosted Python+ JAX + Image classification and helped people learn JAX and how to use it in Colab. They shared knowledge about the difference between JAX and Numpy, the advantages of JAX, and how to use it in Colab.

          Introduction to JAX by ML GDE João Araújo (Brazil) shared the basics of JAX in Deep Learning Indaba 2022.

          A comparison of the performance and overview of issues resulting from changing from NumPy to JAX

          Should I change from NumPy to JAX? by ML GDE Gad Benram (Portugal) compares the performance and overview of the issues that may result from changing from NumPy to JAX.

          Introduction to JAX: efficient and reproducible ML framework by ML GDE Seunghyun Lee (Korea) introduced JAX/Flax and their key features using practical examples. He explained the pure function and PRNG, which make JAX explicit and reproducible, and XLA and mapping functions which make JAX fast and easily parallelized.

          Data2Vec Style pre-training in JAX by ML GDE Vasudev Gupta (India) shares a tutorial for demonstrating how to pre-train Data2Vec using the Jax/Flax version of HuggingFace Transformers.

          Distributed Machine Learning with JAX by ML GDE David Cardozo (Canada) delivered what makes JAX different from TensorFlow.

          Image classification with JAX & Flax by ML GDE Derrick Mwiti (Kenya) explains how to build convolutional neural networks with JAX/Flax. And he wrote several articles about JAX/Flax: What is JAX?, How to load datasets in JAX with TensorFlow, Optimizers in JAX and Flax, Flax vs. TensorFlow, etc..

          Kaggle

          DDPMs – Part 1 by ML GDE Aakash Nain (India) and cait-tf by ML GDE Sayak Paul (India) were announced as Kaggle ML Research Spotlight Winners.

          Forward process in DDPMs from Timestep 0 to 100

          Fresher on Random Variables, All you need to know about Gaussian distribution, and A deep dive into DDPMs by ML GDE Aakash Nain (India) explain the fundamentals of diffusion models.

          In Grandmasters Journey on Kaggle + The Kaggle Book, ML GDE Luca Massaron (Italy) explained how Kaggle helps people in the data science industry and which skills you must focus on apart from the core technical skills.

          Cloud AI

          How Cohere is accelerating language model training with Google Cloud TPUs by ML GDE Joanna Yoo (Canada) explains what Cohere engineers have done to solve scaling challenges in large language models (LLMs).

          ML GDE Hannes Hapke (United States) chats with Fillipo Mandella, Customer Engineering Manager at Google

          In Using machine learning to transform finance with Google Cloud and Digits, ML GDE Hannes Hapke (United States) chats with Fillipo Mandella, Customer Engineering Manager at Google, about how Digits leverages Google Cloud’s machine learning tools to empower accountants and business owners with near-zero latency.

          A tour of Vertex AI by TFUG Chennai for ML, cloud, and DevOps engineers who are working in MLOps. This session was about the introduction of Vertex AI, handling datasets and models in Vertex AI, deployment & prediction, and MLOps.

          TFUG Abidjan hosted two events with GDG Cloud Abidjan for students and professional developers who want to prepare for a Google Cloud certification: Introduction session to certifications and Q&A, Certification Study Group.

          Flow chart showing shows how to deploy a ViT B/16 model on Vertex AI

          Deploying 🤗 ViT on Vertex AI by ML GDE Sayak Paul (India) and Chansung Park (Korea) shows how to deploy a ViT B/16 model on Vertex AI. They cover some critical aspects of a deployment such as auto-scaling, authentication, endpoint consumption, and load-testing.

          Photo collage of AI generated images

          TFUG Singapore hosted The World of Diffusion – DALL-E 2, IMAGEN & Stable Diffusion. ML GDE Martin Andrews (Singapore) and Sam Witteveen (Singapore) gave talks named “How Diffusion Works” and “Investigating Prompt Engineering on Diffusion Models” to bring people up-to-date with what has been going on in the world of image generation.

          ML GDE Martin Andrews (Singapore) have done three projects: GCP VM with Nvidia set-up and Convenience Scripts, Containers within a GCP host server, with Nvidia pass-through, Installing MineRL using Containers – with linked code.

          Jupyter Services on Google Cloud by ML GDE Gad Benram (Portugal) explains the differences between Vertex AI Workbench, Colab, and Deep Learning VMs.

          Google Cloud's Two Towers Recommender and TensorFlow

          Train and Deploy Google Cloud’s Two Towers Recommender by ML GDE Rubens de Almeida Zimbres (Brazil) explains how to implement the model and deploy it in Vertex AI.

          Research & Ecosystem

          WOMEN DATA SCIENCE, LA PAZ Club de lectura de papers de Machine Learning Read, Learn and Share the knowledge #MLPaperReadingClubs, Nathaly Alarcón, @WIDS_LaPaz #MLPaperReadingClubs

          The first session of #MLPaperReadingClubs (video) by ML GDE Nathaly Alarcon Torrico (Bolivia) and Women in Data Science La Paz. Nathaly led the session, and the community members participated in reading the ML paper “Zero-shot learning through cross-modal transfer.”

          In #MLPaperReadingClubs (video) by TFUG Lesotho, Arnold Raphael volunteered to lead the first session “Zero-shot learning through cross-modal transfer.”

          Screenshot of a screenshare of Zero-shot learning through cross-modal transfer to 7 participants in a virtual call

          ML Paper Reading Clubs #1: Zero Shot Learning Paper (video) by TFUG Agadir introduced a model that can recognize objects in images even if no training data is available for the objects. TFUG Agadir prepared this event to make people interested in machine learning research and provide them with a broader vision of differentiating good contributions from great ones.

          Opening of the Machine Learning Paper Reading Club (video) by TFUG Dhaka introduced ML Paper Reading Club and the group’s plan.

          EDA on SpaceX Falcon 9 launches dataset (Kaggle) (video) by TFUG Mysuru & TFUG Chandigarh organizer Aashi Dutt (presenter) walked through exploratory data analysis on SpaceX Falcon 9 launches dataset from Kaggle.

          Screenshot of ML GDE Qinghua Duan (China) showing how to apply the MRC paradigm and BERT to solve the dialogue summarization problem.

          Introduction to MRC-style dialogue summaries based on BERT by ML GDE Qinghua Duan (China) shows how to apply the MRC paradigm and BERT to solve the dialogue summarization problem.

          Plant disease classification using Deep learning model by ML GDE Yannick Serge Obam Akou (Cameroon) talked on plant disease classification using deep learning model : an end to end Android app (open source project) that diagnoses plant diseases.

          TensorFlow/Keras implementation of Nystromformer

          Nystromformer Github repository by Rishit Dagli provides TensorFlow/Keras implementation of Nystromformer, a transformer variant that uses the Nyström method to approximate standard self-attention with O(n) complexity which allows for better scalability.

          Extending support for App Engine bundled services (Module 17)

          Posted by Wesley Chun (@wescpy), Developer Advocate, Google Cloud

          Background

          App Engine initially launched in 2008, providing a suite of bundled services making it convenient for applications to access a database (Datastore), caching service (Memcache), independent task execution (Task Queue), Google Sign-In authentication (Users), or large “blob” storage (Blobstore), or other companion services. However, apps leveraging those services can only run their apps on App Engine.

          To increase app portability and help Google move towards its goal of having the most open cloud on the market, App Engine launched its 2nd-generation service in 2018, initially removing those legacy services. The newer platform allows developers to upgrade apps to the latest language runtimes, such as moving from Python 2 to 3 or Java 8 to 11 (and today, Java 17). One of the major drawbacks to the 1st-generation runtimes is that they’re customized, proprietary, and restrictive in what you can use or can’t.

          Instead, the 2nd-generation platform uses open source runtimes, meaning ability to follow standard development practices, use common/known idioms, and have fewer restrictions of 3rd-party libraries, and obviating the need to copy or “vendor” them with your code. Unfortunately, to use these newer runtimes, migrating away from App Engine services were required because while you could upgrade language releases, there was no access to bundled services, breaking apps or requiring complete rewrites, making it a showstopper for many users.

          Due to their popularity and the desire to ease the upgrade process for customers, the App Engine team restored access to most (but not all) of those services in Fall 2021. Today’s Serverless Migration Station video demonstrates how to continue usage of bundled services available to Python 3 developers.

          Showing App Engine users how to use bundled services on Python 3

          Performing the upgrade

          Modernizing the typical Python 2 App Engine app looks something like this:
          1. Migrate from the webapp2 framework (not available in Python 3)
          2. Port from Python 2 to 3, preserve use of bundled services
          3. Optional migration to Cloud standalone or similar 3rd-party services

          The first step is to move to a standard Python web framework like Flask, Django, Pyramid, etc. Below is some pseudocode from Migration Module 1 demonstrating how to migrate from webapp2 to Flask:

          codeblocks for porting Python 2 sample app from webapp2 to Flask
          Step 1: Port Python 2 sample app from webapp2 to Flask

          The key changes are bolded in the above code snippets. Notice how the App Engine NDB code [the Visit class definition plus store_visit() and fetch_visits() functions] are unaffected by this web framework migration. The full webapp2 code sample can be found in the Module 0 repo folder while the completed migration to Flask sample is located in the Module 1 repo folder.

          After your app has ported frameworks, you’re free to upgrade to Python 3 while preserving access to the bundled services if your app uses any. Below is pseudocode demonstrating how to upgrade the same sample app to Python 3 as well as the code changes needed to continue to use App Engine NDB:

          codeblocks for porting sample app to Python 3, preserving use of NDB bundled service
          Step 2: Port sample app to Python 3, preserving use of NDB bundled service
          The original app was designed to work under both Python 2 and 3 interpreters, so no language changes were required in this case. We added an import of the new App Engine SDK followed by the key update wrapping the WSGI object so the app can access the bundled services. As before, the key updates are bolded. Some updates to configuration are also required, and those are outlined in the documentation and the (Module 17) codelab.

          The NDB code is also left untouched in this migration. Not all of the bundled services feature such a hands-free migration, and we hope to cover some of the more complex ones ahead in Module 22. Java, PHP, and Go users have it even better, requiring fewer or no code changes at all. The Python 2 Flask sample is located in the Module 1 repo folder, and the resulting Python 3 app can be found in the Module 1b repo folder.

          The immediate benefit of step two is the ability to upgrade to a more current version of language runtime. This leaves the third step of migrating off the bundled services as optional, especially if you plan on staying on App Engine for the long-term.

          Additional options

          If you decide to migrate off the bundled services, you can do so on your own timeline. It should be a consideration should you ever want to move to modern serverless platforms such as Cloud Functions or Cloud Run, to lower-level platforms because you want more control, like GKE, our managed Kubernetes service, or Compute Engine VMs.

          Step three is also where the rest of the Serverless Migration Station content may be useful:

          *code samples and codelabs available; videos forthcoming

          As far as moving to modern serverless platforms, if you want to break apart a large App Engine app into multiple microservices, consider Cloud Functions. If your organization has added containerization as part of your software development workflow, consider Cloud Run. It’s suitable for apps if you’re familiar with containers and Docker, but even if you or your team don’t have that experience, Cloud Buildpacks can do the heavy lifting for you. Here are the relevant migration modules to explore:

            Wrap-up

            Early App Engine users appreciate the convenience of the platform’s bundled services, and after listening to user feedback, adding them back to 2nd-generation runtimes is another way we can help developers modernize their apps. Whether upgrading to newer language runtimes to stay on App Engine and continue to use its bundled services, migrating to Cloud standalone products, or shifting to other serverless platforms, the Google Cloud team aims to provide the tools to help streamline your modernization efforts.

            All Serverless Migration Station content (codelabs, videos, source code [when available]) can be accessed at its open source repo. While our content initially focuses on Python users, the Cloud team is working on covering other language runtimes, so stay tuned. Today’s video features a special guest to provide a teaser of what to expect for Java. For additional video content, check out the broader Serverless Expeditions series.

            Dev Library Letters: 14th Issue

            Posted by Garima Mehra, Program Manager

            ‘Google Dev Library letters’ is curated to bring you some of the best projects developed with Google tech that have been submitted to the Dev Library platform. We hope this brings you the inspiration you need for your next project!

            Android

            Image-compressor 
            by Vinod Baste

            Check out Vinod’s Android Image compress library that helps reduce the size of the image by 90% without losing any of its pixels.

            SealedX 
            by Jaewoong Eum

            Learn how to auto-generate extensive sealed classes and interfaces for Android and Kotlin.

            Flutter

            GitHub Actions to deploy
            Flutter Web to gh-pages             
            by Sai Rajendra Immadi

            Tired of manually deploying the app every time? Or do you want to deploy your flutter web applications to gh-pages? Use this blog as your guide.

            Double And Triple Dots in Flutter 
            by Lakshydeep Vikram

            Learn the reason for using double and triple dots in flutter and where to use them.

            Machine Learning

            Nystromformer 
            by Rishit Dagli

            Learn how to use the Nystrom method to approximate standard self-attention. 


            Google Cloud

            by Ezekias Bokove

            Learn how to set up a notification system for Cloud Run services. 

            Switch to GCP for cost savings and better performance
            by Gaurav Madan

            Learn why architects dealing with complex application design and who use well-known Google services should consider the Google Cloud Platform. 


            “The Google community includes people with diverse backgrounds. No matter what an individual circumstance is, the platform should support anyone to explore and be creative. We encourage authors to boldly consider diverse backgrounds and to be inclusive when authoring.”

            Vinesh Prasanna M

            Customer Engineer | Google Cloud 



            “Authoring a good code sample is hard. The difficulty comes from the additional pieces you need to add to your respository to keep the code sample fresh and appealing to your developers.”

            Brett Morgan

            Developer Relations Engineer | Flutter





            Want to read more? 
            Check out the latest projects and community-authored content by visiting Google Dev Library. 
            Submit your projects to showcase your work and inspire developers!

            Google Cloud & Kotlin GDE Kevin Davin helps others learn in the face of challenges

            Posted by Kevin Hernandez, , Developer Relations Community Manager

            Kevin Davin speaking at the SnowCamp Conference in 2019
            Kevin Davin has always had a passion for learning and helping others learn, no matter their background or unique challenges they may face. He explains, “I want to learn something new every day, I want to help others learn, and I’m addicted to learning.” This mantra is evident in everything he does from giving talks at numerous conferences to helping people from underrepresented groups overcome imposter syndrome and even helping them become GDEs. In addition to learning, Kevin is also passionate about diversity and inclusion efforts, partly inspired by navigating the world with partial blindness.
            Kevin has been a professional programmer for 10 years now and has been in the field of Computer Science for about 20 years. Through the years, he has emphasized the importance of learning how and where to learn. For example, while he learned a lot while he was studying at a university, he was able to learn just as much through his colleagues. In fact, it was through his colleagues that he picked up lessons in teamwork and the ability to learn from people with different points of view and experience. Since he was able to learn so much from those around him, Kevin also wanted to pay it forward and started volunteering at a school for people with disabilities. Guided by the Departmental Centers for People with Disabilities, the aim of the program is to teach coding languages and reintegrate students into a technical profession. During his time at this center, Kevin helped students practice what they learned and ultimately successfully transition into a new career.

            During these experiences, Kevin was always involved in the developer community through open-source projects. It was through these projects that he learned about the GDE program and was connected to Google Developer advocates. Kevin was drawn to the GDE program because he wanted to share his knowledge with others and have direct access to Google in order to become an advocate on behalf of developers. In 2016, he discovered Kubernetes and helped his company at the time move to Google Cloud. He always felt like this model was the right solution and invested a lot of time to learn it and practice it. “Google Cloud is made for developers. It’s like a Lego set because you can take the parts you want and put it together,” he remarked.

            The GDE program has given him access to the things he values most: being a part of a developer community, being an advocate for developers, helping people from all backgrounds feel included, and above all, an opportunity to learn something new every day. Kevin’s parting advice for hopeful GDEs is: “Even if you can’t reach the goal of being a GDE now, you can always get accepted in the future. Don’t be afraid to fail because without failure, you won’t learn anything.” With his involvement in the program, Kevin hopes to continue connecting with the developer community and learning while supporting diversity efforts.

            Learn more about Kevin on Twitter & LinkedIn.

            The Google Developer Experts (GDE) program is a global network of highly experienced technology experts, influencers, and thought leaders who actively support developers, companies, and tech communities by speaking at events and publishing content.

            Migrating from App Engine Blobstore to Cloud Storage (Module 16)

            Posted by Wesley Chun (@wescpy), Developer Advocate, Google Cloud

            Introduction and background

            The most recent Serverless Migration Station video demonstrated how to add use of the App Engine’s Blobstore service to a sample Python 2 App Engine app, kicking off the first of a 2-part series on migrating away from Blobstore. In today’s Module 16 video, we complete this journey, arriving at Cloud Storage. Moving away from proprietary App Engine services like Blobstore makes apps more portable, giving them enough flexibility to:

            Showing App Engine users how to migrate to Cloud Storage

            As described previously, a Blobstore for Python 2 dependency on webapp made the Module 15 content more straightforward to implement if it was still using webapp2. To completely modernize this app here in Module 16, the following migrations should be carried out:

            • Migrate from webapp2 (and webapp) to Flask
            • Migrate from App Engine NDB to Cloud NDB
            • Migrate from App Engine Blobstore to Cloud Storage
            • Migrate from Python 2 to Python (2 and) 3

            Performing the migrations

            Prior to modifying the application code, a variety of configuration updates need to be made. Updates applying only to Python 2 feature a “Py2” designation while those migrating to Python 3 will see “Py3” annotations.

            1. Remove the built-in Jinja2 library from app.yaml—Jinja2 already comes with Flask, so remove use of the older built-in version which may possibly conflict with the contemporary Flask version you’re using. (Py2)
            2. Use of Cloud client libraries (such as those for Cloud NDB and Cloud Storage) require a pair of built-in libraries, grpcio and setuptools, so add those to app.yaml (Py2)
            3. Remove everything in app.yaml except for a valid runtime (Py3)
            4. Add Cloud NDB and Cloud Storage client libraries to requirements.txt (Py2 & Py3)
            5. Create an appengine_config.py supporting both built-in (those in app.yaml) and non built-in (those in requirements.txt) libraries used (Py2)

            The Module 15 app already migrated away from webapp2‘s (Django) templating system to Jinja2. This is useful when migrating to Flask because Jinja2 is Flask’s default template system. Switching from App Engine NDB to Cloud NDB is fairly straightforward as the latter was designed to be mostly compatible with the original. The only change visible in this sample app is to move Datastore calls into Python with blocks.

            The most significant changes occur when moving the upload and download handlers from webapp to Cloud Storage. The video and corresponding codelab go more in-depth into the necessary changes, but in summary, these are the updates required in the main application:

            1. webapp2 is replaced by Flask. Instead of using the older built-in version of Jinja2, use the version that comes with Flask.
            2. App Engine Blobstore and NDB are replaced by Cloud NDB and Cloud Storage, respectively.
            3. The webapp Blobstore handler functionality is replaced by a combination of the io standard library module plus components from Flask and Werkzeug. Furthermore, the handler classes and methods are replaced by Flask functions.
            4. The main handler class and corresponding GET and POST methods are all replaced by a single Flask function.

            The results

            With all the changes implemented, the original Module 15 app still operates identically in Module 16, starting with a form requesting a visit artifact followed by the most recents visits page:
            The sample app’s artifact prompt page


            The sample app’s most recent visits page.

            The only difference is that four migrations have been completed where all of the “infrastructure” is now taken care of by non-App Engine legacy services. Furthermore, the Module 16 app could be either a Python 2 or 3 app. As far as the end-user is concerned, “nothing happened.”

            Migrating sample app from App Engine Blobstore to Cloud Storage

            Wrap-up

            Module 16 featured four different migrations, modernizing the Module 15 app from using App Engine legacy services like NDB and Blobstore to Cloud NDB and Cloud Storage, respectively. While we recommend users move to the latest offerings from Google Cloud, migrating from Blobstore to Cloud Storage isn’t required, and should you opt to do so, can do it on your own timeline. In addition to today’s video, be sure to check out the Module 16 codelab which leads you step-by-step through the migrations discussed.

            In Fall 2021, the App Engine team extended support of many of the bundled services to 2nd generation runtimes (that have a 1st generation runtime), meaning you are no longer required to migrate to Cloud Storage when porting your app to Python 3. You can continue using Blobstore in your Python 3 app so long as you retrofit the code to access bundled services from next-generation runtimes.

            If you’re using other App Engine legacy services be sure to check out the other Migration Modules in this series. All Serverless Migration Station content (codelabs, videos, source code [when available]) can be accessed at its open source repo. While our content initially focuses on Python users, the Cloud team is working on covering other language runtimes, so stay tuned. For additional video content, check out our broader Serverless Expeditions series.