Jul '20 tensorwerk Heartbeat

Hey folks! The heat of July didn’t stop us ☀️ We successfully started our Hangar open discussion call series, our Sherin published a nice blog post about deep learning models deployment and we released a new version of Stockroom 🚀

Every month we are sharing news on projects we are working on, conferences and events we attend, what are our plans for the future and everything that might be related to data.

All you need is PyTorch, MLflow, RedisAI and a cup of mocha latte

Deploying a Deep Learning model is still a nightmare to you? RedisAI and MLflow are here for you 🤓 Our Sherin Thomas (aka @hhsecond) in his new blog post explains how easy is becoming a MLOps engineer with the right toolbox!

Check it out 👉 https://bit.ly/mlflow-redisai-deployment

Hangar open discussion calls recap

As promised in the last heartbeat, we successfully started our Public Hangar Developers meeting series!

We published all the recordings on our YouTube channel.

Here is a little recap of what happened:

July 07, 2020

🔗 Video Link

🗣️ Team presentation, Hangar introduction, Stockroom introduction, discussion about Hangar Bulk Importer

July 14, 2020

🔗 Video Link

🗣️ Hangar remote system, Users Q&A

July 22, 2020

🔗 Video Link

🗣️ Machine Learning Data Loaders for Hangar

July 28, 2020

🔗 Video Link

🗣️ Hangar Documentation and tutorials

See you every Tuesday at 11AM EST / 5PM CEST! Please come with questions, we'd love to hear your thoughts and/or issues 🧡

Stockroom 0.2.2 release

We presented Stockroom in our very first heartbeat, when it was just at an early stage. With the latest releases, we introduced a lot of interesting features, such as the ability to import torchvision datasets directly into a Stockroom repository. Import CIFAR-10, MNIST and FashionMNIST (more to come!) simply by using this command:

$ stock import [dataset]

For example, to import CIFAR-10, use:

$ stock import torchvision.cifar10

Check out the new Quick Start tutorial and enjoy your new versioning ally 🙌

Reach out

If you’d like to have a peek into our vision and our upcoming developments, please send us a note at info@tensorwerk.com. In any case, we will be posting our updates regularly here on Substack. Have fun and stay tuned.


If you want to stay up to date with ideas, projects and plans for the future at tensorwerk, subscribe to our publication and receive the Heartbeat directly in your inbox.

Jun '20 tensorwerk Heartbeat

This is our sixth Heartbeat (wow, time flies) and here there are some news about what we did (RedisAI plugin for MLFlow), what we are doing now (RedisAI for training infrastructure), and what we will be doing in the next future (Hangar open discussion calls).

Every month we are sharing news on projects we are working on, conferences and events we attend, what are our plans for the future and everything that might be related to data.

RedisAI plugin for MLFlow

With the latest release of MLFlow (1.9) is now possible to use the new RedisAI plugin and deploy an MLFlow model directly on RedisAI without any extra effort from the user!

Installable with:

pip install mlflow-redisai

Check out the plugin and the documentation at https://github.com/RedisAI/mlflow-redisai. Kudos to Sherin for pulling this off 👏

RedisAI for training infrastructure 🔜

As the anticipation of what you will hear a few months from now, we are working as part of a larger team to provide infrastructure for an upcoming (very cool) technology in the deep learning space. This project takes RedisAI into a new territory, where its ability to efficiently serve tensors and computations mixed with Redis’ unique features make it a perfect tool for the job.

We cannot reveal all the details yet but fasten your seatbelts and be prepared to hear more 😉

Hangar open discussion calls

We want the Hangar community to be involved in the development. We want the Hangar community to be curious. We want the Hangar community to ask questions. We know that it will let us deliver a better product.

That’s why we are starting a series of discussion calls open to the community. We are sharing our technical conversations with you, in order to grow a more participative community. There will be calls where we discuss new features we want to integrate into Hangar; more “educational“ calls where we explain some internals to the curious; Q&A sessions dedicated to users; or maybe a combination of them 😉

It’s also a means to get to know you better, and hopefully also the other way around ❤️

Stay tuned on Twitter to know when we are starting, it will be very soon 📞

P.S.: We plan to record them and post them on YouTube, so don’t worry if you miss one.

Reach out

If you’d like to have a peek into our vision and our upcoming developments, please send us a note at info@tensorwerk.com. In any case, we will be posting our updates regularly here on Substack. Have fun and stay tuned.


If you want to stay up to date with ideas, projects and plans for the future at tensorwerk, subscribe to our publication and receive the Heartbeat directly in your inbox.

May '20 tensorwerk Heartbeat

Hey there! This is our fifth heartbeat and … what a month! We published our brand new website, we were at RedisConf 2020 presenting RedisAI GA 1.0 and our Luca has been interviewed twice (for a podcast and during a meetup). Read below to see what happened!

Every month we are sharing news on projects we are working on, conferences and events we attend, what are our plans for the future and everything that might be related to data.

Website

Now, with a fully-fledged professional website (many thanks to the Evoque team for the great work 👏), we can finally say to the world we are ready to be trusted as a solid company, able to compete for real-world challenges.

Now we have a wonderful contact form and we are ready to hear from you. Of course, we are keeping all of the other channels open, so feel free to get in touch also via email or Twitter.

Interview @ Chai Time Data Science

This month our Luca was invited for an interview on the Chai Time Data Science podcast, together with Eli Stevens and Thomas Viehmann. The topic of discussion was their new Manning book “Deep Learning with PyTorch“, expected to be published during August 2020. It has been recognized as the official PyTorch book and here you can read the Essential Excerpts from the book for free.

Talking about the idea and the proposal of writing a technical book on PyTorch, Luca said:

I really wanted to understand how things really work and it was a good chance to have to explain things to others. It’s a good opportunity to learn them yourself.

Here you can find the full interview (also available as only audio):

How OSS is changing the world? @ AWS User Group Pune

Our Luca has been interviewed also during a meetup of the AWS User Group Pune by Jayesh Ahire and the topic of conversation was Open Source Software.

Open-source software is a virtuous mechanism by which a piece of invention gets alive on its own, independently of the commercial exploitation. The viability and the guarantees you get around that software are prolonged over time because they’re not a result of the need of a company at a certain point in time (which can change anytime). And at the same time, it is an opportunity because it’s something that can be created unifying many minds, so it can attract minds from different backgrounds and something like this is impossible in a pure enterprise environment.

Watch the full interview:

Meet the people: Alessia Marcolini

Alessia (@viperale on Twitter) is a Computer Science undergraduate at UniTN and she is helping tensorwerk to grow the community behind the company and its products, writing tutorials, creating these heartbeats (hello 👋) and managing the social networks’ account.

Alessia is a Junior Research Assistant at MPBA lab @ Fondazione Bruno Kessler, working on machine learning / deep learning frameworks to integrate multiple medical imaging modalities and different clinical data to get more precise prognostic/diagnostic cancer biomarkers.

She is keen on contributing to the Python community: she is already a volunteer of the Italian Python Community since 2017, helping with the organisation of PyCon Italy (the national Python Conference, hosting each year 600+ international delegates). Since 2018, she also joined the organisation committee of EuroSciPy, the European Conference for Python in Science.

When not coding, she loves dancing (she has been studying hip hop for 15 years!) and drinking black tea and good gin.

Reach out

If you’d like to have a peek into our vision and our upcoming developments, please send us a note at info@tensorwerk.com. In any case, we will be posting our updates regularly here on Substack. Have fun and stay tuned.


If you want to stay up to date with ideas, projects and plans for the future at tensorwerk, subscribe to our publication and receive the Heartbeat directly in your inbox.

Apr '20 tensorwerk Heartbeat

Hey Folks! Here we come with our fourth Heartbeat: we are glad to present a super nice use case for Hangar, and yet very contemporary considering the situation we are facing right now with the SARS-CoV-2 global pandemic. Moreover, we will be talking about RedisConf 2020, the international Redis conference which is happening this week.

Every month we are sharing news on projects we are working on, conferences and events we attend, what are our plans for the future and everything that might be related to data.

A collaborative annotation tool for covid19 datasets

Today we are proud to present a practical use case for Hangar we are working on. We are building a collaborative image annotation tool on top of the secure foundations of Hangar, with the hope it could serve the community in these times of emergency.

Using this system, you get annotations versioning and the choice of the best backend storage for your data for free. Moreover, this enables collaborative dataset curation amongst different collaborators, without the hassle of maintaining a collection of (maybe) CSV files with names, timestamps, or even worse … formatted Excel files (yes - there are plenty of people still doing that).

The annotation interface is built on top LOST and it is based on a coarse point counting grid. Stereology has been proved to be an effective approach in reducing the cost of annotation, yet enabling the training of a segmentation model and achieve a satisfying performance.

You could either decide to distribute the annotation workload across annotators, assigning to each of them a different Hangar branch. Each person sees only a subset of the data and can carry out the task individually. When the annotators are done, you can simply merge all the branches and get the annotations for the whole dataset.

Otherwise, you could still assign a different Hangar branch for each person involved with the annotation process, but instead, let them see and annotate the whole dataset. This way you can get a bunch of different annotations for the same image, so you can stop relying on the eyes of a single radiologist. This would also help to overcome the bias coming from having one image seen by only one radiologist and therefore limiting possible batch effects.

If you want to have a closer look at the code, please visit https://github.com/hhsecond/coviddatastore.

RedisConf 2020 Takeaway, May 12-13

Like many other technical conferences in the world, due to the outbreak of COVID-19, RedisConf 2020 became a virtual event. The good news is that it became a free event, so every one of you can actually participate from their own couch, enjoying a live keynote, 50+ breakout sessions, a hackathon, 1:1 office hours with Redis experts, group chats, games, and more. Just tune in on May 12-13!

👉 Registration and more info at the official website. 👈

Our CEO Luca Antiga has also been invited to be a speaker for the conference! Make sure to follow his Breakout Session on RedisAI. You will discover the latest new features coming with the new release of RedisAI 1.0, including auto-batching, DAG commands, MLFlow integration and revamped docs. You’ll also get a glimpse of what’s baking for the next releases. In case you want to find out more about it, please check out our last heartbeat, which was entirely dedicated to RedisAI!

Meet the people: Luca Antiga

Luca (lantiga on Twitter and GitHub) is a co-founder and CEO at Tensorwerk.

As a kid, he started coding on his Sinclair ZX Spectrum 48k in the mid-’80s, but he didn’t do much with it until much later in life (he still thinks that having BASIC instructions stamped on the keyboard was a great way to get a kid’s attention). A bioengineer in training, he went on as a researcher in medical image analysis and cardiovascular biomechanics in the 2000’s. He picked up C++ and Python, and after noodling with connections between vascular morphology, computational geometry and fluid dynamics, he released the Vascular Modeling Toolkit in 2004, an open-source project that is still used to date in bioengineering departments. He later contributed to the Insight Toolkit and 3DSlicer, and authored scientific papers on these subjects.

In 2009 he left research to co-found Orobix, a company based in Bergamo (Italy) initially focused on medical image analysis, and that around 2014 became an AI engineering company operating in different sectors, such as healthcare, manufacturing, gaming, astrophysics. In 2017 Luca started contributing to PyTorch and was a core contributor for a couple of years. In the meantime he started co-authoring Deep Learning with PyTorch for Manning. As Orobix was developing, ideas for new tools filling the gaps in the AI tooling landscape came up. Those ideas converged in an opportunity of a new initiative stemming from the experience at Orobix, but focused on developing core tools for Software 2.0. This is how Tensorwerk came to be. ✨

At Tensorwerk he’s busy with directions and design, and he’s directly involved in the development of RedisAI. Along with family and work, Luca has a passion for listening to jazz and its surroundings. He spends a few hours a week trying to make some sense out of the sounds coming out of his guitar.

Reach out

If you’d like to have a peek into our vision and our upcoming developments, please send us a note at info@tensorwerk.com. In any case, we will be posting our updates regularly here on Substack. Have fun and stay tuned.


If you want to stay up to date with ideas, projects and plans for the future at [tensor]werk, subscribe to our publication and receive the Heartbeat directly in your inbox.

Mar '20 [tensor]werk Heartbeat

Hey there! While this SARS-CoV-2 global pandemic is affecting most of the world population, fortunately we were able to continue with our activities from NY, Italy and Bangalore and here comes the March’s Heartbeat to talk about one of our core products, RedisAI, and, hopefully, to take your mind off things.

Every month we are sharing news on projects we are working on, conferences and events we attend, what are our plans for the future and everything that might be related to data.

What’s going on?

We released Hangar 0.5! 🎉 As we mentioned in our Feb heartbeat, with this release we are introducing our last API breaking change, and for the better. In particular, you will find the new columns API replacing the old arraysets and metadata terminology. With an arrayset you could represent only tensors as a data type, while with a column will be possible to represent also strings, replacing the functionality of metadata in a way that is simpler, more effective and extensible.

This month we also submitted a PR to the MLflow project to support our RedisAI plugin, in order to seamlessly deploy MLflow models directly to RedisAI. Stay tuned for the updates!

RedisAI

While we briefly introduced RedisAI in our very first Heartbeat a couple of months ago, we would like to explain better what kind of problems it solves and why it’s a convenient tool to adopt in your stack.

Before diving into the details, let’s take a step back: what is Redis? Redis, which stands for Remote Dictionary Server, is a fast, open-source, in-memory key-value data store for use as a database, cache, message broker, and queue.

At a glance, RedisAI is a Redis module for serving tensors and executing deep learning models, born from a collaboration between [tensor]werk and RedisLabs. With the RedisAI module, Redis can store another data type, the Tensor.

As our CEO Luca Antiga use to say “Don't say AI until you productionize“, taking your deep learning model prototype out of that Jupyter Notebook and putting it to production is a critical but required step for real-world AI applications.

The strongest point on RedisAI is its easiness:

  • It is easy to work with models defined in different deep learning frameworks. In fact, RedisAI understands PyTorch and TensorFlow models directly, plus models saved in the ONNX interchange format (from almost any machine learning framework, including scikit-learn). RedisAI can actually execute models from multiple frameworks as part of a single pipeline.

  • It is easy to switch between devices you want your model to be executed on and there is no separate workflow for GPU or CPU.

  • It is easy to become an MLOps engineer: if you already have Redis in your stack, setting up RedisAI is a no-brainer and your DevOps engineers don’t need to learn anything else. Also because setting up RedisAI is a matter of 5-6 shell commands (or one Docker command 😉).

  • And since you are using Redis in the back, it is easy to scale your production runtime to a multi-node cluster setup with failover.

  • It is easy to manage the deployment even without Python - the language of choice of deep learning practitioners - if you don’t want to include it in your production tech stack! We have clients available for Python, Go and Java; for other languages, users can use native Redis Client libraries. Have a look at this repo for a showcase of Python, Go, Node.js and bash clients.

  • It is easy to reduce response time: RedisAI is architected in a way that it enables users to keep the data local, keep the model hot and keep the stack short. Fewer moving parts (data) mean less cost and fewer headaches.

Oh, and not to mention that RedisAI is ~3 times faster than a REST API server a user would build using Django or Flask.

Although being written a year ago, read this blog post by our Sherin for a more comprehensive walkthrough of the features of RedisAI, including a comparison of existing deep learning runtimes, details on the installation and a practical example of an object detector with YOLO v3 step by step.

Meet the people: Sherin Thomas

Sherin, a.k.a hhsecond, is a senior developer at [tensor]werk started working with the team even before the company was founded. Sherin had spent his fair share of time on each tool [tensor]werk developed so far. He also created Stockroom, a high-level data + model + parameter versioning platform built on the foundation of Hangar and git. Sherin is expanding our super distributed team across the globe by working (and staying) from Bangalore. He is an author, speaker and also teaches students (and professionals) about programming in general and about different components in software 2.0.
Outside of work, Sherin is still a programmer 😁 He reads a lot (about a multitude of different topics spanning through psychology, tech, black holes, aliens, biology, management, etc), and is quite attached to the Bangalore startup ecosystem. He believes the current education system is still running with the decades-old methodologies and ideas and it should be rebuilt and hence helped to build fullstackengineering.ai. He is also fond of farming (although he never did farming, apparently) and probably use some AI stuff he copied from GitHub to build a robot to disrupt the Indian agriculture industry (that's what he says 😛).

Reach out

If you’d like to have a peek into our vision and our upcoming developments, please send us a note at info@tensorwerk.com. In any case, we will be posting our updates regularly here on Substack. Have fun and stay tuned.


If you want to stay up to date with ideas, projects and plans for the future at [tensor]werk, subscribe to our publication and receive the Heartbeat directly in your inbox.

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