Since our first release in August 2017 a lot of amazing events happened around Supervisely. Community keeps growing and today around 5000 people are using our platform. We have gone a long way, received positive feedback, implemented new features, fixed bugs and now understand the people’s needs much better.
Solving the GPU puzzle
But there was one thing that makes us really sad:
For a long time, Community Edition remained very limited compared to Enterprise Edition. Our enterprise customers had an opportunity to use Supervisely as a complete computer vision platform whereas Community Edition was more like a set of annotation tools. Why is it not enough?
When you build an AI product, you have to go through three main stages one by one — annotation, data preparation and neural network building. If one stage is missing or inefficient, the development process slows down in a significant way.
Supervisely was designed to cover these main stages:
In our case, neural network module was missed in Community Edition and user experience was incomplete. But we managed to find a solution.
The final piece
Today we are happy to announce the final piece of our computer vision platform — now everyone can train & run latest neural networks with Supervisely.
To make it possible, we had to overcome one huge challenge: “Where do we get tons of GPUs resources so that our community could use neural networks?”
Be smart and use bare-metal — it’s OK, we personally know tons of large companies doing this. But, again, you can always deploy an agent in AWS or Azure if don’t have GPU on your hands.
2. No lock-in
Google, Amazon, Microsoft — they all offer cloud AI services. The problem is, essentially, they force you to use their computational resources, store your data on their servers, use their software — to keep you forever.
With Supervisely you can run models and store data anywhere you want — it might be your local PC or AWS server — the choice is yours.
3. Effective usage of GPUs
Have you ever had that unsatisfied feeling that too many research ideas were left untested? Not anymore! Connect as much computers as you want, run training processes with various data samples or training metaparameters and then aggregate and estimate the results.
In other words, connect computers to your private computational cluster and conduct tons of experiments with no costs.
4. Reproducible research
Building computer vision product based on neural networks implies a lot of experiments, especially on the early stages of development. These experiments involves trying to annotate objects in different ways, playing with different neural network architectures, understanding how to better augment the data and so on.
Supervisely was designed to keep track of experiments that users perform. So scripts for data transformations, training metaparameters are kept and organized in the way that it is easy to see and reproduce the actions that lead to “the most accurate model”.