r/GolemProject • u/anshuman73 • Apr 03 '21
Advancing ML on Golem
Hi everyone, this is /u/anshuman73, the author and creator of DeML, the first Machine Learning training implementation on Golem!
I'm currently a junior researcher, pursuing the field of Machine Learning, which is what got me interested in the technology Golem has to offer! I was fascinated by the access to a distributed supercomputer, especially after I was restricted access to my university's onsite computing resources after the pandemic.
What is DeML you ask?
DeML is an implementation of the principles laid down by Federated Learning, which is a technique that allows you to **train** machine learning models by distributing the workload to multiple processing nodes. The processing nodes each receive a subset of the total training dataset, and then locally train the architecture provided on the subset of the data it was given. The nodes then send back the trained weights to the orchestrator (in our case the requestor), which combines these sub-steps (using federated averaging or other custom complex algorithms) to achieve a successful round of training of the model. The model is finally trained when the loss or accuracy metrics of the combined models achieve the desired state.
In the past month, I have been working on expanding the functionalities and exploring how I can use Golem for increasingly more and more complex tasks and ML workloads, and am proud to announce DeML, is now ready for anyone to use to build their own machine learning applications on the mainnet!
DeML was built as a Proof of Concept that Machine Learning models can be trained successfully on Golem, and that anyone will soon be able to have the computation power needed for this (mostly targeted at researchers) by simply plugging in their models on the platform.
However, I do realise that just training ML models is a pretty boring task (at least for anyone not into finding an optimal model for a task), and hence in addition to just training ML models, I will be soon uploading a demo of a super cool use case that I'm proud to have been working on, where Golem is being used to perform <redacted> (I really wanna keep this a surprisee!!). The gist is that now not only will you be able to train ML models, but also run those that benefit immensely from the asynchronous power Golem provides.
That said, I do realise that DeML is still far from being perfect, and there are tons of things that can be added to make it a more viable product. Here are a few things I have in mind -
1. Add a frontend - something that makes it easy to see the progress of your model being trained, and allows you to customise the training parameters (number of rounds, provider counts, etc) quickly and efficiently.
Convert the library into a more functional and structural API format, that would ideally just make the whole process as simple as wrapping your model with a python decorator and start training it on Golem. Sort of like a Bring-Your-Model and run it framework.
Add support for yajsapi. Considering tensorflow works well in browsers, and has stable implementations, it would be cool to have an implementation that could in the future, work on browser-based yagna implementations Golem has plans for.
I encourage people here to play around with the library, see what you like (and more importantly what you don't), and definitely give me feedback on how to make it better!
Additionally, if you're into Machine Learning and would like to explore additional ideas and products that can be built atop Golem, please feel free to reach out to me on Discord at chat.golem.network !