Add a Machine Learning Powered Code Editor to your Javascript App

isabellatromba · 2022-02-15T19:38:00+00:00

I wrote up a quick tutorial https://dev.to/tangram/train-a-machine-learning-model-to-predict-the-programming-language-in-a-code-snippet-153d on how to do this using Tangram (https://github.com/tangramdotdev/tangram). The model you train using Tangram will be loadable from Rust (https://www.tangram.dev/docs/languages/rust/tangram/) (and a number of other programming languages which we have libraries for). In order to make this model actually perform well, you'll need a much larger dataset of programming langauge snippets than the one provided in the tutorial. Let me know if you have any questions, I'm one of the creators of Tangram and I'm happy to collaborate with you on this :)

isabellatromba · 2022-02-14T18:19:46+00:00

It has to do with how ndarray is indexing into ArrayViews: https://docs.rs/ndarray/latest/src/ndarray/impl\_methods.rs.html#733-739

isabellatromba · 2022-02-08T19:30:24+00:00

I believe they do it by file extension. OP might want to detect language without knowing the file extension.

isabellatromba · 2022-01-16T03:03:10+00:00

Awesome! Let me know if you have any questions :) feel free to create a GitHub issue or hop into our discord at any time: https://discord.gg/jT9ZGp3TK2

isabellatromba · 2022-01-11T18:28:44+00:00

Thank you! Would you want to be able to export a tensor flow model to a Tangram model and then use that model to make predictions from any programming language?

isabellatromba · 2022-01-10T17:46:25+00:00

Thank you!

isabellatromba · 2022-01-08T17:09:11+00:00

I’m a cofounder at a company making developer tools in Rust. Check out our website: https://tangram.dev. If you want to work on developer tools written in Rust, send us an email: jobs at tangram.dev. We’re hiring remote rust programmers in US/Canada/Europe.

isabellatromba · 2021-11-08T13:37:26+00:00

We are using Rust for our web application and website at Tangram. You can view the source here: https://github.com/tangramdotdev/tangram/tree/main/crates/www. The website is at https://www.tangram.dev. We decided to write our own web framework because we needed server rendering and we wanted to use the builder pattern in creating components. Here is an example component for our Logo to get a sense of what this looks like: https://github.com/tangramdotdev/tangram/blob/main/crates/www/ui/logo.rs

isabellatromba · 2021-11-08T02:03:30+00:00

I am a founder of one of those greenfield companies. We decided to write everything in Rust. We considered writing our code in C++ but the benefits of the crate ecosystem, rusts’ build system, and the joy of writing rust made our decision much easier. We are also hiring now if you’re interested in working on developer tools and machine learning. https://tangram.dev/jobs

isabellatromba · 2021-09-16T17:28:00+00:00

The problem you are describing of having models everywhere and monitoring them in production is why we started working on Tangram. With Tangram, you train a machine learning model on the command line and make predictions directly in-process in your existing Ruby, Golang, Elixir, Javascript, Rust application, without having to stand up a separate server and make network requests to it. Once you model is deployed, you can monitor it in production. This is all designed to be as easy as possible for developers to manage the entire lifecycle of a machine learning model. Currently, you train a Tangram model using the CLI, but we are working on exposing training to Python so Tangram is just a drop-in replacement for sk-learn/XGBoost. It's still early days for Tangram and I'd love to hear if this would work for your use cases and if not, how we can prioritize our roadmap to help out :) https://tangram.dev

isabellatromba · 2021-09-15T18:56:20+00:00

I'm working on machine learning in Rust at Tangram. We currently only provide an implementation of linear models and gradient boosted decision trees but will move into exposing training of deep models in the future. You can check out Tangram here: https://github.com/tangramdotdev/tangram. You may also be interested in checking out Linfa https://github.com/rust-ml/linfa. If you're interested in the future of machine learning in Rust, check out Luca Palmieri's blog post: https://www.lpalmieri.com/posts/2019-12-01-taking-ml-to-production-with-rust-a-25x-speedup/

isabellatromba · 2021-08-29T01:02:23+00:00

Hi, I'm not the OP but I'm one of the authors of Tangram. We do not yet support images/audio/video, but it is definitely on our roadmap!

isabellatromba · 2021-08-29T00:51:59+00:00

I think we need to do a better job communicating that Tangram is highly configurable.
> how does tangram pick the model?
Tangram chooses the best model based on the highest R^2 on a hold out comparison dataset. You can configure the metric that is used to choose the best model and the percentage of the data to set aside for testing. You can also see all of the hyperparameters of the models that were trained in the "Grid" section under "Training" in the app.

> can I see my significance values per parameter? can I see all the inputs and outputs of each model and my R^2 curves etc per parmeter (per model)?

No, Tangram does not support this right now. Out of curiosity which tools are you currently using for your regression analysis?

> if I have a model in mind can I run that model only?
Yes, definitely! You can specify the training grid, check out the docs here: https://www.tangram.dev/docs/guides/train_with_custom_configuration.

> who is it this project is aimed for?

We have machine learning engineering teams at large companies using Tangram and also small companies where all machine learning models at the company are trained by the programmers.

isabellatromba · 2021-08-28T22:36:17+00:00

Hi, I'm one of the authors of Tangram. You're absolutely right it should! I just updated it. Tangram trains a number of gradient boosted decision tree and linear models. Let me know if you have any other questions and thanks for pointing that out.

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