12 ms·

Show HN: Tangram – Train a model from a CSV file on the command line

94 points by nitsky 5y ago

nitsky 5y ago
Hi HN! We are Isabella and David, and we're excited to share Tangram, our attempt to make ML easy for programmers who are not experts. With Tangram, you train a model from a CSV file on the command line, use your model from one of many languages (so far we have libraries for Elixir, Go, JavaScript, Python, Ruby, and Rust), and learn about your models and monitor them in production from a web app. There's a video on our homepage (https://www.tangram.dev https://www.tangram.dev) and we're on GitHub at https://github.com/tangramdotdev/tangram https://github.com/tangramdotdev/tangram. Over the past few months we have been working with a handful of early users. A team at a small company had a TensorFlow model deployed as a Flask service consumed by their Elixir app. They replaced it with a Tangram model because they didn't want to maintain a server separate from their monolith. A team of front end engineers at a large company was looking for a way to to train and deploy models on their own, without the overhead of involving their data scientists, machine learning engineers, or backend engineers. They trained a model on their own and embedded it directly in their React front-end with the Tangram JavaScript library that makes predictions with WebAssembly. Tangram is written entirely in Rust, from the core machine learning algorithms, to the bindings for each language, to the front and back end of the web application. We have benefited from Rust's fast performance, strong typing, convenient tooling, and high quality libraries (serde, tokio, hyper, sqlx, and more). We hope to make Tangram a sustainable business with the open core business model. The CLI and language libraries are MIT licensed, while the web application is source available, free to use for testing, but requires a paid license to use in production. We would love to hear your feedback. Give it a try and let us know what you think!
emhagman 5y ago
We use this at Drafted (https://www.drafted.us https://www.drafted.us). It's been huge in helping us quickly train and deploy our classification models. The bindings into Elixir and Go helped us keep everything in our code base. We got to get rid of our Python TensorFlow app which we had to deploy multiple instances of for acceptable performance. All gone with Tangram. One model, any language. I would definitely give this a try if you can.
drewcoo 5y ago
name collision: https://mathigon.org/tangram https://mathigon.org/tangram
- jcoder 5y ago
  Isn’t this a bit like saying Ruby collides with a gem, python with a snake, or Java with an island?
  - isabellat 5y ago
    Yes, the name collision was very intentional :).
- jhgb 5y ago
  Personally I'm much more confused by the existence of https://github.com/tangrams/tangram https://github.com/tangrams/tangram .
ujeezy 5y ago
I love the simplicity - this tickles my brain in the same way that Firebase did when I first saw it :) Well done! Looking forward to playing with it.
- nitsky 5y ago
  Cool! Let us know how it works for you, and open an issue on GitHub if you run into any trouble!
tdeck 5y ago
Reminds me of vowpal-wabbit, which also has a command line. Their website looks very corporate now but it's a free tool: https://vowpalwabbit.org/tutorials/cmd_first_steps.html https://vowpalwabbit.org/tutorials/cmd_first_steps.html Not sure how its modeling techniques have kept up with the state of the art.
- nitsky 5y ago
  There are a lot of similarities with vowpal-wabbit. The biggest difference is that tangram has libraries for a lot of popular languages (js, go, ruby, elixir, etc.), and has a web application for learning about your model and monitoring it in production.
Bostonian 5y ago
You could give the user an option to specify that the CSV file represents equally-spaced time series data, in which case you fit a time series model to predict the target column.
- nitsky 5y ago
  Great idea! We definitely want to do this. We have an open issue to track it: https://github.com/tangramdotdev/tangram/issues/28 https://github.com/tangramdotdev/tangram/issues/28.
  - Bostonian 5y ago
    Good. A further thought is that if you allow time series models, you could allow the user to specify a time column in addition to the column of the response variable. Then you can fit seasonal time series models to the data.
    - nitsky 5y ago
      Yes! What we have in mind is this: `tangram train --file data.csv --target price --date date` This would predict the "price" column, using the date in the "date" column. Internally, we can generate features from the date column like "hour of day", "day of week", "season", etc. that can help the model learn periodic patterns. Thanks for the great suggestion.
IAmEveryone 5y ago
Really like the initial impression. I’ve been playing around with Apple’s CreateML tool, which seems to be similar in capability, maybe(?), but is a GUI app that forces me into a process where I need to use switch from ruby to GUIs to XCode and Swift to the cLI… The reference in the name & the monospaced font work well to establish a sense of being a developer‘s company, but not just anyone but someone with a background in math(?). Perfect marketing to get me, although I‘m not a lucrative prospect. Is it possible that you’d catch bigger fish by going corporate? Possible, yes. But it’s my impression this has and still is shifting drastically. One meta-issue for now: somehow, I always read the imprint when I am checking out some software or service. I can’t really make a case why it’s helpful, and you are obviously free to maintain an air of mystery. But if it’s just an oversight or you haven’t gotten around to it, just naming a city & country goes a long way to convincing me that you exist. Short bios are also useful, or links to your Twitter accounts, although J found those with only minimal stalking.(This preference may just be a result from my home country of Germany requiring such data and making it possible to get into the habit).
- nitsky 5y ago
  Hey, you are right, we should definitely add an "about" page to our site. In the mean time... David: https://www.linkedin.com/in/david-yamnitsky https://www.linkedin.com/in/david-yamnitsky Isabella: https://www.linkedin.com/in/isabella-tromba/ https://www.linkedin.com/in/isabella-tromba/ I promise we exist and are real humans! But that is exactly what the aliens would say.
  - IAmEveryone 5y ago
    Yeah, I’ve avoid LinkedIn precisely because I abhor the smell of corpses/corporates it gives off. > I promise we exist and are real humans! But that is exactly what the aliens would say That… is a go-to joke I keep using on OKCupid, only with axe murderers instead of aliens. I’ll stop hitting on you now, but might write up some impressions of the actual product in the next few days.
    - nitsky 5y ago
waterfirezero 5y ago
great works! Really impress with your software. However, how to convert tangram model to scikit model and vice versa?
- nitsky 5y ago
  Thanks! At the moment, we do not have any ability to convert to/from scikit, but this is definitely something we would like to consider in the future. This would give users of scikit the ability to make predictions in production from their preferred language, and access to tangram's reporting and monitoring app.
civilized 5y ago
Why would it be desirable to train a model from a CSV on the command line? Is opening RStudio or IPython and running a couple commands the hard part of data science? Writing a data science pipeline with command line tools really just means writing a data science pipeline in your terminal's shell programming language. If that sounds like a good idea to you, I'd be curious to hear why.
- nitsky 5y ago
  You're right, CLI vs RStudio or IPython is not a big improvement. The real benefit here is that you can use your model from Go, JavaScript, Ruby, and other languages without having to include the Python or R ecosystems in your tech stack.
  - civilized 5y ago
    Having a format that can be used in any language is great, and I agree this is the core problem. How does your .tangram format compare to existing attempts at standardized machine learning model formats? The only one I can think of off the top of my head is PMML and it seems like it never got much traction. Do you think tangram has a better approach to the problem?
    - nitsky 5y ago
      Unfortunately, tangram does not help here, it just adds yet another format to the list. The reason it needs its own format is because .tangram files include all the model reporting information that shows up in the web application.
      - civilized 5y ago
        Another interesting direction would be to make model objects from existing popular tools (sklearn, xgboost, pytorch, etc) more portable to other languages.
        5y ago
anigbrowl 5y ago
This is very cool, finally the blue sky science of 30 years ago has descended to my humble command line. Like the website design too.
- nitsky 5y ago
  Thanks for the kind comment. We spent a lot of time trying to make the website feel hacker friendly, so programmers know that tangram is not made only for someone with a PhD.
- isabellat 5y ago
  We're so happy you mentioned the command line! We are really focused on building ML tools with an interface that programmers are most comfortable with -- a lot of devs don't want to use jupyter notebooks. We've considered building a whole TUI showing all kinds of cool UI like CPU utilization, memory usage, training loss etc while training :)
  - anigbrowl 5y ago
    I'm really more a GUI person tbh but when I'm figuring out a new thing it's nice to play with a working binary before trying to write any code. I don't need to deal with ML so while I find it interesting and cool I've avoided getting very deep into it because it looked like a huge work/resource commitment just to indulge my curiosity. I'm really happy to have something simple I can just mess around with and figure out if/how I want to explore further. Thanks!
surajs 5y ago
this is the kind of tools we need not gpt3 or copilot
tobiasks 5y ago
This is very nice and I really like the CLI aspect. I would have one feature request: Serve the model via GrPC. Im running ML in production for the last 4 years in the field of biology and need > million predictions per hour. Load-balacing on multiple servers (and staying with http request based predictions) is not an options as the latency would kill the application. Am I in such niche? Or other question: are there many more people that run fine-tuned TF servings/TensorRT servers to keep up with the production need?
pavlovskyi 5y ago
Really like the simplicity! At my actual workplace we are dealing with curse of model monitoring for a long time, because there was always a issue with intepretability and ease of adding new features (indicators, etc). And also, expectation of use of monitoring tools are much higher that its usability in real case scenarios. Great work!

94 points by nitsky 5y ago

Hi HN! We are Isabella and David, and we're excited to share Tangram, our attempt to make ML easy for programmers who are not experts. With Tangram, you train a model from a CSV file on the command line, use your model from one of many languages (so far we have libraries for Elixir, Go, JavaScript, Python, Ruby, and Rust), and learn about your models and monitor them in production from a web app. There's a video on our homepage (https://www.tangram.dev https://www.tangram.dev) and we're on GitHub at https://github.com/tangramdotdev/tangram https://github.com/tangramdotdev/tangram. Over the past few months we have been working with a handful of early users. A team at a small company had a TensorFlow model deployed as a Flask service consumed by their Elixir app. They replaced it with a Tangram model because they didn't want to maintain a server separate from their monolith. A team of front end engineers at a large company was looking for a way to to train and deploy models on their own, without the overhead of involving their data scientists, machine learning engineers, or backend engineers. They trained a model on their own and embedded it directly in their React front-end with the Tangram JavaScript library that makes predictions with WebAssembly. Tangram is written entirely in Rust, from the core machine learning algorithms, to the bindings for each language, to the front and back end of the web application. We have benefited from Rust's fast performance, strong typing, convenient tooling, and high quality libraries (serde, tokio, hyper, sqlx, and more). We hope to make Tangram a sustainable business with the open core business model. The CLI and language libraries are MIT licensed, while the web application is source available, free to use for testing, but requires a paid license to use in production. We would love to hear your feedback. Give it a try and let us know what you think!

emhagman 5y ago

We use this at Drafted (https://www.drafted.us https://www.drafted.us). It's been huge in helping us quickly train and deploy our classification models. The bindings into Elixir and Go helped us keep everything in our code base. We got to get rid of our Python TensorFlow app which we had to deploy multiple instances of for acceptable performance. All gone with Tangram. One model, any language. I would definitely give this a try if you can.

drewcoo 5y ago

name collision: https://mathigon.org/tangram https://mathigon.org/tangram

jcoder 5y ago
Isn’t this a bit like saying Ruby collides with a gem, python with a snake, or Java with an island?
- isabellat 5y ago
  Yes, the name collision was very intentional :).
jhgb 5y ago
Personally I'm much more confused by the existence of https://github.com/tangrams/tangram https://github.com/tangrams/tangram .

ujeezy 5y ago

I love the simplicity - this tickles my brain in the same way that Firebase did when I first saw it :) Well done! Looking forward to playing with it.

nitsky 5y ago
Cool! Let us know how it works for you, and open an issue on GitHub if you run into any trouble!

tdeck 5y ago

Reminds me of vowpal-wabbit, which also has a command line. Their website looks very corporate now but it's a free tool: https://vowpalwabbit.org/tutorials/cmd_first_steps.html https://vowpalwabbit.org/tutorials/cmd_first_steps.html Not sure how its modeling techniques have kept up with the state of the art.

nitsky 5y ago
There are a lot of similarities with vowpal-wabbit. The biggest difference is that tangram has libraries for a lot of popular languages (js, go, ruby, elixir, etc.), and has a web application for learning about your model and monitoring it in production.

Bostonian 5y ago

You could give the user an option to specify that the CSV file represents equally-spaced time series data, in which case you fit a time series model to predict the target column.

nitsky 5y ago
Great idea! We definitely want to do this. We have an open issue to track it: https://github.com/tangramdotdev/tangram/issues/28 https://github.com/tangramdotdev/tangram/issues/28.
- Bostonian 5y ago
  Good. A further thought is that if you allow time series models, you could allow the user to specify a time column in addition to the column of the response variable. Then you can fit seasonal time series models to the data.
  - nitsky 5y ago
    Yes! What we have in mind is this: `tangram train --file data.csv --target price --date date` This would predict the "price" column, using the date in the "date" column. Internally, we can generate features from the date column like "hour of day", "day of week", "season", etc. that can help the model learn periodic patterns. Thanks for the great suggestion.

IAmEveryone 5y ago

Really like the initial impression. I’ve been playing around with Apple’s CreateML tool, which seems to be similar in capability, maybe(?), but is a GUI app that forces me into a process where I need to use switch from ruby to GUIs to XCode and Swift to the cLI… The reference in the name & the monospaced font work well to establish a sense of being a developer‘s company, but not just anyone but someone with a background in math(?). Perfect marketing to get me, although I‘m not a lucrative prospect. Is it possible that you’d catch bigger fish by going corporate? Possible, yes. But it’s my impression this has and still is shifting drastically. One meta-issue for now: somehow, I always read the imprint when I am checking out some software or service. I can’t really make a case why it’s helpful, and you are obviously free to maintain an air of mystery. But if it’s just an oversight or you haven’t gotten around to it, just naming a city & country goes a long way to convincing me that you exist. Short bios are also useful, or links to your Twitter accounts, although J found those with only minimal stalking.(This preference may just be a result from my home country of Germany requiring such data and making it possible to get into the habit).

nitsky 5y ago
Hey, you are right, we should definitely add an "about" page to our site. In the mean time... David: https://www.linkedin.com/in/david-yamnitsky https://www.linkedin.com/in/david-yamnitsky Isabella: https://www.linkedin.com/in/isabella-tromba/ https://www.linkedin.com/in/isabella-tromba/ I promise we exist and are real humans! But that is exactly what the aliens would say.
- IAmEveryone 5y ago
  Yeah, I’ve avoid LinkedIn precisely because I abhor the smell of corpses/corporates it gives off. > I promise we exist and are real humans! But that is exactly what the aliens would say That… is a go-to joke I keep using on OKCupid, only with axe murderers instead of aliens. I’ll stop hitting on you now, but might write up some impressions of the actual product in the next few days.
  - nitsky 5y ago

waterfirezero 5y ago

great works! Really impress with your software. However, how to convert tangram model to scikit model and vice versa?

nitsky 5y ago
Thanks! At the moment, we do not have any ability to convert to/from scikit, but this is definitely something we would like to consider in the future. This would give users of scikit the ability to make predictions in production from their preferred language, and access to tangram's reporting and monitoring app.

civilized 5y ago

Why would it be desirable to train a model from a CSV on the command line? Is opening RStudio or IPython and running a couple commands the hard part of data science? Writing a data science pipeline with command line tools really just means writing a data science pipeline in your terminal's shell programming language. If that sounds like a good idea to you, I'd be curious to hear why.

nitsky 5y ago
You're right, CLI vs RStudio or IPython is not a big improvement. The real benefit here is that you can use your model from Go, JavaScript, Ruby, and other languages without having to include the Python or R ecosystems in your tech stack.
- civilized 5y ago
  Having a format that can be used in any language is great, and I agree this is the core problem. How does your .tangram format compare to existing attempts at standardized machine learning model formats? The only one I can think of off the top of my head is PMML and it seems like it never got much traction. Do you think tangram has a better approach to the problem?
  - nitsky 5y ago
    Unfortunately, tangram does not help here, it just adds yet another format to the list. The reason it needs its own format is because .tangram files include all the model reporting information that shows up in the web application.
    - civilized 5y ago
      Another interesting direction would be to make model objects from existing popular tools (sklearn, xgboost, pytorch, etc) more portable to other languages.
      - 5y ago

anigbrowl 5y ago

This is very cool, finally the blue sky science of 30 years ago has descended to my humble command line. Like the website design too.

nitsky 5y ago
Thanks for the kind comment. We spent a lot of time trying to make the website feel hacker friendly, so programmers know that tangram is not made only for someone with a PhD.
isabellat 5y ago
We're so happy you mentioned the command line! We are really focused on building ML tools with an interface that programmers are most comfortable with -- a lot of devs don't want to use jupyter notebooks. We've considered building a whole TUI showing all kinds of cool UI like CPU utilization, memory usage, training loss etc while training :)
- anigbrowl 5y ago
  I'm really more a GUI person tbh but when I'm figuring out a new thing it's nice to play with a working binary before trying to write any code. I don't need to deal with ML so while I find it interesting and cool I've avoided getting very deep into it because it looked like a huge work/resource commitment just to indulge my curiosity. I'm really happy to have something simple I can just mess around with and figure out if/how I want to explore further. Thanks!

surajs 5y ago

this is the kind of tools we need not gpt3 or copilot

tobiasks 5y ago

This is very nice and I really like the CLI aspect. I would have one feature request: Serve the model via GrPC. Im running ML in production for the last 4 years in the field of biology and need > million predictions per hour. Load-balacing on multiple servers (and staying with http request based predictions) is not an options as the latency would kill the application. Am I in such niche? Or other question: are there many more people that run fine-tuned TF servings/TensorRT servers to keep up with the production need?

pavlovskyi 5y ago

Really like the simplicity! At my actual workplace we are dealing with curse of model monitoring for a long time, because there was always a issue with intepretability and ease of adding new features (indicators, etc). And also, expectation of use of monitoring tools are much higher that its usability in real case scenarios. Great work!