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https://github.com/modelfoxdotdev/modelfox
ModelFox makes it easy to train, deploy, and monitor machine learning models.
https://github.com/modelfoxdotdev/modelfox
automl developer-tools elixir elixir-lang go golang javascript js machine-learning mlops python python3 ruby ruby-on-rails rust rust-crate rust-lang rust-library rustlang
Last synced: about 20 hours ago
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ModelFox makes it easy to train, deploy, and monitor machine learning models.
- Host: GitHub
- URL: https://github.com/modelfoxdotdev/modelfox
- Owner: modelfoxdotdev
- License: other
- Created: 2021-04-23T19:25:24.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2024-08-02T17:23:15.000Z (4 months ago)
- Last Synced: 2024-12-04T13:06:01.347Z (8 days ago)
- Topics: automl, developer-tools, elixir, elixir-lang, go, golang, javascript, js, machine-learning, mlops, python, python3, ruby, ruby-on-rails, rust, rust-crate, rust-lang, rust-library, rustlang
- Language: Rust
- Homepage:
- Size: 27.4 MB
- Stars: 1,469
- Watchers: 27
- Forks: 64
- Open Issues: 40
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
ModelFox makes it easy to train, deploy, and monitor machine learning models.
Train a model from a CSV file on the command line. Make predictions from Elixir, Go, JavaScript, PHP, Python, Ruby, or Rust. Learn about your models and monitor them in production from your browser.
# ModelFox
[Discord](https://discord.gg/jT9ZGp3TK2)
ModelFox makes it easy to train, deploy, and monitor machine learning models.
- Run `modelfox train` to train a model from a CSV file on the command line.
- Make predictions with libraries for [Elixir](https://hex.pm/packages/modelfox), [Go](https://pkg.go.dev/github.com/modelfoxdotdev/modelfox-go), [JavaScript](https://www.npmjs.com/package/@modelfoxdotdev/modelfox), [PHP](https://packagist.org/packages/modelfox/modelfox), [Python](https://pypi.org/project/modelfox), [Ruby](https://rubygems.org/gems/modelfox), and [Rust](https://lib.rs/crates/modelfox).
- Run `modelfox app` to learn more about your models and monitor them in production.
### Install
You can install the modelfox CLI by either downloading the binary from the [latest github release](https://github.com/modelfoxdotdev/modelfox/releases/tag/v0.8.0), or by building from source.
### Train
Train a machine learning model by running `modelfox train` with the path to a CSV file and the name of the column you want to predict.
```
$ modelfox train --file heart_disease.csv --target diagnosis --output heart_disease.modelfox
✅ Loading data.
✅ Computing features.
🚂 Training model 1 of 8.
[==========================================> ]
```
The CLI automatically transforms your data into features, trains a number of linear and gradient boosted decision tree models to predict the target column, and writes the best model to a `.modelfox` file. If you want more control, you can provide a config file.
### Predict
Make predictions with libraries for [Elixir](https://hex.pm/packages/modelfox), [Go](https://pkg.go.dev/github.com/modelfoxdotdev/modelfox-go), [JavaScript](https://www.npmjs.com/package/@modelfoxdotdev/modelfox), [PHP](https://packagist.org/packages/modelfox/modelfox), [Python](https://pypi.org/project/modelfox), [Ruby](https://rubygems.org/gems/modelfox), and [Rust](https://lib.rs/modelfox).
```javascript
let modelfox = require("@modelfoxdotdev/modelfox")
let model = new modelfox.Model("./heart_disease.modelfox")
let input = {
age: 63,
gender: "male",
// ...
}
let output = model.predict(input)
console.log(output)
```
```javascript
{ className: 'Negative', probability: 0.9381780624389648 }
```
### Inspect
Run `modelfox app`, open your browser to http://localhost:8080, and upload the model you trained.
- View stats and metrics.
- Tune your model to get the best performance.
- Make example predictions and get detailed explanations.
### Monitor
Once your model is deployed, make sure that it performs as well in production as it did in training. Opt in to logging by calling `logPrediction`.
```javascript
// Log the prediction.
model.logPrediction({
identifier: "6c955d4f-be61-4ca7-bba9-8fe32d03f801",
input,
options,
output,
})
```
Later on, if you find out the true value for a prediction, call `logTrueValue`.
```javascript
// Later on, if we get an official diagnosis for the patient, log the true value.
model.logTrueValue({
identifier: "6c955d4f-be61-4ca7-bba9-8fe32d03f801",
trueValue: "Positive",
})
```
Now you can:
- Look up any prediction by its identifier and get a detailed explanation.
- Get alerts if your data drifts or metrics dip.
- Track production accuracy, precision, recall, etc.
## Building from Source
This repository is a Cargo workspace, and does not require anything other than the latest nightly Rust toolchain to get started with.
1. Install [Rust](rust-lang.org) on Linux, macOS, or Windows.
2. Clone this repo and `cd` into it.
3. Run `cargo run` to run a debug build of the CLI.
If you are working on the app, run `scripts/app/dev`. This rebuilds and reruns the CLI with the `app` subcommand as you make changes.
To install all dependencies necessary to work on the language libraries and build releases, install [Nix](https://nixos.org) with [flake support](https://nixos.wiki/wiki/Flakes), then run `nix develop` or set up [direnv](https://github.com/direnv/direnv).
If you want to submit a pull request, please run `scripts/fmt` and `scripts/check` at the root of the repository to confirm that your changes are formatted correctly and do not have any errors.
## License
All of this repository is MIT licensed, except for the `crates/app` directory, which is source available and free to use for testing, but requires a paid license to use in production.