Made to fulfill the requirements of graduation in Bandung Institute of Technology as an Informatics Engineer (2018). This repository holds a proof-of-concept tool that converts complete ML experiments to a working code. Don't expect this project to be used in production; this was really made to see if a certain tool can be useful and be adopted into a MLOps workflow.
- It accepts a specification file which contains information regarding what service should be made
- In the specification file, users can configure how to process their input before going to the model itself, since there usually exist an extra step in data preprocessing.
- It uses existing scalers/encoders and pretrained models that is used in the training process to make the process more streamlined.
- This tool generates Python code which are widely used in ML services. Currently based only on FastAPI.
- Go v1.18
- Python, pip for installing the requirements
$ make build
$ ./myx
Usage:
myx [flags]
Flags:
-h, --help help for myx
-o, --output string generated code output (default "./")
-v, --verbose verbose output
Build the image and pass the parameters when running through docker run. E.g:
docker build -t myx:latest .
docker run \
-v "$(pwd)/examples/:/root/examples" \
myx:latest --output ./examples/titanic ./examples/titanic/spec.yamlSome examples are generated in the examples directory.
In general, there are five main sections in the directory.
input:
format: <input-format>
meta: <input-metadata>
output:
- name: <output-name>
type: <output-type>
pipeline:
- module: <module-name>
meta: <module-parameters>
model:
format: <model-format>
path: <model-path>
interface:
- type: <interface-type>
port: <interface-port>Specification format is open to changes and additions. Author designed this to be especially extensible.