jams-cli 0.1.6

jams-cli is an easy-to-use CLI application for making predictions.
jams-cli-0.1.6 is not a library.

JAMS-CLI

This crate is part of wider project called J.A.M.S - Just Another Model Server. Please refer here.

This crate provides a simple CLI for making predictions for the following models

  • Tensorflow
  • Torch
  • Catboost
  • LightGBM

Setup

This project relies on couple of shared libraries. In order to easily setup please follow the steps below

  1. Use the bash script here based on your system architecture

  2. Run the following commands or add them to shell profile

# add environment variables
export COMMON_LIBS_PATH=/usr/local/lib
export LIGHTGBM_LIB_DIR=$COMMON_LIBS_PATH
export LIBTORCH=$COMMON_LIBS_PATH/libtorch
export LIBTORCH_INCLUDE=$COMMON_LIBS_PATH/libtorch
export LIBTORCH_LIB=$COMMON_LIBS_PATH/libtorch
export LD_LIBRARY_PATH=$COMMON_LIBS_PATH:$COMMON_LIBS_PATH/libtorch/lib
export LIBRARY_PATH=$LIBRARY_PATH:$COMMON_LIBS_PATH/libtensorflow
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$COMMON_LIBS_PATH/libtensorflow/lib
  1. Ensure that cargo and rust compiler are installed. Follow instructions here if not installed
  2. Run the following command to install jams-cli
cargo install jams-cli

Examples

There are multiple python scripts in examples folder which would allow you to generate different models and their corresponding sample json input. Below are some examples

Tensorflow

  1. Run tensorflow_penguin_multiclass_classification_model.py
  2. This will create two files - a model file and input json file
  3. Run the following command and pass in the path for model file and input file
jams-cli tensorflow --model-path=tensorflow_penguin_functional --input-path=tensorflow_input.json

Torch

  1. Run torch_penguin_multiclass_classification_model.py
  2. This will create two files _ a model file and input json file
  3. Run the following command and pass in the path for model file and input file
jams-cli torch --model-path=torch_penguin.pt --input-path=torch_input.json

Catboost

  1. Run catboost_titanic_binary_classification_model.py
  2. This will create two files - a model file and input json file
  3. Run the following command and pass in the path for model file and input file
jams-cli catboost --model-path=catboost_titanic --input-path=catboost_input.json

LightGBM

  1. Run lightgbm_iris_binary_classification_model.py
  2. This will create two files - a model file and input json file
  3. Run the following command(example) and pass in the path for model file and input file
jams-cli light-gbm --model-path=lightgbm_iris.txt --input-path=lightgbm_input.json

DISCLAIMER: jams-cli is currently unstable and may not run properly on your machines. I have tested the above on apple silicon. Future releases will fix this