JuliusFx131/Helsinki---End-to-End-Machine-Translation-System

This repository features an end-to-end Dyula-to-French translation system built with Joeynmt, addressing low-resource language challenges. It incorporates MLOps best practices for optimizing accuracy, latency, throughput, and cost efficiency. The project is fully documented for reproducibility and deployed on the AWS-powered HighWind platform. # Deployment This folder contains the resources required for deploying the trained model onto Highwind. ## Usage > All commands below are run from this directory. ### Building your model image This step builds the Kserve predictor image that contains your model. 1. First, make sure you have the trained model and tokenizer locally available. To get these, you can do one of the following: - Download the files from the Hugging Face model repo and save them to `saved_model` (in the root directory of this example) - Run the inference notebooks located in the `notebooks` directory. 1. Copy over the trained model and its definition code into this folder so that it can be baked into the Docker container for serving ```bash cp -r ../saved_model . ``` 1. Then build the container locally and give it a tag ```bash docker build -t local/hw-examples/helsinki-nlp-opus-mt-en-fr:latest . ``` ### Local testing 1. After building the Kserve predictor image that contains your model, spin it up to test your model inference ```bash docker compose up -d docker compose logs ``` 1. Finally, send a payload to your model to test its response. To do this, use the `curl` cmmand to send a `POST` request with an example JSON payload. > Run this from another terminal (remember to navigate to this folder first) Linux/Mac Bash/zsh ```bash curl -X POST localhost -H 'Content-Type: application/json' -d @./input.json ``` Windows PowerShell ```PowerShell $json = Get-Content -Raw -Path ./input.json $response = Invoke-WebRequest -Uri localhost -Method Post -ContentType 'application/json' -Body ([System.Text.Encoding]::UTF8.GetBytes($json)) $responseObject = $response.Content | ConvertFrom-Json $responseObject | ConvertTo-Json -Depth 10 ```