Machine Learning in Linux: Argos Translate is an Offline Translation Library

Argos Translate
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Our Machine Learning in Linux series focuses on apps that make it easy to experiment with machine learning.

Machine Learning extends in many directions. We’ve not covered neural networks in this series, so let’s rectify that. A neural network is a method in artificial intelligence that teaches computers to process data in a way that’s inspired by the human brain. It is a type of machine learning process, called deep learning, that uses interconnected nodes or neurons in a layered structure that resembles the human brain.

Google Translate is a popular multilingual neural machine translation service that translates text and websites from one language into another. How about a self-hosted open source alternative?

Argos Translate is state of the art neural machine translation software. It’s written in Python and published under an open source license. Argos Translate uses OpenNMT for translations and can be used as either a Python library, command-line, or GUI application.

Modern machine translation goes beyond simple word-to-word translation to communicate the full meaning of the original language text in the target language. It analyzes all text elements and recognizes how the words influence one another.

Installation

As we’ve explained in previous articles in this series, we don’t recommend using pip to install software unless it’s within a virtual environment. A good solution is to use a conda environment as it helps manage dependencies, isolate projects, and it’s language agnostic.

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If your machine has a dedicated graphics card, you’ll want to enable GPU support. This is enabled by setting the ARGOS_DEVICE_TYPE env variable to cuda or auto.

I actually think this is bad advice, at least for now. I recommend running Argos Translate on a CPU. For whatever reason CTranslate2 doesn’t seem to really run faster on a GPU than a CPU. Libretranslate’s performance on high end CPUs is very good though.

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In my tests the GPU also did not show much increase in performance (for single translations).

I think there’s a good use case for using the GPU to speed-up batch inference via ctranslate (where it’s actually faster), but it’s hard to scale it right, as large batch sizes quickly fill up the GPU memory.

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