ReadNext 0.0.4: Local Embedding Model

I just release ReadNext version 0.0.4. The primary goal of this new version is to remove the dependency on the Cohere Embedding web service endpoint by using a local embedding model by default. To enable that, ReadNext got integrated with Hugging Face and is currently uses the BAAI/bge-base-en model.

Local vs. Remote

This new change remove dependency on one external service which makes it more stable. The processing time is a little bit longer with the local model, but it also depends on the capabilities of your local computer.

In terms of performance, the two systems are comparable. In my experience, about 80% of the propositions are the same, and the remaining 20% that are different yeld no major difference in accuracy. However, I do prefer the BAAI/bge-base-en propositions a little better for what I experienced so far.

You may want to experiment with both to see what works best for you. The only thing you have to do is to change the EMBEDDING_SYSTEM environment variable and to reload your terminal instance.

New Configurations

Two new configuration options have been added to this version:

  1. EMBEDDING_SYSTEM: This is the embedding system you want to use. One of: BAAI/bge-base-en(local) or cohere.
  2. MODELS_PATH: This is the local path where you want the models files to be saved on your local file system (ex: /Users/me/.readnext/models/)

If you already have ReadNext installed on your computer, please make sure to add those two new environment variables to you environment.

New Commands

Two new commands have been added as well. They have been added to help understanding the current status of the ReadNext tool. Those two commands are:

  1. readnext version: this gives the version of ReadNext that you are currently using
  2. readnext config: this gives the configuration parameters, and their values, currently used to run that instance of ReadNext

Literate Programming

While at it, I decided to migrate ReadNext’s Python codebase to use nbdev to continue its development using literate programming

All the literate files (notebooks in this case) where the code is tangled and the documentation weaved from are accessible in the nbs folder. The tangled codebase is available in the readnext folder. Finally, the weaved documentation is available as GitHub pages here.

How to Deploy Hugging Face Models in Azure using Terraform and Docker

In a previous blog post, I explained how we can easily deploy Hugging Face models in Docker containers. In this new post, I will explain how we can easily deploy that container in Azure using Terraform. At the end of this article, we will have end-to-end process that creates a translation service, containerize, deploy in the cloud and that is readily available on the Web in a single command in your terminal.

Create Free Azure Account

If you don’t currently have access to an Azure account, you can easily create one for free here. You get a series of popular services for free for 12 months, 55 services free forever and 200$ USD in credit for a month. This is more than enough to run the commands in this tutorial. Even if none of those benefits would exists, it won’t be that much of a problem since you could create and tears down the services within a minute with terraform destroy which would incurs a few cents of usage.

Finally, make sure that you install the Azure CLI command line tool.

Install Terraform

The next step is to install Terraform on your local computer.

Terraform is an infrastructure-as-code (IaC) tool that allows users to define and manage their cloud infrastructure in a declarative manner (i.e. the infrastructure that is described in a Terraform file is the end-state of that infrastructure in the Cloud). It automates the process of provisioning and managing resources across various cloud providers, enabling consistent and reproducible deployments.

Using Terraform

This blog post is not meant to be an introduction to Terraform, so I will only cover the key commands that  I will be using. There are excellent documentation by HashiCorp for developers and there are excellent books such as Terraform: Up and Running: Writing Infrastructure as Code by Yevgeniy Brikman.

The commands we will be using in this post are:

  • terraform plan: It is used to preview the changes that Terraform will make to the infrastructure. It analyzes the configuration files and compares the desired state with the current state of the resources. It provides a detailed report of what will be added, modified, or deleted. It does not make any actual changes to the infrastructure.

  • terraform apply: It is used to apply the changes defined in the Terraform configuration files to the actual infrastructure. It will do the same as terraform plan, but at the end of the process it will prompt for confirmation before making any modifications. When we say yes, then all hells are breaking loose in the Cloud and the changes are applied by Terraform.

  • terraform destroy: It is used to destroy all the resources created and managed by Terraform. It effectively removes the infrastructure defined in the configuration files. It prompts for confirmation before executing the destruction.

Terraform file to deploy Hugging Face models on Azure

Now, let’s analyze the terraform file that tells Terraform how to create the infrastructure required to run the translation service in the Cloud.

Terraform Block

This terraform block is used to define the specific versions of Terraform and its providers. This ensure the reproducibility of the service over time, just like all the set versions of the libraries used for the translation service in Python.

terraform {
  required_version = ">= 1.5.6" 

  required_providers {
    azurerm = {
      source  = "hashicorp/azurerm"
      version = ">= 3.71.0"
    null = {
      source  = "hashicorp/null"
      version = ">= 3.2.1"

AzureRM Provider Block

This block configures the AzureRM provider. The skip_provider_registration prevents the provider from attempting automatic registration. The features {} specifies that no additional features for the provider are required for this demo.

provider "azurerm" {
  skip_provider_registration = "true"
  features {}

Resource Group Block

This block creates an Azure Resource Group (ARG) named translationarg in the eastus region. The resource group is what is used to bundle all the other resources we will require for the translation service.

resource "azurerm_resource_group" "acr" {
  name     = "translationarg"
  location = "eastus"

Container Registry Block

This block creates an Azure Container Registry (ACR) named translationacr. It associates the ACR with the previously defined resource group, in the same region. The ACR is set to the “Standard” SKU. admin_enabled allows admin access to the registry.

resource "azurerm_container_registry" "acr" {
  name                     = "translationacr"
  resource_group_name      =
  location                 = azurerm_resource_group.acr.location
  sku                      = "Standard"
  admin_enabled            = true

Null Resource for Building and Pushing Docker Image

This block uses the Null Provider and defines a null_resource used for building and pushing the Docker image where the translation service is deployed. It has a dependence on the creation of the Azure Container Registry, which means that the ACR needs to be created before this resource. The triggers section is set to a timestamp, ensuring the Docker build is triggered on every Terraform apply. The local-exec provisioner runs the specified shell commands for building, tagging, and pushing the Docker image.

resource "null_resource" "build_and_push_image" {
  depends_on = [azurerm_container_registry.acr]

  triggers = {
    # Add a trigger to detect changes in your Docker build context
    # The timestamp forces Terraform to trigger the Docker build,
    # every time terraform is applied. The check to see if anything
    # needs to be updated in the Docker container is delegated
    # to Docker.
    build_trigger = timestamp()

  provisioner "local-exec" {
    # Replace with the commands to build and push your Docker image to the ACR
    command = <<EOT
      # Build the Docker image
      docker build -t en-fr-translation-service:v1 ../../
      # Log in to the ACR
      az acr login --name translationacr
      # Tag the Docker image for ACR
      docker tag en-fr-translation-service:v1
      # Push the Docker image to ACR
      docker push

Container Group Block

This block creates an Azure Container Group (ACG). This is the resource used to create a container instance from a Docker container. It depends on the null_resource above for creating the image of the container and to make it available to the ACG.

The lifecycle block ensures that this container group is replaced when the Docker image is updated. Various properties like name, location, resource group, IP address type, DNS label, and operating system are specified. The image_registry_credential section provides authentication details for the Azure Container Registry. A container is defined with its name, image, CPU, memory, and port settings. Those CPU and Memory are required for the service with the current model that is embedded in the Docker container. Lowering those values may result in the container instance to die silently with a out of memory error.

resource "azurerm_container_group" "acr" {
  depends_on = [null_resource.build_and_push_image]

  lifecycle {
    replace_triggered_by = [
      # Replace `azurerm_container_group` each time this instance of
      # the the Docker image is replaced.

  name                = "translation-container-instance"
  location            = azurerm_resource_group.acr.location
  resource_group_name =
  ip_address_type     = "Public"
  dns_name_label      = "en-fr-translation-service"
  restart_policy      = "Never"
  os_type             = "Linux"

  image_registry_credential {
     username = azurerm_container_registry.acr.admin_username
     password = azurerm_container_registry.acr.admin_password
    server   = ""

  container {
    name   = "en-fr-translation-service-container"
    image  = ""
    cpu    = 4
    memory = 8

    ports {
      protocol = "TCP"
      port     = 6000

  tags = {
    environment = "development"

Deploying the English/French Translation Service on Azure

Now that we have a Terraform file that does all the work for us, how can we deploy the service on Azure?

As simply as running this command line from the /terraform/deploy/ folder:

terraform apply

Once we run that command, Terraform will analyze the file, and show everything that will changes in the Cloud infrastructure. In this case, we start from scratch, so all those resources will be created (none will change nor be destroyed):

All the resources will then be created by Terraform. Those resources are created by communicating with Azure’s web service API. The output of each step is displayed in the terminal. The entire process to deploy four resources took about 12 minutes, 4 of which is to create the Docker image and 3 to create the Cloud resources and deploy the service. Most of the time is spent dealing with the somewhat big translation models that we baked in the Docker image:

Testing the Translation Service on Azure

The next step is to test the service we just created on Azure.

curl "" -H "Content-Type: application/json" -d '{"fr_text":"Bonjour le monde!"}'

The result we get from the service:

  "en_text": "Hello, world!"

It works! (well, why would I have spent the time to write this post if it didn’t?)

A single command in your terminal to:

  1. Package a translation service and powerful translation models into a container image
  2. Creating a complete cloud infrastructure to support the service
  3. Deploy the image on the infrastructure and start the service

If this is not like magic, I wonder what that is.

Destroying Cloud Infrastructure

The last step is to destroy the entire infrastructure such that we don’t incur costs for those tests. The only thing that is required is to run the following Terraform command:

terraform destroy

Just like with terraform apply, Terraform will check the current state of the cloud infrastructure (which is defined in the terraform.tfstate JSON file), will show all the resources that will be destroyed, and ask the user to confirm that they want to proceed by answering yes:


I would recommend that you always run your Terraform through a linter. There are several of them existing, none of them are mutually exclusive. Three popular ones are tflint, Checkov and Trivy. Checkov and Trivy are more focused on security risks. 

For this blog post, I will only focus on tflint. Once you installed it, you can run it easily from your terminal:


If I run that command from the /terraform/deploy/ folder, and if I remove the Terraform version from the Terraform block, tflint will return the following error:

You can then follow the link to the Github documentation to understand what the error means and how to fix it.

Run Linter every time you update your repository

The final step is to create a new Github Action that will be triggered every time the main is modified. I simply had to use the setup-tflint action from the marketplace, add it to my repository, and to push it to  GitHub to run it every time the main branch is modified. 

Here is what it looks like when it runs:


This is what finalizes the series of blog posts related to the creation of an English/French translation web service endpoint using Hugging Face models.

As we can see, the current state state of the DevOps and machine learning echo system enables us to create powerful web services, in very little amount of time, with minimal efforts, while following engineering best practices.

None of this would have been as simple and fast just a few years ago. Just think about the amount of work necessary, by millions of people and thousands of business over the years to enable Fred to spawn a translation service in the Cloud, that anyone can access, with a single command in my laptop terminal.

How to Deploy Hugging Face Models in a Docker Container

In this short tutorial, we will explore how Hugging Face models can be deployed in a Docker Container and exposed as a web service endpoint.

The service it exposes is a translation service from English to French and French to English.

Why someone would like to do that? Other than to learn about those specific technologies, it is a very convenient way to try and test the thousands of models that exists on Hugging Face, in a clean and isolated environment that can easily be replicated, shared or deployed elsewhere than on your local computer.

In this tutorial, you will learn how to use Docker to create a container with all the necessary code and artifacts to load Hugging Face models and to expose them as web service endpoints using Flask.

All code and configurations used to write this blog post are available in this GitHub Repository. You simply have to clone it and to run the commands listed in this tutorial to replicate the service on your local machine.

Installing Docker

The first step is to install Docker. The easiest way is by simply installing Docker Desktop which is available on MacOS, Windows and Linux.

Creating the Dockerfile

The next step is to create a new Git repository where you will create a Dockerfile. The Dockerfile is where all instructions are written that tells Docker how to create the container.

I would also strongly encourage you to install and use hadolint, which is a really good Docker linter that helps people to follow Docker best practices. There is also a plugin for VS Code if this is what you use as you development IDE.

Base image and key installs

The first thing you define in a Dockerfile is the base image to use to initialize the container. For this tutorial, we will use Ubuntu’s latest LTS:

# Use Ubuntu's current LTS
FROM ubuntu:jammy-20230804

Since we are working to create a Python web service that expose the predictions of a ML model, the next step is to add they key pieces required for the Python service. Let’s make sure that you only include what is necessary to minimize the size, and complexity, of the container as much as possible:

# Make sure to not install recommends and to clean the 
# install to minimize the size of the container as much as possible.
RUN apt-get update && \
    apt-get install --no-install-recommends -y python3=3.10.6-1~22.04 && \
    apt-get install --no-install-recommends -y python3-pip=22.0.2+dfsg-1ubuntu0.3 && \
    apt-get install --no-install-recommends -y python3-venv=3.10.6-1~22.04 && \
    apt-get clean && \
    rm -rf /var/lib/apt/lists/*

This instruct Docker to install Python3, pip and venv. It also ensures that apt get cleaned of cached files, that nothing more is installed and that we define the exact version of the package we want to install. That is to ensure that we minimize the size of the container, while making sure that the container can easily be reproduced, with the exact same codebase, any time in the future.

Another thing to note: we run multiple commands with a single RUN instruction by piping them together with &&. This is to minimize the number of layers created by Docker for the container, and this is a best practice to follow when creating containers. If you don’t do this and run hadolint, then you will get warning suggesting you to refactor your Dockerfile accordingly.

Copy required files

Now that the base operating system is installed, the next step is to install all the requirements of the Python project we want to deploy in the container:

# Set the working directory within the container

# Copy necessary files to the container
COPY requirements.txt .

First we define the working directory with the WORKDIR instruction. From now on, every other instruction will run from that directory in the container. We copy the local files: requirements.txt, and to the working directory.

Create virtual environment

Before doing anything with those files, we are better creating a virtual environment where to install all those dependencies. Some people may wonder why we create an environment within an environment? It is further isolation between the container and the Python application to make sure that there is no possibility of dependencies clashes. This is a good best practice to adopt.

# Create a virtual environment in the container
RUN python3 -m venv .venv

# Activate the virtual environment
ENV PATH="/app/.venv/bin:$PATH"

Install application requirements

Once the virtual environment is created and activated in the container, the next step is to install all the required dependencies in that new environment:

    # Install Python dependencies from the requirements file
RUN pip install --no-cache-dir -r requirements.txt && \
    # Get the models from Hugging Face to bake into the container

It runs pip install to install all the dependencies listed in requirements.txt. The dependencies are:


Just like the Ubuntu package version, we should (have to!) pin (specify) the exact version of each dependency. This is the best way to ensure that we can reproduce this environment any time in the future and to prevent unexpected crashes because code changed in some downstream dependencies that causes issues with the code.

Downloading all models in the container

As you can see in the previous RUN command, the next step is to download all models and tokenizers in the working directory such that we bake the model’s artifacts directly in the container. That will ensures that we minimize the time it takes to initialize a container. We spend the time to download all those artifacts at build time instead of run time. The downside is that the containers will be much bigger depending on the models that are required.

The file is a utility file used to download the Hugging Face models used by the service directly into the container. The code simply download the models and tokenizer files from Hugging Face and save them locally (in the working directory of the container):

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import os

def download_model(model_path, model_name):
    """Download a Hugging Face model and tokenizer to the specified directory"""
    # Check if the directory already exists
    if not os.path.exists(model_path):
        # Create the directory

    tokenizer = AutoTokenizer.from_pretrained(model_name)
    model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

    # Save the model and tokenizer to the specified directory

# For this demo, download the English-French and French-English models
download_model('models/en_fr/', 'Helsinki-NLP/opus-mt-en-fr')
download_model('models/fr_en/', 'Helsinki-NLP/opus-mt-fr-en')

Creating the Flask translation web service endpoint

The last thing we have to do with the Dockerfile is to expose the port where the web service will be available and to tell the container what to run when it starts:

# Make port 6000 available to the world outside this container

ENTRYPOINT [ "python3" ]

# Run when the container launches
CMD [ "" ]

We expose the port 6000 to the outside world, and we tell Docker to run the python3 command with The file is a very simple file that register the web service’s path using Flask, and that makes the predictions (translations in this case):

from flask import Flask, request, jsonify
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

def get_model(model_path):
    """Load a Hugging Face model and tokenizer from the specified directory"""
    tokenizer = AutoTokenizer.from_pretrained(model_path)
    model = AutoModelForSeq2SeqLM.from_pretrained(model_path)
    return model, tokenizer

# Load the models and tokenizers for each supported language
en_fr_model, en_fr_tokenizer = get_model('models/en_fr/')
fr_en_model, fr_en_tokenizer = get_model('models/fr_en/')

app = Flask(__name__)

def is_translation_supported(from_lang, to_lang):
    """Check if the specified translation is supported"""
    supported_translations = ['en_fr', 'fr_en']
    return f'{from_lang}_{to_lang}' in supported_translations

@app.route('/translate/<from_lang>/<to_lang>/', methods=['POST'])
def translate_endpoint(from_lang, to_lang):
    """Translate text from one language to another. This function is 
    called when a POST request is sent to /translate/<from_lang>/<to_lang>/"""
    if not is_translation_supported(from_lang, to_lang):
        return jsonify({'error': 'Translation not supported'}), 400

    data = request.get_json()
    from_text = data.get(f'{from_lang}_text', '')

    if from_text:
        model = None
        tokenizer = None

        match from_lang:
            case 'en':        
                model = en_fr_model
                tokenizer = en_fr_tokenizer
            case 'fr':
                model = fr_en_model
                tokenizer = fr_en_tokenizer

        to_text = tokenizer.decode(model.generate(tokenizer.encode(from_text, return_tensors='pt')).squeeze(), skip_special_tokens=True)

        return jsonify({f'{to_lang}_text': to_text})
        return jsonify({'error': 'Text to translate not provided'}), 400
if __name__ == '__main__':'', port=6000, debug=True)

Building the container

Now that the Dockerfile is completed, the next step is to use it to have Docker to build the actual image of the container. This is done using this command in the terminal:

docker build -t localbuild:en_fr_translation_service .

Note that we specified a tag to make it easier to manage it in between all the other images that may exists in the environment. The output of the terminal will show every step defined in the Dockerfile, and the processing for each of those step. The final output looks like:

Running and Querying the service

Now that we have a brand new image, the next step is to test it. In this section, I will use Docker Desktop’s user interface to show how we can easily do this, but all those step can easily be done (and automated) using the docker command line application.

After you built the image, it will automatically appear in the images section of Docker Desktop:

You can see the tag of the image, its size, when it was created, etc. To start the container from that image, we simply have to click the play arrow in the Actions column. That will start running a new container using that image.

Docker Desktop will enable you to add some more parameter to start the container with the following window:

The most important thing to define here is to Host port. If you leave it empty, then the port 6000 we exposed in the Docker file will become unbound and we won’t be able to reach the service running in the container.

Once you click the Run button, the container will appear in the Containers section:

And if you click on it’s name’s link, you will have access to the internal of the container (the files it contains, the execution logs, etc.:

Now that the container is running, we can query the endpoint like this:

curl http://localhost:6000/translate/en/fr/ POST -H "Content-Type: application/json" -v -d '{"en_text": "Towards Certification of Machine Learning-Based Distributed Systems Behavior"}'

It returns:

  "fr_text": "Vers la certification des syst\u00e8mes distribu\u00e9s fond\u00e9s sur l'apprentissage automatique"

And then for the French to English translation:

curl http://localhost:6000/translate/fr/en/ POST -H "Content-Type: application/json" -v -d '{"fr_text": "Ce qu'\''il y a d'\''admirable dans le bonheur des autres, c'\''est qu'\''on y croit."}'

It returns:

  "en_text": "What is admirable in the happiness of others is that one believes in it."


As we can see, it is pretty straightforward to create simple Docker containers that turns pretty much any Hugging Face pre-trained models into a web service endpoint.