e2e_nlp

ZenML NLP project

This is a comprehensive supervised ML project built with the ZenML framework and its integration. The project will be a comprehensive starting point for anyone looking to build and deploy NLP models using the ZenML framework by streamlining the process of training, promoting, and deploying NLP models with a focus on reproducibility, scalability, and ease of use. The project was generated from the NLP ZenML project template. with the following properties:

• Project name: ZenML NLP project
• Technical Name: nlp_use_case
• Version: 0.0.1
• Licensed with apache to ZenML GmbH[email protected]
• Deployment environment: staging

Settings of your project are:

• Accelerator: cpu
• Trained model promotion to staging based on accuracy metric vs currently deployed model
• Local deployment enabled
• Deployment to HuggingFace Hub enabled
• Deployment to SkyPilot enabled
• Dataset: airline_reviews
• Model: distilbert-base-uncased
• Notifications about failures enabled

👋 Introduction

Welcome to your newly generated "ZenML NLP project" project! This is a great way to get hands-on with ZenML using production-like template. The project contains a collection of standard and custom ZenML steps, pipelines and other artifacts and useful resources that can serve as a solid starting point for your smooth journey with ZenML.

What to do first? You can start by giving the the project a quick run. The project is ready to be used and can run as-is without any further code changes! You can try it right away by installing ZenML, the needed ZenML integration and then calling the CLI included in the project. We also recommend that you start the ZenML UI locally to get a better sense of what is going on under the hood:

# Set up a Python virtual environment, if you haven't already
python3 -m venv .venv
source .venv/bin/activate
# Install requirements & integrations
make setup
# Optionally, provision default local stack
make install-local-stack
# Start the ZenML UI locally (recommended, but optional);
# the default username is "admin" with an empty password
zenml up
# Run the pipeline included in the project
python run.py

When the pipelines are done running, you can check out the results in the ZenML UI by following the link printed in the terminal (or you can go straight to the ZenML UI pipelines run page.

Next, you should:

• look at the CLI help to see what you can do with the project:

python run.py --help

• go back and try out different parameters for your generated project. For example, you could disable hyperparameters tuning and use your favorite model architecture or promote every trained model, if you haven't already!
• take a look at the project structure and the code itself. The code is heavily commented and should be easy to follow.
• read the ZenML documentation to learn more about various ZenML concepts referenced in the code and to get a better sense of what you can do with ZenML.
• start building your own ZenML project by modifying this code

📦 What's in the box?

The ZenML NLP project project demonstrates how the most important steps of the ML Production Lifecycle can be implemented in a reusable way remaining agnostic to the underlying infrastructure for a Natural Language Processing (NLP) task.

This template uses one of these datasets:

• IMDB Movie Reviews
• Financial News
• Airlines Reviews

and one of these models:

• DistilBERT
• RoBERTa
• BERT

It consists of three pipelines with the following high-level setup:

All pipelines are leveraging the Model Control Plane to bring all parts together - the training pipeline creates and promotes a new Model Control Plane version with a trained model object in it, deployment pipeline uses the inference Model Control Plane version (the one promoted during training) to create a deployment service and inference pipeline using deployment service from the inference Model Control Plane version and store back new set of predictions as a versioned data artifact for future use. This makes those pipelines closely connected while ensuring that only quality-assured Model Control Plane versions are used to produce predictions delivered to stakeholders.

• [CT] Training
  • Load the training dataset from HuggingFace Datasets
  • Load Tokenizer from HuggingFace Models based on the model name
  • Tokenize the training dataset and store the tokenizer as an artifact
  • Train and evaluate a model object using the training dataset and store it as an artifact
  • Register the model object as a new inference Model Control Plane version
• [CD] Promotion
  • Evaluate the latest Model Control Plane version using the evaluation metric
    • Compare the evaluation metric of the latest Model Control Plane version with the evaluation metric of the currently promoted Model Control Plane version
    • If the evaluation metric of the latest Model Control Plane version is better than the evaluation metric of the currently promoted Model Control Plane version, promote the latest Model Control Plane version to the specified stage
    • If the evaluation metric of the latest Model Control Plane version is worse than the evaluation metric of the currently promoted Model Control Plane version, do not promote the latest Model Control Plane version
• [CD] Deployment
  • Load the inference Model Control Plane version
    • Save the Model locally (for that this pipeline needs to be run on the local machine)
  • Deploy the Model to the specified environment
    • If the specified environment is HuggingFace Hub, upload the Model to the HuggingFace Hub
    • If the specified environment is SkyPilot, deploy the Model to the SkyPilot
    • If the specified environment is local, do not deploy the Model

In the repository documentation, you can find more details about every step of this template.

The project code is meant to be used as a template for your projects. For this reason, you will find several places in the code specifically marked to indicate where you can add your code:

### ADD YOUR OWN CODE HERE - THIS IS JUST AN EXAMPLE ###
...
### YOUR CODE ENDS HERE ###

📜 Project Structure

The project loosely follows the recommended ZenML project structure:

.
├── gradio                    # Gradio app for inference
│   ├── __init__.py          # Gradio app initialization
│   ├── app.py               # Gradio app entrypoint
│   ├── Dockerfile           # Gradio app Dockerfile
│   ├── requirements.txt     # Gradio app Python dependencies
│   └── serve.yaml           # Gradio app SkyPilot deployment configuration
├── pipelines                 # `zenml.pipeline` implementations
│   ├── __init__.py
│   ├── deployment.py         # deployment pipeline
│   ├── promotion.py          # promotion pipeline
│   └── training.py           # training pipeline
├── steps                     # `zenml.steps` implementations
│   ├── __init__.py
│   ├── alerts                # `zenml.steps.alerts` implementations
│   │   ├── __init__.py
│   │   └── notify_on.py      # notify step
│   ├── dataset_loader        # `zenml.steps.dataset_loader` implementations
│   │   ├── __init__.py
│   │   └── data_loader.py    # data loader step
│   ├── deploying             # `zenml.steps.deploying` implementations
│   │   ├── __init__.py
│   │   ├── save_model.py     # save model step
│   │   ├── deploy_locally.py # deploy locally step
│   │   ├── deploy_to_huggingface.py  # deploy to HuggingFace Hub step
│   │   └── deploy_to_skypilot.py     # deploy to SkyPilot step
│   ├── promotion             # `zenml.steps.promotion` implementations
│   │   ├── __init__.py
│   │   ├── promote_latest.py # promote latest step
│   │   ├── promote_metric_compare_promoter.py  # metric compare promoter step
│   │   └── promote_get_metrics.py   # get metric step
│   ├── register            # `zenml.steps.register` implementations
│   │   ├── __init__.py
│   │   └── model_log_register.py  # model log register step
│   ├── tokenization         # `zenml.steps.tokenization` implementations
│   │   ├── __init__.py
│   │   └── tokenization.py   # tokenization step
│   ├── tokenizer_loader     # `zenml.steps.tokenizer_loader` implementations
│   │   ├── __init__.py
│   │   └── tokenizer_loader.py  # tokenizer loader step
│   └── training             # `zenml.steps.training` implementations
│       ├── __init__.py
│       └── trainer.py          # train step
└── utils                     # `zenml.utils` implementations
│   └── misc.py               # miscellaneous utilities
├── README.md                 # this file
├── requirements.txt          # extra Python dependencies
├── config.yaml               # ZenML configuration file
└── run.py                    # CLI tool to run pipelines on ZenML Stack