Project templates
Rocketstart your ZenML journey!
Last updated
Rocketstart your ZenML journey!
Last updated
What would you need to get a quick understanding of the ZenML framework and start building your ML pipelines? The answer is one of ZenML project templates to cover major use cases of ZenML: a collection of steps and pipelines and, to top it all off, a simple but useful CLI. This is exactly what the ZenML templates are all about!
Project Template [Short name] | Tags | Description |
---|---|---|
Do you have a personal project powered by ZenML that you would like to see here? At ZenML, we are looking for design partnerships and collaboration to help us better understand the real-world scenarios in which MLOps is being used and to build the best possible experience for our users. If you are interested in sharing all or parts of your project with us in the form of a ZenML project template, please join our Slack and leave us a message!
First, to use the templates, you need to have ZenML and its templates
extras installed:
Note that these templates are not the same thing as the templates used for triggering a pipeline (from the dashboard or via the Python SDK). Those are known as 'Run Templates' and you can read more about them here.
Now, you can generate a project from one of the existing templates by using the --template
flag with the zenml init
command:
Running the command above will result in input prompts being shown to you. If you would like to rely on default values for the ZenML project template - you can add --template-with-defaults
to the same command, like this:
Starter template [starter
]
basic
scikit-learn
All the basic ML ingredients you need to get you started with ZenML: parameterized steps, a model training pipeline, a flexible configuration and a simple CLI. All created around a representative and versatile model training use-case implemented with the scikit-learn library.
E2E Training with Batch Predictions [e2e_batch
]
etl
hp-tuning
model-promotion
drift-detection
batch-prediction
scikit-learn
This project template is a good starting point for anyone starting with ZenML. It consists of two pipelines with the following high-level steps: load, split, and preprocess data; run HP tuning; train and evaluate model performance; promote model to production; detect data drift; run batch inference.
NLP Training Pipeline [nlp
]
nlp
hp-tuning
model-promotion
training
pytorch
gradio
huggingface
This project template is a simple NLP training pipeline that walks through tokenization, training, HP tuning, evaluation and deployment for a BERT or GPT-2 based model and testing locally it with gradio