BudgetML is perfect for practitioners who would like to quickly deploy their models to an endpoint, but not waste a lot of time, money, and effort trying to figure out how to do this end-to-end.
We built BudgetML because it's hard to find a simple way to get a model in production fast and cheaply.
Cloud functions are limited in memory and cost a lot at scale.
Kubernetes clusters are overkill for one single model.
Deploying from scratch involves learning too many different concepts like SSL certificate generation, Docker, REST, Uvicorn/Gunicorn, backend servers etc., that are simply not within the scope of a typical data scientist.
BudgetML is our answer to this challenge. It is supposed to be fast, easy, and developer-friendly. It is by no means meant to be used in a full-fledged production-ready setup. It is simply a means to get a server up and running as fast as possible with the lowest costs possible.
BudgetML lets you deploy your model on a Google Cloud Platform preemptible instance (which is ~80% cheaper than a regular instance) with a secured HTTPS API endpoint. The tool sets it up in a way that the instance autostarts when it shuts down (at least once every 24 hours) with only a few minutes of downtime. BudgetML ensures the cheapest possible API endpoint with the lowest possible downtime.
BudgetML uses Google Cloud Preemptible instances under-the-hood to reduce costs by 80%. This can potentially mean hundreds of dollars
worth of savings. Here is a screenshot of the
e2-highmem GCP series, which is regular family of instances to be using for
memory intense tasks like ML model inference functions. See the following price comparison (as of Jan 31, 2021 [source])
Even with the lowest machine_type, there is a $46/month savings, and with the highest configuration this is $370/month savings!
BudgetML is available for easy installation into your environment via PyPI:
pip install budgetml
Alternatively, if youre feeling brave, feel free to install the bleeding edge:
NOTE: Do so on your own risk, no guarantees given!
pip install git+https://github.com/ebhy/[email protected] --upgrade
BudgetML aims for as simple a process as possible. First set up a predictor:
# predictor.py class Predictor: def load(self): from transformers import pipeline self.model = pipeline(task="sentiment-analysis") async def predict(self, request): # We know we are going to use the `predict_dict` method, so we use # the request.payload pattern req = request.payload return self.model(req["text"])
Then launch it with a simple script:
# deploy.py import budgetml from predictor import Predictor # add your GCP project name here. budgetml = budgetml.BudgetML(project='GCP_PROJECT') # launch endpoint budgetml.launch( Predictor, domain="example.com", subdomain="api", static_ip="22.214.171.1242", machine_type="e2-medium", requirements=['tensorflow==2.3.0', 'transformers'], )
Interactive docs to test endpoints. Support for Images.
Simple prediction interface:
We are proud that BudgetML is actively being used in the following live products:
BudgetML is for users on a budget. If you're working in a more serious production environment, then consider using ZenML as the perfect open-source MLOPs framework for ML production needs. It does more than just deployments, and is more suited for professional workplaces.
We are two brothers who love building products, especially ML-related products that make life easier for people. If you use this tool for any of your products, we would love to hear about it and potentially add it to this space. Please get in touch via email.
Oh and please do consider giving us a GitHub star if you like the repository - open-source is hard, and the support keeps us going.