Automatic Docker Image Creation for Any ML/DL Model 🐳¶
chitra CLI can build docker image for any kind of Machine Learning or Deep Learning Model.
You need to create a main.py file which will contain an object of type chitra.serve.ModelServer and
its name should be app.
If you have any external Python dependency then create a requirements.txt file and keep in the same directory.
If the above conditions are satisfied then just run chitra builder run --path MAIN_FILEPATH
Usage¶
chitra builder create [OPTIONS]
Options:
--path TEXT [default: ./]
--port TEXT
--tag TEXT
--help Show this message and exit.
path is the file location where main.py and requirements.txt is present.
You can specify which port to run your app on. By default, it is 8080.
To set the tag of the docker image use tag argument.
Example: Auto Build Docker Image for HuggingFace Text Classification Model API¶
Create ModelServer¶
First create Text Classification model from HuggingFace. chitra provides create_api method
to create API for any kind of ML/DL model. We specify the api_type="text-classification" and
get an app object.
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
from chitra.serve import create_api
tokenizer = AutoTokenizer.from_pretrained("microsoft/xtremedistil-l6-h256-uncased")
model = AutoModelForSequenceClassification.from_pretrained(
"microsoft/xtremedistil-l6-h256-uncased"
)
classifier = pipeline("text-classification", model=model, tokenizer=tokenizer)
app = create_api(classifier, run=False, api_type="text-classification").app
Use Chitra CLI to auto-build Docker Image¶
Go to terminal and change the directory to the path where main.py is present.
Run chitra builder create --path ./FILEPATH --tag chitra-server.
That's all you need to do! You can run the docker image using docker run -p 8080:8080 chitra-server
from terminal.