An environment variable is a value that affects the way the code runs and is dependent on the environment on which it is running.
For example, you have written a ML model API service that,
- Downloads the model from somewhere.
- Loads the model from disk.
- Serves a
/infer route, which calls the model’s inference function.
Now, your service code may not change if you re-train and update the model. In this case, we can pass the model path via an environment variable.
main.py
import os
from fastapi import FastAPI
import mlfoundry as mlf
client = mlf.get_client()
# NOTE: `MODEL_FQN` variable will now contain the value
# of environment variable `MODEL_FQN`
MODEL_FQN = os.getenv("MODEL_FQN")
MODEL = client.get_model(MODEL_FQN).load()
S3_BUCKET = os.getenv("S3_BUCKET_NAME")
app = FastAPI()
@app.get("/infer")
def infer(...):
...
You can then run this service and inject the environment variable value like below,
MODEL_FQN="YOUR MODEL FQN" S3_BUCKET_NAME="my-s3-bucket" uvicorn main:app --port 8000 --host 0.0.0.0
You can also use a .env file on your local dev environment and use python-dotenv.
.env
MODEL_FQN = "YOUR MODEL FQN FOR LOCAL RUN";
S3_BUCKET_NAME = "S3 bucket for local development";
How to inject environment variables in TrueFoundry
In this guide we will learn how can we inject environment variables in our deployments in TrueFoundry.
"""
Both `Service ` and `Job` classes have an argument `env` where you can pass a dictionary. The dictionary keys will be assumed as environment variable names and the values will be the environment variable values.
"""
import logging
from servicefoundry import Build, Service, DockerFileBuild
logging.basicConfig(level=logging.INFO)
service = Service(
name="my-service",
image=Build(build_spec=DockerFileBuild()),
ports=[{"port": 8501}],
env={
"MODEL_FQN": "YOUR MODEL FQN",
"S3_BUCKET_NAME": "my-s3-bucket"
},
)
service.deploy(workspace_fqn="YOUR_WORKSPACE_FQN")
The variables MODEL_FQN and S3_BUCKET_NAME should be available in your environment on deployment.
