A short tutorial on using pre-trained Huggingface models
The first thing we’ll do is define the environment that our app will run on. For this example, we’re building a Sentiment Analysis model using Huggingface.
First, you’ll define a Runtime
with an Image
.
We’re going to be defining which packages to install in the runtime, and the hardware this code will run on.
Now, we’ll write some code to predict the sentiment of a given text prompt.
Our function takes keyword arguments, as (**inputs)
.
To prepare to deploy the API, we’ll add a rest_api
decorator to our inference function.
Add the following decorator to your predict_sentiment
function.
The complete app.py
file will look like this:
To deploy the model, enter your terminal and cd
to the directory you’re
working on.
Then, run the following:
After running this command, you’ll see some logs in the console that show the progress of your deployment.
Show Logs
At the bottom of the console, you’ll see a URL for invoking your function. Here’s what a cURL request would look like:
A short tutorial on using pre-trained Huggingface models
The first thing we’ll do is define the environment that our app will run on. For this example, we’re building a Sentiment Analysis model using Huggingface.
First, you’ll define a Runtime
with an Image
.
We’re going to be defining which packages to install in the runtime, and the hardware this code will run on.
Now, we’ll write some code to predict the sentiment of a given text prompt.
Our function takes keyword arguments, as (**inputs)
.
To prepare to deploy the API, we’ll add a rest_api
decorator to our inference function.
Add the following decorator to your predict_sentiment
function.
The complete app.py
file will look like this:
To deploy the model, enter your terminal and cd
to the directory you’re
working on.
Then, run the following:
After running this command, you’ll see some logs in the console that show the progress of your deployment.
Show Logs
At the bottom of the console, you’ll see a URL for invoking your function. Here’s what a cURL request would look like: