Batch functions

Gradio supports the ability to pass batch functions. Batch functions are just functions which take in a list of inputs and return a list of predictions.

For example, here is a batched function that takes in two lists of inputs (a list of words and a list of ints), and returns a list of trimmed words as output:

import time

def trim_words(words, lens):
    trimmed_words = []
    time.sleep(5)
    for w, l in zip(words, lens):
        trimmed_words.append(w[:int(l)])
    return [trimmed_words]

The advantage of using batched functions is that if you enable queuing, the Gradio server can automatically batch incoming requests and process them in parallel, potentially speeding up your demo. Here's what the Gradio code looks like (notice the batch=True and max_batch_size=16)

With the gr.Interface class:

demo = gr.Interface(
    fn=trim_words, 
    inputs=["textbox", "number"], 
    outputs=["output"],
    batch=True, 
    max_batch_size=16
)

demo.launch()

With the gr.Blocks class:

import gradio as gr

with gr.Blocks() as demo:
    with gr.Row():
        word = gr.Textbox(label="word")
        leng = gr.Number(label="leng")
        output = gr.Textbox(label="Output")
    with gr.Row():
        run = gr.Button()

    event = run.click(trim_words, [word, leng], output, batch=True, max_batch_size=16)

demo.launch()

In the example above, 16 requests could be processed in parallel (for a total inference time of 5 seconds), instead of each request being processed separately (for a total inference time of 80 seconds). Many Hugging Face transformers and diffusers models work very naturally with Gradio's batch mode: here's an example demo using diffusers to generate images in batches