Announcing 🎤 InfinityWarp, a module that enables you to run inference on AI’s while they are actively training! [Agora]
date
May 1, 2023
slug
train-and-inference
status
Published
tags
Research
summary
run inference on AI’s while they are actively training!
type
Post
When training an AI model, waiting for it to complete training before seeing the results is not only frustrating but also unimaginably inefficient prohibiting atleast a 20% increase in overall model performance.
InfinityWarp addresses this challenge head-on by allowing you to perform real-time inference during the training process.
By bridging the gap between training and inference, InfinityWarp provides an unprecedented level of flexibility and efficiency for AI developers worldwide.
Imagine being able to fine-tune your models in real-time, continuously refining and adapting them to ever-changing conditions.
With InfinityWarp, this vision becomes a reality, giving you a competitive advantage and accelerating your path to success.
To better understand how InfinityWarp solves this problem, let’s delve into its inner workings.
InfinityWarp utilizes multiprocessing to run the training and inference tasks simultaneously, without interfering with each other.
By copying the model’s parameters at regular intervals, it ensures that the latest learned knowledge is available for inference, all while the training process continues uninterrupted.
Usage
Here’s an example of how to use Infinity Warp with your custom training and inference functions:
from infinity_warp import InfinityWarp# Define your custom training and inference functions
def my_train_fn(model, train_data, train_labels):
# Your custom training function logic heredef my_infer_fn(model, infer_data):
# Your custom inference function logic here
return predictions# Instantiate the InfinityWarp class with your model, data, and functions
iw = InfinityWarp(
model=my_model,
train_data=my_train_data,
train_labels=my_train_labels,
infer_data=my_infer_data,
train_fn=my_train_fn,
infer_fn=my_infer_fn
)# Start the concurrent training and inference processes
iw.start()Boom 💥 that’s it.
Now you can interact with an intuitive entity as it learns!
As we look towards the future of InfinityWarp, there are three crucial steps we plan to take to enhance its capabilities:
Roadmap
- Automated adaptation: Implementing an intelligent mechanism that can automatically adapt the training process based on the real-time inference feedback. This will enable the model to learn and improve itself more rapidly, resulting in superior performance.
- Multi-modal support: Extending InfinityWarp to support multi-modal AI models, allowing developers to train and infer across different data types seamlessly. This will open up new possibilities for combining diverse data sources and generating more insightful predictions.
- Scalability and optimization: Further optimizing InfinityWarp for large-scale deployments, ensuring it can handle the demands of modern AI applications. This includes enhancing its performance on multi-GPU setups and distributed systems.
With InfinityWarp, the future of AI development is in your hands. Embrace the change and unleash the full potential of your AI models today! The possibilities are endless, and the journey has just begun.
InfinityWarp: Train, Infer, Conquer.
Join Agora
InfinityWarp is brought to you by Agora, an open source research organization radically devoted to advancing Humanity!
Join us as we set sail on this grand voyage in space and time to battle the forces that seek to destroy you and Humanity!
