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The API for serverless ML compute

Train, predict, and deploy ML models.
Get instant production-grade infrastructure with our API.

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BACKED BY

SUPPORTED BY

We are the GPU lambda functions for ML

Wide library support

The API integrates with PyTorch, TensorFlow2.0, and MXNet. Also 🤗.

Rapid GPU setup

Our API automatically connects your ML task to top-tier servers.

Grow with your model

Our infrastructure scales to support high speed and bandwidth requirements.

Pay as you go

Save money and the environment by paying only for compute time.

Serverless helps you

focus on Machine Learning

optimise your budget

benefit from flexible infrastructure

Serverless helps you

focus on building models

optimise your budget

benefit from flexible infrastructure

HOW NPU WORKS

Focus on building models, not infrastructure.

Compile

Build your ML model and upload it with our API. In the dashboard, you'll be able to check on the model's performance.

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Tensorflow 2.0 / PyTorch / MXNet
Easy training
Easy predicting
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Train or predict

Train or predict with your model from your Python environment of choice. Behind the scenes, we'll spin up the best hardware. Instantly.

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Let Neuro's API do the rest

We'll train or run prediction on the fastest GPU for your task.

FROM PROTOTYPE TO PRODUCTION

Train or deploy your model
in just four lines of code

training

Train your model

Train in parallel

Track any metric

Automatic visualisations

Learn more →
DEPLOYment

Deploy your model

1 line predictions

Scale as you go

JSON support

Learn more →

Frequently asked questions

Where are my datasets and models stored after upload?

Your data will be stored in AWS S3 buckets.

Can I choose the GPU for my task?

After parsing your model, a Neuro algorithm automatically finds and assigns the fastest GPU for your task. If you want to select a specific GPU, contact us about an enterprise solution.

Where should I use the API?

You can use our npu library in any Python environment, including Jupyter notebooks.

THE API for serverless ML compute

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