Tensorfuse now supports working with Secrets.

Deployments often require sensitive information such as passwords, API keys, and access tokens. Hardcoding these in the code is not secure and considered as bad practise.
With Tensorfuse Secrets, you can securely store and use such sensitive data in your code.
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Deploy your first GenAI app, on your own cloud, in minutes. Get started with our docs: tensorfuse.io/docs/concepts/getting_started_tensorkube

Відео

Tensorfuse at SF Awesome AI Dev Tools Meetup

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Tensorfuse at SF Awesome AI Dev Tools Meetup

Переглядів 62День тому

We presented Tensorfuse dev container at the SF Awesome AI Dev Tools Meetup at Github Office. With Tensorfuse dev containers, you can quickly test your ML code on cloud GPUs without having to deal with ssh, cloud console or dangling with CUDA driver and dependencies. Check out the video to see how we tested a qwen model on lamdalabs GPUs without leaving our code editor.

6:38

Tensorfuse Demo at AI Tinkerer 2024

Переглядів 1582 місяці тому

Run Stable Diffusion 3 model on your AWS account via serverless API using Tensorfuse. You only need model inference code wrapped in a FastAPI and Dockerfile to deploy directly to your cloud from the CLI using our runtime, "tensorkube."

Run Llama-3.1 Instruct model on your AWS

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Run Llama-3.1 Instruct model on your AWS

Переглядів 1103 місяці тому

You can now deploy the latest llama-3.1 instruct model on serverless GPUs on your AWS account. The new llama-3.1 family of models are meta’s most capable models till date. Here’s how you can run them on your AWS with Tensorfuse: 1. Download the model from Huggingface and make it part of the Dockerfile. 2. Write a simple FastAPI app for model inference. That’s it! Just run the command, “tensorku...

Serverless GPUs on AWS - Tensorfuse Demo

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Serverless GPUs on AWS - Tensorfuse Demo

Переглядів 1924 місяці тому

Serverless GPUs on AWS - Tensorfuse Demo