Tensorfuse Howto
Tensorfuse is a serverless GPU platform that enables easy deployment and auto-scaling of generative AI models on your own cloud infrastructure.
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Connect your cloud account: Connect your cloud account (AWS, GCP or Azure) to Tensorfuse. Tensorfuse will automatically provision the resources to manage your infrastructure.
Describe your environment: Use Python to describe your container images and hardware specifications. No YAML required. For example, use tensorkube.Image to specify the base image, Python version, apt packages, pip packages, environment variables, etc.
Define your model loading function: Use the @tensorkube.entrypoint decorator to define a function that loads your model onto the GPU. Specify the image and GPU type to use.
Define your inference function: Use the @tensorkube.function decorator to define your inference function. This function will handle incoming requests and return predictions.
Deploy your model: Deploy your ML model to your own cloud via the Tensorfuse SDK. Your model and data will remain within your private cloud.
Start using the API: Begin using your deployment through an OpenAI-compatible API endpoint provided by Tensorfuse.
Monitor and scale: Tensorfuse will automatically scale your deployment in response to incoming traffic, from zero to hundreds of GPU workers in seconds.
Tensorfuse FAQs
Tensorfuse is a platform that allows users to deploy and auto-scale generative AI models on their own cloud infrastructure. It provides serverless GPU computing capabilities on private clouds like AWS, Azure, and GCP.
Tensorfuse Monthly Traffic Trends
Tensorfuse received 17.3k visits last month, demonstrating a Significant Growth of 190.2%. Based on our analysis, this trend aligns with typical market dynamics in the AI tools sector.
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