ezkl takes a high-level description of your program and sets up a zero-knowledge prover and verifier. Our focus is on programs that are expressed as pytorch AI/ML models and other computational graphs. After setup, the prover can prove statements such as the following.
"I ran this publicly available neural network on some private data and it produced this output"
"I ran my private neural network on some public data and it produced this output"
"I correctly ran this publicly available neural network on some public data and it produced this output"
These proofs can be trusted by anyone with a copy of the verifier, and even verified on Ethereum. ezkl can be used as a command-line tool, or directly from Python; see this colab notebook and the python bindings docs.
ezkl can prove an MNIST-sized inference in less than a second and under 180mb of memory and verify it on the Ethereum Virtual Machine (or on the command line, or in the browser using wasm).
For more details on how to use ezkl, we invite you to explore the docs and check out the repo!