ezkl

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ezkl is an engine for doing inference for deep learning models and other computational graphs in a zk-snark (ZKML). Use it from Python, Javascript, or the comma

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ezkl is an engine for doing inference for deep learning models and other computational graphs in a zk-snark (ZKML). Use it from Python, Javascript, or the comma

:thought_balloon:

EZKL

Easy Zero-Knowledge Inference ezkl is a library and command-line tool for doing inference for deep learning models and other computational graphs in a zk-snark (ZKML). It enables the following workflow:

Define a computational graph, for instance a neural network (but really any arbitrary set of operations), as you would normally in pytorch or tensorflow.
Export the final graph of operations as an .onnx file and some sample inputs to a .json file.
Point ezkl to the .onnx and .json files to generate a ZK-SNARK circuit with which you can prove statements such as:

"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" In the backend we use the collaboratively-developed Halo2 as a proof system.

The generated proofs can then be verified with much less computational resources, including on-chain (with the Ethereum Virtual Machine), in a browser, or on a device.

If you have any questions, we'd love for you to open up a discussion topic in Discussions. Alternatively, you can join the ✨EZKL Community Telegram Group.
For more technical writeups and details check out our blog.
To see what you can build with ezkl, check out cryptoidol.tech where ezkl is used to create an AI that judges your singing ... forever.

Getting Started ⚙️

The easiest way to get started is to try out a notebook.

Python

Install the python bindings by calling.

bash

pip install ezkl

Or for the GPU:

bash

pip install ezkl-gpu

Google Colab Example to learn how you can train a neural net and deploy an inference verifier onchain for use in other smart contracts.

More notebook tutorials can be found within examples/notebooks.

CLI

Install the CLI

bash

curl https://raw.githubusercontent.com/zkonduit/ezkl/main/install_ezkl_cli.sh | bash

https://user-images.githubusercontent.com/45801863/236771676-5bbbbfd1-ba6f-418a-902e-20738ce0e9f0.mp4

For more details visit the docs. The CLI is faster than Python, as it has less overhead. For even more speed and convenience, check out the remote proving service, which feels like the CLI but is backed by a tuned cluster.

Build the auto-generated rust documentation and open the docs in your browser locally. cargo doc --open

Building the Project

Rust CLI

You can install the library from source

bash

cargo install --locked --path .

ezkl now auto-manages solc installation for you.

Building Python Bindings

Python bindings exists and can be built using maturin. You will need rust and cargo to be installed.

bash

python -m venv .env
source .env/bin/activate
pip install -r requirements.txt
maturin develop --release --features python-bindings
# dependencies specific to tutorials
pip install torch pandas numpy seaborn jupyter onnx kaggle py-solc-x web3 librosa tensorflow keras tf2onnx

GPU Acceleration

If you have access to NVIDIA GPUs, you can enable acceleration by building with the feature icicle and setting the following environment variable:

bash

export ENABLE_ICICLE_GPU=true

GPU acceleration is provided by Icicle

To go back to running with CPU, the previous environment variable must be unset instead of being switch to a value of false:

bash

unset ENABLE_ICICLE_GPU

NOTE: Even with the above environment variable set, icicle is disabled for circuits where k NOTE: Because operations are quantized when they are converted from an onnx file to a zk-circuit, outputs in python and ezkl may differ slightly.

Check out docs/advanced_security for more advanced information on potential threat vectors that are specific to zero-knowledge inference, quantization, and to machine learning models generally.

No Warranty

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.