DSperse: Community Edition

DSperse is a proving-system-agnostic intelligent slicer for verifiable AI. It decomposes ONNX neural network models into circuit-compatible segments and orchestrates compilation, inference, proving, and verification across pluggable ZK backends.

Features

Model Slicing: Split neural network models into individual layers or custom segments
ONNX Support: Slice and orchestrate ONNX models
Layered Inference: Run inference on sliced models, chaining the output of each segment
Zero-Knowledge Proofs: Generate and verify proofs for model execution via JSTprove
Tiling and Channel Splitting: Automatically decompose large convolutions for circuit-compatible execution
Proof System Agnostic: Pluggable backend architecture supporting Expander and Remainder proof systems

Documentation

Overview: High-level overview of the project, its goals, and features
JSTprove Backend: JSTprove integration and usage

Installation

From PyPI (includes CLI)

pip install dsperse

This installs both the dsperse CLI command and the Python library bindings. No additional dependencies required — everything is compiled into a single native extension.

From source (Rust binary)

cargo install --path crates/dsperse

As a Rust library

[dependencies]
dsperse = { git = "https://github.com/inference-labs-inc/dsperse.git" }

CLI Usage

DSperse provides six subcommands that form a complete pipeline:

Command	Description
`slice`	Split an ONNX model into segments
`compile`	Compile slices into ZK circuits
`run`	Execute chained inference across slices (`--weights` to inject consumer ONNX)
`prove`	Generate ZK proofs for a completed run
`verify`	Verify ZK proofs
`full-run`	Execute compile, run, prove, verify in sequence (supports `--weights`)

Quickstart

dsperse slice --model-dir models/net
dsperse compile --model-dir models/net --parallel 4
dsperse run --model-dir models/net --input-file models/net/input.json
dsperse prove --model-dir models/net --run-dir models/net/run/run_*
dsperse verify --model-dir models/net --run-dir models/net/run/run_*

Or run the entire pipeline at once:

dsperse full-run --model-dir models/net --input-file models/net/input.json

To inject consumer weights from a fine-tuned ONNX model (same architecture, different weights):

dsperse run --model-dir models/net --input-file models/net/input.json --weights path/to/consumer.onnx
dsperse full-run --model-dir models/net --input-file models/net/input.json --weights path/to/consumer.onnx

Python Library Usage

import dsperse

metadata_json = dsperse.slice_model("models/net/model.onnx", output_dir="models/net/slices")
dsperse.compile_slices("models/net/slices", parallel=4)
run_json = dsperse.run_inference("models/net/slices", "models/net/input.json", "models/net/run")
proof_json = dsperse.prove_run("models/net/run", "models/net/slices")
verify_json = dsperse.verify_run("models/net/run", "models/net/slices")

To inject consumer weights at inference time, pass weights_onnx (path to a fine-tuned ONNX with the same architecture):

run_json = dsperse.run_inference(
    "models/net/slices", "models/net/input.json", "models/net/run",
    weights_onnx="path/to/consumer.onnx",
)

slice_model, run_inference, prove_run, and verify_run return JSON strings parseable with json.loads(). compile_slices returns None.

Project Structure

crates/dsperse/
  src/
    cli/          CLI argument parsing and command dispatch
    slicer/       ONNX model analysis, slicing, autotiling, channel splitting
    pipeline/     Compilation, inference, proving, verification orchestration
    backend/      JSTprove backend integration
    schema/       Metadata and execution result types (serde)
    converter.rs  Prepares JSTprove artifacts from ONNX files
    utils/        I/O helpers and path resolution
  tests/          Unit and integration tests
python/           Thin Python wrapper for PyO3 bindings

Contributing

Contributions are welcome. Please open issues and PRs on GitHub.

License

See the LICENSE file for details.

Metadata

Release files for dsperse 3.9.10

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Built distributions (wheels)

Table of built distributions (wheels) for dsperse 3.9.10
File	Interpreter	ABI	Platform	Reset
dsperse-3.9.10-cp312-cp312-manylinux_2_28_x86_64.whl 16.0 MB Jul 13, 2026 Without provenance	CPython 3.12	CPython 3.12	Linux glibc 2.28+ x86-64	Details
dsperse-3.9.10-cp312-cp312-macosx_11_0_arm64.whl 13.6 MB Jul 13, 2026 Without provenance	CPython 3.12	CPython 3.12	macOS 11.0+ ARM64	Details

Total release size: 29.7 MB

Release files / dsperse-3.9.10-cp312-cp312-manylinux_2_28_x86_64.whl

Download URL	dsperse-3.9.10-cp312-cp312-manylinux_2_28_x86_64.whl
Size	16.0 MB
Tags	CPython 3.12 Linux glibc 2.28+ x86-64
SHA-256 checksum How to use checksums	`9b4226eb524c7cc68ad10212bfbd10f6fac58a7cca399a86f13909d178f9075b`
BLAKE2b-256 checksum How to use checksums	`fa1969e0952b1ebbb9f97a8b1a7b89291a52f4ca6a072cd0ebdb26358064bda8`
Upload date	Jul 13, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`uv/0.10.8 {"installer":{"name":"uv","version":"0.10.8","subcommand":["publish"]},"python":null,"implementation":{"name":null,"version":null},"distro":{"name":"Ubuntu","version":"24.04","id":"noble","libc":null},"system":{"name":null,"release":null},"cpu":null,"openssl_version":null,"setuptools_version":null,"rustc_version":null,"ci":true}`

Release files / dsperse-3.9.10-cp312-cp312-macosx_11_0_arm64.whl

Download URL	dsperse-3.9.10-cp312-cp312-macosx_11_0_arm64.whl
Size	13.6 MB
Tags	CPython 3.12 macOS 11.0+ ARM64
SHA-256 checksum How to use checksums	`9bdb38657c2a539fad0b782c396237b1f1dd3ffb773d0d5c07b5546edcb1f079`
BLAKE2b-256 checksum How to use checksums	`cb8f1ef04ddde55c946c7d6307152f607b007e4f97b98a888d72a7963dceef90`
Upload date	Jul 13, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`uv/0.10.8 {"installer":{"name":"uv","version":"0.10.8","subcommand":["publish"]},"python":null,"implementation":{"name":null,"version":null},"distro":{"name":"Ubuntu","version":"24.04","id":"noble","libc":null},"system":{"name":null,"release":null},"cpu":null,"openssl_version":null,"setuptools_version":null,"rustc_version":null,"ci":true}`

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