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Petrify Alternatives
Similar projects and alternatives to petrify
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sherpa-onnx
Speech-to-text, text-to-speech, speaker diarization, speech enhancement, source separation, and VAD using next-gen Kaldi with onnxruntime without Internet connection. Support embedded systems, Android, iOS, HarmonyOS, Raspberry Pi, RISC-V, RK NPU, Axera NPU, Ascend NPU, x86_64 servers, websocket server/client, support 12 programming languages
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timber
Ollama for classical ML models. AOT compiler that turns XGBoost, LightGBM, scikit-learn, CatBoost & ONNX models into native C99 inference code. One command to load, one command to serve. 336x faster than Python inference. (by kossisoroyce)
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HY-World-2.0
HY-World 2.0: A Multi-Modal World Model for Reconstructing, Generating, and Simulating 3D Worlds
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eli5
A library for debugging/inspecting machine learning classifiers and explaining their predictions
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NOTE:
The number of mentions on this list indicates mentions on common posts plus user suggested alternatives.
Hence, a higher number means a better petrify alternative or higher similarity.
petrify discussion
petrify reviews and mentions
Posts with mentions or reviews of petrify.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2026-05-28.
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Endive: A JVM native WebAssembly runtime
Projects like this would be significantly funner and easier to make in Jdk25+(well technically 24+) because of the new Java classfile/bytecode API. It looks like Endive uses OW2 ASM, probably because this supports back to Jdk11. The new jdk API has a minimum target of Jdk17. OW2 ASM is significantly harder to use IMHO though.
What got me into this is I just finished a major release of Petrify (https://github.com/exabrial/petrify) that compiles ML Models to JVM Bytecode. It requires Jdk25 to do the compilation, but the compiled models can run on Jdk17+.
I'm looking for more side projects to use the classfile API on.
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Ask HN: Is Java the ideal language for LLM-assisted coding?
I'm quite a fan of lightgbm4j. It is a JNI Wrapper, but the API is decent. Training itself happens in native code, but the real advantage your support code gets compiled to native through the JVM's compiler.
I've also authored a Machine Learning Model compiler for the JVM: https://github.com/exabrial/petrify . This allows you to take Many Trees/Ensembles/Regressors/Classifiers/etc and compile them as regular Java classes! ...removing the long dependency chain that ML Models drag with them to run in production. It's also very memory efficient; exactly one array allocation invocation, with 0 other Garbage Collector pressure. Your weights and splits are also stored in the constant pool; making it an excellent C2 target and do not add to the additional heap memory pressure!
Overall, I think it's an ideal language for Data Science; but what it lacks compared to Python is community engagement. Oracle has Tribuo, which is actually quite good; but Oracle is still learning to interact with the open source community.
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Show HN: Apple's Sharp Running in the Browser via ONNX Runtime Web
A *2.4gb* ONNX? That is wild. This format continues to impress me. ONNX uses 32bit single precision floats I believe, so thats something like ~644m float params/constants. I recently dove deep 'traditional ML' side of the ONNX serialization format for the purposes of writing an JVM ML compiler for trees and regressions. ONNX actually quite clever the way it serializes trees into parallel arrays (which is then serialized using protobuf). My trees have capped out at < 32mb. I haven't dove into the neural net side of things yet, mainly because I don't have any models to run in prod.(https://github.com/exabrial/petrify if anyone is interested.)
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A note from our sponsor - SaaSHub
www.saashub.com | 15 Jul 2026
Stats
Basic petrify repo stats
3
7
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3 months ago
exabrial/petrify is an open source project licensed under European Union Public License 1.2 which is an OSI approved license.
The primary programming language of petrify is Java.
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