slimnet
Hardware-Aware Neural Architecture Search & Edge AI Deployment Toolkit
End-to-end pipeline to optimize deep learning models for embedded and edge hardware — from differentiable NAS to ONNX deployment.
Installation
pip install slimnet
Commands
# Full optimization pipeline
slimnet --model model.pth --platform local_pc --dataset cifar10
# Latency prediction
slimnet-predict --model model.onnx --platform local_pc
Stages
- Stage 1 — Differentiable NAS (MixedOp + Gumbel-Softmax)
- Stage 2 — Structured channel pruning
- Stage 3 — Post-Training Quantization (FP16 / INT8)
[opt-in] - Stage 4 — ORT compiler tuning
[opt-in]
License
MIT
1 maintainer
Release files for slimnet 0.2.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| slimnet-0.2.1.tar.gz | 1.4 MB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| slimnet-0.2.1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 2.9 MB
Release files / slimnet-0.2.1.tar.gz
| Download URL | slimnet-0.2.1.tar.gz |
|---|---|
| Size | 1.4 MB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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No |
| Uploaded via |
twine/6.2.0 CPython/3.10.20
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Release files / slimnet-0.2.1-py3-none-any.whl
| Download URL | slimnet-0.2.1-py3-none-any.whl |
|---|---|
| Size | 1.5 MB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.2.0 CPython/3.10.20
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