Web DL Compiler - Lightweight deep learning compiler utilities
Project description
What will we need to do?
Implement the major layers in pytorch:
- Linear
- Conv2D
- FC
- ReLU
- Pool
- RNN
- LSTM
- Batch Norm ... etc
-
Support
nn.Sequential -
Support hugging face models
-
Support saved model.
-
Optimization Phase:
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
wdlc-0.1.0.tar.gz
(6.6 kB
view details)
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
wdlc-0.1.0-py3-none-any.whl
(6.7 kB
view details)
File details
Details for the file wdlc-0.1.0.tar.gz.
File metadata
- Download URL: wdlc-0.1.0.tar.gz
- Upload date:
- Size: 6.6 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.2.0 CPython/3.13.7
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
82f3b79696173c6bd1adf45d31873bd0e5b6177012342b17345f9bab86233b5c
|
|
| MD5 |
a8b7b4f642a27f531fa1e7f62871bfcf
|
|
| BLAKE2b-256 |
0f9606a3048f29fc7b8a3d22fc487568b067efcc39e6b08cc906ffccd3a17f92
|
File details
Details for the file wdlc-0.1.0-py3-none-any.whl.
File metadata
- Download URL: wdlc-0.1.0-py3-none-any.whl
- Upload date:
- Size: 6.7 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.2.0 CPython/3.13.7
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
e86f629b06f0cffcbb0b06ee487a3c477d1a68a8404327bd36a1de80b21fdf66
|
|
| MD5 |
e8f1c607da433f5f3a2e42f6aa0675c5
|
|
| BLAKE2b-256 |
b9a5e064c541865a26e4721bd6659646d0aa84fdc8a6dd14cb1b1fe72ef797b8
|
