Utilities for deep learning on multimodal data.
Project description
visiontext
Utilities for deep learning on multimodal data.
- jupyter notebooks / jupyter lab / ipython
- matplotlib
- pandas
- webdataset / tar
- pytorch
Install
Requires python>=3.8, requires pytorch to be installed already,
see https://pytorch.org/
pip install visiontext
Full build
Additionally requires libjpeg-turbo and sqlite
pip install visiontext[full]
Dev install
Clone repository and cd into, then:
pip install pytest pytest-cov pylint black[jupyter]
pylint visiontext
pylint tests
# full build
pip install -e .[full]
python -m pytest --cov
# minimal build
pip install -e .
python -m pytest --cov -m "not full"
Changelog
- 0.10.1: Test with python 3.12
- 0.8.1: Set minimum python version to 3.8 since PyTorch requires it
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
visiontext-0.22.8.tar.gz
(54.9 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
File details
Details for the file visiontext-0.22.8.tar.gz.
File metadata
- Download URL: visiontext-0.22.8.tar.gz
- Upload date:
- Size: 54.9 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.1.0 CPython/3.12.8
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
911a52d7ae4fbb4ab4e550d21601520d7b3f07f28667bbe440df959ea71761b2
|
|
| MD5 |
1f7722ea05c9becd779a419904f1ebec
|
|
| BLAKE2b-256 |
6b546f3579f2c8c30ba1035ea7446281d823b312382ad8dad583379acbc4f442
|
File details
Details for the file visiontext-0.22.8-py3-none-any.whl.
File metadata
- Download URL: visiontext-0.22.8-py3-none-any.whl
- Upload date:
- Size: 64.5 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.1.0 CPython/3.12.8
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
f39bacb19e2591a069bf5a1372f0a72aa37739fd9a8b3bc88b4e1dba66b6643f
|
|
| MD5 |
4c625084ebeefaabcf94202d3143c293
|
|
| BLAKE2b-256 |
c9a641d2524715b013b8956e5d37f580e2933381a5764825ad22c6e4cc7553c0
|