| Name | pyvtorch JSON |
| Version |
1.3.1
JSON |
| download |
| home_page | None |
| Summary | Python tools to work in deep learning with PyTorch |
| upload_time | 2025-09-12 17:26:00 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.12.0 |
| license | MIT License |
| keywords |
deep learning
pytorch
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# PyVTorch
General custom tools to work in deep learning research using Python and PyTorch
## Getting Started
### Installation
This package can easily be installed using `pip`:
```bash
pip install pyvtorch
```
An alternative installation that partially uses Anaconda would involve...
1. First, install some Anaconda distribution, in case you do not have any:
https://docs.anaconda.com/anaconda/install/
2. Then, create an Anaconda environment with Python
```bash
conda create -n dev python
```
3. Activate the environment
```bash
conda activate dev
```
3. Then, install all required packages by running the `install.sh` script:
```bash
yes | . install.sh
```
4. You can make sure that your PyTorch installation has CUDA GPU support by running...
```bash
python -c "import torch; print(torch.cuda.is_available()) \
print([torch.cuda.get_device_name(i) for i in range(torch.cuda.device_count())])"
```
The first line should print `True` if CUDA is supported. And the second line should show you the name/s of your available GPU/s.
5. That's it! You're good to go :)
That second installation procedure is designed to be overly redundant, so please feel free to follow your own installation procedure.
### Requirements
Provided installation steps are only guaranteed to work in Ubuntu 24.04 with NVidia drivers 535.
## Additional information
### Main Author Contact
Valeria Pais - @vrpais - valeriarpais@gmail.com
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