Rockpool supports training spiking neural networks with PyTorch and JAX and deploying them to neuromorphic hardware.
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PyTorch TNT supplies lightweight tools and utilities for training with PyTorch.
About PyTorchNet
PyTorchNet is a GitHub repository for TNT, a library of tools and utilities for training models with PyTorch. The project describes itself as lightweight. It is installed as the torchtnt package with pip or from conda-forge, and documentation is listed at pytorch.org/tnt/.
The repository homepage does not list the individual training tools the library contains, so this entry does not describe them. Installation notes say to install PyTorch first if problems occur.
Key features
- Library of tools and utilities for training with PyTorch
- Installable with pip as the torchtnt package
- Installable with conda from the conda-forge channel
- Option to install the latest development version from the master branch
- Repository includes examples and tests directories
Best for: Python developers who train machine learning models with PyTorch and want a lightweight set of training utilities.
Good for
- Can be installed with pip or conda-forge
- Offers a route to the latest development version from the master branch
- Documentation is linked from the repository
PyTorchNet at a glance
- Website
- github.com/pytorch/tnt
- Type
- Open-source project
- Pricing
- Not confirmed. Check the site
- Open source
- Not stated
- Language
- English
- Last checked
- Oct 6, 2026 · Online when checked
- Listed
- Oct 5, 2026
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PyTorchNet FAQ
How do I install TNT?
Run pip install torchtnt, or run conda install -c conda-forge torchtnt. If you run into issues, make sure PyTorch is installed first.
How can I install the latest development version?
Run pip install git+https://github.com/pytorch/tnt.git@master. To update an existing install, use pip install --upgrade with the same address.
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