GraphSTAM
Graph Based Spatio-Temporal Attention Models
Note: The current implementation works for GPU (CUDA) enabled machines. To run on CPU, install the following dependencies manually:
pip install torch --index-url https://download.pytorch.org/whl/cpu
pip install torch-geometric
# for latest PyG version, install from master: pip install git+https://github.com/pyg-team/pytorch_geometric.git
pip install torch_scatter torch_sparse -f https://data.pyg.org/whl/torch-2.0.0+cpu.html
For usage guide, run:
import graphstam
graphstam.usage()
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Release files for GraphSTAM 1.2.10
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| GraphSTAM-1.2.10.tar.gz | 575.1 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| GraphSTAM-1.2.10-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 1.2 MB
Release files / GraphSTAM-1.2.10.tar.gz
| Download URL | GraphSTAM-1.2.10.tar.gz |
|---|---|
| Size | 575.1 kB |
| Tags | Source |
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SHA-256 checksum How to use checksums |
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Release files / GraphSTAM-1.2.10-py3-none-any.whl
| Download URL | GraphSTAM-1.2.10-py3-none-any.whl |
|---|---|
| Size | 598.4 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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No |
| Uploaded via |
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