PyTorch implementation of two papers: (1) Adaptive checkpoint adjoint method for gradient estimation in Neural ODEs (2) MALI: a memory efficient and reverse accurate integrator for Neural ODEs
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Release files for TorchDiffEqPack 1.0.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| TorchDiffEqPack-1.0.1.tar.gz | 26.1 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| TorchDiffEqPack-1.0.1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 60.1 kB
Release files / TorchDiffEqPack-1.0.1.tar.gz
| Download URL | TorchDiffEqPack-1.0.1.tar.gz |
|---|---|
| Size | 26.1 kB |
| Tags | Source |
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SHA-256 checksum How to use checksums |
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twine/3.2.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/50.3.0 requests-toolbelt/0.9.1 tqdm/4.36.1 CPython/3.7.4
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Release files / TorchDiffEqPack-1.0.1-py3-none-any.whl
| Download URL | TorchDiffEqPack-1.0.1-py3-none-any.whl |
|---|---|
| Size | 34.0 kB |
| Tags | Python 3 |
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SHA-256 checksum How to use checksums |
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| Uploaded via |
twine/3.2.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/50.3.0 requests-toolbelt/0.9.1 tqdm/4.36.1 CPython/3.7.4
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