Optimization in machine learning, both theoretical and applied, is presently dominated by first-order gradient methods such as stochastic gradient descent. Second-order optimization methods, that involve second derivatives and/or second order statistics of the data, are far less prevalent despite strong theoretical properties, due to their prohibitive computation, memory and communication costs.
Here we present a scalable implementation of a second-order preconditioning method (concretely, a variant of full-matrix Adagrad) that provides significant convergence and wall-clock time improvements compared to conventional first-order methods on state-of-the-art deep models.
Paper preprints: https://arxiv.org/abs/2002.09018
1 maintainer
Metadata
1 maintainer
Release files for optax-shampoo 0.0.6
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | Actions |
|---|---|---|---|---|
| optax_shampoo-0.0.6.tar.gz | 31.0 kB | Without provenance | Details for optax_shampoo-0.0.6.tar.gz |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Actions Reset |
|---|---|---|---|---|
| optax_shampoo-0.0.6-py3-none-any.whl | Python 3 | none | any | Details for optax_shampoo-0.0.6-py3-none-any.whl |
Total release size: 63.0 kB
Release files / optax_shampoo-0.0.6.tar.gz
| Download URL | optax_shampoo-0.0.6.tar.gz |
|---|---|
| Size | 31.0 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
0aa9ae859c7be2f29cc3e260931bf7c9560c4357a1fd3675478b20c6d099e3bb
|
|
BLAKE2b-256 checksum How to use checksums |
f4ab5c370d310ace203de7ebf50f0800e1c36041de707e5c7374c978d8d727cf
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/4.0.1 CPython/3.10.7
|
Release files / optax_shampoo-0.0.6-py3-none-any.whl
| Download URL | optax_shampoo-0.0.6-py3-none-any.whl |
|---|---|
| Size | 32.0 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
935bf236149365e6afbaef5de2384008c78cb5a7d2ac74b0c75181fadf14c6aa
|
|
BLAKE2b-256 checksum How to use checksums |
803b6609f0b3a98527a7fe2c2b4b48944b455ab65a3c9223e86d3cd5cf823a5e
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/4.0.1 CPython/3.10.7
|
Security for optax-shampoo 0.0.6
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
No provenance found
None of the files in this release were uploaded with provenance metadata.
A note on package security
PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.
To learn more about protecting yourself from malicious actors, visit our package security documentation.
