amd-lightgbm
LightGBM is a gradient boosting framework that uses tree based learning algorithms. It is designed to be distributed and efficient with the following advantages:
- Faster training speed and higher efficiency.
- Lower memory usage.
- Better accuracy.
- Support of parallel, distributed, and GPU learning.
- Capable of handling large-scale data.
Information
- Homepage: https://github.com/rocm/lightgbm
- License: Apache 2.0
Installation
pip install amd-lightgbm
Metadata
Release files for amd-lightgbm 0.0.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | Actions |
|---|---|---|---|---|
| amd_lightgbm-0.0.1.tar.gz | 1.3 kB | Without provenance | Details for amd_lightgbm-0.0.1.tar.gz |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Actions Reset |
|---|---|---|---|---|
| amd_lightgbm-0.0.1-py3-none-any.whl | Python 3 | none | any | Details for amd_lightgbm-0.0.1-py3-none-any.whl |
Total release size: 2.7 kB
Release files / amd_lightgbm-0.0.1.tar.gz
| Download URL | amd_lightgbm-0.0.1.tar.gz |
|---|---|
| Size | 1.3 kB |
| Tags | Source |
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SHA-256 checksum How to use checksums |
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No |
| Uploaded via |
twine/6.2.0 CPython/3.12.3
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Release files / amd_lightgbm-0.0.1-py3-none-any.whl
| Download URL | amd_lightgbm-0.0.1-py3-none-any.whl |
|---|---|
| Size | 1.4 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
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| Upload date | |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.2.0 CPython/3.12.3
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Security for amd-lightgbm 0.0.1
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