Dask-ML provides scalable machine learning in Python using Dask alongside popular machine learning libraries like Scikit-Learn, XGBoost, and others.
You can try Dask-ML on a small cloud instance by clicking the following button:
LICENSE
New BSD. See License File.
2 maintainers
Metadata
2 maintainers
Release files for dask-ml 2021.11.16
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| dask-ml-2021.11.16.tar.gz | 595.7 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| dask_ml-2021.11.16-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 743.4 kB
Release files / dask-ml-2021.11.16.tar.gz
| Download URL | dask-ml-2021.11.16.tar.gz |
|---|---|
| Size | 595.7 kB |
| Tags | Source |
|
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/3.6.0 importlib_metadata/4.8.2 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.10.0
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Release files / dask_ml-2021.11.16-py3-none-any.whl
| Download URL | dask_ml-2021.11.16-py3-none-any.whl |
|---|---|
| Size | 147.7 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/3.6.0 importlib_metadata/4.8.2 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.10.0
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