Data Science oriented tools, mostly for Apache Spark
- The pipepline for using Python ML models together with Apache Spark
- Command-line tools (see readme)
- demo: usage demos in form of Jupyter notebooks
- model inference on cluster: demo/score-sklearn.ipynb
- quick dataset distribution change detection: demo/datadiff.ipynb
1 maintainer
Metadata
1 maintainer
Release files for spark-pipeline 0.0.4
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| spark-pipeline-0.0.4.tar.gz | 8.8 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| spark_pipeline-0.0.4-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 24.1 kB
Release files / spark-pipeline-0.0.4.tar.gz
| Download URL | spark-pipeline-0.0.4.tar.gz |
|---|---|
| Size | 8.8 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/2.0.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/41.6.0.post20191030 requests-toolbelt/0.9.1 tqdm/4.38.0 CPython/3.7.3
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Release files / spark_pipeline-0.0.4-py3-none-any.whl
| Download URL | spark_pipeline-0.0.4-py3-none-any.whl |
|---|---|
| Size | 15.4 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
86f71882fa4cd334ab1d6813986a3be04d962904feb1238a883bd2b45192369e
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| Upload date | |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/2.0.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/41.6.0.post20191030 requests-toolbelt/0.9.1 tqdm/4.38.0 CPython/3.7.3
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