MLOps ODS
This project for ml model with MLOps instruments
the methodology of repo (GitHub flow - one main branch, and developing in other brunches):
- main branch: 'master'
- other branches: 'fix-', 'feature-', 'model-', 'experiment-'
poetry build --format=wheel
Docker image (build / run):
docker build . -t mlops_ods_image
docker run -it mlops_ods_image /bin/bash
build for linux:
docker build . -t mlops_ods_image --platform linux/amd64
run with port and volumes if necessary:
docker run -p 8888:8888 -v {path-to-local-folder-(pwd)}:/app/volumes -it mlops_ods_image /bin/bash
documentation of jupyter notebooks with quarto:
quarto render
quarto preview src/mlops_ods/notebooks/eda.ipynb
quarto render src/mlops_ods/notebooks/eda.ipynb --to html
snakemake command inside docker:
snakemake --cores 10
snakemake --dag | dot -Tsvg > dag.svg
docker ps
docker cp <CONTAINER ID>:/app/dag.svg .
1 maintainer
Metadata
1 maintainer
Release files for mlops-ods 0.1.202406241835
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 |
|---|---|---|---|---|
| mlops_ods-0.1.202406241835.tar.gz | 1.5 MB | Without provenance | Details for mlops_ods-0.1.202406241835.tar.gz |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Actions Reset |
|---|---|---|---|---|
| mlops_ods-0.1.202406241835-py3-none-any.whl | Python 3 | none | any | Details for mlops_ods-0.1.202406241835-py3-none-any.whl |
Total release size: 3.1 MB
Release files / mlops_ods-0.1.202406241835.tar.gz
| Download URL | mlops_ods-0.1.202406241835.tar.gz |
|---|---|
| Size | 1.5 MB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
94dbde5d3c5e793a4456a3d2297403e89c722e45ed03beb0d17eb70b6d1f5015
|
|
BLAKE2b-256 checksum How to use checksums |
33077be3524c06e78e299fe5ecc3b6d0b777052860bf8a2f3a8063e0079291ae
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/5.1.0 CPython/3.9.19
|
Release files / mlops_ods-0.1.202406241835-py3-none-any.whl
| Download URL | mlops_ods-0.1.202406241835-py3-none-any.whl |
|---|---|
| Size | 1.6 MB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
52cf111c5191c9db01e35c16b389ec210a2a2c2b3d02f1c779e355f7ce8afddd
|
|
BLAKE2b-256 checksum How to use checksums |
7cd4934c1bf2ad3633f0b93a46cb46731d218682962f754e12bec241a3c05ef9
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/5.1.0 CPython/3.9.19
|
Security for mlops-ods 0.1.202406241835
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.
