Skip to main content

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-'

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

Metadata

Release files for mlops-ods 0.1.202404172104

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for mlops-ods 0.1.202404172104
File Size Uploaded Provenance Actions
mlops_ods-0.1.202404172104.tar.gz 1.5 MB Without provenance Details for mlops_ods-0.1.202404172104.tar.gz

Built distribution (wheel)

Table of built distributions (wheels) for mlops-ods 0.1.202404172104
File Interpreter ABI Platform Actions
mlops_ods-0.1.202404172104-py3-none-any.whl Python 3 none any Details for mlops_ods-0.1.202404172104-py3-none-any.whl

Total release size: 3.1 MB

Release files / mlops_ods-0.1.202404172104.tar.gz

Download URL mlops_ods-0.1.202404172104.tar.gz
Size 1.5 MB
Tags Source
SHA-256 checksum
How to use checksums
f77dd72ac917d277a6ad1b813efdfefd51d9e275d6aac3b8709394e8d07831d6
BLAKE2b-256 checksum
How to use checksums
80f39ded8e409093d7c3ff6770a97f498d0feb72c44fbfcfa623be644cc299c3
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/5.0.0 CPython/3.9.19

Release files / mlops_ods-0.1.202404172104-py3-none-any.whl

Download URL mlops_ods-0.1.202404172104-py3-none-any.whl
Size 1.5 MB
Tags Python 3
SHA-256 checksum
How to use checksums
9d1f643f351cc8ff5d5b3da8a52635192485765bacb5a52b5d0f6b05e0bdd4e2
BLAKE2b-256 checksum
How to use checksums
6b6dafa37ff62407555e6f3ab943a52ee32c200cf13692f81a4b8a1820eca563
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/5.0.0 CPython/3.9.19

Security for mlops-ods 0.1.202404172104

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.

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page