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mlflow_tracking | AMP demonstrating using MLFlow to track parameters

 by   fastforwardlabs Python Version: Current License: Apache-2.0

 by   fastforwardlabs Python Version: Current License: Apache-2.0

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kandi X-RAY | mlflow_tracking Summary

mlflow_tracking is a Python library. mlflow_tracking has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.
An AMP demonstrating using MLFlow to track parameters and metrics for scikit-learn models.
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Support
Quality
Quality
Security
Security
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kandi-support Support

  • mlflow_tracking has a low active ecosystem.
  • It has 0 star(s) with 0 fork(s). There are 2 watchers for this library.
  • It had no major release in the last 12 months.
  • There are 0 open issues and 1 have been closed. There are no pull requests.
  • It has a neutral sentiment in the developer community.
  • The latest version of mlflow_tracking is current.
mlflow_tracking Support
Best in #Python
Average in #Python
mlflow_tracking Support
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Average in #Python

quality kandi Quality

  • mlflow_tracking has no bugs reported.
mlflow_tracking Quality
Best in #Python
Average in #Python
mlflow_tracking Quality
Best in #Python
Average in #Python

securitySecurity

  • mlflow_tracking has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
mlflow_tracking Security
Best in #Python
Average in #Python
mlflow_tracking Security
Best in #Python
Average in #Python

license License

  • mlflow_tracking is licensed under the Apache-2.0 License. This license is Permissive.
  • Permissive licenses have the least restrictions, and you can use them in most projects.
mlflow_tracking License
Best in #Python
Average in #Python
mlflow_tracking License
Best in #Python
Average in #Python

buildReuse

  • mlflow_tracking releases are not available. You will need to build from source code and install.
  • Build file is available. You can build the component from source.
mlflow_tracking Reuse
Best in #Python
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mlflow_tracking Reuse
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Average in #Python
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mlflow_tracking Key Features

An AMP demonstrating using MLFlow to track parameters and metrics for scikit-learn models.

Repository structure

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.
├── cml       # This folder contains scripts that facilitate the project launch on CML
└── scripts   # Our analysis code

cml

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cml
├── install_dependencies.py # Script to run pip install of Python dependencies
└── mlflow_ui.py            # Script to launch MLflow ui application.

scripts

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scripts
├── data.py                 # create fake train and test data
├── train_kneighbors.py     # train a k-nearest neighbors classifier
└── train_random_forest.py  # train a random forest classifier

Installation

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!pip3 install -r requirements.txt

Training

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!python3 scripts/train_kneighbors.py --n-neighbors 3

Viewing the MLflow UI

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!mlflow ui --port $CDSW_READONLY_PORT

Community Discussions

No Community Discussions are available at this moment for mlflow_tracking.Refer to stack overflow page for discussions.

No Community Discussions are available at this moment for mlflow_tracking.Refer to stack overflow page for discussions.

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install mlflow_tracking

The code was developed against Python 3.6.9, and will likely work on more later versions. Inside a CML Python 3 session, simply run. In order for Python to pick up the scripts directory when running from the command line (see below), we must set an environment variable for the project, setting the PYTHONPATH to the root directory of the project. Unless you have specifically cloned the project into a different location, this will be /home/cdsw. See the instructions for setting project-level environment variables in CML. Alternately, type export PYTHONPATH=/home/cdsw in a session terminal.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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