Pylint
MLflow
Pylint | MLflow | |
---|---|---|
29 | 56 | |
5,127 | 17,335 | |
0.8% | 1.5% | |
9.6 | 9.9 | |
6 days ago | 3 days ago | |
Python | Python | |
GNU General Public License v3.0 only | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Pylint
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W1203: logging-fstring-interpolation (Solved)
A little introduction about pylint. Pylint is a static code analyzer, it analyses your code without actually running it. Pylint looks for potential errors, gives suggestions on coding standards that your code is not adhering to, potential places where refactoring might help, and also warnings about smelly code.
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Enhancing Python Code Quality: A Comprehensive Guide to Linting with Ruff
Pylint, on the other hand, focuses on code analysis and style checking. It offers extensive customization options and supports various coding standards. Pylint is known for its comprehensive reports and ability to detect a wide range of code issues.
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Options for configuration of python libraries - Stack Overflow
In my opinion, the best way to expose configuration options is to read and parse them from the project's pyproject.toml file. Here's how Pylint handles it.
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Pylint strict base configuration
I even contributed to Pylint by submitting a new rule a few years ago : implicit-str-concat.
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Premier League Project Infrastructure Update
Implemented code formatting with Black and linting with Pylint in my CI pipeline. Here is my updated GitHub Actions Workflow file: ci.yml
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Improve your Django Code with pre-commit
One last thing to do before running the hooks is to create a config file, just like we did with flake8. For this you are going to create a pylintrc file at the roor of your project and copy the contents of the pylintrc file from the pylint repo (here is the link to it).
- Even the Pylint codebase uses Ruff
MLflow
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Observations on MLOps–A Fragmented Mosaic of Mismatched Expectations
How can this be? The current state of practice in AI/ML work requires adaptivity, which is uncommon in classical computational fields. There are myriad tools that capture the work across the many instances of the AI/ML lifecycle. The idea that any one tool could sufficiently capture the dynamic work is unrealistic. Take, for example, an experiment tracking tool like W&B or MLFlow; some form of experiment tracking is necessary in typical model training lifecycles. Such a tool requires some notion of a dataset. However, a tool focusing on experiment tracking is orthogonal to the needs of analyzing model performance at the data sample level, which is critical to understanding the failure modes of models. The way one does this depends on the type of data and the AI/ML task at hand. In other words, MLOps is inherently an intricate mosaic, as the capabilities and best practices of AI/ML work evolve.
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My Favorite DevTools to Build AI/ML Applications!
MLflow is an open-source platform for managing the end-to-end machine learning lifecycle. It includes features for experiment tracking, model versioning, and deployment, enabling developers to track and compare experiments, package models into reproducible runs, and manage model deployment across multiple environments.
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Exploring Open-Source Alternatives to Landing AI for Robust MLOps
Platforms such as MLflow monitor the development stages of machine learning models. In parallel, Data Version Control (DVC) brings version control system-like functions to the realm of data sets and models.
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cascade alternatives - clearml and MLflow
3 projects | 1 Nov 2023
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EL5: Difference between OpenLLM, LangChain, MLFlow
MLFlow - http://mlflow.org
- Explain me how websites like Dall-E, chatgpt, thispersondoesntexit process the user data so quickly
- [D] What licensed software do you use for machine learning experimentation tracking?
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Exploring MLOps Tools and Frameworks: Enhancing Machine Learning Operations
MLflow:
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Options for configuration of python libraries - Stack Overflow
In search for a tool that needs comparable configuration I looked into mlflow and found this. https://github.com/mlflow/mlflow/blob/master/mlflow/environment_variables.py There they define a class _EnvironmentVariable and create many objects out of it, for any variable they need. The get method of this class is in principle a decorated os.getenv. Maybe that is something I can take as orientation.
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[D] Is there a tool to keep track of my ML experiments?
I have been using DVC and MLflow since then DVC had only data tracking and MLflow only model tracking. I can say both are awesome now and maybe the only factor I would like to mention is that IMO, MLflow is a bit harder to learn while DVC is just a git practically.
What are some alternatives?
Flake8 - flake8 is a python tool that glues together pycodestyle, pyflakes, mccabe, and third-party plugins to check the style and quality of some python code.
clearml - ClearML - Auto-Magical CI/CD to streamline your AI workload. Experiment Management, Data Management, Pipeline, Orchestration, Scheduling & Serving in one MLOps/LLMOps solution
pylama - Code audit tool for python.
Sacred - Sacred is a tool to help you configure, organize, log and reproduce experiments developed at IDSIA.
black - The uncompromising Python code formatter
zenml - ZenML 🙏: Build portable, production-ready MLOps pipelines. https://zenml.io.
prospector - Inspects Python source files and provides information about type and location of classes, methods etc
guildai - Experiment tracking, ML developer tools
autopep8 - A tool that automatically formats Python code to conform to the PEP 8 style guide.
dvc - 🦉 ML Experiments and Data Management with Git
ruff - An extremely fast Python linter and code formatter, written in Rust.
tensorflow - An Open Source Machine Learning Framework for Everyone