Python Expert for Model Training
Budget: $8 – $15 USD
I am running an AI project that has reached the core stage of model training and I want a seasoned Python developer to take it from “works locally” to a repeatable, well-evaluated pipeline. Your mission is to refine my existing training code, organise the data loaders, add rigorous validation logic, and make sure each experiment leaves a clear audit trail so we can compare results without guesswork.
The current stack relies heavily on Python 3.x, NumPy and pandas for data handling, and PyTorch for the networks themselves. If you prefer TensorFlow or another deep-learning framework, I am open to a quick discussion—yet PyTorch is what the repository is built on right now. Experience with GPU acceleration, mixed-precision training and common libraries such as scikit-learn for metrics will be invaluable.
Key deliverables will be:
• a cleaned-up, well-documented training script that runs end-to-end from raw data to saved checkpoint
• a concise evaluation report (not just accuracy but loss curves, confusion matrices, or other task-relevant metrics)
• guidance on any hyper-parameter or architecture tweaks you introduce so I can reproduce or extend the work later
I handle version control on GitHub; you will work in a feature branch and submit a pull request so I can review and merge. Acceptance is complete when the training run can be reproduced on my machine, the evaluation metrics match your report, and the code passes flake8 linting.
If this sounds like your kind of challenge, I’m ready to get started right away.
The current stack relies heavily on Python 3.x, NumPy and pandas for data handling, and PyTorch for the networks themselves. If you prefer TensorFlow or another deep-learning framework, I am open to a quick discussion—yet PyTorch is what the repository is built on right now. Experience with GPU acceleration, mixed-precision training and common libraries such as scikit-learn for metrics will be invaluable.
Key deliverables will be:
• a cleaned-up, well-documented training script that runs end-to-end from raw data to saved checkpoint
• a concise evaluation report (not just accuracy but loss curves, confusion matrices, or other task-relevant metrics)
• guidance on any hyper-parameter or architecture tweaks you introduce so I can reproduce or extend the work later
I handle version control on GitHub; you will work in a feature branch and submit a pull request so I can review and merge. Acceptance is complete when the training run can be reproduced on my machine, the evaluation metrics match your report, and the code passes flake8 linting.
If this sounds like your kind of challenge, I’m ready to get started right away.
Related categories:
Python
Software Architecture
Statistics
Machine Learning (ML)
Git
Data Science
Deep Learning