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feat(profiles): add PyTorch and Deep Learning fine-tuning workflow profile

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Assessment

Difficulty
2/5
Estimated time
1-3 hours
Newbie friendliness
72/100
Issue type
Feature
Clarity
Mostly clear
Activity status
Active
Tech stack
python, pytorch
Domain
ai, machine-learning

Research direction

Create src/skills_mcp_server/profiles/pytorch_deeplearning.json, using the field list in the issue and the rules enforced by tests/test_profile_schemas.py. Read that test and one existing profile in the same folder to copy the structure. Done when the JSON parses and pytest tests/test_profile_schemas.py passes.

Written by the indexing model from the issue text.

Description

enhancement good first issue
Summary

Add a curated workflow profile for PyTorch deep learning and model fine-tuning in src/skills_mcp_server/profiles/pytorch_deeplearning.json.

Scope and Requirements

Create src/skills_mcp_server/profiles/pytorch_deeplearning.json following the schema enforced by tests/test_profile_schemas.py:

  1. id: "pytorch_deeplearning"
  2. name: "PyTorch & Deep Learning Fine-Tuning"
  3. description: "Model training, LoRA/PEFT parameter-efficient fine-tuning, evaluation metrics, and GPU acceleration workflows."
  4. version: "1.0.0"
  5. category: "ai"
  6. skills: list of relevant skills (for example pytorch, torchvision, transformers, peft, accelerate, bitsandbytes, tensorboard).
  7. tools: list of training utilities and profilers.
  8. best_practices: list of model training practices (for example mixed precision training with fp16/bf16, gradient checkpointing, validation loss early stopping, reproducible random seeding).
Acceptance Criteria
  • File src/skills_mcp_server/profiles/pytorch_deeplearning.json is created and valid JSON.
  • pytest tests/test_profile_schemas.py passes without error.
Dominant language
Python
Stars
1
Forks
4
Avg merge
5h 58m
Merged PRs (30d)
20

Getting set up

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First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

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