nishide-dev
- 21 skills
- 0 followers
- 12 hours ago last updated
- ▌ Ml Lint · nishide-devRun comprehensive code quality checks with ruff (format, lint) and ty (type checking). Use when checking code quality, fixing linting errors, or ensuring code follows best practices before commits or PRs.
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- ▌ Ml Setup · nishide-devSetup development environment with modern Python tooling (uv/pixi), install dependencies, and configure development tools (ruff, ty, pytest). Use when setting up new ML projects, configuring environments, or installing dependencies.
- ▌ Ml Train · nishide-dev bundleExecute training runs with proper monitoring, checkpointing, and experiment tracking. Use when starting training, resuming training, debugging training issues, or setting up multi-GPU/distributed training with PyTorch Lightning and Hydra.
- ▌ Ml Format · nishide-devFormat Python code with ruff formatter and optionally fix auto-fixable linting issues. Use when formatting code, preparing code for commit, or ensuring consistent code style across the project.
- ▌ Tool Pixi · nishide-dev bundleComprehensive guide for Pixi package manager - Python environment management, CUDA/GPU support, PyPI integration, Docker/Pixi-Pack deployment, and best practices for ML research
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- ▌ Ml Validate · nishide-dev bundleComprehensive validation of ML project structure, configurations, code quality, and training readiness. Use when setting up a new project, before training runs, or debugging configuration issues. Validates config loading, data pipeline, model architecture, and dependencies.
- ▌ Tool Marimo · nishide-devComprehensive guide for marimo - reactive Python notebooks as pure .py files, uv integration, AI-friendly architecture, reproducible data science workflows, and serverless deployment with WASM
- ▌ Ml CLI Tools · nishide-dev bundleBuilding professional CLIs with Typer and Rich - type-safe argument parsing, progress bars, model visualization, Hydra integration, RichHandler logging, and multi-process handling for ML workflows
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- ▌ Ml Hydra Config · nishide-devComprehensive guide for Hydra configuration management, hierarchical configs, experiment management, Optuna integration, and Lightning integration patterns
- ▌ Ml Model Export · nishide-devExport trained PyTorch models to various formats (ONNX, TorchScript, TensorRT) and upload to model registries (Hugging Face Hub, MLflow). Use when deploying models, sharing trained weights, or preparing for production inference.
- ▌ Ml Project Init · nishide-devInitialize a new ML research project using the ML Research template with PyTorch Lightning, Hydra, and modern Python tooling. Use when starting a new ML project from scratch.
- ▌ Ml Transformers · nishide-dev bundleHugging Face Transformers with PyTorch Lightning - LightningModule integration, distributed training (FSDP/DeepSpeed), PEFT (LoRA/QLoRA), data pipelines with HF Datasets, evaluation metrics, and common NLP tasks
- ▌ Ml Data Pipeline · nishide-dev bundleCreate and manage data loading, preprocessing, and augmentation pipelines (DataModule, transforms, data loaders). Use when implementing DataModules, setting up data loaders, or optimizing data pipelines for computer vision, NLP, or graph ML tasks.
- ▌ Tool Uv Monorepo · nishide-devComprehensive guide for building Python monorepos with uv workspaces - unified dependency resolution, shared lock files, editable installs, testing strategies, Docker optimization, and CI/CD patterns for managing multiple packages in a single repository
- ▌ Ml Config Manager · nishide-dev bundleGenerate and manage Hydra configuration files for machine learning experiments. Use when creating new configs (model, data, trainer, logger, experiment, sweep), organizing config hierarchies, or setting up hyperparameter sweeps with Optuna.
- ▌ Ml Wandb Tracking · nishide-devComplete guide for Weights & Biases (W&B) - experiment tracking, hyperparameter sweeps, artifact management, model registry, and PyTorch Lightning integration
- ▌ Ml Lightning Basics · nishide-devComprehensive guide for PyTorch Lightning - LightningModule, Trainer, distributed training, PyTorch 2.0 torch.compile integration, Lightning Fabric, and production best practices
- ▌ Ml Pytorch Geometric · nishide-devComplete guide for PyTorch Geometric (PyG) - graph neural networks, message passing, large-scale distributed graph learning, Lightning integration, and heterogeneous graphs