ml-training

Add a specific feature to an existing ML training project. Features include dataset, model, distributed, finetune, deploy, platform.

**Project Name:** `$0`

GPU selection, PEFT configuration, batch size tuning, and cost estimation for optimal training efficiency

Dataset preparation, Supabase integration, data loading, and data validation

Advanced preprocessing, tokenization, augmentation, and data quality checks

Multi-GPU training with FSDP, DeepSpeed, and Accelerate - handles sharding strategies, gradient accumulation, and performance optimization

BigQuery ML for SQL-based machine learning training - model creation, Vertex AI integration, remote model deployment, and cost estimation

Vertex AI custom training jobs for deep learning - GPU/TPU selection, PyTorch/TensorFlow/Hugging Face integration, distributed training setup

Model deployment for serverless inference, auto-scaling configuration, and endpoint creation

Integrate ML pipeline with FastAPI, Next.js, and Supabase for full-stack ML applications

Lambda Labs cloud instances, API integration, and cost-optimized GPU infrastructure for ML training workloads

High-level ML pipeline design, framework selection, platform recommendation, and project initialization

End-to-end testing of ML pipeline including data validation, training tests, and inference tests

Modal platform deployment, GPU configuration, and serverless ML endpoint setup with cost optimization

Parameter-efficient fine-tuning with LoRA/QLoRA/prefix-tuning

RunPod serverless and on-demand GPU configuration, FlashBoot setup, and deployment

Training configuration, hyperparameter tuning, framework setup, and TrainingArguments creation

Monitor ML training runs, track metrics with TensorBoard and Weights & Biases, implement failure recovery strategies

Platform-specific configuration templates and GPU selection guidance for Modal, Lambda Labs, and RunPod cloud platforms.

Cost estimation scripts and tools for calculating GPU hours, training costs, and inference pricing across Modal, Lambda Labs, and RunPod platforms. Use when estimating ML training costs, comparing platform pricing, calculating GPU hours, budgeting for ML projects, or when user mentions cost estimation, pricing comparison, GPU budgeting, training cost analysis, or inference cost optimization.

Provides three production-ready ML training examples (sentiment classification, text generation, RedAI trade classifier) with complete training scripts, deployment configs, and datasets. Use when user needs example projects, reference implementations, starter templates, or wants to see working code for sentiment analysis, text generation, or financial trade classification.

Google Cloud Platform configuration templates for BigQuery ML and Vertex AI training with authentication setup, GPU/TPU configs, and cost estimation tools. Use when setting up GCP ML training, configuring BigQuery ML models, deploying Vertex AI training jobs, estimating GCP costs, configuring cloud authentication, selecting GPUs/TPUs for training, or when user mentions BigQuery ML, Vertex AI, GCP training, cloud ML setup, TPU training, or Google Cloud costs.

Integration templates for FastAPI endpoints, Next.js UI components, and Supabase schemas for ML model deployment. Use when deploying ML models, creating inference APIs, building ML prediction UIs, designing ML database schemas, integrating trained models with applications, or when user mentions FastAPI ML endpoints, prediction forms, model serving, ML API deployment, inference integration, or production ML deployment.

Training monitoring dashboard setup with TensorBoard and Weights & Biases (WandB) including real-time metrics tracking, experiment comparison, hyperparameter visualization, and integration patterns. Use when setting up training monitoring, tracking experiments, visualizing metrics, comparing model runs, or when user mentions TensorBoard, WandB, training metrics, experiment tracking, or monitoring dashboard.

Templates and patterns for common ML training scenarios including text classification, text generation, fine-tuning, and PEFT/LoRA. Provides ready-to-use training configurations, dataset preparation scripts, and complete training pipelines. Use when building ML training pipelines, fine-tuning models, implementing classification or generation tasks, setting up PEFT/LoRA training, or when user mentions model training, fine-tuning, classification, generation, or parameter-efficient tuning.

Data validation and pipeline testing utilities for ML training projects. Validates datasets, model checkpoints, training pipelines, and dependencies. Use when validating training data, checking model outputs, testing ML pipelines, verifying dependencies, debugging training failures, or ensuring data quality before training.

0 hooks across 6 events