training-pipelines

name	training-pipelines
version	2.0.0
sasmp_version	1.3.0
description	Master training pipelines - orchestration, distributed training, hyperparameter tuning
bonded_agent	04-training-pipelines
bond_type	PRIMARY_BOND
category	training
difficulty	intermediate_to_advanced
estimated_hours	40
prerequisites	["mlops-basics","experiment-tracking"]
validation	{"pre_conditions":["Completed prerequisite skills","Access to GPU resources"],"post_conditions":["Can build Kubeflow pipelines","Can configure distributed training","Can run hyperparameter tuning"]}
observability	{"metrics":["pipelines_created","training_jobs_completed","gpu_utilization"]}

Training Pipelines Skill

Learn: Build production training pipelines with orchestration and distributed training.

Skill Overview

Attribute	Value
Bonded Agent	04-training-pipelines
Difficulty	Intermediate to Advanced
Duration	40 hours
Prerequisites	mlops-basics, experiment-tracking

---

Learning Objectives

1. Design end-to-end training pipelines
2. Implement distributed training with PyTorch DDP
3. Configure hyperparameter tuning with Optuna
4. Deploy pipelines to Kubeflow
5. Optimize GPU utilization and costs

---

Topics Covered

Module 1: Pipeline Design (10 hours)

Pipeline Architecture:

┌────────────────────────────────────────────────────────────────┐
│                    TRAINING PIPELINE                            │
├────────────────────────────────────────────────────────────────┤
│                                                                 │
│  ┌─────────┐  ┌─────────┐  ┌─────────┐  ┌─────────┐  ┌───────┐ │
│  │  Data   │─▶│Preprocess│─▶│  Train  │─▶│ Evaluate│─▶│Register│ │
│  │  Load   │  │         │  │         │  │         │  │       │ │
│  └─────────┘  └─────────┘  └─────────┘  └─────────┘  └───────┘ │
│                              ║                                  │
│                              ▼                                  │
│                        [Hyperparameter]                         │
│                        [   Tuning     ]                         │
│                                                                 │
└────────────────────────────────────────────────────────────────┘

---

Module 2: Distributed Training (12 hours)

PyTorch DDP Setup:

import torch.distributed as dist
from torch.nn.parallel import DistributedDataParallel as DDP

def setup_distributed():
    dist.init_process_group(backend=)
    local_rank = (os.environ[])
    torch.cuda.set_device(local_rank)
     local_rank


model = DDP(model, device_ids=[local_rank])


sampler = DistributedSampler(dataset)
loader = DataLoader(dataset, sampler=sampler)