Comparison of two primary approaches for distributed ML workflows.
Scenario: Large LLM pretraining on multi-node GPU cluster
# Step 1: Submit Job
$ sbatch train_llm.slurm
# Requests N nodes, M GPUs per node, time limit
# Step 2: Job Allocation
# Slurm queues → allocates nodes exclusively
# Wait time depends on cluster load
# Step 3: Launch Training
$ srun python train.py
# Uses MPI/NCCL for multi-node communication
# All nodes must start together
# Step 4: Monitor
# Slurm provides job stats (GPU, memory, time)
# Logs written to shared storage
# Step 5: Checkpoint & Resume
# Manual checkpoints
# Job resubmission if preempted/failsCharacteristics:
- ✅ Throughput: Maximum
- ❌ Flexibility: Low (queue times, static resources)
- ❌ Fault tolerance: Minimal
- 🎯 Best for: Long-running, tightly-coupled jobs
Scenario: LLM fine-tuning or multi-task experiments on cloud GPUs
# Step 1: Launch Cluster
# K8s provisions GPU nodes dynamically
# Ray head + workers started as pods
# Auto-scaling enabled
# Step 2: Submit Training Job
from ray.train.torch import TorchTrainer
trainer = TorchTrainer(...)
result = trainer.fit()
# Step 3: Dynamic Resource Management
# Nodes/pods added or removed based on workload
# Multiple jobs run concurrently
# Hyperparameter tuning jobs start/stop independently
# Step 4: Monitor
# Ray dashboard: http://localhost:8265
# K8s dashboard, Prometheus metrics
# Step 5: Checkpoint & Resume
# Built-in checkpointing
# Elastic rescheduling on node failuresCharacteristics:
- ⚡ Throughput: Slightly lower (network overhead)
- ✅ Flexibility: High (elastic scaling, many workloads)
- ✅ Fault tolerance: Built-in
- 🎯 Best for: Agile experimentation, production ML pipelines
| Feature | HPC + Slurm | Kubernetes + Ray |
|---|---|---|
| Resource allocation | Fixed, queued | Dynamic, elastic |
| Job start | All nodes together | Tasks scheduled as pods |
| Fault tolerance | Minimal | Automatic recovery |
| Scaling | Hard, manual | Easy, auto-scaling |
| Workload type | Single large job | Many simultaneous jobs |
| Monitoring | Slurm logs | Dashboards, metrics |
| Flexibility | Low | High |
| Peak throughput | Max | Slightly lower |
| Network | InfiniBand | Ethernet (+ RDMA in cloud) |
TL;DR: HPC = max performance for long jobs; K8s+Ray = flexible multi-workload environment.