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Multi-cloud orchestration for ML workloads with automatic cost optimization. Use when you need to run training or batch jobs across multiple clouds, leverage spot instances with auto-recovery, or optimize GPU costs across providers.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
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Compiles any research input — PDF papers, GitHub repositories, experiment logs, code directories, or raw notes — into a complete Agent-Native Research Artifact (ARA) with cognitive layer (claims, concepts, heuristics), physical layer (configs, code stubs), exploration graph, and grounded evidence. Use when ingesting a paper or codebase into a structured, machine-executable knowledge package, building an ARA from scratch, or converting research outputs into a falsifiable, agent-traversable form.
Records research provenance as a post-task epilogue, scanning conversation history at the end of a coding or research session to extract decisions, experiments, dead ends, claims, heuristics, and pivots, and writing them into the ara/ directory with user-vs-AI provenance tags. Use as a session epilogue — never during execution — to maintain a faithful, auditable trace of how a research project actually evolved.
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Write publication-ready ML/AI papers for NeurIPS, ICML, ICLR, ACL, AAAI, COLM. Use when drafting papers from research repos, structuring arguments, verifying citations, or preparing camera-ready submissions. For systems venues (OSDI, NSDI, ASPLOS, SOSP), use systems-paper-writing instead.
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| name | skypilot-multi-cloud-orchestration |
| description | Multi-cloud orchestration for ML workloads with automatic cost optimization. Use when you need to run training or batch jobs across multiple clouds, leverage spot instances with auto-recovery, or optimize GPU costs across providers. |
| version | 1.0.0 |
| author | Orchestra Research |
| license | MIT |
| tags | ["Infrastructure","Multi-Cloud","Orchestration","GPU","Cost Optimization","SkyPilot"] |
| dependencies | ["skypilot>=0.7.0"] |
Comprehensive guide to running ML workloads across clouds with automatic cost optimization using SkyPilot.
Use SkyPilot when:
Key features:
Use alternatives instead:
pip install "skypilot[aws,gcp,azure,kubernetes]"
# Verify cloud credentials
sky check
Create hello.yaml:
resources:
accelerators: T4:1
run: |
nvidia-smi
echo "Hello from SkyPilot!"
Launch:
sky launch -c hello hello.yaml
# SSH to cluster
ssh hello
# Terminate
sky down hello
# Task name (optional)
name: my-task
# Resource requirements
resources:
cloud: aws # Optional: auto-select if omitted
region: us-west-2 # Optional: auto-select if omitted
accelerators: A100:4 # GPU type and count
cpus: 8+ # Minimum CPUs
memory: 32+ # Minimum memory (GB)
use_spot: true # Use spot instances
disk_size: 256 # Disk size (GB)
# Number of nodes for distributed training
num_nodes: 2
# Working directory (synced to ~/sky_workdir)
workdir: .
# Setup commands (run once)
setup: |
pip install -r requirements.txt
# Run commands
run: |
python train.py
| Command | Purpose |
|---|---|
sky launch | Launch cluster and run task |
sky exec | Run task on existing cluster |
sky status | Show cluster status |
sky stop | Stop cluster (preserve state) |
sky down | Terminate cluster |
sky logs | View task logs |
sky queue | Show job queue |
sky jobs launch | Launch managed job |
sky serve up | Deploy serving endpoint |
# NVIDIA GPUs
accelerators: T4:1
accelerators: L4:1
accelerators: A10G:1
accelerators: L40S:1
accelerators: A100:4
accelerators: A100-80GB:8
accelerators: H100:8
# Cloud-specific
accelerators: V100:4 # AWS/GCP
accelerators: TPU-v4-8 # GCP TPUs
resources:
accelerators:
H100: 8
A100-80GB: 8
A100: 8
any_of:
- cloud: gcp
- cloud: aws
- cloud: azure
resources:
accelerators: A100:8
use_spot: true
spot_recovery: FAILOVER # Auto-recover on preemption
# Launch new cluster
sky launch -c mycluster task.yaml
# Run on existing cluster (skip setup)
sky exec mycluster another_task.yaml
# Interactive SSH
ssh mycluster
# Stream logs
sky logs mycluster
resources:
accelerators: A100:4
autostop:
idle_minutes: 30
down: true # Terminate instead of stop
# Set autostop via CLI
sky autostop mycluster -i 30 --down
# All clusters
sky status
# Detailed view
sky status -a
resources:
accelerators: A100:8
num_nodes: 4 # 4 nodes × 8 GPUs = 32 GPUs total
setup: |
pip install torch torchvision
run: |
torchrun \
--nnodes=$SKYPILOT_NUM_NODES \
--nproc_per_node=$SKYPILOT_NUM_GPUS_PER_NODE \
--node_rank=$SKYPILOT_NODE_RANK \
--master_addr=$(echo "$SKYPILOT_NODE_IPS" | head -n1) \
--master_port=12355 \
train.py
| Variable | Description |
|---|---|
SKYPILOT_NODE_RANK | Node index (0 to num_nodes-1) |
SKYPILOT_NODE_IPS | Newline-separated IP addresses |
SKYPILOT_NUM_NODES | Total number of nodes |
SKYPILOT_NUM_GPUS_PER_NODE | GPUs per node |
run: |
if [ "${SKYPILOT_NODE_RANK}" == "0" ]; then
python orchestrate.py
fi
# Launch managed job with spot recovery
sky jobs launch -n my-job train.yaml
name: training-job
file_mounts:
/checkpoints:
name: my-checkpoints
store: s3
mode: MOUNT
resources:
accelerators: A100:8
use_spot: true
run: |
python train.py \
--checkpoint-dir /checkpoints \
--resume-from-latest
# List jobs
sky jobs queue
# View logs
sky jobs logs my-job
# Cancel job
sky jobs cancel my-job
workdir: ./my-project # Synced to ~/sky_workdir
file_mounts:
/data/config.yaml: ./config.yaml
~/.vimrc: ~/.vimrc
file_mounts:
# Mount S3 bucket
/datasets:
source: s3://my-bucket/datasets
mode: MOUNT # Stream from S3
# Copy GCS bucket
/models:
source: gs://my-bucket/models
mode: COPY # Pre-fetch to disk
# Cached mount (fast writes)
/outputs:
name: my-outputs
store: s3
mode: MOUNT_CACHED
| Mode | Description | Best For |
|---|---|---|
MOUNT | Stream from cloud | Large datasets, read-heavy |
COPY | Pre-fetch to disk | Small files, random access |
MOUNT_CACHED | Cache with async upload | Checkpoints, outputs |
# service.yaml
service:
readiness_probe: /health
replica_policy:
min_replicas: 1
max_replicas: 10
target_qps_per_replica: 2.0
resources:
accelerators: A100:1
run: |
python -m vllm.entrypoints.openai.api_server \
--model meta-llama/Llama-2-7b-chat-hf \
--port 8000
# Deploy
sky serve up -n my-service service.yaml
# Check status
sky serve status
# Get endpoint
sky serve status my-service
service:
replica_policy:
min_replicas: 1
max_replicas: 10
target_qps_per_replica: 2.0
upscale_delay_seconds: 60
downscale_delay_seconds: 300
load_balancing_policy: round_robin
# SkyPilot finds cheapest option
resources:
accelerators: A100:8
# No cloud specified - auto-select cheapest
# Show optimizer decision
sky launch task.yaml --dryrun
resources:
accelerators: A100:8
any_of:
- cloud: gcp
region: us-central1
- cloud: aws
region: us-east-1
- cloud: azure
envs:
HF_TOKEN: $HF_TOKEN # Inherited from local env
WANDB_API_KEY: $WANDB_API_KEY
# Or use secrets
secrets:
- HF_TOKEN
- WANDB_API_KEY
name: llm-finetune
file_mounts:
/checkpoints:
name: finetune-checkpoints
store: s3
mode: MOUNT_CACHED
resources:
accelerators: A100:8
use_spot: true
setup: |
pip install transformers accelerate
run: |
python train.py \
--checkpoint-dir /checkpoints \
--resume
name: hp-sweep-${RUN_ID}
envs:
RUN_ID: 0
LEARNING_RATE: 1e-4
BATCH_SIZE: 32
resources:
accelerators: A100:1
use_spot: true
run: |
python train.py \
--lr $LEARNING_RATE \
--batch-size $BATCH_SIZE \
--run-id $RUN_ID
# Launch multiple jobs
for i in {1..10}; do
sky jobs launch sweep.yaml \
--env RUN_ID=$i \
--env LEARNING_RATE=$(python -c "import random; print(10**random.uniform(-5,-3))")
done
# SSH to cluster
ssh mycluster
# View logs
sky logs mycluster
# Check job queue
sky queue mycluster
# View managed job logs
sky jobs logs my-job
| Issue | Solution |
|---|---|
| Quota exceeded | Request quota increase, try different region |
| Spot preemption | Use sky jobs launch for auto-recovery |
| Slow file sync | Use MOUNT_CACHED mode for outputs |
| GPU not available | Use any_of for fallback clouds |