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Documentation Index

Fetch the complete documentation index at: https://docs.primeintellect.ai/llms.txt

Use this file to discover all available pages before exploring further.

This guide covers deploying PRIME-RL training infrastructure on Kubernetes clusters using the provided Helm chart.

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Prerequisites

  • Kubernetes cluster with GPU nodes
  • NVIDIA GPU Operator installed
  • Helm 3.x installed
  • Storage class that supports ReadWriteMany (e.g., NFS, CephFS, or cloud provider storage)

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Verify Prerequisites

# Check Helm installation
helm version

# Check GPU operator
kubectl get pods -n gpu-operator

# Check available storage classes
kubectl get storageclass

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Quick Start

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1. Deploy

# Deploy with a release name
helm install my-exp ./k8s/prime-rl -f ./k8s/prime-rl/examples/reverse-text.yaml

# Or with defaults (no example-specific config)
helm install my-exp ./k8s/prime-rl --set trainer.replicas=3 --set inference.replicas=2

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2. Verify deployment

# Check pod status
kubectl get pods -l app.kubernetes.io/instance=my-exp

# Should show 3 pods:
# my-exp-orchestrator-0
# my-exp-inference-0
# my-exp-trainer-0

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3. Run training

# Exec into trainer
kubectl exec -it my-exp-trainer-0 -- bash

# Inside the pod, run training
cd /data
uv run trainer @ /app/examples/reverse_text/configs/train.toml

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4. Monitor progress

# Get logs
kubectl logs my-exp-trainer-0

# Follow logs in real-time
kubectl logs -f my-exp-trainer-0

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Available Examples

The chart includes pre-configured values for each example:

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reverse-text (Small - 1 GPU)

helm install my-exp ./k8s/prime-rl -f ./k8s/prime-rl/examples/reverse-text.yaml
  • Model: Qwen3-0.6B
  • GPUs: 1 per component
  • Runs on consumer GPUs (RTX 3090/4090)
  • Note: You can use any release name - the chart automatically configures service URLs

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Configuration

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Storage Configuration

By default, the chart creates a 1TB PVC with NFS storage. To customize: 
# custom-values.yaml
storage:
  storageClassName: my-storage-class
  size: 500Gi
Deploy with custom storage: 
helm install my-release ./k8s/prime-rl -f custom-values.yaml

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GPU Configuration

Adjust GPU count per component: 
# custom-gpu.yaml
inference:
  gpu:
    count: 4  # Use 4 GPUs for inference

trainer:
  gpu:
    count: 2  # Use 2 GPUs for training

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Resource Limits

Customize memory and CPU: 
# custom-resources.yaml
trainer:
  resources:
    requests:
      memory: "64Gi"
      cpu: "16"
    limits:
      memory: "128Gi"
      cpu: "32"

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Secrets (Optional)

For W&B and HuggingFace authentication: 
# Create secret
kubectl create secret generic prime-rl-secrets \
  --from-literal=wandb-api-key=YOUR_WANDB_KEY \
  --from-literal=hf-token=YOUR_HF_TOKEN

# Enable in values
helm install my-release ./k8s/prime-rl \
  --set config.secrets.enabled=true \
  --set config.secrets.name=prime-rl-secrets

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Common Operations

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Deploy a new experiment

# With example config
helm install my-exp ./k8s/prime-rl -f ./k8s/prime-rl/examples/reverse-text.yaml

# With custom settings
helm install my-exp ./k8s/prime-rl --set trainer.replicas=10 --set inference.replicas=5

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Exec into pods

# Exec into trainer-0
kubectl exec -it my-exp-trainer-0 -- bash

# Exec into specific trainer pod
kubectl exec -it my-exp-trainer-3 -- bash

# Exec into inference
kubectl exec -it my-exp-inference-0 -- bash

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View logs

# Get logs from trainer-0
kubectl logs my-exp-trainer-0

# Follow logs in real-time
kubectl logs -f my-exp-trainer-2

# Get logs from all trainers
kubectl logs -l app.kubernetes.io/instance=my-exp,role=trainer

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List all pods

# List pods for specific experiment
kubectl get pods -l app.kubernetes.io/instance=my-exp

# List all prime-rl pods
kubectl get pods -l app=prime-rl

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Architecture

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Components

The chart deploys three main components (all using StatefulSets):
  1. Orchestrator (StatefulSet) - Coordinates training workflow
    • Always 1 replica: prime-rl-orchestrator-0
    • No GPU required
    • Communicates with trainer and inference
  2. Inference (StatefulSet) - Runs vLLM inference server
    • Scalable replicas with stable pod names: prime-rl-inference-0, prime-rl-inference-1, …
    • Each pod gets predictable DNS: prime-rl-inference-0.prime-rl-inference-headless.default.svc.cluster.local
    • Requires GPU(s)
    • Serves model predictions
  3. Trainer (StatefulSet) - Runs SFT or RL training
    • Scalable replicas with stable pod names: prime-rl-trainer-0, prime-rl-trainer-1, …
    • Each pod gets predictable DNS: prime-rl-trainer-0.prime-rl-trainer-headless.default.svc.cluster.local
    • Requires GPU(s)
    • Updates model weights on shared storage
Why StatefulSets for all components?
  • Consistent naming: All pods have predictable names (orchestrator-0, trainer-0, trainer-1, …)
  • Stable networking: Each pod gets its own DNS hostname via headless service
  • Required for distributed training: PyTorch/vLLM need to discover peers by stable hostname
  • Clean naming: No random pod suffixes, easier to identify and debug

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Shared Storage

All components mount the same PVC at /data for:
  • Model checkpoint sharing
  • Training data
  • Experiment outputs
This is required for coordinating weight updates between trainer and inference.

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Environment Variables

Each pod has these K8s environment variables set:
  • $POD_NAME - Full pod name (e.g., my-exp-trainer-3)
  • $POD_IP - Pod IP address
  • $STATEFUL_REPLICAS - Total number of replicas for that component
  • $HEADLESS_SERVICE - DNS name for peer discovery (e.g., my-exp-trainer-headless.default.svc.cluster.local)
  • $INFERENCE_URL - Full URL to the first inference pod (available in orchestrator and trainer pods)
For distributed training, extract the rank from the pod name: 
# Extract ordinal from pod name
RANK=$(echo $POD_NAME | grep -o '[0-9]*$')  # e.g., "my-exp-trainer-3" -> "3"

# Use in torchrun
torchrun \
  --nnodes=$STATEFUL_REPLICAS \
  --node-rank=$RANK \
  --nproc-per-node=8 \
  --rdzv-endpoint=my-exp-trainer-0.$HEADLESS_SERVICE:29501 \
  src/prime_rl/trainer/sft/train.py @ configs/train.toml

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Troubleshooting

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Can’t access shared storage

Verify PVC is bound: 
kubectl get pvc prime-rl-shared-data
# STATUS should be "Bound"
Check mount inside pod: 
kubectl exec -it prime-rl-trainer-xxx -- df -h /data

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Pod stuck in Pending

Check if GPU resources are available: 
kubectl describe pod my-exp-trainer-0
Look for events like Insufficient nvidia.com/gpu.

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Inference server not responding

Check if the inference pod is ready: 
kubectl get pods -l role=inference
kubectl logs my-exp-inference-0

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Uninstalling

# Remove the Helm release
helm uninstall my-exp

# Delete PVC (data will be lost!)
kubectl delete pvc prime-rl-shared-data