Deploy Stack

Deploy Embodied AI Platform CDK Stacks

Autonomous execution: Execute all phases sequentially from Phase 1 through Phase 8c without pausing for user input, except where a phase says "Prompt the user" — pause and ask before proceeding with those optional phases. Only stop if a phase fails or requires information not available in this document. When a phase depends on a long-running process (e.g. CodeBuild, training job), poll until completion then proceed to the next phase automatically.

This skill walks through deploying the two CDK stacks that make up the platform's AWS infrastructure, setting up SSH access to the GPU workstation, submitting training jobs, visualizing metrics, and running model evaluations.

The two stacks are:

• IsaacGr00tBatchStack — VPC, EFS, ECR, CodeBuild, AWS Batch compute environment and job queue
• IsaacLabDcvStack — GPU-accelerated DCV workstation (EC2 instance, accessed via SSM)

The DCV stack depends on the Batch stack (shares its VPC and EFS), so deploy order matters: Batch first, DCV second. Destroy order is reversed: DCV first, Batch second.

Phase 1: Prerequisites

Before deploying, verify these are in place.

Region selection: Deploy in a region with g6e GPU instances for training (e.g. us-west-2, us-east-1). The DCV instance uses g6 family by default. Set your region: export AWS_DEFAULT_REGION=us-west-2 or use --profile with a configured AWS CLI profile.

# AWS CLI configured with correct account
aws sts get-caller-identity --query '[Account, Arn]' --output text

# CDK available (via npx, no global install needed)
npx cdk --version

# jq for parsing stack outputs
jq --version

# Python venv — CDK uses Python to synthesize CloudFormation templates.
# The venv isolates CDK dependencies from your system Python.
REPO_ROOT=$(git rev-parse --show-toplevel)
ls -la "$REPO_ROOT/.venv/bin/python"

# If venv is missing, create it:
cd "$REPO_ROOT" && python3 -m venv .venv
source .venv/bin/activate
pip install -r training/gr00t/infra/requirements.txt
pip install -r workstation/requirements.txt

Also confirm CDK has been bootstrapped in the target account/region:

aws cloudformation describe-stacks --stack-name CDKToolkit --query 'Stacks[0].StackStatus' --output text

If that fails, run npx cdk bootstrap from training/gr00t/infra/.

Phase 2: Validate with CDK Synth

The default app.py deploys with IsaacSim 5.1.0 / IsaacLab v2.3.0.

Always synth before deploying — it catches version mismatches, missing dependencies, and code errors at zero cost (no AWS resources created).

cd training/gr00t/infra
npx cdk synth --quiet

Expected output: Successfully synthesized to .../cdk.out listing both stack names.

If synth fails, fix the error before proceeding. Common issues:

• Missing Python dependencies -> pip install -r requirements.txt
• Version validation errors -> check workstation/versions.py for supported IsaacSim versions

Phase 2.5: Probe GPU Instance Capacity

--dry-run does not test actual instance capacity — it only validates IAM permissions. The only reliable check is a real launch + immediate terminate. Run this after IsaacGr00tBatchStack deploys (so its VPC subnets exist), before deploying IsaacLabDcvStack.

# Requires $VpcId from the Batch stack outputs (captured below after Phase 3 Step 1)

UBUNTU_AMI=$(aws ec2 describe-images --owners amazon \
  --filters "Name=name,Values=ubuntu/images/hvm-ssd/ubuntu-jammy-22.04-amd64-server-*" \
            "Name=state,Values=available" \
  --query 'sort_by(Images, &CreationDate)[-1].ImageId' --output text)

SUBNETS=$(aws ec2 describe-subnets \
  --filters "Name=vpc-id,Values=$VpcId" "Name=mapPublicIpOnLaunch,Values=true" \
  --query 'Subnets[].{AZ:AvailabilityZone,SubnetId:SubnetId}' --output json)

# Probe in order: preferred first, then fallbacks (all ≥32GiB — minimum for GR00T + sim)
FOUND=false
for ITYPE in g6.4xlarge g6.2xlarge g5.2xlarge; do
  while IFS= read -r row; do
    AZ=$(echo "$row" | jq -r '.AZ')
    SUBNET=$(echo "$row" | jq -r '.SubnetId')
    RESULT=$(aws ec2 run-instances \
      --image-id "$UBUNTU_AMI" --instance-type "$ITYPE" \
      --subnet-id "$SUBNET" --count 1 \
      --no-associate-public-ip-address \
      --query 'Instances[0].InstanceId' --output text 2>&1)
    if [[ "$RESULT" == i-* ]]; then
      aws ec2 terminate-instances --instance-ids "$RESULT" > /dev/null
      echo "✅  $ITYPE in $AZ — available. Set in app.py:"
      echo "    availability_zone=\"$AZ\","
      echo "    instance_type=\"$ITYPE\","
      FOUND=true; break 2
    else
      echo "❌  $ITYPE / $AZ — $(echo "$RESULT" | grep -oE 'InsufficientInstanceCapacity|[A-Za-z]+Error')"
    fi
  done < <(echo "$SUBNETS" | jq -c '.[]')
done
$FOUND || echo "⚠️  No capacity found — try a different region or instance family"

Update training/gr00t/infra/app.py with the printed availability_zone and instance_type values before running Step 2 below.

Phase 3: Deploy Stacks

No context parameters needed — the stacks auto-create VPC, EFS, and ECR.

N1.6 build target: The BatchStack deploy automatically triggers a CodeBuild run. Pass --context build_target=n16 so it builds N16/Dockerfile (not the N1.5 default).

cd training/gr00t/infra

# Step 1: Batch stack (VPC, EFS, ECR, Batch — ~3 min)
# build_target=n16 selects N16/Dockerfile for the CodeBuild run triggered on deploy.
npx cdk deploy IsaacGr00tBatchStack --require-approval=never --context build_target=n16

# Capture VpcId output, then run Phase 2.5 capacity probe, then update app.py.

# Step 2: DCV stack (GPU instance — ~3 min for CFN, then ~15 min bootstrap)
# This blocks until cfn-signal is received from the bootstrap script.
# By default, ports 8443 (DCV) and 8080 (W&B) are open publicly.
# For SSM-only access (no public ports), add: --context public_dcv_access=false
npx cdk deploy IsaacLabDcvStack --require-approval=never

Parallel monitoring during Step 2: Open a second terminal and poll CloudFormation events directly — avoids stdout buffering delays from CDK:

while true; do
  STATUS=$(aws cloudformation describe-stacks --stack-name IsaacLabDcvStack \
    --query 'Stacks[0].StackStatus' --output text 2>/dev/null || echo "CREATING")
  printf '\n=== %s [%s] ===\n' "$(date +%H:%M:%S)" "$STATUS"
  aws cloudformation describe-stack-events --stack-name IsaacLabDcvStack \
    --query 'StackEvents[0:8].[Timestamp,ResourceStatus,LogicalResourceId]' \
    --output table 2>/dev/null
  [[ "$STATUS" =~ (COMPLETE|FAILED) ]] && break
  sleep 20
done

After both deploys complete, capture the stack outputs as shell variables for later phases. The jq approach handles values with spaces safely (unlike eval-based approaches):

# Capture Batch stack outputs
BATCH_OUTPUTS=$(aws cloudformation describe-stacks --stack-name IsaacGr00tBatchStack \
  --query 'Stacks[0].Outputs' --output json)
export EcrImageUri=$(echo "$BATCH_OUTPUTS" | jq -r '.[] | select(.OutputKey=="EcrImageUri") | .OutputValue')
export EFSFileSystemId=$(echo "$BATCH_OUTPUTS" | jq -r '.[] | select(.OutputKey=="EFSFileSystemId") | .OutputValue')
export EFSSecurityGroupId=$(echo "$BATCH_OUTPUTS" | jq -r '.[] | select(.OutputKey=="EFSSecurityGroupId") | .OutputValue')
export VpcId=$(echo "$BATCH_OUTPUTS" | jq -r '.[] | select(.OutputKey=="VpcId") | .OutputValue')
export CodeBuildProjectName=$(echo "$BATCH_OUTPUTS" | jq -r '.[] | select(.OutputKey=="CodeBuildProjectName") | .OutputValue // empty')
export CheckpointS3UploadUri=$(echo "$BATCH_OUTPUTS" | jq -r '.[] | select(.OutputKey=="CheckpointS3UploadUri") | .OutputValue // empty')

# Capture DCV stack outputs
# CDK appends hash suffixes to output keys (e.g. DCVInstanceIdXXX00000),
# so use startswith() instead of exact matching.
DCV_OUTPUTS=$(aws cloudformation describe-stacks --stack-name IsaacLabDcvStack \
  --query 'Stacks[0].Outputs' --output json)
export InstanceId=$(echo "$DCV_OUTPUTS" | jq -r '.[] | select(.OutputKey | startswith("DCVInstanceId")) | .OutputValue')
export InstancePublicIP=$(echo "$DCV_OUTPUTS" | jq -r '.[] | select(.OutputKey | startswith("DCVInstancePublicIP")) | .OutputValue')
export DCVWebURL=$(echo "$DCV_OUTPUTS" | jq -r '.[] | select(.OutputKey | startswith("DCVDCVWebURL")) | .OutputValue')
export DCVCredentials=$(echo "$DCV_OUTPUTS" | jq -r '.[] | select(.OutputKey | startswith("DCVDCVCredentials")) | .OutputValue')

# Verify key values are set
echo "Instance ID: $InstanceId"
echo "Elastic IP:  $InstancePublicIP"
echo "ECR URI:     $EcrImageUri"
echo "EFS ID:      $EFSFileSystemId"
echo "DCV URL:     $DCVWebURL"
echo "DCV Creds:   $DCVCredentials"

The variable names match the CDK CfnOutput keys exactly (PascalCase). These are used in later phases as $InstanceId, $InstancePublicIP, $EcrImageUri, etc.

Phase 4: Monitor Bootstrap Completion

The DCV instance runs a bootstrap that installs NVIDIA drivers, Docker, pulls the IsaacLab container from NGC, installs DCV, and mounts EFS. CloudFormation waits for a cfn-signal before marking CREATE_COMPLETE — so the Phase 3 CDK deploy does not return until bootstrap finishes. Phase 4 monitoring is for a parallel terminal to track mid-progress.

# Auto-poll bootstrap summary every 60 seconds (run in a second terminal during Phase 3)
while true; do
  CMD_ID=$(aws ssm send-command \
    --instance-ids $InstanceId \
    --document-name AWS-RunShellScript \
    --parameters 'commands=["cat /var/log/dcv-bootstrap.summary 2>/dev/null || echo BOOTSTRAP_NOT_STARTED"]' \
    --output text --query 'Command.CommandId')
  sleep 5
  OUTPUT=$(aws ssm get-command-invocation \
    --command-id $CMD_ID --instance-id $InstanceId \
    --query 'StandardOutputContent' --output text)
  printf '\n=== %s ===\n%s\n' "$(date +%H:%M:%S)" "$OUTPUT"
  echo "$OUTPUT" | grep -q "STEP_FAIL" && echo "Bootstrap FAILED" && break
  sleep 55
done

Steps appear as STEP_OK entries. Total time: ~15-20 minutes. You may see STEP_WARN:nvidia-driver-loaded — this is expected on first boot (the NVIDIA kernel module loads after a reboot).

If any step shows STEP_FAIL, check the detailed log via SSM: sudo grep -A 50 "== START: <step-name> ==" /var/log/dcv-bootstrap.log

Phase 5: Set Up SSH via SSM

SSH through SSM tunnels through AWS Session Manager — it doesn't require port 22 to be open in the security group.

5a. Verify Session Manager plugin is installed locally

session-manager-plugin --version

If missing: Ubuntu/Debian sudo dpkg -i session-manager-plugin.deb, macOS brew install --cask session-manager-plugin.

5b. Push SSH public key to the instance

# Generate SSH key if you don't have one
test -f ~/.ssh/id_ed25519.pub || ssh-keygen -t ed25519 -f ~/.ssh/id_ed25519 -N ""

PUBKEY=$(cat ~/.ssh/id_ed25519.pub)
aws ssm send-command \
  --instance-ids $InstanceId \
  --document-name AWS-RunShellScript \
  --parameters "commands=[\"mkdir -p /home/ubuntu/.ssh && echo '$PUBKEY' > /home/ubuntu/.ssh/authorized_keys && chmod 700 /home/ubuntu/.ssh && chmod 600 /home/ubuntu/.ssh/authorized_keys && chown -R ubuntu:ubuntu /home/ubuntu/.ssh\"]" \
  --output text --query 'Command.CommandId'

5c. Configure SSH config

Add or update this entry in ~/.ssh/config:

Host dcv-isaac
  HostName <$InstanceId>
  User ubuntu
  IdentityFile ~/.ssh/id_ed25519
  ProxyCommand aws ssm start-session --target %h --document-name AWS-StartSSHSession --parameters 'portNumber=%p' --region <$AWS_DEFAULT_REGION>

Replace <$InstanceId> and <$AWS_DEFAULT_REGION> with the values from Phase 3.

5d. Test the connection

ssh -o StrictHostKeyChecking=accept-new dcv-isaac "echo 'SSH OK'"

Phase 5e: Wait for Auto-Reboot

The bootstrap automatically reboots the instance after sending cfn-signal. This loads the NVIDIA kernel module (which can't load on the same boot that installs the driver). Wait ~2 minutes after cdk deploy returns, then verify:

ssh dcv-isaac "nvidia-smi --query-gpu=name --format=csv,noheader"
# Expected: NVIDIA L4 (or similar, depending on instance type)

If nvidia-smi fails, the reboot may still be in progress. Wait another minute and retry.

Phase 6: Verify Everything Works

# 1. Bootstrap summary — all named steps should be STEP_OK
ssh dcv-isaac "cat /var/log/dcv-bootstrap.summary"

# 2. EFS mounted
ssh dcv-isaac "mount | grep efs"

# 3. Container image pulled
ssh dcv-isaac "docker images | grep isaac-lab"

# 4. Helper script installed
ssh dcv-isaac "test -x /usr/local/bin/run-isaaclab.sh && echo 'Helper script OK'"

# 5. NVIDIA GPU detected (should work after auto-reboot)
ssh dcv-isaac "nvidia-smi --query-gpu=name --format=csv,noheader"

The DCV web console is available at https://<elastic-ip>:8443 (accept the self-signed certificate warning). DCV is password-protected (credentials in stack outputs).

If you deployed with --context public_dcv_access=false (SSM-only mode), ports 8443/8080 are not open. Use SSH port forwarding instead:

ssh -f -N -L 8443:localhost:8443 -L 8080:localhost:8080 dcv-isaac

Then open https://localhost:8443 for DCV or http://localhost:8080 for W&B. Claude Code does not need these port forwards — it accesses everything via SSH commands.

Phase 6a: Container and LeIsaac Testing

6a.1 Launch IsaacLab Container

The helper script wraps docker run with GPU access, X11 forwarding, cache volumes, and persistent package mounts. On first launch, it auto-installs leisaac and downloads scene assets (~60 seconds total).

ssh dcv-isaac
run-isaaclab.sh

The container Python is at /workspace/isaaclab/_isaac_sim/python.sh (an Isaac Sim wrapper). All Python commands below use this wrapper.

6a.2 Verify Inside Container

# Python wrapper works
/workspace/isaaclab/_isaac_sim/python.sh --version

# GPU accessible
nvidia-smi

Exit the container with exit or Ctrl-D.

6a.3 Verify Policy Client N1.6 Patch

The run-isaaclab.sh helper patches policy_inference.py for headless keyboard support on first launch. Confirm the patch applied:

ssh dcv-isaac "grep -q 'self._appwindow is not None' /home/ubuntu/leisaac-repo/scripts/evaluation/policy_inference.py && echo 'Keyboard patch OK' || echo 'MISSING — re-run run-isaaclab.sh'"

If missing, re-run run-isaaclab.sh -c 'echo patched' and check again.

Phase 7: Submit Training Job

7a. Verify container image is ready

The Batch stack triggers a CodeBuild project that builds and pushes the training container to ECR. If you deployed with --context build_target=n16 in Phase 3, the N1.6 container is already building. This takes ~15 minutes for N1.6 (PyTorch3D compilation). Note: Redeploying BatchStack auto-triggers a new CodeBuild run that may fail due to transient Docker Hub rate limits — check for a recent successful build, not just the latest build status:

aws codebuild batch-get-projects --names "$CodeBuildProjectName" \
  --query 'projects[0].lastSuccessfulBuild.endTime' --output text

# If no successful build yet, check current build status:
BUILD_ID=$(aws codebuild list-builds-for-project --project-name "$CodeBuildProjectName" \
  --query 'ids[0]' --output text)
aws codebuild batch-get-builds --ids "$BUILD_ID" \
  --query 'builds[0].[currentPhase, buildStatus]' --output text

Do not submit a Batch job until the build shows SUCCEEDED.

7b. Submit the training job

JOB_ID=$(aws batch submit-job \
  --job-name "IsaacGr00tFinetuning" \
  --job-queue "IsaacGr00tJobQueue" \
  --job-definition "IsaacGr00tJobDefinition" \
  --query 'jobId' --output text)
echo "Job submitted: $JOB_ID"

Monitor progress:

aws batch describe-jobs --jobs $JOB_ID --query 'jobs[0].status' --output text

# Stream logs once RUNNING:
aws logs tail /aws/batch/job --follow \
  --log-stream-names "$(aws batch describe-jobs --jobs $JOB_ID \
  --query 'jobs[0].container.logStreamName' --output text)"

Default: 6000 steps (~2 hours on g6e.4xlarge). Checkpoints saved every 2000 steps at /mnt/efs/gr00t/checkpoints/$JOB_ID/.

Phase 7a: Visualize Training Metrics (W&B)

Prompt the user: "Would you like to visualize the training loss curves? This uses a local W&B server on the DCV instance — no W&B license or cloud account required." Only proceed with this phase if the user says yes; otherwise skip to Phase 8.

Training logs to W&B in offline mode — run data persists on EFS after the container exits.

# Start local W&B server on DCV instance
ssh dcv-isaac "docker run -d --name wandb-local -p 8080:8080 -v wandb-data:/vol wandb/local:latest"

Access the W&B dashboard:

• If deployed with default public_dcv_access=true: open http://<$InstancePublicIP>:8080
• If deployed with --context public_dcv_access=false: use SSH port forward first: ssh -f -N -L 8080:localhost:8080 dcv-isaac, then open http://localhost:8080

Create an account on the local instance and generate an API key from Settings → API Keys. This is entirely local — no external W&B service is contacted.

# Sync offline runs (replace <your-local-api-key> with the key from the step above)
ssh dcv-isaac "bash -l -c '
  export WANDB_BASE_URL=http://localhost:8080
  export WANDB_API_KEY=<your-local-api-key>
  source /home/ubuntu/.venv/bin/activate
  wandb sync /mnt/efs/gr00t/checkpoints/$JOB_ID/wandb/offline-run-*
'"

View loss curves in the W&B dashboard. Stop/restart the server anytime — data persists in the wandb-data Docker volume.

Phase 8: Start Policy Server

All evaluation phases (8a, 8b, 8c) require a policy server serving the trained checkpoint. Start it once here; it stays running until cleanup.

Serves the checkpoint as a ZMQ policy server on TCP port 5555. Do not use --use-sim-policy-wrapper — the Gr00t16ServicePolicyClient in LeIsaac sends observations in the nested format the server expects natively, and the wrapper would cause a key mismatch error.

CHECKPOINT=/mnt/efs/gr00t/checkpoints/$JOB_ID/checkpoint-6000

ssh dcv-isaac "aws ecr get-login-password --region $AWS_DEFAULT_REGION | \
  docker login --username AWS --password-stdin ${EcrImageUri%%/*}"

ssh dcv-isaac "docker pull $EcrImageUri"

ssh dcv-isaac "docker run --gpus all -d \
  --name gr00t-policy-server \
  --shm-size=8g \
  --network host \
  --entrypoint /bin/sh \
  -v /mnt/efs:/mnt/efs \
  $EcrImageUri \
  -c 'cd /workspace/gr00t-repo && python3 -m gr00t.eval.run_gr00t_server \
    --model-path $CHECKPOINT \
    --embodiment-tag NEW_EMBODIMENT \
    --port 5555'"

Use --entrypoint /bin/sh because the container's default entrypoint invokes a uv-managed Python that can't be exec'd directly. The server module is gr00t.eval.run_gr00t_server which wraps Gr00tPolicy in a PolicyServer (ZMQ). Allow ~60 seconds for model loading before the server begins accepting connections.

Verify the server is listening:

ssh dcv-isaac "docker logs gr00t-policy-server 2>&1 | tail -5"
ssh dcv-isaac "ss -tlnp | grep 5555"

For a direct inference test (without IsaacSim), see ../references/policy-server-test.md.

For observation/response format details, see ../references/eval-format.md.

Phase 8a: Open-Loop Evaluation (Optional)

Prompt the user: "Would you like to run open-loop evaluation? This computes MSE/MAE against a dataset to measure action prediction quality. You can use the included sample dataset or provide your own." If the user declines, skip to Phase 8b.

Ask for dataset: "Which dataset should we use for evaluation?

1. Sample dataset (default) — the included training/sample_dataset/ (57 episodes)
2. Custom dataset — provide a local path or an EFS path (e.g. /mnt/efs/gr00t/my_dataset/)

Custom datasets must have the same LeRobot format with data/, meta/, and videos/ directories, plus meta/modality.json and meta/stats.json."

Prepare dataset on EFS

The training job uses a dataset baked into the container during CodeBuild, but open-loop evaluation runs on the DCV instance and needs the dataset on EFS.

If using the sample dataset:

REPO_ROOT=$(git rev-parse --show-toplevel)

# Pull LFS-tracked files (parquet data, video files). Without this, rsync copies
# LFS pointer files instead of actual data, causing pyarrow read failures.
cd "$REPO_ROOT" && git lfs pull --include="training/sample_dataset/"

# Create EFS target directory with correct ownership (EFS root is owned by root)
ssh dcv-isaac "sudo mkdir -p /mnt/efs/gr00t/sample_dataset && sudo chown -R ubuntu:ubuntu /mnt/efs/gr00t"

# Sync dataset to EFS
rsync -avz "$REPO_ROOT/training/sample_dataset/" dcv-isaac:/mnt/efs/gr00t/sample_dataset/

DATASET_PATH=/mnt/efs/gr00t/sample_dataset

If using a custom local dataset:

# Create EFS target directory
ssh dcv-isaac "sudo mkdir -p /mnt/efs/gr00t/<dataset-name> && sudo chown -R ubuntu:ubuntu /mnt/efs/gr00t"

rsync -avz "<local-dataset-path>/" dcv-isaac:/mnt/efs/gr00t/<dataset-name>/

DATASET_PATH=/mnt/efs/gr00t/<dataset-name>

If the dataset is already on EFS, just set DATASET_PATH to its path.

Generate metadata files (if not already present)

# Create modality.json (maps dataset columns to model modality keys)
ssh dcv-isaac "cat > $DATASET_PATH/meta/modality.json << 'EOF'
{
  \"action\": {
    \"single_arm\": {\"start\": 0, \"end\": 5},
    \"gripper\": {\"start\": 5, \"end\": 6}
  },
  \"state\": {
    \"single_arm\": {\"start\": 0, \"end\": 5},
    \"gripper\": {\"start\": 5, \"end\": 6}
  },
  \"annotation\": {
    \"human.task_description\": {
      \"original_key\": \"task_index\"
    }
  },
  \"video\": {
    \"front\": {
      \"original_key\": \"observation.images.front\",
      \"shape\": [480, 640, 3]
    },
    \"wrist\": {
      \"original_key\": \"observation.images.wrist\",
      \"shape\": [480, 640, 3]
    }
  }
}
EOF"

# Generate stats.json (normalization statistics used by the eval pipeline)
ssh dcv-isaac "docker run --gpus all --rm \
  -v /mnt/efs:/mnt/efs \
  --entrypoint /bin/sh \
  $EcrImageUri \
  -c 'cd /workspace/gr00t-repo && python3 -m gr00t.data.stats \
    --dataset-path $DATASET_PATH \
    --embodiment-tag NEW_EMBODIMENT'"

modality.json maps the dataset's column names to the model's expected modality keys (video cameras, state joints, action joints, language annotations). stats.json contains per-feature normalization statistics. Both are required by open_loop_eval.

Expected warning: The stats command may print KeyError: 'new_embodiment' from generate_rel_stats — this is non-blocking. The main stats.json file is still written successfully. Verify with: ssh dcv-isaac "ls -la $DATASET_PATH/meta/stats.json"

Run the evaluation

ssh dcv-isaac "docker run --gpus all --rm \
  --network host \
  -v /mnt/efs:/mnt/efs:ro \
  --shm-size=8g \
  --entrypoint /bin/sh \
  $EcrImageUri \
  -c 'cd /workspace/gr00t-repo && python3 -m gr00t.eval.open_loop_eval \
    --host 127.0.0.1 \
    --port 5555 \
    --model-path None \
    --embodiment-tag NEW_EMBODIMENT \
    --dataset-path $DATASET_PATH \
    --modality-keys single_arm gripper'"

Note: The module is gr00t.eval.open_loop_eval (not robot_eval). Use --entrypoint /bin/sh because the container's default entrypoint invokes a uv-managed Python that can't be exec'd directly. The --embodiment-tag is case-sensitive and must be NEW_EMBODIMENT (uppercase). --model-path None tells the eval to use the running policy server (--host/--port) instead of loading the model directly.

Phase 8b: Closed-Loop Smoke Test (Headless)

This runs a single headless simulation episode to verify that IsaacSim launches, connects to the policy server, and completes without crashing. It is a smoke test only — success rate is irrelevant at this stage.

Important: Do NOT use run-isaaclab.sh for automated evaluation — it launches an interactive bash shell and routes arguments through runheadless.sh (the Kit launcher), which does not execute the Python eval script. Use the direct docker run command below with --entrypoint set to python.sh.

run-isaaclab.sh handles leisaac package install, scene asset download, and repo clone on first launch. Run it once interactively to set up prerequisites if they haven't been installed yet:

ssh dcv-isaac "run-isaaclab.sh -c 'echo prerequisites installed'"

Run one episode headlessly via SSH:

# Detect the DCV display number (varies between deployments: :0 or :1)
DCV_DISPLAY=$(ssh dcv-isaac "ls /tmp/.X11-unix/ | sed 's/X/:/'" | head -1)
echo "Using DISPLAY=$DCV_DISPLAY"

ssh dcv-isaac "docker run --gpus all --rm --network host \
  -e ACCEPT_EULA=Y -e PRIVACY_CONSENT=Y \
  -e PYTHONPATH=/workspace/isaaclab-pkgs \
  -e PYTHONUNBUFFERED=1 \
  -e DISPLAY=$DCV_DISPLAY \
  -v /tmp/.X11-unix:/tmp/.X11-unix:ro \
  -v /home/ubuntu/isaaclab-pkgs:/workspace/isaaclab-pkgs:rw \
  -v /home/ubuntu/leisaac-repo/scripts:/workspace/scripts:ro \
  -v /home/ubuntu/leisaac-assets:/assets:ro \
  -v /mnt/efs:/mnt/efs \
  -e LEISAAC_ASSETS_ROOT=/assets \
  --shm-size=8g \
  --entrypoint /workspace/isaaclab/_isaac_sim/python.sh \
  nvcr.io/nvidia/isaac-lab:2.3.0 \
  /workspace/scripts/evaluation/policy_inference.py \
    --task=LeIsaac-SO101-PickOrange-v0 \
    --eval_rounds=1 \
    --policy_type=gr00tn1.6 \
    --policy_host=localhost \
    --policy_port=5555 \
    --policy_action_horizon=16 \
    --policy_language_instruction='Pick up the orange and place it on the plate' \
    --device=cuda \
    --enable_cameras 2>&1 | tee /mnt/efs/gr00t/eval-results.log"

DISPLAY and the X11 socket mount are required because --enable_cameras uses Vulkan rendering for camera frames, which needs an active display. DCV picks display :0 or :1 depending on what's available at boot — the detection command above reads it from /tmp/.X11-unix/. Without these flags, the render loop blocks silently with 0% GPU utilization despite allocating VRAM.

PYTHONUNBUFFERED=1 ensures eval output streams in real time over SSH. First run takes ~5 minutes for IsaacSim shader compilation before the episode begins.

Expected: The episode completes (success or timeout) without errors. If it runs to completion, the simulation pipeline is working correctly. Proceed to Phase 8c for visual inspection.

For eval parameters and format details, see ../references/eval-format.md.

Phase 8c: Visual Policy Inspection (GUI)

Prompt the user: "Would you like to visually inspect the trained policy in the simulator? This opens the DCV desktop where you can watch the robot arm execute actions in real time — the best way to qualitatively evaluate policy performance."

This is the primary way to evaluate whether the policy has learned useful behavior. The DCV desktop renders the full IsaacSim environment so you can watch the robot arm interact with objects in real time.

Connect to the DCV desktop

• If deployed with default public_dcv_access=true: open https://<$InstancePublicIP>:8443 (accept the self-signed certificate warning)
• If deployed with --context public_dcv_access=false: start an SSH port forward first: ssh -f -N -L 8443:localhost:8443 dcv-isaac, then open https://localhost:8443

Log in with the DCV credentials from the stack outputs ($DCVCredentials).

Run the evaluation from the DCV terminal

1. Open a terminal in the DCV desktop
2. Launch the IsaacLab container:

   run-isaaclab.sh
   

3. Inside the container, run the evaluation:

   /workspace/isaaclab/_isaac_sim/python.sh \
     /workspace/scripts/evaluation/policy_inference.py \
       --task=LeIsaac-SO101-PickOrange-v0 \
       --eval_rounds=1 \
       --policy_type=gr00tn1.6 \
       --policy_host=localhost \
       --policy_port=5555 \
       --policy_action_horizon=16 \
       --policy_language_instruction='Pick up the orange and place it on the plate' \
       --device=cuda \
       --enable_cameras
   

First GUI run takes ~5 minutes for IsaacSim shader compilation. These shaders are cached in the Docker volumes mounted by run-isaaclab.sh, so subsequent runs start much faster.

A simulation window will appear showing the robot arm, the table scene, and the orange. Watch the arm's behavior to judge whether the policy has learned the intended task. Increase --eval_rounds to run multiple episodes for a more thorough assessment.

Cleanup: Tearing Down Stacks

Step 1: Stop running containers

ssh dcv-isaac "docker rm -f gr00t-policy-server wandb-local 2>/dev/null; echo 'Containers stopped'"

Step 2: Destroy stacks (DCV first, then Batch)

aws ec2 modify-instance-attribute --instance-id $InstanceId --no-disable-api-termination

cd training/gr00t/infra
npx cdk destroy IsaacLabDcvStack --force
npx cdk destroy IsaacGr00tBatchStack --force

Step 3: Clean up retained resources

Three resources have RemovalPolicy.RETAIN and survive stack deletion:

# S3 checkpoint bucket
BUCKET=<bucket-name-from-stack-outputs>
aws s3 rb s3://$BUCKET --force

# ECR repository
aws ecr delete-repository --repository-name gr00t-finetune --force --region $AWS_DEFAULT_REGION

# EFS file system
aws efs delete-file-system --file-system-id $EFSFileSystemId

If EFS deletion fails with "FileSystemInUse", delete lingering mount targets first: aws efs describe-mount-targets --file-system-id $EFSFileSystemId --query 'MountTargets[].MountTargetId' --output text | xargs -n1 aws efs delete-mount-target --mount-target-id

See ../references/troubleshooting.md for common issues and fixes.