aws-samples RLinf Functional Testing
Purpose
RLinf-specific test configurations for the validation harness (L2-L6). These complement the generic testing framework in the shared functional-testing skill with concrete RLinf values.
L2: Container Validation
The container test (infrastructure/manifests/container-test.yaml) runs 10 tests verifying:
- Multi-venv availability (openvla, openvla-oft, openpi)
- EFA libraries (libfabric, aws-ofi-nccl)
- CUDA/GPU detection
- RLinf framework imports (
import verl,from flash_attn import flash_attn_func) - MuJoCo/ManiSkill rendering (
MUJOCO_GL=osmesa)
L4: Model Download
The model download jobs (examples/<example>/manifests/model-download.yaml) download models to FSx:
| Model | FSx Path | Size |
|---|---|---|
RLinf/Openvla-oft-SFT-libero10-trajall |
/fsx/models/openvla-oft-sft-libero10 |
~14 GB |
RLinf/RLinf-Pi0-SFT-Spatial-Object-Goal |
/fsx/models/rlinf-pi0-sft-spatial |
~14 GB |
L5: Per-Example Training Step Tests
Each example runs 1 PPO training step via HYDRA_OVERRIDES="runner.max_steps=1 runner.save_interval=-1 runner.val_check_interval=-1".
Example Configurations
Defined in tests/lib/cluster.sh L5_EXAMPLE_LIST:
| Example Name | VENV_NAME |
CONFIG_NAME |
MODEL_PATH |
|---|---|---|---|
maniskill-openvlaoft |
openvla-oft |
maniskill_ppo_openvlaoft_quickstart |
/fsx/models/openvla-oft-sft-libero10 |
libero-pi0 |
openpi |
libero_spatial_ppo_openpi_quickstart |
/fsx/models/rlinf-pi0-sft-spatial |
Running a Specific Example
./validate.sh --mode cluster --level 5 --skip-to 5 --example maniskill-openvlaoft
Expected Success Indicators
In the pod logs, look for:
TRAINING_STEP_TEST_PASSED-- explicit success markerGlobal Step: 1/1-- training step completed- No
Traceback,ERROR:, orFAILEDin output
Common Failures
| Symptom | Cause | Fix |
|---|---|---|
| OOM during FSDP init | GPU too small for 7B model | Use p-class instances (not g6) |
ModuleNotFoundError: verl |
Wrong venv activated | Check VENV_NAME matches example |
input() hang (no output) |
LIBERO import without LIBERO_DATASET_DIR |
Set env var and pre-create config |
FileNotFoundError: config.json |
Model not downloaded | Run L4 first |
L6: Multi-Node Training
Uses infrastructure/manifests/training-statefulset.yaml + training-service.yaml. Requires 2+ GPU nodes. Ray head starts on pod-0, workers connect via headless service training-svc.
The test patches HYDRA_OVERRIDES onto the StatefulSet for single-step validation.
Related Skills
functional-testing-- generic testing framework and validation levelsrlinf-pytorch-training-scripts-- Hydra configs and training entrypointsrlinf-dataset-preparation-- model download details
Validation Checklist
- L2 container test passes all 10 tests
- L4 all 3 models downloaded and verified on FSx
- L5 each example completes 1 training step without OOM or error
- L6 multi-node Ray cluster forms and completes 1 training step (if 2+ GPU nodes)