rl-agentic-launch-iris

name

rl-agentic-launch-iris

description

Launch / relaunch agentic MarinSkyRL (SkyRL GRPO) RL on Marin's Iris / CoreWeave GPU cluster (cw-us-east-02a, 8x H100-80GB + InfiniBand per node) via `python -m cloud.iris.launch_rl_iris` (run from the MarinSkyRL repo root) + the gpu-rl Docker image (NO Apptainer SIF). Covers the MoE 30B-A3B arms (CP + DCP=2 + R3 @ 131k) and the dense 8B arms (seqnorm + TIS); the iris configs default to the **Megatron** training backend (`trainer.strategy: megatron`, FSDP2 selectable) — the exact launcher flag set (`--rl_config`, `--model_path`, `--train_data`, `--num-nodes`, `--rendezvous-dir`, `--job-name`, `--priority`, `--cpu`, `--max-retries`), the gang/leafgroup/Kueue multi-node Ray rendezvous, the iris config-authoring rules (NO container block, load-bearing top-level `extra_env:` forwarding, disaggregated placement + explicit `num_inference_engines`, SIF→Docker env translation), and the bring-up gotchas (`--cpu 48`, `--max-retries ≥1`). Use when asked to launch / relaunch an agentic SkyRL RL run on Iris / CoreWeave. Cluster access/hardware particulars live in .agents/ops/iris/ops.md (this skill defers to it). Reference: cloud/iris/launch_rl_iris.py, cloud/iris/start_rl_iris_controller.py, cloud/iris/configs/, .agents/ops/iris/ops.md.

⚠ Do not add comments to YAMLs. Report recommendations directly to the supervisor.

rl-agentic-launch-iris

Read first: .agents/ops/iris/ops.md for Iris access, preamble, gotchas, and helper scripts.

⚠ Local clone = ground truth (CLAUDE.md §Always). ALL code/config edits go in the local Mac checkouts → commit. The Iris launcher uploads the LOCAL workspace to /app (PYTHONPATH puts /app + /opt/skyrl/skyrl-train first), so a local commit takes effect on the next launch immediately — no push-then-pull-on-cluster (there is no Iris clone to pull). Still never hand-edit on a remote, leave divergent state, or patch-by-rsync. The vLLM fork (compiled) is fixed only by an image rebuild + digest bump.

Agentic SkyRL/GRPO RL runs via python -m cloud.iris.launch_rl_iris from the MarinSkyRL repo root. Each rollout is a Harbor agent episode in a Daytona sandbox (terminal_bench). Target is CoreWeave cw-us-east-02a, with exclusive 8×H100-80GB nodes and gang/leafgroup scheduling. The gpu-rl Docker image is the runtime: no Apptainer SIF or hpc.launch. Iris configs default to Megatron; set trainer.strategy: fsdp2 for FSDP2.

1. Prereqs (the pre-launch preamble)

Launch from the local Mac, otagent py3.12 conda env, from the MarinSkyRL repo root:

cd ~/Documents/MarinSkyRL                              # `-m cloud.iris.launch_rl_iris` resolves from here; on main (or a cloud/iris branch)
source "${DC_AGENT_SECRET_ENV:?set DC_AGENT_SECRET_ENV to the secrets file first}"   # HF_TOKEN, WANDB_*, DAYTONA_* (forwarded into the pod)
export KUBECONFIG=~/.kube/coreweave-iris-gpu           # CoreWeave GPU cluster kubeconfig (see ops doc)
# otagent python = /Users/benjaminfeuer/miniconda3/envs/otagent/bin/python (symlinks fail in the sandbox); the `iris` SDK is installed in this env

• Confirm access before submitting; run iris and kubectl synchronously. The headroom check is in the ops doc.
• Cluster config auto-resolves to ~/Documents/marin/lib/iris/config/cw-us-east-02a.yaml; override with --cluster-config only if it moved.
• gpu-rl image: deps-only (RL venv /opt/openthoughts/envs/rl + vLLM fork + MarinSkyRL editable /opt/skyrl + harbor), pinned by immutable @sha256: digest in cloud/iris/launch_rl_iris.py:DEFAULT_RL_DOCKER_IMAGE (NOT the floating :gpu-rl tag — it stale-caches). Source syncs at runtime so first-party edits live without a rebuild; bump the digest on an image rebuild.

2. The canonical launch

Lifted from each iris config's header (carries its own ready-to-run command):

cd ~/Documents/MarinSkyRL   # canonical launcher repo (on main / a cloud/iris branch)
source "${DC_AGENT_SECRET_ENV:?set DC_AGENT_SECRET_ENV to the secrets file first}"
python -m cloud.iris.launch_rl_iris \
  --rl_config cloud/iris/configs/<cfg>.yaml \
  --model_path <hf-id> \
  --train_data '["<HF-repo-or-harbor-task-set>"]' \
  --num-nodes N \
  --gpus_per_node 8 \
  --cpu 48 \
  --max-retries 1 \
  --rendezvous-dir s3://marin-us-east-02a/iris/rl-<slug>/<run> \
  --job-name <name> \
  --priority interactive \
  --no-wait

Varies per arm: --rl_config, --model_path, --train_data, and --num-nodes (§3). Fixed on Iris: --gpus_per_node 8, --cpu 48, --max-retries 1, --priority interactive, and --no-wait. Flag glossary:

• --rl_config — repo-relative path under cloud/iris/configs/; it must exist in synced /app.
• --model_path — HF id (e.g. Qwen/Qwen3-8B, Qwen/Qwen3-Coder-30B-A3B-Instruct). CoreWeave nodes have egress → model pulled from HF online (do NOT set HF_HUB_OFFLINE).
• --train_data — JSON-list string '["..."]' (HF repo DCAgent/… / laion/…, or a harbor task set).
• --num-nodes N — number of whole, exclusive H100 nodes; one Iris task and eight GPUs per node.
• --rendezvous-dir — REQUIRED for --num-nodes>1 (launcher hard-errors otherwise). Shared store the multi-node Ray head/workers rendezvous through. Build this off marin_prefix() (rigging.filesystem — auto-resolves the active cluster's storage root, so a store migration is followed automatically); the literal below is an illustrative fallback. On cw-us-east-02a use an s3:// URI under the marin-us-east-02a bucket (e.g. s3://marin-us-east-02a/iris/rl-<slug>/<run>); the cluster injects working creds into every task pod (the iris-task-env Secret) so no external creds are needed and you must NOT forward AWS_*/R2_* (clobbers the pod's injected creds and silently targets real AWS S3). Use a fresh sub-path per run so a stale head file from a prior attempt isn't picked up. ⚠ Store is s3://marin-us-east-02a (CW), NOT the old s3://marin-na (R2) — a marin-na rendezvous PUT resolves to the nonexistent marin-na.cwlota.com and STALLS.
• --job-name — controls /benjaminfeuer/<name>; set it explicitly for monitoring and teardown.
• --priority — production / interactive / batch band.
• --cpu / / — per-node resources (defaults 64 / 512GB / 512GB; ).

3. Config map + node count (--num-nodes MUST match the config)

--num-nodes = total_GPUs_in_config / 8. Derive the GPU budget from the yaml (policy_num_nodes, ref_num_nodes, num_inference_engines × inference_engine_tensor_parallel_size):

Config (cloud/iris/configs/…, MarinSkyRL)	Model	Layout	GPUs → --num-nodes
56GPU_qwen3_8b.yaml	dense 8B (seqnorm + TIS)	disaggregated: 1 node policy + 1 node ref + 48×TP1 engines	56 → 7
32GPU_qwen3_coder_30b_a3b_ep4.yaml	Qwen3-Coder-30B-A3B (MoE) — CANONICAL 30B (EP4)	disaggregated (EP4)	32 → 4

• Only these configs are in MarinSkyRL cloud/iris/configs/. Port any OT-Agent config into that directory and confirm its referenced assets are in the MarinSkyRL tree before use.
• Smoke first. No dedicated toy smoke config ships. Validate via --dry-run + a short real arm before a full run. Author a throwaway cloud/iris/configs/*.yaml (colocate_all, small max_steps, trace_upload.enabled: false) if you need a live bring-up check.
• Dense-8B is typically Qwen/Qwen3-8B (or laion/… 8B); the MoE arm is Qwen/Qwen3-Coder-30B-A3B-Instruct. Read each config header.

4. Config-authoring rules for cloud/iris/configs/ (MarinSkyRL)

Porting a Jupiter (Apptainer SIF) config to Iris (Docker) — the load-bearing rules:

• Every referenced asset must exist in the MarinSkyRL tree. run_rl.py parses /app and opens asset paths relative to it. Use the root chat_templates/qwen3_thinking_acc.jinja2, not the terminal-bench example.
• NO container: / SIF / apptainer / conda / binds / pydeps block. The gpu-rl image IS the container (RL venv /opt/openthoughts/envs/rl, MarinSkyRL /opt/skyrl, workspace synced to /app).
• Top-level extra_env: is load-bearing. load_config_extra_env() merges it into EnvironmentSpec; the Iris path does not use container.extra_env. Values are coerced to strings and launcher values win collisions.
• SIF→Docker env translation (what to DROP / KEEP / hardcode in extra_env:):
  • DROP all APPTAINERENV_* duplicates, TRITON_LIBCUDA_PATH, LIBRARY_PATH=/.singularity-d/libs (SIF-only), and HF_HUB_OFFLINE/TRANSFORMERS_OFFLINE (CoreWeave has egress).
  • DROP the GH200/SIF NCCL disables NCCL_P2P_DISABLE / NCCL_NVLS_ENABLE / NCCL_COLLNET_ENABLE — on H100+IB they CRIPPLE the intra-node NVLink all-reduce a TP=8 engine (DCP) depends on. Use NCCL defaults (NVLink intra-node + IB inter-node); keep NCCL_DEBUG=INFO + the observability/raised timeouts (SKYRL_WORKER_NCCL_TIMEOUT_IN_S, TORCH_NCCL_*). disable_custom_all_reduce: true STAYS. Do NOT add these for MoE (falsified A/B — the salad was the w13 gate/up swap below).
  • KEEP SKYRL_W13_RELOAD_BRACKET ON (default 1) for MoE. Re-applies the FusedMoE w13 gate/up kernel swap (process_weights_after_loading) on the disaggregated RL weight update. WITHOUT it, the served MoE policy emits CJK token-salad (100% reward-0) on H100/FlashInfer-CUTLASS (fixed MarinSkyRL 2bb70a88). Swap-inert on triton/dense → byte-identical there, so just leave it on; do NOT set 0. Bring-up check: engine log shows initialize_layerwise_reload / .

5. Gang scheduling + multi-node Ray rendezvous

• --num-nodes N → replicas=N whole H100x8 tasks. For GPUs with replicas>1, resolve_multinode_defaults returns CoschedulingConfig(group_by="leafgroup") — all N nodes co-scheduled on one InfiniBand leaf fabric, all-or-nothing. cw-us-east-02a enables Kueue gang admission (host_network: true for NCCL/IB) → the N-task gang is admitted atomically (all N whole nodes granted, or the job queues). At submit: replicas=N, coscheduling=leafgroup, then pods sit SchedulingGated (normal Kueue gang pre-admit) until admitted.
• Every node runs start_rl_iris_controller.py; Iris injects IRIS_TASK_ID, IRIS_NUM_TASKS, and IRIS_ADVERTISE_HOST. Rank 0 starts Ray, writes ray_head.json, waits for all nodes, and runs run_rl.py. Workers join via the rendezvous then wait for ray_head.done; rank 0 clears stale rendezvous on retry.

6. Bring-up gotchas (the pre-flight checklist)

• --cpu 48, not 64. Persistent system overhead leaves too few 64-core nodes for an N-node leafgroup gang; 48-core requests fit all nodes. Memory 512GB is fine.
• --max-retries ≥1 for the transient HF weight-resolution flake. At scale (e.g. 32 FSDP ranks each resolving sharded safetensors online) a single rank can hit a transient HF Hub HTTP/EOF failure → transformers reports … does not appear to have a file named model.safetensors; with max_retries=0 that one rank kills the gang. --max-retries 1 re-brings-up the gang (time-only cost). A first-party retry-wrapper landed in MarinSkyRL (0b2b05b); keep --max-retries ≥1 as belt-and-suspenders. (DURABLE alternative: pre-stage the model into the image's HF cache / a shared snapshot before the FSDP workers start, or raise HF_HUB_DOWNLOAD_TIMEOUT.)
• TP=8 must place intra-node on an 8-GPU node (NVLink decode) — guaranteed by --gpus_per_node 8 + the per-engine STRICT_PACK PG; do not split a TP=8 engine across nodes.
• DCP / CP / R3 / EPDIAG env must reach the pod — that's the extra_env: forwarding (§4). After a launch, confirm from the rank-0 log that the guard ENGAGED (not rejected): SkyRL _validate_dcp_cfg VLLM_ALLOW_ROUTED_EXPERTS_DCP=1: allowing … AND vLLM-fork vllm.py … allowing --enable-return-routed-experts with decode_context_parallel_size=2. The envs.py "Unknown vLLM environment variable detected: VLLM_ALLOW_ROUTED_EXPERTS_DCP" line is ONLY the env-registry whitelist not listing the fork-added var — it is NOT a no-op when the fork's own code reads the var (it does).
• ghcr.io blob EOF ImagePullBackOff self-heals. shm_broadcast: No available shared memory broadcast block found in 60s is benign while engines wait for policy weights; do not salvage either.

7. Standing constraints (do NOT violate)

• Daytona RL concurrency ≤ 6 RUNNING per cluster. PENDING/SchedulingGated gangs that haven't admitted don't count, but a RUNNING gang does — don't launch a 7th concurrent RL job.
• enable_db_registration: false in every iris RL yaml (it is). DB registration is a manual cleanup step, never a launch flag.
• The a3 series is CONCLUDED — do NOT launch / refill / auto-advance a3 rows. Active iris arms: the seqnorm/TIS dense-8B port + the CP+DCP2+R3 MoE port.
• Never alter config/hparams mid-series — propose a separate experiment.
• Daytona snapshot caps are HARD — clean STALE snapshots, never raise the cap (.agents/projects/daytona/daytona.md).
• Never kill a RUNNING job without explicit permission; never iris cluster restart/stop/bounce (kills every running job). iris job kill /benjaminfeuer/<job> is job-scoped (with permission).

8. Monitoring + completion

Use authoritative lifecycle state for liveness and terminal detection: iris job summary --json → state. Use log greps only for sel_rows, EPDIAG, and throughput analysis.

• Watch a run (the primitive — use this, do not grep logs for liveness):

  PY=/Users/benjaminfeuer/miniconda3/envs/otagent/bin/python   # otagent env: ships iris + a WORKING kubernetes
  # one-shot authoritative state (state + error + per-task + pod cross-check):
  $PY scripts/iris/iris_ops.py /benjaminfeuer/<job> --once --json
  # watch until the run leaves RUNNING, emit a line on every transition, exit terminal:
  $PY scripts/iris/iris_ops.py /benjaminfeuer/<job> --interval 60
  #   exit 0 succeeded · 1 failed/killed/worker_failed/unschedulable · 2 absent (disappeared/0-pods) · 3 error
  

  It polls iris job summary --json (falls back to the SQL query) and treats "no controller record AND 0 pods" as a TERMINAL absent verdict — the case the old content-watch missed. Importable: from scripts.iris.iris_ops import get_job_state, watch.

⚠ FRESH-LAUNCH 15-MIN + 30-MIN CHECK-INS MUST PARSE THE ROLLOUT LOGS — lifecycle state is necessary but NOT sufficient. iris_ops reports running, pods=8, failure_count=0 for a job that is silently dead: a throughput-starvation wedge (engines decode but an oversubscribed queue never drains → the step-0 batch never assembles), node-local data starvation (a rank-0-only task-dataset stage → 7 ranks see empty task dirs → every rollout reward 0), or vLLM simply never serving. State-polling alone reports all of these as "healthy." For ANY fresh Iris RL launch, the 15-min AND 30-min check-ins capture + parse the logs. Dispatch a subagent armed with rl-job-health-deep-dive (operationalizes the ladder below into a KILL/NO-KILL: syncs trace_jobs + logs via peek … pull, live-polls the GPUs against the serving-throughput LUT, reads the literal rollouts). Procedure:

# capture finelog + per-rank pod logs (our RL jobs use a REMOTE s3 trials_dir, so peek's ls/cat/grep bail —
# `pull` still grabs the logs, where the live bring-up signal is). Override IRIS_BIN: the script defaults to
# the marin .venv iris, which CANNOT drive cw (broken kubernetes import).
IRIS_BIN=/Users/benjaminfeuer/miniconda3/envs/otagent/bin/iris \
  bash scripts/iris/analyze_coreweave_rl_job_live.sh <pod-name-substring> pull     # → ~/Documents/experiments/traces/<job>_<stamp>/logs/iris_finelog.log

Then grep the finelog for the milestone ladder — each rung rules out a specific silent-death mode:

1. [rl-iris] MarinSkyRL now at <sha> (+ HEAD is now at <sha>) — confirms --skyrl-ref took (the fix is live). Absent ⇒ forgot the flag / it failed → stale baked code.
2. Staging train_data on this node (rank N/8) for ALL N ranks — else node-local data starvation → silent reward 0. Must see every rank.
3. Ray nodes alive: N/N — rendezvous complete.
4. HF weight load: an OSError … does not appear to have a file named model.safetensors FOLLOWED by load_pretrained_with_retry … retrying → the 0b2b05b retry catching it (BENIGN). The SAME error with no retry wrapper + a task failure ~70s in = the stale-image crash. (MoE arm: is the stale-image crash — must be ABSENT.)

• Manual state / logs (synchronous calls only — no background iris/kubectl; use the otagent iris binary — the bare marin .venv/bin/iris lacks a working kubernetes for the cw k8s controller backend):

  # iris-side state (0=UNSPECIFIED 1=PENDING 2=BUILDING 3=RUNNING 4=SUCCEEDED 5=FAILED 6=KILLED
  #                  7=WORKER_FAILED 8=UNSCHEDULABLE) — the watcher already wraps this:
  /Users/benjaminfeuer/miniconda3/envs/otagent/bin/iris --cluster=cw-us-east-02a query \
    "SELECT job_id,state FROM jobs WHERE job_id LIKE '/benjaminfeuer/<job>%'" -f csv
  # richest single-job authoritative call (state + error + exit + per-task states + finished_at):
  /Users/benjaminfeuer/miniconda3/envs/otagent/bin/iris --cluster=cw-us-east-02a job summary /benjaminfeuer/<job> --json
  # via the python SDK (matches the launcher's client):
  #   IrisClient.status(JobName.from_wire("/benjaminfeuer/<job>"))
  #   IrisClient.fetch_task_logs(JobName.from_wire("/benjaminfeuer/<job>/0"))   # rank 0 = the driver
  

  Rank-0 (/…/<job>/0) is the training driver; ranks 1..N-1 are Ray workers that clean-exit on the head's done-marker (a healthy run's head failing shows 1 task failed, N-1 succeeded).
• Healthy bring-up: gang admitted → N pods running → Ray nodes alive: N/N → driver → engines → policy mesh → first step. A 30B/131k arm can take ~1h to first rollout and ~2.5–3h to gs1 forward.
• Mandatory progress columns (per monitor-job-tables): entropy / log_ratio / grad_norm + reward — not just step. For full-history science (sel_rows / EPDIAG / throughput stats) use the windowed pagination (scripts/iris/analyze_iris_harbor_job.py), NOT iris job logs --tail (under-samples 10–100×).
• On completion → rl-agentic-job-cleanup (best-ckpt selection, HF upload, the manual Supabase DB registration, trace export). enable_db_registration stays false at launch.
• Teardown: iris job kill /benjaminfeuer/<job> (with permission). Rescue banked traces from the s3:// jobs/rendezvous path if the trace upload didn't auto-run (ops.md §4).

---

Final checks

• Always --dry-run a new or edited config. Check colocate_all, policy_num_nodes, explicit num_inference_engines, TP/DCP/CP/EP/FSDP, and intended extra_env: with no NCCL_P2P_DISABLE.
• This is the terminal_bench/Harbor/Daytona path. Forward DAYTONA_*; do not forward AWS_* or R2_*.