flex-pi git:20261025.fb45527A

Immutable. This exact content never changes and is served at /api/v1/blob/d5ace98bc1c6af05.

---
name: flex-pi
description: Run, package, validate, or troubleshoot flex-pi inference and pinned public YAM training in NPA, including runtime-fetch assets, GPU workflows, and verified artifacts.
---

# flex-pi

Run the genuine upstream 6B world-action checkpoint. Do not replace policy
inference with an import check, random action fixture, or manifest-only smoke.

## Boundaries

- Source: `geyan21/flex-pi@20c1b2b71ea35a415d5d47c39b04443cfadad7a1`,
  MIT, baked without the vendored simulators.
- Checkpoint: `flex-pi/flexpi-robotwin@87d3833ea3bd89c4922945631db81b346e780785`,
  MIT-labelled, runtime fetch.
- Observation: `flex-pi/robotwin_3d@bee164afe94041d8c3d7dd1203725c41163fc3f4`,
  episode 0 frame 0. Its dataset card declares no license. Use only under the
  operator's authorized scope, fetch at runtime, verify each byte hash, and do
  not publish the media.
- DINOv3 uses its separate model license. The pinned converted Wan VAE/T5 and
  tokenizer repositories are Apache-2.0. They are operator runtime fetches;
  immutable revisions and heavyweight-file hashes are enforced before loading.
- Before GPU allocation, run the HF credential preflight and the exact pinned
  DINOv3 weight HEAD probe in `docs/workbench/flex-pi.md#packaging-and-terms`.
  Flex-Pi is not in the generic health access catalog; an identity check or
  repository listing does not prove payload access.
- The public image must contain none of those weights, media, credentials, or
  populated caches. Run `npa/scripts/scan_image_flex_pi_payload.py` against the
  built image before any registry write.

## Choose the regime and GPU

For real training use `workbench.flex_pi.train` and the maintained
`flex-pi-b200-public-training.yaml`. Its public `sort_utensils` dataset has
136/115,620 train and 16/12,390 validation episodes/anchors, separate CC-BY-4.0
rights, and the official YAM32 transforms. Require native four-GPU DDP,
BF16, default microbatch one, accumulation 24, and effective batch 96.
Require the adapter's strict deterministic algorithms and pinned cuBLAS
workspace for both baseline and candidate. Never use warning-only determinism
or accept nondeterministic attention backward as a bitwise resume proof.
Keep the guarded fixed DDP bucket policy for all comparisons. Before a full
epoch, use `--mode profile-resume` to prove the exact next 96 anchors and full
fresh-process state/update parity after the real 30-update profile. This early
diagnostic does not satisfy full-epoch, validation or final-resume acceptance.
The final 36-sample update must preserve every anchor and use its actual
denominator. Microbatch one and eager execution are the defaults. Microbatch
three and native MoT CUDA graphs are separate opt-in candidates; qualify each
against its matched control. The recorded faster microbatch-three full run
failed the unchanged held-out quality ceiling and must not be recommended.
Freeze the baseline's `dataset_stats.json` and provide its exact S3 object with
`--normalization-path` and `--normalization-sha256` for every independent
comparison run. Checkpoints include these bytes and fresh resume must reload
them; never recompute normalization or relax its content-hash check on resume.
Keep the default optimizer unless separate default-versus-selected optimizer
parity is proven.
Activation checkpointing defaults to `on` for both experts and mixed attention.
Use `--activation-checkpointing off` only as an explicit memory/performance
candidate: retain the fixed batch, precision, optimizer and eager execution,
prove memory fit and exact cross-policy state/update parity, then require the
same checkpoint, fresh-resume and full-epoch quality gates. Verify the actual
model flags in each phase's `activation_checkpointing` receipt. Durable
checkpoints remain mandatory with either activation policy.

Training requires the full Wan VideoDiT and official derived ActionDiT
initialization; the checkpoint-only inference instructions below apply only to
the released RoboTwin inference checkpoint.

For four separate B300 instances use `flex-pi-b300-multinode-public-training.yaml`:
`training_nodes: "4"`, `num_nodes: 4`, and one requested GPU per host. Supply the
original normalization object and hash. The current source overlay validates
SkyPilot's peer allocation, uses one global four-rank DDP group, joins saved RNG
files from all hosts, and restores complete checkpoint bytes independently before
fresh continuation. Require distinct-host placement and collective receipts,
then profile/resume before a full epoch. Report socket communication and topology
separately from the single-host NVLink result; local CPU tests are not GPU
acceptance. A preempted peer must fail the gang instead of leaving surviving
phase workers waiting indefinitely.

On B300, diagnostic profiles fetch hash-pinned CUPTI 13.0.85 and its Python
13.0.0 interface into a private directory. The original training compute stack
remains in use. Require actual GPU kernel events and zero dropped records;
the released CUPTI 12.8 binding can emit `CUPTI_ERROR_INVALID_DEVICE` and a
CPU-only trace despite successful training. A library preload alone does not
repair that binding. Keep the fetched SDKs and their license files outside the
public image. Profile update five and retain all six excluded initial updates.
GPU profiling support is diagnostic evidence, not a throughput improvement.

Freeze the bundled source/split manifests before profiling. Measure the
six excluded startup/trace updates and three eight-update steady windows
separately. Accept an execution-only optimization only after fixed-input
numerical and synchronized model-state parity. Full acceptance additionally
requires the complete epoch and validation split, finite gradients, non-regressed
validation, immutable checkpoint readback and matching fresh-process continuation.
Keep public fallback results explicitly non-comparable to any unverified private
reference, regardless of their numerical throughput. Record source-overlay
hashes separately from image qualification. See `docs/workbench/flex-pi.md`.

Use the released action-only regime for policy-serving validation: video and
DINO are observed, pointmap is absent, and all future-stream joint-denoising
flags are false. It produces the same 32-step, 14D bimanual action contract as
upstream evaluation without pretending an isolated observation proves simulator
success. The upstream model card reports equal average RoboTwin success for
action-only and full-joint inference, while action-only is substantially faster.

Route a reference workflow to one RTX PRO 6000 or one B200. The workload is
headless and does not need RT cores. These targets independently prove CUDA
`sm_120` and `sm_100`; neither substitutes for the other. Flex-pi inference
requires exactly one visible CUDA GPU. To exercise more capacity, fan out
independent one-GPU replicas with unique seeds and output prefixes. Never claim
that replica fan-out is model parallelism.

## Preflight and run

Before build, provisioning, or submission, prove Nebius and selected-project S3
credentials with `npa workbench health preflight --checks nebius,s3 --json`.
Probe the exact anonymous/public model and input objects before allocating a GPU.
Do not treat a generic Hugging Face token check as proof of those payloads.

Validate and plan the reference spec, then submit it with the selected project's
bucket and exact immutable image:

```bash
npa workbench workflow validate-spec workflows/testing/flex-pi-b200-inference.yaml
npa workbench workflow plan-spec workflows/testing/flex-pi-b200-inference.yaml
npa workbench workflow submit workflows/testing/flex-pi-b200-inference.yaml \
  --infra "$CONFIGURED_TARGET" --var "bucket=$OPERATOR_BUCKET" \
  --secret-env HF_TOKEN
```

Use `flex-pi-rtxpro-inference.yaml` for the independent RTX PRO 6000 path.
Use `flex-pi-b300-inference.yaml` for the experimental one-B300 path. It
requires the current NPA source overlay and runtime-fetches a hash-pinned private
PyTorch 2.13.0/CUDA 12.9/Triton 3.7.1 stack and the upstream-compatible CPU
TorchCodec 0.13 decoder, plus CUDA 12.9.86 assembler for native
`sm_103a` compilation. Require its compiled reduction preflight before model fetch. Keep its NVIDIA CUDA Toolkit
EULA beside the private runtime binary; do not bake or publish that binary.
Submit automatically stages source when the workflow requests an overlay, even
with a pinned image. Confirm the rendered task carries `NPA_SRC_S3_URI` and
`NPA_SRC_OVERLAY=1`; record the source fingerprint and compiler hashes separately
from the base-image digest. A B300 result with this overlay does not qualify the
unmodified released image. Four one-GPU hosts also require independent multi-node
training validation; they are not a performance substitute for four GPUs sharing
one host's NVLink fabric.

The one-preemptible-B300 path passed on 2026-09-23 with native compiled-kernel
preflight, full compiled policy inference, live-verified terminal success, and
three read-back-verified JSON objects. The single warmed observation took
0.151714 seconds with 25,270,528,512 bytes peak allocated GPU memory. Keep this
acceptance bound to the source/runtime hashes in `docs/workbench/flex-pi.md`;
it establishes neither an unchanged-image qualification nor a training speedup.

The maintained inference workflow specs resolve through a toolRef that passes the
literal `--torch-compile` flag. This compiles only the upstream denoising step
after complete checkpoint load and performs its warmup before timed inference.
For a direct CLI or SDK call, opt in explicitly; do not infer compile mode from
the GPU name.

The cache may be run-owned and persistent across retries. Never place it in the
image build context or upload it as evidence. The checkpoint is public and the
token is not an access acceptance; forward an operator read token through this
secret-only channel to avoid anonymous rate limits during its multi-shard fetch.
The image defaults `MODELSCOPE_DOWNLOAD_PARALLELS=16`, the SDK's supported
maximum, because the converted UMT5 asset is one roughly 11 GB object. Retain
that default for normal cold starts; an operator may lower it for a constrained
network.

The released checkpoint contains the trained VideoDiT and ActionDiT experts.
Deployment must keep the maintained checkpoint-only patch enabled so upstream
constructs those experts with `skip_dit_load_from_pretrain=True` and immediately
loads the released checkpoint. Do not fetch or generate the training-only
ActionDiT initialization, and do not restore the redundant Wan VideoDiT shard
download. The strict-loader patch must reject every missing or unexpected MoT
key and require proprio, DINO, and pointmap state. Treat any checkpoint mismatch
as a failed run; `strict=False` in the upstream call is not permission to accept
partial model state.

## Artifacts and acceptance

Require all of the following from the exact image digest:

- terminal workflow/job success on the requested one-GPU B200 or RTX PRO 6000
  target;
- `actions.json` with schema `npa.flex_pi.actions.v1`, action-only regime, and
  exactly 32 rows of 14 finite numbers;
- positive inference latency and peak allocated GPU memory;
- CUDA device name and compute capability matching the request: B200 10.0 or
  RTX PRO 6000 12.0;
- pinned source/checkpoint/dataset identity plus checkpoint, stats, and input
  SHA-256 provenance;
- non-empty `result.json` and read-after-write verification in operator storage.
- exactly one `FLEX_PI_REAL_INFERENCE_PASSED` marker after strict state and
  artifact validation, with no traceback or checkpoint mismatch in the logs.

Keep raw logs and object locations access-controlled. Repository and PR evidence
may report only sanitized status, metrics, hashes, and generic GPU class.

The current `0.1.0-cu128-r2` release (`sha256:e27978b682056339fb332acfdd0df2369af1f180d93ba1e0fd86772d6649efe6`)
passed every publication gate and independent real compiled inference on one
B200 and one RTX PRO 6000 on 2026-09-17. Each exact-image worker passed native
architecture and baked-source checks, produced finite 32x14 actions, and ended
successfully with zero restarts and three read-back-verified JSON objects.
See `docs/workbench/flex-pi.md#r2-exact-image-acceptance` for measured values.

The following fan-out and paired benchmarks are historical evidence for
`0.1.0-cu128` (`sha256:88359258470d9622d9fb5274d8ad39627a57a5682cb8630c7ac85a3f303c7b91`), not r2 qualification.

The accepted 2026-09-16 RTX PRO 6000 run used the exact `0.1.0-cu128` digest,
torch 2.7.1+cu128, four Euler steps, 0.678 seconds of inference, and
25,268,430,336 bytes peak allocated GPU memory. It produced a finite 32×14
action chunk and three read-back-verified JSON objects.

The independent B200 validation used the same exact digest on the maximum live
capacity: 23 newly allocated reservation-backed devices plus one scheduler-free
device on a pre-existing shared node, for 24 requested/allocated/used GPUs.
Every one-GPU replica loaded the complete checkpoint, produced finite 32×14
actions, and published three durable objects. The run had 24 successes, zero
failures/restarts/tracebacks, 24 unique placement hashes, 24 unique action
hashes, and 72 unique object keys. It completed in 132.0 seconds at 0.1818
replicas/s, a 16.70× wall-throughput speedup and 69.56% efficiency versus the
91.823-second cached single-B200 baseline. Per-replica inference latency was
0.579–1.053 seconds (0.752-second median, 1.028-second p95), with
25,268,430,336 bytes peak allocated memory per replica. This was replica
fan-out, not model parallelism.

The 2026-09-17 runtime optimization retained the exact image, checkpoint,
public observation, seed, four Euler steps, and 32x14 output contract. With five
fixed-seed warm samples per mode, the maintained `--torch-compile` path reduced
median / p95 latency from 0.2701 / 0.2989 to 0.1021 / 0.1278 seconds on one RTX
PRO 6000 (2.645x), and from 0.1990 / 0.2075 to 0.08738 / 0.08759 seconds on one
B200 (2.278x). Peak allocated memory changed from 25,268,430,336 to
25,270,528,512 bytes. Require the measured correctness envelope as well as
finite actions: `atol=rtol=0.01`, relative L2 at most 0.5%, and action-L2 drift
at most 0.1%. The observed relative-L2 maxima were 0.2563% and 0.2927%,
respectively. Compile setup is not free: it added 30.94 seconds on RTX PRO 6000
and 24.16 seconds on B200, so keep eager mode available for one-shot callers.

Keep these historical observations tied to their exact digest. They are not latency SLAs or
closed-loop task-success claims; other GPU classes remain unmeasured for this
release.

Cancel the exact run before removing only run-owned infrastructure. Never tear
down a shared or pre-existing cluster.

## Troubleshoot

- ModelScope VAE/T5 fetch failures: this repository exposes only `master` to the
  SDK. Do not pass its raw commit as `revision`; require `master` to match the
  pinned Git commit, then enforce the two maintained SHA-256 file digests.
- Missing DINO weights: confirm access to the timm DINOv3 repository and its
  model terms before retrying.
- Missing ActionDiT initialization: confirm the checkpoint-only deployment patch
  is present. The released inference checkpoint replaces that training-only
  initialization; do not source an unrelated third-party copy.
- CUDA OOM: verify action-only flags and absence of competing workloads. Do not
  silently reduce cameras, horizon, action dimensions, or checkpoint fidelity.
- GPU mismatch: inspect scheduler labels and the artifact's device name. Do not
  infer RTX support from a B200 result, or B200 support from an RTX result.
- B300 `sm_103a` assembler rejection: confirm the source overlay was staged and
  the pinned runtime compiler was selected. Do not disable compilation or
  substitute another architecture to make the run pass.
- Invalid actions: retain the failed artifact privately and diagnose upstream;
  never coerce, clip, or fabricate values to pass validation.

## Verify changes

```bash
npa/.venv/bin/python /home/ubuntu/.codex/skills/.system/skill-creator/scripts/quick_validate.py skills/tools/flex-pi
npa/.venv/bin/python -m pytest \
  npa/tests/workbench/test_flex_pi.py \
  npa/tests/docker/test_flex_pi_image_contract.py \
  npa/tests/guardrails/test_skills_index.py \
  npa/tests/guardrails/test_three_tier_contract.py \
  npa/tests/orchestration/npa_workflow/test_catalog_doc_sync.py -q
```