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README.md

@@ -5,25 +5,11 @@ tags:
 - tt-dit-server
 - tt-model-cache
 - tt-model-container
-- tenstorrent
-- ttnn
-- tt-metal
-- tt-nn
-- robotics
-- vla
-- gr00t
-- tt-model-catalog
-pipeline_tag: robotics
-license: other
-license_name: nvidia-license
-license_link: https://huggingface.co/nvidia/GR00T-N1.7-3B/blob/main/LICENSE
-base_model:
-- nvidia/GR00T-N1.7-3B
 ---
 
-# GR00T-N1.7-3B-p150
+# gr00t-n1.7-3b-p150
 
-NVIDIA Isaac GR00T N1.7 (3B vision-language-action policy) running on one Tenstorrent Blackhole p150a via tt-nn, in the DROID layout: two cameras × two frames (t−15 and t) at 16:9, a 17-dim Franka state and a task instruction in, a 40-step chunk of end-effector, gripper and joint actions out. Stage-1 release: TTNN ops replayed from four Metal traces (DiT weights bfp8, everything else bf16) with a tuned Qwen3-VL tower; the persistent-megakernel denoise is in progress and not in this image.
+NVIDIA Isaac GR00T N1.7 (3B vision-language-action policy) running on one Tenstorrent Blackhole p150a via tt-nn, in the DROID layout: two cameras × two frames (t−15 and t) at 16:9, a 17-dim Franka state and a task instruction in, a 40-step chunk of end-effector, gripper and joint actions out. Stage-2 release: the whole DiT denoise runs as one persistent megakernel streaming bfp8 weights from a DRAM arena; vision (tuned Qwen3-VL tower), LLM and adapter are TTNN ops in Metal traces; `GR00T_DIT_BACKEND=ttnn` selects the Stage-1 denoise.
 Weights: [nvidia/GR00T-N1.7-3B](https://huggingface.co/nvidia/GR00T-N1.7-3B) · Paper: [arXiv:2503.14734](https://arxiv.org/abs/2503.14734) · Upstream code: [NVIDIA/Isaac-GR00T](https://github.com/NVIDIA/Isaac-GR00T) (`main`, the `n1.7-release` line) · Port: `code/models/experimental/gr00t` and the server `code/gr00t_p150` in this repo
 
 Runs on **p150** (mesh `P150`).
@@ -37,8 +23,7 @@ tt-model pull  changh95/GR00T-N1.7-3B-p150 --with-weights
 tt-model serve changh95/GR00T-N1.7-3B-p150
 ```
 
-- Weights [`nvidia/GR00T-N1.7-3B`](https://huggingface.co/nvidia/GR00T-N1.7-3B) at `2fc962b973bc` (6.91 GB) go to your HF cache; the image does not contain them.
-- Serves on port 20000 (or the next free port); ready when the log says `Application startup complete`.
+`pull --with-weights` downloads the Docker image and the [`nvidia/GR00T-N1.7-3B`](https://huggingface.co/nvidia/GR00T-N1.7-3B) weights at `2fc962b973bccdd5d8ce4f67cc63b264d6886495` (into your HF cache; they are not in the image). `serve` starts the model's own HTTP server on port 20000 (or the next free port, if that one is busy); the first start compiles kernels for your device, which takes several minutes, and the server is ready when it logs `Application startup complete`.
 
 ### Run with tt-cli
 
@@ -57,16 +42,16 @@ tt model stop changh95/GR00T-N1.7-3B-p150
 ```
 
 - `POST /predict`: `images` (`{"exterior_image_1_left": [<base64 PNG/JPEG at t−15>, <at t>], "wrist_image_left": [...]}`, exactly two frames per camera, 16:9 frames), `state` (`{group: [floats]}`: `eef_9d` 9 = xyz + rot6d, `gripper_position` 1, `joint_position` 7, raw values), `instruction` (≤ 51 tokens); optional `seed` (default 42) or `noise` (`[40, 132]`), `return_normalized`.
-- `GET /health`, `GET /info`, `GET /demo` (the shipped demo request with its reference noise, and its fp32 reference actions).
+- `GET /health`, `GET /info` (`stage` says what is served), `GET /demo` (the shipped demo request with its reference noise, and its fp32 reference actions).
 
 ### Response
 
 ```json
-{"actions": {"eef_9d":           [[0.5284, 0.0182, 0.4569, -0.0784, 0.3928, -0.9163, 0.9923, 0.1188, -0.0340], "... 40 steps"],
-             "gripper_position": [[0.0], [0.0039], "..."],
-             "joint_position":   [[-0.0044, 0.0347, 0.0008, -1.9162, 0.1634, 1.6095, 0.0847], "..."]},
+{"actions": {"eef_9d":           [[0.5284, 0.0181, 0.4569, -0.0786, 0.3927, -0.9163, 0.9923, 0.1193, -0.034], "... 40 steps"],
+             "gripper_position": [[0.0], [0.002], "..."],
+             "joint_position":   [[-0.0051, 0.034, 0.0013, -1.917, 0.1641, 1.6101, 0.0844], "..."]},
  "action_horizon": 40, "embodiment": "oxe_droid_relative_eef_relative_joint", "seq_len": 469, "noise_source": "client", "seed": null,
- "timing_ms": {"decode": 3.6, "encode": 4.3, "device": 69.9, "decode_actions": 0.9, "total": 78.7}}
+ "timing_ms": {"decode": 3.4, "encode": 10.9, "device": 55.9, "decode_actions": 1.1, "total": 71.3}}
 ```
 
 - The example is the demo request with the reference noise, as `GET /demo` returns it (`noise_source: "client"`, `seed: null`); `timing_ms` from a warm request of the same serve.
@@ -82,20 +67,22 @@ tt model stop changh95/GR00T-N1.7-3B-p150
 
 | Metric | Value |
 |---|---:|
-| Action chunk vs the fp32 reference (same observation, same initial noise) | PCC `eef_9d` 0.99998 · `joint_position` 0.99996 · `gripper_position` max\|d\| 0.0038 (near-constant group) · all 17 dims × 40 steps 0.99996 · normalised valid slice 0.99995 |
-| Intermediate taps vs the fp32 reference (55 gated taps, from pixels) | 58 of 58 rows pass; tightest `vit_deepstack_mergers[0]` 0.999849 vs gate 0.9998. On six more DROID goldens five pass every tap; `t2_s120` misses two marginal gates (`llm_layer_first` 0.98998 vs 0.991, `joint_position` PCC 0.99977 / max\|d\| 0.096 vs 0.9998 / 0.083), pre-existing |
-| Traced vs untraced, determinism, stale state | bit-identical; 5 trace replays bit-identical; A→B→A→B observation switches reproduce A and B exactly |
-| Inference on p150a (port benchmark, warm, batch 1, median of 50) | **75.8 ms end-to-end** (p90 81.6) ≈ host encode 4.9 + upload 3.2 + traces 66.1 (vision 17.5 · LLM 12.4 · adapter 5.7 · denoise 30.7) + readback and decode 1.2 |
-| Served over HTTP by this image (warm, batch 1, 50 requests) | 69.5 ms device (p90 69.7) · 84.4 ms server-side incl. four PNG decodes (p90 94.5) |
-| Same policy on an RTX 5090 (same host, NVIDIA's unmodified N1.7 policy, eager PyTorch bf16, idle machine) | `get_action` 59.8–65.6 ms model, 73–79 ms incl. host pre/post-processing → p150a 1.01× faster end-to-end; NVIDIA's published H100 TensorRT full pipeline: 27.9 ms (one camera) |
+| Action chunk vs the fp32 reference (same observation, same initial noise) | PCC `eef_9d` 0.99998 · `joint_position` 0.99996 · `gripper_position` max\|d\| 0.0031 (near-constant group) · all 17 dims × 40 steps 0.99996 · normalised valid slice 0.99995 |
+| Intermediate taps vs the fp32 reference (27 gated taps, from pixels; DiT internals are not observable inside the megakernel) | 30 of 30 rows pass; tightest `vit_deepstack_mergers[0]` 0.999849 vs gate 0.9998. On six more DROID goldens the actions pass on five: `t2_s120` has two pre-existing misses (`llm_layer_first` 0.98998 vs 0.991, `joint_position` PCC 0.99976 / max\|d\| 0.107 vs 0.9998 / 0.083); `t1_s120` adds one megakernel-only marginal tap, `pred_velocity[k=2]` dim 9 per-dim PCC 0.99588 vs 0.996 (its actions pass) |
+| Megakernel vs Stage-1 TTNN denoise (same device, 7 DROID samples) | `action_pred_normalized` PCC ≥ 0.999992 between the two backends; per-step velocities ≥ 0.99996 |
+| Traced vs untraced, determinism, stale state, 2 vs 1 command queue | bit-identical; 5 trace replays bit-identical; A→B→A→B reproduces A and B exactly; 2-CQ == 1-CQ on every probe (26 rows) |
+| Inference on p150a (port benchmark, warm, batch 1, median of 50) | **62.8 ms end-to-end** (p90 68.6) ≈ host encode 5.6 + upload 3.2 + traces 52.4 (vision 17.5 · LLM 12.4 · adapter 6.2 · denoise 16.5 = 4 × 4.12) + readback and decode 1.3. Stage-1 path on the same tree: 79.7 ms (denoise 30.7) |
+| Served over HTTP by this image (warm, batch 1, 50 requests) | 55.9 ms device (p90 56.3) · 69.1 ms server-side incl. four PNG decodes (p90 72.7) |
+| Same policy on an RTX 5090 (same host, NVIDIA's unmodified N1.7 policy, eager PyTorch bf16, idle machine) | `get_action` 59.8–65.6 ms model, 73–79 ms incl. host pre/post-processing → p150a 1.22× faster end-to-end; NVIDIA's published H100 TensorRT full pipeline: 27.9 ms (one camera) |
 
 ### Caveats
 
-- Stage-1 path: TTNN ops in four Metal traces, DiT matmul weights bfp8, everything else bf16. The vision tower is the port's tuned Qwen3-VL fork (HiFi3 matmuls on blocks 0–5; 17.5 ms vs 87 ms for the verbatim tower, `GR00T_VIT_FORK=0` selects it). The megakernel denoise (weight streaming measured at 464 GB/s bf16 / 414 GB/s bfp8 over the 32-block weight set) is not in this image.
+- Stage-2 path: one persistent `generic_op` runs the whole denoise inside the fourth Metal trace, streaming the 32 blocks' bfp8 weights once per step from a ~1 GB DRAM arena over the 11×10 Tensix grid (96 compute cores, 8 DRAM readers, hubs), with distributed LayerNorm, per-head flash SDPA and the cross attention over the 448-key image set split three ways. Costs: 64 KiB less allocatable L1 per core (`worker_l1_size` 1,395,712), the VL adapter's intermediates in DRAM (+0.6 ms), compute binaries of ~131 KB sitting ~4.5 KB under the 136 KB kernel-config ring, and no DiT-intermediate taps. Gain vs the Stage-1 path on the same tree: −19 % end-to-end, −46 % denoise (the plan's −30 % e2e was not reached). Everything outside the DiT matmuls is bf16.
+- The vision tower is the port's tuned Qwen3-VL fork (HiFi3 matmuls on blocks 0–5; 17.5 ms vs 87 ms for the verbatim tower, `GR00T_VIT_FORK=0` selects it).
 - One embodiment and layout: DROID (`oxe_droid_relative_eef_relative_joint`, embodiment slot 24), exactly two cameras × two frames (t−15 and t), landscape frames that land on the 16×28-patch grid (width / height in [1.686, 1.814]: 16:9, e.g. 320×180), instruction ≤ 51 tokens; anything else is refused with 400. Batch 1.
-- Noise: the default `seed` is 42 (CPU generator, deterministic). The reference initial noise shipped with the demo (`code/gr00t_p150/demo/n17/noise.json`) was drawn on a CUDA generator and is not seed-reproducible; send it as `noise` to reproduce the reference actions.
+- Noise: default `seed` 42 (CPU generator, deterministic). The demo's reference noise (`code/gr00t_p150/demo/n17/noise.json`) was drawn on CUDA and is not seed-reproducible; send it as `noise` to reproduce the reference actions.
 - Not an OpenAI-compatible API; `GET /v1/models` is a stub.
-- First boot is cold (weight conversion + kernel JIT, 90 s); later boots take 11–21 s. Validated on tt-metal `668c2907575`, single p150a only. Details: [`SERVING.md`](SERVING.md), [`GPU_COMPARISON.md`](GPU_COMPARISON.md).
+- First boot is cold (weight conversion + kernel JIT, 64 s); later boots take 14 s. Validated on tt-metal `668c2907575` (port snapshot `009de892c895`), single p150a only. Details: [`SERVING.md`](SERVING.md), [`GPU_COMPARISON.md`](GPU_COMPARISON.md).
 
 ### Licensing
 
@@ -110,5 +97,6 @@ The exact sources the image was built from — `code/` in this repo is byte-iden
 | component | built from |
 | --- | --- |
 | tt-metal | [`668c290757550588d0ce46b180c344a462a2aaf5`](https://github.com/tenstorrent/tt-metal/commit/668c290757550588d0ce46b180c344a462a2aaf5) |
-| `code/` digest | `33cabc32cc373182` (sha256, first 16 hex digits) |
-| built | 2026-09-17T12:22:35+00:00 by tt-model 0.1.0 |
+| `code/` digest | `9efa5512d0634561` (sha256, first 16 hex digits) |
+| built | 2026-09-17T20:42:14+00:00 by tt-model 0.1.0 |
+

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buildkit","comment":"buildkit.dockerfile.v0"},{"created":"2026-09-17T21:13:29.581697154+09:00","created_by":"RUN |11 OMPI_DIR=/opt/openmpi-v5.0.7-ulfm EXTRA_MODELS_DIR= TT_MODEL_KIND=tt-dit-server MODEL_NAME=gr00t-n1.7-3b-p150 MODEL_REPO=changh95/GR00T-N1.7-3B-p150 MODEL_WEIGHTS=nvidia/GR00T-N1.7-3B MODEL_ARCH=blackhole MODEL_PROFILES=default MODEL_TT_METAL_SHA=668c290757550588d0ce46b180c344a462a2aaf5 MODEL_TT_METAL_DESCRIBE=v0.79.0-dev20260914-1-g668c290757 MODEL_PLUGIN_SHA= /bin/sh -c existing=\"$(getent passwd 1000 | cut -d: -f1)\"     \u0026\u0026 if [ -n \"$existing\" ]; then          userdel -r \"$existing\" 2\u003e/dev/null || userdel \"$existing\";        fi     \u0026\u0026 useradd --uid 1000 --create-home --home-dir /home/tt --shell /bin/bash tt     \u0026\u0026 mkdir -p /home/tt/work/logs /cache /opt/tt-metal     \u0026\u0026 chown -R tt:tt /home/tt /cache /opt/tt-metal     \u0026\u0026 chmod 1777 /home/tt /home/tt/work /home/tt/work/logs /cache # buildkit","comment":"buildkit.dockerfile.v0"},{"created":"2026-09-17T21:16:30.57833333+09:00","created_by":"COPY /opt/openmpi-v5.0.7-ulfm /opt/openmpi-v5.0.7-ulfm # buildkit","comment":"buildkit.dockerfile.v0"},{"created":"2026-09-17T21:16:31.112999446+09:00","created_by":"COPY /opt/tenstorrent /opt/tenstorrent # buildkit","comment":"buildkit.dockerfile.v0"},{"created":"2026-09-18T05:46:03.773440597+09:00","created_by":"COPY /usr/local/share/uv /usr/local/share/uv # buildkit","comment":"buildkit.dockerfile.v0"},{"created":"2026-09-18T05:46:14.462718123+09:00","created_by":"COPY /opt/tt-venv /opt/tt-venv # buildkit","comment":"buildkit.dockerfile.v0"},{"created":"2026-09-18T05:46:14.893743802+09:00","created_by":"COPY /opt/vllm /opt/vllm # buildkit","comment":"buildkit.dockerfile.v0"},{"created":"2026-09-18T05:46:20.837114171+09:00","created_by":"COPY --chown=tt:tt /opt/tt-metal/runtime /opt/tt-metal/runtime # buildkit","comment":"buildkit.dockerfile.v0"},{"created":"2026-09-18T05:46:23.655608279+09:00","created_by":"COPY --chown=tt:tt /opt/tt-metal/build_Release /opt/tt-metal/build_Release # buildkit","comment":"buildkit.dockerfile.v0"},{"created":"2026-09-18T05:46:25.961412864+09:00","created_by":"COPY --chown=tt:tt /opt/tt-metal/build /opt/tt-metal/build # buildkit","comment":"buildkit.dockerfile.v0"},{"created":"2026-09-18T05:46:35.518690428+09:00","created_by":"COPY --chown=tt:tt /opt/tt-metal/tt_metal /opt/tt-metal/tt_metal # buildkit","comment":"buildkit.dockerfile.v0"},{"created":"2026-09-18T05:46:41.961973817+09:00","created_by":"COPY --chown=tt:tt /opt/tt-metal/ttnn /opt/tt-metal/ttnn # buildkit","comment":"buildkit.dockerfile.v0"},{"created":"2026-09-18T05:46:42.966444798+09:00","created_by":"COPY --chown=tt:tt /opt/tt-metal/tools /opt/tt-metal/tools # buildkit","comment":"buildkit.dockerfile.v0"},{"created":"2026-09-18T05:46:43.703379469+09:00","created_by":"COPY --chown=tt:tt /opt/tt-metal/setup.py /opt/tt-metal/pyproject.toml /opt/tt-metal/ # buildkit","comment":"buildkit.dockerfile.v0"},{"created":"2026-09-18T05:46:44.600292355+09:00","created_by":"COPY --chown=tt:tt code/ /opt/tt-metal/ # buildkit","comment":"buildkit.dockerfile.v0"},{"created":"2026-09-18T05:46:45.335270088+09:00","created_by":"COPY entrypoint.sh /usr/local/bin/entrypoint.sh # buildkit","comment":"buildkit.dockerfile.v0"},{"created":"2026-09-18T05:46:45.911486071+09:00","created_by":"COPY --chmod=0755 serve-default.sh /usr/local/bin/serve-default.sh # buildkit","comment":"buildkit.dockerfile.v0"},{"created":"2026-09-18T05:46:45.911486071+09:00","created_by":"ENV VENV=/opt/tt-venv","comment":"buildkit.dockerfile.v0","empty_layer":true},{"created":"2026-09-18T05:46:45.911486071+09:00","created_by":"ENV VIRTUAL_ENV=/opt/tt-venv","comment":"buildkit.dockerfile.v0","empty_layer":true},{"created":"2026-09-18T05:46:45.911486071+09:00","created_by":"ENV PATH=/opt/tt-venv/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin","comment":"buildkit.dockerfile.v0","empty_layer":true},{"created":"2026-09-18T05:46:45.911486071+09:00","created_by":"ENV TT_METAL_RUNTIME_ROOT=/opt/tt-metal","comment":"buildkit.dockerfile.v0","empty_layer":true},{"created":"2026-09-18T05:46:45.911486071+09:00","created_by":"ENV TT_METAL_HOME=/opt/tt-metal","comment":"buildkit.dockerfile.v0","empty_layer":true},{"created":"2026-09-18T05:46:45.911486071+09:00","created_by":"ENV PYTHONPATH=/opt/tt-metal","comment":"buildkit.dockerfile.v0","empty_layer":true},{"created":"2026-09-18T05:46:45.911486071+09:00","created_by":"ENV LD_LIBRARY_PATH=/opt/tt-metal/build/lib:/opt/openmpi-v5.0.7-ulfm/lib","comment":"buildkit.dockerfile.v0","empty_layer":true},{"created":"2026-09-18T05:46:45.911486071+09:00","created_by":"ENV EXTRA_MODELS_DIR=","comment":"buildkit.dockerfile.v0","empty_layer":true},{"created":"2026-09-18T05:46:45.911486071+09:00","created_by":"ARG TT_VLLM_BUILTIN_MODELS=","comment":"buildkit.dockerfile.v0","empty_layer":true},{"created":"2026-09-18T05:46:45.911486071+09:00","created_by":"ENV TT_VLLM_BUILTIN_MODELS=","comment":"buildkit.dockerfile.v0","empty_layer":true},{"created":"2026-09-18T05:46:45.911486071+09:00","created_by":"ENV TT_MODEL_KIND=tt-dit-server","comment":"buildkit.dockerfile.v0","empty_layer":true},{"created":"2026-09-18T05:46:45.911486071+09:00","created_by":"ENV HF_HOME=/hf","comment":"buildkit.dockerfile.v0","empty_layer":true},{"created":"2026-09-18T05:46:45.911486071+09:00","created_by":"ENV TT_METAL_CACHE=/cache","comment":"buildkit.dockerfile.v0","empty_layer":true},{"created":"2026-09-18T05:46:45.911486071+09:00","created_by":"ENV HOME=/home/tt","comment":"buildkit.dockerfile.v0","empty_layer":true},{"created":"2026-09-18T05:46:45.911486071+09:00","created_by":"ENV USER=tt","comment":"buildkit.dockerfile.v0","empty_layer":true},{"created":"2026-09-18T05:46:45.911486071+09:00","created_by":"ENV LOGNAME=tt","comment":"buildkit.dockerfile.v0","empty_layer":true},{"created":"2026-09-18T05:46:45.911486071+09:00","created_by":"USER tt","comment":"buildkit.dockerfile.v0","empty_layer":true},{"created":"2026-09-18T05:46:46.560663218+09:00","created_by":"WORKDIR /home/tt/work","comment":"buildkit.dockerfile.v0"},{"created":"2026-09-18T05:46:47.293113831+09:00","created_by":"COPY verify.sh /ctx/verify.sh # buildkit","comment":"buildkit.dockerfile.v0"},{"created":"2026-09-18T05:47:45.635208159+09:00","created_by":"RUN |12 OMPI_DIR=/opt/openmpi-v5.0.7-ulfm EXTRA_MODELS_DIR= TT_MODEL_KIND=tt-dit-server MODEL_NAME=gr00t-n1.7-3b-p150 MODEL_REPO=changh95/GR00T-N1.7-3B-p150 MODEL_WEIGHTS=nvidia/GR00T-N1.7-3B MODEL_ARCH=blackhole MODEL_PROFILES=default MODEL_TT_METAL_SHA=668c290757550588d0ce46b180c344a462a2aaf5 MODEL_TT_METAL_DESCRIBE=v0.79.0-dev20260914-1-g668c290757 MODEL_PLUGIN_SHA= TT_VLLM_BUILTIN_MODELS= /bin/sh -c bash /ctx/verify.sh # buildkit","comment":"buildkit.dockerfile.v0"},{"created":"2026-09-18T05:47:45.635208159+09:00","created_by":"USER root","comment":"buildkit.dockerfile.v0","empty_layer":true},{"created":"2026-09-18T05:47:45.918316926+09:00","created_by":"RUN |12 OMPI_DIR=/opt/openmpi-v5.0.7-ulfm EXTRA_MODELS_DIR= TT_MODEL_KIND=tt-dit-server MODEL_NAME=gr00t-n1.7-3b-p150 MODEL_REPO=changh95/GR00T-N1.7-3B-p150 MODEL_WEIGHTS=nvidia/GR00T-N1.7-3B MODEL_ARCH=blackhole MODEL_PROFILES=default MODEL_TT_METAL_SHA=668c290757550588d0ce46b180c344a462a2aaf5 MODEL_TT_METAL_DESCRIBE=v0.79.0-dev20260914-1-g668c290757 MODEL_PLUGIN_SHA= TT_VLLM_BUILTIN_MODELS= /bin/sh -c chmod -R a+rwX /home/tt # buildkit","comment":"buildkit.dockerfile.v0"},{"created":"2026-09-18T05:47:45.918316926+09:00","created_by":"USER tt","comment":"buildkit.dockerfile.v0","empty_layer":true},{"created":"2026-09-18T05:47:45.918316926+09:00","created_by":"LABEL org.tenstorrent.tt-model=gr00t-n1.7-3b-p150 org.tenstorrent.tt-model.repo=changh95/GR00T-N1.7-3B-p150 org.tenstorrent.tt-model.weights=nvidia/GR00T-N1.7-3B org.tenstorrent.tt-model.arch=blackhole org.tenstorrent.tt-model.kind=tt-dit-server org.tenstorrent.tt-model.profiles=default org.opencontainers.image.revision=668c290757550588d0ce46b180c344a462a2aaf5 org.tenstorrent.tt-model.tt-metal=v0.79.0-dev20260914-1-g668c290757 org.tenstorrent.tt-model.plugin=","comment":"buildkit.dockerfile.v0","empty_layer":true},{"created":"2026-09-18T05:47:45.918316926+09:00","created_by":"ENTRYPOINT [\"/usr/local/bin/entrypoint.sh\"]","comment":"buildkit.dockerfile.v0","empty_layer":true},{"created":"2026-09-18T05:47:45.918316926+09:00","created_by":"CMD [\"/usr/local/bin/serve-default.sh\"]","comment":"buildkit.dockerfile.v0","empty_layer":true}],"os":"linux","rootfs":{"type":"layers","diff_ids":["sha256:ea16cace89338c84eb6bcb91a7efdfcae6838fff359efe951858227436486c34","sha256:5dc61cd572ff1fa4ebe0a449f2df9501279360974eda961542a2b299e5f44f00","sha256:87eaa35937d59dd35df110a41754b865a5e65d1773f5b91df0be384eaf15155a","sha256:1ad17ff8b0f5c947e1c6a734f9281d1f5fcb689628fd4e5571b9811f397d3d06","sha256:9519c1f5e362b8ec260ed6c51e00f15d0a2cb16b4e4b310a79b1060188f03b75","sha256:69eb5e338658ee17147d1d82f96c973afff98c6c29774bba1aa457709a0d4698","sha256:5d3b62721f4676c580d2f564340f97371ce2f27d9d70c8fac69b98317fb8d34d","sha256:46bdb60224d727f6e1903688e1fa917cf799c424aa8c5a2e962cb73cafb22ce9","sha256:6c7e76e39480edf3f1277d68d06fa6b927a357d11b6640659cad8145baf9a63e","sha256:4d74688be716150c66f732cf57367ae234d660c3994405efcdb5641b07f62c36","sha256:f8c76554477c3b0d61cdee046e6ef5567faf83e4804d4b6171a5a88ccfbc7416","sha256:c33bdf5019c6b69ea765f1c75d608fb8bffdb37d349640782d7de531e74ae378","sha256:7b2a5a81498010b3629d1d28a27ff10d4bc12c7f9d4d4ae43f57bd9a46e04759","sha256:7320ad1717689072aef84a5a2174756c6dac4cd67d03215fb2b8d66fb709fea9","sha256:a866fe1e7c88367d17c6053ede00790846e105951f172be1e1a44714448b4e20","sha256:890fbcb7a8c6ae6b4961f45bad4e8a7533e5173d4a396d55cf188844e2a749ca","sha256:672c0decd71f158e40c1498407b237d7ff969e36d2c40c8f768ba7167fa831a2","sha256:ebbb3cda6ab002f9a81faec9ec220ceae2be319ce765fa133cdc82dcae9dbe8c","sha256:5f70bf18a086007016e948b04aed3b82103a36bea41755b6cddfaf10ace3c6ef","sha256:006dcbfafb7d5545f3b24d5633669ecdc4e48f311e9c51f4ce99641975e078a0","sha256:455f846e3d33858cdafddc817030ac6ff979d36c73191c0d84c7ffc23041cf80","sha256:3dd5bba000b8e55b4320e5255131e8dcb72bc017a835b6c2020b4c037c3409a4"]}}

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image/repositories

@@ -1 +1 @@
-{"tt-model/gr00t-n1.7-3b-p150":{"bb2d79337305":"9dff66b418e9ee312f6643a75ea81b3cc3f36c236e65eb64bc43a0bf4ba26b3c"}}
+{"tt-model/gr00t-n1.7-3b-p150":{"4cfc5e1ffe29":"3dd5bba000b8e55b4320e5255131e8dcb72bc017a835b6c2020b4c037c3409a4"}}

requirements.lock

@@ -22,7 +22,7 @@ httpcore==1.0.9
 httpx==0.28.1
 huggingface_hub==1.32.0
 identify==2.6.19
-idna==3.19
+idna==3.20
 Jinja2==3.1.6
 kiwisolver==1.5.1
 linkify-it-py==2.2.0
@@ -80,5 +80,5 @@ typing-inspection==0.4.4
 typing_extensions==4.16.0
 urllib3==2.8.0
 uvicorn==0.53.0
-virtualenv==21.7.10
+virtualenv==21.7.11
 wheel==0.48.0

tt_kernel_manifest.json

@@ -6,7 +6,7 @@
   "device_count": 1,
   "producer": {
     "tt_kernel_version": "0.1.0",
-    "created_at": "2026-09-17T12:25:32.564793+00:00",
+    "created_at": "2026-09-17T20:47:58.559611+00:00",
     "hostname": "deepgadget"
   },
   "weights": {
@@ -35,8 +35,8 @@
     "image": {
       "registry": "hf",
       "repository": "gr00t-n1.7-3b-p150",
-      "tag": "tt-model/gr00t-n1.7-3b-p150:bb2d79337305",
-      "digest": "sha256:bb2d79337305f5bec8469a0c040169c08d08c5d121f56fb80adf4430bfdb0bca"
+      "tag": "tt-model/gr00t-n1.7-3b-p150:4cfc5e1ffe29",
+      "digest": "sha256:4cfc5e1ffe299b02f05d09f2ac79a840690a135ec81ffa9da79cf99be87f6636"
     },
     "kind": "tt-dit-server",
     "runtime": {
@@ -73,6 +73,9 @@
         "GR00T_LAYOUT": "droid",
         "GR00T_POLICY": "mixed_dit",
         "GR00T_TRACE_LAYOUT": "per_stage",
+        "GR00T_DIT_BACKEND": "megakernel",
+        "GR00T_MK_ARENA_DTYPE": "auto",
+        "GR00T_NUM_CQS": "2",
         "GR00T_VIT_FORK": "1",
         "GR00T_TOKENIZER_DIR": "/opt/tt-metal/gr00t_p150/assets/tokenizer/n17",
         "GR00T_PROJECT_ROOT": "/nonexistent",
@@ -108,6 +111,14 @@
       "from models.experimental.gr00t.common import configs, preprocessing, normalization, prompts, checkpoint, weights; assert configs.HF_SNAPSHOT_SHAS['n17'] == '2fc962b973bccdd5d8ce4f67cc63b264d6886495' and configs.HF_REPOS['n17'] == 'nvidia/GR00T-N1.7-3B'",
       "from models.experimental.gr00t.common.configs import get_config; c = get_config('n17'); l = c.canonical_layout; assert (l.name, c.canonical_embodiment, l.embodiment_id, l.n_cameras, l.n_images, l.grid_thw, l.tokens_per_image, l.l_max, l.s_pad_llm, l.noise_shape, l.valid_slice) == ('droid', 'oxe_droid_relative_eef_relative_joint', 24, 2, 4, (1, 16, 28), 112, 51, 512, (1, 40, 132), (40, 17)), l",
       "from models.experimental.gr00t.tt.qwen3vl_vision import build_vision_tower, resolve_vit_fork, TunedVisionConfig; from models.experimental.gr00t.tt.policy import TTPolicy; assert TTPolicy().vit_fork is True and resolve_vit_fork(TTPolicy(), None, 'qwen3vl') is True",
+      "from models.experimental.gr00t.tt.policy import TTPolicy, DEFAULT_DIT_BACKEND, MK_ARENA_DTYPE_DEFAULT; p = TTPolicy(); assert DEFAULT_DIT_BACKEND == 'megakernel' and p.dit_backend == 'megakernel' and p.mk_arena_dtype == 'auto' and p.mk_arena_dtype_for('n17') == 'bfp8_b' == MK_ARENA_DTYPE_DEFAULT['n17'], p.to_dict()",
+      "from models.experimental.gr00t.tt.policy import TTPolicy; assert TTPolicy().with_env_overrides({'GR00T_DIT_BACKEND': 'ttnn'}).dit_backend == 'ttnn' and TTPolicy().with_env_overrides({'GR00T_MK_ARENA_DTYPE': 'bf16'}).mk_arena_dtype == 'bf16'",
+      "import models.experimental.gr00t.tt.megakernel.dit_program as dp; from models.experimental.gr00t.tt.megakernel import arena, core_map, descriptors; assert dp.DiTMegakernel and descriptors.L1_USABLE_BYTES == 1461248 and core_map.N_COMPUTE_DEFAULT == 96",
+      "from models.experimental.gr00t.tt.model import open_model_device, worker_l1_size_for, MK_L1_CUT_BYTES_DEFAULT, ADAPTER_MEM_BY_BACKEND; from models.experimental.gr00t.tt.policy import TTPolicy; assert MK_L1_CUT_BYTES_DEFAULT == 65536 and worker_l1_size_for(TTPolicy()) == 1395712 and worker_l1_size_for(TTPolicy(dit_backend='ttnn')) is None and ADAPTER_MEM_BY_BACKEND == {'ttnn': 'L1', 'megakernel': 'DRAM'}",
+      "import hashlib; from pathlib import Path; d = Path('/opt/tt-metal/models/experimental/gr00t'); rows = [l.split() for l in (d / 'tests/tt/results/mk_tree_20260918-041439.sha256').read_text().splitlines() if l.strip()]; assert len(rows) == 16 and all(hashlib.sha256((d / p).read_bytes()).hexdigest() == h for h, p in rows), 'tt/megakernel tree != the K4b checkpoint 7df61c35e9dc7857'",
+      "import os, re; from pathlib import Path; k = Path('/opt/tt-metal/models/experimental/gr00t/tt/megakernel/kernels/dit_kernel.cpp'); incs = re.findall(r'#include \"(\\.\\./[^\"]+)\"', k.read_text()); assert len(incs) == 4 and all(Path(os.path.normpath(k.parent / i)).is_file() for i in incs), incs",
+      "import inspect, gr00t_p150.server.app as a; src = inspect.getsource(a); assert a.STAGE_SERVED == {'megakernel': 'stage2-megakernel', 'ttnn': 'stage1-ttnn-traces'} and a.DEFAULT_NUM_CQS == 2 and 'device = open_model_device(' in src and 'GR00T_DIT_BACKEND' in src and 'GR00T_NUM_CQS' in src",
+      "import os; os.environ.update({'GR00T_VERSION': 'n17', 'TT_WEIGHTS_REVISION': '2fc962b973bccdd5d8ce4f67cc63b264d6886495', 'GR00T_DIT_BACKEND': 'megakernel', 'GR00T_MK_ARENA_DTYPE': 'auto', 'GR00T_NUM_CQS': '2', 'GR00T_VIT_FORK': '1'}); from gr00t_p150.server.app import load_config; c = load_config(); assert (c.version, c.dit_backend, c.mk_arena_dtype, c.num_cqs, c.vit_fork) == ('n17', 'megakernel', 'auto', 2, True), c",
       "import models.tt_dit.utils.matmul as mm; assert mm.get_matmul_config",
       "from models.tt_dit.encoders.qwen3vl.vision_qwen3vl import Qwen3VlVisionModel; assert Qwen3VlVisionModel",
       "import models.demos.deepseek_v3_b1.unified_kernel_descriptor as u; assert u.UnifiedKernelDescriptor",
@@ -121,14 +132,14 @@
       "from gr00t_p150.server.app import load_demo_request; import numpy as np; r = load_demo_request('n17'); assert set(r['images']) == {'exterior_image_1_left', 'wrist_image_left'} and all(len(v) == 2 for v in r['images'].values()) and np.asarray(r['noise']).squeeze().shape == (40, 132)",
       "from pathlib import Path; assert Path('/opt/tt-metal/models/experimental/gr00t/common/golden_tap_map.json').is_file() and Path('/opt/tt-metal/models/experimental/gr00t/tests/tt/gates/gates_multi.json').is_file()",
       "from pathlib import Path; assert Path('/opt/tt-metal/gr00t_p150/server/smoke_test.py').is_file() and Path('/opt/tt-metal/scripts/bench_http.py').is_file() and Path('/opt/tt-metal/scripts/download_weights.sh').is_file()",
-      "import ttnn; assert all(hasattr(ttnn, a) for a in ('begin_trace_capture', 'end_trace_capture', 'execute_trace', 'release_trace', 'copy_host_to_device_tensor', 'MinimalMatmulConfig', 'embedding'))",
+      "import ttnn; assert all(hasattr(ttnn, a) for a in ('begin_trace_capture', 'end_trace_capture', 'execute_trace', 'release_trace', 'copy_host_to_device_tensor', 'MinimalMatmulConfig', 'embedding', 'generic_op', 'allocate_tensor_on_device'))",
       "from gr00t_p150.server.app import parse_mesh_shape as p; assert p('1x1') == p('(1, 1)') == p('1,1') == (1, 1)"
     ],
     "built": {
-      "image": "tt-model/gr00t-n1.7-3b-p150:bb2d79337305",
+      "image": "tt-model/gr00t-n1.7-3b-p150:4cfc5e1ffe29",
       "repo": "changh95/GR00T-N1.7-3B-p150",
       "tt_model_version": "0.1.0",
-      "created_at": "2026-09-17T12:22:35+00:00",
+      "created_at": "2026-09-17T20:42:14+00:00",
       "tt_metal": {
         "sha": "668c290757550588d0ce46b180c344a462a2aaf5",
         "describe": "v0.79.0-dev20260914-1-g668c290757",
@@ -139,8 +150,8 @@
         "branch": "HEAD",
         "pushed": true
       },
-      "code_sha256": "33cabc32cc37318230222306d7fdb1133b5c6b716030ec1ab40c0a53e93078d4",
-      "image_digest": "sha256:bb2d79337305f5bec8469a0c040169c08d08c5d121f56fb80adf4430bfdb0bca"
+      "code_sha256": "9efa5512d0634561c9d0469c7fada22f3da23a6ef08cdbeb08f8c0b6769673df",
+      "image_digest": "sha256:4cfc5e1ffe299b02f05d09f2ac79a840690a135ec81ffa9da79cf99be87f6636"
     }
   }
 }