# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# Example manifest for `lerobot-policy-server --manifest server.yaml`.
#
# One process = one (model, revision, dtype, device) on one GPU. Dynamic
# model loading is deliberately unsupported: pre-warmed processes keep
# capacity planning honest. Every field below can also be overridden on
# the command line via draccus, e.g. --model.repo_or_path=... or
# --zenoh.connect_endpoints='["tcp/other-router:7447"]'.
#
# Field names mirror the dataclasses in src/lerobot/policy_server/manifest.py.

# --- Which policy this process serves, and where it runs ------------------
model:
  # Hub repo id (org/name) or a local checkpoint directory. Required.
  repo_or_path: lerobot/pi0_towels
  # Hub revision: branch, tag, or commit sha.
  revision: main
  # Optional torch dtype cast applied after load (e.g. "bfloat16",
  # "float16"). null keeps the checkpoint's native dtype.
  dtype: bfloat16
  # Inference device, e.g. "cuda", "cuda:1", "cpu".
  device: cuda

# --- Task namespace --------------------------------------------------------
# The task this service is published under. VLA clients may override the
# task per session unless `pin_task` is true, in which case session opens
# with a different task string are rejected.
default_task: "fold the towel"
pin_task: false
# Optional override for the <task_slug> key segment of the Zenoh prefix
# (defaults to a slug of `default_task`).
service_name: ""

# --- Serving mode & capacity ------------------------------------------------
# "auto" resolves from the policy classification: shared for verified
# chunk-stateless policies (act/pi0/pi05, smolvla with n_obs_steps=1),
# exclusive otherwise. Chunk-stateful policies — e.g. diffusion, whose
# predict_action_chunk reads select_action-fed queues — are always forced
# to "exclusive" (max_sessions=1); "shared" cannot override that.
serving_mode: auto

# Capacity rule-of-thumb: with t = server seconds per inference, r = each
# client's request rate (self-clocked to ~1-4 Hz, not the control rate),
# H = RTC execution horizon, and dt = control period:
#   max_sessions ~= min( 0.8 / (r*t),  (H*dt/2 - network RTT) / t )
# e.g. ACT @ 20 ms, 1 Hz refresh -> ~40 clients/GPU; Pi0 @ 150 ms -> ~5.
# Session opens beyond this are rejected with the current load in the
# reply, so clients retry another replica.
max_sessions: 5

# Dummy inferences run at startup so the first real request does not pay
# for CUDA graph/kernel warmup.
warmup_inferences: 2

# --- FPS contract -----------------------------------------------------------
# Control rate the policy was trained at. Clients reporting a different
# fps get a warning — or a hard reject when `strict_fps` is true.
trained_fps: 30.0
strict_fps: false

# --- Real Time Chunking (RTC) -----------------------------------------------
# Global to this process: init_rtc_processor mutates the policy instance,
# so RTC is a per-process decision, not per-session. Only rtc-capable
# families (pi0/pi05/smolvla) honor it; others are downgraded to plain
# chunk-append at session open.
rtc:
  enabled: true
  # Number of actions executed from each chunk before the next chunk is
  # blended in (the H in the capacity formula above).
  execution_horizon: 10

# --- Housekeeping ------------------------------------------------------------
# Sessions with no liveliness token and no traffic for this long are
# garbage-collected (belt-and-braces behind liveliness GC).
session_idle_timeout_s: 300.0

# --- Transport ----------------------------------------------------------------
# Robots and servers both *dial out* to a zenohd router in production
# (mode: client). mode: peer + listen_endpoints supports router-less LAN
# and loopback test deployments. Multicast scouting is always disabled:
# fleet discovery is configuration, not protocol magic.
zenoh:
  mode: client
  connect_endpoints:
    - tcp/router.gpu-cluster.internal:7447
  listen_endpoints: []
  # mTLS material (PEM paths). All three are required for tls/ endpoints;
  # leave them null for plain tcp/ inside a trusted network.
  # tls_root_ca_certificate: /etc/lerobot/tls/ca.pem
  # tls_connect_certificate: /etc/lerobot/tls/server.pem
  # tls_connect_private_key: /etc/lerobot/tls/server.key
  # Escape hatch: raw JSON5 merged into the zenoh config last.
  # extra_config_json5: '{transport: {link: {tx: {queue: {size: {data: 4}}}}}}'

# --- Observability -------------------------------------------------------------
# HTTP health + Prometheus metrics port; 0 disables the endpoint.
health_port: 9100

# Optional bounded request/response capture for offline replay.
debug:
  capture_dir: null
  capture_max: 256