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700dd75 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 | # SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# 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.
from dataclasses import dataclass
from io import BytesIO
from typing import Any, Callable, Dict
import torch
import zmq
class TorchSerializer:
@staticmethod
def to_bytes(data: dict) -> bytes:
buffer = BytesIO()
torch.save(data, buffer)
return buffer.getvalue()
@staticmethod
def from_bytes(data: bytes) -> dict:
buffer = BytesIO(data)
obj = torch.load(buffer, weights_only=False)
return obj
@dataclass
class EndpointHandler:
handler: Callable
requires_input: bool = True
class BaseInferenceServer:
"""
An inference server that spin up a ZeroMQ socket and listen for incoming requests.
Can add custom endpoints by calling `register_endpoint`.
"""
def __init__(self, host: str = "*", port: int = 5555):
self.running = True
self.context = zmq.Context()
self.socket = self.context.socket(zmq.REP)
self.socket.bind(f"tcp://{host}:{port}")
self._endpoints: dict[str, EndpointHandler] = {}
# Register the ping endpoint by default
self.register_endpoint("ping", self._handle_ping, requires_input=False)
self.register_endpoint("kill", self._kill_server, requires_input=False)
def _kill_server(self):
"""
Kill the server.
"""
self.running = False
def _handle_ping(self) -> dict:
"""
Simple ping handler that returns a success message.
"""
return {"status": "ok", "message": "Server is running"}
def register_endpoint(self, name: str, handler: Callable, requires_input: bool = True):
"""
Register a new endpoint to the server.
Args:
name: The name of the endpoint.
handler: The handler function that will be called when the endpoint is hit.
requires_input: Whether the handler requires input data.
"""
self._endpoints[name] = EndpointHandler(handler, requires_input)
def run(self):
addr = self.socket.getsockopt_string(zmq.LAST_ENDPOINT)
print(f"Server is ready and listening on {addr}")
while self.running:
try:
message = self.socket.recv()
request = TorchSerializer.from_bytes(message)
endpoint = request.get("endpoint", "get_action")
if endpoint not in self._endpoints:
raise ValueError(f"Unknown endpoint: {endpoint}")
handler = self._endpoints[endpoint]
result = (
handler.handler(request.get("data", {}))
if handler.requires_input
else handler.handler()
)
self.socket.send(TorchSerializer.to_bytes(result))
except Exception as e:
print(f"Error in server: {e}")
import traceback
print(traceback.format_exc())
self.socket.send(b"ERROR")
class BaseInferenceClient:
def __init__(self, host: str = "localhost", port: int = 5555, timeout_ms: int = 15000):
self.context = zmq.Context()
self.host = host
self.port = port
self.timeout_ms = timeout_ms
self._init_socket()
def _init_socket(self):
"""Initialize or reinitialize the socket with current settings"""
self.socket = self.context.socket(zmq.REQ)
self.socket.connect(f"tcp://{self.host}:{self.port}")
def ping(self) -> bool:
try:
self.call_endpoint("ping", requires_input=False)
return True
except zmq.error.ZMQError:
self._init_socket() # Recreate socket for next attempt
return False
def kill_server(self):
"""
Kill the server.
"""
self.call_endpoint("kill", requires_input=False)
def call_endpoint(
self, endpoint: str, data: dict | None = None, requires_input: bool = True
) -> dict:
"""
Call an endpoint on the server.
Args:
endpoint: The name of the endpoint.
data: The input data for the endpoint.
requires_input: Whether the endpoint requires input data.
"""
request: dict = {"endpoint": endpoint}
if requires_input:
request["data"] = data
self.socket.send(TorchSerializer.to_bytes(request))
message = self.socket.recv()
if message == b"ERROR":
raise RuntimeError("Server error")
return TorchSerializer.from_bytes(message)
def __del__(self):
"""Cleanup resources on destruction"""
self.socket.close()
self.context.term()
class ExternalRobotInferenceClient(BaseInferenceClient):
"""
Client for communicating with the RealRobotServer
"""
def set_observation(self, observation: dict[str, Any]):
self.call_endpoint("set_observation", data=observation)
def get_action(self, time: float | None = None) -> Dict[str, Any]:
"""
Get the action from the server.
The exact definition of the observations is defined
by the policy, which contains the modalities configuration.
"""
return self.call_endpoint("get_action", data={"time": time})
def get_modality_config(self) -> dict[str, Any]:
return self.call_endpoint("get_modality_config")
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