# Copyright 2018-2020 Amazon.com, Inc. or its affiliates. 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. A copy of # the License is located at # # http://aws.amazon.com/apache2.0/ # # or in the "license" file accompanying this file. This file 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. import boto3 import logging import os import re import signal import subprocess import tfs_utils from contextlib import contextmanager logging.basicConfig(level=logging.INFO) log = logging.getLogger(__name__) JS_PING = "js_content tensorflowServing.ping" JS_INVOCATIONS = "js_content tensorflowServing.invocations" GUNICORN_PING = "proxy_pass http://gunicorn_upstream/ping" GUNICORN_INVOCATIONS = "proxy_pass http://gunicorn_upstream/invocations" MULTI_MODEL = "s" if os.environ.get("SAGEMAKER_MULTI_MODEL", "False").lower() == "true" else "" CODE_DIR = ( "/opt/ml/code" if os.environ.get("SAGEMAKER_MULTI_MODEL", "False").lower() == "true" else "/opt/ml/model/code" ) PYTHON_LIB_PATH = os.path.join(CODE_DIR, "lib") REQUIREMENTS_PATH = os.path.join(CODE_DIR, "requirements.txt") INFERENCE_PATH = os.path.join(CODE_DIR, "inference.py") class ServiceManager(object): def __init__(self): self._state = "initializing" self._nginx = None self._tfs = [] self._gunicorn = None self._gunicorn_command = None self._enable_python_service = False self._tfs_version = os.environ.get("SAGEMAKER_TFS_VERSION", "1.13") self._nginx_http_port = os.environ.get("SAGEMAKER_BIND_TO_PORT", "8080") self._nginx_loglevel = os.environ.get("SAGEMAKER_TFS_NGINX_LOGLEVEL", "error") self._tfs_default_model_name = os.environ.get("SAGEMAKER_TFS_DEFAULT_MODEL_NAME", "None") self._sagemaker_port_range = os.environ.get("SAGEMAKER_SAFE_PORT_RANGE", None) self._gunicorn_workers = os.environ.get("SAGEMAKER_GUNICORN_WORKERS", None) if self._gunicorn_workers is None: num_host_cores = os.environ.get("NEURON_CORE_HOST_TOTAL") if num_host_cores is None: self._gunicorn_workers = 1 else: self._gunicorn_workers = num_host_cores self._gunicorn_threads = os.environ.get("SAGEMAKER_GUNICORN_THREADS", 1) self._gunicorn_loglevel = os.environ.get("SAGEMAKER_GUNICORN_LOGLEVEL", "info") self._tfs_config_path = "/sagemaker/model-config.cfg" self._tfs_batching_config_path = "/sagemaker/batching-config.cfg" self._user_ncgs = os.environ.get("NEURONCORE_GROUP_SIZES", None) if self._user_ncgs is None: os.environ["NEURONCORE_GROUP_SIZES"] = "1" self._user_ncgs = 1 _enable_batching = os.environ.get("SAGEMAKER_TFS_ENABLE_BATCHING", "false").lower() _enable_multi_model_endpoint = os.environ.get("SAGEMAKER_MULTI_MODEL", "false").lower() self._tfs_wait_time_seconds = int(os.environ.get("SAGEMAKER_TFS_WAIT_TIME_SECONDS", 300)) self._tfs_inter_op_parallelism = os.environ.get("SAGEMAKER_TFS_INTER_OP_PARALLELISM", 0) self._tfs_intra_op_parallelism = os.environ.get("SAGEMAKER_TFS_INTRA_OP_PARALLELISM", 0) self._gunicorn_worker_class = os.environ.get("SAGEMAKER_GUNICORN_WORKER_CLASS", "gevent") self._gunicorn_timeout_seconds = int( os.environ.get("SAGEMAKER_GUNICORN_TIMEOUT_SECONDS", 30) ) self._nginx_proxy_read_timeout_seconds = int( os.environ.get("SAGEMAKER_NGINX_PROXY_READ_TIMEOUT_SECONDS", 60) ) # Nginx proxy read timeout should not be less than the GUnicorn timeout. If it is, this # can result in upstream time out errors. if self._gunicorn_timeout_seconds > self._nginx_proxy_read_timeout_seconds: log.info( "GUnicorn timeout was higher than Nginx proxy read timeout." " Setting Nginx proxy read timeout from {} seconds to {} seconds" " to match GUnicorn timeout.".format( self._nginx_proxy_read_timeout_seconds, self._gunicorn_timeout_seconds ) ) self._nginx_proxy_read_timeout_seconds = self._gunicorn_timeout_seconds if os.environ.get("OMP_NUM_THREADS") is None: os.environ["OMP_NUM_THREADS"] = "1" if _enable_multi_model_endpoint not in ["true", "false"]: raise ValueError("SAGEMAKER_MULTI_MODEL must be 'true' or 'false'") self._tfs_enable_multi_model_endpoint = _enable_multi_model_endpoint == "true" self._need_python_service() log.info("PYTHON SERVICE: {}".format(str(self._enable_python_service))) if _enable_batching not in ["true", "false"]: raise ValueError("SAGEMAKER_TFS_ENABLE_BATCHING must be 'true' or 'false'") self._tfs_enable_batching = _enable_batching == "true" if _enable_multi_model_endpoint not in ["true", "false"]: raise ValueError("SAGEMAKER_MULTI_MODEL must be 'true' or 'false'") self._tfs_enable_multi_model_endpoint = _enable_multi_model_endpoint == "true" self._use_gunicorn = self._enable_python_service or self._tfs_enable_multi_model_endpoint if self._sagemaker_port_range is not None: parts = self._sagemaker_port_range.split("-") low = int(parts[0]) hi = int(parts[1]) self._tfs_grpc_ports = [] self._tfs_rest_ports = [] if low + 2 > hi: raise ValueError( "not enough ports available in SAGEMAKER_SAFE_PORT_RANGE ({})".format( self._sagemaker_port_range ) ) self._tfs_grpc_ports.append(str(low)) self._tfs_rest_ports.append(str(low + 1)) # concat selected ports respectively in order to pass them to python service self._tfs_grpc_concat_ports = self._concat_ports(self._tfs_grpc_ports) self._tfs_rest_concat_ports = self._concat_ports(self._tfs_rest_ports) else: # just use the standard default ports self._tfs_grpc_ports = ["9000"] self._tfs_rest_ports = ["8501"] # provide single concat port here for default case self._tfs_grpc_concat_ports = "9000" self._tfs_rest_concat_ports = "8501" # set environment variable for python service os.environ["TFS_GRPC_PORTS"] = self._tfs_grpc_concat_ports os.environ["TFS_REST_PORTS"] = self._tfs_rest_concat_ports def _need_python_service(self): if ( os.path.exists(INFERENCE_PATH) or os.path.exists(REQUIREMENTS_PATH) or os.path.exists(PYTHON_LIB_PATH) ): self._enable_python_service = True if os.environ.get("SAGEMAKER_MULTI_MODEL_UNIVERSAL_BUCKET") and os.environ.get( "SAGEMAKER_MULTI_MODEL_UNIVERSAL_PREFIX" ): self._enable_python_service = True def _concat_ports(self, ports): str_ports = [str(port) for port in ports] concat_str_ports = ",".join(str_ports) return concat_str_ports def _create_tfs_config(self): models = tfs_utils.find_models() if not models: raise ValueError("no SavedModel bundles found!") if self._tfs_default_model_name == "None": default_model = os.path.basename(models[0]) if default_model: self._tfs_default_model_name = default_model log.info("using default model name: {}".format(self._tfs_default_model_name)) else: log.info("no default model detected") # config (may) include duplicate 'config' keys, so we can't just dump a dict config = "model_config_list: {\n" for m in models: config += " config: {\n" config += " name: '{}'\n".format(os.path.basename(m)) config += " base_path: '{}'\n".format(m) config += " model_platform: 'tensorflow'\n" config += " model_version_policy: {\n" config += " specific: {\n" for version in tfs_utils.find_model_versions(m): config += " versions: {}\n".format(version) config += " }\n" config += " }\n" config += " }\n" config += "}\n" log.info("tensorflow serving model config: \n%s\n", config) with open(self._tfs_config_path, "w", encoding="utf8") as f: f.write(config) def _setup_gunicorn(self): python_path_content = [] python_path_option = "" bucket = os.environ.get("SAGEMAKER_MULTI_MODEL_UNIVERSAL_BUCKET", None) prefix = os.environ.get("SAGEMAKER_MULTI_MODEL_UNIVERSAL_PREFIX", None) if not os.path.exists(CODE_DIR) and bucket and prefix: self._download_scripts(bucket, prefix) if self._enable_python_service: lib_path_exists = os.path.exists(PYTHON_LIB_PATH) requirements_exists = os.path.exists(REQUIREMENTS_PATH) python_path_content = ["/opt/ml/model/code"] python_path_option = "--pythonpath " if lib_path_exists: python_path_content.append(PYTHON_LIB_PATH) if requirements_exists: if lib_path_exists: log.warning( "loading modules in '{}', ignoring requirements.txt".format(PYTHON_LIB_PATH) ) else: log.info("installing packages from requirements.txt...") pip_install_cmd = "pip3 install -r {}".format(REQUIREMENTS_PATH) try: subprocess.check_call(pip_install_cmd.split()) except subprocess.CalledProcessError: log.error("failed to install required packages, exiting.") self._stop() raise ChildProcessError("failed to install required packages.") gunicorn_command = ( "gunicorn -b unix:/tmp/gunicorn.sock -k {} --chdir /sagemaker " "--workers {} --threads {} --log-level {} --timeout {} " "{}{} -e TFS_GRPC_PORTS={} -e TFS_REST_PORTS={} " "-e SAGEMAKER_MULTI_MODEL={} -e SAGEMAKER_SAFE_PORT_RANGE={} " "-e SAGEMAKER_TFS_WAIT_TIME_SECONDS={} " "-e SAGEMAKER_TFS_INTER_OP_PARALLELISM={} " "-e SAGEMAKER_TFS_INTRA_OP_PARALLELISM={} " "python_service:app" ).format( self._gunicorn_worker_class, self._gunicorn_workers, self._gunicorn_threads, self._gunicorn_loglevel, self._gunicorn_timeout_seconds, python_path_option, ",".join(python_path_content), self._tfs_grpc_concat_ports, self._tfs_rest_concat_ports, self._tfs_enable_multi_model_endpoint, self._sagemaker_port_range, self._tfs_wait_time_seconds, self._tfs_inter_op_parallelism, self._tfs_intra_op_parallelism, ) log.info("gunicorn command: {}".format(gunicorn_command)) self._gunicorn_command = gunicorn_command def _download_scripts(self, bucket, prefix): log.info("checking boto session region ...") boto_session = boto3.session.Session() boto_region = boto_session.region_name if boto_region in ("us-iso-east-1", "us-gov-west-1"): raise ValueError("Universal scripts is not supported in us-iso-east-1 or us-gov-west-1") log.info("downloading universal scripts ...") client = boto3.client("s3") resource = boto3.resource("s3") # download files paginator = client.get_paginator("list_objects") for result in paginator.paginate(Bucket=bucket, Delimiter="/", Prefix=prefix): for file in result.get("Contents", []): destination = os.path.join(CODE_DIR, file.get("Key").split("/")[-1]) if not os.path.exists(os.path.dirname(destination)): os.makedirs(os.path.dirname(destination)) resource.meta.client.download_file(bucket, file.get("Key"), destination) def _create_nginx_tfs_upstream(self): indentation = " " tfs_upstream = "" for port in self._tfs_rest_ports: tfs_upstream += "{}server localhost:{};\n".format(indentation, port) tfs_upstream = tfs_upstream[len(indentation) : -2] return tfs_upstream def _create_nginx_config(self): template = self._read_nginx_template() pattern = re.compile(r"%(\w+)%") template_values = { "TFS_VERSION": self._tfs_version, "TFS_UPSTREAM": self._create_nginx_tfs_upstream(), "TFS_DEFAULT_MODEL_NAME": self._tfs_default_model_name, "NGINX_HTTP_PORT": self._nginx_http_port, "NGINX_LOG_LEVEL": self._nginx_loglevel, "FORWARD_PING_REQUESTS": GUNICORN_PING if self._use_gunicorn else JS_PING, "FORWARD_INVOCATION_REQUESTS": GUNICORN_INVOCATIONS if self._use_gunicorn else JS_INVOCATIONS, "PROXY_READ_TIMEOUT": str(self._nginx_proxy_read_timeout_seconds), } config = pattern.sub(lambda x: template_values[x.group(1)], template) log.info("nginx config: \n%s\n", config) with open("/sagemaker/nginx.conf", "w", encoding="utf8") as f: f.write(config) def _read_nginx_template(self): with open("/sagemaker/nginx.conf.template", "r", encoding="utf8") as f: template = f.read() if not template: raise ValueError("failed to read nginx.conf.template") return template def _start_tfs(self): self._log_version("tensorflow_model_server_neuron --version", "tensorflow version info:") p = self._start_single_tfs(0) self._tfs.append(p) def _start_gunicorn(self): self._log_version("gunicorn --version", "gunicorn version info:") env = os.environ.copy() env["TFS_DEFAULT_MODEL_NAME"] = self._tfs_default_model_name p = subprocess.Popen(self._gunicorn_command.split(), env=env) log.info("started gunicorn (pid: %d)", p.pid) self._gunicorn = p def _start_nginx(self): self._log_version("/usr/sbin/nginx -V", "nginx version info:") p = subprocess.Popen("/usr/sbin/nginx -c /sagemaker/nginx.conf".split()) log.info("started nginx (pid: %d)", p.pid) self._nginx = p def _log_version(self, command, message): try: output = ( subprocess.check_output(command.split(), stderr=subprocess.STDOUT) .decode("utf-8", "backslashreplace") .strip() ) log.info("{}\n{}".format(message, output)) except subprocess.CalledProcessError: log.warning("failed to run command: %s", command) def _stop(self, *args): # pylint: disable=W0613 self._state = "stopping" log.info("stopping services") try: os.kill(self._nginx.pid, signal.SIGQUIT) except OSError: pass try: if self._gunicorn: os.kill(self._gunicorn.pid, signal.SIGTERM) except OSError: pass try: for tfs in self._tfs: os.kill(tfs.pid, signal.SIGTERM) except OSError: pass self._state = "stopped" log.info("stopped") def _wait_for_gunicorn(self): while True: if os.path.exists("/tmp/gunicorn.sock"): log.info("gunicorn server is ready!") return def _wait_for_tfs(self): tfs_utils.wait_for_model( self._tfs_rest_ports[0], self._tfs_default_model_name, self._tfs_wait_time_seconds ) @contextmanager def _timeout(self, seconds): def _raise_timeout_error(signum, frame): raise TimeoutError("time out after {} seconds".format(seconds)) try: signal.signal(signal.SIGALRM, _raise_timeout_error) signal.alarm(seconds) yield finally: signal.alarm(0) def _is_tfs_process(self, pid): for p in self._tfs: if p.pid == pid: return True return False def _find_tfs_process(self, pid): for index, p in enumerate(self._tfs): if p.pid == pid: return index return None def _restart_single_tfs(self, pid): instance_id = self._find_tfs_process(pid) if instance_id is None: raise ValueError("Cannot find tfs with pid: {};".format(pid)) p = self._start_single_tfs(instance_id) self._tfs[instance_id] = p def _start_single_tfs(self, instance_id): cmd = tfs_utils.tfs_command( self._tfs_grpc_ports[instance_id], self._tfs_rest_ports[instance_id], self._tfs_config_path, self._tfs_enable_batching, self._tfs_batching_config_path, tfs_intra_op_parallelism=self._tfs_intra_op_parallelism, tfs_inter_op_parallelism=self._tfs_inter_op_parallelism, ) log.info("tensorflow serving command: {}".format(cmd)) # cpu and single gpu p = subprocess.Popen(cmd.split()) log.info("started tensorflow serving (pid: {})".format(p.pid)) return p def _monitor(self): while True: pid, status = os.wait() if self._state != "started": break if pid == self._nginx.pid: log.warning("unexpected nginx exit (status: {}). restarting.".format(status)) self._start_nginx() elif self._is_tfs_process(pid): log.warning( "unexpected tensorflow serving exit (status: {}). restarting.".format(status) ) try: self._restart_single_tfs(pid) except (ValueError, OSError) as error: log.error("Failed to restart tensorflow serving. {}".format(error)) elif self._gunicorn and pid == self._gunicorn.pid: log.warning("unexpected gunicorn exit (status: {}). restarting.".format(status)) self._start_gunicorn() def start(self): log.info("starting services") log.info("NEURONCORE_GROUP_SIZES {}".format(self._user_ncgs)) log.info("SAGEMAKER_GUNICORN_WORKERS {}".format(self._gunicorn_workers)) self._state = "starting" signal.signal(signal.SIGTERM, self._stop) if self._tfs_enable_batching: log.info("batching is enabled") tfs_utils.create_batching_config(self._tfs_batching_config_path) if self._tfs_enable_multi_model_endpoint: log.info("multi-model endpoint is enabled, TFS model servers will be started later") else: self._create_tfs_config() # Start TFS workers for each gunicorn worker for tf_worker_num in range(int(self._gunicorn_workers)): self._start_tfs() self._wait_for_tfs() print("all TFS PIDs {}".format(self._tfs)) self._create_nginx_config() if self._use_gunicorn: self._setup_gunicorn() self._start_gunicorn() # make sure gunicorn is up with self._timeout(seconds=self._gunicorn_timeout_seconds): self._wait_for_gunicorn() self._start_nginx() self._state = "started" self._monitor() self._stop() if __name__ == "__main__": ServiceManager().start()