Source code for graphscope.client.session

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Copyright 2020 Alibaba Group Holding Limited. 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
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.

""" Manage sessions to the GraphScope coordinator.

import atexit
import base64
import contextlib
import copy
import json
import logging
import os
import random
import signal
import socket
import subprocess
import sys
import threading
import time
import warnings
from queue import Empty as EmptyQueue

    from kubernetes import config as kube_config
except ImportError:
    kube_config = None

import graphscope
from graphscope.client.rpc import GRPCClient
from graphscope.client.utils import CaptureKeyboardInterrupt
from graphscope.client.utils import GSLogger
from graphscope.client.utils import set_defaults
from graphscope.config import GSConfig as gs_config
from graphscope.deploy.kubernetes.cluster import KubernetesCluster
from graphscope.framework.errors import ConnectionError
from graphscope.framework.errors import FatalError
from graphscope.framework.errors import GRPCError
from graphscope.framework.errors import InteractiveEngineInternalError
from graphscope.framework.errors import InvalidArgumentError
from graphscope.framework.errors import K8sError
from graphscope.framework.errors import LearningEngineInternalError
from graphscope.framework.errors import check_argument
from graphscope.framework.operation import Operation
from graphscope.interactive.query import InteractiveQuery
from graphscope.interactive.query import InteractiveQueryStatus
from graphscope.proto import message_pb2
from graphscope.proto import op_def_pb2
from graphscope.proto import types_pb2

    import gscoordinator

    COORDINATOR_HOME = os.path.abspath(os.path.join(gscoordinator.__file__, "..", ".."))
except ModuleNotFoundError:
    # If gscoordinator is not installed, try to locate it by relative path,
    # which is strong related with the directory structure of GraphScope
    COORDINATOR_HOME = os.path.abspath(
        os.path.join(__file__, "..", "..", "..", "..", "coordinator")

DEFAULT_CONFIG_FILE = os.environ.get(
    "GS_CONFIG_PATH", os.path.expanduser("~/.graphscope/session.json")

_session_dict = {}

logger = logging.getLogger("graphscope")

[docs]class Session(object): """A class for interacting with GraphScope graph computation service cluster. A :class:`Session` object encapsulates the environment in which :class:`Operation` objects are executed/evaluated. A session may own resources. It is important to release these resources when they are no longer required. To do this, invoke the :meth:`close` method on the session. A Session can register itself as default session with :meth:`as_default`, and all operations after that will use the default session. Session deregister itself as a default session when closed. The following example demonstrates its usage: .. code:: python >>> import graphscope as gs >>> # use session object explicitly >>> s = gs.session() >>> g = s.load_from('xxx') >>> r = s.sssp(g, 4) >>> s.close() >>> # or use a session as default >>> s = gs.session().as_default() >>> g = gs.load_from('xxx') >>> r = gs.sssp(g, 4) >>> s.close() We support setup a service cluster and create a RPC session in following ways: - GraphScope graph computation service run in cluster managed by kubernetes. >>> s = graphscope.session() Also, :class:`Session` provides several keyword params for users to define the cluster. You may use the param :code:`k8s_gs_image` to specify the image for all engine pod, and param :code:`k8s_engine_cpu` or :code:`k8s_engine_mem` to specify the resources. More, you can find all params detail in :meth:`__init__` method. >>> s = graphscope.session( ... k8s_gs_image="", ... k8s_vineyard_cpu=0.1, ... k8s_vineyard_mem="256Mi", ... k8s_vineyard_shared_mem="4Gi", ... k8s_engine_cpu=0.1, ... k8s_engine_mem="256Mi") - or all params can be provided by a json configuration file or configuration dict. >>> s = graphscope.session(config='/tmp/config.json') >>> # Or >>> s = graphscope.session(config={'k8s_engine_cpu': 5, 'k8s_engine_mem': '5Gi'}) """
[docs] @set_defaults(gs_config) def __init__( self, config=None, num_workers=gs_config.num_workers, k8s_namespace=gs_config.k8s_namespace, k8s_service_type=gs_config.k8s_service_type, k8s_gs_image=gs_config.k8s_gs_image, k8s_etcd_image=gs_config.k8s_etcd_image, k8s_gie_graph_manager_image=gs_config.k8s_gie_graph_manager_image, k8s_zookeeper_image=gs_config.k8s_zookeeper_image, k8s_image_pull_policy=gs_config.k8s_image_pull_policy, k8s_image_pull_secrets=gs_config.k8s_image_pull_secrets, k8s_coordinator_cpu=gs_config.k8s_coordinator_cpu, k8s_coordinator_mem=gs_config.k8s_coordinator_mem, k8s_etcd_cpu=gs_config.k8s_etcd_cpu, k8s_etcd_mem=gs_config.k8s_etcd_mem, k8s_zookeeper_cpu=gs_config.k8s_zookeeper_cpu, k8s_zookeeper_mem=gs_config.k8s_zookeeper_mem, k8s_gie_graph_manager_cpu=gs_config.k8s_gie_graph_manager_cpu, k8s_gie_graph_manager_mem=gs_config.k8s_gie_graph_manager_mem, k8s_vineyard_cpu=gs_config.k8s_vineyard_cpu, k8s_vineyard_mem=gs_config.k8s_vineyard_mem, k8s_vineyard_shared_mem=gs_config.k8s_vineyard_shared_mem, k8s_engine_cpu=gs_config.k8s_engine_cpu, k8s_engine_mem=gs_config.k8s_engine_mem, k8s_volumes=gs_config.k8s_volumes, k8s_waiting_for_delete=gs_config.k8s_waiting_for_delete, timeout_seconds=gs_config.timeout_seconds, **kw ): """Construct a new GraphScope session. Args: config (dict or str, optional): The configuration dict or file about how to launch the GraphScope instance. For str, it will identify it as a path and read the configuration file to build a session if file exist. If not specified, the global default configuration :code:`DEFAULT_CONFIG_FILE` will be used, which get value of GS_CONFIG_PATH in environment. Note that it will overwrite explicit parameters. Defaults to None. num_workers (int, optional): The number of workers to launch GraphScope engine. Defaults to 2. k8s_namespace (str, optional): Contains the namespace to create all resource inside. If param missing, it will try to read namespace from kubernetes context, or a random namespace will be created and deleted if namespace not exist. Defaults to None. k8s_service_type (str, optional): Type determines how the GraphScope service is exposed. Valid options are NodePort, and LoadBalancer. Defaults to NodePort. k8s_gs_image (str, optional): The GraphScope engine's image. k8s_etcd_image (str, optional): The image of etcd, which used by vineyard. k8s_image_pull_policy (str, optional): Kubernetes image pull policy. Defaults to "IfNotPresent". k8s_image_pull_secrets (list[str], optional): A list of secret name used to authorize pull image. k8s_gie_graph_manager_image (str, optional): The GraphScope interactive engine's graph manager image. k8s_zookeeper_image (str, optional): The image of zookeeper, which used by GIE graph manager. k8s_vineyard_cpu (float, optional): Minimum number of CPU cores request for vineyard container. Defaults to 0.5. k8s_vineyard_mem (str, optional): Minimum number of memory request for vineyard container. Defaults to '512Mi'. k8s_vineyard_shared_mem (str, optional): Init size of vineyard shared memory. Defaults to '4Gi'. k8s_engine_cpu (float, optional): Minimum number of CPU cores request for engine container. Defaults to 0.5. k8s_engine_mem (str, optional): Minimum number of memory request for engine container. Defaults to '4Gi'. k8s_coordinator_cpu (float, optional): Minimum number of CPU cores request for coordinator pod. Defaults to 1.0. k8s_coordinator_mem (str, optional): Minimum number of memory request for coordinator pod. Defaults to '4Gi'. k8s_etcd_cpu (float, optional): Minimum number of CPU cores request for etcd pod. Defaults to 0.5. k8s_etcd_mem (str, optional): Minimum number of memory request for etcd pod. Defaults to '128Mi'. k8s_zookeeper_cpu (float, optional): Minimum number of CPU cores request for zookeeper container. Defaults to 0.5. k8s_zookeeper_mem (str, optional): Minimum number of memory request for zookeeper container. Defaults to '256Mi'. k8s_gie_graph_manager_cpu (float, optional): Minimum number of CPU cores request for graphmanager container. Defaults to 1.0. k8s_gie_graph_manager_mem (str, optional): Minimum number of memory request for graphmanager container. Defaults to '4Gi'. k8s_volumes (dict, optional): A dict of k8s volume which represents a directory containing data, accessible to the containers in a pod. Defaults to {}. For example, you can mount host path with: k8s_volumes = { "my-data": { "type": "hostPath", "field": { "path": "<path>", "type": "Directory" }, "mounts": [ { "mountPath": "<path1>" }, { "mountPath": "<path2>" } ] } } Or you can mount PVC with: k8s_volumes = { "my-data": { "type": "persistentVolumeClaim", "field": { "claimName": "your-pvc-name" }, "mounts": [ { "mountPath": "<path1>" } ] } } Also, you can mount a single volume with: k8s_volumes = { "my-data": { "type": "hostPath", "field": {xxx}, "mounts": { "mountPath": "<path1>" } } } timeout_seconds (int, optional): For waiting service ready (or waiting for delete if k8s_waiting_for_delete is True). Also, after seconds of client disconnect, coordinator will clean up this graphscope instance. Defaults to 600. k8s_waiting_for_delete (bool, optional): Waiting for service delete or not. Defaults to False. **kw (dict, optional): Other optional parameters will be put to :code:`**kw`. - k8s_minikube_vm_driver: Deprecated. - k8s_client_config (dict, optional): Provide configurable parameters for connecting to remote k8s, which strongly relies on the `kube_config.new_client_from_config` function. eg: {"config_file": "~/.kube/config", "context": None, "persist_config": True} config_file: Name of the kube-config file. context: set the active context. If is set to None, current_context from config file will be used. persist_config: If True, config file will be updated when changed(e.g GCP token refresh). - log_level: Deprecated. Move this param as a global configuration. Set via `graphscope.set_option(log_level='DEBUG')` - show_log: Deprecated. Move this param as a global configuration.Set via `graphscope.set_option(show_log=True)` Raises: TypeError: If the given argument combination is invalid and cannot be used to create a GraphScope session. """ num_workers = int(num_workers) self._config_params = {} self._accessable_params = ( "num_workers", "k8s_namespace", "k8s_service_type", "k8s_gs_image", "k8s_etcd_image", "k8s_image_pull_policy", "k8s_image_pull_secrets", "k8s_gie_graph_manager_image", "k8s_zookeeper_image", "k8s_coordinator_cpu", "k8s_coordinator_mem", "k8s_etcd_cpu", "k8s_etcd_mem", "k8s_zookeeper_cpu", "k8s_zookeeper_mem", "k8s_gie_graph_manager_cpu", "k8s_gie_graph_manager_mem", "k8s_vineyard_cpu", "k8s_vineyard_mem", "k8s_vineyard_shared_mem", "k8s_engine_cpu", "k8s_engine_mem", "k8s_volumes", "k8s_waiting_for_delete", "timeout_seconds", ) saved_locals = locals() for param in self._accessable_params: self._config_params[param] = saved_locals[param] # parse config, which should be a path to config file, or dict # config has highest priority if isinstance(config, dict): self._config_params.update(config) elif isinstance(config, str): self._load_config(config, False) elif DEFAULT_CONFIG_FILE: self._load_config(DEFAULT_CONFIG_FILE) # update other optional params self._config_params.update(kw) self._config_params["addr"] = kw.pop("addr", None) # Reserved keyword for local testing. run_on_local = kw.pop("run_on_local", False) if not run_on_local: self._config_params["enable_k8s"] = True else: self._run_on_local() # deprecated params handle if "show_log" in kw: warnings.warn( "The `show_log` parameter has been deprecated and has no effect, " "please use `graphscope.set_option(show_log=%s)` instead." % kw.pop("show_log", None), category=DeprecationWarning, ) if "log_level" in kw: warnings.warn( "The `log_level` parameter has been deprecated and has no effect, " "please use `graphscope.set_option(log_level=%r)` instead." % kw.pop("show_log", None), category=DeprecationWarning, ) # update k8s_client_config params self._config_params["k8s_client_config"] = kw.pop("k8s_client_config", {}) # There should be no more custom keyword arguments. if kw: raise ValueError("Not recognized value: ", list(kw.keys())) "Initializing graphscope session with parameters: %s", self._config_params ) self._closed = False # set _session_type in self._connect() self._session_type = types_pb2.INVALID_SESSION # need clean up when session exit self._proc = None self._endpoint = None self._k8s_cluster = None self._heartbeat_sending_thread = None self._grpc_client = None self._session_id = None # unique identifier across sessions # engine config: # # { # "experiment": "ON/OFF", # "vineyard_socket": "...", # "vineyard_rpc_endpoint": "..." # } self._engine_config = None # interactive instance related graph map self._interactive_instance_dict = {} # learning engine related graph map self._learning_instance_dict = {} self._default_session = None atexit.register(self.close) # create and connect session with CaptureKeyboardInterrupt(self.close): self._proc, self._endpoint = self._connect() self._disconnected = False # heartbeat self._heartbeat_interval_seconds = 5 self._heartbeat_sending_thread = threading.Thread( target=self._send_heartbeat, args=() ) self._heartbeat_sending_thread.daemon = True self._heartbeat_sending_thread.start()
def __repr__(self): return str( def __str__(self): return repr(self) @property def session_id(self): return self._session_id def _load_config(self, path, slient=True): config_path = os.path.expandvars(os.path.expanduser(path)) try: with open(config_path, "r") as f: data = json.load(f) self._config_params.update(data) except Exception as exp: # noqa if not slient: raise exp @property def info(self): """Show all resources info associated with session in json format.""" info = {} if self._closed: info["status"] = "closed" elif self._grpc_client is None or self._disconnected: info["status"] = "disconnected" else: info["status"] = "active" if self._session_type == types_pb2.K8S: info["type"] = "k8s" info["engine_hosts"] = ",".join(self._pod_name_list) info["namespace"] = self._config_params["k8s_namespace"] else: info["type"] = "hosts" if self._config_params["addr"] is not None: info["engine_hosts"] = self._engine_config["engine_hosts"] else: info["engine_hosts"] = ",".join(self._config_params["hosts"]) info["num_workers"] = self._config_params["num_workers"] info["coordinator_endpoint"] = self._endpoint info["engine_config"] = self._engine_config return info def _send_heartbeat(self): while not self._closed: if self._grpc_client: try: self._grpc_client.send_heartbeat() except GRPCError as exc: logger.warning(exc) self._disconnected = True else: self._disconnected = False time.sleep(self._heartbeat_interval_seconds)
[docs] def close(self): """Closes this session. This method frees all resources associated with the session. """ if self._closed: return self._closed = True self._endpoint = None self._deregister_default() if self._heartbeat_sending_thread: self._heartbeat_sending_thread.join( timeout=self._heartbeat_interval_seconds ) self._heartbeat_sending_thread = None self._disconnected = True # close all interactive instances for instance in self._interactive_instance_dict.values(): try: if instance is not None: instance.close() except InteractiveEngineInternalError: pass self._interactive_instance_dict.clear() # close all learning instances for instance in self._learning_instance_dict.values(): try: if instance is not None: instance.close() except LearningEngineInternalError: pass self._learning_instance_dict.clear() if self._grpc_client: self._grpc_client.close() self._grpc_client = None _session_dict.pop(self._session_id, None) # clean up if self._proc is not None: # coordinator's GRPCServer.wait_for_termination works for SIGINT (Ctrl-C) self._proc.send_signal(signal.SIGINT) self._proc.wait() self._proc = None if self._config_params["enable_k8s"]: if self._k8s_cluster: self._k8s_cluster.stop() self._pod_name_list = []
def _close_interactive_instance(self, instance): """Close a interactive instance.""" if self._grpc_client: self._grpc_client.close_interactive_engine(instance.object_id) self._interactive_instance_dict[instance.object_id] = None def _close_learning_instance(self, instance): """Close a learning instance.""" if self._grpc_client: self._grpc_client.close_learning_engine(instance.object_id) self._learning_instance_dict[instance.object_id] = None def __del__(self): # cleanly ignore all exceptions try: self.close() except Exception: # pylint: disable=broad-except pass
[docs] def as_default(self): """Obtain a context manager that make this object as default session. This method is used when a Session is constructed, which will immediately install self as a default session. Raises: ValueError: If default session exist in current context. Returns: A context manager using this session as the default session. """ if not _default_session_stack.is_cleared(): raise ValueError( "A default session is already active. You must explicitly call Session.close()." ) # session context manager self._default_session = default_session(self) self._default_session.__enter__()
def _deregister_default(self): """Remove self from the default session stack.""" if self._default_session: self._default_session.__exit__(None, None, None) self._default_session = None def run(self, fetch): """Run operations of `fetch`. Args: fetch: :class:`Operation` Raises: RuntimeError: Client disconnect to the service. Or run on a closed session. ValueError: If fetch is not a instance of :class:`Operation`. Or the fetch has been evaluated. InvalidArgumentError: Not recognized on output type. Returns: Different values for different output types of :class:`Operation` """ # prepare names to run and fetch if hasattr(fetch, "op"): fetch = fetch.op if not isinstance(fetch, Operation): raise ValueError("Expect a `Operation`") if fetch.output is not None: raise ValueError("The op <%s> are evaluated duplicated." % fetch.key) # convert to list to be compatible with rpc client method signature fetch_ops = [fetch] dag = op_def_pb2.DagDef() for op in fetch_ops: dag.op.extend([copy.deepcopy(op.as_op_def())]) if self._closed: raise RuntimeError("Attempted to use a closed Session.") if not self._grpc_client: raise RuntimeError("Session disconnected.") # execute the query try: response = except FatalError: self.close() raise check_argument( len(fetch_ops) == 1, "Cannot execute multiple ops at the same time" ) return self._parse_value(fetch_ops[0], response) def _parse_value(self, op, response: message_pb2.RunStepResponse): # attach an output to op, indicating the op is already run. op.set_output(response.metrics) # if loads a arrow property graph, will return {'object_id': xxxx} if op.output_types == types_pb2.GRAPH: return response.graph_def if op.output_types == types_pb2.APP: return response.result.decode("utf-8") if op.output_types in ( types_pb2.RESULTS, types_pb2.VINEYARD_TENSOR, types_pb2.VINEYARD_DATAFRAME, ): return response.result.decode("utf-8") if op.output_types in (types_pb2.TENSOR, types_pb2.DATAFRAME): return response.result else: raise InvalidArgumentError( "Not recognized output type: %s", op.output_types ) def _connect(self): if self._config_params["addr"] is not None: # try connect to exist coordinator self._session_type = types_pb2.HOSTS proc, endpoint = None, self._config_params["addr"] elif self._config_params["enable_k8s"]: if ( self._config_params["k8s_etcd_image"] is None or self._config_params["k8s_gs_image"] is None ): raise K8sError("None image found.") api_client = kube_config.new_client_from_config( **self._config_params["k8s_client_config"] ) proc = None self._session_type = types_pb2.K8S self._k8s_cluster = KubernetesCluster( api_client=api_client, namespace=self._config_params["k8s_namespace"], service_type=self._config_params["k8s_service_type"], num_workers=self._config_params["num_workers"], gs_image=self._config_params["k8s_gs_image"], etcd_image=self._config_params["k8s_etcd_image"], gie_graph_manager_image=self._config_params[ "k8s_gie_graph_manager_image" ], zookeeper_image=self._config_params["k8s_zookeeper_image"], image_pull_policy=self._config_params["k8s_image_pull_policy"], image_pull_secrets=self._config_params["k8s_image_pull_secrets"], vineyard_cpu=self._config_params["k8s_vineyard_cpu"], vineyard_mem=self._config_params["k8s_vineyard_mem"], vineyard_shared_mem=self._config_params["k8s_vineyard_shared_mem"], etcd_cpu=self._config_params["k8s_etcd_cpu"], etcd_mem=self._config_params["k8s_etcd_mem"], zookeeper_cpu=self._config_params["k8s_zookeeper_cpu"], zookeeper_mem=self._config_params["k8s_zookeeper_mem"], gie_graph_manager_cpu=self._config_params["k8s_gie_graph_manager_cpu"], gie_graph_manager_mem=self._config_params["k8s_gie_graph_manager_mem"], engine_cpu=self._config_params["k8s_engine_cpu"], engine_mem=self._config_params["k8s_engine_mem"], coordinator_cpu=float(self._config_params["k8s_coordinator_cpu"]), coordinator_mem=self._config_params["k8s_coordinator_mem"], volumes=self._config_params["k8s_volumes"], waiting_for_delete=self._config_params["k8s_waiting_for_delete"], timeout_seconds=self._config_params["timeout_seconds"], ) endpoint = self._k8s_cluster.start() if self._config_params["k8s_namespace"] is None: self._config_params["k8s_namespace"] = self._k8s_cluster.get_namespace() elif ( isinstance(self._config_params["hosts"], list) and len(self._config_params["hosts"]) != 0 and self._config_params["num_workers"] > 0 ): # lanuch coordinator with hosts proc, endpoint = _launch_coordinator_on_local(self._config_params) self._session_type = types_pb2.HOSTS else: raise RuntimeError("Session initialize failed.") # waiting service ready self._grpc_client = GRPCClient(endpoint) self._grpc_client.waiting_service_ready( timeout_seconds=self._config_params["timeout_seconds"], enable_k8s=self._config_params["enable_k8s"], ) # connect to rpc server try: ( self._session_id, self._engine_config, self._pod_name_list, ) = self._grpc_client.connect() _session_dict[self._session_id] = self except Exception: if proc is not None and proc.poll() is None: try: proc.terminate() except: # noqa: E722 pass raise return proc, endpoint
[docs] def get_config(self): """Get configuration of the session.""" return self._config_params
[docs] def load_from(self, *args, **kwargs): """Load a graph within the session. See more information in :meth:`graphscope.load_from`. """ with default_session(self): return graphscope.load_from(*args, **kwargs)
def _run_on_local(self): self._config_params["hosts"] = ["localhost"] self._config_params["port"] = None self._config_params["vineyard_socket"] = "" self._config_params["enable_k8s"] = False def _get_gl_handle(self, graph): """Dump a handler for GraphLearn for interaction. Fields in :code:`schema` are: + the name of node type or edge type + whether the graph is weighted graph + whether the graph is labeled graph + the number of int attributes + the number of float attributes + the number of string attributes An example of the graph handle: .. code:: python { "server": ",", "client_count": 1, "vineyard_socket": "/var/run/vineyard.sock", "vineyard_id": 13278328736, "node_schema": [ "user:false:false:10:0:0", "item:true:false:0:0:5" ], "edge_schema": [ "user:click:item:true:false:0:0:0", "user:buy:item:true:true:0:0:0", "item:similar:item:false:false:10:0:0" ], "node_attribute_types": { "person": { "age": "i", "name": "s", }, }, "edge_attribute_types": { "knows": { "weight": "f", }, }, } The handle can be decoded using: .. code:: python base64.b64decode(handle.encode('ascii')).decode('ascii') Note that the ports are selected from a range :code:`(8000, 9000)`. Args: graph (:class:`Graph`): A Property Graph. client_number (int): Number of client. Returns: str: Base64 encoded handle Raises: InvalidArgumentError: If the graph is not loaded, or graph_type isn't `ARROW_PROPERTY`. """ if not graph.loaded(): raise InvalidArgumentError("The graph has already been unloaded") if not graph.graph_type == types_pb2.ARROW_PROPERTY: raise InvalidArgumentError("The graph should be a property graph.") def group_property_types(props): weighted, labeled, i, f, s, attr_types = "false", "false", 0, 0, 0, {} for field_name, field_type in props.items(): if field_type in [types_pb2.STRING]: s += 1 attr_types[field_name] = "s" elif field_type in (types_pb2.FLOAT, types_pb2.DOUBLE): f += 1 attr_types[field_name] = "f" else: i += 1 attr_types[field_name] = "i" if field_name == "weight": weighted = "true" elif field_name == "label": labeled = "true" return weighted, labeled, i, f, s, attr_types node_schema, node_attribute_types = [], dict() for index, label in enumerate(graph.schema.vertex_labels): weighted, labeled, i, f, s, attr_types = group_property_types( graph.schema.vertex_properties[index] ) node_schema.append( "{}:{}:{}:{}:{}:{}".format(label, weighted, labeled, i, f, s) ) node_attribute_types[label] = attr_types edge_schema, edge_attribute_types = [], dict() for index, label in enumerate(graph.schema.edge_labels): weighted, labeled, i, f, s, attr_types = group_property_types( graph.schema.edge_properties[index] ) for rel in graph.schema.edge_relationships[index]: edge_schema.append( "{}:{}:{}:{}:{}:{}:{}:{}".format( rel[0], label, rel[1], weighted, labeled, i, f, s ) ) edge_attribute_types[label] = attr_types handle = { "hosts":["engine_hosts"], "client_count": 1, "vineyard_id": graph.vineyard_id, "vineyard_socket": self._engine_config["vineyard_socket"], "node_schema": node_schema, "edge_schema": edge_schema, "node_attribute_types": node_attribute_types, "edge_attribute_types": edge_attribute_types, } handle_json_string = json.dumps(handle) return base64.b64encode(handle_json_string.encode("utf-8")).decode("utf-8")
[docs] @set_defaults(gs_config) def gremlin(self, graph, engine_params=None): """Get a interactive engine handler to execute gremlin queries. Note that this method will be executed implicitly when a property graph created and cache a instance of InteractiveQuery in session if `initializing_interactive_engine` is True. If you want to create a new instance under the same graph by different params, you should close the instance first. .. code:: python >>> # close and recreate InteractiveQuery. >>> interactive_query = sess.gremlin(g) >>> interactive_query.close() >>> interactive_query = sess.gremlin(g, engine_params={"xxx":"xxx"}) Args: graph (:class:`Graph`): Use the graph to create interactive instance. engine_params (dict, optional): Configure startup parameters of interactive engine. You can also configure this param by `graphscope.set_option(engine_params={})`. See a list of configurable keys in `interactive_engine/deploy/docker/dockerfile/` Raises: InvalidArgumentError: :code:`graph` is not a property graph or unloaded. Returns: :class:`InteractiveQuery` """ # self._interactive_instance_dict[graph.vineyard_id] will be None if # InteractiveQuery closed if ( graph.vineyard_id in self._interactive_instance_dict and self._interactive_instance_dict[graph.vineyard_id] is not None ): interactive_query = self._interactive_instance_dict[graph.vineyard_id] if interactive_query.status == InteractiveQueryStatus.Running: return interactive_query elif interactive_query.status == InteractiveQueryStatus.Failed: raise InteractiveEngineInternalError(interactive_query.error_msg) else: # Initializing. # while True is ok, as the status is either running or failed eventually after timeout. while True: time.sleep(1) if interactive_query.status == InteractiveQueryStatus.Running: return interactive_query elif interactive_query.status == InteractiveQueryStatus.Failed: raise InteractiveEngineInternalError( interactive_query.error_msg ) if not graph.loaded(): raise InvalidArgumentError("The graph has already been unloaded") if not graph.graph_type == types_pb2.ARROW_PROPERTY: raise InvalidArgumentError("The graph should be a property graph.") interactive_query = InteractiveQuery(session=self, object_id=graph.vineyard_id) self._interactive_instance_dict[graph.vineyard_id] = interactive_query if engine_params is not None: engine_params = { str(key): str(value) for key, value in engine_params.items() } else: engine_params = {} try: response = self._grpc_client.create_interactive_engine( object_id=graph.vineyard_id, schema_path=graph.schema_path, gremlin_server_cpu=gs_config.k8s_gie_gremlin_server_cpu, gremlin_server_mem=gs_config.k8s_gie_gremlin_server_mem, engine_params=engine_params, ) except Exception as e: interactive_query.status = InteractiveQueryStatus.Failed interactive_query.error_msg = str(e) raise InteractiveEngineInternalError(str(e)) from e else: interactive_query.set_frontend( front_ip=response.frontend_host, front_port=response.frontend_port ) interactive_query.status = InteractiveQueryStatus.Running graph.attach_interactive_instance(interactive_query) return interactive_query
[docs] def learning(self, graph, nodes=None, edges=None, gen_labels=None): """Start a graph learning engine. Args: nodes (list): The node types that will be used for gnn training. edges (list): The edge types that will be used for gnn training. gen_labels (list): Extra node and edge labels on original graph for gnn training. Returns: `graphscope.learning.Graph`: An instance of `graphscope.learning.Graph` that could be feed to the learning engine. """ if ( graph.vineyard_id in self._learning_instance_dict and self._learning_instance_dict[graph.vineyard_id] is not None ): return self._learning_instance_dict[graph.vineyard_id] if sys.platform != "linux" and sys.platform != "linux2": raise RuntimeError( "The learning engine currently supports Linux only, doesn't support %s" % sys.platform ) if not graph.loaded(): raise InvalidArgumentError("The graph has already been unloaded") if not graph.graph_type == types_pb2.ARROW_PROPERTY: raise InvalidArgumentError("The graph should be a property graph.") from graphscope.learning.graph import Graph as LearningGraph handle = self._get_gl_handle(graph) config = LearningGraph.preprocess_args(handle, nodes, edges, gen_labels) config = base64.b64encode(json.dumps(config).encode("utf-8")).decode("utf-8") endpoints = self._grpc_client.create_learning_engine( graph.vineyard_id, handle, config ) handle = json.loads(base64.b64decode(handle.encode("utf-8")).decode("utf-8")) handle["server"] = endpoints handle["client_count"] = 1 learning_graph = LearningGraph(handle, config, graph.vineyard_id, self) self._learning_instance_dict[graph.vineyard_id] = learning_graph graph.attach_learning_instance(learning_graph) return learning_graph
session = Session def _is_port_in_use(port): """Check whether port of localhost is used Returns: bool True if port already in used. """ with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: return s.connect_ex(("localhost", port)) == 0 def _launch_coordinator_on_local(config_params): """Launch coordinator locally using specific configuration. Args: config_params (dict): Specific configurations, in which we will look at several specific keys: port: Port used to launch coordinator, use random port if None. num_workers: Workers number. hosts: Hosts name of workers. timeout_seconds: Wait until reached timeout. vineyard_socket: Vineyard socket path. Use default path if None. Returns: process: instance of Popen object. endpoint (str): The endpoint to connect to coordinator. """ port = config_params["port"] if port is None: # use random port port = random.randint(60801, 63801) while _is_port_in_use(port): port = random.randint(60801, 63801) else: # check port conflict if _is_port_in_use(port): raise ConnectionError("Port already used.") cmd = [ sys.executable, "-m", "gscoordinator", "--num_workers", "{}".format(str(config_params["num_workers"])), "--hosts", "{}".format(",".join(config_params["hosts"])), "--log_level", "{}".format(gs_config.log_level), "--timeout_seconds", "{}".format(config_params["timeout_seconds"]), "--port", "{}".format(str(port)), ] if config_params["vineyard_socket"]: cmd.extend(["--vineyard_socket", "{}".format(config_params["vineyard_socket"])])"Client is initializing coordinator.") env = os.environ.copy() env["PYTHONUNBUFFERED"] = "TRUE" process = subprocess.Popen( cmd, cwd=COORDINATOR_HOME, universal_newlines=True, encoding="utf-8", stdin=subprocess.DEVNULL, stdout=sys.stdout if gs_config.show_log else subprocess.DEVNULL, stderr=sys.stderr if gs_config.show_log else subprocess.DEVNULL, bufsize=1, env=env, ) return process, "localhost:%s" % port
[docs]def set_option(**kwargs): """Set the value of specified options. Available options: - num_workers - log_level - show_log - k8s_namespace - k8s_service_type - k8s_gs_image - k8s_etcd_image - k8s_gie_graph_manager_image - k8s_zookeeper_image - k8s_image_pull_policy - k8s_image_pull_secrets - k8s_coordinator_cpu - k8s_coordinator_mem - k8s_vineyard_cpu - k8s_vineyard_mem - k8s_vineyard_shared_mem - k8s_engine_cpu - k8s_engine_mem - k8s_waiting_for_delete - engine_params - initializing_interactive_engine - timeout_seconds Args: kwargs: dict kv pair of GraphScope config you want to set. Raises: ValueError: If no such option exists. Returns: None """ # check exists for k, v in kwargs.items(): if not hasattr(gs_config, k): raise ValueError("No such option {} exists.".format(k)) for k, v in kwargs.items(): setattr(gs_config, k, v) GSLogger.update()
[docs]def get_option(key): """Get the value of specified option. Available options: - num_workers - log_level - show_log - k8s_namespace - k8s_service_type - k8s_gs_image - k8s_etcd_image - k8s_gie_graph_manager_image - k8s_zookeeper_image - k8s_image_pull_policy - k8s_image_pull_secrets - k8s_coordinator_cpu - k8s_coordinator_mem - k8s_vineyard_cpu - k8s_vineyard_mem - k8s_vineyard_shared_mem - k8s_engine_cpu - k8s_engine_mem - k8s_waiting_for_delete - engine_params - initializing_interactive_engine - timeout_seconds Args: key: str Key of GraphScope config you want to get. Raises: ValueError: If no such option exists. Returns: result: the value of the option """ if hasattr(gs_config, key): return getattr(gs_config, key) else: raise ValueError("No such option {} exists.".format(key))
def default_session(session): """Python's :code:`with` handler for defining a default session. This function provides a means of registering a session for handling and code that need a default session calls. The :code:`with` keyword to specify that code invocations within the scope of a block should be executed by a particular session. Args: session: :class:`Session` The session to be installed as the default session. Returns: A context manager for the default session. """ return _default_session_stack.get_controller(session)
[docs]def get_default_session(): """Returns the default session for the current context. Raises: RuntimeError: Default session is not exist. Returns: The default :class:`Session`. """ return _default_session_stack.get_default()
def get_session_by_id(handle): """Return the session by handle.""" if handle not in _session_dict: raise ValueError("Session not exists.") return _session_dict.get(handle) class _DefaultSessionStack(object): """A stack of objects for providing implicit defaults.""" def __init__(self): super().__init__() self.stack = [] def get_default(self): if not self.stack: raise RuntimeError("No default session found.") return self.stack[-1] def reset(self): self.stack = [] def is_cleared(self): return not self.stack @contextlib.contextmanager def get_controller(self, default): """A context manager for manipulating a default stack.""" self.stack.append(default) try: yield default finally: # stack may be empty if reset() was called if self.stack: self.stack.remove(default) _default_session_stack = _DefaultSessionStack() # pylint: disable=protected-access