coach.py

# Copyright (c) 2017 Intel Corporation
#
# 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.
#

import sys
sys.path.append('.')

import copy
from configparser import ConfigParser, Error
from rl_coach.core_types import EnvironmentSteps
import os
from rl_coach import logger
import traceback
from rl_coach.logger import screen, failed_imports
import argparse
import atexit
import time
import sys
import json
from rl_coach.base_parameters import Frameworks, VisualizationParameters, TaskParameters, DistributedTaskParameters
from multiprocessing import Process
from multiprocessing.managers import BaseManager
import subprocess
from rl_coach.graph_managers.graph_manager import HumanPlayScheduleParameters, GraphManager
from rl_coach.utils import list_all_presets, short_dynamic_import, get_open_port, SharedMemoryScratchPad, get_base_dir
from rl_coach.agents.human_agent import HumanAgentParameters
from rl_coach.graph_managers.basic_rl_graph_manager import BasicRLGraphManager
from rl_coach.environments.environment import SingleLevelSelection
from rl_coach.orchestrators.kubernetes_orchestrator import KubernetesParameters, Kubernetes, RunType, RunTypeParameters
from rl_coach.memories.backend.redis import RedisPubSubMemoryBackendParameters
from rl_coach.memories.backend.memory_impl import construct_memory_params
from rl_coach.data_stores.data_store import DataStoreParameters
from rl_coach.data_stores.s3_data_store import S3DataStoreParameters
from rl_coach.data_stores.data_store_impl import get_data_store, construct_data_store_params
from rl_coach.training_worker import training_worker
from rl_coach.rollout_worker import rollout_worker, wait_for_checkpoint


if len(set(failed_imports)) > 0:
    screen.warning("Warning: failed to import the following packages - {}".format(', '.join(set(failed_imports))))


def get_graph_manager_from_args(args: argparse.Namespace) -> 'GraphManager':
    """
    Return the graph manager according to the command line arguments given by the user
    :param args: the arguments given by the user
    :return: the updated graph manager
    """

    graph_manager = None

    # if a preset was given we will load the graph manager for the preset
    if args.preset is not None:
        graph_manager = short_dynamic_import(args.preset, ignore_module_case=True)

    # for human play we need to create a custom graph manager
    if args.play:
        env_params = short_dynamic_import(args.environment_type, ignore_module_case=True)()
        env_params.human_control = True
        schedule_params = HumanPlayScheduleParameters()
        graph_manager = BasicRLGraphManager(HumanAgentParameters(), env_params, schedule_params, VisualizationParameters())

    # Set framework
    # Note: Some graph managers (e.g. HAC preset) create multiple agents and the attribute is called agents_params
    if hasattr(graph_manager, 'agent_params'):
        for network_parameters in graph_manager.agent_params.network_wrappers.values():
            network_parameters.framework = args.framework
    elif hasattr(graph_manager, 'agents_params'):
        for ap in graph_manager.agents_params:
            for network_parameters in ap.network_wrappers.values():
                network_parameters.framework = args.framework

    if args.level:
        if isinstance(graph_manager.env_params.level, SingleLevelSelection):
            graph_manager.env_params.level.select(args.level)
        else:
            graph_manager.env_params.level = args.level

    # set the seed for the environment
    if args.seed is not None:
        graph_manager.env_params.seed = args.seed

    # visualization
    graph_manager.visualization_parameters.dump_gifs = graph_manager.visualization_parameters.dump_gifs or args.dump_gifs
    graph_manager.visualization_parameters.dump_mp4 = graph_manager.visualization_parameters.dump_mp4 or args.dump_mp4
    graph_manager.visualization_parameters.render = args.render
    graph_manager.visualization_parameters.tensorboard = args.tensorboard
    graph_manager.visualization_parameters.print_networks_summary = args.print_networks_summary

    # update the custom parameters
    if args.custom_parameter is not None:
        unstripped_key_value_pairs = [pair.split('=') for pair in args.custom_parameter.split(';')]
        stripped_key_value_pairs = [tuple([pair[0].strip(), pair[1].strip()]) for pair in
                                    unstripped_key_value_pairs if len(pair) == 2]

        # load custom parameters into run_dict
        for key, value in stripped_key_value_pairs:
            exec("graph_manager.{}={}".format(key, value))

    return graph_manager


def display_all_presets_and_exit():
    # list available presets
    screen.log_title("Available Presets:")
    for preset in sorted(list_all_presets()):
        print(preset)
    sys.exit(0)


def expand_preset(preset):
    if preset.lower() in [p.lower() for p in list_all_presets()]:
        preset = "{}.py:graph_manager".format(os.path.join(get_base_dir(), 'presets', preset))
    else:
        preset = "{}".format(preset)
        # if a graph manager variable was not specified, try the default of :graph_manager
        if len(preset.split(":")) == 1:
            preset += ":graph_manager"

    # verify that the preset exists
    preset_path = preset.split(":")[0]
    if not os.path.exists(preset_path):
        screen.error("The given preset ({}) cannot be found.".format(preset))

    # verify that the preset can be instantiated
    try:
        short_dynamic_import(preset, ignore_module_case=True)
    except TypeError as e:
        traceback.print_exc()
        screen.error('Internal Error: ' + str(e) + "\n\nThe given preset ({}) cannot be instantiated."
                     .format(preset))

    return preset


def parse_arguments(parser: argparse.ArgumentParser) -> argparse.Namespace:
    """
    Parse the arguments that the user entered
    :param parser: the argparse command line parser
    :return: the parsed arguments
    """
    args = parser.parse_args()

    # if no arg is given
    if len(sys.argv) == 1:
        parser.print_help()
        sys.exit(0)

    # list available presets
    if args.list:
        display_all_presets_and_exit()

    # Read args from config file for distributed Coach.
    if args.distributed_coach and args.distributed_coach_run_type == RunType.ORCHESTRATOR:
        coach_config = ConfigParser({
            'image': '',
            'memory_backend': 'redispubsub',
            'data_store': 's3',
            's3_end_point': 's3.amazonaws.com',
            's3_bucket_name': '',
            's3_creds_file': ''
        })
        try:
            coach_config.read(args.distributed_coach_config_path)
            args.image = coach_config.get('coach', 'image')
            args.memory_backend = coach_config.get('coach', 'memory_backend')
            args.data_store = coach_config.get('coach', 'data_store')
            args.s3_end_point = coach_config.get('coach', 's3_end_point')
            args.s3_bucket_name = coach_config.get('coach', 's3_bucket_name')
            args.s3_creds_file = coach_config.get('coach', 's3_creds_file')
        except Error as e:
            screen.error("Error when reading distributed Coach config file: {}".format(e))

        if args.image == '':
            screen.error("Image cannot be empty.")

        data_store_choices = ['s3']
        if args.data_store not in data_store_choices:
            screen.warning("{} data store is unsupported.".format(args.data_store))
            screen.error("Supported data stores are {}.".format(data_store_choices))

        memory_backend_choices = ['redispubsub']
        if args.memory_backend not in memory_backend_choices:
            screen.warning("{} memory backend is not supported.".format(args.memory_backend))
            screen.error("Supported memory backends are {}.".format(memory_backend_choices))

        if args.s3_bucket_name == '':
            screen.error("S3 bucket name cannot be empty.")

        if args.s3_creds_file == '':
            args.s3_creds_file = None

    if args.play and args.distributed_coach:
        screen.error("Playing is not supported in distributed Coach.")

    # replace a short preset name with the full path
    if args.preset is not None:
        args.preset = expand_preset(args.preset)

    # validate the checkpoints args
    if args.checkpoint_restore_dir is not None and not os.path.exists(args.checkpoint_restore_dir):
        screen.error("The requested checkpoint folder to load from does not exist.")

    # no preset was given. check if the user requested to play some environment on its own
    if args.preset is None and args.play and not args.environment_type:
        screen.error('When no preset is given for Coach to run, and the user requests human control over '
                     'the environment, the user is expected to input the desired environment_type and level.'
                     '\nAt least one of these parameters was not given.')
    elif args.preset and args.play:
        screen.error("Both the --preset and the --play flags were set. These flags can not be used together. "
                     "For human control, please use the --play flag together with the environment type flag (-et)")
    elif args.preset is None and not args.play:
        screen.error("Please choose a preset using the -p flag or use the --play flag together with choosing an "
                     "environment type (-et) in order to play the game.")

    # get experiment name and path
    args.experiment_name = logger.get_experiment_name(args.experiment_name)
    args.experiment_path = logger.get_experiment_path(args.experiment_name)

    if args.play and args.num_workers > 1:
        screen.warning("Playing the game as a human is only available with a single worker. "
                       "The number of workers will be reduced to 1")
        args.num_workers = 1

    args.framework = Frameworks[args.framework.lower()]

    # checkpoints
    args.checkpoint_save_dir = os.path.join(args.experiment_path, 'checkpoint') if args.checkpoint_save_secs is not None else None

    if args.export_onnx_graph and not args.checkpoint_save_secs:
        screen.warning("Exporting ONNX graphs requires setting the --checkpoint_save_secs flag. "
                       "The --export_onnx_graph will have no effect.")

    return args


def add_items_to_dict(target_dict, source_dict):
    updated_task_parameters = copy.copy(source_dict)
    updated_task_parameters.update(target_dict)
    return updated_task_parameters


def open_dashboard(experiment_path):
    """
    open X11 based dashboard in a new process (nonblocking)
    """
    dashboard_path = 'python {}/dashboard.py'.format(get_base_dir())
    cmd = "{} --experiment_dir {}".format(dashboard_path, experiment_path)
    screen.log_title("Opening dashboard - experiment path: {}".format(experiment_path))
    # subprocess.Popen(cmd, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL, shell=True, executable="/bin/bash")
    subprocess.Popen(cmd, shell=True, executable="/bin/bash")


def start_graph(graph_manager: 'GraphManager', task_parameters: 'TaskParameters'):
    graph_manager.create_graph(task_parameters)

    # let the adventure begin
    if task_parameters.evaluate_only:
        graph_manager.evaluate(EnvironmentSteps(sys.maxsize), keep_networks_in_sync=True)
    else:
        graph_manager.improve()


def handle_distributed_coach_tasks(graph_manager, args):
        ckpt_inside_container = "/checkpoint"

        memory_backend_params = None
        if args.memory_backend_params:
            memory_backend_params = json.loads(args.memory_backend_params)
            memory_backend_params['run_type'] = str(args.distributed_coach_run_type)
            graph_manager.agent_params.memory.register_var('memory_backend_params', construct_memory_params(memory_backend_params))

        data_store_params = None
        if args.data_store_params:
            data_store_params = construct_data_store_params(json.loads(args.data_store_params))
            data_store_params.checkpoint_dir = ckpt_inside_container
            graph_manager.data_store_params = data_store_params

        if args.distributed_coach_run_type == RunType.TRAINER:
            training_worker(
                graph_manager=graph_manager,
                checkpoint_dir=ckpt_inside_container
            )

        if args.distributed_coach_run_type == RunType.ROLLOUT_WORKER:
            data_store = None
            if args.data_store_params:
                data_store = get_data_store(data_store_params)
                wait_for_checkpoint(checkpoint_dir=ckpt_inside_container, data_store=data_store)

            rollout_worker(
                graph_manager=graph_manager,
                checkpoint_dir=ckpt_inside_container,
                data_store=data_store,
                num_workers=args.num_workers
            )


def handle_distributed_coach_orchestrator(graph_manager, args):
        ckpt_inside_container = "/checkpoint"
        rollout_command = ['python3', 'rl_coach/coach.py', '--distributed_coach_run_type', str(RunType.ROLLOUT_WORKER)] + sys.argv[1:]
        trainer_command = ['python3', 'rl_coach/coach.py', '--distributed_coach_run_type', str(RunType.TRAINER)] + sys.argv[1:]

        if '--experiment_name' not in rollout_command:
            rollout_command = rollout_command + ['--experiment_name', args.experiment_name]

        if '--experiment_name' not in trainer_command:
            trainer_command = trainer_command + ['--experiment_name', args.experiment_name]

        memory_backend_params = None
        if args.memory_backend == "redispubsub":
            memory_backend_params = RedisPubSubMemoryBackendParameters()

        ds_params_instance = None
        if args.data_store == "s3":
            ds_params = DataStoreParameters("s3", "", "")
            ds_params_instance = S3DataStoreParameters(ds_params=ds_params, end_point=args.s3_end_point, bucket_name=args.s3_bucket_name,
                                                       creds_file=args.s3_creds_file, checkpoint_dir=ckpt_inside_container)

        worker_run_type_params = RunTypeParameters(args.image, rollout_command, run_type=str(RunType.ROLLOUT_WORKER), num_replicas=args.num_workers)
        trainer_run_type_params = RunTypeParameters(args.image, trainer_command, run_type=str(RunType.TRAINER))

        orchestration_params = KubernetesParameters([worker_run_type_params, trainer_run_type_params],
                                                    kubeconfig='~/.kube/config',
                                                    memory_backend_parameters=memory_backend_params,
                                                    data_store_params=ds_params_instance)
        orchestrator = Kubernetes(orchestration_params)
        if not orchestrator.setup():
            print("Could not setup.")
            return

        if orchestrator.deploy_trainer():
            print("Successfully deployed trainer.")
        else:
            print("Could not deploy trainer.")
            return

        if orchestrator.deploy_worker():
            print("Successfully deployed rollout worker(s).")
        else:
            print("Could not deploy rollout worker(s).")
            return

        try:
            orchestrator.trainer_logs()
        except KeyboardInterrupt:
            pass

        orchestrator.undeploy()


def main():
    parser = argparse.ArgumentParser()
    parser.add_argument('-p', '--preset',
                        help="(string) Name of a preset to run (class name from the 'presets' directory.)",
                        default=None,
                        type=str)
    parser.add_argument('-l', '--list',
                        help="(flag) List all available presets",
                        action='store_true')
    parser.add_argument('-e', '--experiment_name',
                        help="(string) Experiment name to be used to store the results.",
                        default='',
                        type=str)
    parser.add_argument('-r', '--render',
                        help="(flag) Render environment",
                        action='store_true')
    parser.add_argument('-f', '--framework',
                        help="(string) Neural network framework. Available values: tensorflow",
                        default='tensorflow',
                        type=str)
    parser.add_argument('-n', '--num_workers',
                        help="(int) Number of workers for multi-process based agents, e.g. A3C",
                        default=1,
                        type=int)
    parser.add_argument('-c', '--use_cpu',
                        help="(flag) Use only the cpu for training. If a GPU is not available, this flag will have no "
                             "effect and the CPU will be used either way.",
                        action='store_true')
    parser.add_argument('-ew', '--evaluation_worker',
                        help="(int) If multiple workers are used, add an evaluation worker as well which will "
                             "evaluate asynchronously and independently during the training. NOTE: this worker will "
                             "ignore the evaluation settings in the preset's ScheduleParams.",
                        action='store_true')
    parser.add_argument('--play',
                        help="(flag) Play as a human by controlling the game with the keyboard. "
                             "This option will save a replay buffer with the game play.",
                        action='store_true')
    parser.add_argument('--evaluate',
                        help="(flag) Run evaluation only. This is a convenient way to disable "
                             "training in order to evaluate an existing checkpoint.",
                        action='store_true')
    parser.add_argument('-v', '--verbosity',
                        help="(flag) Sets the verbosity level of Coach print outs. Can be either low or high.",
                        default="low",
                        type=str)
    parser.add_argument('-tfv', '--tf_verbosity',
                        help="(flag) TensorFlow verbosity level",
                        default=3,
                        type=int)
    parser.add_argument('--nocolor',
                        help="(flag) Turn off color-codes in screen logging.  Ascii text only",
                        action='store_true')
    parser.add_argument('-s', '--checkpoint_save_secs',
                        help="(int) Time in seconds between saving checkpoints of the model.",
                        default=None,
                        type=int)
    parser.add_argument('-crd', '--checkpoint_restore_dir',
                        help='(string) Path to a folder containing a checkpoint to restore the model from.',
                        type=str)
    parser.add_argument('-dg', '--dump_gifs',
                        help="(flag) Enable the gif saving functionality.",
                        action='store_true')
    parser.add_argument('-dm', '--dump_mp4',
                        help="(flag) Enable the mp4 saving functionality.",
                        action='store_true')
    parser.add_argument('-et', '--environment_type',
                        help="(string) Choose an environment type class to override on top of the selected preset.",
                        default=None,
                        type=str)
    parser.add_argument('-lvl', '--level',
                        help="(string) Choose the level that will be played in the environment that was selected."
                             "This value will override the level parameter in the environment class."
                        ,
                        default=None,
                        type=str)
    parser.add_argument('-cp', '--custom_parameter',
                        help="(string) Semicolon separated parameters used to override specific parameters on top of"
                             " the selected preset (or on top of the command-line assembled one). "
                             "Whenever a parameter value is a string, it should be inputted as '\\\"string\\\"'. "
                             "For ex.: "
                             "\"visualization.render=False; num_training_iterations=500; optimizer='rmsprop'\"",
                        default=None,
                        type=str)
    parser.add_argument('--print_networks_summary',
                        help="(flag) Print network summary to stdout",
                        action='store_true')
    parser.add_argument('-tb', '--tensorboard',
                        help="(flag) When using the TensorFlow backend, enable TensorBoard log dumps. ",
                        action='store_true')
    parser.add_argument('-ns', '--no_summary',
                        help="(flag) Prevent Coach from printing a summary and asking questions at the end of runs",
                        action='store_true')
    parser.add_argument('-d', '--open_dashboard',
                        help="(flag) Open dashboard with the experiment when the run starts",
                        action='store_true')
    parser.add_argument('--seed',
                        help="(int) A seed to use for running the experiment",
                        default=None,
                        type=int)
    parser.add_argument('-onnx', '--export_onnx_graph',
                        help="(flag) Export the ONNX graph to the experiment directory. "
                             "This will have effect only if the --checkpoint_save_secs flag is used in order to store "
                             "checkpoints, since the weights checkpoint are needed for the ONNX graph. "
                             "Keep in mind that this can cause major overhead on the experiment. "
                             "Exporting ONNX graphs requires manually installing the tf2onnx package "
                             "(https://github.com/onnx/tensorflow-onnx).",
                        action='store_true')
    parser.add_argument('-dc', '--distributed_coach',
                        help="(flag) Use distributed Coach.",
                        action='store_true')
    parser.add_argument('-dcp', '--distributed_coach_config_path',
                        help="(string) Path to config file when using distributed rollout workers."
                             "Only distributed Coach parameters should be provided through this config file."
                             "Rest of the parameters are provided using Coach command line options."
                             "Used only with --distributed_coach flag."
                             "Ignored if --distributed_coach flag is not used.",
                        type=str)
    parser.add_argument('--memory_backend_params',
                        help=argparse.SUPPRESS,
                        type=str)
    parser.add_argument('--data_store_params',
                        help=argparse.SUPPRESS,
                        type=str)
    parser.add_argument('--distributed_coach_run_type',
                        help=argparse.SUPPRESS,
                        type=RunType,
                        default=RunType.ORCHESTRATOR,
                        choices=list(RunType))

    args = parse_arguments(parser)

    if args.nocolor:
        screen.set_use_colors(False)

    graph_manager = get_graph_manager_from_args(args)

    if args.distributed_coach and not graph_manager.agent_params.algorithm.distributed_coach_synchronization_type:
        screen.error("{} preset is not supported using distributed Coach.".format(args.preset))

    # Intel optimized TF seems to run significantly faster when limiting to a single OMP thread.
    # This will not affect GPU runs.
    os.environ["OMP_NUM_THREADS"] = "1"

    # turn TF debug prints off
    if args.framework == Frameworks.tensorflow:
        os.environ['TF_CPP_MIN_LOG_LEVEL'] = str(args.tf_verbosity)

    # turn off the summary at the end of the run if necessary
    if not args.no_summary and not args.distributed_coach:
        atexit.register(logger.summarize_experiment)
        screen.change_terminal_title(args.experiment_name)

    # open dashboard
    if args.open_dashboard:
        open_dashboard(args.experiment_path)

    if args.distributed_coach and args.distributed_coach_run_type != RunType.ORCHESTRATOR:
        handle_distributed_coach_tasks(graph_manager, args)
        return

    if args.distributed_coach and args.distributed_coach_run_type == RunType.ORCHESTRATOR:
        handle_distributed_coach_orchestrator(graph_manager, args)
        return

    # Single-threaded runs
    if args.num_workers == 1:
        # Start the training or evaluation
        task_parameters = TaskParameters(
            framework_type=args.framework,
            evaluate_only=args.evaluate,
            experiment_path=args.experiment_path,
            seed=args.seed,
            use_cpu=args.use_cpu,
            checkpoint_save_secs=args.checkpoint_save_secs,
            checkpoint_restore_dir=args.checkpoint_restore_dir,
            checkpoint_save_dir=args.checkpoint_save_dir
        )

        start_graph(graph_manager=graph_manager, task_parameters=task_parameters)

    # Multi-threaded runs
    else:
        total_tasks = args.num_workers
        if args.evaluation_worker:
            total_tasks += 1

        ps_hosts = "localhost:{}".format(get_open_port())
        worker_hosts = ",".join(["localhost:{}".format(get_open_port()) for i in range(total_tasks)])

        # Shared memory
        class CommManager(BaseManager):
            pass
        CommManager.register('SharedMemoryScratchPad', SharedMemoryScratchPad, exposed=['add', 'get', 'internal_call'])
        comm_manager = CommManager()
        comm_manager.start()
        shared_memory_scratchpad = comm_manager.SharedMemoryScratchPad()

        def start_distributed_task(job_type, task_index, evaluation_worker=False,
                                   shared_memory_scratchpad=shared_memory_scratchpad):
            task_parameters = DistributedTaskParameters(
                framework_type=args.framework,
                parameters_server_hosts=ps_hosts,
                worker_hosts=worker_hosts,
                job_type=job_type,
                task_index=task_index,
                evaluate_only=evaluation_worker,
                use_cpu=args.use_cpu,
                num_tasks=total_tasks,  # training tasks + 1 evaluation task
                num_training_tasks=args.num_workers,
                experiment_path=args.experiment_path,
                shared_memory_scratchpad=shared_memory_scratchpad,
                seed=args.seed+task_index if args.seed is not None else None,  # each worker gets a different seed
                checkpoint_save_secs=args.checkpoint_save_secs,
                checkpoint_restore_dir=args.checkpoint_restore_dir,
                checkpoint_save_dir=args.checkpoint_save_dir
            )

            # we assume that only the evaluation workers are rendering
            graph_manager.visualization_parameters.render = args.render and evaluation_worker
            p = Process(target=start_graph, args=(graph_manager, task_parameters))
            # p.daemon = True
            p.start()
            return p

        # parameter server
        parameter_server = start_distributed_task("ps", 0)

        # training workers
        # wait a bit before spawning the non chief workers in order to make sure the session is already created
        workers = []
        workers.append(start_distributed_task("worker", 0))
        time.sleep(2)
        for task_index in range(1, args.num_workers):
            workers.append(start_distributed_task("worker", task_index))

        # evaluation worker
        if args.evaluation_worker or args.render:
            evaluation_worker = start_distributed_task("worker", args.num_workers, evaluation_worker=True)

        # wait for all workers
        [w.join() for w in workers]
        if args.evaluation_worker:
            evaluation_worker.terminate()


if __name__ == "__main__":
    main()