# Copyright (c) 2022 PaddlePaddle Authors. 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.

from .context import Context


def launch():
    """
    Paddle distribution training entry ``python -m paddle.distributed.launch``.

    Usage:
        .. code-block:: bash
            :name: code-block-bash1

            python -m paddle.distributed.launch [-h] [--master MASTER] [--rank RANK]
                   [--log_level LOG_LEVEL] [--nnodes NNODES]
                   [--nproc_per_node NPROC_PER_NODE] [--log_dir LOG_DIR]
                   [--run_mode RUN_MODE] [--job_id JOB_ID] [--devices DEVICES]
                   [--host HOST] [--servers SERVERS] [--trainers TRAINERS]
                   [--trainer_num TRAINER_NUM] [--server_num SERVER_NUM]
                   [--gloo_port GLOO_PORT] [--with_gloo WITH_GLOO]
                   [--max_restart MAX_RESTART] [--elastic_level ELASTIC_LEVEL]
                   [--elastic_timeout ELASTIC_TIMEOUT]
                   training_script ...


    Base Parameters:
        - ``--master``: The master/rendezvous server, support ``http://`` and ``etcd://``, default with ``http://``. e.g., ``--master=127.0.0.1:8080``. Default ``--master=None``.

        - ``--rank``: The rank of the node, can be auto assigned by master. Default ``--rank=-1``.

        - ``--log_level``: The log level to set for logging.setLevel which can be CRITICAL/ERROR/WARNING/INFO/DEBUG/NOTSET, case insensitive. Default ``--log_level=INFO``.

        - ``--nnodes``: The number of nodes for a distributed job, it can be a range in elastic mode, e.g., ``--nnodes=2:3``. Default ``--nnodes=1``.

        - ``--nproc_per_node``: The number of processes to launch on a node. In gpu training, it should be less or equal to the gpus number of you system.  e.g., ``--nproc_per_node=8``

        - ``--log_dir``: The path for each process's log. e.g., ``--log_dir=output_dir``. Default ``--log_dir=log``.

        - ``--run_mode``: The run mode of job, can be:collective/ps/ps-heter/rpc. e.g., ``--run_mode=ps``. Default ``--run_mode=collective``.

        - ``--job_id``: The job unique id, it affects the log files' name. e.g., ``--job_id=job1``. Default ``--job_id=default``.

        - ``--devices``: The selected accelerate devices on nodes, can be gpu/xpu etc.. e.g., ``--devices=0,1,2,3`` will launch four training processes each bound to one device.

        - ``training_script``: The full path to the single GPU training program/script to be launched in parallel, followed by all the arguments for the training script. e.g., ``training.py``

        - ``training_script_args``: The args of training_script. e.g., ``--lr=0.1``

    Collective Parameters:
        - ``--ips``: [DEPRECATED] Paddle cluster nodes ips, e.g., ``--ips=192.168.0.16,192.168.0.17``. Default ``--ips=127.0.0.1``.

    Parameter-Server Parameters:
        - ``--servers``: User defined servers ip:port, e.g., ``--servers="192.168.0.16:6170,192.168.0.17:6170"``

        - ``--trainers``: User defined trainers ip:port, e.g., ``--trainers="192.168.0.16:6171,192.168.0.16:6172,192.168.0.17:6171,192.168.0.17:6172"``

        - ``--workers``: [DEPRECATED] The same as trainers.

        - ``--trainer_num``: Number of trainers on each node, can be 0.

        - ``--worker_num``: [DEPRECATED] The same as trainer_num.

        - ``--server_num``: Number of servers on each node, can be 0.

        - ``--heter_workers``: User defined heter workers ip1:port1;ip2:port2, e.g., ``--heter_workers="192.168.0.16:6172;192.168.0.17:6172"``

        - ``--heter_worker_num``: Number of heter_workers in each stage (It recommend to set when in the emulated distributed environment using single node)

        - ``--heter_devices``: Type of heter_device in each stage

        - ``--gloo_port``: Gloo http Port. Default ``--gloo_port=6767``.

        - ``--with_gloo``: Using gloo or not. Default ``--with_gloo=0``.

    Elastic Parameters:
        - ``--max_restart``: The maximum restart times for an elastic job. Default ``--max_restart=3``.

        - ``--elastic_level``: The elastic level: -1: disable, 0: failed exit, peers hold, 1: internal restart. Default ``--elastic_level=-1``.

        - ``--elastic_timeout``: Seconds to wait before elastic job begin to train. Default ``--elastic_timeout=30``.

    IPU Parameters:
        IPU distributed launch only requires and allowes three arguments ``--devices``, ``training_script`` and ``training_script_args``.
        The ``--devices`` is the number of IPU devices. e.g., ``--devices=4`` will launch the training program with four IPU devices.
        The ``training_script`` is only allowed to set as ``ipu``.
        The ``training_script_args`` includes arguments required by IPU distributed launch and illustrated as below.
        ``Examples 10`` has provided a example of paddle.distributed.launch with IPUs.

        - ``--hosts``: The hosts for IPU distributd training. Each host is able to include multiple processes.

        - ``--nproc_per_host``: The number of processes launched per host. Each process is able to include multiple replicas.

        - ``--ipus_per_replica``: The number of IPUs requested per replica. Each replica is able to include multiple IPUs.

        - ``--ipu_partition``: The partition name of IPU devices.

        - ``--vipu_server``: The ip of the IPU device manager.

        - ``training_script``: The full path to the IPU distributed training program/script to be launched in parallel. e.g., ``training.py``.

        - ``training_script_args``: The args of the IPU distributed training program/script. e.g., ``--lr=0.1``.

    Returns:
        - ``None``

    Examples 0 (master, ip/port auto detection):
        .. code-block:: bash
            :name: code-block-example-bash0

            # For training on multi node, run the following command in one of the nodes

            python -m paddle.distributed.launch --nnodes 2 train.py

            # Then the following info will be print

            # Copy the following command to other nodes to run.
            # --------------------------------------------------------------------------------
            # python -m paddle.distributed.launch --master 10.0.0.1:38714 --nnodes 2 train.py
            # --------------------------------------------------------------------------------

            # Follow the instruction above and paste the command in other nodes can launch a multi nodes training job.

            # There are two ways to launch a job with the same command for multi nodes training
            # 1) using the following command in every nodes, make sure the ip is one of the training node and the port is available on that node
            # python -m paddle.distributed.launch --master 10.0.0.1:38714 --nnodes 2 train.py
            # 2) using the following command in every nodes with a independent etcd service
            # python -m paddle.distributed.launch --master etcd://10.0.0.1:2379 --nnodes 2 train.py

            # This functionality works will for both collective and ps mode and even with other arguments.


    Examples 1 (collective, single node):
        .. code-block:: bash
            :name: code-block-example-bash1

            # For training on single node using 4 gpus.

            python -m paddle.distributed.launch --devices=0,1,2,3 train.py --lr=0.01

    Examples 2 (collective, multi node):
        .. code-block:: bash
            :name: code-block-example-bash2

            # For training on multiple nodes, e.g., 192.168.0.16, 192.168.0.17

            # On 192.168.0.16:

            python -m paddle.distributed.launch --devices=0,1,2,3 --master=192.168.0.16:8090 train.py --lr=0.01

            # On 192.168.0.17:
            python -m paddle.distributed.launch --devices=0,1,2,3 --master=192.168.0.16:8090 train.py --lr=0.01

    Examples 3 (ps, cpu, single node):
        .. code-block:: bash
            :name: code-block-example-bash3

            # To simulate distributed environment using single node, e.g., 2 servers and 4 workers.

            python -m paddle.distributed.launch --server_num=2 --worker_num=4 train.py --lr=0.01

    Examples 4 (ps, cpu, multi node):
        .. code-block:: bash
            :name: code-block-example-bash4

            # For training on multiple nodes, e.g., 192.168.0.16, 192.168.0.17 where each node with 1 server and 2 workers.

            # On 192.168.0.16:

            python -m paddle.distributed.launch --servers="192.168.0.16:6170,192.168.0.17:6170" --workers="192.168.0.16:6171,192.168.0.16:6172,192.168.0.17:6171,192.168.0.17:6172" train.py --lr=0.01

            # On 192.168.0.17:

            python -m paddle.distributed.launch --servers="192.168.0.16:6170,192.168.0.17:6170" --workers="192.168.0.16:6171,192.168.0.16:6172,192.168.0.17:6171,192.168.0.17:6172" train.py --lr=0.01

            # Or with master, the following command run 2 server and 2 trainer on each node.

            python -m paddle.distributed.launch --master 192.168.0.16:9090 --server_num=2 --trainer_num=2 --nnodes 2 train.py


    Examples 5 (ps, gpu, single node):
        .. code-block:: bash
            :name: code-block-example-bash5

            # To simulate distributed environment using single node, e.g., 2 servers and 4 workers, each worker use single gpu.

            export CUDA_VISIBLE_DEVICES=0,1,2,3
            python -m paddle.distributed.launch --server_num=2 --worker_num=4 train.py --lr=0.01

    Examples 6 (ps, gpu, multi node):
        .. code-block:: bash
            :name: code-block-example-bash6

            # For training on multiple nodes, e.g., 192.168.0.16, 192.168.0.17 where each node with 1 server and 2 workers.

            # On 192.168.0.16:

            export CUDA_VISIBLE_DEVICES=0,1
            python -m paddle.distributed.launch --servers="192.168.0.16:6170,192.168.0.17:6170" --workers="192.168.0.16:6171,192.168.0.16:6172,192.168.0.17:6171,192.168.0.17:6172" train.py --lr=0.01

            # On 192.168.0.17:

            export CUDA_VISIBLE_DEVICES=0,1
            python -m paddle.distributed.launch --servers="192.168.0.16:6170,192.168.0.17:6170" --workers="192.168.0.16:6171,192.168.0.16:6172,192.168.0.17:6171,192.168.0.17:6172" train.py --lr=0.01

    Examples 7 (ps-heter, cpu + gpu, single node):
        .. code-block:: bash
            :name: code-block-example-bash7

            # To simulate distributed environment using single node, e.g., 2 servers and 4 workers, two workers use gpu, two workers use cpu.

            export CUDA_VISIBLE_DEVICES=0,1
            python -m paddle.distributed.launch --server_num=2 --worker_num=2 --heter_worker_num=2 train.py --lr=0.01

    Examples 8 (ps-heter, cpu + gpu, multi node):
        .. code-block:: bash
            :name: code-block-example-bash8

            # For training on multiple nodes, e.g., 192.168.0.16, 192.168.0.17 where each node with 1 server, 1 gpu worker, 1 cpu worker.

            # On 192.168.0.16:

            export CUDA_VISIBLE_DEVICES=0
            python -m paddle.distributed.launch --servers="192.168.0.16:6170,192.168.0.17:6170" --workers="192.168.0.16:6171,192.168.0.17:6171" --heter_workers="192.168.0.16:6172,192.168.0.17:6172" train.py --lr=0.01

            # On 192.168.0.17:

            export CUDA_VISIBLE_DEVICES=0
            python -m paddle.distributed.launch --servers="192.168.0.16:6170,192.168.0.17:6170" --workers="192.168.0.16:6171,192.168.0.17:6171" --heter_workers="192.168.0.16:6172,192.168.0.17:6172" train.py --lr=0.01

    Examples 9 (elastic):
        .. code-block:: bash
            :name: code-block-example-bash9

            # With the following command, the job will begin to run immediately if 4 nodes are ready,
            # or it will run after elastic_timeout if only 2 or 3 nodes ready
            python -m paddle.distributed.launch --master etcd://10.0.0.1:2379 --nnodes 2:4 train.py

            # once the number of nodes changes between 2:4 during training, the strategy holds

    Examples 10 (ipu):
        .. code-block:: bash
            :name: code-block-example-bash10

            # With the following command, the job will begin to run the distributhed program with IPUs
            # Require `devices` as the number of IPUs
            # Require `training_script` to be set as `ipu`
            # Require `training_script_args` as the arguments of IPU distributed training instead of the arguments of the training program/script
            # Please Check the `IPU Parameters` for details
            python -m paddle.distributed.launch --devices 4 ipu --hosts=localhost --nproc_per_host=2 --ipus_per_replica=1 --ipu_partition=pod16 --vipu_server=127.0.0.1 train.py

    Examples 11 (rpc, cpu, single node):
        .. code-block:: bash
            :name: code-block-example-bash11

            # Training on single node with two local servers
            python -m paddle.distributed.launch --master 127.0.0.1:8765 --nnodes 1 --nproc_per_node 2 --rank 0 --run_mode rpc train.py

    Examples 12 (rpc, cpu, multi node):
        .. code-block:: bash
            :name: code-block-example-bash12

            # For training on multiple nodes, e.g., 192.168.0.16, 192.168.0.17 where each node with 2 servers.

            # On 192.168.0.16

            python -m paddle.distributed.launch --master 192.168.0.16:8765 --nnodes 2 --nproc_per_node 2 --rank 0 --run_mode rpc train.py

            # On 192.168.0.17

            python -m paddle.distributed.launch --master 192.168.0.16:8765 --nnodes 2 --nproc_per_node 2 --rank 1 --run_mode rpc train.py

    """

    # initialize the context to run
    ctx = Context()

    if ctx.is_legacy_mode():
        # legacy mode
        from paddle.distributed.fleet import launch

        launch.launch()

    elif ctx.is_auto_tuner_mode():
        import copy
        import json
        import sys
        import time

        from ..auto_tuner.recorder import HistoryRecorder
        from ..auto_tuner.tuner import AutoTuner
        from ..auto_tuner.utils import gen_new_args, read_log
        from . import controllers

        start_time = time.time()
        # read user defined tuner config json
        try:
            with open(ctx.args.auto_tuner_json, "r") as f:
                tuner_cfg = json.load(f)
        except:
            raise ValueError("Please check your auto tuner json whether valid.")

        # copy training script args
        if ctx.args.training_script.endswith('.py'):
            entrypoint = [sys.executable, "-u", ctx.args.training_script]
        else:
            entrypoint = [ctx.args.training_script]
        entrypoint.extend(ctx.args.training_script_args)
        raw_args = copy.deepcopy(ctx.args.training_script_args)

        # get nodes and gpus from args
        if not ctx.args.devices:
            gpus_per_node = 8
        else:
            gpus_per_node = len(ctx.args.devices.split(","))
        nnodes = ctx.args.nnodes
        if isinstance(nnodes, str):
            nnodes = int(nnodes.split(":")[0])
        else:
            nnodes = int(nnodes)
        tuner_cfg["nodes"] = nnodes
        tuner_cfg["num_gpus"] = gpus_per_node * tuner_cfg["nodes"]

        if nnodes > 1:
            import etcd3

            assert "etcd://" in ctx.args.master
            master_ip, port = ctx.args.master.strip("etcd://").split(':')
            client = etcd3.client(host=master_ip, port=port)
            client.delete("best_cfg")

        # get max time per task run
        max_time_per_task = tuner_cfg.get("max_time_per_task", 1800)
        ctx.max_time_per_task = max_time_per_task

        # warmup
        warmup_time = (
            max_time_per_task
            if "warmup_time" not in tuner_cfg
            else tuner_cfg.get("warmup_time")
        )

        is_first_task = True
        # build history recorder
        recorder = HistoryRecorder()

        job_id = 0
        ctx.args.max_restart = -1
        raw_ctx = copy.deepcopy(ctx)

        # gbs search
        if (
            tuner_cfg.get('model_cfg', {}).get('global_batch_size', 'auto')
            == "auto"
        ):
            # adjust micron batch size until out of memory to get best global batch size
            gbs_tuner_cfg = copy.deepcopy(tuner_cfg)
            gbs_tuner_cfg["search_algo"] = "gbs"
            gbs_tuner = AutoTuner(gbs_tuner_cfg)

            gbs_cur_cfg = gbs_tuner.search_once()
            best_gbs = None
            while gbs_cur_cfg:
                ctx = copy.deepcopy(raw_ctx)
                log_dir = "GBSSearch/GBS{}_DP{}_MP{}_PP{}_Sharding_degree_{}_stage_{}_MBS_{}_Recompute_{}_granularity_{}".format(
                    gbs_cur_cfg["global_batch_size"],
                    gbs_cur_cfg["dp_degree"],
                    gbs_cur_cfg["mp_degree"],
                    gbs_cur_cfg["pp_degree"],
                    gbs_cur_cfg["sharding_degree"],
                    gbs_cur_cfg["sharding_stage"],
                    gbs_cur_cfg["micro_batch_size"],
                    gbs_cur_cfg["use_recompute"],
                    gbs_cur_cfg["recompute_granularity"],
                )
                ctx.args.log_dir = log_dir

                # every task has own job id
                job_id += 1
                task_job_id = "gbs_tuner_" + str(job_id)
                ctx.args.job_id = task_job_id

                # generate script args of task
                gbs_new_args = gen_new_args(
                    raw_args, gbs_cur_cfg, gbs_tuner_cfg
                )
                ctx.args.training_script_args = gbs_new_args

                # launch task
                ctx.logger.info(
                    "Launch task from auto tuner: job_id {}, log_dir {}, config {}".format(
                        task_job_id, log_dir, gbs_cur_cfg
                    )
                )
                c = controllers.init(ctx)
                c.run()

                # process generated result
                # TODO diffentiate out of memory and no loss(maybe over time)
                # TODO integragte memory and metric read
                metric, mem, err = read_log(
                    path=ctx.args.log_dir,
                    metric_file="workerlog.0",
                    target_metric=tuner_cfg["metric_cfg"]["name"],
                    memory_file=f"{ctx.args.job_id}.gpu.log",
                )

                if err & (1 << 0):
                    ctx.logger.warning(
                        f"Read metric failed for parameters: {log_dir}"
                    )
                    # for pruner use
                    gbs_cur_cfg['time'] = -1
                    gbs_cur_cfg[tuner_cfg['metric_cfg']['name']] = None
                    gbs_cur_cfg["max_mem_usage"] = mem

                if err & (1 << 1):
                    ctx.logger.warning(
                        f"Out of memory for parameters: {log_dir}"
                    )
                    # for pruner use
                    gbs_cur_cfg['time'] = -1
                    gbs_cur_cfg[tuner_cfg['metric_cfg']['name']] = None
                    gbs_cur_cfg["max_mem_usage"] = "OOM"

                # not err & (1 << 1): do not record memory usage when out of memory
                if err & (1 << 2) and not err & (1 << 1):
                    ctx.logger.warning(
                        f"Read memory usage failed for parameters: {log_dir}"
                    )
                    gbs_cur_cfg["max_mem_usage"] = None

                if not err:
                    # for pruner use
                    gbs_cur_cfg['time'] = metric
                    gbs_cur_cfg[tuner_cfg['metric_cfg']['name']] = metric
                    gbs_cur_cfg["max_mem_usage"] = mem

                if err & (1 << 0) or err & (1 << 1):
                    # no metric or out of memory, end gbs search
                    break

                # store and update args for next round
                gbs_cur_cfg["job_id"] = job_id
                best_gbs = gbs_cur_cfg["global_batch_size"]
                recorder.add_cfg(**gbs_cur_cfg)
                c.finalize(exit=False)
                recorder.store_history("./tuner_gbs_history.csv")

                # new cfgs for next round
                gbs_new_cfg = gbs_tuner.search_once()
                gbs_cur_cfg = copy.deepcopy(gbs_new_cfg)
                gbs_tuner.add_cfg(gbs_cur_cfg)

                # per task launch interval
                time.sleep(3)
            # prevent no valid global batch size found
            if best_gbs is None:
                raise ValueError(
                    "No valid global batch size found, check memory or valid search time. cur_tuner_cfg{}".format(
                        gbs_tuner_cfg
                    )
                )
            # set best global batch size to tuner cfg
            tuner_cfg["model_cfg"]["global_batch_size"] = best_gbs

            recorder.store_history("./tuner_gbs_history.csv")
            recorder.clean_history()

            end_time = time.time()
            ctx.logger.info(
                f"AtuoTuner for GBS search ends in {end_time-start_time}s."
            )
        # build AutoTuner to get new config
        auto_tuner = AutoTuner(tuner_cfg)
        cur_cfg = auto_tuner.search_once()
        auto_tuner.add_cfg(cur_cfg)

        while cur_cfg:
            ctx = copy.deepcopy(raw_ctx)
            if is_first_task:
                ctx.max_time_per_task = warmup_time
            is_first_task = False
            # auto tuner supports dp, mp, pp, micro batch size, sharding, recompute by default and every task has own log dir
            log_dir = "DP{}_MP{}_PP{}_Sharding_degree_{}_stage_{}_MBS_{}_Recompute_{}_granularity_{}".format(
                cur_cfg["dp_degree"],
                cur_cfg["mp_degree"],
                cur_cfg["pp_degree"],
                cur_cfg["sharding_degree"],
                cur_cfg["sharding_stage"],
                cur_cfg["micro_batch_size"],
                cur_cfg["use_recompute"],
                cur_cfg["recompute_granularity"],
            )

            ctx.args.log_dir = log_dir

            # every task has own job id
            job_id += 1
            task_job_id = "auto_tuner_" + str(job_id)
            ctx.args.job_id = task_job_id

            # generate script args of task
            new_args = gen_new_args(raw_args, cur_cfg, tuner_cfg)
            ctx.args.training_script_args = new_args

            # launch task
            ctx.logger.info(
                "Launch task from auto tuner: job_id {}, log_dir {}, config {}".format(
                    task_job_id, log_dir, cur_cfg
                )
            )
            c = controllers.init(ctx)
            c.run()

            # process generated result

            metric, mem, err = read_log(
                path=ctx.args.log_dir,
                metric_file="workerlog.0",
                target_metric=tuner_cfg["metric_cfg"]["name"],
                memory_file=f"{ctx.args.job_id}.gpu.log",
            )

            if err & (1 << 0):
                ctx.logger.warning(
                    f"Read metric failed for parameters: {log_dir}"
                )
                # for pruner use
                cur_cfg['time'] = -1
                cur_cfg[tuner_cfg['metric_cfg']['name']] = None
                cur_cfg["max_mem_usage"] = mem

            if err & (1 << 1):
                ctx.logger.warning(f"Out of memory for parameters: {log_dir}")
                # for pruner use
                cur_cfg['time'] = -1
                cur_cfg[tuner_cfg['metric_cfg']['name']] = None
                cur_cfg["max_mem_usage"] = "OOM"

            # not err & (1 << 1): do not record memory usage when out of memory
            if err & (1 << 2) and not err & (1 << 1):
                ctx.logger.warning(
                    f"Read memory usage failed for parameters: {log_dir}"
                )
                cur_cfg["max_mem_usage"] = None

            if not err:
                # for pruner use
                cur_cfg['time'] = metric
                cur_cfg[tuner_cfg['metric_cfg']['name']] = metric
                cur_cfg["max_mem_usage"] = mem

            # record history
            cur_cfg['job_id'] = job_id
            recorder.add_cfg(**cur_cfg)
            cur_best_cfgs, err = recorder.get_best(
                metric=tuner_cfg['metric_cfg']['name'],
                direction=tuner_cfg['metric_cfg']['OptimizationDirection'],
            )
            if not err:
                ctx.logger.info(f"Current best config: {cur_best_cfgs}")
                recorder.store_history(
                    ctx.args.auto_tuner_json.split(".")[0] + "_history.csv"
                )
            else:
                ctx.logger.info(
                    "Get best config failed. Currently there are no appropriate configs."
                )
            c.finalize(exit=False)

            # generate a new config
            new_cfg = auto_tuner.search_once()
            cur_cfg = copy.deepcopy(new_cfg)
            auto_tuner.add_cfg(cur_cfg)

            # per task launch interval
            time.sleep(3)
        recorder.store_history()

        # get best config to run
        best_cfg = None
        ctx = copy.deepcopy(raw_ctx)
        if nnodes > 1:
            import socket

            ip = None
            try:
                hostname = socket.gethostname()
                ip = socket.gethostbyname(socket.getfqdn(hostname))
            except:
                ip = '127.0.0.1'
            if ip == master_ip:
                best_cfg, err = recorder.get_best(
                    metric=tuner_cfg['metric_cfg']['name'],
                    direction=tuner_cfg['metric_cfg']['OptimizationDirection'],
                )
                if err:
                    raise ValueError(
                        "Get best config failed. Currently there are no appropriate configs."
                    )
                data = json.dumps(best_cfg)
                while not client.put("best_cfg", data):
                    time.sleep(1)
                    continue
            else:
                for i in range(10):
                    try:
                        data = client.get("best_cfg")[0].decode()
                        best_cfg = json.loads(data)
                    except Exception as e:
                        ctx.logger.warning(e)
                        time.sleep(2)
                    if best_cfg:
                        break
                assert best_cfg
        else:
            best_cfg, err = recorder.get_best(
                metric=tuner_cfg['metric_cfg']['name'],
                direction=tuner_cfg['metric_cfg']['OptimizationDirection'],
            )
            if err:
                raise ValueError(
                    "Get best config failed. Currently there are no appropriate configs."
                )
        assert best_cfg

        end_time = time.time()
        ctx.logger.info(f"AutoTuner ends in {end_time-start_time}s.")
        # launch best cfg
        new_args = gen_new_args(raw_args, best_cfg, tuner_cfg)
        ctx.run_best = True
        ctx.args.training_script_args = new_args
        ctx.args.job_id = "best_cfg"
        ctx.logger.info(f"Launch best cfg from auto tuner: {best_cfg}")
        ctx.args.log_dir = "best_cfg"
        # run best cfg
        c = controllers.init(ctx)
        c.run()
        c.finalize(exit=True)

    else:
        from . import controllers

        # initialize the selected controller
        c = controllers.init(ctx)

        # run the pods
        c.run()

        # manager or just wait pod
        c.finalize()


if __name__ == "__main__":
    launch()
