wehub-resource-sync
114 lines
3.7 KiB
Python
Executable File
114 lines
3.7 KiB
Python
Executable File
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# TODO: define distributed api under this directory,
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from . import metrics # noqa: F401
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from .base.distributed_strategy import DistributedStrategy
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from .base.role_maker import (
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PaddleCloudRoleMaker,
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Role,
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UserDefinedRoleMaker,
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)
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from .base.topology import CommunicateTopology, HybridCommunicateGroup
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from .base.util_factory import UtilBase
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from .data_generator.data_generator import (
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MultiSlotDataGenerator,
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MultiSlotStringDataGenerator,
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)
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from .dataset import ( # noqa: F401
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DatasetBase,
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FileInstantDataset,
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InMemoryDataset,
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QueueDataset,
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)
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from .fleet import Fleet
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from .model import distributed_model
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from .optimizer import distributed_optimizer
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from .scaler import distributed_scaler
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from .utils import log_util
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__all__ = [
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"CommunicateTopology",
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"UtilBase",
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"HybridCommunicateGroup",
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"MultiSlotStringDataGenerator",
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"UserDefinedRoleMaker",
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"DistributedStrategy",
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"Role",
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"MultiSlotDataGenerator",
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"PaddleCloudRoleMaker",
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"Fleet",
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]
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fleet = Fleet()
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_final_strategy = fleet._final_strategy
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_get_applied_meta_list = fleet._get_applied_meta_list
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_get_applied_graph_list = fleet._get_applied_graph_list
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init = fleet.init
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is_first_worker = fleet.is_first_worker
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worker_index = fleet.worker_index
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worker_num = fleet.worker_num
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node_num = fleet.node_num
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rank = fleet.worker_index
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nranks = fleet.worker_num
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world_size = fleet.worker_num
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# device id in current trainer
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local_device_ids = fleet.local_device_ids
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# device ids in world
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world_device_ids = fleet.world_device_ids
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# rank in node
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local_rank = fleet.local_rank
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rank_in_node = local_rank
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is_worker = fleet.is_worker
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is_coordinator = fleet.is_coordinator
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init_coordinator = fleet.init_coordinator
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make_fl_strategy = fleet.make_fl_strategy
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get_fl_client = fleet.get_fl_client
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worker_endpoints = fleet.worker_endpoints
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server_num = fleet.server_num
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server_index = fleet.server_index
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server_endpoints = fleet.server_endpoints
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is_server = fleet.is_server
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util = UtilBase()
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barrier_worker = fleet.barrier_worker
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all_reduce = fleet.all_reduce
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init_worker = fleet.init_worker
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init_server = fleet.init_server
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run_server = fleet.run_server
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stop_worker = fleet.stop_worker
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distributed_optimizer = distributed_optimizer
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save_inference_model = fleet.save_inference_model
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save_persistables = fleet.save_persistables
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save_cache_model = fleet.save_cache_model
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check_save_pre_patch_done = fleet.check_save_pre_patch_done
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save_one_table = fleet.save_one_table
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save_dense_params = fleet.save_dense_params
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load_model = fleet.load_model
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load_inference_model = fleet.load_inference_model
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load_one_table = fleet.load_one_table
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set_date = fleet.set_date
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print_table_stat = fleet.print_table_stat
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minimize = fleet.minimize
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distributed_model = distributed_model
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shrink = fleet.shrink
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get_hybrid_communicate_group = fleet.get_hybrid_communicate_group
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distributed_scaler = distributed_scaler
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set_log_level = log_util.set_log_level
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get_log_level_code = log_util.get_log_level_code
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get_log_level_name = log_util.get_log_level_name
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check_memory_usage = log_util.check_memory_usage
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save_cache_table = fleet.save_cache_table
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collective_perf = fleet.collective_perf
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from .. import auto_parallel as auto # noqa: F401
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