Fleet is High-Level API for distributed training in PaddlePaddle. The name of Fleet means that a large crowd of ships working together to finish a large scale job. The design of Fleet makes a trade-off between easy-to-use and algorithmic extensibility and is highly efficient. First, a user can shift from local machine paddlepaddle code to distributed code within ten lines of code. Second, different algorithms can be easily defined through distributed strategy through Fleet API. Finally, distributed training is extremely fast with Fleet and just enjoy it.
Note: all the examples here should be replicated from develop branch of Paddle
To show how to setup distributed training with fleet, we introduce a small library call fleet-lightning. fleet-lightning helps industrial users to directly train a specific standard model such as Resnet50 without learning to write a Paddle Model.
pip install fleet-lightning
import os import fleet_lightning as lightning import paddle.fluid as fluid from paddle.fluid.incubate.fleet.collective import fleet, DistributedStrategy import paddle.fluid.incubate.fleet.base.role_maker as role_maker configs = lightning.parse_train_configs() role = role_maker.PaddleCloudRoleMaker(is_collective=True) fleet.init(role) model = lightning.applications.Resnet50() loader = model.load_imagenet_from_file("/pathto/imagenet/train.txt") optimizer = fluid.optimizer.Momentum(learning_rate=configs.lr, momentum=configs.momentum) optimizer = fleet.distributed_optimizer(optimizer) optimizer.minimize(model.loss) place = fluid.CUDAPlace(int(os.environ.get('FLAGS_selected_gpus', 0))) exe = fluid.Executor(place) exe.run(fluid.default_startup_program()) epoch = 30 for i in range(epoch): for data in loader(): cost_val = exe.run(fleet.main_program, feed=data, fetch_list=[model.loss.name])
Deep neural networks training with Fleet API is highly efficient in PaddlePaddle. We benchmark serveral standard models here.
Parameter server training benchmark is performed on click through rate estimation task on Criteo Dataset and Semantic Representation Learning on One-billion word Dataset. Details of hardware and software information for this benchmark can be found in parameter server benchmark.
Collective Training is usually used in GPU training in PaddlePaddle. Benchmark of collective training with Fleet is as follows. Details of hardware and software information for this benchmark can be found in benchmark environment.
