def distribute_train(args):
# 根据环境变量确定当前机器/进程在分布式训练中扮演的角色
# 然后使用 fleet api的 init()方法初始化这个节点
role = role_maker.PaddleCloudRoleMaker()
fleet.init(role)
# 我们还可以进一步指定分布式的运行模式，通过 DistributeTranspilerConfig进行配置
# 如下，我们设置分布式运行模式为异步(async)，同时将参数进行切分，以分配到不同的节点
strategy = DistributeTranspilerConfig()
strategy.sync_mode = False
strategy.runtime_split_send_recv = True
ctr_model = CTR()
inputs = ctr_model.input_data(args)
avg_cost, auc_var = ctr_model.net(inputs, args)
# 配置分布式的optimizer，传入我们指定的strategy，构建program
optimizer = fluid.optimizer.Adam(args.learning_rate)
optimizer = fleet.distributed_optimizer(optimizer, strategy)
optimizer.minimize(avg_cost)
# 根据节点角色，分别运行不同的逻辑
if fleet.is_server():
# 初始化及运行参数服务器节点
fleet.init_server()
fleet.run_server()
elif fleet.is_worker():
# 初始化工作节点
fleet.init_worker()
exe = fluid.Executor(fluid.CPUPlace())
# 初始化含有分布式流程的fleet.startup_program
exe.run(fleet.startup_program)
dataset, file_list = get_dataset(inputs, args)
for epoch in range(args.epochs):
# 以文件为粒度进行shuffle
random.shuffle(file_list)
dataset.set_filelist(file_list)
# 训练节点运行的是经过分布式裁剪的fleet.mian_program
start_time = time.time()
exe.train_from_dataset(program=fleet.main_program,
dataset=dataset,
fetch_list=[auc_var],
fetch_info=["Epoch {} auc ".format(epoch)],
print_period=100,
debug=False)
end_time = time.time()
logger.info("epoch %d finished, use time=%d\n" %
((epoch), end_time - start_time))
# 默认使用0号节点保存模型
if args.save_model and fleet.is_first_worker():
model_path = os.path.join(str(args.model_path),
"epoch_" + str(epoch))
fleet.save_persistables(executor=exe, dirname=model_path)
fleet.stop_worker()
logger.info("Distribute Train Success!")
def run_pserver(self, args):
if args.role.upper() != "PSERVER":
raise ValueError("args role must be PSERVER")
role = role_maker.UserDefinedRoleMaker(
current_id=args.current_id,
role=role_maker.Role.SERVER,
worker_num=args.trainers,
server_endpoints=args.endpoints.split(","))
fleet.init(role)
strategy = DistributeTranspilerConfig()
strategy.sync_mode = args.sync_mode
strategy.geo_sgd_mode = args.geo_sgd_mode
strategy.geo_sgd_need_push_nums = args.geo_sgd_need_push_nums
avg_cost = self.net()
optimizer = fluid.optimizer.SGD(LEARNING_RATE)
optimizer = fleet.distributed_optimizer(optimizer, strategy)
optimizer.minimize(avg_cost)
fleet.init_server()
fleet.run_server()
def main(args):
log.info("start")
worker_num = int(os.getenv("PADDLE_TRAINERS_NUM", "0"))
num_devices = int(os.getenv("CPU_NUM", 10))
model = GraphsageModel(args)
loss = model.forward()
train_iter = reader.get_iter(args, model.graph_wrapper, 'train')
pyreader = fake_py_reader(train_iter, num_devices)
# init fleet
init_role()
optimization(args.lr, loss, args.optimizer)
# init and run server or worker
if fleet.is_server():
fleet.init_server(args.warm_start_from_dir)
fleet.run_server()
if fleet.is_worker():
log.info("start init worker done")
fleet.init_worker()
#just the worker, load the sample
log.info("init worker done")
exe = F.Executor(F.CPUPlace())
exe.run(fleet.startup_program)
log.info("Startup done")
compiled_prog = build_complied_prog(fleet.main_program, loss)
train_prog(exe, compiled_prog, model, pyreader, args)
def run_pserver(self, args):
fleet.init(self.build_role(args))
strategy = self.build_strategy(args)
avg_cost = self.net(args)
self.build_optimizer(avg_cost, strategy)
fleet.init_server()
fleet.run_server()
def init_and_run_ps_worker(self, ckpt_path):
# init and run server or worker
self.exe = F.Executor(F.CPUPlace())
if tfleet.is_server():
tfleet.init_server()
self.warmstart(tfleet.startup_program, path=ckpt_path)
tfleet.run_server()
exit()
if tfleet.is_worker():
log.info("start init worker done")
tfleet.init_worker()
self.exe.run(tfleet.startup_program)
def run_pserver(self, args):
"""run pserver"""
from paddle.fluid.incubate.fleet.parameter_server.distribute_transpiler import fleet
import paddle.fluid as fluid
from paddle.fluid.transpiler.ps_dispatcher import RoundRobin
from paddle.fluid.transpiler.ps_dispatcher import HashName
fluid.default_startup_program().random_seed = 1
fluid.default_main_program().random_seed = 1
if args.role.upper() != "PSERVER":
raise ValueError("args role must be PSERVER")
role = role_maker.UserDefinedRoleMaker(
current_id=args.current_id,
role=role_maker.Role.SERVER,
worker_num=args.trainers,
server_endpoints=args.endpoints.split(","))
fleet.init(role)
strategy = DistributeTranspilerConfig()
strategy.sync_mode = args.run_params["sync_mode"]
strategy.async_mode = args.run_params["async_mode"]
strategy.mode = "pserver"
strategy.slice_var_up = args.run_params['slice_var_up']
strategy.enable_dc_asgd = args.run_params['enable_dc_asgd']
if args.run_params['split_method']:
strategy.split_method = HashName
strategy.split_method = RoundRobin
strategy.wait_port = args.run_params['wait_port']
strategy.runtime_split_send_recv = args.run_params['runtime_split_send_recv']
strategy.use_hierarchical_allreduce = args.run_params['use_hierarchical_allreduce']
#strategy.hierarchical_allreduce_exter_nranks = args.run_params['hierarchical_allreduce_exter_nranks']
#strategy.hierarchical_allreduce_inter_nranks = args.run_params['hierarchical_allreduce_inter_nranks']
strategy.geo_sgd_mode = args.run_params['geo_sgd']
strategy.geo_sgd_need_push_nums = args.run_params['push_nums']
avg_cost = self.net(args)
optimizer = fluid.optimizer.SGD(LEARNING_RATE)
optimizer = fleet.distributed_optimizer(optimizer, strategy)
optimizer.minimize(avg_cost)
fleet.init_server()
fleet.run_server()
def train(args):
import logging
log.setLevel(logging.DEBUG)
log.info("start")
worker_num = int(os.getenv("PADDLE_TRAINERS_NUM", "0"))
num_devices = int(os.getenv("CPU_NUM", 10))
model = DeepwalkModel(args.num_nodes, args.hidden_size, args.neg_num,
args.is_sparse, args.is_distributed, 1.)
pyreader = model.pyreader
loss = model.forward()
# init fleet
init_role()
train_steps = math.ceil(1. * args.num_nodes * args.epoch /
args.batch_size / num_devices / worker_num)
log.info("Train step: %s" % train_steps)
if args.optimizer == "sgd":
args.lr *= args.batch_size * args.walk_len * args.win_size
optimization(args.lr, loss, train_steps, args.optimizer)
# init and run server or worker
if fleet.is_server():
fleet.init_server(args.warm_start_from_dir)
fleet.run_server()
if fleet.is_worker():
log.info("start init worker done")
fleet.init_worker()
#just the worker, load the sample
log.info("init worker done")
exe = F.Executor(F.CPUPlace())
exe.run(fleet.startup_program)
log.info("Startup done")
if args.dataset is not None:
if args.dataset == "BlogCatalog":
graph = data_loader.BlogCatalogDataset().graph
elif args.dataset == "ArXiv":
graph = data_loader.ArXivDataset().graph
else:
raise ValueError(args.dataset + " dataset doesn't exists")
log.info("Load buildin BlogCatalog dataset done.")
elif args.walkpath_files is None or args.walkpath_files == "None":
graph = build_graph(args.num_nodes, args.edge_path)
log.info("Load graph from '%s' done." % args.edge_path)
else:
graph = build_fake_graph(args.num_nodes)
log.info("Load fake graph done.")
# bind gen
gen_func = build_gen_func(args, graph)
pyreader.decorate_tensor_provider(gen_func)
pyreader.start()
compiled_prog = build_complied_prog(fleet.main_program, loss)
train_prog(exe, compiled_prog, loss, pyreader, args, train_steps)
def server(self, context):
fleet.init_server()
fleet.run_server()
context['is_exit'] = True
def fit():
role = role_maker.UserDefinedRoleMaker(
current_id=current_id,
role=role_maker.Role.WORKER if bool(1==int(roles)) else role_maker.Role.SERVER,
worker_num=2,
server_endpoints=["127.0.0.1:36011"])
fleet.init(role)
BATCH_SIZE = 128
type_size=createDataList(in_file_path,in_file_path+'.data'+"/")
# 用于训练的数据提供器
train_reader=paddle.batch(reader=paddle.reader.shuffle(reader=dataReader(in_file_path+".data/trainer.list"),buf_size=BATCH_SIZE*100), batch_size=BATCH_SIZE)
test_reader=paddle.batch(reader=paddle.reader.shuffle(reader=dataReader(in_file_path+".data/test.list"),buf_size=BATCH_SIZE*100), batch_size=BATCH_SIZE)
data_shape = [3, 32, 32]
images = fluid.layers.data(name='images', shape=data_shape, dtype='float32')
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
# 获取分类器
predict = networkConfiguration(images,type_size)
# 定义损失函数和准确率
cost = fluid.layers.cross_entropy(input=predict, label=label) # 交叉熵
avg_cost = fluid.layers.mean(cost) # 计算cost中所有元素的平均值
acc = fluid.layers.accuracy(input=predict, label=label) # 使用输入和标签计算准确率
# 定义优化方法
test_program = fluid.default_main_program().clone(for_test=True) # 获取测试程序
optimizer = fluid.optimizer.Adam(learning_rate=0.001)
strategy = DistributeTranspilerConfig()
strategy.sync_mode = True
optimizer = fleet.distributed_optimizer(optimizer,strategy)
# 定义优化方法
optimizer.minimize(avg_cost)
if fleet.is_server():
print("启动server")
fleet.init_server()
fleet.run_server()
elif fleet.is_worker():
print("启动worker")
fleet.init_worker()
print(fleet.worker_endpoints())
########## 模型训练＆模型评估 ##########
# 创建Executor
use_cuda = False # 定义使用CPU还是GPU，使用CPU时use_cuda=False
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
print("cpu")
# 定义数据映射器
feeder = fluid.DataFeeder(feed_list=[images, label], place=place)
print("数据映射")
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
for pass_id in range(EPOCH_NUM):
print(pass_id)
# 开始训练
for batch_id, data in enumerate(train_reader()): # 遍历train_reader
train_cost, train_acc = exe.run(program=fluid.default_main_program(), # 运行主程序
feed=feeder.feed(data), # 喂入一个batch的数据
fetch_list=[avg_cost, acc]) # fetch均方误差和准确率 # fetch均方误差和准确率
# 每100次batch打印一次训练、进行一次测试
if batch_id % 20 == 0:
print('Pass:%d, Batch:%d, Cost:%0.5f, Accuracy:%0.5f' %(pass_id, batch_id, train_cost[0], train_acc[0]))
# 开始测试
test_costs = [] # 测试的损失值
test_accs = [] # 测试的准确率
for batch_id, data in enumerate(test_reader()):
test_cost, test_acc = exe.run(program=test_program, # 执行训练程序
feed=feeder.feed(data), # 喂入数据
fetch_list=[avg_cost, acc]) # fetch误差、准确率
test_costs.append(test_cost[0]) # 记录每个batch的损失值
test_accs.append(test_acc[0]) # 记录每个batch的准确率
test_cost = (sum(test_costs) / len(test_costs)) # 计算误差平均值
test_acc = (sum(test_accs) / len(test_accs)) # 计算准确率平均值
print('Test:%d, Cost:%0.5f, ACC:%0.5f' % (pass_id, test_cost, test_acc))
save(predict,model_file_path,exe)
fleet.stop_worker()
def train(use_cuda, save_dirname, is_local, is_increment):
"""
train
"""
# predict, avg_cost, feed_order, auc_var, auc_batch, auc_states = model()
old_model = None
model_args = model()
predict = model_args['predict']
avg_cost = model_args['avg_cost']
feed_order = model_args['feed_order']
loader = model_args['loader']
auc_batch = model_args['auc'][1]
# 加入 fleet distributed_optimizer 加入分布式策略配置及多机优化
sgd_optimizer = AdamOptimizer(learning_rate=2e-4)
# sgd_optimizer = fluid.optimizer.Adam(learning_rate=2e-5)
if is_local:
sgd_optimizer.minimize(avg_cost)
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
exe = Executor(place)
readers = []
for i in range(16):
readers.append(data_reader(cluster_train_dir))
multi_readers = paddle.reader.multiprocess_reader(readers)
loader.set_sample_generator(
multi_readers, batch_size=BATCH_SIZE, places=fluid.cpu_places(CPU_NUM))
# data_reader(cluster_train_dir), batch_size=BATCH_SIZE, places=fluid.cpu_places(CPU_NUM))
# feeder = fluid.DataFeeder(feed_order, place)
# train_reader = feeder.decorate_reader(
# paddle.batch(paddle.reader.shuffle(
# data_reader(cluster_train_dir), buf_size=8192), batch_size=BATCH_SIZE),
# multi_devices=False, drop_last=True)
start_program = fluid.default_startup_program()
exe.run(start_program)
main_prog = fluid.default_main_program()
exec_strategy = fluid.ExecutionStrategy()
exec_strategy.num_threads = CPU_NUM * 2
build_strategy = fluid.BuildStrategy()
build_strategy.reduce_strategy = fluid.BuildStrategy.ReduceStrategy.Reduce # cpu reduce faster
build_strategy.fuse_broadcast_ops = True
# build_strategy.async_mode = True
main_program = fluid.CompiledProgram(main_prog).with_data_parallel(
loss_name=avg_cost.name, exec_strategy=exec_strategy, build_strategy=build_strategy)
#loss_name=avg_cost.name, exec_strategy=exec_strategy, build_strategy=build_strategy, places=fluid.cpu_places(CPU_NUM))
if is_increment: # load model to fine-tune
fluid.io.load_params(exe, old_model, main_program)
for auc_state in model_args['auc'][2]:
set_zero(place, fluid.global_scope(), auc_state.name)
# 并行训练，速度更快
# train_pe = fluid.ParallelExecutor(use_cuda=use_cuda,
# main_program=main_program, loss_name=avg_cost.name,
# exec_strategy=exec_strategy, build_strategy=build_strategy)
cost_list = []
auc_list = []
import time
pass_s_time = time.time()
for pass_id in range(PASS_NUM):
s_time = time.time()
for batch_id, data in enumerate(loader()):
r_time = time.time() - s_time
st_time = time.time()
cost_value, auc_value = exe.run(
program=main_program,
feed=data,
fetch_list=[avg_cost.name, auc_batch.name])
t_time = time.time() - st_time
cost_list.append(np.array(cost_value))
auc_list.append(np.array(auc_value))
if batch_id % 10 == 0 and batch_id != 0:
print "Pass %d, batch %d, cost %s auc %s readtime %f triantime %f" % \
(pass_id, batch_id, np.array(cost_list).mean(),
np.array(auc_list).mean(), r_time, t_time)
cost_list = []
auc_list = []
if batch_id % 1000 == 0:
if save_dirname is not None:
fluid.io.save_inference_model(
save_dirname,
feed_order,
[predict, avg_cost, auc_batch], exe
)
fluid.io.save_persistables(exe, save_dirname)
infer(cluster_test_dir, save_dirname, feed_order)
s_time = time.time()
pass_time = time.time() - pass_s_time
print("Pass train time: %f" % pass_time)
else:
role = role_maker.PaddleCloudRoleMaker()
# 全异步训练
config = DistributeTranspilerConfig()
config.sync_mode = False
config.runtime_split_send_recv = True
# 加入 fleet init 初始化环境
fleet.init(role)
optimizer = fleet.distributed_optimizer(sgd_optimizer, config)
optimizer.minimize(avg_cost)
if fleet.is_server():
fleet.init_server()
fleet.run_server()
# 启动worker
if fleet.is_worker():
# 初始化worker配置
fleet.init_worker()
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
exe = Executor(place)
feeder = fluid.DataFeeder(feed_order, place)
train_reader = feeder.decorate_reader(
paddle.batch(paddle.reader.shuffle(
data_reader(cluster_train_dir), buf_size=8192), batch_size=BATCH_SIZE),
multi_devices=False, drop_last=True)
exe.run(fleet.startup_program)
exec_strategy = fluid.ExecutionStrategy()
exec_strategy.num_threads = CPU_NUM
build_strategy = fluid.BuildStrategy()
build_strategy.async_mode = True
if CPU_NUM > 1:
build_strategy.reduce_strategy = fluid.BuildStrategy.ReduceStrategy.Reduce
compiled_prog = fluid.compiler.CompiledProgram(
fleet.main_program).with_data_parallel(
loss_name=avg_cost.name, build_strategy=build_strategy, exec_strategy=exec_strategy)
for pass_id in range(PASS_NUM):
cost_list = []
auc_list = []
import time
s_time = time.time()
for batch_id, data in enumerate(train_reader()):
r_time = time.time() - s_time
cost_value, auc_value = exe.run(
program=compiled_prog, feed=data,
fetch_list=[avg_cost.name, auc_batch.name])
t_time = time.time() - r_time
cost_list.append(np.array(cost_value))
auc_list.append(np.array(auc_value))
if batch_id % 10 == 0 and batch_id != 0:
print "Pass %d, batch %d, cost %s auc %s readtime %f traintime %f" % \
(pass_id, batch_id, np.array(cost_list).mean(),
np.array(auc_list).mean(), r_time, t_time)
cost_list = []
auc_list = []
if batch_id % 1000 == 0 and fleet.is_first_worker():
if save_dirname is not None:
fleet.save_inference_model(
exe,
save_dirname,
feed_order,
[predict, avg_cost, auc_batch]
)
fleet.save_persistables(exe, save_dirname)
infer(cluster_test_dir, save_dirname, feed_order)
s_time = time.time()
fleet.stop_worker()
def train(args):
"""run train"""
# set random
program = fluid.default_main_program()
program.random_seed = args.random_seed
# 根据环境变量确定当前机器/进程在分布式训练中扮演的角色
# 然后使用 fleet api的 init()方法初始化这个节点
role = role_maker.PaddleCloudRoleMaker()
fleet.init(role)
# 我们还可以进一步指定分布式的运行模式，通过 DistributeTranspilerConfig进行配置
# 如下，我们设置分布式运行模式为异步(async)，同时将参数进行切分，以分配到不同的节点
if args.sync_mode == "sync":
strategy = StrategyFactory.create_sync_strategy()
elif args.sync_mode == "half_async":
strategy = StrategyFactory.create_half_async_strategy()
elif args.sync_mode == "async":
strategy = StrategyFactory.create_async_strategy()
# set model
logger.info("TDM Begin build network.")
tdm_model = TdmTrainNet(args)
inputs = tdm_model.input_data()
logger.info("TDM Begin load tree travel & layer.")
avg_cost, acc = tdm_model.tdm(inputs)
logger.info("TDM End build network.")
# 配置分布式的optimizer，传入我们指定的strategy，构建program
optimizer = fluid.optimizer.AdamOptimizer(learning_rate=args.learning_rate,
lazy_mode=True)
optimizer = fleet.distributed_optimizer(optimizer, strategy)
optimizer.minimize(avg_cost)
logger.info("TDM End append backward.")
# 根据节点角色，分别运行不同的逻辑
if fleet.is_server():
logger.info("TDM Run server ...")
# 初始化及运行参数服务器节点
logger.info("TDM init model path: {}".format(
args.init_model_files_path))
# 模型中除了tdm树结构相关的变量都应该在此处初始化
fleet.init_server(args.init_model_files_path)
lr = fluid.global_scope().find_var("learning_rate_0")
if lr:
lr.get_tensor().set(
np.array(args.learning_rate).astype('float32'),
fluid.CPUPlace())
logger.info("TDM Set learning rate {}".format(args.learning_rate))
else:
logger.info("TDM Didn't find learning_rate_0 param")
logger.info("TDM load End")
fleet.run_server()
logger.info("TDM Run server success!")
elif fleet.is_worker():
logger.info("TDM Run worker ...")
# 初始化工作节点
fleet.init_worker()
place = fluid.CPUPlace()
exe = fluid.Executor(place)
logger.info("TDM Run Startup Begin")
# 初始化含有分布式流程的fleet.startup_program
exe.run(fleet.startup_program)
# Set Learning Rate
lr = fluid.global_scope().find_var("learning_rate_0")
if lr:
lr.get_tensor().set(
np.array(args.learning_rate).astype('float32'), place)
logger.info("TDM Set learning rate {}".format(args.learning_rate))
# Set TDM Variable
logger.info("TDM Begin load parameter.")
# Set TDM_Tree_Info
# 树结构相关的变量不参与网络更新，不存储于参数服务器，因此需要在本地手动Set
tdm_param_prepare_dict = tdm_sampler_prepare(args)
tdm_param_prepare_dict['info_array'] = tdm_child_prepare(args)
Numpy_model = {}
Numpy_model['TDM_Tree_Travel'] = tdm_param_prepare_dict['travel_array']
Numpy_model['TDM_Tree_Layer'] = tdm_param_prepare_dict['layer_array']
Numpy_model['TDM_Tree_Info'] = tdm_param_prepare_dict['info_array']
# Numpy_model['TDM_Tree_Emb'] = tdm_emb_prepare(args)
# 分布式训练中，Emb存储与参数服务器，无需在本地set
for param_name in Numpy_model:
param_t = fluid.global_scope().find_var(param_name).get_tensor()
param_t.set(Numpy_model[str(param_name)].astype('int32'), place)
logger.info("TDM Run Startup End")
# Train loop
dataset, file_list, example_num = get_dataset(inputs, args)
logger.info("TDM Distributed training begin ...")
for epoch in range(args.epoch_num):
# local shuffle
random.shuffle(file_list)
dataset.set_filelist(file_list)
# 训练节点运行的是经过分布式裁剪的fleet.mian_program
start_time = time.time()
exe.train_from_dataset(program=fleet.main_program,
dataset=dataset,
fetch_list=[acc, avg_cost],
fetch_info=[
"Epoch {} acc ".format(epoch),
"Epoch {} loss ".format(epoch)
],
print_period=1,
debug=False)
end_time = time.time()
logger.info(
"Epoch {} finished, use time {} second, speed {} example/s".
format(epoch, end_time - start_time,
example_num * 1.0 / (end_time - start_time)))
# 默认使用0号节点保存模型
if fleet.is_first_worker():
model_path = os.path.join(args.model_files_path,
"epoch_" + str(epoch))
fleet.save_persistables(executor=exe, dirname=model_path)
logger.info("Begin upload files")
# upload_files(model_path, warm_up=False)
# 在分布式环境下时，支持上传模型到hdfs
logger.info("TDM Before stop worker")
fleet.stop_worker()
logger.info("TDM Distributed training success!")
def train(use_cuda, train_sample_dir, test_sample_dir, old_model, output_model,
is_local, is_increment):
"""
train
"""
# predict, avg_cost, feed_order, auc_var, auc_batch, auc_states = model()
model_args = model()
navi_predict = model_args['predict'][0]
voice_navi_predict = model_args['predict'][1]
speed_navi_predict = model_args['predict'][2]
avg_cost = model_args['avg_cost']
feed_order = model_args['feed_order']
role = role_maker.PaddleCloudRoleMaker()
# 全异步训练
config = DistributeTranspilerConfig()
config.sync_mode = False
config.runtime_split_send_recv = True
sgd_optimizer = AdamOptimizer(learning_rate=2e-4)
if is_local:
sgd_optimizer.minimize(avg_cost)
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
exe = Executor(place)
# train_reader = paddle.batch(
# paddle.reader.shuffle(
# streaming_data_reader(), buf_size=8192), batch_size=BATCH_SIZE)
feeder = fluid.DataFeeder(feed_order, place)
train_reader = feeder.decorate_reader(paddle.batch(
paddle.reader.shuffle(streaming_data_reader(), buf_size=8192),
batch_size=BATCH_SIZE),
multi_devices=False,
drop_last=True)
start_program = fluid.default_startup_program()
exe.run(start_program)
main_program = fluid.default_main_program()
if is_increment: # load model to fine-tune
fluid.io.load_params(exe, old_model, main_program)
# for auc_state in model_args['auc'][2]:
# set_zero(place, fluid.global_scope(), auc_state.name)
exec_strategy = fluid.ExecutionStrategy()
exec_strategy.num_threads = CPU_NUM
main_program.num_threads = CPU_NUM
build_strategy = fluid.BuildStrategy()
build_strategy.async_mode = True
# 并行训练，速度更快
train_pe = fluid.ParallelExecutor(use_cuda=use_cuda,
main_program=main_program,
loss_name=avg_cost.name)
cost_list = []
for pass_id in range(PASS_NUM):
for batch_id, data in enumerate(train_reader()):
cost_value = train_pe.run(feed=data,
fetch_list=[avg_cost.name])
cost_list.append(np.array(cost_value))
if batch_id % 100 == 0 and batch_id != 0:
print "Pass %d, batch %d, cost %s" % \
(pass_id, batch_id, np.array(cost_list).mean())
cost_list = []
if batch_id % 2000 == 0:
if output_model is not None:
fluid.io.save_inference_model(
output_model, feed_order, [
navi_predict, voice_navi_predict,
speed_navi_predict, avg_cost
], exe)
fluid.io.save_persistables(exe, output_model)
infer(test_sample_dir, output_model, feed_order)
else:
# 加入 fleet init 初始化环境
fleet.init(role)
# 加入 fleet distributed_optimizer 加入分布式策略配置及多机优化
optimizer = fleet.distributed_optimizer(sgd_optimizer, config)
optimizer.minimize(avg_cost)
if fleet.is_server():
if is_increment:
fleet.init_server(old_model)
else:
fleet.init_server()
fleet.run_server()
# 启动worker
if fleet.is_worker():
# 初始化worker配置
fleet.init_worker()
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
exe = Executor(place)
# train_reader = paddle.batch(
# paddle.reader.shuffle(
# data_reader(train_sample_dir), buf_size=8192), batch_size=BATCH_SIZE)
feeder = fluid.DataFeeder(feed_order, place)
train_reader = feeder.decorate_reader(paddle.batch(
paddle.reader.shuffle(data_reader(train_sample_dir),
buf_size=8192),
batch_size=BATCH_SIZE),
multi_devices=False,
drop_last=True)
exe.run(fleet.startup_program)
exec_strategy = fluid.ExecutionStrategy()
exec_strategy.num_threads = CPU_NUM
build_strategy = fluid.BuildStrategy()
build_strategy.async_mode = True
if CPU_NUM > 1:
build_strategy.reduce_strategy = fluid.BuildStrategy.ReduceStrategy.Reduce
compiled_prog = fluid.compiler.CompiledProgram(
fleet.main_program).with_data_parallel(
loss_name=avg_cost.name,
build_strategy=build_strategy,
exec_strategy=exec_strategy)
cost_list = []
for pass_id in range(PASS_NUM):
for batch_id, data in enumerate(train_reader()):
cost_value = exe.run(program=compiled_prog,
feed=data,
fetch_list=[avg_cost.name])
cost_list.append(np.array(cost_value))
if batch_id % 100 == 0 and batch_id != 0:
print "Pass %d, batch %d, cost %s" % \
(pass_id, batch_id, np.array(cost_list).mean())
cost_list = []
if batch_id % 1000 == 0 and fleet.is_first_worker():
if output_model is not None:
fleet.save_inference_model(
exe, output_model, feed_order, [
navi_predict, voice_navi_predict,
speed_navi_predict, avg_cost
])
fleet.save_persistables(exe, output_model)
infer(test_sample_dir, output_model, feed_order)
fleet.stop_worker()
