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PaddleRec
提交 154e5da2 编写于 作者: T tangwei
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rename to eleps

上级 dc9f2dac
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Showing with 92 addition and 51 deletion
models/base.py
浏览文件 @ 154e5da2
......@@ -5,7 +5,7 @@ import abc
import copy
import yaml
import paddle.fluid as fluid
import kagle.utils.kagle_table as kagle_table
from ..utils import table as table
from paddle.fluid.incubate.fleet.parameter_server.pslib import fleet
......@@ -187,7 +187,7 @@ class YamlModel(Model):
if self._build_nodes[phase] is None:
continue
for node in self._build_nodes[phase]:
exec("""layer=kagle_layer.{}(node)""".format(node['class']))
exec("""layer=layer.{}(node)""".format(node['class']))
layer_output, extend_output = layer.generate(self._config['mode'], self._build_param)
self._build_param['layer'][node['name']] = layer_output
self._build_param['layer_extend'][node['name']] = extend_output
......@@ -208,7 +208,7 @@ class YamlModel(Model):
param_name = inference_param['name']
if param_name not in self._build_param['table']:
self._build_param['table'][param_name] = {'params' :[]}
table_meta = kagle_table.TableMeta.alloc_new_table(inference_param['table_id'])
table_meta = table.TableMeta.alloc_new_table(inference_param['table_id'])
self._build_param['table'][param_name]['_meta'] = table_meta
self._build_param['table'][param_name]['params'] += inference_param['params']
pass
......
models/ctr_dnn/model.py
浏览文件 @ 154e5da2
class TrainModel(object):
def input(self):
pass
def net(self):
pass
def net(self):
pass
def loss(self):
pass
def optimizer(self):
pass
class InferModel(object):
def input(self):
pass
def net(self):
pass
def net(self):
pass
def loss(self):
pass
def optimizer(self):
pass
models/ctr_dnn/reader.py
浏览文件 @ 154e5da2
def TrainReader():
pass
def InferReader():
pass
reader/dataset.py
浏览文件 @ 154e5da2
......@@ -7,8 +7,8 @@ import yaml
import time
import datetime
import paddle.fluid as fluid
import kagle.utils.kagle_fs as kagle_fs
import kagle.utils.kagle_util as kagle_util
from .. utils import fs as fs
from .. utils import util as util
class Dataset(object):
......@@ -61,16 +61,16 @@ class TimeSplitDataset(Dataset):
Dataset.__init__(self, config)
if 'data_donefile' not in config or config['data_donefile'] is None:
config['data_donefile'] = config['data_path'] + "/to.hadoop.done"
self._path_generator = kagle_util.PathGenerator({'templates': [
self._path_generator = util.PathGenerator({'templates': [
{'name': 'data_path', 'template': config['data_path']},
{'name': 'donefile_path', 'template': config['data_donefile']}
]})
self._split_interval = config['split_interval'] # data split N mins per dir
self._data_file_handler = kagle_fs.FileHandler(config)
self._data_file_handler = fs.FileHandler(config)
def _format_data_time(self, daytime_str, time_window_mins):
""" """
data_time = kagle_util.make_datetime(daytime_str)
data_time = util.make_datetime(daytime_str)
mins_of_day = data_time.hour * 60 + data_time.minute
begin_stage = mins_of_day / self._split_interval
end_stage = (mins_of_day + time_window_mins) / self._split_interval
......
trainer/ctr_trainer.py
浏览文件 @ 154e5da2
......@@ -12,12 +12,14 @@ import datetime
import numpy as np
import paddle.fluid as fluid
import kagle.utils.kagle_fs as kagle_fs
import kagle.utils.kagle_util as kagle_util
import kagle.kagle_model as kagle_model
import kagle.kagle_metric as kagle_metric
import kagle.reader.dataset as kagle_dataset
import kagle.trainer.kagle_trainer as kagle_trainer
from .. utils import fs as fs
from .. utils import util as util
from .. metrics.auc_metrics import AUCMetric
from .. models import base as model_basic
from .. reader import dataset
from . import trainer
from paddle.fluid.incubate.fleet.parameter_server.pslib import fleet
from paddle.fluid.incubate.fleet.base.role_maker import GeneralRoleMaker
......@@ -62,22 +64,22 @@ def worker_numric_max(value, env="mpi"):
return wroker_numric_opt(value, env, "max")
class CtrPaddleTrainer(kagle_trainer.Trainer):
class CtrPaddleTrainer(trainer.Trainer):
"""R
"""
def __init__(self, config):
"""R
"""
kagle_trainer.Trainer.__init__(self, config)
config['output_path'] = kagle_util.get_absolute_path(
trainer.Trainer.__init__(self, config)
config['output_path'] = util.get_absolute_path(
config['output_path'], config['io']['afs'])
self.global_config = config
self._place = fluid.CPUPlace()
self._exe = fluid.Executor(self._place)
self._exector_context = {}
self._metrics = {}
self._path_generator = kagle_util.PathGenerator({
self._path_generator = util.PathGenerator({
'templates': [
{'name': 'xbox_base_done', 'template': config['output_path'] + '/xbox_base_done.txt'},
{'name': 'xbox_delta_done', 'template': config['output_path'] + '/xbox_patch_done.txt'},
......@@ -116,7 +118,7 @@ class CtrPaddleTrainer(kagle_trainer.Trainer):
scope = fluid.Scope()
self._exector_context[executor['name']] = {}
self._exector_context[executor['name']]['scope'] = scope
self._exector_context[executor['name']]['model'] = kagle_model.create(executor)
self._exector_context[executor['name']]['model'] = model_basic.create(executor)
model = self._exector_context[executor['name']]['model']
self._metrics.update(model.get_metrics())
runnnable_scope.append(scope)
......@@ -127,7 +129,7 @@ class CtrPaddleTrainer(kagle_trainer.Trainer):
data_var_list.append(var)
data_var_name_dict[var.name] = var
optimizer = kagle_model.FluidModel.build_optimizer({
optimizer = model_basic.YamlModel.build_optimizer({
'metrics': self._metrics,
'optimizer_conf': self.global_config['optimizer']
})
......@@ -153,7 +155,7 @@ class CtrPaddleTrainer(kagle_trainer.Trainer):
dataset_item['data_vars'] = data_var_list
dataset_item.update(self.global_config['io']['afs'])
dataset_item["batch_size"] = self.global_config['batch_size']
self._dataset[dataset_item['name']] = kagle_dataset.FluidTimeSplitDataset(dataset_item)
self._dataset[dataset_item['name']] = dataset.FluidTimeSplitDataset(dataset_item)
# if config.need_reqi_changeslot and config.reqi_dnn_plugin_day >= last_day and config.reqi_dnn_plugin_pass >= last_pass:
# util.reqi_changeslot(config.hdfs_dnn_plugin_path, join_save_params, common_save_params, update_save_params, scope2, scope3)
fleet.init_worker()
......@@ -176,7 +178,7 @@ class CtrPaddleTrainer(kagle_trainer.Trainer):
"""R
"""
metrics = model.get_metrics()
metric_calculator = kagle_metric.AUCMetric(None)
metric_calculator = AUCMetric(None)
for metric in metrics:
metric_param = {'label': metric, 'metric_dict': metrics[metric]}
metric_calculator.calculate(scope, metric_param)
......@@ -188,13 +190,13 @@ class CtrPaddleTrainer(kagle_trainer.Trainer):
def save_model(self, day, pass_index, base_key):
"""R
"""
cost_printer = kagle_util.CostPrinter(kagle_util.print_cost,
cost_printer = util.CostPrinter(util.print_cost,
{'master': True, 'log_format': 'save model cost %s sec'})
model_path = self._path_generator.generate_path('batch_model', {'day': day, 'pass_id': pass_index})
save_mode = 0 # just save all
if pass_index < 1: # batch_model
save_mode = 3 # unseen_day++, save all
kagle_util.rank0_print("going to save_model %s" % model_path)
util.rank0_print("going to save_model %s" % model_path)
fleet.save_persistables(None, model_path, mode=save_mode)
if fleet._role_maker.is_first_worker():
self._train_pass.save_train_progress(day, pass_index, base_key, model_path, is_checkpoint=True)
......@@ -206,11 +208,11 @@ class CtrPaddleTrainer(kagle_trainer.Trainer):
"""
stdout_str = ""
xbox_patch_id = str(int(time.time()))
kagle_util.rank0_print("begin save delta model")
util.rank0_print("begin save delta model")
model_path = ""
xbox_model_donefile = ""
cost_printer = kagle_util.CostPrinter(kagle_util.print_cost, {'master': True, \
cost_printer = util.CostPrinter(util.print_cost, {'master': True, \
'log_format': 'save xbox model cost %s sec',
'stdout': stdout_str})
if pass_index < 1:
......@@ -225,23 +227,23 @@ class CtrPaddleTrainer(kagle_trainer.Trainer):
total_save_num = fleet.save_persistables(None, model_path, mode=save_mode)
cost_printer.done()
cost_printer = kagle_util.CostPrinter(kagle_util.print_cost, {'master': True,
cost_printer = util.CostPrinter(util.print_cost, {'master': True,
'log_format': 'save cache model cost %s sec',
'stdout': stdout_str})
model_file_handler = kagle_fs.FileHandler(self.global_config['io']['afs'])
model_file_handler = fs.FileHandler(self.global_config['io']['afs'])
if self.global_config['save_cache_model']:
cache_save_num = fleet.save_cache_model(None, model_path, mode=save_mode)
model_file_handler.write(
"file_prefix:part\npart_num:16\nkey_num:%d\n" % cache_save_num,
model_path + '/000_cache/sparse_cache.meta', 'w')
cost_printer.done()
kagle_util.rank0_print("save xbox cache model done, key_num=%s" % cache_save_num)
util.rank0_print("save xbox cache model done, key_num=%s" % cache_save_num)
save_env_param = {
'executor': self._exe,
'save_combine': True
}
cost_printer = kagle_util.CostPrinter(kagle_util.print_cost, {'master': True,
cost_printer = util.CostPrinter(util.print_cost, {'master': True,
'log_format': 'save dense model cost %s sec',
'stdout': stdout_str})
if fleet._role_maker.is_first_worker():
......@@ -269,8 +271,8 @@ class CtrPaddleTrainer(kagle_trainer.Trainer):
"monitor_data": monitor_data,
"mpi_size": str(fleet.worker_num()),
"input": model_path.rstrip("/") + "/000",
"job_id": kagle_util.get_env_value("JOB_ID"),
"job_name": kagle_util.get_env_value("JOB_NAME")
"job_id": util.get_env_value("JOB_ID"),
"job_name": util.get_env_value("JOB_NAME")
}
if fleet._role_maker.is_first_worker():
model_file_handler.write(json.dumps(xbox_done_info) + "\n", xbox_model_donefile, 'a')
......@@ -289,7 +291,7 @@ class CtrPaddleTrainer(kagle_trainer.Trainer):
scope = self._exector_context[executor_name]['scope']
model = self._exector_context[executor_name]['model']
with fluid.scope_guard(scope):
kagle_util.rank0_print("Begin " + executor_name + " pass")
util.rank0_print("Begin " + executor_name + " pass")
begin = time.time()
program = model._build_param['model']['train_program']
self._exe.train_from_dataset(program, dataset, scope,
......@@ -299,12 +301,12 @@ class CtrPaddleTrainer(kagle_trainer.Trainer):
avg_cost = worker_numric_avg(local_cost)
min_cost = worker_numric_min(local_cost)
max_cost = worker_numric_max(local_cost)
kagle_util.rank0_print("avg train time %s mins, min %s mins, max %s mins" % (avg_cost, min_cost, max_cost))
util.rank0_print("avg train time %s mins, min %s mins, max %s mins" % (avg_cost, min_cost, max_cost))
self._exector_context[executor_name]['cost'] = max_cost
monitor_data = ""
self.print_global_metrics(scope, model, monitor_data, stdout_str)
kagle_util.rank0_print("End " + executor_name + " pass")
util.rank0_print("End " + executor_name + " pass")
if self._train_pass.need_dump_inference(pass_id) and executor_config['dump_inference_model']:
stdout_str += self.save_xbox_model(day, pass_id, xbox_base_key, monitor_data)
fleet._role_maker._barrier_worker()
......@@ -317,9 +319,9 @@ class CtrPaddleTrainer(kagle_trainer.Trainer):
context['status'] = 'wait'
return
stdout_str = ""
self._train_pass = kagle_util.TimeTrainPass(self.global_config)
self._train_pass = util.TimeTrainPass(self.global_config)
if not self.global_config['cold_start']:
cost_printer = kagle_util.CostPrinter(kagle_util.print_cost,
cost_printer = util.CostPrinter(util.print_cost,
{'master': True, 'log_format': 'load model cost %s sec',
'stdout': stdout_str})
self.print_log("going to load model %s" % self._train_pass._checkpoint_model_path, {'master': True})
......@@ -358,8 +360,8 @@ class CtrPaddleTrainer(kagle_trainer.Trainer):
xbox_base_key = int(time.time())
context['status'] = 'begin_day'
kagle_util.rank0_print("shrink table")
cost_printer = kagle_util.CostPrinter(kagle_util.print_cost,
util.rank0_print("shrink table")
cost_printer = util.CostPrinter(util.print_cost,
{'master': True, 'log_format': 'shrink table done, cost %s sec'})
fleet.shrink_sparse_table()
for executor in self._exector_context:
......@@ -370,9 +372,9 @@ class CtrPaddleTrainer(kagle_trainer.Trainer):
cost_printer.done()
next_date = self._train_pass.date(delta_day=1)
kagle_util.rank0_print("going to save xbox base model")
util.rank0_print("going to save xbox base model")
self.save_xbox_model(next_date, 0, xbox_base_key, "")
kagle_util.rank0_print("going to save batch model")
util.rank0_print("going to save batch model")
self.save_model(next_date, 0, xbox_base_key)
self._train_pass._base_key = xbox_base_key
fleet._role_maker._barrier_worker()
......@@ -388,7 +390,7 @@ class CtrPaddleTrainer(kagle_trainer.Trainer):
self.print_log(" ==== begin delta:%s ========" % pass_id, {'master': True, 'stdout': stdout_str})
train_begin_time = time.time()
cost_printer = kagle_util.CostPrinter(kagle_util.print_cost, \
cost_printer = util.CostPrinter(util.print_cost, \
{'master': True, 'log_format': 'load into memory done, cost %s sec',
'stdout': stdout_str})
current_dataset = {}
......@@ -400,8 +402,8 @@ class CtrPaddleTrainer(kagle_trainer.Trainer):
fleet._role_maker._barrier_worker()
cost_printer.done()
kagle_util.rank0_print("going to global shuffle")
cost_printer = kagle_util.CostPrinter(kagle_util.print_cost, {
util.rank0_print("going to global shuffle")
cost_printer = util.CostPrinter(util.print_cost, {
'master': True, 'stdout': stdout_str,
'log_format': 'global shuffle done, cost %s sec'})
for name in current_dataset:
......@@ -423,7 +425,7 @@ class CtrPaddleTrainer(kagle_trainer.Trainer):
pure_train_begin = time.time()
for executor in self.global_config['executor']:
self.run_executor(executor, current_dataset[executor['dataset_name']], stdout_str)
cost_printer = kagle_util.CostPrinter(kagle_util.print_cost, \
cost_printer = util.CostPrinter(util.print_cost, \
{'master': True, 'log_format': 'release_memory cost %s sec'})
for name in current_dataset:
current_dataset[name].release_memory()
......@@ -439,8 +441,8 @@ class CtrPaddleTrainer(kagle_trainer.Trainer):
for executor in self._exector_context:
log_str += '[' + executor + ':' + str(self._exector_context[executor]['cost']) + ']'
log_str += '[other_cost:' + str(other_cost) + ']'
kagle_util.rank0_print(log_str)
stdout_str += kagle_util.now_time_str() + log_str
util.rank0_print(log_str)
stdout_str += util.now_time_str() + log_str
sys.stdout.write(stdout_str)
fleet._role_maker._barrier_worker()
stdout_str = ""
......
utils/kagle_util.py → utils/util.py
浏览文件 @ 154e5da2
......@@ -4,7 +4,7 @@ Util lib
import os
import time
import datetime
import kagle.utils.kagle_fs as kagle_fs
from .. utils import fs as fs
def get_env_value(env_name):
......@@ -168,10 +168,10 @@ class TimeTrainPass(object):
self._pass_donefile_handler = None
if 'pass_donefile_name' in self._config:
self._train_pass_donefile = global_config['output_path'] + '/' + self._config['pass_donefile_name']
if kagle_fs.is_afs_path(self._train_pass_donefile):
self._pass_donefile_handler = kagle_fs.FileHandler(global_config['io']['afs'])
if fs.is_afs_path(self._train_pass_donefile):
self._pass_donefile_handler = fs.FileHandler(global_config['io']['afs'])
else:
self._pass_donefile_handler = kagle_fs.FileHandler(global_config['io']['local_fs'])
self._pass_donefile_handler = fs.FileHandler(global_config['io']['local_fs'])
last_done = self._pass_donefile_handler.cat(self._train_pass_donefile).strip().split('\n')[-1]
done_fileds = last_done.split('\t')
......
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