- 18 10月, 2019 1 次提交
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由 xujiaqi01 提交于
* add check nan / inf in downpour worker during training * test=develop
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- 14 10月, 2019 1 次提交
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由 Thunderbrook 提交于
* support dump multi file test=develop * dump fix num file test=develop
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- 29 8月, 2019 1 次提交
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由 Thunderbrook 提交于
* dump slot * test * proto * dump slot * test * proto * code style * code style * code style * style * add delete after unseen days * add unseen days * code style * conflict solve test=develop * add clear model * code style test=develop * code style test=develop * support debug tensor of each ins test=develop * support debug tensor of each ins test=develop * learning rate * code style * code style * code style * code style * code style * code style * code style * code style * code style * code style * code style * code style * code style test=develop * code style test=develop * unitest * style * style * multi phase * add channel * code style * style * style * unitest * style * define * define test=develop * style test=develop * rm define test=develop * linux * linux test=develop * style test=develop * output format test=develop * windows ci test=develop
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- 01 8月, 2019 1 次提交
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由 jiaqi 提交于
adjust ins weight according to nid slot , user can specify adjust_ins_weight in strategy
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- 25 7月, 2019 1 次提交
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由 fuyinno4 提交于
Fix FleetWrapper: 1. fix shrink dense: just scale show 2. add datanorm scale: divide datanorm's gradient by batch_size
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- 24 7月, 2019 1 次提交
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由 Thunderbrook 提交于
The change includes 2 things: 1. save delta model and shrink table are control by the same parameter before, now add delete_after_unseen_days to control shrink table. 2. value in sparse table has no slot before, now add slot in sparse table, and add DownpureCtrAccessor to support the new meta. test=develop
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- 23 7月, 2019 1 次提交
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由 jiaqi 提交于
（1）support patch data （merge slots of instances of same line id, modify dense layer which changes its size） （2）add fleet load_one_table interface, support load from paddle model and load from pslib model （3）fix push sparse bug which cause push sparse cost more time（about 10% in my testcase） （4）when some slots are not in one of your network (join/update, etc.)，data feed、collect label info、push/pull sparse will skip these slots， instead of throw error. （5）add more debug info in TrainFilesWithProfiler
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- 21 6月, 2019 1 次提交
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由 hutuxian 提交于
* fix relative import error in python3 * fix debug string info
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- 11 6月, 2019 1 次提交
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由 hutuxian 提交于
Add Pipeline Concurrency Train Mode: - Cpp: pipeline_trainer & section_worker - Python: PipelineOptimizer - Add a new data_feed type: PrivateInstantDataFeed - Add a test demo of pipeline trainer and the test model is gnn - Do not support win32 now
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- 15 5月, 2019 1 次提交
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由 jiaqi 提交于
* add save/load model, shrink table, cvm, config file & fix pull dense bug test=develop * fix global shuffle bug, fix pull dense bug, fix release memeory bug, fix shrink error add client flush, add get data size test=develop * fix global shuffle bug test=develop * fix global shuffle bug test=develop * fix code style test=develop * fix code style & modify pslib cmake test=develop * fix error of _role_maker test=develop * fix code style test=develop * fix code style test=develop * fix code style test=develop * fix code style test=develop * fix code style test=develop * fix windows compile error of fleet test=develop * fix global shuffle bug * add comment test=develop * update pslib.cmake test=develop * fix fill sparse bug test=develop * fix push sparse bug test=develop
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- 11 4月, 2019 2 次提交
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由 dongdaxiang 提交于
test=develop
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由 dongdaxiang 提交于
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- 04 4月, 2019 2 次提交
- 29 3月, 2019 20 次提交
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由 dongdaxiang 提交于
test=develop
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由 dongdaxiang 提交于
test=develop
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由 dongdaxiang 提交于
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由 dongdaxiang 提交于
test=develop
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由 dongdaxiang 提交于
test=develop
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由 dongdaxiang 提交于
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由 dongdaxiang 提交于
test=develop
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由 dongdaxiang 提交于
test=develop
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由 dongdaxiang 提交于
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由 dongdaxiang 提交于
test=develop
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由 dongdaxiang 提交于
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由 dongdaxiang 提交于
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由 dongdaxiang 提交于
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由 xujiaqi01 提交于
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由 dongdaxiang 提交于
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由 heqiaozhi 提交于
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由 dongdaxiang 提交于
test=develop
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由 dongdaxiang 提交于
test=develop
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由 dongdaxiang 提交于
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由 dongdaxiang 提交于
add dist_multi_trainer for distributed training, add trainer_factory and device_worker_factory so that we can easily extend new training mode, add pull dense worker which is a singleton for parameter fetching
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