- 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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- 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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- 30 3月, 2019 2 次提交
- 29 3月, 2019 15 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 xjqbest 提交于
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由 xujiaqi01 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 xujiaqi01 提交于
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由 xujiaqi01 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 dongdaxiang 提交于
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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
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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