- 25 7月, 2019 1 次提交
-
-
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
-
- 24 7月, 2019 1 次提交
-
-
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
-
- 11 6月, 2019 1 次提交
-
-
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
-
- 15 5月, 2019 1 次提交
-
-
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
-
- 11 4月, 2019 1 次提交
-
-
由 dongdaxiang 提交于
test=develop
-
- 29 3月, 2019 11 次提交
-
-
由 dongdaxiang 提交于
test=develop
-
由 dongdaxiang 提交于
-
由 dongdaxiang 提交于
test=develop
-
由 dongdaxiang 提交于
-
由 dongdaxiang 提交于
test=develop
-
由 dongdaxiang 提交于
test=develop
-
由 heqiaozhi 提交于
-
由 dongdaxiang 提交于
-
由 dongdaxiang 提交于
test=develop
-
由 dongdaxiang 提交于
-
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
-