- 23 10月, 2022 1 次提交
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由 Nyakku Shigure 提交于
* update config * re-blacken python code * temporarily disable date and diff_py_file * skip a format
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- 27 9月, 2022 1 次提交
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由 Nyakku Shigure 提交于
* [CodeStyle] remove all future import * revert test_error.py * restore future import in example code
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- 20 9月, 2022 1 次提交
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由 Roc 提交于
uniform logger manager in FleetAPI. hidde API under distributed/utils which users don't need.
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- 05 6月, 2022 1 次提交
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由 Sing_chan 提交于
* use yapf to format all python file * yapf exclude two unittests file for they rely on writing and reading file, and format will break them * disable diff_py_file because too many diff files cause command following failed
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- 13 5月, 2022 1 次提交
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由 Weilong Wu 提交于
* [Eager] Support test_dist_hapi_model under eager mode * [Eager] Polish code * Fix code-format issue, coverage-ci issue
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- 15 4月, 2021 1 次提交
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由 Jiaqi Liu 提交于
* make hapi support amp, and add unittest * make unittest only support GPU * update parameters for amp in hapi.Model * update hapi.Model.prepare interface, and update unittest * fix test_model.py unittest bug * add grad clear in dygraph * use_fp16_guard defaults to True, which could avoid nan * add input check, and add internal doc link to low level api * update doc, and decrease the sample num of dataset to avoid timeout * make hapi amp param support str 'O1' or 'O2' * resume calling , modify the code of the check part * upgrade the usage of Fleet API, and disable 'pure_fp16' param
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- 16 9月, 2020 1 次提交
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由 danleifeng 提交于
* fix ports conflict when launching multi-nodes in paddlecloud;test=develop * add DISTRIBUTED_TRAINER_ENDPOINTS env for cloud;test=develop
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- 31 8月, 2020 1 次提交
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由 qingqing01 提交于
* Move hapi form paddle/incubate to paddle * Remove vision/datasets/utils.py and clean code * Add sample code for conll05 * Print pull path when saving model * Fix sample code after paramter_list of SGD is changed to parameters * Fix bug in wmt16 datase
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- 11 5月, 2020 1 次提交
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由 qingqing01 提交于
* Merge hapi into Paddle Hapi is a high level API for training and inference. The main modules include Model, Loss, Metrics, Dataset. Also includes common modules and models in NLP and computer vision, such as BERT, ResNet. These modules are developed by: 0YuanZhang0, guoshengCS heavengate, LielinJiang, qingqing01, xyzhou-puck huangjun12, wangxiao1021, zhangyang.
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