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2017 06 07
Porting Majel and improving the building system
- https://github.com/PaddlePaddle/Paddle/pull/2339
- https://github.com/PaddlePaddle/Paddle/pull/2326
- https://github.com/PaddlePaddle/Paddle/pull/2343#pullrequestreview-41423553
- https://github.com/dmlc/mshadow/issues/254
Errands
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Setup Teamcity CI to check PaddlePaddle Pull Requests.
- It runs build and GPU/CPU tests. There are 3 GPU enabled build agents, build statistics (click login as guest).
- Fix frequent Teamcity CI test failures: https://github.com/PaddlePaddle/Paddle/pull/2414
- Make Teamcity visible outside of Baidu: http://19196ef4.ngrok.io/ , people can login as a guest.
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Create recordio repo, add Python binding, wheel packaging and CI test:
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Code Reviews
- Optimizer: https://github.com/PaddlePaddle/Paddle/pull/2386#pullrequestreview-42115122
- File Manager: https://github.com/PaddlePaddle/cloud/pull/112#pullrequestreview-41651168
- New remote parameter updater: https://github.com/PaddlePaddle/Paddle/pull/2413#pullrequestreview-42699278
- Code Reviews
- add script to package recordio into wheel: https://github.com/PaddlePaddle/recordio/pull/5
- improve doc and travis CI: https://github.com/PaddlePaddle/recordio/pull/4
- Submit job with upload user's package:https://github.com/PaddlePaddle/cloud/pull/128
- Pull Request
- change restclient file: https://github.com/PaddlePaddle/cloud/pull/123
- Add convert function: https://github.com/PaddlePaddle/Paddle/pull/2407
- merge filemanager module: https://github.com/PaddlePaddle/cloud/pull/112
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Develop
- polish and add more features to the optimizer library https://github.com/PaddlePaddle/Paddle/pull/2386
- develop go part based optimizer library, will send PR after optimizer merged.
- trainer design doc discussion https://github.com/PaddlePaddle/Paddle/pull/2363 https://github.com/PaddlePaddle/Paddle/issues/2364 https://github.com/PaddlePaddle/Paddle/issues/2347
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Code Review
- PaddleCloud
- Add a demo fit_a_line in PaddleCloud, https://github.com/PaddlePaddle/cloud/pull/130
- [Reviewing] A doc about how to develop cloud dataset,https://github.com/PaddlePaddle/cloud/pull/134/files
- PaddleCloud mount public dataset, https://github.com/PaddlePaddle/cloud/pull/122
- Code Reviews:
- MPI
- [WIP]Test V2 API receiver and deploy for hlan and 10g MPI server
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Github PR
- add OBJECT property in generic CMake https://github.com/PaddlePaddle/Paddle/pull/2359
- rename CMAKE_CURRENT_LIST_DIR to CMAKE_CURRENT_SOURCE_DIR https://github.com/PaddlePaddle/Paddle/pull/2383
- Survey of Eigen in order to compare with Majel https://github.com/PaddlePaddle/Paddle/wiki/A-Survey-and-Taxonomy-of-Majel https://github.com/PaddlePaddle/Paddle/wiki/A-Survey-and-Taxonomy-of-Eigen
- TeamCity CI only output failure https://github.com/PaddlePaddle/Paddle/pull/2390
- update docker docs https://github.com/PaddlePaddle/Paddle/pull/2362
- update book docker docs https://github.com/PaddlePaddle/book/pull/329
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GitHub issue
- Is cuDNN/BLAS, MKL, Neon all BLAS compatible? https://github.com/PaddlePaddle/Paddle/issues/2397
- pr
- improve pruning https://github.com/PaddlePaddle/Paddle/pull/2354 user need to specify a sparisity_ratio for each layer.
- auto pruning
- Investigation on dynamic network surgery
- Investigation on direct convolution
- the whole pruning process for a model takes several stages. For example, if u want to reach the ratio of sparse of 0.7, u must not directly specify the ratio of 0.7 for fine-tuning. It will cause a lot of accuracy drop. So, it would be better to increase the sparsity in several stages. This will keep the accuracy loss within a reasonable range, but it will take a lot of time. I have did a demo on caffe platform to complete it in a whole process. it's have a perfect result on oxford flowers 102 reasult see here, the test on larger dataset is in the process. If test well, i'll update it to paddle.
- DeepSpeech2:
- Make DS2 run on Kubernates clusters: https://github.com/PaddlePaddle/Paddle/issues/2381
- other:
- simple document about attributes of nnvm: doc
ParameterUpdater using go:
- issue: https://github.com/PaddlePaddle/Paddle/issues/2274
- can use go cclient to communicate with pserver now: https://github.com/PaddlePaddle/Paddle/pull/2413
Code review:
- Optimizer Lib: https://github.com/PaddlePaddle/Paddle/pull/2339
survey and ducument:
- survey of tensorflow: https://github.com/PaddlePaddle/Paddle/wiki/TensorFlow%E8%B0%83%E7%A0%94
- document of parameter updater in Paddle: https://github.com/PaddlePaddle/Paddle/wiki/Paddle%E7%9B%AE%E5%89%8D%E7%9A%84%E5%AE%9E%E7%8E%B0%E7%9A%84%E5%90%84%E4%B8%AA%E6%A8%A1%E5%9D%97%E7%BB%86%E8%8A%82#parameterupdater
- remove duplicated examples among demo/models/book, and rename remain demo to v1_api_demo: #2357
- fix bugs:
- Wechat PaddlePaddle:
- support artical with latex formulas
- 179 fans -> 216 fans
- survey dynet: https://github.com/PaddlePaddle/Paddle/wiki/dynet%E8%B0%83%E7%A0%94
- survey caffe2: https://github.com/PaddlePaddle/Paddle/wiki/Caffe2%E8%B0%83%E7%A0%94
- survey operator register framework of mxnet
- survey on mshadow and lazy operation: https://github.com/PaddlePaddle/Paddle/wiki/mshadow%E8%B0%83%E7%A0%94
- survey on Caffe2, with Qijun: https://github.com/PaddlePaddle/Paddle/wiki/Caffe2%E8%B0%83%E7%A0%94
- update the demo script of cluster job, fix bugs: http://wiki.baidu.com/pages/viewpage.action?pageId=327596461
- Issue:https://github.com/PaddlePaddle/book/issues/328
- 手音:Began to use convolution neural network training model, the accuracy rate rose to 67%
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DeepSpeech2
- Finish the row convolution operation for both CPU and GPU implementation.
- Support variable-length input and SortaGrad.
- https://github.com/PaddlePaddle/models/pull/74
- https://github.com/PaddlePaddle/models/issues/75
- The speed of variable-length input is faster based on the train-clean-100 dataset of LibriSpeech.
- 3752 sec/epoc: pad all samples to 2000
- 3286 sec/epoc: shuffle all samples -> make mini-batch -> pad each batch to same size.
- 2861 sec/epoc: sort all samples by length -> make mini-batch -> shuffle batches -> pad each batch to same size.
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Traffic lights detection
- Verify the training on the refactored code.
- help Yaming to solve some problems when porting the v1's demo to v2 API.
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PR View
- DeepSpeech2
- Solved the training convergence problem.
- https://github.com/PaddlePaddle/models/pull/55
- Trained a toy model with full LibriSpeech 960-Hours dataset (WER 22, without beam search and LM).
- Reading speech_dl codes from SVAIL.
- Working on decoder, batch arrangement, augmentation pipeline, Kubernetes running (with Qingqing, Yibing, Yaming, Shaoyong, Haoshuang).
- Other Pull Requests
- PR Reviews
- Solved the training convergence problem.
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DS2: CTC Beam Search Decoder
- PR: https://github.com/PaddlePaddle/models/pull/59
- Design Doc: https://github.com/PaddlePaddle/Paddle/pull/2423
- Confirm correctness by comparing with the decoder in Tensorflow
- Read speech_dl code for decoder part
- Integrate with CTC network and language model
- Two things remaining:
- Optimizie decoding efficiency
- Tune parameters by quantitative index(WER/CER)
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Code Review:
- ComputationGraph Refactorization.
- Survey on Tensorflow/Caffe2/MXNet/DyNet/PyTorch and give two talks this week.
- design overview of these frameworks
- Computation graph implementation survey of these frameworks.
- Survey on Tensorflow/Caffe2/MXNet/DyNet/PyTorch and give two talks this week.
- Keep servuy on mxnet, caff2's computation graph implementation.
- Writting a toy project to help me thinking how to implement a compuatation graph.
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A simple survey of torch, the second part is written by @yanchunwei
- https://github.com/PaddlePaddle/Paddle/wiki/PyTorch-Survey
- Pytorch is designed in quite a different way to PaddlePaddle, I think we can learn from it, but not in a direct way. So I currently lower the priority of this work.
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some modifications to refine the model project
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fix bugs in PaddlePaddle:
- fix the bug in parsing the network configuration for machine translation and SRL
- add an helper for
prelu
layer and refine the API doc: - Last week, we've discussed to fix the way V2 api parses the network configuration. This work is already done by this commit. I learn this commit, and fix some small bugs.
- DeepSpeech: Audio data augmentation
- Read speech_dl code for augmentation part
- Construct the code of augmentation part of speech_dl:https://github.com/chrisxu2016/models/tree/develop/deep_speech_2/augmentation
- Design doc: https://github.com/chrisxu2016/models/blob/develop/deep_speech_2/augmentation/README.md
- code view:
- Paddle cloud:
- bug fixes and enhancements and public datasets: https://github.com/PaddlePaddle/cloud/pulls?q=is%3Apr+is%3Aclosed
- pfs review and merged. need more tests
- MPI cluster v2 training issues
- Test for running kubernetes on baidu cloud bare-metal servers with hardware VxLAN environment
- Document for distributed training with v2 API update: https://github.com/PaddlePaddle/Paddle/pull/2072
- Look into Tensorflow to catch up current NN framework designs, layer->op, tensor->variable
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Convolution Function and Reconstruction Convolution Layer
https://github.com/PaddlePaddle/Paddle/pull/2282
https://github.com/PaddlePaddle/Paddle/issues/2424
https://github.com/PaddlePaddle/Paddle/issues/2425 -
Code Review
https://github.com/PaddlePaddle/Paddle/pull/2373
https://github.com/PaddlePaddle/Paddle/pull/2299
https://github.com/PaddlePaddle/Paddle/pull/2341
- Feature
- DetectionUtil (Some utility functions)
https://github.com/PaddlePaddle/Paddle/pull/2357 - DS2 (Support initializing model from a pre-trained model)
https://github.com/PaddlePaddle/models/pull/72
- DetectionUtil (Some utility functions)
- Traffic light detection demo for ADU
- v2 network configuration (done)
- training、test、eval and infer scripts (done)
- usage document (MIP)
- PR
- Created Reinforcement learning demo using v2 api: to be uploaded
- Refine the docmentation of layers
- fix some typo and add more detail about the usage of warp_ctc layer https://github.com/PaddlePaddle/Paddle/pull/2376
- Build and install Paddle on NVIDIA DRIVE PX2
- Remove
DYNAMIC_ARCH
option of buliding openblas https://github.com/PaddlePaddle/Paddle/pull/2393 - Remove the dependency of opencv-python for arm-based platforms https://github.com/PaddlePaddle/Paddle/pull/2394
- Remove
- Code review
- issue
- finish CTC tutorial, waiting for PR
- finish CTR tutorial, merged into paddle/models
- survey PyTorch from user's view
- wiki was merged into PyTorch-Survey
- survey mxnet::engine