[GitHub] javelinjs commented on issue #7417: Update mxnet in maven timely?

[git]

javelinjs commented on issue #7417: Update mxnet in maven timely?
URL: 
https://github.com/apache/incubator-mxnet/issues/7417#issuecomment-322661354
 
 
   @szha Thanks for invitation to the deployment project.
 

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[GitHub] szha commented on issue #7417: Update mxnet in maven timely?

[git]

szha commented on issue #7417: Update mxnet in maven timely?
URL: 
https://github.com/apache/incubator-mxnet/issues/7417#issuecomment-322658768
 
 
   @javelinjs let me know if need any help on this.
 

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[GitHub] javelinjs commented on a change in pull request #7411: [scala-package][spark] fix example script

[git]

javelinjs commented on a change in pull request #7411: [scala-package][spark] 
fix example script
URL: https://github.com/apache/incubator-mxnet/pull/7411#discussion_r133352986
 
 

 ##
 File path: scala-package/spark/bin/run-mnist-example.sh
 ##
 @@ -18,47 +18,62 @@
 # under the License.
 
 CURR_DIR=$(cd `dirname $0`; pwd)
-MODULE_DIR=$(cd $CURR_DIR/../; pwd)
-ROOT_DIR=$(cd $CURR_DIR/../../; pwd)
+SPARK_MODULE_DIR=$(cd $CURR_DIR/../; pwd)
+SCALA_PKG_DIR=$(cd $CURR_DIR/../../; pwd)
 
+OS=""
 
-LIB_DIR=${MODULE_DIR}/target/classes/lib
-JAR=${MODULE_DIR}/target/mxnet-spark_2.10-0.1.2-SNAPSHOT.jar
+if [ "$(uname)" == "Darwin" ]; then
+   # Do something under Mac OS X platform
+   OS='osx-x86_64-cpu' 
 
 Review comment:
   Could you make all indent to 2 spaces?
 

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[GitHub] javelinjs commented on issue #7417: Update mxnet in maven timely?

[git]

javelinjs commented on issue #7417: Update mxnet in maven timely?
URL: 
https://github.com/apache/incubator-mxnet/issues/7417#issuecomment-322657600
 
 
   Sure. I'll work on this.
   BTW, are we going to change package name from `ml.dmlc` to `org.apache`? cc 
@mli 
 

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[GitHub] starimpact commented on issue #7445: Using cuDNN for CTC Loss

[git]

starimpact commented on issue #7445: Using cuDNN for CTC Loss
URL: 
https://github.com/apache/incubator-mxnet/issues/7445#issuecomment-322657512
 
 
   so, the ctc of cudnn7 supports neither variable lengths inputs nor longer 
labellengths than 256.
 

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[GitHub] szha commented on issue #7488: Fixes scaling issue identified in #7455

[git]

szha commented on issue #7488: Fixes scaling issue identified in #7455
URL: https://github.com/apache/incubator-mxnet/pull/7488#issuecomment-322656625
 
 
   Thanks for bringing this up @solin319
 

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[GitHub] jeremiedb commented on issue #7476: R-package RNN refactor

[git]

jeremiedb commented on issue #7476: R-package RNN refactor
URL: https://github.com/apache/incubator-mxnet/pull/7476#issuecomment-322632234
 
 
   @thirdwing `source()` and `library()` calls removed. 
   
   Functions `mx.model.train.rnn.buckets` and `mx.rnn.buckets` merged into 
`model.rnn.R` in order to better align with `model.R`. 
   
   Sorry for multiple commits - I struggled a bit with rebasing the submodules. 
   
 

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[GitHub] piiswrong closed pull request #7484: add gluon resnet18_v2, resnet34_v2 models

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piiswrong closed pull request #7484: add gluon resnet18_v2, resnet34_v2 models
URL: https://github.com/apache/incubator-mxnet/pull/7484
 
 
   
 

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[incubator-mxnet] branch master updated: add gluon resnet18_v2, resnet34_v2 models (#7484)

[jxie]

This is an automated email from the ASF dual-hosted git repository.

jxie pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/incubator-mxnet.git


The following commit(s) were added to refs/heads/master by this push:
 new bca9c4c  add gluon resnet18_v2, resnet34_v2 models (#7484)
bca9c4c is described below

commit bca9c4cf0b7c90374557170eec088a2b30b8bb72
Author: Joshua Z. Zhang 
AuthorDate: Tue Aug 15 19:22:06 2017 -0700

add gluon resnet18_v2, resnet34_v2 models (#7484)
---
 python/mxnet/gluon/model_zoo/model_store.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/python/mxnet/gluon/model_zoo/model_store.py 
b/python/mxnet/gluon/model_zoo/model_store.py
index 67ba572..e524f21 100644
--- a/python/mxnet/gluon/model_zoo/model_store.py
+++ b/python/mxnet/gluon/model_zoo/model_store.py
@@ -36,6 +36,8 @@ _model_sha1 = {name: checksum for checksum, name in [
 ('38d6d423c22828718ec3397924b8e116a03e6ac0', 'resnet18_v1'),
 ('4dc2c2390a7c7990e0ca1e53aeebb1d1a08592d1', 'resnet34_v1'),
 ('2a903ab21260c85673a78fe65037819a843a1f43', 'resnet50_v1'),
+('8aacf80ff4014c1efa2362a963ac5ec82cf92d5b', 'resnet18_v2'),
+('0ed3cd06da41932c03dea1de7bc2506ef3fb97b3', 'resnet34_v2'),
 ('264ba4970a0cc87a4f15c96e25246a1307caf523', 'squeezenet1.0'),
 ('33ba0f93753c83d86e1eb397f38a667eaf2e9376', 'squeezenet1.1'),
 ('dd221b160977f36a53f464cb54648d227c707a05', 'vgg11'),

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[GitHub] kevinthesun closed pull request #7419: Add resnet50_v2, resnet101_V2 and resnet152_v2 gluon pre-trained model

[git]

kevinthesun closed pull request #7419: Add resnet50_v2, resnet101_V2 and 
resnet152_v2 gluon pre-trained model
URL: https://github.com/apache/incubator-mxnet/pull/7419
 
 
   
 

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[GitHub] madjam opened a new pull request #7488: Fixes scaling issue identified in #7455

[git]

madjam opened a new pull request #7488: Fixes scaling issue identified in #7455
URL: https://github.com/apache/incubator-mxnet/pull/7488
 
 
   @mli @ptrendx @szha 
 

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[GitHub] starimpact commented on issue #7455: Distributed training is slow

[git]

starimpact commented on issue #7455: Distributed training is slow
URL: 
https://github.com/apache/incubator-mxnet/issues/7455#issuecomment-322638762
 
 
   in mxnet0.8.0 there is no "send_buf.WaitToReadd()".
   lucky for me.^_^
   
https://github.com/starimpact/mxnet_v0.8.0/blob/bProxy_Weight/src/kvstore/kvstore_dist.h#L412
   my mxnet support partial parameters update. welcome to use it.
   haha
 

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[GitHub] starimpact commented on issue #7455: Distributed training is slow

[git]

starimpact commented on issue #7455: Distributed training is slow
URL: 
https://github.com/apache/incubator-mxnet/issues/7455#issuecomment-322638762
 
 
   in mxnet0.8.0 there is no "send_buf.WaitToReadd()".
   lucky for me.^_^
 

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[GitHub] jeremiedb commented on issue #7476: R-package RNN refactor

[git]

jeremiedb commented on issue #7476: R-package RNN refactor
URL: https://github.com/apache/incubator-mxnet/pull/7476#issuecomment-322632234
 
 
   @thirdwing `source()` and `library()` calls removed. 
   
   Functions `mx.model.train.rnn.buckets` and `mx.rnn.buckets` merged into 
`model.rnn.R` in order to better align with `model.R`. 
   
 

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[GitHub] szha commented on issue #7486: Quick question about LSTM parameters

[git]

szha commented on issue #7486: Quick question about LSTM parameters
URL: 
https://github.com/apache/incubator-mxnet/issues/7486#issuecomment-322618950
 
 
   No problem. And the reason that you see i2h_f_bias and h2h_f_bias being the 
same could be that they were initialized with the same value. Since they are 
added together with the same weight 1, what ends up happening is that the 
gradients they received will always be the same.
 

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[GitHub] szha commented on issue #7486: Quick question about LSTM parameters

[git]

szha commented on issue #7486: Quick question about LSTM parameters
URL: 
https://github.com/apache/incubator-mxnet/issues/7486#issuecomment-322618950
 
 
   No problem. And the reason that you see i2h_f_bias and h2h_f_bias being the 
same could be that they were initialized the same way. Since they are added 
together with the same weight 1, what ends up happening is that the gradients 
they received will always be the same.
 

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[GitHub] aspzest commented on issue #7486: Quick question about LSTM parameters

[git]

aspzest commented on issue #7486: Quick question about LSTM parameters
URL: 
https://github.com/apache/incubator-mxnet/issues/7486#issuecomment-322617307
 
 
   Okay. Thanks a lot!
 

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[GitHub] mli commented on issue #7455: Distributed training is slow

[git]

mli commented on issue #7455: Distributed training is slow
URL: 
https://github.com/apache/incubator-mxnet/issues/7455#issuecomment-322612600
 
 
   https://github.com/apache/incubator-mxnet/issues/6975
 

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[GitHub] aspzest commented on issue #7486: Quick question about LSTM parameters

[git]

aspzest commented on issue #7486: Quick question about LSTM parameters
URL: 
https://github.com/apache/incubator-mxnet/issues/7486#issuecomment-322611872
 
 
   @szha So, b_f = i2h_f_bias + h2h_f_bias?
 

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[GitHub] mli commented on issue #7455: Distributed training is slow

[git]

mli commented on issue #7455: Distributed training is slow
URL: 
https://github.com/apache/incubator-mxnet/issues/7455#issuecomment-322609681
 
 
   @madjam 's test case is that `send_buf` maybe not ready to get `data()`
   
   agree with @ptrendx that we should remove this WaitToRead. One solution is 
moving 
https://github.com/madjam/mxnet/blob/0012f7722d97238a84c33f1bee8cd2926707a7e9/src/kvstore/kvstore_dist.h#L221
 into the captured function.
   
   Can someone help contribute a PR for it?
   
 

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[GitHub] leoxiaobin opened a new issue #7455: Distributed training is slow

[git]

leoxiaobin opened a new issue #7455: Distributed training is slow
URL: https://github.com/apache/incubator-mxnet/issues/7455
 
 
   ## Environment info
   Operating System: Ubuntu 16.4
   
   Compiler: gcc 5.4
   
   Package used (Python/R/Scala/Julia): Python
   
   MXNet version: Last code
   
   Or if installed from source: installed from source
   
   MXNet commit hash (`git rev-parse HEAD`): 1a3faa
   
   If you are using python package, please provide
   
   Python version and distribution: Python 2.7.13 :: Anaconda custom (64-bit)
   
   I tried to train image classification model using two servers with 
infiniband cards. But the speed is a little slow, just like using one server. I 
used the code of example/image-classifaction. 
   
   when training on one server, the training command is
   ```
   python train_imagenet.py --benchmark 1 --gpus 0,1,2,3,4,5,6,7 --kv-store 
device --network inception-v3 --batch-size 256   --image-shape 3,299,299
   ```
   
   the speed is
   ```
   INFO:root:start with arguments Namespace(batch_size=256, benchmark=1, 
data_nthreads=4, data_train=None, data_val=None, disp_batches=20, 
dtype='float32', gpus='0,1,2,3,4,5,6,7', image_shape='3,299,299', 
kv_store='device', load_epoch=None, lr=0.1, lr_factor=0.1, 
lr_step_epochs='30,60', max_random_aspect_ratio=0.25, max_random_h=36, 
max_random_l=50, max_random_rotate_angle=10, max_random_s=50, 
max_random_scale=1, max_random_shear_ratio=0.1, min_random_scale=1, 
model_prefix=None, mom=0.9, monitor=0, network='inception-v3', 
num_classes=1000, num_epochs=80, num_examples=1281167, num_layers=50, 
optimizer='sgd', pad_size=0, random_crop=1, random_mirror=1, 
rgb_mean='123.68,116.779,103.939', test_io=0, top_k=0, wd=0.0001)
   [22:35:19] src/operator/././cudnn_algoreg-inl.h:112: Running performance 
tests to find the best convolution algorithm, this can take a while... (setting 
env variable MXNET_CUDNN_AUTOTUNE_DEFAULT to 0 to disable)
   [22:35:40] src/kvstore/././comm.h:327: only 24 out of 56 GPU pairs are 
enabled direct access. It may affect the performance. You can set 
MXNET_ENABLE_GPU_P2P=0 to turn it off
   [22:35:40] src/kvstore/././comm.h:336: .vvv
   [22:35:40] src/kvstore/././comm.h:336: v.vv
   [22:35:40] src/kvstore/././comm.h:336: vv.v
   [22:35:40] src/kvstore/././comm.h:336: vvv.
   [22:35:40] src/kvstore/././comm.h:336: .vvv
   [22:35:40] src/kvstore/././comm.h:336: v.vv
   [22:35:40] src/kvstore/././comm.h:336: vv.v
   [22:35:40] src/kvstore/././comm.h:336: vvv.
   INFO:root:Epoch[0] Batch [20]   Speed: 1065.93 samples/sec  
accuracy=0.165365
   INFO:root:Epoch[0] Batch [40]   Speed: 1033.22 samples/sec  
accuracy=0.989648
   INFO:root:Epoch[0] Batch [60]   Speed: 1029.90 samples/sec  
accuracy=1.00
   INFO:root:Epoch[0] Batch [80]   Speed: 1029.80 samples/sec  
accuracy=1.00
   INFO:root:Epoch[0] Batch [100]  Speed: 1028.05 samples/sec  
accuracy=1.00
   INFO:root:Epoch[0] Batch [120]  Speed: 1019.75 samples/sec  
accuracy=1.00
   INFO:root:Epoch[0] Batch [140]  Speed: 1025.79 samples/sec  
accuracy=1.00
   INFO:root:Epoch[0] Batch [160]  Speed: 1027.82 samples/sec  
accuracy=1.00
   INFO:root:Epoch[0] Batch [180]  Speed: 1021.11 samples/sec  
accuracy=1.00
   INFO:root:Epoch[0] Batch [200]  Speed: 1025.14 samples/sec  
accuracy=1.00
   INFO:root:Epoch[0] Batch [220]  Speed: 1017.72 samples/sec  
accuracy=1.00
   INFO:root:Epoch[0] Batch [240]  Speed: 1021.09 samples/sec  
accuracy=1.00
   INFO:root:Epoch[0] Batch [260]  Speed: 1024.25 samples/sec  
accuracy=1.00
   ```
   
   When training with 2 servers, the command is
   ```
python ../../tools/launch.py -n 2 --launcher ssh -H hosts python 
train_imagenet.py --benchmark 1 --gpus 0,1,2,3,4,5,6,7 --kv-store dist_sync 
--network inception-v3 --num-layers 50 --batch-size 256 --sync-dst-dir 
/tmp/mxnet  --image-shape 3,299,299
   ```
   
   And the speed is
   ```
   INFO:root:Epoch[0] Batch [20]   Speed: 609.31 samples/sec   
accuracy=0.056920
   INFO:root:Epoch[0] Batch [20]   Speed: 610.12 samples/sec   
accuracy=0.050967
   INFO:root:Epoch[0] Batch [40]   Speed: 608.68 samples/sec   
accuracy=0.854883
   INFO:root:Epoch[0] Batch [40]   Speed: 608.19 samples/sec   
accuracy=0.868164
   INFO:root:Epoch[0] Batch [60]   Speed: 602.48 samples/sec   
accuracy=1.00
   INFO:root:Epoch[0] Batch [60]   Speed: 603.86 samples/sec   
accuracy=1.00
   INFO:root:Epoch[0] Batch [80]   Speed: 603.11 samples/sec   
accuracy=1.00
   INFO:root:Epoch[0] Batch [80]   Speed: 603.87 samples/sec   
accuracy=1.00
   INFO:root:Epoch[0] Batch [100]  Speed: 607.30 samples/sec   
accuracy=1.00
   INFO:root:Epoch[0] Batch [100]  Speed: 606.54 samples/sec   
accuracy=1.00
   INFO:root:Epoch[0] Batch [120]  Speed: 604.53 samples/sec   
accuracy=1.00
   INFO:root:Epoch[0] Batch [120]  

[GitHub] aspzest opened a new issue #7486: Quick question about LSTM parameters

[git]

aspzest opened a new issue #7486: Quick question about LSTM parameters
URL: https://github.com/apache/incubator-mxnet/issues/7486
 
 
   I am using LSTM from mxnet and was able to get the parameters of the LSTM 
block. I have a question about the biases. According to the equation below 
taken from [here](http://colah.github.io/posts/2015-08-Understanding-LSTMs/), 
there is a single bias_f defined to get f_t. But, mxnet's LSTM parameters 
contain two biases for this equation: 1. i2h_f_bias and 2. h2h_f_bias. Is b_f 
here simply i2h_f_bias + h2h_f_bias? Or there is some other relation?
   
   ![screen shot 2017-08-15 at 3 13 07 
pm](https://user-images.githubusercontent.com/29802784/29338879-4f8cadbc-81cc-11e7-992c-8dd4ce63896d.png)
   
   I am also seeing that i2h_f_bias and h2h_f_bias are same sometimes.
   
   Thank You
   
 

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[GitHub] lxn2 closed pull request #10: Fix more links

[git]

lxn2 closed pull request #10: Fix more links
URL: https://github.com/apache/incubator-mxnet-site/pull/10
 
 
   
 

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[GitHub] madjam commented on issue #7455: Distributed training is slow

[git]

madjam commented on issue #7455: Distributed training is slow
URL: 
https://github.com/apache/incubator-mxnet/issues/7455#issuecomment-322593478
 
 
   For context, that barrier was added since an operation such as:
   ```
   kv.init(2, mx.nd.zeros((50, 50)))
   ```
   would access memory that is not fully initialized and therefore causes a 
segfault.
 

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[GitHub] kevinthesun opened a new pull request #7485: Fix more links

[git]

kevinthesun opened a new pull request #7485: Fix more links
URL: https://github.com/apache/incubator-mxnet/pull/7485
 
 
   
 

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[GitHub] zhreshold commented on issue #7419: Add resnet50_v2, resnet101_V2 and resnet152_v2 gluon pre-trained model

[git]

zhreshold commented on issue #7419: Add resnet50_v2, resnet101_V2 and 
resnet152_v2 gluon pre-trained model
URL: https://github.com/apache/incubator-mxnet/pull/7419#issuecomment-322586548
 
 
   @kevinthesun @szha  Validation on these three models are bad, basically 
around 0.001 accuracy. So I guess these weights are not correctly handled.
   The iterator I used:
   ```
   val_iter = mx.image.ImageIter(args.batch_size, data_shape, 
path_imgrec=args.val_rec,
 shuffle=False, mean=True, std=True, resize=256)
   
   ```
   I've used the same iterator to test v1 models, which are good.
 

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[GitHub] zhreshold opened a new pull request #7484: add gluon resnet18_v2, resnet34_v2 models

[git]

zhreshold opened a new pull request #7484: add gluon resnet18_v2, resnet34_v2 
models
URL: https://github.com/apache/incubator-mxnet/pull/7484
 
 
   resnet18v2: validation: accuracy=0.696827, top_k_accuracy_5=0.888473
   
   resnet34v2: validation: accuracy=0.732103, top_k_accuracy_5=0.910415
 

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[incubator-mxnet] branch master updated (a21d3e0 -> 7d6385a)

[jxie]

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jxie pushed a change to branch master
in repository https://gitbox.apache.org/repos/asf/incubator-mxnet.git.


from a21d3e0  Fix more broken links (#7480)
 add 7d6385a  fix autograd memory cost (#7478)

No new revisions were added by this update.

Summary of changes:
 nnvm|  2 +-
 python/mxnet/gluon/data/dataloader.py   | 10 +++-
 python/mxnet/gluon/data/dataset.py  |  7 ++-
 python/mxnet/gluon/nn/basic_layers.py   | 12 +++--
 python/mxnet/gluon/parameter.py | 75 
 src/executor/attach_op_execs_pass.cc| 40 +--
 src/ndarray/autograd.cc | 87 +++--
 src/ndarray/autograd.h  |  2 +-
 tests/python/gpu/test_operator_gpu.py   | 11 +
 tests/python/unittest/test_gluon.py | 13 -
 tests/python/unittest/test_gluon_rnn.py |  1 +
 11 files changed, 211 insertions(+), 49 deletions(-)

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[GitHub] piiswrong closed pull request #7478: fix autograd memory cost

[git]

piiswrong closed pull request #7478: fix autograd memory cost
URL: https://github.com/apache/incubator-mxnet/pull/7478
 
 
   
 

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[GitHub] piiswrong opened a new pull request #7478: fix autograd memory cost

[git]

piiswrong opened a new pull request #7478: fix autograd memory cost
URL: https://github.com/apache/incubator-mxnet/pull/7478
 
 
   
 

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[GitHub] piiswrong commented on issue #7434: fix a formula typo in doc

[git]

piiswrong commented on issue #7434: fix a formula typo in doc
URL: https://github.com/apache/incubator-mxnet/pull/7434#issuecomment-322556923
 
 
   Looks like should be channel instead of num_channel
 

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[GitHub] zjjxsjh opened a new pull request #7434: fix a formula typo in doc

[git]

zjjxsjh opened a new pull request #7434: fix a formula typo in doc
URL: https://github.com/apache/incubator-mxnet/pull/7434
 
 
   
 

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[GitHub] thirdwing commented on a change in pull request #7476: R-package RNN refactor

[git]

thirdwing commented on a change in pull request #7476: R-package RNN refactor
URL: https://github.com/apache/incubator-mxnet/pull/7476#discussion_r133259264
 
 

 ##
 File path: R-package/R/rnn.graph.R
 ##
 @@ -0,0 +1,123 @@
+library(mxnet)
+
+# RNN graph design
+rnn.graph <- function(num.rnn.layer, 
+  input.size,
+  num.embed, 
+  num.hidden,
+  num.label,
+  dropout = 0,
+  ignore_label = 0,
+  init.state = NULL,
+  config,
+  cell.type="gru",
+  masking = T,
+  output_last_state = F) {
+  
+  # define input arguments
+  label <- mx.symbol.Variable("label")
+  data <- mx.symbol.Variable("data")
+  seq.mask <- mx.symbol.Variable("seq.mask")
+  
+  embed.weight <- mx.symbol.Variable("embed.weight")
+  rnn.params.weight <- mx.symbol.Variable("rnn.params.weight")
+  rnn.state.weight <- mx.symbol.Variable("rnn.state.weight")
+  if (cell.type == "lstm") rnn.state.cell.weight <- 
mx.symbol.Variable("rnn.state.cell.weight")
+  cls.weight <- mx.symbol.Variable("cls.weight")
+  cls.bias <- mx.symbol.Variable("cls.bias")
+  
+  data <- mx.symbol.transpose(data=data)  
+  # seq.mask <- mx.symbol.stop_gradient(seq.mask, name="seq.mask")
+  
+  embed <- mx.symbol.Embedding(data=data, input_dim=input.size,
+   weight=embed.weight, output_dim=num.embed, 
name="embed")
+  
+  if (cell.type == "lstm") {
+rnn <- mx.symbol.RNN(data=embed, state=rnn.state.weight, state_cell = 
rnn.state.cell.weight, parameters=rnn.params.weight, state.size=num.hidden, 
num.layers=num.rnn.layer, bidirectional=F, mode=cell.type, state.outputs=F, 
p=dropout, name=paste(cell.type, num.rnn.layer, "layer", sep="_"))
+
+  } else {
+rnn <- mx.symbol.RNN(data=embed, state=rnn.state.weight, 
parameters=rnn.params.weight, state.size=num.hidden, num.layers=num.rnn.layer, 
bidirectional=F, mode=cell.type, state.outputs=F, p=dropout, 
name=paste(cell.type, num.rnn.layer, "layer", sep="_"))
+  }
+  
+  if (config=="seq-to-one") {
+
+if (masking) mask <- mx.symbol.SequenceLast(data=rnn[[1]], 
use.sequence.length = T, sequence_length = seq.mask, name = "mask") else
+  mask <- mx.symbol.identity(data = rnn[[1]], name = "mask")
+
+fc <- mx.symbol.FullyConnected(data=mask,
+   weight=cls.weight,
+   bias=cls.bias,
+   num.hidden=num.label,
+   name = "decode")
+
+loss <- mx.symbol.SoftmaxOutput(data=fc, name="sm", label=label, 
ignore_label=ignore_label)
+
+  } else if (config=="one-to-one"){
+
+if (masking) mask <- mx.symbol.SequenceMask(data = rnn[[1]], 
use.sequence.length = T, sequence_length = seq.mask, name = "mask") else
+  mask <- mx.symbol.identity(data = rnn[[1]], name = "mask")
+
+reshape = mx.symbol.reshape(mask, shape=c(num.hidden, -1))
+
+fc <- mx.symbol.FullyConnected(data=reshape,
+   weight=cls.weight,
+   bias=cls.bias,
+   num.hidden=num.label,
+   name = "decode")
+
+label <- mx.symbol.reshape(data=label, shape=c(-1))
+loss <- mx.symbol.SoftmaxOutput(data=fc, name="sm", label=label, 
ignore_label=ignore_label)
+
+  }
+  
+  if (output_last_state){
+# group <- mx.symbol.Group(c(unlist(last.states), loss))
+# return(group)
+return(loss)
+  } else return(loss)
+}
+
+
+
+# data <- mx.symbol.Variable("data")
 
 Review comment:
   Can we remove the testing code or move into unit test?
 

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[incubator-mxnet-site] branch asf-site updated: Fix broken links

[lxn2]

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lxn2 pushed a commit to branch asf-site
in repository https://gitbox.apache.org/repos/asf/incubator-mxnet-site.git


The following commit(s) were added to refs/heads/asf-site by this push:
 new d777c9c  Fix broken links
d777c9c is described below

commit d777c9c8cb629d0e93ba61c7fbbaf73a5f5fc6ec
Author: Wang 
AuthorDate: Tue Aug 15 11:06:05 2017 -0700

Fix broken links
---
 get_started/windows_setup.html |  2 +-
 model_zoo/index.html   | 10 +-
 versions/master/get_started/windows_setup.html |  2 +-
 versions/master/model_zoo/index.html   | 10 +-
 4 files changed, 12 insertions(+), 12 deletions(-)

diff --git a/get_started/windows_setup.html b/get_started/windows_setup.html
index 7645a4e..ff5d687 100644
--- a/get_started/windows_setup.html
+++ b/get_started/windows_setup.html
@@ -259,7 +259,7 @@ This produces a library called 
 To build and install MXNet yourself, you need the following dependencies. 
Install the required dependencies:
 
 If https://www.visualstudio.com/downloads/;>Microsoft Visual Studio 2013 
is not already installed, download and install it. You can download and install 
the free community edition.
-Install https://www.microsoft.com/en-us/download/details.aspx?id=41151;>Visual 
C++ Compiler Nov 2013 CTP.
+Install http://landinghub.visualstudio.com/visual-cpp-build-tools;>Visual C++ 
Compiler.
 Back up all of the files in the C:\Program Files (x86)\Microsoft Visual Studio 12.0\VC folder to a 
different location.
 Copy all of the files in the C:\Program Files (x86)\Microsoft Visual C++ Compiler Nov 2013 CTP folder (or the folder where you extracted the zip 
archive) to the C:\ProgramDownload and install http://sourceforge.net/projects/opencvlibrary/files/opencv-win/3.0.0/opencv-3.0.0.exe/download;>OpenCV.
diff --git a/model_zoo/index.html b/model_zoo/index.html
index 5b72005..7b69d56 100644
--- a/model_zoo/index.html
+++ b/model_zoo/index.html
@@ -269,7 +269,7 @@ ongoing project to collect complete models, with python 
scripts, pre-trained wei
 http://places2.csail.mit.edu/download.html;>Places2: There are 1.6 
million train images from 365 scene categories in the Places365-Standard, which 
are used to train the Places365 CNNs. There are 50 images per category in the 
validation set and 900 images per category in the testing set. Compared to the 
train set of Places365-Standard, the train set of Places365-Challenge has 6.2 
million extra images, leading to totally 8 million train images fo [...]
 https://aws.amazon.com/public-datasets/multimedia-commons/;>Multimedia 
Commons: YFCC100M (99.2 million images and 0.8 million videos from Flickr) 
and supplemental material (pre-extracted features, additional annotations).
 
-For instructions on using these models, see https://mxnet.incubator.apache.org/tutorials/python/predict_imagenet.html;>the
 python tutorial on using pre-trained ImageNet models.
+For instructions on using these models, see https://mxnet.incubator.apache.org/tutorials/python/predict_image.html;>the
 python tutorial on using pre-trained ImageNet models.
 
 
 
@@ -364,12 +364,12 @@ ongoing project to collect complete models, with python 
scripts, pre-trained wei
 
 
 Recurrent 
Neural Networks (RNNs) including LSTMs¶
-MXNet supports many types of recurrent neural networks (RNNs), including 
Long Short-Term Memory (http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf;>LSTM)
+MXNet supports many types of recurrent neural networks (RNNs), including 
Long Short-Term Memory (http://www.bioinf.jku.at/publications/older/2604.pdf;>LSTM)
 and Gated Recurrent Units (GRU) networks. Some available datasets include:
 
-https://www.cis.upenn.edu/~treebank/;>Penn Treebank (PTB): Text 
corpus with ~1 million words. Vocabulary is limited to 10,000 words. The task 
is predicting downstream words/characters.
+https://catalog.ldc.upenn.edu/LDC95T7;>Penn Treebank (PTB): Text 
corpus with ~1 million words. Vocabulary is limited to 10,000 words. The task 
is predicting downstream words/characters.
 http://cs.stanford.edu/people/karpathy/char-rnn/;>Shakespeare: 
Complete text from Shakespeare’s works.
-https://s3.amazonaws.com/text-datasets;>IMDB reviews: 25,000 movie 
reviews, labeled as positive or negative
+https://getsatisfaction.com/imdb/topics/imdb-data-now-available-in-amazon-s3;>IMDB
 reviews: 25,000 movie reviews, labeled as positive or negative
 https://research.facebook.com/researchers/1543934539189348;>Facebook 
bAbI: As a set of 20 question  answer tasks, each with 1,000 training 
examples.
 http://mscoco.org/;>Flickr8k, 
COCO: Images with associated caption (sentences). Flickr8k consists of 
8,092 images captioned by AmazonTurkers with ~40,000 captions. COCO has 328,000 
images, each with 5 captions. The COCO images also come with labeled objects 
using segmentation algorithms.
 
@@ -393,7 +393,7 @@ and Gated 

[GitHub] piiswrong commented on issue #7393: add depthwise convolution's gpu version optimization

[git]

piiswrong commented on issue #7393: add depthwise convolution's gpu version 
optimization
URL: https://github.com/apache/incubator-mxnet/pull/7393#issuecomment-322552001
 
 
   Could you rebase to master and push again? Somehow test is failing
 

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[GitHub] cheshirecats commented on issue #7481: [v0.11.0] Amalgamation for Javascript JS has unresolved symbol: __cxa_thread_atexit

[git]

cheshirecats commented on issue #7481: [v0.11.0] Amalgamation for Javascript JS 
has unresolved symbol: __cxa_thread_atexit
URL: 
https://github.com/apache/incubator-mxnet/issues/7481#issuecomment-322547610
 
 
   For now I am using #define DMLC_CXX11_THREAD_LOCAL 0 in amalgamation.py to 
solve it.
 

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[GitHub] statist-bhfz opened a new issue #7483: MXNet - R API broken link

[git]

statist-bhfz opened a new issue #7483: MXNet - R API broken link
URL: https://github.com/apache/incubator-mxnet/issues/7483
 
 
   "MXNet R Reference Manual" on http://www.mxnet.io/api/r/index.html actually 
contains Julia reference.
 

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[GitHub] sandeep-krishnamurthy opened a new pull request #7482: Adding developer keys for sandeep

[git]

sandeep-krishnamurthy opened a new pull request #7482: Adding developer keys 
for sandeep
URL: https://github.com/apache/incubator-mxnet/pull/7482
 
 
   @nswamy @lxn2 
 

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[GitHub] szha commented on issue #7455: Distributed training is slow

[git]

szha commented on issue #7455: Distributed training is slow
URL: 
https://github.com/apache/incubator-mxnet/issues/7455#issuecomment-322544218
 
 
   Thanks, @ptrendx. @madjam for more context. 
 

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[GitHub] kevinthesun opened a new pull request #9: Fix broken links

[git]

kevinthesun opened a new pull request #9: Fix broken links
URL: https://github.com/apache/incubator-mxnet-site/pull/9
 
 
   
 

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[GitHub] thirdwing commented on a change in pull request #7476: R-package RNN refactor

[git]

thirdwing commented on a change in pull request #7476: R-package RNN refactor
URL: https://github.com/apache/incubator-mxnet/pull/7476#discussion_r133259264
 
 

 ##
 File path: R-package/R/rnn.graph.R
 ##
 @@ -0,0 +1,123 @@
+library(mxnet)
+
+# RNN graph design
+rnn.graph <- function(num.rnn.layer, 
+  input.size,
+  num.embed, 
+  num.hidden,
+  num.label,
+  dropout = 0,
+  ignore_label = 0,
+  init.state = NULL,
+  config,
+  cell.type="gru",
+  masking = T,
+  output_last_state = F) {
+  
+  # define input arguments
+  label <- mx.symbol.Variable("label")
+  data <- mx.symbol.Variable("data")
+  seq.mask <- mx.symbol.Variable("seq.mask")
+  
+  embed.weight <- mx.symbol.Variable("embed.weight")
+  rnn.params.weight <- mx.symbol.Variable("rnn.params.weight")
+  rnn.state.weight <- mx.symbol.Variable("rnn.state.weight")
+  if (cell.type == "lstm") rnn.state.cell.weight <- 
mx.symbol.Variable("rnn.state.cell.weight")
+  cls.weight <- mx.symbol.Variable("cls.weight")
+  cls.bias <- mx.symbol.Variable("cls.bias")
+  
+  data <- mx.symbol.transpose(data=data)  
+  # seq.mask <- mx.symbol.stop_gradient(seq.mask, name="seq.mask")
+  
+  embed <- mx.symbol.Embedding(data=data, input_dim=input.size,
+   weight=embed.weight, output_dim=num.embed, 
name="embed")
+  
+  if (cell.type == "lstm") {
+rnn <- mx.symbol.RNN(data=embed, state=rnn.state.weight, state_cell = 
rnn.state.cell.weight, parameters=rnn.params.weight, state.size=num.hidden, 
num.layers=num.rnn.layer, bidirectional=F, mode=cell.type, state.outputs=F, 
p=dropout, name=paste(cell.type, num.rnn.layer, "layer", sep="_"))
+
+  } else {
+rnn <- mx.symbol.RNN(data=embed, state=rnn.state.weight, 
parameters=rnn.params.weight, state.size=num.hidden, num.layers=num.rnn.layer, 
bidirectional=F, mode=cell.type, state.outputs=F, p=dropout, 
name=paste(cell.type, num.rnn.layer, "layer", sep="_"))
+  }
+  
+  if (config=="seq-to-one") {
+
+if (masking) mask <- mx.symbol.SequenceLast(data=rnn[[1]], 
use.sequence.length = T, sequence_length = seq.mask, name = "mask") else
+  mask <- mx.symbol.identity(data = rnn[[1]], name = "mask")
+
+fc <- mx.symbol.FullyConnected(data=mask,
+   weight=cls.weight,
+   bias=cls.bias,
+   num.hidden=num.label,
+   name = "decode")
+
+loss <- mx.symbol.SoftmaxOutput(data=fc, name="sm", label=label, 
ignore_label=ignore_label)
+
+  } else if (config=="one-to-one"){
+
+if (masking) mask <- mx.symbol.SequenceMask(data = rnn[[1]], 
use.sequence.length = T, sequence_length = seq.mask, name = "mask") else
+  mask <- mx.symbol.identity(data = rnn[[1]], name = "mask")
+
+reshape = mx.symbol.reshape(mask, shape=c(num.hidden, -1))
+
+fc <- mx.symbol.FullyConnected(data=reshape,
+   weight=cls.weight,
+   bias=cls.bias,
+   num.hidden=num.label,
+   name = "decode")
+
+label <- mx.symbol.reshape(data=label, shape=c(-1))
+loss <- mx.symbol.SoftmaxOutput(data=fc, name="sm", label=label, 
ignore_label=ignore_label)
+
+  }
+  
+  if (output_last_state){
+# group <- mx.symbol.Group(c(unlist(last.states), loss))
+# return(group)
+return(loss)
+  } else return(loss)
+}
+
+
+
+# data <- mx.symbol.Variable("data")
 
 Review comment:
   Can we remove the testing code?
 

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[GitHub] cheshirecats opened a new issue #7481: [v0.11.0] Amalgamation for Javascript JS has unresolved symbol: __cxa_thread_atexit

[git]

cheshirecats opened a new issue #7481: [v0.11.0] Amalgamation for Javascript JS 
has unresolved symbol: __cxa_thread_atexit
URL: https://github.com/apache/incubator-mxnet/issues/7481
 
 
   The amalgamation for Javascript in mxnet v0.9.2 worked fine, however for the 
latest 0.11.0 version, 
   I got this error while running "make clean libmxnet_predict.js MIN=1":
   `warning: unresolved symbol: __cxa_thread_atexit`
   and the compiled js code crashed upon loading (with the same unresolved 
symbol error).
   
   It seems this may have something to do with C++11 features support in emcc.
 

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[GitHub] thirdwing commented on a change in pull request #7476: R-package RNN refactor

[git]

thirdwing commented on a change in pull request #7476: R-package RNN refactor
URL: https://github.com/apache/incubator-mxnet/pull/7476#discussion_r133259192
 
 

 ##
 File path: R-package/R/rnn.infer.R
 ##
 @@ -0,0 +1,77 @@
+library(mxnet)
+
+source("rnn.R")
 
 Review comment:
   Please remove these two lines.
 

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[GitHub] thirdwing commented on a change in pull request #7476: R-package RNN refactor

[git]

thirdwing commented on a change in pull request #7476: R-package RNN refactor
URL: https://github.com/apache/incubator-mxnet/pull/7476#discussion_r133259025
 
 

 ##
 File path: R-package/R/rnn.graph.R
 ##
 @@ -0,0 +1,123 @@
+library(mxnet)
 
 Review comment:
   Please remove "library(mxnet)".
 

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[GitHub] piiswrong closed pull request #7480: Fix more broken links

[git]

piiswrong closed pull request #7480: Fix more broken links
URL: https://github.com/apache/incubator-mxnet/pull/7480
 
 
   
 

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[GitHub] thirdwing commented on a change in pull request #7476: R-package RNN refactor

[git]

thirdwing commented on a change in pull request #7476: R-package RNN refactor
URL: https://github.com/apache/incubator-mxnet/pull/7476#discussion_r133258978
 
 

 ##
 File path: R-package/R/rnn.R
 ##
 @@ -1,342 +1,101 @@
-# rnn cell symbol
-rnn <- function(num.hidden, indata, prev.state, param, seqidx, 
-layeridx, dropout=0., batch.norm=FALSE) {
-if (dropout > 0. )
-indata <- mx.symbol.Dropout(data=indata, p=dropout)
-i2h <- mx.symbol.FullyConnected(data=indata,
-weight=param$i2h.weight,
-bias=param$i2h.bias,
-num.hidden=num.hidden,
-name=paste0("t", seqidx, ".l", layeridx, 
".i2h"))
-h2h <- mx.symbol.FullyConnected(data=prev.state$h,
-weight=param$h2h.weight,
-bias=param$h2h.bias,
-num.hidden=num.hidden,
-name=paste0("t", seqidx, ".l", layeridx, 
".h2h"))
-hidden <- i2h + h2h
+library(mxnet)
 
 Review comment:
   Please remove "library(mxnet)".
 

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[GitHub] thirdwing commented on a change in pull request #7476: R-package RNN refactor

[git]

thirdwing commented on a change in pull request #7476: R-package RNN refactor
URL: https://github.com/apache/incubator-mxnet/pull/7476#discussion_r133258942
 
 

 ##
 File path: R-package/R/mx.io.bucket.iter.R
 ##
 @@ -0,0 +1,110 @@
+library(mxnet)
 
 Review comment:
   Please remove "library(mxnet)".
 

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[GitHub] sandeep-krishnamurthy opened a new pull request #7480: Fix more broken links

[git]

sandeep-krishnamurthy opened a new pull request #7480: Fix more broken links
URL: https://github.com/apache/incubator-mxnet/pull/7480
 
 
   @kevinthesun @lxn2 
 

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[GitHub] larroy commented on a change in pull request #7416: update submoules with android fixes

[git]

larroy commented on a change in pull request #7416: update submoules with 
android fixes
URL: https://github.com/apache/incubator-mxnet/pull/7416#discussion_r133256329
 
 

 ##
 File path: src/operator/c_lapack_api.h
 ##
 @@ -73,8 +73,13 @@ using namespace mshadow;
 extern "C" {
 
   // Fortran signatures
-  #define MXNET_LAPACK_FSIGNATURE1(func, dtype) \
-void func##_(char *uplo, int *n, dtype *a, int *lda, int *info);
+  #ifdef __ANDROID__
+#define MXNET_LAPACK_FSIGNATURE1(func, dtype) \
+  int func##_(char* uplo, int* n, dtype* a, int* lda, int *info);
 
 Review comment:
   https://github.com/xianyi/OpenBLAS/blob/develop/common_lapack.h#L43
 

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[GitHub] thirdwing commented on issue #7461: [R] update tutorial link. close #6536

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thirdwing commented on issue #7461: [R] update tutorial link. close #6536
URL: https://github.com/apache/incubator-mxnet/pull/7461#issuecomment-322538191
 
 
   @sandeep-krishnamurthy Please have a look at this.
 

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[GitHub] thirdwing commented on issue #7461: [R] update tutorial link. close #6536

[git]

thirdwing commented on issue #7461: [R] update tutorial link. close #6536
URL: https://github.com/apache/incubator-mxnet/pull/7461#issuecomment-322538068
 
 
   This will add an index page at http://mxnet.io/tutorials/r/index.html
   
   All the R tutorials are already on our website 
(http://mxnet.io/tutorials/r/), but no links to them.
   
   ![screenshot from 2017-08-15 
10-44-53](https://user-images.githubusercontent.com/1547093/29328282-e4909e8a-81a6-11e7-821c-01025d649fc6.png)
   
 

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[GitHub] piiswrong closed pull request #7478: fix autograd memory cost

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piiswrong closed pull request #7478: fix autograd memory cost
URL: https://github.com/apache/incubator-mxnet/pull/7478
 
 
   
 

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[GitHub] piiswrong opened a new pull request #7479: Fix autograd memory

[git]

piiswrong opened a new pull request #7479: Fix autograd memory
URL: https://github.com/apache/incubator-mxnet/pull/7479
 
 
   
 

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[GitHub] piiswrong commented on a change in pull request #7416: update submoules with android fixes

[git]

piiswrong commented on a change in pull request #7416: update submoules with 
android fixes
URL: https://github.com/apache/incubator-mxnet/pull/7416#discussion_r133249270
 
 

 ##
 File path: src/operator/c_lapack_api.h
 ##
 @@ -73,8 +73,13 @@ using namespace mshadow;
 extern "C" {
 
   // Fortran signatures
-  #define MXNET_LAPACK_FSIGNATURE1(func, dtype) \
-void func##_(char *uplo, int *n, dtype *a, int *lda, int *info);
+  #ifdef __ANDROID__
+#define MXNET_LAPACK_FSIGNATURE1(func, dtype) \
+  int func##_(char* uplo, int* n, dtype* a, int* lda, int *info);
 
 Review comment:
   why does it need to be int?
 

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[GitHub] piiswrong opened a new pull request #7478: fix autograd memory cost

[git]

piiswrong opened a new pull request #7478: fix autograd memory cost
URL: https://github.com/apache/incubator-mxnet/pull/7478
 
 
   
 

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[GitHub] piiswrong closed pull request #7477: fix autograd memory cost

[git]

piiswrong closed pull request #7477: fix autograd memory cost
URL: https://github.com/apache/incubator-mxnet/pull/7477
 
 
   
 

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[GitHub] thomasmooon opened a new issue #7475: Paradox VRAM demand as a function of batch size: Low batch size, high VRAM demand

[git]

thomasmooon opened a new issue #7475: Paradox VRAM demand as a function of 
batch size: Low batch size, high VRAM demand
URL: https://github.com/apache/incubator-mxnet/issues/7475
 
 
   Dear community,
   
   I'm running mxnet on the environment as mentioned below with the following 
hardware:
   - 32 GB RAM
   - 8 x NVIDIA 1080 Ti, 8 GB VRAM
   - 12 CPUs
   
   MXnet is utlized to train a stacked denoising autoencoder reconstruction of 
an array with dimension about 62.000 x 100.
   
   I was looking for a batch size with minimum VRAM demand. Therefore I 
measured the VRAM demand per single Card for a given combination of "number of 
GPUs" and "array.batch.size".
   
   The results are shown in the attached table:
   Columns indicate array.batch.size and rows indicate number of GPUs.
   Each entry indicates the GPU memory usage per Card.
   
   
![paradox_vram_batchsize](https://user-images.githubusercontent.com/29228225/29312390-5b2b6c60-81b5-11e7-9258-f877551427e1.jpg)
   
   As one can see if array.batch.size is below or above a particular value, 
VRAM demand rises.
   Given an array.batch.size of 12, computation crashes due to "cuda memory 
allocation error" (red style).
   Given a array.batch.size of 128 (2 and 4 GPUS) or 256 (6) results in a 
minimum VRAM demand of about 3GB per card (green style).
   
   My question is:
   **Why does VRAM hunger rise below a particular array.batch.size value?**
   
   ## Environment info
   Operating System: REDHAT 7.3
   Package used (Python/R/Scala/Julia): R
   MXNet version: 0.1.0.1
   
   R `sessionInfo()`:
   R version 3.4.0 (2017-04-21)
   Platform: x86_64-redhat-linux-gnu (64-bit)
   Running under: Red Hat Enterprise Linux Server 7.3 (Maipo)
   
   Matrix products: default
   BLAS/LAPACK: /usr/lib64/R/lib/libRblas.so
   
   locale:
[1] LC_CTYPE=en_GB.UTF-8   LC_NUMERIC=C   
LC_TIME=en_GB.UTF-8LC_COLLATE=en_GB.UTF-8 LC_MONETARY=en_GB.UTF-8   
 LC_MESSAGES=en_GB.UTF-8LC_PAPER=en_GB.UTF-8  
[8] LC_NAME=C  LC_ADDRESS=C   LC_TELEPHONE=C
 LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C   
   
   attached base packages:
   [1] stats graphics  grDevices utils datasets  methods   base 
   
   loaded via a namespace (and not attached):
   [1] compiler_3.4.0 tools_3.4.0   
   
   
   
 

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[GitHub] wanderingpj opened a new issue #7474: Training error when using cifar100.

[git]

wanderingpj opened a new issue #7474: Training error when using cifar100.
URL: https://github.com/apache/incubator-mxnet/issues/7474
 
 
   I train the network given in
   
https://github.com/dmlc/mxnet-notebooks/blob/master/python/moved-from-mxnet/cifar-100.ipynb
   with cifar100. But here comes a training error like this.
   ![qq 
20170815175608](https://user-images.githubusercontent.com/24189081/29311528-df0311c6-81e4-11e7-9613-ff7c055b7a12.png)
   What's the problem?@aileli
   
 

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[GitHub] wanderingpj closed issue #7473: Training error when using cifar100.

[git]

wanderingpj closed issue #7473: Training error when using cifar100.
URL: https://github.com/apache/incubator-mxnet/issues/7473
 
 
   
 

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[GitHub] wanderingpj opened a new issue #7473: Training error when using cifar100.

[git]

wanderingpj opened a new issue #7473: Training error when using cifar100.
URL: https://github.com/apache/incubator-mxnet/issues/7473
 
 
   I train the network given in 
   
https://github.com/dmlc/mxnet-notebooks/blob/master/python/moved-from-mxnet/cifar-100.ipynb
   with cifar100. But here comes a training error like this.
   file:///C:/Users/Administrator/Desktop/QQ%E6%88%AA%E5%9B%BE20170815173949.png
   What's the problem?@aileli
 

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[GitHub] starimpact commented on issue #7455: Distributed training is slow

[git]

starimpact commented on issue #7455: Distributed training is slow
URL: 
https://github.com/apache/incubator-mxnet/issues/7455#issuecomment-322418256
 
 
   I am using mxnet0.8.0, HAHAHA...
   I noticed that your "one server " is actually "local", because that your 
"kvstore=device". the kvstore will use gpu to update parameters.
   And , your "two server" is really the distributed mode. in the "dist ..." 
mode,  kvstore will use cpu to update the parameters.
   So... your speed descending is normal.
 

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[GitHub] squidszyd commented on issue #7427: how to set dataiter with multi data?

[git]

squidszyd commented on issue #7427: how to set dataiter with multi data?
URL: 
https://github.com/apache/incubator-mxnet/issues/7427#issuecomment-322420119
 
 
   Use collections.namedtuple:
   Batch = namedtuple('Batch',['data', 'label'])
   def __iter__(self):
   ...
   yield Batch(data=[data1, data2, data3,...], label=[...])
   @property
   def provide_data(self):
return [('data1', shape1), ('data2', shape2), ('data3', shape3), ...]
   
 

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[GitHub] starimpact commented on issue #7455: Distributed training is slow

[git]

starimpact commented on issue #7455: Distributed training is slow
URL: 
https://github.com/apache/incubator-mxnet/issues/7455#issuecomment-322418256
 
 
   I am using mxnet0.8.0, HAHAHA...
 

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[GitHub] kurt-o-sys opened a new issue #7472: continuously train rnn - training data stream?

[git]

kurt-o-sys opened a new issue #7472: continuously train rnn - training data 
stream?
URL: https://github.com/apache/incubator-mxnet/issues/7472
 
 
   ## Question
   
   Usually, a neural network is trained by using a training, validation and 
test set. 
   Having a continuous series of data, an event (new training data) occurring 
every 1-5 seconds, is it possible to continuously train (update) a recurrent 
neural network using mxnet? I don't need to care to reuse previous (training) 
data points: I just want to update the weights slightly(!) on each new event.
   
   It's for a behaviour/game like system: depending on the 
(expressed/intentional) behaviour of the players (the features), the output of 
the system should be estimated and continuously adapted (for further 
processing). The system has to learn on the way, and being able to cope with, 
to a certain extend, changing player behaviour and it needs to remember certain 
patterns from weeks and if possible, months, ago. (I'd probably be mainly an 
LSTM.)
   
   Storing all data and retrain the system on that data is close to impossible 
because:
   1. I estimate there's about 10-100GB of data per day (will be varying)
   2. retraining every time, let's say, 10 seconds, on all existing data would 
take too long.
   
   I want a system that continuously trains itself on the real data, not 
splitting into training/testing/validation sets:
   1. The training set is the real data, comparing the actual state of the 
system with the prediction previously made
   2. There's not validation, besides the fact that the system validates itself
   3. Testing is done on every new event. The predictive power will be 
continuously determined.
   
   Can this be done with mxnet, having a training data stream?
   
   ## Environment info
   This is not really relevant, but well, I don't mind providing it :)
   
   Operating System:
   ```
   $ uname -ar
   Linux flipflap 4.4.0-57-generic #78-Ubuntu SMP Fri Dec 9 23:50:32 UTC 2016 
x86_64 x86_64 x86_64 GNU/Linux
   ```
   
   Compiler: ?
   
   Package used (Python/R/Scala/Julia): R
   
   MXNet version:
   ```
   > packageVersion("mxnet")
   [1] ?0.10.1?
   > sessionInfo()
   R version 3.4.1 (2017-06-30)
   Platform: x86_64-pc-linux-gnu (64-bit)
   Running under: Linux Mint 18
   
   Matrix products: default
   BLAS: /usr/lib/openblas-base/libblas.so.3
   LAPACK: /usr/lib/libopenblasp-r0.2.18.so
   
   locale:
[1] LC_CTYPE=en_US.UTF-8   LC_NUMERIC=C   
LC_TIME=en_US.UTF-8LC_COLLATE=en_US.UTF-8 LC_MONETARY=de_BE.UTF-8   
[6] LC_MESSAGES=en_US.UTF-8LC_PAPER=de_BE.UTF-8   LC_NAME=C 
 LC_ADDRESS=C   LC_TELEPHONE=C
   [11] LC_MEASUREMENT=de_BE.UTF-8 LC_IDENTIFICATION=C   
   
   attached base packages:
   [1] stats graphics  grDevices utils datasets  methods   base 
   
   other attached packages:
   [1] mxnet_0.10.1 httr_1.2.1   jsonlite_1.5
   
   loaded via a namespace (and not attached):
[1] Rcpp_0.12.12   compiler_3.4.1 RColorBrewer_1.1-2 
influenceR_0.1.0   plyr_1.8.4 bindr_0.1  viridis_0.4.0 
[8] tools_3.4.1digest_0.6.12  tibble_1.3.3   gtable_0.2.0   
viridisLite_0.2.0  rgexf_0.15.3   pkgconfig_2.0.1   
   [15] rlang_0.1.1igraph_1.1.2   rstudioapi_0.6 curl_2.4   
bindrcpp_0.2   gridExtra_2.2.1stringr_1.2.0 
   [22] DiagrammeR_0.9.0   dplyr_0.7.2htmlwidgets_0.9grid_3.4.1 
glue_1.1.1 R6_2.2.2   Rook_1.1-1
   [29] XML_3.98-1.9   ggplot2_2.2.1  magrittr_1.5   
codetools_0.2-15   scales_0.4.1   htmltools_0.3.6assertthat_0.1
   [36] colorspace_1.3-2   brew_1.0-6 stringi_1.1.5  
visNetwork_2.0.1   lazyeval_0.2.0 munsell_0.4.3
   ```
 

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[GitHub] cuteding closed issue #7471: Why are resnet's RELU and BN set before CONV?

[git]

cuteding closed issue #7471: Why are resnet's RELU and BN set before CONV?
URL: https://github.com/apache/incubator-mxnet/issues/7471
 
 
   
 

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[GitHub] thirdwing commented on issue #7470: R-package RNN refactor

[git]

thirdwing commented on issue #7470: R-package RNN refactor
URL: https://github.com/apache/incubator-mxnet/pull/7470#issuecomment-322392069
 
 
   Thank you for this. I suggest you not update submodules in this PR.
 

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[GitHub] leoxiaobin commented on issue #7455: Distributed training is slow

[git]

leoxiaobin commented on issue #7455: Distributed training is slow
URL: 
https://github.com/apache/incubator-mxnet/issues/7455#issuecomment-322390365
 
 
   @starimpact , I have tried to use 4 servers per machine, I got almost the 
same result.
 

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[GitHub] leoxiaobin commented on issue #7455: Distributed training is slow

[git]

leoxiaobin commented on issue #7455: Distributed training is slow
URL: 
https://github.com/apache/incubator-mxnet/issues/7455#issuecomment-322390219
 
 
   @szha , every server has 8 TitanXp GPUs and 2 Intel Xeon CPU E5-2650 v2@ 
2.60GHz.
   The two servers are connected with IB cards. 
   The test is using --benchmark = 1 configuration, so there is no disk I/O 
operation. 
 

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