[GitHub] piiswrong closed pull request #7147: cuda support for linalg-functions, restructuring of linalg interfaces

[git]

piiswrong closed pull request #7147: cuda support for linalg-functions, 
restructuring of linalg interfaces
URL: https://github.com/apache/incubator-mxnet/pull/7147
 
 
   
 

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[GitHub] piiswrong commented on issue #7425: Tensorcore fullyconnected support

[git]

piiswrong commented on issue #7425: Tensorcore fullyconnected support
URL: https://github.com/apache/incubator-mxnet/pull/7425#issuecomment-322000376
 
 
   The linalg_gemm PR is merged. Please use that for tensorcore support
 

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[GitHub] lxn2 commented on issue #5: Change to 0.11-RC

[git]

lxn2 commented on issue #5: Change to 0.11-RC
URL: 
https://github.com/apache/incubator-mxnet-site/pull/5#issuecomment-321989354
 
 
   Does this not list 0.10 as one of the versions? I wiped it out and pushed 
your changes but tags.txt only has .11
 

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[GitHub] lxn2 commented on issue #5: Change to 0.11-RC

[git]

lxn2 commented on issue #5: Change to 0.11-RC
URL: 
https://github.com/apache/incubator-mxnet-site/pull/5#issuecomment-321993040
 
 
   That's not my understanding...did Steffen suggest to delete 0.10 anywhere? I 
thought we wanted to have all the versions including .10 so far?
 

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[GitHub] asmushetzel commented on issue #7147: cuda support for linalg-functions, restructuring of linalg interfaces

[git]

asmushetzel commented on issue #7147: cuda support for linalg-functions, 
restructuring of linalg interfaces
URL: https://github.com/apache/incubator-mxnet/pull/7147#issuecomment-321958146
 
 
   So this is done from my point of view. Jenkins failed on some nodes in some 
tests but that is all due to an unrelated problem with some Gluon testcase (I 
examined all the logfiles). The relevant la_op tests  pass on all nodes  
successfully. 
   
   Eric, from my point of view this can be merged now. 
 

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[GitHub] goodtogood commented on issue #7154: mx.contrib.sym.ctc_loss slow down extremely on gpu with large alphabet_size,but faster on cpu

[git]

goodtogood commented on issue #7154: mx.contrib.sym.ctc_loss slow down 
extremely on gpu with large alphabet_size,but faster on cpu
URL: 
https://github.com/apache/incubator-mxnet/issues/7154#issuecomment-321994447
 
 
   same problem, would you like to share your code ?
   thx!
 

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[GitHub] lxn2 closed pull request #6: Fix example link

[git]

lxn2 closed pull request #6: Fix example link
URL: https://github.com/apache/incubator-mxnet-site/pull/6
 
 
   
 

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[GitHub] szha opened a new pull request #7442: contrib ctc interface changes for compatibility, and gluon CTC

[git]

szha opened a new pull request #7442: contrib ctc interface changes for 
compatibility, and gluon CTC
URL: https://github.com/apache/incubator-mxnet/pull/7442
 
 
   This change is to make current contrib CTC compatible with the cudnn7 CTC 
interface, and to add CTC loss layer for gluon.
 

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[GitHub] piiswrong closed pull request #7441: broken link in readme

[git]

piiswrong closed pull request #7441: broken link in readme
URL: https://github.com/apache/incubator-mxnet/pull/7441
 
 
   
 

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[GitHub] ufukcbicici opened a new issue #7443: tf.boolean_mask equivalent in MxNet

[git]

ufukcbicici opened a new issue #7443: tf.boolean_mask equivalent in MxNet
URL: https://github.com/apache/incubator-mxnet/issues/7443
 
 
   In my archictecture, I want to pick same of the samples in a minibatch at 
different stages of computation, based on a binary vector produced elsewhere in 
the network. In Tensorflow, tf.boolean_mask is generally used for that purpose. 
How can I achieve the similar effect in MxNet, is it possible?
 

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[GitHub] piiswrong commented on a change in pull request #7082: Sparse Tensor: request for reviews

[git]

piiswrong commented on a change in pull request #7082: Sparse Tensor: request 
for reviews
URL: https://github.com/apache/incubator-mxnet/pull/7082#discussion_r132833058
 
 

 ##
 File path: python/mxnet/ndarray/sparse_ndarray.py
 ##
 @@ -0,0 +1,906 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+# coding: utf-8
+"""SparseNDArray API of mxnet."""
+from __future__ import absolute_import
+from __future__ import division
+try:
+from __builtin__ import slice as py_slice
+except ImportError:
+from builtins import slice as py_slice
+
+import ctypes
+import warnings
+
+import os as _os
+import sys as _sys
+
+# import operator
+import numpy as np
+from ..base import NotSupportedForSparseNDArray
+from ..base import _LIB, numeric_types
+from ..base import c_array, mx_real_t
+from ..base import mx_uint, NDArrayHandle, check_call
+from ..context import Context
+from . import _internal
+from .ndarray import _DTYPE_NP_TO_MX, _DTYPE_MX_TO_NP
+from .ndarray import _STORAGE_TYPE_STR_TO_ID
+from .ndarray import NDArray, _storage_type, _zeros_ndarray, _array
+from . import cast_storage
+from . import slice as nd_slice
+
+# Use different verison of SymbolBase
+# When possible, use cython to speedup part of computation.
+# pylint: disable=unused-import
+try:
+if int(_os.environ.get("MXNET_ENABLE_CYTHON", True)) == 0:
+from .._ctypes.ndarray import NDArrayBase, _set_ndarray_class
+elif _sys.version_info >= (3, 0):
+from .._cy3.ndarray import NDArrayBase, _set_ndarray_class
 
 Review comment:
   why not import these from ndarray.py to void try except?
 
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[GitHub] piiswrong commented on a change in pull request #7082: Sparse Tensor: request for reviews

[git]

piiswrong commented on a change in pull request #7082: Sparse Tensor: request 
for reviews
URL: https://github.com/apache/incubator-mxnet/pull/7082#discussion_r132833058
 
 

 ##
 File path: python/mxnet/ndarray/sparse_ndarray.py
 ##
 @@ -0,0 +1,906 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+# coding: utf-8
+"""SparseNDArray API of mxnet."""
+from __future__ import absolute_import
+from __future__ import division
+try:
+from __builtin__ import slice as py_slice
+except ImportError:
+from builtins import slice as py_slice
+
+import ctypes
+import warnings
+
+import os as _os
+import sys as _sys
+
+# import operator
+import numpy as np
+from ..base import NotSupportedForSparseNDArray
+from ..base import _LIB, numeric_types
+from ..base import c_array, mx_real_t
+from ..base import mx_uint, NDArrayHandle, check_call
+from ..context import Context
+from . import _internal
+from .ndarray import _DTYPE_NP_TO_MX, _DTYPE_MX_TO_NP
+from .ndarray import _STORAGE_TYPE_STR_TO_ID
+from .ndarray import NDArray, _storage_type, _zeros_ndarray, _array
+from . import cast_storage
+from . import slice as nd_slice
+
+# Use different verison of SymbolBase
+# When possible, use cython to speedup part of computation.
+# pylint: disable=unused-import
+try:
+if int(_os.environ.get("MXNET_ENABLE_CYTHON", True)) == 0:
+from .._ctypes.ndarray import NDArrayBase, _set_ndarray_class
+elif _sys.version_info >= (3, 0):
+from .._cy3.ndarray import NDArrayBase, _set_ndarray_class
 
 Review comment:
   why not import these from ndarray to void try except?
 
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[GitHub] xzqjack opened a new issue #7444: define a new Parametrized symbol layer and how to use (bind, init, set learning rate ) it?

[git]

peration is ok.
   
   Can anyone show me how to use (bind, init, set learning rate) a new 
parameterizd layer ? Thanks.
   
   
 
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[GitHub] punisher-n commented on issue #6023: pip install error: No matching distribution found for mxnet-cu80

[git]

punisher-n commented on issue #6023: pip install error: No matching 
distribution found for mxnet-cu80
URL: 
https://github.com/apache/incubator-mxnet/issues/6023#issuecomment-321838490
 
 
   I am having the same issues with my machine
   --
   
 Could not find a version that satisfies the requirement setup.py (from 
versions: )
   No matching distribution found for setup.py
   
   please help me on this..
 

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[GitHub] lxn2 opened a new pull request #7429: Add more license files

[git]

lxn2 opened a new pull request #7429:  Add more license files
URL: https://github.com/apache/incubator-mxnet/pull/7429
 
 
   Added more extension handlers to license_header.py. Made it verbose so we 
keep track of what we're whitelisting/skipping.
   
   Added license headers to files with newly added extension headers.
 

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[GitHub] ArturIndio opened a new issue #7446: format input data using mx.rnn

[git]

ArturIndio opened a new issue #7446: format input data using mx.rnn
URL: https://github.com/apache/incubator-mxnet/issues/7446
 
 
   
   
[data_ex_git.zip](https://github.com/apache/incubator-mxnet/files/1220978/data_ex_git.zip)
   I've trying to forecast time-series using mx.rnn model but I can't shape the 
data to input  format asked from mx.rnn. I want do predict a variable Q_t using 
some past values from itself and another variables. Using 
mx.model.FeedForward.create is very easy to define. I can't understand the 
"labels" input on mx.rnn model. This is my code:
   
   ```
   load("data_ex_git.RData") # attached
   
   
   train <- data[1:dias_train,]
   test <- data[(dias_train+1):nrow(data),]
   
   # Neural net fitting
   
   # Scaling data for the NN
   maxs <- apply(data, 2, max)
   mins <- apply(data, 2, min)
   scaled <- as.data.frame(scale(data, center = mins, scale = maxs - mins))
   
   train_ <- scaled[1:dias_train,]
   test_ <- scaled[(dias_train+1):nrow(data),]
   
   library(mxnet)
   train.x <- data.matrix(train_[,-1])
   train.y <- train_[,1]
   test.x <- data.matrix(test_[,-1])
   test.y <- test_[,1]
   
   X.train <- list(data=t(train.x), label=t(train.y))
   X.val <- list(data=t(test.x), label=t(test.y))
   
   batch.size = 5
   seq.len = 5
   num.hidden = 3
   num.embed = 3
   num.rnn.layer = 1
   num.lstm.layer = 1
   num.round = 1
   update.period = 1
   learning.rate= 0.1
   wd=0.1
   clip_gradient=1
   
   mx.set.seed(0)
   model <- mx.rnn(X.train, NULL, num.rnn.layer=num.rnn.layer, seq.len=seq.len, 
num.hidden=num.hidden,
   num.embed=num.embed, num.label=5, batch.size=batch.size, 
input.size=5, ctx = mx.cpu(),
   num.round = num.round, update.period = update.period, 
initializer = mx.init.uniform(0.01),
   dropout = 0, optimizer = "sgd", batch.norm = FALSE,
   learning.rate=learning.rate, wd=wd, 
clip_gradient=clip_gradient)
   
   preds = predict(model,t(test.x))
   ```
   That's my error:
   
   `[19:10:06] d:\program files 
(x86)\jenkins\workspace\mxnet\mxnet\src\operator\tensor\./matrix_op-inl.h:141: 
Using target_shape will be deprecated.
   [19:10:06] d:\program files 
(x86)\jenkins\workspace\mxnet\mxnet\src\operator\tensor\./matrix_op-inl.h:141: 
Using target_shape will be deprecated.
   [19:10:06] d:\program files 
(x86)\jenkins\workspace\mxnet\mxnet\src\operator\tensor\./matrix_op-inl.h:141: 
Using target_shape will be deprecated.
   [19:10:06] D:\Program Files 
(x86)\Jenkins\workspace\mxnet\mxnet\dmlc-core\include\dmlc/logging.h:308: 
[19:10:06] D:\Program Files 
(x86)\Jenkins\workspace\mxnet\mxnet\src\ndarray\ndarray.cc:329: Check failed: 
from.shape() == to->shape() operands shape mismatchfrom.shape = (5,14) 
to.shape=(5,5)
   Error in exec$update.arg.arrays(arg.arrays, match.name, skip.null) : 
 [19:10:06] D:\Program Files 
(x86)\Jenkins\workspace\mxnet\mxnet\src\ndarray\ndarray.cc:329: Check failed: 
from.shape() == to->shape() operands shape mismatchfrom.shape = (5,14) 
to.shape=(5,5)`
 
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[GitHub] ysh329 commented on issue #7424: train_mnist.py failed: TypeError: __init__() got an unexpected keyword argument 'multi_precision'

[git]

ysh329 commented on issue #7424: train_mnist.py failed: TypeError: __init__() 
got an unexpected keyword argument 'multi_precision'
URL: 
https://github.com/apache/incubator-mxnet/issues/7424#issuecomment-322086687
 
 
   @ptrendx So stupid I am, I forgot `make` and `pip install -e .`. It's okay 
on current master branch. Issue closed. :rofl: 
 

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[GitHub] ysh329 closed issue #7424: train_mnist.py failed: TypeError: __init__() got an unexpected keyword argument 'multi_precision'

[git]

ysh329 closed issue #7424: train_mnist.py failed: TypeError: __init__() got an 
unexpected keyword argument 'multi_precision'
URL: https://github.com/apache/incubator-mxnet/issues/7424
 
 
   
 

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[GitHub] sbodenstein opened a new issue #7445: Using cuDNN for CTC Loss

[git]

sbodenstein opened a new issue #7445: Using cuDNN for CTC Loss
URL: https://github.com/apache/incubator-mxnet/issues/7445
 
 
   @piiswrong, @szha: Now that cuDNN 7 supports CTC loss, perhaps we should 
discard the current GPU implementation in contrib.ctc_loss (adapted from the 
WarpCTC implementation) and only use cuDNN for GPU? The main reasons:
   1) it requires maintenance effort to ensure the GPU implementation works on 
new GPU architectures, requiring careful updating of dependencies (like modern 
gpu). 
   2) Users are still reporting problems with memset issues when using the 
WarpCTC plugin (#6121)
   
   I don't think the maintenance effort is worthwhile if almost every single 
user training with CUDA will have cuDNN.
   
   What are your thoughts?
   
   
 

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[GitHub] thirdwing closed pull request #7437: [R] vignette update

[git]

thirdwing closed pull request #7437: [R] vignette update
URL: https://github.com/apache/incubator-mxnet/pull/7437
 
 
   
 

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

[git]

szha commented on issue #7445: Using cuDNN for CTC Loss
URL: 
https://github.com/apache/incubator-mxnet/issues/7445#issuecomment-322064100
 
 
   Thanks for raising this, @sbodenstein. I'm working on using the cudnn7 
implementation of CTC for GPU.
 

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[GitHub] ysh329 commented on a change in pull request #7363: Add tensorboard configure into ./common/fit.py and ./train_mnist.py

[git]

ysh329 commented on a change in pull request #7363: Add tensorboard configure 
into ./common/fit.py and ./train_mnist.py
URL: https://github.com/apache/incubator-mxnet/pull/7363#discussion_r132864621
 
 

 ##
 File path: example/image-classification/train_mnist.py
 ##
 @@ -75,5 +75,13 @@ def get_mnist_iter(args, kv):
 net = import_module('symbols.'+args.network)
 sym = net.get_symbol(**vars(args))
 
+# tensorboard logs
+train_log = 'logs/mnist/train'
+eval_log = 'logs/mnist/eval'
+batch_end_callbacks = 
[mx.contrib.tensorboard.LogMetricsCallback(train_log)]
 
 Review comment:
   So stupid I am, I forgot `make` and `pip install -e .`. It's okay on current 
master branch.
 

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[GitHub] DickJC123 opened a new pull request #7447: Tensorcore fullyconnected support2

[git]

DickJC123 opened a new pull request #7447: Tensorcore fullyconnected support2
URL: https://github.com/apache/incubator-mxnet/pull/7447
 
 
   Consider this an alternative approach to getting TensorCore working with 
FullyConnected.  It is far simpler than my first PR for this new functionality. 
 If anything, this is my proof that one can invoke TensorCore algos through 
manipulation of the cublas handle along with the existing dot function's use of 
Hgemm and SgemmEx.  This PR also shows the type of per-instance handle 
manipulations that are necessary, since blindly setting the handle globally to 
enable TensorCore will have the unfortunate side-effect of introducing 
fp16-casts on the inputs of fp32-I/O gemms.  Bottom line, I wouldn't expect you 
to accept this PR without a discussion.
   
   I have begun studying the new linear algebra code with the idea of producing 
an enable-TensorCore PR for this new approach.  I notice the new LA code 
doesn't support fp16 I/O gemms yet, and the solution there will not fit the 
mold of the existing function templates.  Also, what is the plan for switching 
over MXNET's use of dot() to use the new functions?
 

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[GitHub] formath opened a new pull request #7452: rm not use variables

[git]

formath opened a new pull request #7452: rm not use variables
URL: https://github.com/apache/incubator-mxnet/pull/7452
 
 
   @piiswrong 
 

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[GitHub] BenLag2906 opened a new issue #7453: Pb in import of mxnet

[git]

BenLag2906 opened a new issue #7453: Pb in import of mxnet
URL: https://github.com/apache/incubator-mxnet/issues/7453
 
 
   I have a Pb in import of mxnet in python
   
   ## Environment info
   Operating System: windows 10
   
   Compiler:
   visual 2015 64
   
   Package used (Python/R/Scala/Julia): Python
   
   MXNet version:
   0.10 or 0.11
   installed from source:
   
   If you are using python package, please provide
   
   Python version and distribution:
   2.7.13
   
   
   
   ## Error Message:
   Python 2.7.13 (v2.7.13:a06454b1afa1, Dec 17 2016, 20:53:40) [MSC v.1500 64 
bit (AMD64)] on win32
   Type "help", "copyright", "credits" or "license" for more information.
   
   import mxnet
   Traceback (most recent call last):
   File "", line 1, in 
   File "mxnet_init_.py", line 25, in 
   from .base import MXNetError
   File "mxnet\base.py", line 86, in 
   _LIB = _load_lib()
   File "mxnet\base.py", line 78, in load_lib
   lib = ctypes.CDLL(lib_path[0], ctypes.RTLD_LOCAL)
   File "C:\Python27\lib\ctypes_init.py", line 362, in init
   self._handle = _dlopen(self._name, mode)
   WindowsError: [Error 126] Le module sp?cifi? est introuvable
   
   ## Minimum reproducible example
   if you are using your own code, please provide a short script that 
reproduces the error.
   
   ## Steps to reproduce
   or if you are running standard examples, please provide the commands you 
have run that lead to the error.
   
   1. AFTER COMPILATION : Import mxnet
   2.
   3.
   
   ## What have you tried to solve it?
   
   1. different version
   2.
   3.
   
 
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[GitHub] houkai opened a new issue #7450: reporting bugs: pbegin_ <= pend_. Two thread conflicts.

[git]

;
 }
 return parser_.ParseNext(*dptr);
 },
 [this]() { parser_.BeforeFirst(); });
   }
   ```
   this thread produce data and put data into buffer(16 size), next function 
will consume data from buffer.
   3. Two thread use same fs_. The first one will get 2 data(buffer size 2) and 
stop as no consumer read from its queue. but the first thread change fs_.
   If before the second thread run(read data), the first has read 2 data. Then, 
the first will not effect the second.
   However, if the first running when second thread begin, they are in 
conflict. As a result, the second thread calculate the position in file(fs_) is 
error.
   
   ## What have you tried to solve it?
   
   1. close the first thread in dmlc.Because i train in one computer and don't 
use cache_file, returns the object in advance. It's solved.
   InputSplit::Create in io.cc
   ```
   InputSplit* InputSplit::Create(const char *uri_,
  unsigned part,
  unsigned nsplit,
  const char *type) {
 using namespace std;
 using namespace dmlc::io;
 // allow cachefile in format path#cachefile
 io::URISpec spec(uri_, part, nsplit);
 if (!strcmp(spec.uri.c_str(), "stdin")) {
   return new SingleFileSplit(spec.uri.c_str());
 }
 CHECK(part < nsplit) << "invalid input parameter for InputSplit::Create";
 URI path(spec.uri.c_str());
 InputSplitBase *split = NULL;
 if (!strcmp(type, "text")) {
   split =  new LineSplitter(FileSystem::GetInstance(path),
 spec.uri.c_str(), part, nsplit);
 } else if (!strcmp(type, "recordio")) {
   split =  new RecordIOSplitter(FileSystem::GetInstance(path),
 spec.uri.c_str(), part, nsplit);
 } else {
   LOG(FATAL) << "unknown input split type " << type;
 }
   return split;
   /*#if DMLC_ENABLE_STD_THREAD
 if (spec.cache_file.length() == 0) {
   return new ThreadedInputSplit(split);
 } else {
   return new CachedInputSplit(split, spec.cache_file.c_str());
 }
   #else
 CHECK(spec.cache_file.length() == 0)
 << "to enable cached file, compile with c++11";
 return split;
   #endif*/
   }
   ```
   2. I don't know the impact of  DMLC_ENABLE_STD_THREAD, so keep 
DMLC_ENABLE_STD_THREAD = 1.
 

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[GitHub] chinakook commented on issue #7361: training speed of batch-norm is less than batch-norm-v1

[git]

chinakook commented on issue #7361: training speed of batch-norm is less than 
batch-norm-v1
URL: 
https://github.com/apache/incubator-mxnet/issues/7361#issuecomment-322138483
 
 
   Is the cudnn_off = False ?
 

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[GitHub] aktiger commented on issue #3239: I found my mxnet starts very slow, takes about 5 minutes before the gpus begin to run

[git]

aktiger commented on issue #3239: I found my mxnet starts very slow, takes 
about 5 minutes before the gpus begin to run
URL: 
https://github.com/apache/incubator-mxnet/issues/3239#issuecomment-322136683
 
 
   @sxjscience  thanks, this solved my problem. The first time is still slow, 
but after the first time, it runs very fast. 
 

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[GitHub] ZiyueHuang commented on issue #7449: Fix a bug in SequentialRNNCell.reset()

[git]

ZiyueHuang commented on issue #7449: Fix a bug in SequentialRNNCell.reset()
URL: https://github.com/apache/incubator-mxnet/pull/7449#issuecomment-322136929
 
 
   ping @piiswrong @sxjscience 
 

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[GitHub] aktiger commented on issue #3239: I found my mxnet starts very slow, takes about 5 minutes before the gpus begin to run

[git]

aktiger commented on issue #3239: I found my mxnet starts very slow, takes 
about 5 minutes before the gpus begin to run
URL: 
https://github.com/apache/incubator-mxnet/issues/3239#issuecomment-322136683
 
 
   @sxjscience  thanks, this solved my problem. 
 

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[GitHub] ZhaoxiaZhang commented on issue #7406: inconsistent accuracy: ImageRecordIter vs ImageIter

[git]

ZhaoxiaZhang commented on issue #7406: inconsistent accuracy: ImageRecordIter 
vs ImageIter
URL: 
https://github.com/apache/incubator-mxnet/issues/7406#issuecomment-322140620
 
 
   Thanks for that! let me try and will update the results later.
 

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[GitHub] piiswrong closed pull request #7304: gluon bce loss

[git]

piiswrong closed pull request #7304: gluon bce loss
URL: https://github.com/apache/incubator-mxnet/pull/7304
 
 
   
 

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[GitHub] ZhaoxiaZhang commented on issue #7406: inconsistent accuracy: ImageRecordIter vs ImageIter

[git]

ZhaoxiaZhang commented on issue #7406: inconsistent accuracy: ImageRecordIter 
vs ImageIter
URL: 
https://github.com/apache/incubator-mxnet/issues/7406#issuecomment-322109650
 
 
   @melody-rain Thanks for your comments. I will try the imageiter.
   BTW, any insights why ImageRecordIter does,'t work? I'm reallly confused 
abot this.
 

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[GitHub] ZiyueHuang opened a new pull request #7449: Fix a bug in SequentialRNNCell.reset()

[git]

ZiyueHuang opened a new pull request #7449: Fix a bug in 
SequentialRNNCell.reset()
URL: https://github.com/apache/incubator-mxnet/pull/7449
 
 
   SequentialRNNCell.reset() should invoke reset in all layer cells. This is an 
issue in 
https://github.com/awslabs/sockeye/blob/master/sockeye/decoder.py#L379. It is 
wrong if someone use SequentialRNNCell.__call__() to unroll the network as the 
way in BaseRNNCell.unroll(). Although SequentialRNNCell.unroll() is correct now 
because it uses unroll in all layer cells (cell.unroll will reset at the 
beginning).
 

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[GitHub] piiswrong commented on issue #7319: [RoadMap] Legacy issue resolution before 1.0 release

[git]

piiswrong commented on issue #7319: [RoadMap] Legacy issue resolution before 
1.0 release
URL: 
https://github.com/apache/incubator-mxnet/issues/7319#issuecomment-322110140
 
 
   @ptrendx @madjam @bhavinthaker The removed tutorials need to be brought back 
ASAP!
 

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[GitHub] KeyKy opened a new issue #7448: out of memory when training imagenet with .rec file.

[git]

KeyKy opened a new issue #7448: out of memory when training imagenet with .rec 
file.
URL: https://github.com/apache/incubator-mxnet/issues/7448
 
 
   ## Environment info
   
   Operating System: ubuntu 16.04
   
   Compiler: gcc (Ubuntu 5.4.0-6ubuntu1~16.04.4) 5.4.0 20160609
   
   Package used (Python/R/Scala/Julia): Python
   
   MXNet commit hash: 8ad3c8a7a98dfa6bd6f5065cf9c3688f2414c3d4
   
   Python version and distribution: Python2.7.12
   
   ## Error Message:
   
   The usage of memory, at the beginning of training is 5.7%-7.9% , after a 
while (3~4h) it takes 27% and finally it run out of my memory (waked up by the 
alarm message).
   
   ## Steps to reproduce
   or if you are running standard examples, please provide the commands you 
have run that lead to the error.
   
   1. cd examples/image-classification && python train_imagenet.py --network 
my_net --gpus 0,1,2,3 --num-epochs 100 --lr 0.01 --lr-step-epochs 30,60,80,110 
--batch-size 256 --top-k 5 --data-train 
/data_shared/datasets/ILSVRC2015/rec/train_480_q100.rec --data-val 
/data_shared/datasets/ILSVRC2015/rec/val_480_q100.rec --rgb-mean 
123.68,116.779,103.939 --data-nthreads 4 --model-prefix ./my_net
   
   ## What have you tried to solve it?
   
   1. set the prefetch_buffer = 1 but the accuracy of my model drop to 20% and 
set prefetch_buffer back to 2,4,8, the accuracy is right!
   2. cpu memory increase continually when set the prefetch_buffer = 1
   3. 
   
   also find some similar issues:
   
   https://github.com/apache/incubator-mxnet/issues/1411
   https://github.com/apache/incubator-mxnet/issues/3183
   https://github.com/apache/incubator-mxnet/issues/2969
   https://github.com/apache/incubator-mxnet/issues/2111
   https://github.com/apache/incubator-mxnet/issues/2099
 

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[GitHub] CodingCat commented on issue #7411: [scala-package][spark] fix example script

[git]

CodingCat commented on issue #7411: [scala-package][spark] fix example script
URL: https://github.com/apache/incubator-mxnet/pull/7411#issuecomment-322097900
 
 
   @javelinjs @terrytangyuan  any comments on this?
 

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[GitHub] dabraude opened a new issue #7457: Minimal C example fails to register operators

[git]

dabraude opened a new issue #7457: Minimal C example fails to register operators
URL: https://github.com/apache/incubator-mxnet/issues/7457
 
 
   This is the most minimal example of what should be used to load a symbol 
with the c_api and it doesn't work. Unless I'm missing something completely 
like a compiler flag, otherwise
   
   
   ## Environment info
   Operating System:
   
   centos
   
   Compiler:
   
   gcc
   
   Package used (Python/R/Scala/Julia):
   
   C
   
   MXNet version:
   
   0.9.3
   
   ## Error Message:
   Please paste the full error message, including stack trace.
   
   [16:17:17] /share/tools/mxnet/dmlc-core/include/dmlc/./logging.h:300: 
[16:17:17] src/core/op.cc:55: Check failed: op != nullptr Operator 
FullyConnected is not registered
   
   Stack trace returned 10 entries:
   [bt] (0) /share/tools/mxnet/lib/libmxnet.so(_ZN4nnvm2Op3GetERKSs+0x329) 
[0x7fcb0323f179]
   [bt] (1) /share/tools/mxnet/lib/libmxnet.so(+0xef8268) [0x7fcb03227268]
   [bt] (2) 
/share/tools/mxnet/lib/libmxnet.so(_ZN4dmlc20JSONObjectReadHelper13ReadAllFieldsEPNS_10JSONReaderE+0x100)
 [0x7fcb0322d680]
   [bt] (3) /share/tools/mxnet/lib/libmxnet.so(+0xef70ef) [0x7fcb032260ef]
   [bt] (4) 
/share/tools/mxnet/lib/libmxnet.so(_ZNSt17_Function_handlerIFN4nnvm5GraphES1_EPS2_E9_M_invokeERKSt9_Any_dataS1_+0x11f)
 [0x7fcb02e8c3ef]
   [bt] (5) 
/share/tools/mxnet/lib/libmxnet.so(_ZN4nnvm11ApplyPassesENS_5GraphERKSt6vectorISsSaISsEE+0x501)
 [0x7fcb03232b51]
   [bt] (6) 
/share/tools/mxnet/lib/libmxnet.so(_ZN5mxnet18LoadLegacyJSONPassEN4nnvm5GraphE+0x180)
 [0x7fcb02e851c0]
   [bt] (7) 
/share/tools/mxnet/lib/libmxnet.so(_ZNSt17_Function_handlerIFN4nnvm5GraphES1_EPS2_E9_M_invokeERKSt9_Any_dataS1_+0x11f)
 [0x7fcb02e8c3ef]
   [bt] (8) 
/share/tools/mxnet/lib/libmxnet.so(_ZN4nnvm11ApplyPassesENS_5GraphERKSt6vectorISsSaISsEE+0x501)
 [0x7fcb03232b51]
   [bt] (9) 
/share/tools/mxnet/lib/libmxnet.so(_ZN4nnvm9ApplyPassENS_5GraphERKSs+0x8e) 
[0x7fcb0318006e]
   
   
   
   ## Minimum reproducible example
   test.c : 
   
   #include 
   #include "mxnet/c_api.h"
   
   int main(void)
   {
   const char * symfn = net_symbol.json";
   SymbolHandle sym;
   MXSymbolCreateFromFile(symfn, );
   return 0;
   }
   
   
   ## Steps to reproduce
   or if you are running standard examples, please provide the commands you 
have run that lead to the error.
   
   1. compiled with   gcc -I../include -L. -Wl,--whole-archive -lmxnet 
-Wl,--no-whole-archive test.c -o testrun
   2. run 
   
   ## What have you tried to solve it?
   
   1. including every header in the include/mxnet directory
   2. copying all compiler flags from the make file 
   
   
 

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[GitHub] wanderingpj opened a new issue #7454: Question about networks for cifar100.

[git]

wanderingpj opened a new issue #7454: Question about networks for cifar100.
URL: https://github.com/apache/incubator-mxnet/issues/7454
 
 
   What networks are suitable for cifar100 with relatively high testing 
accuracy?
   
 

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[GitHub] ZiyueHuang closed pull request #7449: Fix a bug in SequentialRNNCell.reset()

[git]

ZiyueHuang closed pull request #7449: Fix a bug in SequentialRNNCell.reset()
URL: https://github.com/apache/incubator-mxnet/pull/7449
 
 
   
 

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[GitHub] ZiyueHuang opened a new pull request #7449: Fix a bug in SequentialRNNCell.reset()

[git]

ZiyueHuang opened a new pull request #7449: Fix a bug in 
SequentialRNNCell.reset()
URL: https://github.com/apache/incubator-mxnet/pull/7449
 
 
   `SequentialRNNCell.reset()` should invoke reset in all layer cells. This is 
an issue in 
https://github.com/awslabs/sockeye/blob/master/sockeye/decoder.py#L379. It is 
wrong if someone use `SequentialRNNCell.__call__()` to unroll the network as 
the way in `BaseRNNCell.unroll()`. Although `SequentialRNNCell.unroll()` is 
correct now because it uses unroll in all layer cells (cell.unroll will reset 
at the beginning).
 

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[GitHub] saswatac opened a new pull request #7456: make MXDataIter work without indices

[git]

saswatac opened a new pull request #7456: make MXDataIter work without indices
URL: https://github.com/apache/incubator-mxnet/pull/7456
 
 
   indices are optional, custom cpp iterators providing data batches
   without indices should work while using MXDataIter.
   
   ##Testing
   
   python tests/python/unittest/test_io.py 
   [10:58:26] src/io/iter_mnist.cc:91: MNISTIter: load 6 images, shuffle=1, 
shape=(100,784)
 

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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] ZiyueHuang commented on issue #7449: Fix a bug in SequentialRNNCell.reset()

[git]

ZiyueHuang commented on issue #7449: Fix a bug in SequentialRNNCell.reset()
URL: https://github.com/apache/incubator-mxnet/pull/7449#issuecomment-322136929
 
 
   ping @piiswrong @sxjscience 
 

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

[git]

xzqjack commented on issue #7427: how to set dataiter with multi data?
URL: 
https://github.com/apache/incubator-mxnet/issues/7427#issuecomment-322027400
 
 
   You can look up the definition of class module 
(python/mxnet/module/module.py), and init module by
   `mod = mx.mod.Module(..., data_names=('data1', 'data2'), ...)`
 

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[GitHub] xzqjack commented on issue #7426: mx random seed doesn't work for random_uniform/random_normal on gpu

[git]

xzqjack commented on issue #7426: mx random seed doesn't work for 
random_uniform/random_normal on gpu
URL: 
https://github.com/apache/incubator-mxnet/issues/7426#issuecomment-322027770
 
 
   The same error happened in my service (ubuntu16.04, gpu, 
mxnet-version:0.10.1)
 

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[GitHub] ZhaoxiaZhang commented on issue #7406: inconsistent accuracy: ImageRecordIter vs ImageIter

[git]

ZhaoxiaZhang commented on issue #7406: inconsistent accuracy: ImageRecordIter 
vs ImageIter
URL: 
https://github.com/apache/incubator-mxnet/issues/7406#issuecomment-322030133
 
 
   HI, I met something maybe similar with you. I trained  ImageRecordIter and 
get a very good accuracy. However when I tested with the same data, the 
accuracy is much lower. I don't kown why. 
   BTW, how did you get the mean r, g b.? 
 

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[GitHub] ZhaoxiaZhang commented on issue #7398: inconsistent results when infering

[git]

ZhaoxiaZhang commented on issue #7398: inconsistent results when infering
URL: 
https://github.com/apache/incubator-mxnet/issues/7398#issuecomment-322030662
 
 
   HI, I have met the similar problems. Have you figured this out?
 

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[GitHub] szha commented on a change in pull request #7449: Fix a bug in SequentialRNNCell.reset()

[git]

szha commented on a change in pull request #7449: Fix a bug in 
SequentialRNNCell.reset()
URL: https://github.com/apache/incubator-mxnet/pull/7449#discussion_r133002287
 
 

 ##
 File path: python/mxnet/rnn/rnn_cell.py
 ##
 @@ -788,6 +788,12 @@ def unpack_weights(self, args):
 def pack_weights(self, args):
 return _cells_pack_weights(self._cells, args)
 
+def reset(self):
+super(SequentialRNNCell, self).reset()
+if hasattr(self, '_cells'):
 
 Review comment:
   Alternatively you can put this in the BaseRNNCell so that other container 
cells (e.g. BidirectionalCell) can be patched up together.
   Would you mind doing the same for python/mxnet/gluon/rnn/rnn_cell.py?
 

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[GitHub] szha commented on a change in pull request #7449: Fix a bug in SequentialRNNCell.reset()

[git]

szha commented on a change in pull request #7449: Fix a bug in 
SequentialRNNCell.reset()
URL: https://github.com/apache/incubator-mxnet/pull/7449#discussion_r133002287
 
 

 ##
 File path: python/mxnet/rnn/rnn_cell.py
 ##
 @@ -788,6 +788,12 @@ def unpack_weights(self, args):
 def pack_weights(self, args):
 return _cells_pack_weights(self._cells, args)
 
+def reset(self):
+super(SequentialRNNCell, self).reset()
+if hasattr(self, '_cells'):
 
 Review comment:
   Alternatively you can put this in the BaseRNNCell so that other container 
cells (e.g. BidirectionalCell) can be patched up together.
   Would you mind doing the same for python/mxnet/gluon/rnn/rnn_cell.py? The 
container attribute there is `_children`.
 

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[GitHub] piiswrong closed pull request #7439: Small doc cleanups

[git]

piiswrong closed pull request #7439: Small doc cleanups
URL: https://github.com/apache/incubator-mxnet/pull/7439
 
 
   
 

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[GitHub] CodingCat opened a new pull request #7440: [scala-package] clean up pom (remove duplicate definition)

[git]

CodingCat opened a new pull request #7440: [scala-package] clean up pom (remove 
duplicate definition)
URL: https://github.com/apache/incubator-mxnet/pull/7440
 
 
   
 

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[GitHub] terrytangyuan commented on issue #7440: [scala-package] clean up pom (remove duplicate definition)

[git]

terrytangyuan commented on issue #7440: [scala-package] clean up pom (remove 
duplicate definition)
URL: https://github.com/apache/incubator-mxnet/pull/7440#issuecomment-322011136
 
 
   @javelinjs Sorry just realized that I merged it without your approval. 
Should we change the setting so we can only merge when ALL requested reviewers 
have approved?
 

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[GitHub] KeyKy commented on issue #3183: Large Rec File Memory and Speed Issues

[git]

KeyKy commented on issue #3183: Large Rec File Memory and Speed Issues
URL: 
https://github.com/apache/incubator-mxnet/issues/3183#issuecomment-322011807
 
 
   I have the same problem when i train ssd or imagenet and i check the usage 
of memory in top. At the beginning of training is 5.7% , after a while (3~4h) 
it takes 27% and finally it run out of my memory (waked up by the alarm message)
 

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[GitHub] KeyKy commented on issue #3183: Large Rec File Memory and Speed Issues

[git]

KeyKy commented on issue #3183: Large Rec File Memory and Speed Issues
URL: 
https://github.com/apache/incubator-mxnet/issues/3183#issuecomment-322011807
 
 
   I have the same problem when i train ssd or imagenet using .rec file and i 
check the usage of memory in top. At the beginning of training is 5.7% , after 
a while (3~4h) it takes 27% and finally it run out of my memory (waked up by 
the alarm message)
 

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[GitHub] ZiyueHuang commented on a change in pull request #7449: Fix a bug in SequentialRNNCell.reset()

[git]

ZiyueHuang commented on a change in pull request #7449: Fix a bug in 
SequentialRNNCell.reset()
URL: https://github.com/apache/incubator-mxnet/pull/7449#discussion_r133008855
 
 

 ##
 File path: python/mxnet/rnn/rnn_cell.py
 ##
 @@ -788,6 +788,12 @@ def unpack_weights(self, args):
 def pack_weights(self, args):
 return _cells_pack_weights(self._cells, args)
 
+def reset(self):
+super(SequentialRNNCell, self).reset()
+if hasattr(self, '_cells'):
 
 Review comment:
   SequentialRNNCell's constructor will call BaseRNNCell's constructor first 
before it initializes `self._cells`, BaseRNNCell's constructor will call 
`self.reset()`. So at first time `hasattr(self, '_cells')` should be false. 
@szha 
 

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[GitHub] cloudhan commented on issue #3724: asnumpy() of NDArray @cpu halted

[git]

cloudhan commented on issue #3724: asnumpy() of NDArray @cpu  halted
URL: 
https://github.com/apache/incubator-mxnet/issues/3724#issuecomment-322980863
 
 
   @maxenceliu nah, I lost the script and forgot how to reproduce...
 

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[GitHub] eric-haibin-lin commented on a change in pull request #7082: Sparse Tensor: request for reviews

[git]

eric-haibin-lin commented on a change in pull request #7082: Sparse Tensor: 
request for reviews
URL: https://github.com/apache/incubator-mxnet/pull/7082#discussion_r133617511
 
 

 ##
 File path: python/mxnet/model.py
 ##
 @@ -113,25 +127,36 @@ def _update_params_on_kvstore(param_arrays, grad_arrays, 
kvstore, param_names):
 # push gradient, priority is negative index
 kvstore.push(name, grad_list, priority=-index)
 # pull back the weights
-kvstore.pull(name, arg_list, priority=-index)
+if _contains_non_default_storage(arg_list):
 
 Review comment:
   Normally given a key, the ndarrays in the arg_list will be either all dense 
or all sparse, right? 
 

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[GitHub] zihaolucky commented on a change in pull request #7503: log epoch number for tensorboard

[git]

zihaolucky commented on a change in pull request #7503: log epoch number for 
tensorboard
URL: https://github.com/apache/incubator-mxnet/pull/7503#discussion_r133611060
 
 

 ##
 File path: python/mxnet/contrib/tensorboard.py
 ##
 @@ -70,4 +70,4 @@ def __call__(self, param):
 for name, value in name_value:
 if self.prefix is not None:
 name = '%s-%s' % (self.prefix, name)
-self.summary_writer.add_scalar(name, value)
+self.summary_writer.add_scalar(name, value, 
global_step=param.epoch)
 
 Review comment:
   What would happened if we use this in `batch_end_callback`?
 

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[GitHub] zihaolucky commented on a change in pull request #7503: log epoch number for tensorboard

[git]

zihaolucky commented on a change in pull request #7503: log epoch number for 
tensorboard
URL: https://github.com/apache/incubator-mxnet/pull/7503#discussion_r133611060
 
 

 ##
 File path: python/mxnet/contrib/tensorboard.py
 ##
 @@ -70,4 +70,4 @@ def __call__(self, param):
 for name, value in name_value:
 if self.prefix is not None:
 name = '%s-%s' % (self.prefix, name)
-self.summary_writer.add_scalar(name, value)
+self.summary_writer.add_scalar(name, value, 
global_step=param.epoch)
 
 Review comment:
   What would happened if we use this in batch_end_callback?
 

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[GitHub] zihaolucky commented on issue #7503: log epoch number for tensorboard

[git]

zihaolucky commented on issue #7503: log epoch number for tensorboard
URL: https://github.com/apache/incubator-mxnet/pull/7503#issuecomment-322955043
 
 
   I've checked this change. LGTM.
   
   Notice that, after we add `global_step`:
   
* In `RELATIVE` mode, the training curve still logs the metric every batch, 
the curve looks more smooth. Same with the older version, great.
* In `STEP` mode, it shows the metric in that epoch.
 

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[GitHub] maxenceliu commented on issue #3724: asnumpy() of NDArray @cpu halted

[git]

maxenceliu commented on issue #3724: asnumpy() of NDArray @cpu  halted
URL: 
https://github.com/apache/incubator-mxnet/issues/3724#issuecomment-322966428
 
 
   @zihaolucky naive engine has not been implemented?
 

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[GitHub] zihaolucky commented on issue #3724: asnumpy() of NDArray @cpu halted

[git]

zihaolucky commented on issue #3724: asnumpy() of NDArray @cpu  halted
URL: 
https://github.com/apache/incubator-mxnet/issues/3724#issuecomment-322957118
 
 
   @maxenceliu I use the Scala package for a while in production. With version 
0.7 it works okay. Or maybe you should try `naive engine`. 
 

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[GitHub] StatML opened a new issue #7506: build error: expected ?}? at end of input

[git]

StatML opened a new issue #7506: build error: expected ?}? at end of input
URL: https://github.com/apache/incubator-mxnet/issues/7506
 
 
   For bugs or installation issues, please provide the following information.
   The more information you provide, the more likely people will be able to 
help you.
   
   ## Environment info
   Operating System: Ubuntu 16.04.4
   
   Compiler: G++ 5.4
   
   Package used (Python/R/Scala/Julia): Python
   
   MXNet version: 
   
   Or if installed from source: source
   
   MXNet commit hash (`git rev-parse HEAD`):
   1286809a1fc76c0b808b988084fc0950300f40d4
   
   If you are using python package, please provide
   
   Python version and distribution: 3.5.2
   
   If you are using R package, please provide
   
   R `sessionInfo()`:
   
   ## Error Message:
   
   > In file included from src/operator/random/./../operator_common.h:37:0,
   >  from src/operator/random/./sample_multinomial_op.h:31,
   >  from src/operator/random/sample_multinomial_op.cc:24:
   > src/operator/random/./../../common/cuda_utils.h:95:43: error: 
?mxnet::common::cuda::CusolverGetErrorString? declared as an ?inline? variable
   >  inline const char* CusolverGetErrorString(cusolverStatus_t error) {
   >^
   > src/operator/random/./../../common/cuda_utils.h:95:43: error: 
?cusolverStatus_t? was not declared in this scope
   > src/operator/random/./../../common/cuda_utils.h:95:67: error: expected ?,? 
or ?;? before ?{? token
   >  inline const char* CusolverGetErrorString(cusolverStatus_t error) {
   >^
   > src/operator/random/sample_multinomial_op.cc:111:1: error: expected ?}? at 
end of input
   >  }  // namespace mxnet
   >  ^
   > src/operator/random/sample_multinomial_op.cc:111:1: error: expected ?}? at 
end of input
   > src/operator/random/sample_multinomial_op.cc:111:1: error: expected ?}? at 
end of input
   > Makefile:275: recipe for target 
'build/src/operator/random/sample_multinomial_op.o' failed
   > make: *** [build/src/operator/random/sample_multinomial_op.o] Error 1
   > 
   
   ## Steps to reproduce
   or if you are running standard examples, please provide the commands you 
have run that lead to the error.
   
   1. make -j
   
   ## What have you tried to solve it?
   
   N/A
 
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[GitHub] maxenceliu commented on issue #3724: asnumpy() of NDArray @cpu halted

[git]

maxenceliu commented on issue #3724: asnumpy() of NDArray @cpu  halted
URL: 
https://github.com/apache/incubator-mxnet/issues/3724#issuecomment-322959344
 
 
   @zihaolucky Now I use version 1.10, problem appear again?!! What do you mean 
by naive engine?
 

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[GitHub] cloudhan commented on issue #3724: asnumpy() of NDArray @cpu halted

[git]

cloudhan commented on issue #3724: asnumpy() of NDArray @cpu  halted
URL: 
https://github.com/apache/incubator-mxnet/issues/3724#issuecomment-322962289
 
 
   @zihaolucky actually pre 0.9 version works okey. Seems there have been 
deadlock since NVVM refactor
 

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[GitHub] maxenceliu commented on issue #3724: asnumpy() of NDArray @cpu halted

[git]

maxenceliu commented on issue #3724: asnumpy() of NDArray @cpu  halted
URL: 
https://github.com/apache/incubator-mxnet/issues/3724#issuecomment-322965165
 
 
   @cloudhan Have you tried 0.10.1 version? Still will block for several 
seconds. It will call `WaitToRead()` when copy data to Cpu. I don't understand 
if all the dependency need to be wait for this copy.
 

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[GitHub] peiyunh opened a new pull request #7507: Fix description of argument parser

[git]

peiyunh opened a new pull request #7507: Fix description of argument parser
URL: https://github.com/apache/incubator-mxnet/pull/7507
 
 
   
 

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[GitHub] piiswrong commented on issue #7502: Change git clone to specific tag for installation

[git]

piiswrong commented on issue #7502: Change git clone to specific tag for 
installation
URL: https://github.com/apache/incubator-mxnet/pull/7502#issuecomment-322939359
 
 
   Is branch name guaranteed to match with version?
 

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[GitHub] hpsoar commented on issue #7491: GPUDeviceStorage is not used? why?

[git]

hpsoar commented on issue #7491: GPUDeviceStorage is not used? why?
URL: 
https://github.com/apache/incubator-mxnet/issues/7491#issuecomment-322953488
 
 
   I didn't compile with CUDA, I'm reading the code, I see that when CUDA is 
used, it actually use GPUPooledStorageManager to do the allocation, on the 
other hand, CPUDeviceStorage is used no where.
   
   I'm just curious why CPUDeviceStorage is there, and not in use.
   
   I'm on the master branch.
 

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[GitHub] CNevd commented on a change in pull request #7082: Sparse Tensor: request for reviews

[git]

CNevd commented on a change in pull request #7082: Sparse Tensor: request for 
reviews
URL: https://github.com/apache/incubator-mxnet/pull/7082#discussion_r133614687
 
 

 ##
 File path: python/mxnet/model.py
 ##
 @@ -113,25 +127,36 @@ def _update_params_on_kvstore(param_arrays, grad_arrays, 
kvstore, param_names):
 # push gradient, priority is negative index
 kvstore.push(name, grad_list, priority=-index)
 # pull back the weights
-kvstore.pull(name, arg_list, priority=-index)
+if _contains_non_default_storage(arg_list):
 
 Review comment:
   Can we just skip the row_sparse weights and pull the dense weights normally 
or any other prettier way to do this?
 

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[GitHub] maxenceliu commented on issue #3724: asnumpy() of NDArray @cpu halted

[git]

maxenceliu commented on issue #3724: asnumpy() of NDArray @cpu  halted
URL: 
https://github.com/apache/incubator-mxnet/issues/3724#issuecomment-322955687
 
 
   @zihaolucky So, you give up to deploy mxnet  on Server at last and use 
another platform?
 

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

[git]

maxenceliu commented on issue #7417: Update mxnet in maven timely?
URL: 
https://github.com/apache/incubator-mxnet/issues/7417#issuecomment-322957351
 
 
   @javelinjs @szha @piiswrong Thanks for your paying attention to this issue. 
Another question about scala version is: NDarray.toArray method could be block 
on cpu version mxnet? 
   
   This method will block me for about 10 seconds each time I run it on the 
Centos server, which could be neglected when I run it on my Mac cpu only.
 

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[GitHub] ZiyueHuang commented on issue #7449: Fix a bug in SequentialRNNCell.reset()

[git]

ZiyueHuang commented on issue #7449: Fix a bug in SequentialRNNCell.reset()
URL: https://github.com/apache/incubator-mxnet/pull/7449#issuecomment-322959352
 
 
   @piiswrong 
 

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

[git]

maxenceliu commented on issue #7417: Update mxnet in maven timely?
URL: 
https://github.com/apache/incubator-mxnet/issues/7417#issuecomment-322965667
 
 
   I Read the code
   `if (this->ctx().dev_mask() == cpu::kDevMask) {
   **this->WaitToRead();**
   RunContext rctx{this->ctx(), nullptr};
   ndarray::Copy<cpu, cpu>(this->data(), ,
   Context::CPU(), Context::CPU(), rctx);
 } `
   I guess it is blocked here. I don't no why we have to wait to read, if there 
is something we don't have to wait to do this copy?
 

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[GitHub] kevinthesun commented on issue #7502: Change git clone to specific tag for installation

[git]

kevinthesun commented on issue #7502: Change git clone to specific tag for 
installation
URL: https://github.com/apache/incubator-mxnet/pull/7502#issuecomment-322940936
 
 
   Yes.
 

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[GitHub] maxenceliu commented on issue #3724: asnumpy() of NDArray @cpu halted

[git]

maxenceliu commented on issue #3724: asnumpy() of NDArray @cpu  halted
URL: 
https://github.com/apache/incubator-mxnet/issues/3724#issuecomment-322952953
 
 
   @zihaolucky Hi, How do you resolve this problem? I met the same occasion. 
Each time I call NDArray.toArray in scala, it will block at least 10 seconds 
when I run this on a cpu-Server with Centos. however, in my Macbook cpu only, 
its time cost can be noticed!
 

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[GitHub] reminisce commented on a change in pull request #7082: Sparse Tensor: request for reviews

[git]

reminisce commented on a change in pull request #7082: Sparse Tensor: request 
for reviews
URL: https://github.com/apache/incubator-mxnet/pull/7082#discussion_r133617599
 
 

 ##
 File path: include/mxnet/c_api.h
 ##
 @@ -321,6 +353,17 @@ MXNET_DLL int MXNDArraySyncCopyToCPU(NDArrayHandle handle,
  void *data,
  size_t size);
 /*!
+ * \brief Copy src.data() to dst.data() if i = -1, else dst.aux_data(i) if i 
>= 0
+ * This function blocks. Do not use it in performance critical code.
+ * \param handle_dst handle of a dst ndarray whose data/aux_data has been 
allocated
+ * \param handle_src handle of a src ndarray which has default storage type
+ * \param i dst data blob indicator
+ */
+MXNET_DLL int MXNDArraySyncCopyFromNDArray(NDArrayHandle handle_dst,
+   const NDArrayHandle handle_src,
+   const int i);
 
 Review comment:
   Agree with @eric-haibin-lin. As long as it's documented clearly in the 
comment, it's fine to use `i`.
 

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[GitHub] DickJC123 opened a new pull request #7505: Changed FullyConnected to use new linalg gemm, plus TensorCore if fp16 I/O.

[git]

DickJC123 opened a new pull request #7505: Changed FullyConnected to use new 
linalg gemm, plus TensorCore if fp16 I/O.
URL: https://github.com/apache/incubator-mxnet/pull/7505
 
 
   GEMMs within the FullyConnected operator switched from using mshadow::dot() 
to the new linalg_gemm().  After a trial-run, this can be the model for 
removing all uses of dot() within MXNet.  Added a specialization 
linalg_gemm<gpu, half_t> that includes use of TensorCore algos by default.  
Users can disable TensorCore on Volta by setting the environment variable 
MXNET_CUDA_ALLOW_TENSOR_CORE=0.
 

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[GitHub] jinfagang opened a new issue #7509: SSD example error

[git]

jinfagang opened a new issue #7509: SSD example error
URL: https://github.com/apache/incubator-mxnet/issues/7509
 
 
   Simply run SSD example got this error:
   
   ```
   [01:09:24] src/nnvm/legacy_json_util.cc:185: Warning: loading symbol saved 
by MXNet version 1001 with lower version of MXNet v1000. May cause undefined 
behavior. Please update MXNet if you encounter any issue
   terminate called after throwing an instance of 'std::bad_alloc'
 what():  std::bad_alloc
   
   ```
 

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[GitHub] CNevd commented on a change in pull request #7082: Sparse Tensor: request for reviews

[git]

CNevd commented on a change in pull request #7082: Sparse Tensor: request for 
reviews
URL: https://github.com/apache/incubator-mxnet/pull/7082#discussion_r133667882
 
 

 ##
 File path: python/mxnet/model.py
 ##
 @@ -113,25 +127,36 @@ def _update_params_on_kvstore(param_arrays, grad_arrays, 
kvstore, param_names):
 # push gradient, priority is negative index
 kvstore.push(name, grad_list, priority=-index)
 # pull back the weights
-kvstore.pull(name, arg_list, priority=-index)
+if _contains_non_default_storage(arg_list):
 
 Review comment:
   Consider arg_list has 1  row_sparse weight and many dense weights, 
```_contains_non_default_storage(arg_list)``` will be true, so user have to 
call  ```kvstore.pull``` for all the dense weights manually out of 
```model.py```.  maybe there is some way to split them (row_sparse and dense) 
so we can only pull row_sparse weights manually?
 

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[GitHub] jinfagang commented on issue #7509: SSD example error

[git]

jinfagang commented on issue #7509: SSD example error
URL: 
https://github.com/apache/incubator-mxnet/issues/7509#issuecomment-323004047
 
 
   Well, I know it's version issue, but I just installed from pip which gives 
me 0.10.0, the newest version is 0.11, but why not update pip? I really cannot 
build from source, the official doc is so out-of-date and messy.
 

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[GitHub] lxn2 closed pull request #11: Fix git clone links

[git]

lxn2 closed pull request #11: Fix git clone links
URL: https://github.com/apache/incubator-mxnet-site/pull/11
 
 
   
 

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[GitHub] maxenceliu opened a new issue #7417: Update mxnet in maven timely?

[git]

maxenceliu opened a new issue #7417: Update mxnet in maven timely?
URL: https://github.com/apache/incubator-mxnet/issues/7417
 
 
   in the maven repository, the mxnet version is much delayed. Furmore, it 
depends on opencv, in which the case our server doesn't need opencv.
 

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[GitHub] haimeh commented on issue #6629: Not enough information to get shape

[git]

haimeh commented on issue #6629: Not enough information to get shape
URL: 
https://github.com/apache/incubator-mxnet/issues/6629#issuecomment-321613657
 
 
   Also, a mistake one might make, so for reference I am adding it here.
   The shape inference may not always work with a chained composition such as:
 ```
   discouragedSelfMatches <- mx.symbol.broadcast_mul(lhs = 
mx.symbol.broadcast_equal(lhs = distances,

   rhs = mx.symbol.min(distances),
   rhs = 
mx.symbol.max(distances))
   ```
   but it will work if the symbols are separated, for instance:
 ```
   selfMatches <- mx.symbol.broadcast_equal(lhs = distances,
  rhs = mx.symbol.min(distances))
 discouragedSelfMatches <- mx.symbol.broadcast_mul(lhs=selfMatches, 
 
rhs=mx.symbol.max(distances))
   ```
 

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[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-321722267
 
 
   @crazy-cat Still not compiling on windows: 
https://builds.apache.org/blue/organizations/jenkins/incubator-mxnet/detail/PR-7393/2/pipeline
 

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[GitHub] regzhuce commented on issue #7375: Can I set instance weight when training?

[git]

regzhuce commented on issue #7375: Can I set instance weight when training?
URL: 
https://github.com/apache/incubator-mxnet/issues/7375#issuecomment-321723588
 
 
   @thirdwing Thanks
   Any proposals for my problem?
 

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[GitHub] ysh329 commented on issue #7424: train_mnist.py failed: TypeError: __init__() got an unexpected keyword argument 'multi_precision'

[git]

ysh329 commented on issue #7424: train_mnist.py failed: TypeError: __init__() 
got an unexpected keyword argument 'multi_precision'
URL: 
https://github.com/apache/incubator-mxnet/issues/7424#issuecomment-321723869
 
 
   @ptrendx @mli @howard0su 
   After I comment this line below about `multi_precision`, it's fixed. It 
seems a not complete feature?
   ```Python
   optimizer_params = {
   'learning_rate': lr,
   'momentum' : args.mom,
   'wd' : args.wd,
   'lr_scheduler': lr_scheduler,}
   #'multi_precision': True}
   ```
 

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[GitHub] DickJC123 opened a new pull request #7425: Tensorcore fullyconnected support

[git]

DickJC123 opened a new pull request #7425: Tensorcore fullyconnected support
URL: https://github.com/apache/incubator-mxnet/pull/7425
 
 
   Adds TensorCore algo support to the FullyConnected operator for users with 
NVIDIA Volta / cuda9 / cudnn7.  On by default, this can be disabled through an 
environment variable as in:
   
   export MXNET_CUDA_ALLOW_TENSOR_CORE=0
   
   Applies to float16 I/O instances only.
 

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[GitHub] ysh329 commented on a change in pull request #7363: Add tensorboard configure into ./common/fit.py and ./train_mnist.py

[git]

ysh329 commented on a change in pull request #7363: Add tensorboard configure 
into ./common/fit.py and ./train_mnist.py
URL: https://github.com/apache/incubator-mxnet/pull/7363#discussion_r132612116
 
 

 ##
 File path: example/image-classification/common/fit.py
 ##
 @@ -168,10 +168,16 @@ def fit(args, network, data_loader, **kwargs):
 
 # callbacks that run after each batch
 batch_end_callbacks = [mx.callback.Speedometer(args.batch_size, 
args.disp_batches)]
+eval_end_callbacks = []
 if 'batch_end_callback' in kwargs:
 cbs = kwargs['batch_end_callback']
 batch_end_callbacks += cbs if isinstance(cbs, list) else [cbs]
 
 Review comment:
   @zihaolucky 
   I assigned `eval_end_callbacks` with same value as `batch_end_ballbacks`, 
but log about validation set still print for each epoch, not 
`args.disp_batches`.
   ```Python
batch_end_callbacks = [mx.callback.Speedometer(args.batch_size, 
args.disp_batches)]
   eval_end_callbacks = [mx.callback.Speedometer(args.batch_size, 
args.disp_batches)]
   ```
 

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[GitHub] EsraaRagaa opened a new issue #7422: Data provided by data_shapes don't match names specified by data_names

[git]

EsraaRagaa opened a new issue #7422: Data provided by data_shapes don't match 
names specified by data_names
URL: https://github.com/apache/incubator-mxnet/issues/7422
 
 
   I am trying to train my data with mxnet, the data are 20x20 images
   I put my data in a csv files in the form:
   label, pixel1, pixel2, ..., pixel400
   0,...
   1,...
   and my labels are 0 or 1,
   this is the code I used:
   batch_size = 2
   
   
   train_iterator = mx.io.NDArrayIter(X_train, Y_train, batch_size=batch_size)
   validate_iterator = mx.io.NDArrayIter(X_validate, Y_validate, 
batch_size=batch_size)
   
   ##first convelutional layer
   conv1 = mx.sym.Convolution(data=data, kernel=(3,3), num_filter=6)
   relu1 = mx.sym.Activation(data=conv1, act_type="relu")
   pool1 = mx.sym.Pooling(data=relu1, pool_type="max", kernel=(2,2), 
stride=(2,2))
   
   ##second convelutional layer
   conv2 = mx.sym.Convolution(data=pool1, kernel=(6,6), num_filter=12)
   relu2 = mx.sym.Activation(data=conv2, act_type="relu")
   pool2 = mx.sym.Pooling(data=relu2, pool_type="max", kernel=(2,2), 
stride=(2,2))
   
   ##first fully connected layer
   flatten = mx.sym.flatten(data=pool2)
   fc1 = mx.symbol.FullyConnected(data=flatten, num_hidden=12 )
   
   ##softmax loss
   lenet = mx.sym.SoftmaxOutput(data=fc1, name='softmax')
   
   ##create a trainable module on CPU 0
   lenet_model = mx.mod.Module(symbol=lenet, context=mx.cpu())
   device = mx.cpu()
   
   ##train using parameters
   
   '''
   model = mx.model.FeedForward.create(lenet_model,
   X = X_train,
   y = Y_train,
   ctx = device,
   num_epoch = 10)
   '''
   lenet_model.fit(train_iterator,
eval_data=validate_iterator,
optimizer='sgd',
optimizer_params={'learning_rate':0.1},
eval_metric='acc',
batch_end_callback = 
mx.callback.Speedometer(batch_size, 100),
num_epoch=10)
   # the error is:
Traceback (most recent call last):
   
 File "", line 7, in 
   num_epoch=10)
   
 File 
"C:\Users\...\Anaconda2\lib\site-packages\mxnet-0.10.1-py2.7.egg\mxnet\module\base_module.py",
 line 459, in fit
   for_training=True, force_rebind=force_rebind)
   
 File 
"C:\Users\...\Anaconda2\lib\site-packages\mxnet-0.10.1-py2.7.egg\mxnet\module\module.py",
 line 372, in bind
   self.data_names, self.label_names, data_shapes, label_shapes)
   
 File 
"C:\Users\...\Anaconda2\lib\site-packages\mxnet-0.10.1-py2.7.egg\mxnet\module\base_module.py",
 line 70, in _parse_data_desc
   _check_names_match(data_names, data_shapes, 'data', True)
   
 File 
"C:\Users\...\Anaconda2\lib\site-packages\mxnet-0.10.1-py2.7.egg\mxnet\module\base_module.py",
 line 62, in _check_names_match
   raise ValueError(msg)
   
   ValueError: Data provided by data_shapes don't match names specified by 
data_names ([DataDesc[_0_data,(2,),,NCHW], 
DataDesc[_1_data,(2,),,NCHW], DataDesc[_2_data,(2,),,NCHW], DataDesc[_3_data,(2,),,NCHW], 
DataDesc[_4_data,(2,),,NCHW], DataDesc[_5_data,(2,),,NCHW], DataDesc[_6_data,(2,),,NCHW], 
DataDesc[_7_data,(2,),,NCHW], DataDesc[_8_data,(2,),,NCHW], DataDesc[_9_data,(2,),,NCHW], 
DataDesc[_10_data,(2,),,NCHW], 
DataDesc[_11_data,(2,),,NCHW], 
DataDesc[_12_data,(2,),,NCHW], 
DataDesc[_13_data,(2,),,NCHW], 
DataDesc[_14_data,(2,),,NCHW], 
DataDesc[_15_data,(2,),,NCHW], 
DataDesc[_16_data,(2,),,NCHW], DataDesc[_17_data,(
 2,),,NCHW],
   ...
   ...
   ...
   ...
   , DataDesc[_2773_data,(2,),,NCHW], 
DataDesc[_2774_data,(2,),,NCHW], 
DataDesc[_2775_data,(2,),,NCHW], 
DataDesc[_2776_data,(2,),,NCHW], 
DataDesc[_2777_data,(2,),,NCHW]] vs. ['data'])
   
   # What should the data look like, what is wrong in the code, please?
   I am using win10, python2, mxnet 0.10, anaconda2
   Thanks in advance
   
 
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[GitHub] chinakook commented on issue #7393: add depthwise convolution's gpu version optimization

[git]

chinakook commented on issue #7393: add depthwise convolution's gpu version 
optimization
URL: https://github.com/apache/incubator-mxnet/pull/7393#issuecomment-321719808
 
 
   Nice work, It's a good feature for mobilenet!
 

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[GitHub] ysh329 opened a new issue #7424: train_mnist.py failed: TypeError: __init__() got an unexpected keyword argument 'multi_precision'

[git]

ysh329 opened a new issue #7424: train_mnist.py failed: TypeError: __init__() 
got an unexpected keyword argument 'multi_precision'
URL: https://github.com/apache/incubator-mxnet/issues/7424
 
 
   Release 0.10.0 is okay.   
   However, Current master branch (git clone --recursive 
https://github.com/apache/incubator-mxnet.git) occurs this problem as below 
(running in the official docker, then I cloned from github.com and run this 
train_mnist.py) :   
   ```Shell
   root@8acd2b8afd12:~/incubator-mxnet-origin/example/image-classification# 
python train_mnist.py
   INFO:root:start with arguments Namespace(add_stn=False, batch_size=64, 
disp_batches=100, dtype='float32', gpus=None, kv_store='device', 
load_epoch=None, lr=0.05, lr_factor=0.1, lr_step_epochs='10', 
model_prefix=None, mom=0.9, monitor=0, network='mlp', num_classes=10, 
num_epochs=20, num_examples=6, num_layers=None, optimizer='sgd', test_io=0, 
top_k=0, wd=0.0001)
   Traceback (most recent call last):
 File "train_mnist.py", line 91, in 
   eval_end_callback = eval_end_callbacks)
 File 
"/root/incubator-mxnet-origin/example/image-classification/common/fit.py", line 
207, in fit
   monitor= monitor)
 File "/mxnet/python/mxnet/module/base_module.py", line 465, in fit
   optimizer_params=optimizer_params)
 File "/mxnet/python/mxnet/module/module.py", line 478, in init_optimizer
   **optimizer_params)
 File "/mxnet/python/mxnet/optimizer.py", line 128, in create_optimizer
   return Optimizer.opt_registry[name.lower()](**kwargs)
 File "/mxnet/python/mxnet/optimizer.py", line 328, in __init__
   super(SGD, self).__init__(**kwargs)
   TypeError: __init__() got an unexpected keyword argument 'multi_precision'
   ```
 
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[GitHub] EsraaRagaa opened a new issue #7420: How to pass CSVIter (images flatten in .csv file) to MXNet ?

[git]

EsraaRagaa opened a new issue #7420: How to pass CSVIter (images flatten in 
.csv file) to MXNet ?
URL: https://github.com/apache/incubator-mxnet/issues/7420
 
 
   # Hello, 
   I am trying to train my data with mxnet, the data are 20x20 images 
   I put my data in a csv files in the form:
   label, pixel1, pixel2, ..., pixel400
   0,...
   1,...
   and my labels are 0 or 1, 
   # this is the code I used:
   -
   train_data = pd.read_csv("training_set_flatten_rows_mxnet.csv")
   keys = ['pixel.'+str(i) for i in range(1,402)]
   X_train = train_data[keys[1:]].get_values()
   X_train = X_train.reshape((2779,1,20,20))
   Y_train = train_data['pixel.1'].get_values().reshape((2779,1))
   
   validate_data = pd.read_csv("validation_set_flatten_rows_mxnet.csv")
   keys = ['pixel.'+str(i) for i in range(1,402)]
   X_validate = validate_data[keys[1:]].get_values()
   X_validate = X_validate.reshape((692,1,20,20))
   Y_validate = validate_data['pixel.1'].get_values().reshape((692,1))
   
   train_iterator = mx.io.NDArrayIter(X_train, Y_train, batch_size=batch_size)
   validate_iterator = mx.io.NDArrayIter(X_validate, Y_validate, 
batch_size=batch_size)
   
   # first convelutional layer
   conv1 = mx.sym.Convolution(data=data, kernel=(3,3), num_filter=6)
   relu1 = mx.sym.Activation(data=conv1, act_type="relu")
   pool1 = mx.sym.Pooling(data=relu1, pool_type="max", kernel=(2,2), 
stride=(2,2))
   
   # second convelutional layer
   conv2 = mx.sym.Convolution(data=pool1, kernel=(6,6), num_filter=12)
   relu2 = mx.sym.Activation(data=conv2, act_type="relu")
   pool2 = mx.sym.Pooling(data=relu2, pool_type="max", kernel=(2,2), 
stride=(2,2))
   
   # first fully connected layer
   flatten = mx.sym.flatten(data=pool2)
   fc1 = mx.symbol.FullyConnected(data=flatten, num_hidden=12 )
   # softmax loss
   lenet = mx.sym.SoftmaxOutput(data=fc1, name='softmax')
   # create a trainable module on CPU 0
   lenet_model = mx.mod.Module(symbol=lenet, context=mx.cpu())
   device = mx.cpu()
   # train using parameters
   '''
   model = mx.model.FeedForward.create(lenet_model,
   X = X_train,
   y = Y_train,
   ctx = device,
   num_epoch = 10)
   '''
   lenet_model.fit(train_iterator,
   eval_data=validate_iterator,
   optimizer='sgd',
   optimizer_params={'learning_rate':0.1},
   eval_metric='acc',
   batch_end_callback = mx.callback.Speedometer(batch_size, 
100),
   num_epoch=10)
   
   -
   
   # the error is:
   
   Traceback (most recent call last):
   
 File "", line 7, in 
   num_epoch=10)
   
 File 
"C:\Users\...\Anaconda2\lib\site-packages\mxnet-0.10.1-py2.7.egg\mxnet\module\base_module.py",
 line 459, in fit
   for_training=True, force_rebind=force_rebind)
   
 File 
"C:\Users\...\Anaconda2\lib\site-packages\mxnet-0.10.1-py2.7.egg\mxnet\module\module.py",
 line 388, in bind
   state_names=self._state_names)
   
 File 
"C:\Users\...\Anaconda2\lib\site-packages\mxnet-0.10.1-py2.7.egg\mxnet\module\executor_group.py",
 line 214, in __init__
   self.bind_exec(data_shapes, label_shapes, shared_group)
   
 File 
"C:\Users\...\Anaconda2\lib\site-packages\mxnet-0.10.1-py2.7.egg\mxnet\module\executor_group.py",
 line 310, in bind_exec
   shared_group))
   
 File 
"C:\Users\...\Anaconda2\lib\site-packages\mxnet-0.10.1-py2.7.egg\mxnet\module\executor_group.py",
 line 582, in _bind_ith_exec
   shared_buffer=shared_data_arrays, **input_shapes)
   
 File 
"C:\Users\...\Anaconda2\lib\site-packages\mxnet-0.10.1-py2.7.egg\mxnet\symbol.py",
 line 1375, in simple_bind
   raise RuntimeError('simple_bind failed')
   
   RuntimeError: simple_bind failed
   
   # If I comment this line in the model.fit ::
#batch_end_callback = mx.callback.Speedometer(batch_size, 100),
   
   # I get error: 
   WARNING:root:Already bound, ignoring bind()
   Traceback (most recent call last):
   
 File "", line 7, in 
   num_epoch=10)
   
 File 
"C:\Users\...\Anaconda2\lib\site-packages\mxnet-0.10.1-py2.7.egg\mxnet\module\base_module.py",
 line 463, in fit
   allow_missing=allow_missing, force_init=force_init)
   
 File 
"C:\Users\...\Anaconda2\lib\site-packages\mxnet-0.10.1-py2.7.egg\mxnet\module\module.py",
 line 272, in init_params
   for name, arr in self._arg_params.items():
   
   AttributeError: 'NoneType' object has no attribute 'items'
   
   I guess the error is from the the data load and passing to mxnet. 
   I don't know what does this mean and what shall I do, help me, please.
   
   
   I am using win10, python2, mxnet 0.10, anaconda2

[GitHub] rahul003 opened a new pull request #7421: Resolve more compile warnings

[git]

rahul003 opened a new pull request #7421: Resolve more compile warnings
URL: https://github.com/apache/incubator-mxnet/pull/7421
 
 
   All sign compare warnings. Warnings in fIrst two files showed up on GPU and 
the last file is a test file which had sign compare warnings. All of these 
occurrences don't need sign for the variable.
 

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[GitHub] larroy opened a new pull request #7416: update submoules with android fixes

[git]

larroy opened a new pull request #7416: update submoules with android fixes
URL: https://github.com/apache/incubator-mxnet/pull/7416
 
 
   
 

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[GitHub] idealboy commented on issue #5452: When executing "train_mnist.py" with two machines, no response returns

[git]

idealboy commented on issue #5452: When executing "train_mnist.py" with two 
machines, no response returns
URL: 
https://github.com/apache/incubator-mxnet/issues/5452#issuecomment-321508451
 
 
   it is probably because of the firewall in the machine, try to close it.
 

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[GitHub] eric-haibin-lin commented on issue #7414: why rnn train speed is not stable,sometims very slow?

[git]

eric-haibin-lin commented on issue #7414: why rnn train speed is not 
stable,sometims very slow?
URL: 
https://github.com/apache/incubator-mxnet/issues/7414#issuecomment-321729637
 
 
   Is 30 samples/sec the speed for a batch? Do you have variable sequence 
length? 
 

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[GitHub] thirdwing commented on issue #7375: Can I set instance weight when training?

[git]

thirdwing commented on issue #7375: Can I set instance weight when training?
URL: 
https://github.com/apache/incubator-mxnet/issues/7375#issuecomment-321731082
 
 
   Can you give more details on what you mean by "set instance weight"?
   
   I am sorry that I don't understand your problem.
   
   On 10 Aug 2017 8:10 p.m., "reg.zhuce" <notificati...@github.com> wrote:
   
   @thirdwing <https://github.com/thirdwing> Thanks
   Any proposals for my problem?
   
   ?
   You are receiving this because you were mentioned.
   Reply to this email directly, view it on GitHub
   
<https://github.com/apache/incubator-mxnet/issues/7375#issuecomment-321723588>,
   or mute the thread
   
<https://github.com/notifications/unsubscribe-auth/ABebVczWwmvWZBZDoSqE8DBQs5w3Q6vzks5sW8YagaJpZM4OwL1A>
   .
   
 

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[GitHub] jmacglashan commented on issue #7319: [RoadMap] Legacy issue resolution before 1.0 release

[git]

jmacglashan commented on issue #7319: [RoadMap] Legacy issue resolution before 
1.0 release
URL: 
https://github.com/apache/incubator-mxnet/issues/7319#issuecomment-320315492
 
 
   Yes, an operator file (or otherwise) to support IDE code completion would be 
greatly welcomed.
 

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