[GitHub] lupesko commented on issue #9378: Mxnet C API CPU Mode segmentation fault

[GitBox]

lupesko commented on issue #9378: Mxnet C API CPU Mode segmentation fault
URL: 
https://github.com/apache/incubator-mxnet/issues/9378#issuecomment-366889618
 
 
   @sandeep-krishnamurthy can you please close?


This is an automated message from the Apache Git Service.
To respond to the message, please log on GitHub and use the
URL above to go to the specific comment.
 
For queries about this service, please contact Infrastructure at:
us...@infra.apache.org


With regards,
Apache Git Services

[GitHub] lupesko commented on issue #9378: Mxnet C API CPU Mode segmentation fault

[GitBox]

lupesko commented on issue #9378: Mxnet C API CPU Mode segmentation fault
URL: 
https://github.com/apache/incubator-mxnet/issues/9378#issuecomment-366889574
 
 
   @pitLog I verified today that the C API with the example code works 
perfectly well.
   It is most likely that the problem is in your model, and/or with the model 
input shape.
   
   We will be closing the issue, feel free to re-open if you think this was 
closed in error. 


This is an automated message from the Apache Git Service.
To respond to the message, please log on GitHub and use the
URL above to go to the specific comment.
 
For queries about this service, please contact Infrastructure at:
us...@infra.apache.org


With regards,
Apache Git Services

[GitHub] rimusolem opened a new issue #9836: Allow -2, -3, and -4 as a shape for NDArray.reshape

[GitBox]

rimusolem opened a new issue #9836: Allow -2, -3, and -4 as a shape for 
NDArray.reshape
URL: https://github.com/apache/incubator-mxnet/issues/9836
 
 
   This is a feature request. `mxnet.ndarray.reshape` and  
`mxnet.symbol.reshape` allow 0, -1, -2, -3, and -4 as valid shapes with special 
meanings. However, `NDArray.reshape` only accepts 0 and -1 among the special 
shapes. It would be convenient if `NDArray.reshape` also allows the rests.


This is an automated message from the Apache Git Service.
To respond to the message, please log on GitHub and use the
URL above to go to the specific comment.
 
For queries about this service, please contact Infrastructure at:
us...@infra.apache.org


With regards,
Apache Git Services

[GitHub] szha commented on issue #9835: Failed to export gluon model zoo vision model to symbol file

[GitBox]

szha commented on issue #9835: Failed to export gluon model zoo vision model to 
symbol file
URL: 
https://github.com/apache/incubator-mxnet/issues/9835#issuecomment-366882796
 
 
   @zheng-da would you take a look?


This is an automated message from the Apache Git Service.
To respond to the message, please log on GitHub and use the
URL above to go to the specific comment.
 
For queries about this service, please contact Infrastructure at:
us...@infra.apache.org


With regards,
Apache Git Services

[GitHub] marcoabreu commented on issue #9705: Added unittest for benchmarking metric performance

[GitBox]

marcoabreu commented on issue #9705: Added unittest for benchmarking metric 
performance
URL: https://github.com/apache/incubator-mxnet/pull/9705#issuecomment-366881982
 
 
   It's planned for end of Q1
   
   Am 20.02.2018 5:47 vorm. schrieb "Sheng Zha" :
   
   > When will nightly tests be moved to public CI?
   >
   > ?
   > You are receiving this because you commented.
   > Reply to this email directly, view it on GitHub
   > 
,
   > or mute the thread
   > 

   > .
   >
   


This is an automated message from the Apache Git Service.
To respond to the message, please log on GitHub and use the
URL above to go to the specific comment.
 
For queries about this service, please contact Infrastructure at:
us...@infra.apache.org


With regards,
Apache Git Services

[GitHub] kevinthesun opened a new issue #9835: Failed to export gluon model zoo vision model to symbol file

[GitBox]

kevinthesun opened a new issue #9835: Failed to export gluon model zoo vision 
model to symbol file
URL: https://github.com/apache/incubator-mxnet/issues/9835
 
 
   ## Description
   Gluon block export function returned error while trying to export mobilenet, 
alexnet, densenet, squeezenet and resnet_v2 models. MXNet is built from source 
with mkldnn.
   
   MXNet commit hash:
   af0c3b4e9bcb41734375470f965bba3c5731b1d0
   
   ## Error Message:
   ```
   Traceback (most recent call last):
 File "test.py", line 18, in 
   block.export(model)
 File "/home/ubuntu/mxnet/python/mxnet/gluon/block.py", line 558, in export
   ndarray.save('%s-%04d.params'%(path, epoch), arg_dict)
 File "/home/ubuntu/mxnet/python/mxnet/ndarray/utils.py", line 236, in save
   keys))
 File "/home/ubuntu/mxnet/python/mxnet/base.py", line 148, in check_call
   raise MXNetError(py_str(_LIB.MXGetLastError()))
   mxnet.base.MXNetError: [06:26:59] 
src/operator/tensor/./././elemwise_unary_op.h:301: Check failed: 
inputs[0].dptr_ == outputs[0].dptr_ (0x7fbc19d37000 vs. 0x7fbc19d39000) 
   
   Stack trace returned 10 entries:
   [bt] (0) 
/home/ubuntu/mxnet/python/mxnet/../../lib/libmxnet.so(dmlc::StackTrace[abi:cxx11]()+0x5b)
 [0x7fbcedf144fb]
   [bt] (1) 
/home/ubuntu/mxnet/python/mxnet/../../lib/libmxnet.so(dmlc::LogMessageFatal::~LogMessageFatal()+0x28)
 [0x7fbcedf15518]
   [bt] (2) /home/ubuntu/mxnet/python/mxnet/../../lib/libmxnet.so(void 
mxnet::op::UnaryOp::IdentityCompute(nnvm::NodeAttrs const&, 
mxnet::OpContext const&, std::vector 
> const&, std::vector > 
const&, std::vector > const&)+0xa99) 
[0x7fbcee3b8c19]
   [bt] (3) 
/home/ubuntu/mxnet/python/mxnet/../../lib/libmxnet.so(mxnet::imperative::PushFCompute(std::function > const&, std::vector > const&, std::vector > const&)> const&, nnvm::Op const*, 
nnvm::NodeAttrs const&, mxnet::Context const&, std::vector > const&, std::vector > const&, std::vector > const&, std::vector > const&, std::vector > const&, std::vector > const&, std::vector > 
const&)::{lambda(mxnet::RunContext)#1}::operator()(mxnet::RunContext) 
const+0x1067) [0x7fbcf06f7ec7]
   [bt] (4) 
/home/ubuntu/mxnet/python/mxnet/../../lib/libmxnet.so(std::_Function_handler, mxnet::Context, std::vector > const&, std::vector > 
const&)::{lambda(mxnet::RunContext)#1}>::_M_invoke(std::_Any_data const&, 
mxnet::RunContext&&)+0x68) [0x7fbcf0b6ba98]
   [bt] (5) 
/home/ubuntu/mxnet/python/mxnet/../../lib/libmxnet.so(std::_Function_handler, mxnet::Context, std::vector > const&, std::vector > 
const&)::{lambda(mxnet::RunContext)#1}>::_M_invoke(std::_Any_data const&, 
mxnet::RunContext&&)+0x47) [0x7fbcf0b6ba77]
   [bt] (6) 
/home/ubuntu/mxnet/python/mxnet/../../lib/libmxnet.so(std::_Function_handler::_M_invoke(std::_Any_data const&, 
mxnet::RunContext&&, mxnet::engine::CallbackOnComplete&&)+0x4b) [0x7fbcf0b5794b]
   [bt] (7) 
/home/ubuntu/mxnet/python/mxnet/../../lib/libmxnet.so(mxnet::engine::ThreadedEngine::ExecuteOprBlock(mxnet::RunContext,
 mxnet::engine::OprBlock*)+0x2be) [0x7fbcf0b5c2ee]
   [bt] (8) 
/home/ubuntu/mxnet/python/mxnet/../../lib/libmxnet.so(std::_Function_handler), 
mxnet::engine::ThreadedEnginePerDevice::PushToExecute(mxnet::engine::OprBlock*, 
bool)::{lambda()#1}::operator()() 
const::{lambda(std::shared_ptr)#1}>::_M_invoke(std::_Any_data
 const&, std::shared_ptr&&)+0x133) 
[0x7fbcf0b79d73]
   [bt] (9) 
/home/ubuntu/mxnet/python/mxnet/../../lib/libmxnet.so(std::thread::_Impl)> 
(std::shared_ptr)> >::_M_run()+0x4a) 
[0x7fbcf0b7590a]
   ```
   
   ## Minimum reproducible example
   ```python
   import numpy as np
   import mxnet as mx
   
   from mxnet.gluon.model_zoo.vision import get_model
   
   model = "mobilenet1.0"
   batch_size = 1
   
   image_shape = (3, 224, 224)
   data_shape = (batch_size,) + image_shape
   
   data_array = np.random.uniform(0, 255, size=data_shape).astype("float32")
   mx_data = mx.nd.array(data_array)
   
   block = get_model(model, pretrained=True)
   block.hybridize()
   block(mx_data)
   block.export(model)
   ```
   


This is an automated message from the Apache Git Service.
To respond to the message, please log on GitHub and use the
URL above to go to the specific comment.
 
For queries about this service, please contact Infrastructure at:
us...@infra.apache.org


With regards,
Apache Git Services

[GitHub] anirudh2290 commented on issue #9826: Crash Mxnet: Error in `python3': corrupted double-linked list: 0x00007f1c4b2e09d0

[GitBox]

anirudh2290 commented on issue #9826: Crash Mxnet: Error in `python3': 
corrupted double-linked list: 0x7f1c4b2e09d0
URL: 
https://github.com/apache/incubator-mxnet/issues/9826#issuecomment-366877966
 
 
   What version of mxnet are you using ? Please provide commit hash ? Please 
provide reproduction steps.


This is an automated message from the Apache Git Service.
To respond to the message, please log on GitHub and use the
URL above to go to the specific comment.
 
For queries about this service, please contact Infrastructure at:
us...@infra.apache.org


With regards,
Apache Git Services

[GitHub] rafaelsimonmaia commented on issue #9217: Installing GPU support on Mac

[GitBox]

rafaelsimonmaia commented on issue #9217: Installing GPU support on Mac
URL: 
https://github.com/apache/incubator-mxnet/issues/9217#issuecomment-366876266
 
 
   Hi @helloniklas, could you remove the space after  `USE_CUDA = 1`. It is 
just a minor detail that may make it fail to compile if someone just copies 
that code without paying attention.  Thanks!


This is an automated message from the Apache Git Service.
To respond to the message, please log on GitHub and use the
URL above to go to the specific comment.
 
For queries about this service, please contact Infrastructure at:
us...@infra.apache.org


With regards,
Apache Git Services

[GitHub] cjolivier01 commented on a change in pull request #9832: Ability to not build examples during the build of the cpp package.

[GitBox]

cjolivier01 commented on a change in pull request #9832: Ability to not build 
examples during the build of the cpp package.
URL: https://github.com/apache/incubator-mxnet/pull/9832#discussion_r169219022
 
 

 ##
 File path: cpp-package/CMakeLists.txt
 ##
 @@ -1,21 +1,23 @@
 if(USE_CPP_PACKAGE)
 
-set(CPP_PACKAGE_OP_H_HEADER ${CMAKE_CURRENT_LIST_DIR}/include/mxnet-cpp/op.h)
+  set(CPP_PACKAGE_OP_H_HEADER ${CMAKE_CURRENT_LIST_DIR}/include/mxnet-cpp/op.h)
 
-if(MSVC)
-  set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} /utf-8")
-endif(MSVC)
+  if(MSVC)
+set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} /utf-8")
+  endif(MSVC)
 
-add_custom_target(
-  cpp_package_op_h ALL
-  BYPRODUCTS ${CPP_PACKAGE_OP_H_HEADER}
-  MAIN_DEPENDENCY mxnet
-  DEPENDS mxnet ${CMAKE_CURRENT_SOURCE_DIR}/scripts/OpWrapperGenerator.py
-  COMMAND echo "Running: OpWrapperGenerator.py"
-  COMMAND python OpWrapperGenerator.py $
-  WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}/scripts
-)
+  add_custom_target(
+cpp_package_op_h ALL
+BYPRODUCTS ${CPP_PACKAGE_OP_H_HEADER}
+MAIN_DEPENDENCY mxnet
+DEPENDS mxnet ${CMAKE_CURRENT_SOURCE_DIR}/scripts/OpWrapperGenerator.py
+COMMAND echo "Running: OpWrapperGenerator.py"
+COMMAND python OpWrapperGenerator.py $
+WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}/scripts
+  )
 
-add_subdirectory(example)
+  if(NOT DO_NOT_BUILD_EXAMPLES)
 
 Review comment:
   naming seems kind of generic and not specific to cop-package
   
   what current use cases don?t build?
   for test target probably they should build?


This is an automated message from the Apache Git Service.
To respond to the message, please log on GitHub and use the
URL above to go to the specific comment.
 
For queries about this service, please contact Infrastructure at:
us...@infra.apache.org


With regards,
Apache Git Services

[GitHub] szha commented on issue #9705: Added unittest for benchmarking metric performance

[GitBox]

szha commented on issue #9705: Added unittest for benchmarking metric 
performance
URL: https://github.com/apache/incubator-mxnet/pull/9705#issuecomment-366865940
 
 
   When will nightly tests be moved to public CI?


This is an automated message from the Apache Git Service.
To respond to the message, please log on GitHub and use the
URL above to go to the specific comment.
 
For queries about this service, please contact Infrastructure at:
us...@infra.apache.org


With regards,
Apache Git Services

[GitHub] szha commented on issue #9805: Enable the reporting of cross-entropy or nll loss value when training CNN network using the models defined by example/image-classification

[GitBox]

szha commented on issue #9805: Enable the reporting of cross-entropy or nll 
loss value when training CNN network using the models defined by 
example/image-classification
URL: https://github.com/apache/incubator-mxnet/pull/9805#issuecomment-366863435
 
 
   Yes, that test is a flaky test #9820 


This is an automated message from the Apache Git Service.
To respond to the message, please log on GitHub and use the
URL above to go to the specific comment.
 
For queries about this service, please contact Infrastructure at:
us...@infra.apache.org


With regards,
Apache Git Services

[GitHub] juliusshufan commented on issue #9805: Enable the reporting of cross-entropy or nll loss value when training CNN network using the models defined by example/image-classification

[GitBox]

juliusshufan commented on issue #9805: Enable the reporting of cross-entropy or 
nll loss value when training CNN network using the models defined by 
example/image-classification
URL: https://github.com/apache/incubator-mxnet/pull/9805#issuecomment-366863185
 
 
   I rerun the failed GPU case "test_gluon_model_zoo_gpu" on my machine, with 
an GTX1070, the test results with/w.o my submissions are same: 
   --
   Ran 2 tests in 9.589s
   
   OK (SKIP=1)
   
   Looks like my submission is not relevant to the failed case during pre-ci.
   
   Thanks.
   


This is an automated message from the Apache Git Service.
To respond to the message, please log on GitHub and use the
URL above to go to the specific comment.
 
For queries about this service, please contact Infrastructure at:
us...@infra.apache.org


With regards,
Apache Git Services

[GitHub] eric-haibin-lin commented on a change in pull request #9747: Add contrib.rand_zipfian

[GitBox]

eric-haibin-lin commented on a change in pull request #9747: Add 
contrib.rand_zipfian
URL: https://github.com/apache/incubator-mxnet/pull/9747#discussion_r169206557
 
 

 ##
 File path: python/mxnet/ndarray/contrib.py
 ##
 @@ -18,9 +18,81 @@
 # coding: utf-8
 # pylint: disable=wildcard-import, unused-wildcard-import
 """Contrib NDArray API of MXNet."""
+import math
+from ..context import current_context
+from ..random import uniform
 try:
 from .gen_contrib import *
 except ImportError:
 pass
 
-__all__ = []
+__all__ = ["log_uniform_candidate_sampler"]
+
+# pylint: disable=line-too-long
+def log_uniform_candidate_sampler(true_classes, num_sampled, range_max, 
ctx=None):
 
 Review comment:
   Name changed to rand_zipfian to follow the convention. Extra namespaces such 
as `contrib.random` might over-complicate APIs since there are just a few 
operators in nd.contrib. 


This is an automated message from the Apache Git Service.
To respond to the message, please log on GitHub and use the
URL above to go to the specific comment.
 
For queries about this service, please contact Infrastructure at:
us...@infra.apache.org


With regards,
Apache Git Services

[GitHub] szha closed pull request #9834: minor spelling tweaks for docs

[GitBox]

szha closed pull request #9834: minor spelling tweaks for docs
URL: https://github.com/apache/incubator-mxnet/pull/9834
 
 
   

This is a PR merged from a forked repository.
As GitHub hides the original diff on merge, it is displayed below for
the sake of provenance:

As this is a foreign pull request (from a fork), the diff is supplied
below (as it won't show otherwise due to GitHub magic):

diff --git a/docs/tutorials/basic/data.md b/docs/tutorials/basic/data.md
index 1a88242592..54ee334f97 100644
--- a/docs/tutorials/basic/data.md
+++ b/docs/tutorials/basic/data.md
@@ -416,7 +416,7 @@ data_iter = mx.io.ImageRecordIter(
 data_shape=(3, 227, 227), # output data shape. An 227x227 region will be 
cropped from the original image.
 batch_size=4, # number of samples per batch
 resize=256 # resize the shorter edge to 256 before cropping
-# ... you can add more augumentation options as defined in ImageRecordIter.
+# ... you can add more augmentation options as defined in ImageRecordIter.
 )
 data_iter.reset()
 batch = data_iter.next()
diff --git a/docs/tutorials/basic/image_io.md b/docs/tutorials/basic/image_io.md
index 8d60ee8fc0..092affbc74 100644
--- a/docs/tutorials/basic/image_io.md
+++ b/docs/tutorials/basic/image_io.md
@@ -85,7 +85,7 @@ data_iter = mx.io.ImageRecordIter(
 data_shape=(3, 227, 227), # output data shape. An 227x227 region will be 
cropped from the original image.
 batch_size=4, # number of samples per batch
 resize=256 # resize the shorter edge to 256 before cropping
-# ... you can add more augumentation options here. use 
help(mx.io.ImageRecordIter) to see all possible choices
+# ... you can add more augmentation options here. use 
help(mx.io.ImageRecordIter) to see all possible choices
 )
 data_iter.reset()
 batch = data_iter.next()
diff --git a/docs/tutorials/basic/record_io.md 
b/docs/tutorials/basic/record_io.md
index e415d9448b..9ba6fa6e25 100644
--- a/docs/tutorials/basic/record_io.md
+++ b/docs/tutorials/basic/record_io.md
@@ -2,7 +2,7 @@
 
 This tutorial will walk through the python interface for reading and writing
 record io files. It can be useful when you need more more control over the
-details of data pipeline. For example, when you need to augument image and 
label
+details of data pipeline. For example, when you need to augment image and label
 together for detection and segmentation, or when you need a custom data 
iterator
 for triplet sampling and negative sampling.
 
@@ -16,7 +16,7 @@ import numpy as np
 import matplotlib.pyplot as plt
 ```
 
-The relevent code is under `mx.recordio`. There are two classes: `MXRecordIO`,
+The relevant code is under `mx.recordio`. There are two classes: `MXRecordIO`,
 which supports sequential read and write, and `MXIndexedRecordIO`, which
 supports random read and sequential write.
 
diff --git a/docs/tutorials/gluon/customop.md b/docs/tutorials/gluon/customop.md
index dbb1907bad..e10f3987ee 100644
--- a/docs/tutorials/gluon/customop.md
+++ b/docs/tutorials/gluon/customop.md
@@ -171,7 +171,7 @@ class DenseProp(mx.operator.CustomOpProp):
 
 ### Use CustomOp together with Block
 
-Parameterized CustomOp are ususally used together with Blocks, which holds the 
parameter.
+Parameterized CustomOp are usually used together with Blocks, which holds the 
parameter.
 
 
 ```python
diff --git a/docs/tutorials/gluon/mnist.md b/docs/tutorials/gluon/mnist.md
index 0bd616c369..fc2271999f 100644
--- a/docs/tutorials/gluon/mnist.md
+++ b/docs/tutorials/gluon/mnist.md
@@ -50,7 +50,7 @@ val_data = mx.io.NDArrayIter(mnist['test_data'], 
mnist['test_label'], batch_size
 
 ## Approaches
 
-We will cover a couple of approaches for performing the hand written digit 
recognition task. The first approach makes use of a traditional deep neural 
network architecture called Multilayer Percepton (MLP). We'll discuss its 
drawbacks and use that as a motivation to introduce a second more advanced 
approach called Convolution Neural Network (CNN) that has proven to work very 
well for image classification tasks.
+We will cover a couple of approaches for performing the hand written digit 
recognition task. The first approach makes use of a traditional deep neural 
network architecture called Multilayer Perceptron (MLP). We'll discuss its 
drawbacks and use that as a motivation to introduce a second more advanced 
approach called Convolution Neural Network (CNN) that has proven to work very 
well for image classification tasks.
 
 Now, let's import required nn modules
 
@@ -142,7 +142,7 @@ for i in range(epoch):
 z = net(x)
 # Computes softmax cross entropy loss.
 loss = gluon.loss.softmax_cross_entropy_loss(z, y)
-# Backpropogate the error for one iteration.
+# Backpropagate the error for one iteration.
 ag.backward([loss])
 outputs.append(z)
 # Updates internal evaluation
diff --git a/docs/tutorials/pyt

[incubator-mxnet] branch master updated: minor spelling tweaks for docs (#9834)

[zhasheng]

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

zhasheng 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 af0c3b4  minor spelling tweaks for docs (#9834)
af0c3b4 is described below

commit af0c3b4e9bcb41734375470f965bba3c5731b1d0
Author: brett koonce 
AuthorDate: Mon Feb 19 18:41:16 2018 -0800

minor spelling tweaks for docs (#9834)
---
 docs/tutorials/basic/data.md  | 2 +-
 docs/tutorials/basic/image_io.md  | 2 +-
 docs/tutorials/basic/record_io.md | 4 ++--
 docs/tutorials/gluon/customop.md  | 2 +-
 docs/tutorials/gluon/mnist.md | 4 ++--
 docs/tutorials/python/mnist.md| 2 +-
 docs/tutorials/sparse/csr.md  | 2 +-
 7 files changed, 9 insertions(+), 9 deletions(-)

diff --git a/docs/tutorials/basic/data.md b/docs/tutorials/basic/data.md
index 1a88242..54ee334 100644
--- a/docs/tutorials/basic/data.md
+++ b/docs/tutorials/basic/data.md
@@ -416,7 +416,7 @@ data_iter = mx.io.ImageRecordIter(
 data_shape=(3, 227, 227), # output data shape. An 227x227 region will be 
cropped from the original image.
 batch_size=4, # number of samples per batch
 resize=256 # resize the shorter edge to 256 before cropping
-# ... you can add more augumentation options as defined in ImageRecordIter.
+# ... you can add more augmentation options as defined in ImageRecordIter.
 )
 data_iter.reset()
 batch = data_iter.next()
diff --git a/docs/tutorials/basic/image_io.md b/docs/tutorials/basic/image_io.md
index 8d60ee8..092affb 100644
--- a/docs/tutorials/basic/image_io.md
+++ b/docs/tutorials/basic/image_io.md
@@ -85,7 +85,7 @@ data_iter = mx.io.ImageRecordIter(
 data_shape=(3, 227, 227), # output data shape. An 227x227 region will be 
cropped from the original image.
 batch_size=4, # number of samples per batch
 resize=256 # resize the shorter edge to 256 before cropping
-# ... you can add more augumentation options here. use 
help(mx.io.ImageRecordIter) to see all possible choices
+# ... you can add more augmentation options here. use 
help(mx.io.ImageRecordIter) to see all possible choices
 )
 data_iter.reset()
 batch = data_iter.next()
diff --git a/docs/tutorials/basic/record_io.md 
b/docs/tutorials/basic/record_io.md
index e415d94..9ba6fa6 100644
--- a/docs/tutorials/basic/record_io.md
+++ b/docs/tutorials/basic/record_io.md
@@ -2,7 +2,7 @@
 
 This tutorial will walk through the python interface for reading and writing
 record io files. It can be useful when you need more more control over the
-details of data pipeline. For example, when you need to augument image and 
label
+details of data pipeline. For example, when you need to augment image and label
 together for detection and segmentation, or when you need a custom data 
iterator
 for triplet sampling and negative sampling.
 
@@ -16,7 +16,7 @@ import numpy as np
 import matplotlib.pyplot as plt
 ```
 
-The relevent code is under `mx.recordio`. There are two classes: `MXRecordIO`,
+The relevant code is under `mx.recordio`. There are two classes: `MXRecordIO`,
 which supports sequential read and write, and `MXIndexedRecordIO`, which
 supports random read and sequential write.
 
diff --git a/docs/tutorials/gluon/customop.md b/docs/tutorials/gluon/customop.md
index dbb1907..e10f398 100644
--- a/docs/tutorials/gluon/customop.md
+++ b/docs/tutorials/gluon/customop.md
@@ -171,7 +171,7 @@ class DenseProp(mx.operator.CustomOpProp):
 
 ### Use CustomOp together with Block
 
-Parameterized CustomOp are ususally used together with Blocks, which holds the 
parameter.
+Parameterized CustomOp are usually used together with Blocks, which holds the 
parameter.
 
 
 ```python
diff --git a/docs/tutorials/gluon/mnist.md b/docs/tutorials/gluon/mnist.md
index 0bd616c..fc22719 100644
--- a/docs/tutorials/gluon/mnist.md
+++ b/docs/tutorials/gluon/mnist.md
@@ -50,7 +50,7 @@ val_data = mx.io.NDArrayIter(mnist['test_data'], 
mnist['test_label'], batch_size
 
 ## Approaches
 
-We will cover a couple of approaches for performing the hand written digit 
recognition task. The first approach makes use of a traditional deep neural 
network architecture called Multilayer Percepton (MLP). We'll discuss its 
drawbacks and use that as a motivation to introduce a second more advanced 
approach called Convolution Neural Network (CNN) that has proven to work very 
well for image classification tasks.
+We will cover a couple of approaches for performing the hand written digit 
recognition task. The first approach makes use of a traditional deep neural 
network architecture called Multilayer Perceptron (MLP). We'll discuss its 
drawbacks and use that as a motivation to introduce a second more advanced 
approach called Convolution Neural Network (CNN) that has proven to work very 
well for image classification tasks.
 
 Now, let's import required nn modules
 
@@ -142,7 +142,7 @@ for i in range(epoch

svn commit: r25153 - /dev/incubator/mxnet/1.1.0/ /release/incubator/mxnet/1.1.0/

[liuyizhi]

Author: liuyizhi
Date: Tue Feb 20 01:37:51 2018
New Revision: 25153

Log:
1.1.0 release moving from dev to release repo

Added:
release/incubator/mxnet/1.1.0/
  - copied from r25152, dev/incubator/mxnet/1.1.0/
Removed:
dev/incubator/mxnet/1.1.0/

svn commit: r25152 - in /dev/incubator/mxnet/1.1.0: ./ apache-mxnet-src-1.1.0-incubating.tar.gz apache-mxnet-src-1.1.0-incubating.tar.gz.asc apache-mxnet-src-1.1.0-incubating.tar.gz.md5 apache-mxnet-s

[liuyizhi]

Author: liuyizhi
Date: Tue Feb 20 01:32:40 2018
New Revision: 25152

Log:
Add mxnet-1.1.0

Added:
dev/incubator/mxnet/1.1.0/
dev/incubator/mxnet/1.1.0/apache-mxnet-src-1.1.0-incubating.tar.gz   (with 
props)
dev/incubator/mxnet/1.1.0/apache-mxnet-src-1.1.0-incubating.tar.gz.asc
dev/incubator/mxnet/1.1.0/apache-mxnet-src-1.1.0-incubating.tar.gz.md5
dev/incubator/mxnet/1.1.0/apache-mxnet-src-1.1.0-incubating.tar.gz.sha512

Added: dev/incubator/mxnet/1.1.0/apache-mxnet-src-1.1.0-incubating.tar.gz
==
Binary file - no diff available.

Propchange: dev/incubator/mxnet/1.1.0/apache-mxnet-src-1.1.0-incubating.tar.gz
--
svn:mime-type = application/octet-stream

Added: dev/incubator/mxnet/1.1.0/apache-mxnet-src-1.1.0-incubating.tar.gz.asc
==
--- dev/incubator/mxnet/1.1.0/apache-mxnet-src-1.1.0-incubating.tar.gz.asc 
(added)
+++ dev/incubator/mxnet/1.1.0/apache-mxnet-src-1.1.0-incubating.tar.gz.asc Tue 
Feb 20 01:32:40 2018
@@ -0,0 +1,16 @@
+-BEGIN PGP SIGNATURE-
+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+=nxKe
+-END PGP SIGNATURE-

Added: dev/incubator/mxnet/1.1.0/apache-mxnet-src-1.1.0-incubating.tar.gz.md5
==
--- dev/incubator/mxnet/1.1.0/apache-mxnet-src-1.1.0-incubating.tar.gz.md5 
(added)
+++ dev/incubator/mxnet/1.1.0/apache-mxnet-src-1.1.0-incubating.tar.gz.md5 Tue 
Feb 20 01:32:40 2018
@@ -0,0 +1 @@
+MD5 (apache-mxnet-src-1.1.0-incubating.tar.gz) = 
01bc96415e372d2afad1e2669fae91de

Added: dev/incubator/mxnet/1.1.0/apache-mxnet-src-1.1.0-incubating.tar.gz.sha512
==
--- dev/incubator/mxnet/1.1.0/apache-mxnet-src-1.1.0-incubating.tar.gz.sha512 
(added)
+++ dev/incubator/mxnet/1.1.0/apache-mxnet-src-1.1.0-incubating.tar.gz.sha512 
Tue Feb 20 01:32:40 2018
@@ -0,0 +1 @@
+a641d212ba1ecb0e4852ff0f4cdcfedfcce6b7a00130251131f826bb4f1eda995cb4c9acd08d3794ca3d9820af10d188c729bc63de8b0a7cef2dd66debcf2061
  apache-mxnet-src-1.1.0-incubating.tar.gz

[GitHub] szha commented on a change in pull request #9833: [Metric] Accelerate the calculation of F1

[GitBox]

szha commented on a change in pull request #9833: [Metric] Accelerate the 
calculation of F1
URL: https://github.com/apache/incubator-mxnet/pull/9833#discussion_r169199762
 
 

 ##
 File path: python/mxnet/metric.py
 ##
 @@ -510,16 +510,10 @@ def update_binary_stats(self, label, pred):
 if len(numpy.unique(label)) > 2:
 raise ValueError("%s currently only supports binary 
classification."
  % self.__class__.__name__)
-
-for y_pred, y_true in zip(pred_label, label):
-if y_pred == 1 and y_true == 1:
-self.true_positives += 1.
-elif y_pred == 1 and y_true == 0:
-self.false_positives += 1.
-elif y_pred == 0 and y_true == 1:
-self.false_negatives += 1.
-else:
-self.true_negatives += 1.
+self.true_positives += ((pred_label == 1) * (label == 1)).sum()
 
 Review comment:
   you can cache the computation such as `predicted_true = pred_label == 1` and 
use later.


This is an automated message from the Apache Git Service.
To respond to the message, please log on GitHub and use the
URL above to go to the specific comment.
 
For queries about this service, please contact Infrastructure at:
us...@infra.apache.org


With regards,
Apache Git Services

[GitHub] pengzhao-intel commented on issue #9828: Building with MKL fails on OSX

[GitBox]

pengzhao-intel commented on issue #9828: Building with MKL fails on OSX
URL: 
https://github.com/apache/incubator-mxnet/issues/9828#issuecomment-366838868
 
 
   @TaoLv @ashokei, please take look this compatibility issue.
   
   @marcoabreu I see there is **NO** OSX build and test in Jenkin. 
   It will be risk w/o building and testing. 
   Is it possible to set up an OSX environment for CI process?


This is an automated message from the Apache Git Service.
To respond to the message, please log on GitHub and use the
URL above to go to the specific comment.
 
For queries about this service, please contact Infrastructure at:
us...@infra.apache.org


With regards,
Apache Git Services

[GitHub] pengzhao-intel commented on issue #9828: Building with MKL fails on OSX

[GitBox]

pengzhao-intel commented on issue #9828: Building with MKL fails on OSX
URL: 
https://github.com/apache/incubator-mxnet/issues/9828#issuecomment-366838868
 
 
   @TaoLv @ashokei, please take look this compatibility issue.
   
   @marcoabreu I see there is **NO** OSX build and test in Jenkin. 
   It will be risk w/o build and testing. 
   Is it possible to set up an OSX environment for CI process?


This is an automated message from the Apache Git Service.
To respond to the message, please log on GitHub and use the
URL above to go to the specific comment.
 
For queries about this service, please contact Infrastructure at:
us...@infra.apache.org


With regards,
Apache Git Services

[GitHub] brettkoonce opened a new pull request #9834: minor spelling tweaks for docs

[GitBox]

brettkoonce opened a new pull request #9834: minor spelling tweaks for docs
URL: https://github.com/apache/incubator-mxnet/pull/9834
 
 
   


This is an automated message from the Apache Git Service.
To respond to the message, please log on GitHub and use the
URL above to go to the specific comment.
 
For queries about this service, please contact Infrastructure at:
us...@infra.apache.org


With regards,
Apache Git Services

[GitHub] rahul003 commented on issue #9831: add USE_MKLDNN to config.mk

[GitBox]

rahul003 commented on issue #9831: add USE_MKLDNN to config.mk
URL: https://github.com/apache/incubator-mxnet/pull/9831#issuecomment-366836229
 
 
   https://github.com/apache/incubator-mxnet/pull/9810 does this and more. 
Closing


This is an automated message from the Apache Git Service.
To respond to the message, please log on GitHub and use the
URL above to go to the specific comment.
 
For queries about this service, please contact Infrastructure at:
us...@infra.apache.org


With regards,
Apache Git Services

[GitHub] rahul003 closed pull request #9831: add USE_MKLDNN to config.mk

[GitBox]

rahul003 closed pull request #9831: add USE_MKLDNN to config.mk
URL: https://github.com/apache/incubator-mxnet/pull/9831
 
 
   

This is a PR merged from a forked repository.
As GitHub hides the original diff on merge, it is displayed below for
the sake of provenance:

As this is a foreign pull request (from a fork), the diff is supplied
below (as it won't show otherwise due to GitHub magic):

diff --git a/make/config.mk b/make/config.mk
index 1591d2ad60..5173aec707 100644
--- a/make/config.mk
+++ b/make/config.mk
@@ -95,6 +95,9 @@ USE_LIBJPEG_TURBO_PATH = NONE
 # use openmp for parallelization
 USE_OPENMP = 1
 
+# Use Intel MKL DNN library
+USE_MKLDNN = 0
+
 # MKL ML Library for Intel CPU/Xeon Phi
 # Please refer to MKL_README.md for details
 


 


This is an automated message from the Apache Git Service.
To respond to the message, please log on GitHub and use the
URL above to go to the specific comment.
 
For queries about this service, please contact Infrastructure at:
us...@infra.apache.org


With regards,
Apache Git Services

[GitHub] rahul003 commented on issue #9774: mx.io.ImageRecordIter does not respect dtype argument

[GitBox]

rahul003 commented on issue #9774: mx.io.ImageRecordIter does not respect dtype 
argument
URL: 
https://github.com/apache/incubator-mxnet/issues/9774#issuecomment-366835890
 
 
   But for Resnet 110 on Cifar10, fp16 is much slower. Do you see something 
fishy here? There are barely any operations with s884 in their names. All the 
top ones don't. So fp16 would not help us with small networks/models? 
   
   ```
   fp16
GPU activities:   64.80%  28.7596s 87602  328.30us  24.128us  444.10us  
void cudnn::detail::wgrad_alg0_engine<__half, int=512, int=6, int=5, int=3, 
int=3, int=3, bool=1, int=512>(int, int, int, __half const *, int, 
cudnn::detail::wgrad_alg0_engine<__half, int=512, int=6, int=5, int=3, int=3, 
int=3, bool=1, int=512>*, __half const , kernel_grad_params, int, float, int, 
int, int, int)
  11.44%  5.07525s120474  42.127us  27.776us  70.752us  
void cudnn::winograd::winograd3x3Kernel<__half, float, int=4, int=1, int=8, 
bool=0>(cudnn::maxwell::winograd::KernelParams)
   9.65%  4.28153s 61959  69.102us  66.112us  88.128us  
void cudnn::winograd::winograd3x3Kernel<__half, float, int=2, int=2, int=8, 
bool=0>(cudnn::maxwell::winograd::KernelParams)
   1.90%  845.06ms182434  4.6320us  3.4240us  12.256us  
void cudnn::winograd::generateWinogradTilesKernel(cudnn::winograd::GenerateWinogradTilesParams<__half, float>)
   1.40%  621.33ms 29716  20.909us  19.744us  26.400us  
void cudnn::detail::bn_bw_1C11_singleread_fp16(float, float, float, float, cudnnTensorStruct, __half2 const *, 
cudnn::detail::bn_bw_1C11_singleread_fp16, 
__half2 const , cudnn::detail::bn_bw_1C11_singleread_fp16, cudnnTensorStruct*, float const *, float*, float const *, float 
const , float const , float, cudnn::reduced_divisor, int, float*, 
cudnn::detail::bnBwPersistentState*, int, float, float, float, int, float, 
cudnnStatus_t*, bool)
   1.39%  615.34ms 85238  7.2190us  3.8080us  11.776us  
void cudnn::detail::activation_bw_4d_kernel<__half, float, int=128, int=1, 
int=4, cudnn::detail::relu_func>(cudnnTensorStruct, __half const *, __half const , 
cudnn::detail::activation_bw_4d_kernel<__half, float, int=128, int=1, int=4, 
cudnn::detail::relu_func>, __half const 
, cudnnTensorStruct*, float, cudnnTensorStruct*, int, cudnnTensorStruct*)
   1.27%  564.01ms 29716  18.980us  11.648us  22.080us  
void cudnn::detail::bn_fw_tr_1C11_singleread_fp16(cudnnTensorStruct, __half2 const *, 
cudnn::detail::bn_fw_tr_1C11_singleread_fp16, 
cudnnTensorStruct*, float const *, float const , float, float, float*, float 
const *, float const *, float const *, float, float, cudnn::reduced_divisor, 
int, float*, cudnn::detail::bnFwPersistentState*, int, float, float, float, 
int, float, float, cudnnStatus_t*, bool)
   1.01%  446.51ms102351  4.3620us  3.1360us  11.296us  
void cudnn::detail::activation_fw_4d_kernel<__half, float, int=128, int=1, 
int=4, cudnn::detail::relu_func>(cudnnTensorStruct, __half const *, 
cudnn::detail::activation_fw_4d_kernel<__half, float, int=128, int=1, int=4, 
cudnn::detail::relu_func>, 
cudnnTensorStruct*, float, cudnnTensorStruct*, int, cudnnTensorStruct*)
   
   API calls:   37.66%  44.8500s   1042145  43.036us  5.0250us  2.6695ms  
cudaStreamSynchronize
  25.68%  30.5900s   1391180  21.988us  6.6070us  12.689ms  
cudaLaunch
  11.41%  13.5923s266335  51.034us  7.7360us  9.7191ms  
cudaMemcpy2DAsync
 
   
   ```
   ```
   fp32
   GPU activities:   29.29%  6.95644s 87602  79.409us  19.072us  133.28us  
void cudnn::detail::wgrad_alg0_engine(int, int, int, float const *, int, 
cudnn::detail::wgrad_alg0_engine*, float const , kernel_grad_params, int, float, int, 
int, int, int)
  16.85%  4.00136s 87609  45.672us  36.960us  71.200us  
void cudnn::winograd::winograd3x3Kernel(cudnn::maxwell::winograd::KernelParams)
   9.11%  2.16456s 28155  76.879us  72.737us  92.384us  
void cudnn::winograd::winograd3x3Kernel(cudnn::maxwell::winograd::KernelParams)
   8.96%  2.12819s 33807  62.951us  22.560us  65.729us  
volta_scudnn_128x32_relu_small_nn_v1
   4.66%  1.10676s 86020  12.866us  7.4880us  16.224us  
void cudnn::detail::bn_fw_tr_1C11_singleread(cudnnTensorStruct, float const *, 
cudnn::detail::bn_fw_tr_1C11_singleread, cudnnTensorStruct*, float const *, float const , float, float, float*, 
float const *, float const *, float const *, float, float, 
cudnn::reduced_divisor, int, float*, cudnn::detail::bnFwPersistentState*, int, 
float, float, float, int, float, float, cudnnStatus_t*, bool)
   4.43%  1.05193s 86020  12.228us  7.5520us  24.448us  
void cudnn::detail::bn_bw_1C11_singleread(float, float, float, float, cudnnTensorStruct, float const *, 
cudnn::detail::bn_bw_1C11_singlere

[GitHub] rahul003 commented on issue #9774: mx.io.ImageRecordIter does not respect dtype argument

[GitBox]

rahul003 commented on issue #9774: mx.io.ImageRecordIter does not respect dtype 
argument
URL: 
https://github.com/apache/incubator-mxnet/issues/9774#issuecomment-366823518
 
 
   Hey @KellenSunderland 
   
   I ran Resnet50 with Imagenet and got about 70% speedup. Some of the top ones 
don't seem to have s884 but some operations do. Can I improve the speed further?
   Here's a log of the profiler
   ```
GPU activities:7.58%  179.746s965213  186.22us  1.3440us  30.955ms  
[CUDA memcpy HtoD]
   6.60%  156.366s   1547648  101.03us  2.0480us  711.93us  
void nchwToNhwcKernel<__half, __half, float, bool=1>(int, int, int, int, __half 
const *, __half*, float, float)
   5.84%  138.416s108321  1.2778ms  314.97us  2.4579ms  
volta_fp16_scudnn_fp16_128x64_relu_interior_nn_v1
   5.02%  119.036s336336  353.92us  88.063us  1.2733ms  
void cudnn::detail::bn_bw_1C11_singleread_fp16(float, float, float, float, cudnnTensorStruct, __half2 const *, 
cudnn::detail::bn_bw_1C11_singleread_fp16, 
__half2 const , cudnn::detail::bn_bw_1C11_singleread_fp16, cudnnTensorStruct*, float const *, float*, float const *, float 
const , float const , float, cudnn::reduced_divisor, int, float*, 
cudnn::detail::bnBwPersistentState*, int, float, float, float, int, float, 
cudnnStatus_t*, bool)
   4.83%  114.400s400400  285.71us  33.088us  1.2040ms  
void cudnn::detail::activation_bw_4d_kernel<__half, float, int=128, int=1, 
int=4, cudnn::detail::relu_func>(cudnnTensorStruct, __half const *, __half const , 
cudnn::detail::activation_bw_4d_kernel<__half, float, int=128, int=1, int=4, 
cudnn::detail::relu_func>, __half const 
, cudnnTensorStruct*, float, cudnnTensorStruct*, int, cudnnTensorStruct*)
   4.72%  111.789s 40190  2.7815ms  1.4254ms  8.5978ms  
void cudnn::detail::wgrad_alg0_engine<__half, int=128, int=6, int=8, int=3, 
int=3, int=5, bool=1, int=512>(int, int, int, __half const *, int, 
cudnn::detail::wgrad_alg0_engine<__half, int=128, int=6, int=8, int=3, int=3, 
int=5, bool=1, int=512>*, __half const , kernel_grad_params, int, float, int, 
int, int, int)
   4.71%  111.533s 16040  6.9534ms  4.7594ms  10.580ms  
void cudnn::detail::dgrad2d_alg1_1<__half, int=0, int=6, int=7, int=5, int=4, 
int=5, bool=1, bool=1>(int, int, int, __half const *, int, __half const , int, 
cudnn::detail::dgrad2d_alg1_1<__half, int=0, int=6, int=7, int=5, int=4, int=5, 
bool=1, bool=1>*, kernel_grad_params, int, int, float, int, int)
   4.14%  98.2065s304388  322.64us  198.69us  723.45us  
volta_s884cudnn_fp16_128x128_ldg8_wgrad_exp_interior_nhwc_nt_v1
   3.60%  85.2418s 56116  1.5190ms  625.92us  2.4792ms  
volta_fp16_scudnn_fp16_128x128_stridedB_interior_nn_v1
   3.54%  83.8923s336336  249.43us  65.888us  908.99us  
void cudnn::detail::bn_fw_tr_1C11_singleread_fp16(cudnnTensorStruct, __half2 const *, 
cudnn::detail::bn_fw_tr_1C11_singleread_fp16, 
cudnnTensorStruct*, float const *, float const , float, float, float*, float 
const *, float const *, float const *, float, float, cudnn::reduced_divisor, 
int, float*, cudnn::detail::bnFwPersistentState*, int, float, float, float, 
int, float, float, cudnnStatus_t*, bool)
   3.36%  79.5726s 40168  1.9810ms  801.53us  10.105ms  
void cudnn::detail::dgrad_engine<__half, int=128, int=6, int=7, int=3, int=3, 
int=5, bool=1>(int, int, int, __half const *, int, __half const , int, 
cudnn::detail::dgrad_engine<__half, int=128, int=6, int=7, int=3, int=3, int=5, 
bool=1>*, kernel_grad_params, int, int, float, int, int, int)
   2.96%  70.1196s416400  168.39us  22.303us  684.35us  
void cudnn::detail::activation_fw_4d_kernel<__half, float, int=128, int=1, 
int=4, cudnn::detail::relu_func>(cudnnTensorStruct, __half const *, 
cudnn::detail::activation_fw_4d_kernel<__half, float, int=128, int=1, int=4, 
cudnn::detail::relu_func>, 
cudnnTensorStruct*, float, cudnnTensorStruct*, int, cudnnTensorStruct*)
   2.89%  68.4710s 16082  4.2576ms  1.6871ms  6.6937ms  
void cudnn::detail::dgrad_engine<__half, int=512, int=6, int=5, int=3, int=3, 
int=3, bool=1>(int, int, int, __half const *, int, __half const , int, 
cudnn::detail::dgrad_engine<__half, int=512, int=6, int=5, int=3, int=3, int=3, 
bool=1>*, kernel_grad_params, int, int, float, int, int, int)
   2.86%  67.7144s174945  387.06us  290.78us  902.65us  
volta_fp16_s884cudnn_fp16_256x128_ldg8_relu_f2f_exp_interior_nhwc2nchw_tn_v1
   2.81%  66.5956s  8008  8.3161ms  8.2356ms  8.5289ms  
void cudnn::detail::bn_bw_1C11_kernel_new<__half, float, float2, int=512, 
bool=1, int=1>(float, cudnn::detail::bn_bw_1C11_kernel_new<__half, float, 
float2, int=512, bool=1, int=1>, cudnn::detail::bn_bw_1C11_kernel_new<__half, 
float, float2, int=512, bool=

[GitHub] sxjscience opened a new pull request #9833: [Metric] Accelerate the calculation of F1

[GitBox]

sxjscience opened a new pull request #9833: [Metric] Accelerate the calculation 
of F1
URL: https://github.com/apache/incubator-mxnet/pull/9833
 
 
   ## Description ##
   Accelerate the calculation of F1 by removing the for-loop. I find that using 
GPU will in fact make the code slower :sweat_smile:.
   
   ## Checklist ##
   ### Essentials ###
   - [x] Passed code style checking (`make lint`)
   - [x] Changes are complete (i.e. I finished coding on this PR)
   - [x] All changes have test coverage:
   - Unit tests are added for small changes to verify correctness (e.g. adding 
a new operator)
   - Nightly tests are added for complicated/long-running ones (e.g. changing 
distributed kvstore)
   - Build tests will be added for build configuration changes (e.g. adding a 
new build option with NCCL)
   - [x] Code is well-documented: 
   - For user-facing API changes, API doc string has been updated. 
   - For new C++ functions in header files, their functionalities and arguments 
are documented. 
   - For new examples, README.md is added to explain the what the example does, 
the source of the dataset, expected performance on test set and reference to 
the original paper if applicable
   - [x] To the my best knowledge, examples are either not affected by this 
change, or have been fixed to be compatible with this change
   
   ### Changes ###
   - [x] Accelerate F1
   


This is an automated message from the Apache Git Service.
To respond to the message, please log on GitHub and use the
URL above to go to the specific comment.
 
For queries about this service, please contact Infrastructure at:
us...@infra.apache.org


With regards,
Apache Git Services

[GitHub] zhechen closed issue #9823: RCNN example fails for using latest mxnet

[GitBox]

zhechen closed issue #9823: RCNN example fails for using latest mxnet
URL: https://github.com/apache/incubator-mxnet/issues/9823
 
 
   


This is an automated message from the Apache Git Service.
To respond to the message, please log on GitHub and use the
URL above to go to the specific comment.
 
For queries about this service, please contact Infrastructure at:
us...@infra.apache.org


With regards,
Apache Git Services

[GitHub] zhechen commented on issue #9823: RCNN example fails for using latest mxnet

[GitBox]

zhechen commented on issue #9823: RCNN example fails for using latest mxnet
URL: 
https://github.com/apache/incubator-mxnet/issues/9823#issuecomment-366833092
 
 
   I somehow found a solution to this. Since I observed that this issue is 
caused by cudnn_softmax_activation function, both disabling cudnn and dropping 
the cudnn implementation of softmax will solve the problem. This mainly happens 
when using asnumpy() function for softmax results. Maybe someone can help check 
the real problem out and fix it. Thanks!


This is an automated message from the Apache Git Service.
To respond to the message, please log on GitHub and use the
URL above to go to the specific comment.
 
For queries about this service, please contact Infrastructure at:
us...@infra.apache.org


With regards,
Apache Git Services

[GitHub] b0noI opened a new pull request #9832: Ability to not build examples during the build of the cpp package.

[GitBox]

b0noI opened a new pull request #9832: Ability to not build examples during the 
build of the cpp package.
URL: https://github.com/apache/incubator-mxnet/pull/9832
 
 
   ## Description ##
   Not everyone need to build all the examples together with the cpp_package. 
Not building examples saves time and several Gig of data.


This is an automated message from the Apache Git Service.
To respond to the message, please log on GitHub and use the
URL above to go to the specific comment.
 
For queries about this service, please contact Infrastructure at:
us...@infra.apache.org


With regards,
Apache Git Services

[GitHub] samhodge commented on issue #9813: Unable to save gluon model to symbolic network : neural style

[GitBox]

samhodge commented on issue #9813: Unable to save gluon model to symbolic 
network : neural style
URL: 
https://github.com/apache/incubator-mxnet/issues/9813#issuecomment-366830758
 
 
   thanks for the help, I will continue with this advice.


This is an automated message from the Apache Git Service.
To respond to the message, please log on GitHub and use the
URL above to go to the specific comment.
 
For queries about this service, please contact Infrastructure at:
us...@infra.apache.org


With regards,
Apache Git Services

[GitHub] sxjscience commented on issue #9816: Dropout may mask values even when ratio=0.0

[GitBox]

sxjscience commented on issue #9816: Dropout may mask values even when ratio=0.0
URL: 
https://github.com/apache/incubator-mxnet/issues/9816#issuecomment-366827980
 
 
   There was an issue in mshadow about this 
https://github.com/dmlc/mshadow/issues/213. The cuda rng generates (0.0, 1.0].


This is an automated message from the Apache Git Service.
To respond to the message, please log on GitHub and use the
URL above to go to the specific comment.
 
For queries about this service, please contact Infrastructure at:
us...@infra.apache.org


With regards,
Apache Git Services

[GitHub] anirudh2290 commented on issue #9813: Unable to save gluon model to symbolic network : neural style

[GitBox]

anirudh2290 commented on issue #9813: Unable to save gluon model to symbolic 
network : neural style
URL: 
https://github.com/apache/incubator-mxnet/issues/9813#issuecomment-366826156
 
 
   @samhodge Can you just reuse the InstanceNorm in 
python/mxnet/gluon/nn/basic_layers.py ? your Net class seems to be taking 
incorrect number of inputs for hybrid forward. GramMatrix doesnt seem to be 
initializing the super class HybridBlock. Also, for the Bottleneck class the 
residual parameter set only based on whether self.downsample is not None. for 
other cases it breaks. 


This is an automated message from the Apache Git Service.
To respond to the message, please log on GitHub and use the
URL above to go to the specific comment.
 
For queries about this service, please contact Infrastructure at:
us...@infra.apache.org


With regards,
Apache Git Services

[GitHub] Roshrini commented on issue #9813: Unable to save gluon model to symbolic network : neural style

[GitBox]

Roshrini commented on issue #9813: Unable to save gluon model to symbolic 
network : neural style
URL: 
https://github.com/apache/incubator-mxnet/issues/9813#issuecomment-366824341
 
 
   @samhodge  Not sure if this helps but HybridBlock has export method 
https://mxnet.incubator.apache.org/api/python/gluon/gluon.html#mxnet.gluon.HybridBlock.export
 to export model


This is an automated message from the Apache Git Service.
To respond to the message, please log on GitHub and use the
URL above to go to the specific comment.
 
For queries about this service, please contact Infrastructure at:
us...@infra.apache.org


With regards,
Apache Git Services

[GitHub] Roshrini commented on issue #9813: Unable to save gluon model to symbolic network : neural style

[GitBox]

Roshrini commented on issue #9813: Unable to save gluon model to symbolic 
network : neural style
URL: 
https://github.com/apache/incubator-mxnet/issues/9813#issuecomment-366824341
 
 
   samhodge  Not sure if this helps but HybridBlock has export method 
https://mxnet.incubator.apache.org/api/python/gluon/gluon.html#mxnet.gluon.HybridBlock.export
 to export model


This is an automated message from the Apache Git Service.
To respond to the message, please log on GitHub and use the
URL above to go to the specific comment.
 
For queries about this service, please contact Infrastructure at:
us...@infra.apache.org


With regards,
Apache Git Services

[GitHub] Roshrini commented on issue #9813: Unable to save gluon model to symbolic network : neural style

[GitBox]

Roshrini commented on issue #9813: Unable to save gluon model to symbolic 
network : neural style
URL: 
https://github.com/apache/incubator-mxnet/issues/9813#issuecomment-366824341
 
 
   Not sure if this helps but HybridBlock has export method 
https://mxnet.incubator.apache.org/api/python/gluon/gluon.html#mxnet.gluon.HybridBlock.export
 to export model


This is an automated message from the Apache Git Service.
To respond to the message, please log on GitHub and use the
URL above to go to the specific comment.
 
For queries about this service, please contact Infrastructure at:
us...@infra.apache.org


With regards,
Apache Git Services

[GitHub] DickJC123 commented on issue #9816: Dropout may mask values even when ratio=0.0

[GitBox]

DickJC123 commented on issue #9816: Dropout may mask values even when ratio=0.0
URL: 
https://github.com/apache/incubator-mxnet/issues/9816#issuecomment-366824118
 
 
   I like making ratio=0 a special case with an identity pass-through.  During 
the same fix, how about making ratio=1 a special case also to output all 0's 
(current behavior is to pass inf or nan I think)?


This is an automated message from the Apache Git Service.
To respond to the message, please log on GitHub and use the
URL above to go to the specific comment.
 
For queries about this service, please contact Infrastructure at:
us...@infra.apache.org


With regards,
Apache Git Services

[GitHub] rahul003 commented on issue #9774: mx.io.ImageRecordIter does not respect dtype argument

[GitBox]

rahul003 commented on issue #9774: mx.io.ImageRecordIter does not respect dtype 
argument
URL: 
https://github.com/apache/incubator-mxnet/issues/9774#issuecomment-366823518
 
 
   Hey @KellenSunderland 
   
   These are the top 30 activities on the GPU. I'm running Resnet 50 on 
imagenet data with 8 GPUs. Some of the top ones don't seem to have s884 but 
some operations do.
   Could you please take a look and suggest ways to improve the speed?
   
   Thanks!
   
   ```
GPU activities:7.58%  179.746s965213  186.22us  1.3440us  30.955ms  
[CUDA memcpy HtoD]
   6.60%  156.366s   1547648  101.03us  2.0480us  711.93us  
void nchwToNhwcKernel<__half, __half, float, bool=1>(int, int, int, int, __half 
const *, __half*, float, float)
   5.84%  138.416s108321  1.2778ms  314.97us  2.4579ms  
volta_fp16_scudnn_fp16_128x64_relu_interior_nn_v1
   5.02%  119.036s336336  353.92us  88.063us  1.2733ms  
void cudnn::detail::bn_bw_1C11_singleread_fp16(float, float, float, float, cudnnTensorStruct, __half2 const *, 
cudnn::detail::bn_bw_1C11_singleread_fp16, 
__half2 const , cudnn::detail::bn_bw_1C11_singleread_fp16, cudnnTensorStruct*, float const *, float*, float const *, float 
const , float const , float, cudnn::reduced_divisor, int, float*, 
cudnn::detail::bnBwPersistentState*, int, float, float, float, int, float, 
cudnnStatus_t*, bool)
   4.83%  114.400s400400  285.71us  33.088us  1.2040ms  
void cudnn::detail::activation_bw_4d_kernel<__half, float, int=128, int=1, 
int=4, cudnn::detail::relu_func>(cudnnTensorStruct, __half const *, __half const , 
cudnn::detail::activation_bw_4d_kernel<__half, float, int=128, int=1, int=4, 
cudnn::detail::relu_func>, __half const 
, cudnnTensorStruct*, float, cudnnTensorStruct*, int, cudnnTensorStruct*)
   4.72%  111.789s 40190  2.7815ms  1.4254ms  8.5978ms  
void cudnn::detail::wgrad_alg0_engine<__half, int=128, int=6, int=8, int=3, 
int=3, int=5, bool=1, int=512>(int, int, int, __half const *, int, 
cudnn::detail::wgrad_alg0_engine<__half, int=128, int=6, int=8, int=3, int=3, 
int=5, bool=1, int=512>*, __half const , kernel_grad_params, int, float, int, 
int, int, int)
   4.71%  111.533s 16040  6.9534ms  4.7594ms  10.580ms  
void cudnn::detail::dgrad2d_alg1_1<__half, int=0, int=6, int=7, int=5, int=4, 
int=5, bool=1, bool=1>(int, int, int, __half const *, int, __half const , int, 
cudnn::detail::dgrad2d_alg1_1<__half, int=0, int=6, int=7, int=5, int=4, int=5, 
bool=1, bool=1>*, kernel_grad_params, int, int, float, int, int)
   4.14%  98.2065s304388  322.64us  198.69us  723.45us  
volta_s884cudnn_fp16_128x128_ldg8_wgrad_exp_interior_nhwc_nt_v1
   3.60%  85.2418s 56116  1.5190ms  625.92us  2.4792ms  
volta_fp16_scudnn_fp16_128x128_stridedB_interior_nn_v1
   3.54%  83.8923s336336  249.43us  65.888us  908.99us  
void cudnn::detail::bn_fw_tr_1C11_singleread_fp16(cudnnTensorStruct, __half2 const *, 
cudnn::detail::bn_fw_tr_1C11_singleread_fp16, 
cudnnTensorStruct*, float const *, float const , float, float, float*, float 
const *, float const *, float const *, float, float, cudnn::reduced_divisor, 
int, float*, cudnn::detail::bnFwPersistentState*, int, float, float, float, 
int, float, float, cudnnStatus_t*, bool)
   3.36%  79.5726s 40168  1.9810ms  801.53us  10.105ms  
void cudnn::detail::dgrad_engine<__half, int=128, int=6, int=7, int=3, int=3, 
int=5, bool=1>(int, int, int, __half const *, int, __half const , int, 
cudnn::detail::dgrad_engine<__half, int=128, int=6, int=7, int=3, int=3, int=5, 
bool=1>*, kernel_grad_params, int, int, float, int, int, int)
   2.96%  70.1196s416400  168.39us  22.303us  684.35us  
void cudnn::detail::activation_fw_4d_kernel<__half, float, int=128, int=1, 
int=4, cudnn::detail::relu_func>(cudnnTensorStruct, __half const *, 
cudnn::detail::activation_fw_4d_kernel<__half, float, int=128, int=1, int=4, 
cudnn::detail::relu_func>, 
cudnnTensorStruct*, float, cudnnTensorStruct*, int, cudnnTensorStruct*)
   2.89%  68.4710s 16082  4.2576ms  1.6871ms  6.6937ms  
void cudnn::detail::dgrad_engine<__half, int=512, int=6, int=5, int=3, int=3, 
int=3, bool=1>(int, int, int, __half const *, int, __half const , int, 
cudnn::detail::dgrad_engine<__half, int=512, int=6, int=5, int=3, int=3, int=3, 
bool=1>*, kernel_grad_params, int, int, float, int, int, int)
   2.86%  67.7144s174945  387.06us  290.78us  902.65us  
volta_fp16_s884cudnn_fp16_256x128_ldg8_relu_f2f_exp_interior_nhwc2nchw_tn_v1
   2.81%  66.5956s  8008  8.3161ms  8.2356ms  8.5289ms  
void cudnn::detail::bn_bw_1C11_kernel_new<__half, float, float2, int=512, 
bool=1, int=1>(float, cudnn::detail::bn_bw_1C11_kernel_new<__half, float, 
float2, int=512, bool=1, int=1>, cudnn::de

[GitHub] rahul003 commented on issue #9774: mx.io.ImageRecordIter does not respect dtype argument

[GitBox]

rahul003 commented on issue #9774: mx.io.ImageRecordIter does not respect dtype 
argument
URL: 
https://github.com/apache/incubator-mxnet/issues/9774#issuecomment-366788577
 
 
   Thanks @KellenSunderland . I am unable to run nvprof . Looks like this is a 
known issue 
(https://devtalk.nvidia.com/default/topic/1028871/cuda-setup-and-installation/nvprof-core-dumps-on-ubuntu-16-04/)
 
   
   How are you profiling the code? What setup do you have?
   
   EDIT: Looks like this is an older version issue. Will update and profile it
   


This is an automated message from the Apache Git Service.
To respond to the message, please log on GitHub and use the
URL above to go to the specific comment.
 
For queries about this service, please contact Infrastructure at:
us...@infra.apache.org


With regards,
Apache Git Services

[GitHub] Roshrini commented on issue #9804: [R] mx.io.arrayiter shuffing is disabled

[GitBox]

Roshrini commented on issue #9804: [R] mx.io.arrayiter shuffing is disabled
URL: 
https://github.com/apache/incubator-mxnet/issues/9804#issuecomment-366818160
 
 
   @thirdwing Can you please take a look?


This is an automated message from the Apache Git Service.
To respond to the message, please log on GitHub and use the
URL above to go to the specific comment.
 
For queries about this service, please contact Infrastructure at:
us...@infra.apache.org


With regards,
Apache Git Services

[GitHub] rahul003 opened a new pull request #9831: add USE_MKLDNN to config.mk

[GitBox]

rahul003 opened a new pull request #9831: add USE_MKLDNN to config.mk
URL: https://github.com/apache/incubator-mxnet/pull/9831
 
 
   ## Description ##
   (Brief description on what this PR is about)
   
   ## Checklist ##
   ### Essentials ###
   - [ ] Passed code style checking (`make lint`)
   - [ ] Changes are complete (i.e. I finished coding on this PR)
   - [ ] All changes have test coverage:
   - Unit tests are added for small changes to verify correctness (e.g. adding 
a new operator)
   - Nightly tests are added for complicated/long-running ones (e.g. changing 
distributed kvstore)
   - Build tests will be added for build configuration changes (e.g. adding a 
new build option with NCCL)
   - [ ] Code is well-documented: 
   - For user-facing API changes, API doc string has been updated. 
   - For new C++ functions in header files, their functionalities and arguments 
are documented. 
   - For new examples, README.md is added to explain the what the example does, 
the source of the dataset, expected performance on test set and reference to 
the original paper if applicable
   - [ ] To the my best knowledge, examples are either not affected by this 
change, or have been fixed to be compatible with this change
   
   ### Changes ###
   - [ ] change config.mk
   


This is an automated message from the Apache Git Service.
To respond to the message, please log on GitHub and use the
URL above to go to the specific comment.
 
For queries about this service, please contact Infrastructure at:
us...@infra.apache.org


With regards,
Apache Git Services

[GitHub] szha commented on issue #9830: Macro F1 score depends on the size of the minibatch

[GitBox]

szha commented on issue #9830: Macro F1 score depends on the size of the 
minibatch
URL: 
https://github.com/apache/incubator-mxnet/issues/9830#issuecomment-366817125
 
 
   Fixing this requires api change, so we should do that in 2.0. #9686 


This is an automated message from the Apache Git Service.
To respond to the message, please log on GitHub and use the
URL above to go to the specific comment.
 
For queries about this service, please contact Infrastructure at:
us...@infra.apache.org


With regards,
Apache Git Services

[GitHub] marcoabreu commented on issue #9492: fix print_summary bug and add groups of convolution

[GitBox]

marcoabreu commented on issue #9492: fix print_summary bug and add groups of 
convolution
URL: https://github.com/apache/incubator-mxnet/pull/9492#issuecomment-366816544
 
 
   Wait, in how far does this have test coverage? 


This is an automated message from the Apache Git Service.
To respond to the message, please log on GitHub and use the
URL above to go to the specific comment.
 
For queries about this service, please contact Infrastructure at:
us...@infra.apache.org


With regards,
Apache Git Services

[GitHub] marcoabreu commented on a change in pull request #9809: register optimizers only once in CPP-Package

[GitBox]

marcoabreu commented on a change in pull request #9809: register optimizers 
only once in CPP-Package
URL: https://github.com/apache/incubator-mxnet/pull/9809#discussion_r169180595
 
 

 ##
 File path: cpp-package/example/test_optimizer.cpp
 ##
 @@ -0,0 +1,33 @@
+/*
+ * 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.
+ */
+#include "mxnet-cpp/MxNetCpp.h"
+
+using namespace std;
+using namespace mxnet::cpp;
+
+int main(int argc, char** argv) {
+  // Confirm >1 optimizers can be created w/o error
+  Optimizer* opt = OptimizerRegistry::Find("sgd");
+  opt = OptimizerRegistry::Find("adam");
+  int ret = (opt == 0) ? 1 : 0;
+
+  MXNotifyShutdown();
+  return ret;
+  return 0;
 
 Review comment:
   Double return statement


This is an automated message from the Apache Git Service.
To respond to the message, please log on GitHub and use the
URL above to go to the specific comment.
 
For queries about this service, please contact Infrastructure at:
us...@infra.apache.org


With regards,
Apache Git Services

[GitHub] sxjscience opened a new issue #9830: Macro F1 score depends on the size of the minibatch

[GitBox]

sxjscience opened a new issue #9830: Macro F1 score depends on the size of the 
minibatch
URL: https://github.com/apache/incubator-mxnet/issues/9830
 
 
   I find our current implementation of the macro F1 score is dependent to the 
size of the minibatch. Our strategy is to average the F1 scores of each 
minibatch. However, the result would be different if we choose different batch 
size and order of the samples 
(https://github.com/apache/incubator-mxnet/blob/master/python/mxnet/metric.py#L623-L626).
 This will certainly confuse the users. I suggest marking the macro F1 as "Not 
Recommended" and set micro F1 as the default.


This is an automated message from the Apache Git Service.
To respond to the message, please log on GitHub and use the
URL above to go to the specific comment.
 
For queries about this service, please contact Infrastructure at:
us...@infra.apache.org


With regards,
Apache Git Services

[GitHub] chsin commented on issue #9809: register optimizers only once in CPP-Package

[GitBox]

chsin commented on issue #9809: register optimizers only once in CPP-Package
URL: https://github.com/apache/incubator-mxnet/pull/9809#issuecomment-366808870
 
 
   @marcoabreu, test was made, checks passes, now what?


This is an automated message from the Apache Git Service.
To respond to the message, please log on GitHub and use the
URL above to go to the specific comment.
 
For queries about this service, please contact Infrastructure at:
us...@infra.apache.org


With regards,
Apache Git Services

[GitHub] piiswrong commented on issue #9816: Dropout may mask values even when ratio=0.0

[GitBox]

piiswrong commented on issue #9816: Dropout may mask values even when ratio=0.0
URL: 
https://github.com/apache/incubator-mxnet/issues/9816#issuecomment-366808616
 
 
   This needs to be fix.
   One possible fix is to use identity pass through when rate=0


This is an automated message from the Apache Git Service.
To respond to the message, please log on GitHub and use the
URL above to go to the specific comment.
 
For queries about this service, please contact Infrastructure at:
us...@infra.apache.org


With regards,
Apache Git Services

[GitHub] piiswrong closed pull request #9492: fix print_summary bug and add groups of convolution

[GitBox]

piiswrong closed pull request #9492: fix print_summary bug and add groups of 
convolution
URL: https://github.com/apache/incubator-mxnet/pull/9492
 
 
   

This is a PR merged from a forked repository.
As GitHub hides the original diff on merge, it is displayed below for
the sake of provenance:

As this is a foreign pull request (from a fork), the diff is supplied
below (as it won't show otherwise due to GitHub magic):

diff --git a/python/mxnet/visualization.py b/python/mxnet/visualization.py
index 8c4cc3b920..47b712c872 100644
--- a/python/mxnet/visualization.py
+++ b/python/mxnet/visualization.py
@@ -134,20 +134,24 @@ def print_layer_summary(node, out_shape):
 pre_filter = pre_filter + int(shape[0])
 cur_param = 0
 if op == 'Convolution':
-if ("no_bias" in node["attrs"]) and int(node["attrs"]["no_bias"]):
-cur_param = pre_filter * int(node["attrs"]["num_filter"])
+if "no_bias" in node["attrs"] and node["attrs"]["no_bias"] == 
'True':
+num_group = int(node['attrs'].get('num_group', '1'))
+cur_param = pre_filter * int(node["attrs"]["num_filter"]) \
+   // num_group
 for k in _str2tuple(node["attrs"]["kernel"]):
 cur_param *= int(k)
 else:
-cur_param = pre_filter * int(node["attrs"]["num_filter"])
+num_group = int(node['attrs'].get('num_group', '1'))
+cur_param = pre_filter * int(node["attrs"]["num_filter"]) \
+   // num_group
 for k in _str2tuple(node["attrs"]["kernel"]):
 cur_param *= int(k)
 cur_param += int(node["attrs"]["num_filter"])
 elif op == 'FullyConnected':
-if ("no_bias" in node["attrs"]) and int(node["attrs"]["no_bias"]):
-cur_param = pre_filter * (int(node["attrs"]["num_hidden"]))
+if "no_bias" in node["attrs"] and node["attrs"]["no_bias"] == 
'True':
+cur_param = pre_filter * int(node["attrs"]["num_hidden"])
 else:
-cur_param = (pre_filter+1) * (int(node["attrs"]["num_hidden"]))
+cur_param = (pre_filter+1) * int(node["attrs"]["num_hidden"])
 elif op == 'BatchNorm':
 key = node["name"] + "_output"
 if show_shape:


 


This is an automated message from the Apache Git Service.
To respond to the message, please log on GitHub and use the
URL above to go to the specific comment.
 
For queries about this service, please contact Infrastructure at:
us...@infra.apache.org


With regards,
Apache Git Services

[incubator-mxnet] branch master updated: fix print_summary bug and add groups of convolution (#9492)

[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 f14b2eb  fix print_summary bug and add groups of convolution (#9492)
f14b2eb is described below

commit f14b2ebcb5475b0cde205648d0b80e3378e3dbed
Author: chinakook 
AuthorDate: Tue Feb 20 04:11:43 2018 +0800

fix print_summary bug and add groups of convolution (#9492)

* fix print_summary bug and add groups of convolution

1. fix "int(node["attrs"]["no_bias"])" bug
2. add groups of convolution param calculation

* Update visualization.py

lint

* Update visualization.py

* Update visualization.py

* Update visualization.py

* Update visualization.py

* Update visualization.py

* Update visualization.py
---
 python/mxnet/visualization.py | 16 ++--
 1 file changed, 10 insertions(+), 6 deletions(-)

diff --git a/python/mxnet/visualization.py b/python/mxnet/visualization.py
index 64ac77e..2b9da15 100644
--- a/python/mxnet/visualization.py
+++ b/python/mxnet/visualization.py
@@ -134,20 +134,24 @@ def print_summary(symbol, shape=None, line_length=120, 
positions=[.44, .64, .74,
 pre_filter = pre_filter + int(shape[0])
 cur_param = 0
 if op == 'Convolution':
-if ("no_bias" in node["attrs"]) and int(node["attrs"]["no_bias"]):
-cur_param = pre_filter * int(node["attrs"]["num_filter"])
+if "no_bias" in node["attrs"] and node["attrs"]["no_bias"] == 
'True':
+num_group = int(node['attrs'].get('num_group', '1'))
+cur_param = pre_filter * int(node["attrs"]["num_filter"]) \
+   // num_group
 for k in _str2tuple(node["attrs"]["kernel"]):
 cur_param *= int(k)
 else:
-cur_param = pre_filter * int(node["attrs"]["num_filter"])
+num_group = int(node['attrs'].get('num_group', '1'))
+cur_param = pre_filter * int(node["attrs"]["num_filter"]) \
+   // num_group
 for k in _str2tuple(node["attrs"]["kernel"]):
 cur_param *= int(k)
 cur_param += int(node["attrs"]["num_filter"])
 elif op == 'FullyConnected':
-if ("no_bias" in node["attrs"]) and int(node["attrs"]["no_bias"]):
-cur_param = pre_filter * (int(node["attrs"]["num_hidden"]))
+if "no_bias" in node["attrs"] and node["attrs"]["no_bias"] == 
'True':
+cur_param = pre_filter * int(node["attrs"]["num_hidden"])
 else:
-cur_param = (pre_filter+1) * (int(node["attrs"]["num_hidden"]))
+cur_param = (pre_filter+1) * int(node["attrs"]["num_hidden"])
 elif op == 'BatchNorm':
 key = node["name"] + "_output"
 if show_shape:

-- 
To stop receiving notification emails like this one, please contact
j...@apache.org.

[GitHub] rahul003 commented on issue #9774: mx.io.ImageRecordIter does not respect dtype argument

[GitBox]

rahul003 commented on issue #9774: mx.io.ImageRecordIter does not respect dtype 
argument
URL: 
https://github.com/apache/incubator-mxnet/issues/9774#issuecomment-366788577
 
 
   Thanks @KellenSunderland . I am unable to run nvprof . Looks like this is a 
known issue 
(https://devtalk.nvidia.com/default/topic/1028871/cuda-setup-and-installation/nvprof-core-dumps-on-ubuntu-16-04/)
 
   
   How are you profiling the code? What setup do you have?
   
   EDIT: Looks like this is an older version issue. Will update and profile it
   


This is an automated message from the Apache Git Service.
To respond to the message, please log on GitHub and use the
URL above to go to the specific comment.
 
For queries about this service, please contact Infrastructure at:
us...@infra.apache.org


With regards,
Apache Git Services

[GitHub] piiswrong commented on issue #9718: organized installation instructions to fix render issue

[GitBox]

piiswrong commented on issue #9718: organized installation instructions to fix 
render issue
URL: https://github.com/apache/incubator-mxnet/pull/9718#issuecomment-366790805
 
 
   ping


This is an automated message from the Apache Git Service.
To respond to the message, please log on GitHub and use the
URL above to go to the specific comment.
 
For queries about this service, please contact Infrastructure at:
us...@infra.apache.org


With regards,
Apache Git Services

[incubator-mxnet] branch master updated: Use get_bz2_data in test_utils (#9776)

[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 50f326f  Use get_bz2_data in test_utils (#9776)
50f326f is described below

commit 50f326ff92de6037152ae30da31f104be4e2d2fe
Author: Anirudh Subramanian 
AuthorDate: Mon Feb 19 11:53:55 2018 -0800

Use get_bz2_data in test_utils (#9776)
---
 benchmark/python/sparse/sparse_op.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/benchmark/python/sparse/sparse_op.py 
b/benchmark/python/sparse/sparse_op.py
index ebe62af..95ea7d5 100644
--- a/benchmark/python/sparse/sparse_op.py
+++ b/benchmark/python/sparse/sparse_op.py
@@ -24,7 +24,8 @@ import time
 import argparse
 
 from mxnet.base import check_call, _LIB
-from util import get_data, estimate_density
+from mxnet.test_utils import get_bz2_data
+from util import estimate_density
 
 parser = argparse.ArgumentParser(description="Benchmark sparse operators",
  
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
@@ -78,7 +79,7 @@ def test_dot_real(data_dict):
 
 path = os.path.join(data_dir, data_dict['data_name'])
 if not os.path.exists(path):
-get_data(
+get_bz2_data(
 data_dir,
 data_dict['data_name'],
 data_dict['url'],

-- 
To stop receiving notification emails like this one, please contact
j...@apache.org.

[GitHub] piiswrong closed pull request #9776: Use get_bz2_data from test_utils for sparse_op script

[GitBox]

piiswrong closed pull request #9776: Use get_bz2_data from test_utils for 
sparse_op script
URL: https://github.com/apache/incubator-mxnet/pull/9776
 
 
   

This is a PR merged from a forked repository.
As GitHub hides the original diff on merge, it is displayed below for
the sake of provenance:

As this is a foreign pull request (from a fork), the diff is supplied
below (as it won't show otherwise due to GitHub magic):

diff --git a/benchmark/python/sparse/sparse_op.py 
b/benchmark/python/sparse/sparse_op.py
index ebe62af05d..95ea7d54e2 100644
--- a/benchmark/python/sparse/sparse_op.py
+++ b/benchmark/python/sparse/sparse_op.py
@@ -24,7 +24,8 @@
 import argparse
 
 from mxnet.base import check_call, _LIB
-from util import get_data, estimate_density
+from mxnet.test_utils import get_bz2_data
+from util import estimate_density
 
 parser = argparse.ArgumentParser(description="Benchmark sparse operators",
  
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
@@ -78,7 +79,7 @@ def get_iter(path, data_shape, batch_size):
 
 path = os.path.join(data_dir, data_dict['data_name'])
 if not os.path.exists(path):
-get_data(
+get_bz2_data(
 data_dir,
 data_dict['data_name'],
 data_dict['url'],


 


This is an automated message from the Apache Git Service.
To respond to the message, please log on GitHub and use the
URL above to go to the specific comment.
 
For queries about this service, please contact Infrastructure at:
us...@infra.apache.org


With regards,
Apache Git Services

[GitHub] rahul003 commented on issue #9774: mx.io.ImageRecordIter does not respect dtype argument

[GitBox]

rahul003 commented on issue #9774: mx.io.ImageRecordIter does not respect dtype 
argument
URL: 
https://github.com/apache/incubator-mxnet/issues/9774#issuecomment-366788577
 
 
   Thanks @KellenSunderland . I am unable to run nvprof . Looks like this is a 
known issue 
(https://devtalk.nvidia.com/default/topic/1028871/cuda-setup-and-installation/nvprof-core-dumps-on-ubuntu-16-04/)
 
   
   How are you profiling the code? What setup do you have?


This is an automated message from the Apache Git Service.
To respond to the message, please log on GitHub and use the
URL above to go to the specific comment.
 
For queries about this service, please contact Infrastructure at:
us...@infra.apache.org


With regards,
Apache Git Services

[GitHub] piiswrong closed pull request #9790: make array.reshape compatible with numpy

[GitBox]

piiswrong closed pull request #9790: make array.reshape compatible with numpy
URL: https://github.com/apache/incubator-mxnet/pull/9790
 
 
   

This is a PR merged from a forked repository.
As GitHub hides the original diff on merge, it is displayed below for
the sake of provenance:

As this is a foreign pull request (from a fork), the diff is supplied
below (as it won't show otherwise due to GitHub magic):

diff --git a/python/mxnet/ndarray/ndarray.py b/python/mxnet/ndarray/ndarray.py
index b089cc5117..4c5273fd40 100644
--- a/python/mxnet/ndarray/ndarray.py
+++ b/python/mxnet/ndarray/ndarray.py
@@ -926,12 +926,12 @@ def _at(self, idx):
 self.handle, mx_uint(idx), ctypes.byref(handle)))
 return NDArray(handle=handle, writable=self.writable)
 
-def reshape(self, shape):
+def reshape(self, *shape, **kwargs):
 """Returns a **view** of this array with a new shape without altering 
any data.
 
 Parameters
 --
-shape : tuple of int
+shape : tuple of int, or n ints
 The new shape should not change the array size, namely
 ``np.prod(new_shape)`` should be equal to ``np.prod(self.shape)``.
 
@@ -960,6 +960,11 @@ def reshape(self, shape):
[ 4.,  5.]], dtype=float32)
 >>> y = x.reshape((3,-1))
 >>> y.asnumpy()
+array([[ 0.,  1.],
+   [ 2.,  3.],
+   [ 4.,  5.]], dtype=float32)
+>>> y = x.reshape(3,2)
+>>> y.asnumpy()
 array([[ 0.,  1.],
[ 2.,  3.],
[ 4.,  5.]], dtype=float32)
@@ -968,6 +973,17 @@ def reshape(self, shape):
 array([[-1., -1., -1.],
[-1., -1., -1.]], dtype=float32)
 """
+if len(shape) == 1 and isinstance(shape[0], (list, tuple)):
+shape = shape[0]
+elif not shape:
+shape = kwargs.get('shape')
+assert shape, "Shape must be provided."
+if len(kwargs) != 1:
+raise TypeError("Only 'shape' is supported as keyword 
argument. Got: {}."
+.format(', '.join(kwargs.keys(
+else:
+assert not kwargs,\
+"Specifying both positional and keyword arguments is not 
allowed in reshape."
 handle = NDArrayHandle()
 
 # Actual reshape
diff --git a/python/mxnet/ndarray/sparse.py b/python/mxnet/ndarray/sparse.py
index 62ee32a9e3..c65d7ce408 100644
--- a/python/mxnet/ndarray/sparse.py
+++ b/python/mxnet/ndarray/sparse.py
@@ -138,7 +138,7 @@ def _at(self, idx):
 def _slice(self, start, stop):
 raise NotSupportedForSparseNDArray(self._slice, None, start, stop)
 
-def reshape(self, shape):
+def reshape(self, *shape, **kwargs):
 raise NotSupportedForSparseNDArray(self.reshape, None, shape)
 
 @property
diff --git a/tests/python/unittest/test_ndarray.py 
b/tests/python/unittest/test_ndarray.py
index 22ff6e8cf5..78804244a2 100644
--- a/tests/python/unittest/test_ndarray.py
+++ b/tests/python/unittest/test_ndarray.py
@@ -154,6 +154,11 @@ def test_ndarray_reshape():
  [5, 6],
  [7, 8]])
 assert same(tensor.reshape((-1, 2)).asnumpy(), true_res.asnumpy())
+assert same(tensor.reshape(4, 2).asnumpy(), true_res.asnumpy())
+assert same(tensor.reshape(-1, 2).asnumpy(), true_res.asnumpy())
+true_res = mx.nd.arange(8) + 1
+assert same(tensor.reshape(-1).asnumpy(), true_res.asnumpy())
+assert same(tensor.reshape(8).asnumpy(), true_res.asnumpy())
 
 
 def test_ndarray_choose():


 


This is an automated message from the Apache Git Service.
To respond to the message, please log on GitHub and use the
URL above to go to the specific comment.
 
For queries about this service, please contact Infrastructure at:
us...@infra.apache.org


With regards,
Apache Git Services

[incubator-mxnet] branch master updated: make array.reshape compatible with numpy (#9790)

[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 9348a3a  make array.reshape compatible with numpy (#9790)
9348a3a is described below

commit 9348a3ab9491a2835ab3b9652e2a1f2750ac1823
Author: Sheng Zha 
AuthorDate: Mon Feb 19 11:50:36 2018 -0800

make array.reshape compatible with numpy (#9790)

* make array.reshape compatible with numpy

* update

* add exception when both *args and **kwargs are specified

* update
---
 python/mxnet/ndarray/ndarray.py   | 20 ++--
 python/mxnet/ndarray/sparse.py|  2 +-
 tests/python/unittest/test_ndarray.py |  5 +
 3 files changed, 24 insertions(+), 3 deletions(-)

diff --git a/python/mxnet/ndarray/ndarray.py b/python/mxnet/ndarray/ndarray.py
index b089cc5..4c5273f 100644
--- a/python/mxnet/ndarray/ndarray.py
+++ b/python/mxnet/ndarray/ndarray.py
@@ -926,12 +926,12 @@ fixed-size items.
 self.handle, mx_uint(idx), ctypes.byref(handle)))
 return NDArray(handle=handle, writable=self.writable)
 
-def reshape(self, shape):
+def reshape(self, *shape, **kwargs):
 """Returns a **view** of this array with a new shape without altering 
any data.
 
 Parameters
 --
-shape : tuple of int
+shape : tuple of int, or n ints
 The new shape should not change the array size, namely
 ``np.prod(new_shape)`` should be equal to ``np.prod(self.shape)``.
 
@@ -963,11 +963,27 @@ fixed-size items.
 array([[ 0.,  1.],
[ 2.,  3.],
[ 4.,  5.]], dtype=float32)
+>>> y = x.reshape(3,2)
+>>> y.asnumpy()
+array([[ 0.,  1.],
+   [ 2.,  3.],
+   [ 4.,  5.]], dtype=float32)
 >>> y[:] = -1
 >>> x.asnumpy()
 array([[-1., -1., -1.],
[-1., -1., -1.]], dtype=float32)
 """
+if len(shape) == 1 and isinstance(shape[0], (list, tuple)):
+shape = shape[0]
+elif not shape:
+shape = kwargs.get('shape')
+assert shape, "Shape must be provided."
+if len(kwargs) != 1:
+raise TypeError("Only 'shape' is supported as keyword 
argument. Got: {}."
+.format(', '.join(kwargs.keys(
+else:
+assert not kwargs,\
+"Specifying both positional and keyword arguments is not 
allowed in reshape."
 handle = NDArrayHandle()
 
 # Actual reshape
diff --git a/python/mxnet/ndarray/sparse.py b/python/mxnet/ndarray/sparse.py
index 62ee32a..c65d7ce 100644
--- a/python/mxnet/ndarray/sparse.py
+++ b/python/mxnet/ndarray/sparse.py
@@ -138,7 +138,7 @@ class BaseSparseNDArray(NDArray):
 def _slice(self, start, stop):
 raise NotSupportedForSparseNDArray(self._slice, None, start, stop)
 
-def reshape(self, shape):
+def reshape(self, *shape, **kwargs):
 raise NotSupportedForSparseNDArray(self.reshape, None, shape)
 
 @property
diff --git a/tests/python/unittest/test_ndarray.py 
b/tests/python/unittest/test_ndarray.py
index 9fae7ab..6c10487 100644
--- a/tests/python/unittest/test_ndarray.py
+++ b/tests/python/unittest/test_ndarray.py
@@ -158,6 +158,11 @@ def test_ndarray_reshape():
  [5, 6],
  [7, 8]])
 assert same(tensor.reshape((-1, 2)).asnumpy(), true_res.asnumpy())
+assert same(tensor.reshape(4, 2).asnumpy(), true_res.asnumpy())
+assert same(tensor.reshape(-1, 2).asnumpy(), true_res.asnumpy())
+true_res = mx.nd.arange(8) + 1
+assert same(tensor.reshape(-1).asnumpy(), true_res.asnumpy())
+assert same(tensor.reshape(8).asnumpy(), true_res.asnumpy())
 
 
 @with_seed()

-- 
To stop receiving notification emails like this one, please contact
j...@apache.org.

[incubator-mxnet] branch master updated: temporary solution for instancenorm, will refactor using backend (#9807)

[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 d048615  temporary solution for instancenorm, will refactor using 
backend (#9807)
d048615 is described below

commit d04861575f18cd476132e56f4e738e9e6771338b
Author: Hang Zhang <8041160+zhanghang1...@users.noreply.github.com>
AuthorDate: Mon Feb 19 11:47:14 2018 -0800

temporary solution for instancenorm, will refactor using backend (#9807)

* temporary solution for instancenorm, will refactor using backend

* fix typo

* rm space

* fix doc

* fix doc

* Update conv_layers.py

* Update basic_layers.py

* Update conv_layers.py

* Update basic_layers.py

* fix typo

* fix typo
---
 python/mxnet/gluon/nn/basic_layers.py | 19 +++
 python/mxnet/gluon/nn/conv_layers.py  | 20 ++--
 2 files changed, 29 insertions(+), 10 deletions(-)

diff --git a/python/mxnet/gluon/nn/basic_layers.py 
b/python/mxnet/gluon/nn/basic_layers.py
index 0f38119..b61540d 100644
--- a/python/mxnet/gluon/nn/basic_layers.py
+++ b/python/mxnet/gluon/nn/basic_layers.py
@@ -420,6 +420,10 @@ class InstanceNorm(HybridBlock):
 
 Parameters
 --
+axis : int, default 1
+The axis that should be normalized. This is typically the channels
+(C) axis. For instance, after a `Conv2D` layer with `layout='NCHW'`,
+set `axis=1` in `InstanceNorm`. If `layout='NHWC'`, then set `axis=3`.
 epsilon: float, default 1e-5
 Small float added to variance to avoid dividing by zero.
 center: bool, default True
@@ -439,6 +443,7 @@ class InstanceNorm(HybridBlock):
 initialization will be deferred to the first time `forward` is called
 and `in_channels` will be inferred from the shape of input data.
 
+
 Inputs:
 - **data**: input tensor with arbitrary shape.
 
@@ -463,11 +468,13 @@ class InstanceNorm(HybridBlock):
  [[-0.8319  0.8361]]]
 
 """
-def __init__(self, epsilon=1e-5, center=True, scale=False,
+def __init__(self, axis=1, epsilon=1e-5, center=True, scale=False,
  beta_initializer='zeros', gamma_initializer='ones',
  in_channels=0, **kwargs):
 super(InstanceNorm, self).__init__(**kwargs)
-self._kwargs = {'eps': epsilon}
+self._kwargs = {'eps': epsilon, 'axis': axis}
+self._axis = axis
+self._epsilon = epsilon
 self.gamma = self.params.get('gamma', grad_req='write' if scale else 
'null',
  shape=(in_channels,), 
init=gamma_initializer,
  allow_deferred_init=True)
@@ -476,8 +483,12 @@ class InstanceNorm(HybridBlock):
 allow_deferred_init=True)
 
 def hybrid_forward(self, F, x, gamma, beta):
-return F.InstanceNorm(x, gamma, beta,
-  name='fwd', **self._kwargs)
+if self._axis == 1:
+return F.InstanceNorm(x, gamma, beta,
+  name='fwd', eps=self._epsilon)
+x = x.swapaxes(1, self._axis)
+return F.InstanceNorm(x, gamma, beta, name='fwd',
+  eps=self._epsilon).swapaxes(1, self._axis)
 
 def __repr__(self):
 s = '{name}({content}'
diff --git a/python/mxnet/gluon/nn/conv_layers.py 
b/python/mxnet/gluon/nn/conv_layers.py
index a69cb8a..87a62bc 100644
--- a/python/mxnet/gluon/nn/conv_layers.py
+++ b/python/mxnet/gluon/nn/conv_layers.py
@@ -1011,18 +1011,26 @@ class GlobalAvgPool3D(_Pooling):
 
 
 class ReflectionPad2D(HybridBlock):
-"""Pads the input tensor using the reflection of the input boundary.
+r"""Pads the input tensor using the reflection of the input boundary.
 
 Parameters
 --
 padding: int
 An integer padding size
 
-Shape:
-- Input: :math:`(N, C, H_{in}, W_{in})`
-- Output: :math:`(N, C, H_{out}, W_{out})` where
-  :math:`H_{out} = H_{in} + 2 * padding
-  :math:`W_{out} = W_{in} + 2 * padding
+
+Inputs:
+- **data**: input tensor with the shape :math:`(N, C, H_{in}, W_{in})`.
+
+Outputs:
+- **out**: output tensor with the shape :math:`(N, C, H_{out}, 
W_{out})`, where
+
+  .. math::
+
+H_{out} = H_{in} + 2 \cdot padding
+
+W_{out} = W_{in} + 2 \cdot padding
+
 
 Examples
 

-- 
To stop receiving notification emails like this one, please contact
j...@apache.org.

[GitHub] piiswrong closed pull request #9807: temporary solution for instancenorm, will refactor using backend

[GitBox]

piiswrong closed pull request #9807: temporary solution for instancenorm, will 
refactor using backend
URL: https://github.com/apache/incubator-mxnet/pull/9807
 
 
   

This is a PR merged from a forked repository.
As GitHub hides the original diff on merge, it is displayed below for
the sake of provenance:

As this is a foreign pull request (from a fork), the diff is supplied
below (as it won't show otherwise due to GitHub magic):

diff --git a/python/mxnet/gluon/nn/basic_layers.py 
b/python/mxnet/gluon/nn/basic_layers.py
index 0f38119af8..b61540dd61 100644
--- a/python/mxnet/gluon/nn/basic_layers.py
+++ b/python/mxnet/gluon/nn/basic_layers.py
@@ -420,6 +420,10 @@ class InstanceNorm(HybridBlock):
 
 Parameters
 --
+axis : int, default 1
+The axis that should be normalized. This is typically the channels
+(C) axis. For instance, after a `Conv2D` layer with `layout='NCHW'`,
+set `axis=1` in `InstanceNorm`. If `layout='NHWC'`, then set `axis=3`.
 epsilon: float, default 1e-5
 Small float added to variance to avoid dividing by zero.
 center: bool, default True
@@ -439,6 +443,7 @@ class InstanceNorm(HybridBlock):
 initialization will be deferred to the first time `forward` is called
 and `in_channels` will be inferred from the shape of input data.
 
+
 Inputs:
 - **data**: input tensor with arbitrary shape.
 
@@ -463,11 +468,13 @@ class InstanceNorm(HybridBlock):
  [[-0.8319  0.8361]]]
 
 """
-def __init__(self, epsilon=1e-5, center=True, scale=False,
+def __init__(self, axis=1, epsilon=1e-5, center=True, scale=False,
  beta_initializer='zeros', gamma_initializer='ones',
  in_channels=0, **kwargs):
 super(InstanceNorm, self).__init__(**kwargs)
-self._kwargs = {'eps': epsilon}
+self._kwargs = {'eps': epsilon, 'axis': axis}
+self._axis = axis
+self._epsilon = epsilon
 self.gamma = self.params.get('gamma', grad_req='write' if scale else 
'null',
  shape=(in_channels,), 
init=gamma_initializer,
  allow_deferred_init=True)
@@ -476,8 +483,12 @@ def __init__(self, epsilon=1e-5, center=True, scale=False,
 allow_deferred_init=True)
 
 def hybrid_forward(self, F, x, gamma, beta):
-return F.InstanceNorm(x, gamma, beta,
-  name='fwd', **self._kwargs)
+if self._axis == 1:
+return F.InstanceNorm(x, gamma, beta,
+  name='fwd', eps=self._epsilon)
+x = x.swapaxes(1, self._axis)
+return F.InstanceNorm(x, gamma, beta, name='fwd',
+  eps=self._epsilon).swapaxes(1, self._axis)
 
 def __repr__(self):
 s = '{name}({content}'
diff --git a/python/mxnet/gluon/nn/conv_layers.py 
b/python/mxnet/gluon/nn/conv_layers.py
index a69cb8a060..87a62bc8c7 100644
--- a/python/mxnet/gluon/nn/conv_layers.py
+++ b/python/mxnet/gluon/nn/conv_layers.py
@@ -1011,18 +1011,26 @@ def __init__(self, layout='NCDHW', **kwargs):
 
 
 class ReflectionPad2D(HybridBlock):
-"""Pads the input tensor using the reflection of the input boundary.
+r"""Pads the input tensor using the reflection of the input boundary.
 
 Parameters
 --
 padding: int
 An integer padding size
 
-Shape:
-- Input: :math:`(N, C, H_{in}, W_{in})`
-- Output: :math:`(N, C, H_{out}, W_{out})` where
-  :math:`H_{out} = H_{in} + 2 * padding
-  :math:`W_{out} = W_{in} + 2 * padding
+
+Inputs:
+- **data**: input tensor with the shape :math:`(N, C, H_{in}, W_{in})`.
+
+Outputs:
+- **out**: output tensor with the shape :math:`(N, C, H_{out}, 
W_{out})`, where
+
+  .. math::
+
+H_{out} = H_{in} + 2 \cdot padding
+
+W_{out} = W_{in} + 2 \cdot padding
+
 
 Examples
 


 


This is an automated message from the Apache Git Service.
To respond to the message, please log on GitHub and use the
URL above to go to the specific comment.
 
For queries about this service, please contact Infrastructure at:
us...@infra.apache.org


With regards,
Apache Git Services

[GitHub] terrytangyuan commented on issue #9829: Simplify R package installation instruction

[GitBox]

terrytangyuan commented on issue #9829: Simplify R package installation 
instruction
URL: https://github.com/apache/incubator-mxnet/pull/9829#issuecomment-366786645
 
 
   Actually this messes up with other default repos. Closing this now. 


This is an automated message from the Apache Git Service.
To respond to the message, please log on GitHub and use the
URL above to go to the specific comment.
 
For queries about this service, please contact Infrastructure at:
us...@infra.apache.org


With regards,
Apache Git Services

[GitHub] terrytangyuan closed pull request #9829: Simplify R package installation instruction

[GitBox]

terrytangyuan closed pull request #9829: Simplify R package installation 
instruction
URL: https://github.com/apache/incubator-mxnet/pull/9829
 
 
   


This is an automated message from the Apache Git Service.
To respond to the message, please log on GitHub and use the
URL above to go to the specific comment.
 
For queries about this service, please contact Infrastructure at:
us...@infra.apache.org


With regards,
Apache Git Services

[GitHub] terrytangyuan commented on issue #9829: Simplify R package installation instruction

[GitBox]

terrytangyuan commented on issue #9829: Simplify R package installation 
instruction
URL: https://github.com/apache/incubator-mxnet/pull/9829#issuecomment-366786334
 
 
   Because it's shorter and you don't have to instantiate a variable


This is an automated message from the Apache Git Service.
To respond to the message, please log on GitHub and use the
URL above to go to the specific comment.
 
For queries about this service, please contact Infrastructure at:
us...@infra.apache.org


With regards,
Apache Git Services

[GitHub] piiswrong commented on issue #9829: Simplify R package installation instruction

[GitBox]

piiswrong commented on issue #9829: Simplify R package installation instruction
URL: https://github.com/apache/incubator-mxnet/pull/9829#issuecomment-366785886
 
 
   why?


This is an automated message from the Apache Git Service.
To respond to the message, please log on GitHub and use the
URL above to go to the specific comment.
 
For queries about this service, please contact Infrastructure at:
us...@infra.apache.org


With regards,
Apache Git Services

[GitHub] samhodge commented on issue #9813: Unable to save gluon model to symbolic network : neural style

[GitBox]

samhodge commented on issue #9813: Unable to save gluon model to symbolic 
network : neural style
URL: 
https://github.com/apache/incubator-mxnet/issues/9813#issuecomment-366778617
 
 
   I have continued on further see
   
   
   `python main.py train --dataset dataset --style-folder images/styles 
--save-model-dir models --cuda 0
   `
   with
   
   
https://github.com/samhodge/incubator-mxnet/commit/949993f486eee117111bfeb801bea3acce417951
   
   yields
   
   
   ```
 File "main.py", line 229, in 
   main()
 File "main.py", line 214, in main
   train(args)
 File "main.py", line 82, in train
   style_model.setTarget(style_image)
 File "/Users/sam/dev/incubator-mxnet/example/gluon/style_transfer/net.py", 
line 236, in setTarget
   F = self.model1(Xs)
 File 
"/Users/sam/anaconda2/lib/python2.7/site-packages/mxnet/gluon/block.py", line 
304, in __call__
   return self.forward(*args)
 File 
"/Users/sam/anaconda2/lib/python2.7/site-packages/mxnet/gluon/block.py", line 
516, in forward
   return self.hybrid_forward(ndarray, x, *args, **params)
 File 
"/Users/sam/anaconda2/lib/python2.7/site-packages/mxnet/gluon/nn/basic_layers.py",
 line 111, in hybrid_forward
   x = block(x)
 File 
"/Users/sam/anaconda2/lib/python2.7/site-packages/mxnet/gluon/block.py", line 
304, in __call__
   return self.forward(*args)
 File 
"/Users/sam/anaconda2/lib/python2.7/site-packages/mxnet/gluon/block.py", line 
507, in forward
   params = {i: j.data(ctx) for i, j in self._reg_params.items()}
 File 
"/Users/sam/anaconda2/lib/python2.7/site-packages/mxnet/gluon/block.py", line 
507, in 
   params = {i: j.data(ctx) for i, j in self._reg_params.items()}
 File 
"/Users/sam/anaconda2/lib/python2.7/site-packages/mxnet/gluon/parameter.py", 
line 389, in data
   return self._check_and_get(self._data, ctx)
 File 
"/Users/sam/anaconda2/lib/python2.7/site-packages/mxnet/gluon/parameter.py", 
line 189, in _check_and_get
   "nested child Blocks"%(self.name,type(self)))
   RuntimeError: Parameter net0_instancenorm0_beta  has not been initialized. Note that you 
should initialize parameters and create Trainer with Block.collect_params() 
instead of Block.params because the later does not include Parameters of nested 
child Blocks
   
   ```


This is an automated message from the Apache Git Service.
To respond to the message, please log on GitHub and use the
URL above to go to the specific comment.
 
For queries about this service, please contact Infrastructure at:
us...@infra.apache.org


With regards,
Apache Git Services

[GitHub] samhodge commented on issue #9813: Unable to save gluon model to symbolic network : neural style

[GitBox]

samhodge commented on issue #9813: Unable to save gluon model to symbolic 
network : neural style
URL: 
https://github.com/apache/incubator-mxnet/issues/9813#issuecomment-366778617
 
 
   I have continued on further see
   
   
   `python main.py train --dataset dataset --style-folder images/styles 
--save-model-dir models --cuda 0
   `
   with
   
   
https://github.com/samhodge/incubator-mxnet/commit/949993f486eee117111bfeb801bea3acce417951
   
   yields
   
   `
 File "main.py", line 229, in 
   main()
 File "main.py", line 214, in main
   train(args)
 File "main.py", line 82, in train
   style_model.setTarget(style_image)
 File "/Users/sam/dev/incubator-mxnet/example/gluon/style_transfer/net.py", 
line 236, in setTarget
   F = self.model1(Xs)
 File 
"/Users/sam/anaconda2/lib/python2.7/site-packages/mxnet/gluon/block.py", line 
304, in __call__
   return self.forward(*args)
 File 
"/Users/sam/anaconda2/lib/python2.7/site-packages/mxnet/gluon/block.py", line 
516, in forward
   return self.hybrid_forward(ndarray, x, *args, **params)
 File 
"/Users/sam/anaconda2/lib/python2.7/site-packages/mxnet/gluon/nn/basic_layers.py",
 line 111, in hybrid_forward
   x = block(x)
 File 
"/Users/sam/anaconda2/lib/python2.7/site-packages/mxnet/gluon/block.py", line 
304, in __call__
   return self.forward(*args)
 File 
"/Users/sam/anaconda2/lib/python2.7/site-packages/mxnet/gluon/block.py", line 
507, in forward
   params = {i: j.data(ctx) for i, j in self._reg_params.items()}
 File 
"/Users/sam/anaconda2/lib/python2.7/site-packages/mxnet/gluon/block.py", line 
507, in 
   params = {i: j.data(ctx) for i, j in self._reg_params.items()}
 File 
"/Users/sam/anaconda2/lib/python2.7/site-packages/mxnet/gluon/parameter.py", 
line 389, in data
   return self._check_and_get(self._data, ctx)
 File 
"/Users/sam/anaconda2/lib/python2.7/site-packages/mxnet/gluon/parameter.py", 
line 189, in _check_and_get
   "nested child Blocks"%(self.name,type(self)))
   RuntimeError: Parameter net0_instancenorm0_beta  has not been initialized. Note that you 
should initialize parameters and create Trainer with Block.collect_params() 
instead of Block.params because the later does not include Parameters of nested 
child Blocks
   `


This is an automated message from the Apache Git Service.
To respond to the message, please log on GitHub and use the
URL above to go to the specific comment.
 
For queries about this service, please contact Infrastructure at:
us...@infra.apache.org


With regards,
Apache Git Services

[GitHub] marcoabreu commented on issue #9828: Building with MKL fails on OSX

[GitBox]

marcoabreu commented on issue #9828: Building with MKL fails on OSX
URL: 
https://github.com/apache/incubator-mxnet/issues/9828#issuecomment-366763226
 
 
   @zheng-da 


This is an automated message from the Apache Git Service.
To respond to the message, please log on GitHub and use the
URL above to go to the specific comment.
 
For queries about this service, please contact Infrastructure at:
us...@infra.apache.org


With regards,
Apache Git Services

[GitHub] shivonkar commented on issue #8575: mxnet multicore on LInux in R

[GitBox]

shivonkar commented on issue #8575: mxnet multicore on LInux in R
URL: 
https://github.com/apache/incubator-mxnet/issues/8575#issuecomment-366755177
 
 
   Here is solution.
   
   First set the environment variable
   Sys.setenv(MXNET_CPU_WORKER_NTHREADS = core); 
   
   Use ctx= mx.cpu() depending upon cpu/gpu
   
   It is working on both Windows and Linux


This is an automated message from the Apache Git Service.
To respond to the message, please log on GitHub and use the
URL above to go to the specific comment.
 
For queries about this service, please contact Infrastructure at:
us...@infra.apache.org


With regards,
Apache Git Services

[GitHub] terrytangyuan opened a new pull request #9829: Simplify R package installation instruction

[GitBox]

terrytangyuan opened a new pull request #9829: Simplify R package installation 
instruction
URL: https://github.com/apache/incubator-mxnet/pull/9829
 
 
   


This is an automated message from the Apache Git Service.
To respond to the message, please log on GitHub and use the
URL above to go to the specific comment.
 
For queries about this service, please contact Infrastructure at:
us...@infra.apache.org


With regards,
Apache Git Services

[GitHub] lebeg commented on a change in pull request #9799: Cleaned up image classification cpp example

[GitBox]

lebeg commented on a change in pull request #9799: Cleaned up image 
classification cpp example
URL: https://github.com/apache/incubator-mxnet/pull/9799#discussion_r169129782
 
 

 ##
 File path: example/image-classification/predict-cpp/CMakeLists.txt
 ##
 @@ -1,34 +1,28 @@
-if(USE_OPENCV)
-  find_package(OpenCV QUIET COMPONENTS core highgui imgproc imgcodecs)
-  if(NOT OpenCV_FOUND) # if not OpenCV 3.x, then imgcodecs are not found
-find_package(OpenCV REQUIRED COMPONENTS core highgui imgproc)
-  endif()
-
-  if(NOT MSVC)
-set(UNITTEST_STATIC_LINK ON)
-  endif()
+# ---[ OpenCV
+if(NOT USE_OPENCV OR NOT OpenCV_FOUND)
+  message(WARNING "\
+OpenCV should be enabled and found to build image classification example, 
skipping...")
 
 Review comment:
   The example will just not be build and it will not break the whole project 
configuration.


This is an automated message from the Apache Git Service.
To respond to the message, please log on GitHub and use the
URL above to go to the specific comment.
 
For queries about this service, please contact Infrastructure at:
us...@infra.apache.org


With regards,
Apache Git Services

[GitHub] stonedl3 closed issue #9766: DeepLearning on Imagenet with mxnet issues translating .lst to .rec files

[GitBox]

stonedl3 closed issue #9766: DeepLearning on Imagenet with mxnet issues 
translating .lst to .rec files
URL: https://github.com/apache/incubator-mxnet/issues/9766
 
 
   


This is an automated message from the Apache Git Service.
To respond to the message, please log on GitHub and use the
URL above to go to the specific comment.
 
For queries about this service, please contact Infrastructure at:
us...@infra.apache.org


With regards,
Apache Git Services

[GitHub] sbodenstein opened a new issue #9828: Building with MKL fails on OSX

[GitBox]

sbodenstein opened a new issue #9828: Building with MKL fails on OSX
URL: https://github.com/apache/incubator-mxnet/issues/9828
 
 
   ## Description
   When building MXNet on OSX with the flag `USE_MKLDNN=1` in the `config.m` 
file, I get a build failure.
   
   ## Environment info (Required)
   
   ```
   --System Info--
   Platform : Darwin-16.7.0-x86_64-i386-64bit
   system   : Darwin
   node : sebastianbmaclap.local
   release  : 16.7.0
   version  : Darwin Kernel Version 16.7.0: Thu Jun 15 17:36:27 PDT 2017; 
root:xnu-3789.70.16~2/RELEASE_X86_64
   --Hardware Info--
   machine  : x86_64
   processor: i386
   b'machdep.cpu.extfeatures: SYSCALL XD 1GBPAGE EM64T LAHF LZCNT RDTSCP TSCI'
   b'machdep.cpu.leaf7_features: SMEP ERMS RDWRFSGS TSC_THREAD_OFFSET BMI1 HLE 
AVX2 BMI2 INVPCID RTM FPU_CSDS'
   b'machdep.cpu.features: FPU VME DE PSE TSC MSR PAE MCE CX8 APIC SEP MTRR PGE 
MCA CMOV PAT PSE36 CLFSH DS ACPI MMX FXSR SSE SSE2 SS HTT TM PBE SSE3 PCLMULQDQ 
DTES64 MON DSCPL VMX SMX EST TM2 SSSE3 FMA CX16 TPR PDCM SSE4.1 SSE4.2 x2APIC 
MOVBE POPCNT AES PCID XSAVE OSXSAVE SEGLIM64 TSCTMR AVX1.0 RDRAND F16C'
   b'machdep.cpu.brand_string: Intel(R) Core(TM) i7-4980HQ CPU @ 2.80GHz'
   ```
   
   ## Build info (Required if built from source)
   
   ```
   clang --version
   Apple LLVM version 9.0.0 (clang-900.0.39.2)
   Target: x86_64-apple-darwin16.7.0
   Thread model: posix
   InstalledDir: 
/Applications/Xcode.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/bin
   ```
   
   MXNet commit hash: `f33591f37da73965e50446704d6b1a73331df2c0`
   
   ## Error Message:
   ```
   In file included from src/operator/nn/mkldnn/mkldnn_pooling.cc:28:
   In file included from 
/Users/sebastianb/Software/incubator-mxnet/src/operator/nn/mkldnn/./mkldnn_pooling-inl.h:30:
   
/Users/sebastianb/Software/incubator-mxnet/3rdparty/mkldnn//install/include/mkldnn.hpp:159:17:
 error: implicit instantiation of undefined template 
'std::__1::basic_string,
 std::__1::allocator >'
   std::string message;
   ^
   
/Applications/Xcode.app/Contents/Developer/Toolchains/XcodeDefault.xctoolchain/usr/include/c++/v1/iosfwd:193:32:
 note: template is declared here
   class _LIBCPP_TEMPLATE_VIS basic_string;
  ^
   In file included from src/operator/nn/mkldnn/mkldnn_pooling.cc:28:
   In file included from 
/Users/sebastianb/Software/incubator-mxnet/src/operator/nn/mkldnn/./mkldnn_pooling-inl.h:30:
   
/Users/sebastianb/Software/incubator-mxnet/3rdparty/mkldnn//install/include/mkldnn.hpp:169:48:
 error: implicit instantiation of undefined template 
'std::__1::basic_string,
 std::__1::allocator >'
   error(mkldnn_status_t astatus, std::string amessage,
   ```
   
   


This is an automated message from the Apache Git Service.
To respond to the message, please log on GitHub and use the
URL above to go to the specific comment.
 
For queries about this service, please contact Infrastructure at:
us...@infra.apache.org


With regards,
Apache Git Services

[GitHub] marcoabreu commented on a change in pull request #9799: Cleaned up image classification cpp example

[GitBox]

marcoabreu commented on a change in pull request #9799: Cleaned up image 
classification cpp example
URL: https://github.com/apache/incubator-mxnet/pull/9799#discussion_r169122011
 
 

 ##
 File path: example/image-classification/predict-cpp/CMakeLists.txt
 ##
 @@ -1,34 +1,28 @@
-if(USE_OPENCV)
-  find_package(OpenCV QUIET COMPONENTS core highgui imgproc imgcodecs)
-  if(NOT OpenCV_FOUND) # if not OpenCV 3.x, then imgcodecs are not found
-find_package(OpenCV REQUIRED COMPONENTS core highgui imgproc)
-  endif()
-
-  if(NOT MSVC)
-set(UNITTEST_STATIC_LINK ON)
-  endif()
+# ---[ OpenCV
+if(NOT USE_OPENCV OR NOT OpenCV_FOUND)
+  message(WARNING "\
+OpenCV should be enabled and found to build image classification example, 
skipping...")
 
 Review comment:
   This is a critical error, right? 


This is an automated message from the Apache Git Service.
To respond to the message, please log on GitHub and use the
URL above to go to the specific comment.
 
For queries about this service, please contact Infrastructure at:
us...@infra.apache.org


With regards,
Apache Git Services

[GitHub] lebeg commented on issue #9799: Cleaned up image classification cpp example

[GitBox]

lebeg commented on issue #9799: Cleaned up image classification cpp example
URL: https://github.com/apache/incubator-mxnet/pull/9799#issuecomment-366740410
 
 
   Added windows to tested platforms


This is an automated message from the Apache Git Service.
To respond to the message, please log on GitHub and use the
URL above to go to the specific comment.
 
For queries about this service, please contact Infrastructure at:
us...@infra.apache.org


With regards,
Apache Git Services

[GitHub] jmacglashan commented on issue #9822: gluon HybridBlock wrapper of constant nd.array, is it possible?

[GitBox]

jmacglashan commented on issue #9822: gluon HybridBlock wrapper of constant 
nd.array, is it possible?
URL: 
https://github.com/apache/incubator-mxnet/issues/9822#issuecomment-366731099
 
 
   I would like better constant support for symbols (and consequently, 
`HybridBlock`s) as well.
   
   A workaround you can use for now though is to do it as follows. In the block 
that needs them, make your constants parameters; name each constant parameter 
something like 'constant_x' (this naming makes it impossible for usual 
initializer patterns to init them), assign the parameter a custom initializer 
that will set it to the right value, and have your block wrap the constant 
usage with a block grad operation so that no optimization will ever touch them.


This is an automated message from the Apache Git Service.
To respond to the message, please log on GitHub and use the
URL above to go to the specific comment.
 
For queries about this service, please contact Infrastructure at:
us...@infra.apache.org


With regards,
Apache Git Services

[GitHub] jmacglashan commented on issue #9822: gluon HybridBlock wrapper of constant nd.array, is it possible?

[GitBox]

jmacglashan commented on issue #9822: gluon HybridBlock wrapper of constant 
nd.array, is it possible?
URL: 
https://github.com/apache/incubator-mxnet/issues/9822#issuecomment-366731099
 
 
   I would like better constant support for symbols (and consequently, 
`HybridBlock`s) as well.
   
   A workaround you can use for now though is to do it as follows. In the block 
that needs them, make your constants parameters; name each constant parameter 
something like 'constant_x' (this naming makes it impossible for usual 
initializer patterns to init them), assign the parameter a custom initializer 
that will set it to the right value and have your constant block wrap them with 
a block grad operation so that no optimization will ever touch them.


This is an automated message from the Apache Git Service.
To respond to the message, please log on GitHub and use the
URL above to go to the specific comment.
 
For queries about this service, please contact Infrastructure at:
us...@infra.apache.org


With regards,
Apache Git Services

[GitHub] stonedl3 commented on issue #9766: DeepLearning on Imagenet with mxnet issues translating .lst to .rec files

[GitBox]

stonedl3 commented on issue #9766: DeepLearning on Imagenet with mxnet issues 
translating .lst to .rec files
URL: 
https://github.com/apache/incubator-mxnet/issues/9766#issuecomment-366729741
 
 
   I have resolved the issue.  I had used resize=256 while my training script 
was using 3x227x227 image.  You can close the issue.


This is an automated message from the Apache Git Service.
To respond to the message, please log on GitHub and use the
URL above to go to the specific comment.
 
For queries about this service, please contact Infrastructure at:
us...@infra.apache.org


With regards,
Apache Git Services

[GitHub] gigasquid commented on issue #9794: MKL linker error on macOS 10.13 despite using configuration without MKL

[GitBox]

gigasquid commented on issue #9794: MKL linker error on macOS 10.13 despite 
using configuration without MKL
URL: 
https://github.com/apache/incubator-mxnet/issues/9794#issuecomment-366727882
 
 
   fwiw - I just ran into this trying out the gpu build as well


This is an automated message from the Apache Git Service.
To respond to the message, please log on GitHub and use the
URL above to go to the specific comment.
 
For queries about this service, please contact Infrastructure at:
us...@infra.apache.org


With regards,
Apache Git Services

[GitHub] jmacglashan opened a new issue #9827: Request: to_bytes and from_bytes variant of ndarray.save and ndarray.load.

[GitBox]

jmacglashan opened a new issue #9827: Request: to_bytes and from_bytes variant 
of ndarray.save and ndarray.load.
URL: https://github.com/apache/incubator-mxnet/issues/9827
 
 
   Mxnet has convenient `ndarray` (and `ParameterDict`) save/load functions, 
but they always go through files specified by a path. Because of a 
serialization pipeline my project is using, it would be convenient to have a 
helper utility for `to_bytes` and `from_bytes` that operates just like the 
current `ndarray.save` and `ndarray.load` utilities, but rather than saving to 
a file, `ndarray.to_bytes` would return the byte stream that `save` would write 
to file and `ndarray.from_bytes` would return the dict/ndarray from a 
bytestream that would have been loaded by the file with the corresponding bytes.


This is an automated message from the Apache Git Service.
To respond to the message, please log on GitHub and use the
URL above to go to the specific comment.
 
For queries about this service, please contact Infrastructure at:
us...@infra.apache.org


With regards,
Apache Git Services

[GitHub] KellenSunderland commented on issue #9753: Make fail for MXNET

[GitBox]

KellenSunderland commented on issue #9753: Make fail for MXNET
URL: 
https://github.com/apache/incubator-mxnet/issues/9753#issuecomment-366725803
 
 
   I usually create a softlink from openblas to blas.  Should be something like 
   ```bash
   ln - s /usr/lib/libopenblas.so /usr/lib/libcblas.so
   ```


This is an automated message from the Apache Git Service.
To respond to the message, please log on GitHub and use the
URL above to go to the specific comment.
 
For queries about this service, please contact Infrastructure at:
us...@infra.apache.org


With regards,
Apache Git Services

[GitHub] marcoabreu commented on issue #9794: MKL linker error on macOS 10.13 despite using configuration without MKL

[GitBox]

marcoabreu commented on issue #9794: MKL linker error on macOS 10.13 despite 
using configuration without MKL
URL: 
https://github.com/apache/incubator-mxnet/issues/9794#issuecomment-366725166
 
 
   @zheng-da could you take a look?


This is an automated message from the Apache Git Service.
To respond to the message, please log on GitHub and use the
URL above to go to the specific comment.
 
For queries about this service, please contact Infrastructure at:
us...@infra.apache.org


With regards,
Apache Git Services

[GitHub] KellenSunderland commented on issue #9762: Should MXNet also support logical operations?

[GitBox]

KellenSunderland commented on issue #9762: Should MXNet also support logical 
operations?
URL: 
https://github.com/apache/incubator-mxnet/issues/9762#issuecomment-366725239
 
 
   +1.  This comes in quite handy for NLP problems where the sequence length is 
variable.


This is an automated message from the Apache Git Service.
To respond to the message, please log on GitHub and use the
URL above to go to the specific comment.
 
For queries about this service, please contact Infrastructure at:
us...@infra.apache.org


With regards,
Apache Git Services

[GitHub] edmBernard opened a new issue #9826: Crash Mxnet: Error in `python3': corrupted double-linked list: 0x00007f1c4b2e09d0

[GitBox]

edmBernard opened a new issue #9826: Crash Mxnet: Error in `python3': corrupted 
double-linked list: 0x7f1c4b2e09d0
URL: https://github.com/apache/incubator-mxnet/issues/9826
 
 
   I try to test Mtcnn face detection from this repository: 
https://github.com/pangyupo/mxnet_mtcnn_face_detection
   
   After some iteration I got a severe crash of Mxnet with this error : 
https://gist.github.com/edmBernard/91731e795decd7b7c5456cb0d7a1d303
   
   I was not able to reproduce this out of my code. All the code was in python 
only Mxnet use C++.
   Does someone have a idea where this can come from ?


This is an automated message from the Apache Git Service.
To respond to the message, please log on GitHub and use the
URL above to go to the specific comment.
 
For queries about this service, please contact Infrastructure at:
us...@infra.apache.org


With regards,
Apache Git Services

[GitHub] lebeg opened a new issue #9825: Google index points to old version of install instructions

[GitBox]

lebeg opened a new issue #9825: Google index points to old version of install 
instructions
URL: https://github.com/apache/incubator-mxnet/issues/9825
 
 
   ## Description
   
   ![screenshot from 2018-02-19 
15-36-47](https://user-images.githubusercontent.com/1753787/36383002-1a4d3dd0-158b-11e8-8fe1-074ce39e7a65.png)
   
   Leads to
   
   ![screenshot from 2018-02-19 
15-40-00](https://user-images.githubusercontent.com/1753787/36383061-4c876e74-158b-11e8-85de-6a34a5367243.png)
   
   


This is an automated message from the Apache Git Service.
To respond to the message, please log on GitHub and use the
URL above to go to the specific comment.
 
For queries about this service, please contact Infrastructure at:
us...@infra.apache.org


With regards,
Apache Git Services

[GitHub] lebeg opened a new issue #9824: Windows build instructions broken

[GitBox]

lebeg opened a new issue #9824: Windows build instructions broken
URL: https://github.com/apache/incubator-mxnet/issues/9824
 
 
   ## Description
   
   Build from source instructions for Windows point to MAC OS build:
   
   https://mxnet.incubator.apache.org/versions/master/install/index.html


This is an automated message from the Apache Git Service.
To respond to the message, please log on GitHub and use the
URL above to go to the specific comment.
 
For queries about this service, please contact Infrastructure at:
us...@infra.apache.org


With regards,
Apache Git Services

[GitHub] stonedl3 commented on issue #9766: DeepLearning on Imagenet with mxnet issues translating .lst to .rec files

[GitBox]

stonedl3 commented on issue #9766: DeepLearning on Imagenet with mxnet issues 
translating .lst to .rec files
URL: 
https://github.com/apache/incubator-mxnet/issues/9766#issuecomment-366689907
 
 
   I have oosted the script.
   
   On Feb 13, 2018 7:48 PM, "David Stone"  wrote:
   
   > I am traveling for the next couple of days.  I will post the script when I
   > return.  I have not tried vs 1.0.
   > Dave
   >
   > On Feb 13, 2018 1:26 PM, "Anirudh Subramanian" 
   > wrote:
   >
   >> Can you please also provide the script that you ran to reproduce the
   >> issue ? I see that you are using older version of MXNet : 0.11.0 . Have 
you
   >> tried 1.0.0 ?
   >>
   >> ?
   >> You are receiving this because you authored the thread.
   >> Reply to this email directly, view it on GitHub
   >> 
,
   >> or mute the thread
   >> 

   >> .
   >>
   >
   


This is an automated message from the Apache Git Service.
To respond to the message, please log on GitHub and use the
URL above to go to the specific comment.
 
For queries about this service, please contact Infrastructure at:
us...@infra.apache.org


With regards,
Apache Git Services

[GitHub] szha commented on issue #8630: How to reassign a new input symbol when load symbol from json file

[GitBox]

szha commented on issue #8630: How to reassign a new input symbol when load 
symbol from json file
URL: 
https://github.com/apache/incubator-mxnet/issues/8630#issuecomment-366678989
 
 
   @apache/mxnet-committers: This issue has been inactive for the past 90 days. 
It has no label and needs triage.
   
   For general "how-to" questions, our [user forum](https://discuss.mxnet.io/) 
(and [Chinese version](https://discuss.gluon.ai/)) is a good place to get help.


This is an automated message from the Apache Git Service.
To respond to the message, please log on GitHub and use the
URL above to go to the specific comment.
 
For queries about this service, please contact Infrastructure at:
us...@infra.apache.org


With regards,
Apache Git Services

[GitHub] gigasquid commented on issue #8971: Clojure Library for mxnet

[GitBox]

gigasquid commented on issue #8971: Clojure Library for mxnet
URL: 
https://github.com/apache/incubator-mxnet/issues/8971#issuecomment-366543008
 
 
   An update - I've ported over the MnistModule example to clojure - yay! ? 
   
https://github.com/gigasquid/incubator-mxnet/blob/clojure-package/clojure-package/examples/module/mnist_mlp.clj
   
   @yzhliu I'm currently developing on a Mac and would like to get GPU going 
there if possible so I can test that out too. Can I help with a PR to add this 
to the Scala build? https://github.com/apache/incubator-mxnet/issues/4469? 
   
   EDIT - taking a quick look at it and seeing if I can help out in this area. 
If not, I can test the GPU on the Clojure package on AWS.


This is an automated message from the Apache Git Service.
To respond to the message, please log on GitHub and use the
URL above to go to the specific comment.
 
For queries about this service, please contact Infrastructure at:
us...@infra.apache.org


With regards,
Apache Git Services

[GitHub] welnaseth commented on issue #621: Support for other Device Types, OpenCL AMD GPU

[GitBox]

welnaseth commented on issue #621: Support for other Device Types, OpenCL AMD 
GPU
URL: https://github.com/apache/incubator-mxnet/issues/621#issuecomment-366623893
 
 
   Any updates on this? @kpot makes a good point above, tvm (and nnvm due to 
being built off of it) supports opencl, so to me it seems like it shouldn't be 
too hard to implement opencl as an option. It would be nice to have a timeline 
for when this can be implemented and if not, what things are blocking it?


This is an automated message from the Apache Git Service.
To respond to the message, please log on GitHub and use the
URL above to go to the specific comment.
 
For queries about this service, please contact Infrastructure at:
us...@infra.apache.org


With regards,
Apache Git Services