piiswrong closed pull request #7147: cuda support for linalg-functions,
restructuring of linalg interfaces
URL: https://github.com/apache/incubator-mxnet/pull/7147
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lxn2 closed pull request #6: Fix example link
URL: https://github.com/apache/incubator-mxnet-site/pull/6
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piiswrong closed pull request #7441: broken link in readme
URL: https://github.com/apache/incubator-mxnet/pull/7441
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in
the network. In Tensorflow, tf.boolean_mask is generally used for that purpose.
How can I achieve the similar effect in MxNet, is it possible?
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why not import these from ndarray.py to void try except?
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why not import these from ndarray to void try except?
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yone show me how to use (bind, init, set learning rate) a new
parameterizd layer ? Thanks.
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the requirement setup.py (from
versions: )
No matching distribution found for setup.py
please help me on this..
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added extension headers.
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\mxnet\src\ndarray\ndarray.cc:329: Check failed:
from.shape() == to->shape() operands shape mismatchfrom.shape = (5,14)
to.shape=(5,5)`
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on current master branch. Issue closed. :rofl:
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the
WarpCTC plugin (#6121)
I don't think the maintenance effort is worthwhile if almost every single
user training with CUDA will have cuDNN.
What are your thoughts?
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thirdwing closed pull request #7437: [R] vignette update
URL: https://github.com/apache/incubator-mxnet/pull/7437
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(train_log)]
Review comment:
So stupid I am, I forgot `make` and `pip install -e .`. It's okay on current
master branch.
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formath opened a new pull request #7452: rm not use variables
URL: https://github.com/apache/incubator-mxnet/pull/7452
@piiswrong
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have run that lead to the error.
1. AFTER COMPILATION : Import mxnet
2.
3.
## What have you tried to solve it?
1. different version
2.
3.
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it, spec.cache_file.c_str());
}
#else
CHECK(spec.cache_file.length() == 0)
<< "to enable cached file, compile with c++11";
return split;
#endif*/
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```
2. I don't know the impact of DMLC_ENABLE_STD_THREAD, so keep
DMLC_ENABLE_STD_THREAD = 1.
-
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the first time, it runs very fast.
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piiswrong closed pull request #7304: gluon bce loss
URL: https://github.com/apache/incubator-mxnet/pull/7304
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work? I'm reallly confused
abot this.
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).
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ues/2099
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on this?
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m the make file
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ZiyueHuang closed pull request #7449: Fix a bug in SequentialRNNCell.reset()
URL: https://github.com/apache/incubator-mxnet/pull/7449
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tests/python/unittest/test_io.py
[10:58:26] src/io/iter_mnist.cc:91: MNISTIter: load 6 images, shuffle=1,
shape=(100,784)
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Or if installed from source: installed from source
MXNet commit hash (`git rev-parse HEAD`): 1a3faa
If you are using python package, please provide
Python version and distribution: Python 2.7.13 :: Anaconda custom (64-bit)
I tried to train image classification model
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(..., data_names=('data1', 'data2'), ...)`
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)
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tested with the same data, the
accuracy is much lower. I don't kown why.
BTW, how did you get the mean r, g b.?
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(e.g. BidirectionalCell) can be patched up together.
Would you mind doing the same for python/mxnet/gluon/rnn/rnn_cell.py?
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(e.g. BidirectionalCell) can be patched up together.
Would you mind doing the same for python/mxnet/gluon/rnn/rnn_cell.py? The
container attribute there is `_children`.
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piiswrong closed pull request #7439: Small doc cleanups
URL: https://github.com/apache/incubator-mxnet/pull/7439
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CodingCat opened a new pull request #7440: [scala-package] clean up pom (remove
duplicate definition)
URL: https://github.com/apache/incubator-mxnet/pull/7440
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only merge when ALL requested reviewers
have approved?
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% , after a while (3~4h)
it takes 27% and finally it run out of my memory (waked up by the alarm message)
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of training is 5.7% , after
a while (3~4h) it takes 27% and finally it run out of my memory (waked up by
the alarm message)
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before it initializes `self._cells`, BaseRNNCell's constructor will call
`self.reset()`. So at first time `hasattr(self, '_cells')` should be false.
@szha
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Review comment:
Normally given a key, the ndarrays in the arg_list will be either all dense
or all sparse, right?
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would happened if we use this in `batch_end_callback`?
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would happened if we use this in batch_end_callback?
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the metric every batch,
the curve looks more smooth. Same with the older version, great.
* In `STEP` mode, it shows the metric in that epoch.
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`.
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you.
## Environment info
Operating System: Ubuntu 16.04.4
Compiler: G++ 5.4
Package used (Python/R/Scala/Julia): Python
MXNet version:
Or if installed from source: source
MXNet commit hash (`git rev-parse HEAD
?
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don't understand
if all the dependency need to be wait for this copy.
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peiyunh opened a new pull request #7507: Fix description of argument parser
URL: https://github.com/apache/incubator-mxnet/pull/7507
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piiswrong commented on issue #7502: Change git clone to specific tag for
installation
URL: https://github.com/apache/incubator-mxnet/pull/7502#issuecomment-322939359
Is branch name guaranteed to match with version
the allocation, on the
other hand, CPUDeviceStorage is used no where.
I'm just curious why CPUDeviceStorage is there, and not in use.
I'm on the master branch.
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comment:
Can we just skip the row_sparse weights and pull the dense weights normally
or any other prettier way to do this?
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ZiyueHuang commented on issue #7449: Fix a bug in SequentialRNNCell.reset()
URL: https://github.com/apache/incubator-mxnet/pull/7449#issuecomment-322959352
@piiswrong
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it to do this copy?
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kevinthesun commented on issue #7502: Change git clone to specific tag for
installation
URL: https://github.com/apache/incubator-mxnet/pull/7502#issuecomment-322940936
Yes.
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at least 10 seconds
when I run this on a cpu-Server with Centos. however, in my Macbook cpu only,
its time cost can be noticed!
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CORE=0.
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so we can only pull row_sparse weights manually?
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build from source, the official doc is so out-of-date and messy.
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lxn2 closed pull request #11: Fix git clone links
URL: https://github.com/apache/incubator-mxnet-site/pull/11
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atches <- mx.symbol.broadcast_mul(lhs=selfMatches,
rhs=mx.symbol.max(distances))
```
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/PR-7393/2/pipeline
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}
```
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an
environment variable as in:
export MXNET_CUDA_ALLOW_TENSOR_CORE=0
Applies to float16 I/O instances only.
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batch_end_callbacks = [mx.callback.Speedometer(args.batch_size,
args.disp_batches)]
eval_end_callbacks = [mx.callback.Speedometer(args.batch_size,
args.disp_batches)]
```
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the code, please?
I am using win10, python2, mxnet 0.10, anaconda2
Thanks in advance
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ysh329 opened a new issue #7424: train_mnist.py failed: TypeError: __init__()
got an unexpected keyword argument 'multi_precision'
URL: https://github.com/apache/incubator-mxnet/issues/7424
Release 0.10.0 is okay.
However, Current master branch (git clone --recursive
https
guess the error is from the the data load and passing to mxnet.
I don't know what does this mean and what shall I do, help me, please.
I am using win10, python2, mxnet 0.10, anaconda2
Thanks in advance
This is an a
of these
occurrences don't need sign for the variable.
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larroy opened a new pull request #7416: update submoules with android fixes
URL: https://github.com/apache/incubator-mxnet/pull/7416
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y to close it.
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