Hello! I use neon to train a model on three backends: CPU, MKL and GPU. All the settings are the same when running with these backends. I got very similar costs from CPU and GPU, while the cost from MKL backend was usually higher than the previous two, sometimes even nan. Anybody has an idea why does that happen?

The CPU is an Intel i7; the GPU is a Nvidia GTX 1050; the code is running on Ubuntu 16.04. Here is the printed result of the code...

Use cpu as backend.

DISPLAY:neon:-------------------------------------------------------------------------------------
DISPLAY:neon:|    Func     |    Mean     |   Median    |     Min     |     Max     |    Units    |
DISPLAY:neon:-------------------------------------------------------------------------------------
DISPLAY:neon:| fprop       |  456.74     |  452.61     |  439.07     |  501.7      |    msec     |
DISPLAY:neon:| bprop       |  819.21     |  796.45     |  772.53     |  979.8      |    msec     |
DISPLAY:neon:| iteration   |  1276       |  1250       |  1213.5     |  1457       |    msec     |
DISPLAY:neon:-------------------------------------------------------------------------------------

Epoch 0   [Train |████████████████████|  246/246  batches, 3.51 cost, 303.30s] [CrossEntropyMulti Loss 0.00, 0.00s]
Epoch 1   [Train |████████████████████|  245/245  batches, 3.49 cost, 301.14s] [CrossEntropyMulti Loss 0.00, 0.00s]
Epoch 2   [Train |████████████████████|  245/245  batches, 3.47 cost, 301.43s] [CrossEntropyMulti Loss 0.00, 0.00s]
Epoch 3   [Train |████████████████████|  245/245  batches, 3.46 cost, 302.56s] [CrossEntropyMulti Loss 0.00, 0.00s]
Epoch 4   [Train |████████████████████|  245/245  batches, 3.44 cost, 302.91s] [CrossEntropyMulti Loss 0.00, 0.00s]
Neon training finishes in 1646.99 seconds.
Misclassification error = 91.2%. Finished in 26.86 seconds.
Top 3 Misclassification error = 78.1%. Finished in 27.36 seconds.
Top 5 Misclassification error = 65.7%. Finished in 27.36 seconds.
Misclassification error = 91.7% on test set. Finished in 43.54 seconds.
Top 3 Misclassification error = 79.8% on test set. Finished in 43.60 seconds.
Top 5 Misclassification error = 67.3% on test set. Finished in 43.76 seconds.


Use mkl as backend.

DISPLAY:neon:-------------------------------------------------------------------------------------
DISPLAY:neon:|    Func     |    Mean     |   Median    |     Min     |     Max     |    Units    |
DISPLAY:neon:-------------------------------------------------------------------------------------
DISPLAY:neon:| fprop       |  119.82     |  120.03     |  111.14     |  130.82     |    msec     |
DISPLAY:neon:| bprop       |  157.51     |  156.32     |  151.81     |  165.86     |    msec     |
DISPLAY:neon:| iteration   |  277.33     |  280.49     |  264.03     |  285.16     |    msec     |
DISPLAY:neon:-------------------------------------------------------------------------------------

Epoch 0   [Train |████████████████████|  246/246  batches, 48.12 cost, 70.76s] [CrossEntropyMulti Loss 0.00, 0.00s]
Epoch 1   [Train |████████████████████|  245/245  batches, 47.54 cost, 73.94s] [CrossEntropyMulti Loss 0.00, 0.00s]
Epoch 2   [Train |████████████████████|  245/245  batches, 48.52 cost, 77.99s] [CrossEntropyMulti Loss 0.00, 0.00s]
Epoch 3   [Train |████████████████████|  245/245  batches, 48.09 cost, 74.04s] [CrossEntropyMulti Loss 0.00, 0.00s]
Epoch 4   [Train |████████████████████|  245/245  batches, 48.20 cost, 79.86s] [CrossEntropyMulti Loss 0.00, 0.00s]
Neon training finishes in 422.74 seconds.
Misclassification error = 94.6%. Finished in 9.29 seconds.
Top 3 Misclassification error = 90.1%. Finished in 9.56 seconds.
Top 5 Misclassification error = 85.6%. Finished in 9.78 seconds.
Misclassification error = 94.5% on test set. Finished in 15.48 seconds.
Top 3 Misclassification error = 90.0% on test set. Finished in 15.47 seconds.
Top 5 Misclassification error = 85.5% on test set. Finished in 14.99 seconds.


Use gpu as backend.

DISPLAY:neon:-------------------------------------------------------------------------------------
DISPLAY:neon:|    Func     |    Mean     |   Median    |     Min     |     Max     |    Units    |
DISPLAY:neon:-------------------------------------------------------------------------------------
DISPLAY:neon:| fprop       |  6.1057     |  6.0366     |  5.8992     |  6.3699     |    msec     |
DISPLAY:neon:| bprop       |  10.76      |  10.753     |  9.9809     |  11.841     |    msec     |
DISPLAY:neon:| iteration   |  16.865     |  16.783     |  15.88      |  18.185     |    msec     |
DISPLAY:neon:-------------------------------------------------------------------------------------

Epoch 0   [Train |████████████████████|  246/246  batches, 3.51 cost, 3.98s] [CrossEntropyMulti Loss 0.00, 0.00s]
Epoch 1   [Train |████████████████████|  245/245  batches, 3.48 cost, 3.97s] [CrossEntropyMulti Loss 0.00, 0.00s]
Epoch 2   [Train |████████████████████|  245/245  batches, 3.47 cost, 3.98s] [CrossEntropyMulti Loss 0.00, 0.00s]
Epoch 3   [Train |████████████████████|  245/245  batches, 3.46 cost, 3.98s] [CrossEntropyMulti Loss 0.00, 0.00s]
Epoch 4   [Train |████████████████████|  245/245  batches, 3.44 cost, 3.98s] [CrossEntropyMulti Loss 0.00, 0.00s]
Neon training finishes in 21.84 seconds.
Misclassification error = 91.2%. Finished in 0.38 seconds.
Top 3 Misclassification error = 78.0%. Finished in 0.38 seconds.
Top 5 Misclassification error = 65.6%. Finished in 0.38 seconds.
Misclassification error = 91.6% on test set. Finished in 0.60 seconds.
Top 3 Misclassification error = 79.8% on test set. Finished in 0.60 seconds.
Top 5 Misclassification error = 67.4% on test set. Finished in 0.60 seconds.

I'm using Neon for my deep Q-learning code - https://github.com/tambetm/simple_dqn. Recently I noticed an issue, that prediction of my network drops to 0 after certain number of epochs. This can be seen from Q-value graph:

Normally it would look like this: This plot was produced using Neon commit hash 7a56fa9645a51e97c05f2e5afbbd1df7057ae832 from October 30th. My code is exactly the same.

The most plausible explanation would be, that weights are truncated to 0 at some point. Because my code hasn't changed, I suspect something in Neon code related to saving and loading weights repeatedly. In my code I need to clone a model, and simplest (and most compatible) way of doing that is to just save and load the model. I do this ~45 times before network prediction drops (it doesn't drop always at the same moment).

Any ideas what change could have resulted in such a behavior and how to debug it?

Hi, Just installed neon on Ubuntu 14 python 3.4 with the following command: [email protected]:~/neon$ neon examples/mlp/mnist-small.yaml and getting an error message: Traceback (most recent call last): File "/home/nir/anaconda3/bin/neon", line 240, in experiment, result, status = main() File "/home/nir/anaconda3/bin/neon", line 126, in main experiment = deserialize(args.yaml_file) File "/home/nir/anaconda3/lib/python3.4/site-packages/neon/util/persist.py", line 183, in deserialize if not isinstance(load_path, file): NameError: name 'file' is not defined I check in the directory - this file exists. Appreciate your assistance thanks N

I've updated my Docker builds for version 1.0 - one for the cpu backend, and a new one for the gpu backend. The GPU images referenced in the 0.9 docs are still available, but with a note about deprecation.

I've tested the cpu version with neon examples/mnist_mlp.yaml and python examples/mnist_mlp.py, and it appears fine. However, the gpu version builds the cpu version because of https://github.com/NervanaSystems/neon/issues/83. When building the code to check for the GPU capabilities, please keep https://github.com/NervanaSystems/neon/issues/19 in mind.

I install cuda and environment set,

nvidia-smi 
Wed Dec  6 13:53:24 2017       
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 384.81                 Driver Version: 384.81                    |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|===============================+======================+======================|
|   0  Tesla P100-PCIE...  Off  | 00000000:2F:00.0 Off |                    0 |
| N/A   33C    P0    31W / 250W |  15553MiB / 16276MiB |      0%      Default |
+-------------------------------+----------------------+----------------------+
|   1  Tesla P100-PCIE...  Off  | 00000000:86:00.0 Off |                    0 |
| N/A   36C    P0    31W / 250W |  15479MiB / 16276MiB |      0%      Default |
+-------------------------------+----------------------+----------------------+

pip install nervananeon

env | grep PATH
LD_LIBRARY_PATH=/usr/local/cuda/lib64:/usr/local/cuda/lib:/usr/lib/python2.7/site-packages/mklml_lnx_2018.0.1.20171007/lib:/usr/local/nvidia/lib:/usr/local/nvidia/lib64:/.singularity.d/libs
PATH=/usr/local/nvidia/bin:/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin

python  mnist.py -b gpu
python : error: argument -b/--backend: invalid choice: 'gpu' (choose from 'cpu', 'mkl')

I don't understand why CPU and GPU install the same package,It should be used when the GPU package is installed by TensorFlow.

pip install tensorflow-gpu

You have this cool function by dividing by integers by using bitshift, and first multiplying by another number, so you're not limited to dividing by powers of 2, as described in https://gmplib.org/~tege/divcnst-pldi94.pdf

At the moment, this function is called 'magic', but I'm not sure it's very descriptive? I've renamed it to get_div_mul_shift in my own branch: https://github.com/hughperkins/winogradCl/blob/api/winogradcl/util/math_helper.py#L33

def get_div_mul_shift_32(nmax, d)

There appears to be a race condition in certain expressions, such as the denominator of softmax with axis=1 (unlike in neon.transforms.activation):

https://gist.github.com/oleg-trott/30b802902fd8c63ce002

I tried this on several GTX 980 cards and see it on all of them. The errors are rare, about 0.1-0.2%. However, when they happen, they are usually dramatic.

I also see these errors on GTX 750, but they are about 100 times less frequent.

How do I start to implement a new layer? I want to implement a few more exotic pooling functions, so basically I want to change the function that takes the max / average and maybe even the gradient for that layer. It would be great to get a step by step recommendation on how to do such things.

$make HAS_GPU=true
Building MKL Engine...
which: no icc in (/usr/local/nvidia/bin:/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin)
using GNU compiler...
basename: missing operand
Try 'basename --help' for more information.
mkl root: /opt/neon/
make[1]: Entering directory `/opt/neon/neon/backends/mklEngine'
make[1]: Leaving directory `/opt/neon/neon/backends/mklEngine'
make[1]: Entering directory `/opt/neon/neon/backends/mklEngine'
Building mklEngine.so...
gcc src/conv.c src/pooling.c src/relu.c src/batchNorm.c src/concat.c src/softmax.c src/MKLDNN.h -shared -o mklEngine.so -std=c99 -O3 -I/opt/neon//include -L/opt/neon//lib -Wl,-rpath=/opt/neon//lib -fopenmp -lmklml_gnu -fPIC -march=native -g -liomp5
In file included from src/conv.c:15:0:
src/MKLDNN.h:23:21: fatal error: mkl_dnn.h: No such file or directory
 #include <mkl_dnn.h>
                     ^
compilation terminated.
In file included from src/pooling.c:15:0:
src/MKLDNN.h:23:21: fatal error: mkl_dnn.h: No such file or directory
 #include <mkl_dnn.h>
                     ^
compilation terminated.
In file included from src/relu.c:15:0:
src/MKLDNN.h:23:21: fatal error: mkl_dnn.h: No such file or directory
 #include <mkl_dnn.h>
                     ^

$ bash prepare_mkl.sh

Checking MKLML dependencies...
Downloading required MKLML version mklml_lnx_2018.0.1.20171007 ...
MKLML dependencies installed: MKLROOT=/opt/neon/
basename: missing operand
Try 'basename --help' for more information.
/opt/neon/ 1

I need help,thanks

Hi guys,

An assertion error is thrown when I run this line of code: layer.load_weights(params)

I looked at the source code and it looks like I might be missing an argument in the function called 'self'. Not sure what is meant by self and if I missed the documentation for it I apologize. I know I do not need the load_states argument since it defaults to true.

Full code here, similar to VGG example: param_layers = [l for l in model.layers.layers] param_dict_list = trained_vgg['model']['config']['layers'] for layer, params in zip(param_layers, param_dict_list): if(layer.name == 'class_layer'): break print(params) layer.load_weights(params)

Ubuntu 16.04: unsupported GNU version! gcc versions later than 4.9 are not supported!

I have gcc4.9 installed. In Torch, I can select gcc4.9 by doing:

export CC=gcc-4.9
export CXX=g++-4.9

Unclear how to do this for Neon?

Hi All,

I am getting this error when I am running the script that has the following line, from neon.util.compat import range, StringIO. And I get the error that there isn't a module named neon.util.compat. How do I fix this error?

And help will be appreciated

Hi! my name is keval pandya . I have issue about neon that . how can I install neon on my python in windows of . the python version is 3.8.5. so can you tell me how can I install neon because I am learning deep-learning from intel and there is use of neon framework so please help me fast as possible so I can learn . please provide me solution to install it on windows

There is a small typo in examples/ssd/mboxloss.py.

Should read scale rather than sclae.

Semi-automated pull request generated by https://github.com/timgates42/meticulous/blob/master/docs/NOTE.md

I have been doing the codes as -

df=pd.DataFrame(adata) df.head()

xq=adata['batch size'].values.reshape(-1,1) yq=adata['Price'].values.reshape(-1,1)

mod = smf.quantreg('yq ~ xq', df) res = mod.fit(q=.5) print(res.summary())

quantiles = [.05, .25, .50, .75, .95] def fit_model(q): res = mod.fit(q=q) return [q, res.params['Intercept'], res.params[xq]] + res.conf_int().ix[xq].tolist() models = [fit_model(xq) for xq in quantiles]

However am getting the error as IndexError: index 200 is out of bounds for axis 0 with size 2

Hi, I am running my capstone project and working on my dataset. When I tried to clean my dataset removing the outliers, I am getting this error. I am attaching the code as below.

#Removing Outliers #Tukey Method

import required libraries

from collections import Counter

Outlier detection

def detect_outliers(df,n,features):

outlier_indices = []

# iterate over features(columns)
for col in features:
    # 1st quartile (25%)
    Q1 = np.percentile(df[col], 25)
    # 3rd quartile (75%)
    Q3 = np.percentile(df[col],75)
    # Interquartile range (IQR)
    IQR = Q3 - Q1
    
    # outlier step
    outlier_step = 1.5 * IQR
    
    # Determine a list of indices of outliers for feature col
    outlier_list_col = df[(df[col] < Q1 - outlier_step) | (df[col] > Q3 + outlier_step )].index
    
    # append the found outlier indices for col to the list of outlier indices 
    outlier_indices.extend(outlier_list_col)
    
# select observations containing more than 2 outliers
outlier_indices = Counter(outlier_indices)        
multiple_outliers = list( k for k, v in outlier_indices.items() if v > n )

return multiple_outliers   

List of Outliers

Outliers_to_drop = detect_outliers(data1.drop('Class',axis=1),0,list(data1.drop('Class',axis=1))) data1.drop('Class',axis=1).loc[Outliers_to_drop]

#Create New Dataset without Outliers good_data = data1.drop(data1.index[Outliers_to_drop]).reset_index(drop = True) good_data.info()

IndexError Traceback (most recent call last) in 1 #Create New Dataset without Outliers ----> 2 good_data = data1.drop(data1.index[Outliers_to_drop]).reset_index(drop = True) 3 good_data.info()

~\Anaconda3\lib\site-packages\pandas\core\indexes\base.py in getitem(self, key) 4289 4290 key = com.values_from_object(key) -> 4291 result = getitem(key) 4292 if not is_scalar(result): 4293 return promote(result)

IndexError: index 5000 is out of bounds for axis 0 with size 5000

​Can any one help me to fix this and code it properly.

pip install nervananeon failed.

posix_ipc_module.c(37): fatal error C1083: 无法打开包括文件: “sys/time.h” no such file or directory

Mine is CPU x86-64

How can I do?