CNTK | Microsoft Cognitive Toolkit , an open source deep | Machine Learning library

by microsoft C++ Version: v2.7 License: Non-SPDX

X-Ray Key Features Code Snippets Community Discussions(10)Vulnerabilities Install Support

kandi X-RAY | CNTK Summary

CNTK is a C++ library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Tensorflow applications. CNTK has no bugs, it has no vulnerabilities and it has medium support. However CNTK has a Non-SPDX License. You can download it from GitHub.

The Microsoft Cognitive Toolkit (is a unified deep learning toolkit that describes neural networks as a series of computational steps via a directed graph. In this directed graph, leaf nodes represent input values or network parameters, while other nodes represent matrix operations upon their inputs. CNTK allows users to easily realize and combine popular model types such as feed-forward DNNs, convolutional nets (CNNs), and recurrent networks (RNNs/LSTMs). It implements stochastic gradient descent (SGD, error backpropagation) learning with automatic differentiation and parallelization across multiple GPUs and servers. CNTK has been available under an open-source license since April 2015. It is our hope that the community will take advantage of CNTK to share ideas more quickly through the exchange of open source working code.

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CNTK has a medium active ecosystem.

It has 17369 star(s) with 4378 fork(s). There are 1265 watchers for this library.

It had no major release in the last 12 months.

There are 752 open issues and 2541 have been closed. On average issues are closed in 790 days. There are 85 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of CNTK is v2.7

Quality

CNTK has no bugs reported.

Security

CNTK has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

CNTK has a Non-SPDX License.

Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.

Reuse

CNTK releases are available to install and integrate.

Installation instructions, examples and code snippets are available.

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CNTK Key Features

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CNTK Examples and Code Snippets

No Code Snippets are available at this moment for CNTK.

Community Discussions

Trending Discussions on CNTK

Getting multiple variables in TensorFlow and other frameworks (overheads)

CNTK NVidia RTX 3060 Cublas Failure 13 with layers larger than 512

How to save CNTK model as ONNX in C#

Error installing CNTK on win10 via scripted install

how do I install parckage(such as mmlspark) to CDH cluster without network access?

Throw exception 'cuDNN failure 8: CUDNN_STATUS_EXECUTION_FAILED' in training ONNX's pretrained model Emotion FerPlus

PySpark ALSModel load fails in deployment over Azure ML service with error java.util.NoSuchElementException: Param blockSize does not exist

Python - TensorFlow and Keras

Are there syntax differences between using Keras with a Tensorflow 2, Theano, or CNTK backend?

Import error even though file is clearly in path

QUESTION

Getting multiple variables in TensorFlow and other frameworks (overheads)

Asked 2021-Oct-06 at 08:10

If I have the following graph and want to get the values of tensors T1 and T2 in TF without eager execution, how would I do this? I only know of eval() or session.run() (running that twice could be an option) or tf.print(), but printing is not desired (for performance reasons).

Especially, how is this functionality implemented in TensorFlow? Does this impose a big overhead towards just getting T2? I would be happy to be pointed to relevant resources as well.

I'm generally looking for discussions on this -- if people want to add comparisons to how other frameworks do this (Caffe, Torch, CNTK, Theano, Chainer, DyNet, etc.), that's great! In the end, I am trying to understand how these frameworks could be expanded by operators that return operator-specfic metrics that a user can use to monitor training.

Thanks!

...

ANSWER

Answered 2021-Oct-06 at 08:10

you can pass multiple parameters to session.run, and it will run the network once and return each of those parameters.

For example (from the docs):

Source https://stackoverflow.com/questions/69445120

QUESTION

CNTK NVidia RTX 3060 Cublas Failure 13 with layers larger than 512

Asked 2021-Mar-16 at 13:11

I have a LSTM network with 2000 neurons in CNTK 2.7 using EasyCNTK C# which is working fine with CPU and with Gigabyte NVidia RTX 2060 6GB, but with Gigabyte NVidia RTX 3060 12GB I get this error if I increase the number of neurons over 512 (using the same NVidia driver version 461.72 on both cards)

This is my neural network configuration

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ANSWER

Answered 2021-Mar-16 at 13:11

Looks like CNTK is not supporting CUDA 11 and RTX 3060 is not working with CUDA 10 or older.

Source https://stackoverflow.com/questions/66610939

QUESTION

How to save CNTK model as ONNX in C#

Asked 2021-Mar-05 at 12:58

The only function I know to save a trained model is trainer.SaveCheckpoint which can save the CNTK model, but I cannot find how to save the model to ONNX format in C#

On the documentation site here https://docs.microsoft.com/en-us/cognitive-toolkit/serialization

I can find only the python method to save it as ONNX

z.save("myModel.onnx", format=C.ModelFormat.ONNX)

But this does not work in C#

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ANSWER

Answered 2021-Feb-27 at 10:21

You can consider using MMdnn from microsoft to convert your CNTK model to onnx.

MMdnn is a comprehensive and cross-framework tool to convert, visualize and diagnose deep learning (DL) models. The "MM" stands for model management, and "dnn" is the acronym of deep neural network.

Source https://stackoverflow.com/questions/66393414

QUESTION

Error installing CNTK on win10 via scripted install

Asked 2020-Dec-08 at 02:08

I have been trying to install the CNTK via scripted install on windows 10 and have come across a strange error.

I got the install from https://docs.microsoft.com/en-us/cognitive-toolkit/Setup-CNTK-on-your-machine

CNTK-2-7-Windows-64bit-GPU.zip

I was kind of expecting the install to work out of the box, has anyone come across any similar errors? Am I missing something simple here?

Thanks,

When attempting to run the install again I get the following:

...

ANSWER

Answered 2020-Dec-08 at 02:08

I managed to get it all running by doing the pip install as suggested by @snowflake. thank you.

There was still a number of steps I needed to perform, which I will list below.

Preparation.

Get Anaconda.

from https://docs.microsoft.com/en-us/cognitive-toolkit/setup-windows-python?tabs=cntkpy26#anaconda3 https://repo.continuum.io/archive/Anaconda3-4.1.1-Windows-x86_64.exe

Get Instructions.

https://microsoft.github.io/CNTK-R/articles/installation.html

Get wheel location.

cntk 2.7 from https://docs.microsoft.com/en-us/cognitive-toolkit/setup-windows-python?tabs=cntkpy26#anaconda3

3.5 CPU-Only https://cntk.ai/PythonWheel/CPU-Only/cntk-2.7.post1-cp35-cp35m-win_amd64.whl GPU https://cntk.ai/PythonWheel/GPU/cntk_gpu-2.7.post1-cp35-cp35m-win_amd64.whl

Get CUDA.

https://developer.nvidia.com/cuda-90-download-archive?target_os=Windows&target_arch=x86_64&target_version=10&target_type=exenetwork

Get CNTK

I got the install from https://docs.microsoft.com/en-us/cognitive-toolkit/Setup-CNTK-on-your-machine

CNTK-2-7-Windows-64bit-GPU.zip

Installation.

Install anaconda3.
open cmd prompt in admin mode
navigate to c:\anaconda3\
create anaconda virtual environment: conda create -n cntk-py35 python=3.5 anaconda
activate environment: activate cntk-py35
install cntk: pip install https://cntk.ai/PythonWheel/GPU/cntk_gpu-2.7.post1-cp35-cp35m-win_amd64.whl

Running.

when running c# example: Cntk.Core.CSBinding-2.8-rc0.dev20200201.dll not found...

Actual fix before it started working:

copy all dll files from "CNTK-2-7-Windows-64bit-GPU\cntk\cntk" into bin of project
change solution to x64 explicitly https://github.com/microsoft/CNTK/issues/3369 It looks as if you need to set the x64 on the solution via the Configuration manager, not the project.

Source https://stackoverflow.com/questions/65117588

QUESTION

how do I install parckage(such as mmlspark) to CDH cluster without network access?

Asked 2020-Nov-16 at 17:24

Because it is hard to connect maven.org in China , I can't not install mmlspark by

...

ANSWER

Answered 2020-Aug-11 at 10:18

Finally I got it around. The key is pass .jar to pyFiles, this is very surprise me that python can read .jar .

bash:

Source https://stackoverflow.com/questions/63146931

QUESTION

Throw exception 'cuDNN failure 8: CUDNN_STATUS_EXECUTION_FAILED' in training ONNX's pretrained model Emotion FerPlus

Asked 2020-Oct-29 at 20:03

I am testing to train Emotion FerPlus emotion recognition model. Training has cuDNN failure 8: CUDNN_STATUS_EXECUTION_FAILED error. I am using Nvidia GPU TitanRTX 24G. Then change the minibatch_size from 32 to 1. But still have error. I am using CNTK-GPU docker. The complete error messages are

...

ANSWER

Answered 2020-Oct-29 at 20:03

CNTK is in maintenance mode now (basically deprecated). While CNTK can export to ONNX pretty OK, importing ONNX models is not really well-supported.

ONNX Runtime https://github.com/microsoft/onnxruntime now supports training, so please try it. ONNX Runtime training is actively developing and is supported, so if something doesn't quite work, it's likely the issues will be resolved fast.

Source https://stackoverflow.com/questions/64592316

QUESTION

PySpark ALSModel load fails in deployment over Azure ML service with error java.util.NoSuchElementException: Param blockSize does not exist

Asked 2020-Aug-03 at 18:16

I am trying to deploy an ALS model trained using PySpark on Azure ML service. I am providing a score.py file that loads the trained model using ALSModel.load() function. Following is the code of my score.py file.

...

ANSWER

Answered 2020-Aug-03 at 18:16

A couple of things to check:

Is your model registered in the workspace? AZUREML_MODEL_DIR only works for registered models. See this link for information about registering a model
Are you specifying the same version of pyspark.ml.recommendation in your InferenceConfig as you use locally? This kind of error might be due to a difference in versions
Have you looked at the output of print(service.get_logs())? Check out our troubleshoot and debugging documentation here for other things you can try

Source https://stackoverflow.com/questions/63204081

QUESTION

Python - TensorFlow and Keras

Asked 2020-Jun-08 at 01:05

I've been searching all about What is Tensorflow and Keras. They say that Tensorflow runs on top of Keras which means that Tensorflow is the "BACKEND" of KERAS. (Although you can use others like Theano and CNTK)

Does the "BACKEND" term here mean it does the whole mathematical process thing behind a deep learning model ? What I mean is like Tensorflow is the one who does the complicated stuffs like processing the matrices (tensor) , doing all the math stuff?

On the other hand KERAS is the guy that is needed ONLY for us to create a MODEL, right? and ONCE the model is created, its "BACKEND" is Tensorflow, right? cause for the MODEL TO WORK AS IT SHOULD BE (like process matrices and do all the math stuffs) it needs a "BACKEND" which is Tensorflow.

That is what I understand based on some open forums and the Keras documentation:

Keras is a model-level library, providing high-level building blocks for developing deep learning models. It does not handle itself low-level operations such as tensor products, convolutions and so on. Instead, it relies on a specialized, well-optimized tensor manipulation library to do so, serving as the “backend engine” of Keras.

Is my understanding correct? Please enlighten me if you have other answers.

...

ANSWER

Answered 2020-Jun-08 at 01:05

When we say that Tensorflow is the backend of Keras, we mean that Keras do not do calculations by itself. Keras just provide easy to use functions that use more complex Tensorflow code. It is then a little less powerful but it is sufficient in most cases. And when it is not, you can add Tensorflow code in your Keras Code as they both use The same Tensorflow objects in background.

Keras is now the official Tensorflow High level API, it is then a part of Tensorflow.

Source https://stackoverflow.com/questions/62148112

QUESTION

Are there syntax differences between using Keras with a Tensorflow 2, Theano, or CNTK backend?

Asked 2020-Apr-02 at 05:26

It looks like tf.keras is suggested if you're using a Tensorflow 2 backend, but what about using Theano or CNTK as a backend? I have never used Keras or any DL library.

...

ANSWER

Answered 2020-Apr-02 at 05:26

Keras has officially decided to drop support of CNTK and Theano moving forward. Therefore, if you are using keras with tensorflow as the backend, you should use tf.keras.

For older versions for keras, you can use all three backend with no syntax change in your keras code.

Keras 2.2.5 was the last release of Keras implementing the 2.2.* API. It was the last release to only support TensorFlow 1 (as well as Theano and CNTK).

The current release is Keras 2.3.0, which makes significant API changes and add support for TensorFlow 2.0. The 2.3.0 release will be the last major release of multi-backend Keras. Multi-backend Keras is superseded by tf.keras.

You can find the above information here.

Source https://stackoverflow.com/questions/60983054

QUESTION

Import error even though file is clearly in path

Asked 2020-Mar-06 at 20:58

I am running a script and I am getting the error:

...

ANSWER

Answered 2020-Mar-06 at 19:45

Just remove 'common' while importing because you are already in 'common' directory

Source https://stackoverflow.com/questions/60570009

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install CNTK

Setup CNTK Windows (Python-only / Script-driven / Manual) Linux (Python-only / Script-driven / Manual / Docker)
CNTK backend for Keras
Setup CNTK development environment Windows (Script-driven / Manual) Linux (Manual)

Support

To setup build and runtime environment on Windows:. To setup build and runtime environment on Linux using docker, please build Unbuntu 16.04 docker image using Dockerfiles here. For other Linux systems, please refer to the Dockerfiles to setup dependent libraries for CNTK.

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