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CNTK

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microsoft /CNTK

Microsoft Cognitive Toolkit (CNTK), an open source deep-learning toolkit

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CNTK

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The Microsoft Cognitive Toolkit (https://cntk.ai) 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.

Installation

Installing nightly packages

If you prefer to use latest CNTK bits from master, use one of the CNTK nightly packages:

Learning CNTK

You can learn more about using and contributing to CNTK with the following resources:

More information

Disclaimer

Dear community,

With our ongoing contributions to ONNX and the ONNX Runtime, we have made it easier to interoperate within the AI framework ecosystem and to access high performance, cross-platform inferencing capabilities for both traditional ML models and deep neural networks. Over the last few years we have been privileged to develop such key open-source machine learning projects, including the Microsoft Cognitive Toolkit, which has enabled its users to leverage industry-wide advancements in deep learning at scale.

Today’s 2.7 release will be the last main release of CNTK. We may have some subsequent minor releases for bug fixes, but these will be evaluated on a case-by-case basis. There are no plans for new feature development post this release.

The CNTK 2.7 release has full support for ONNX 1.4.1, and we encourage those seeking to operationalize their CNTK models to take advantage of ONNX and the ONNX Runtime. Moving forward, users can continue to leverage evolving ONNX innovations via the number of frameworks that support it. For example, users can natively export ONNX models from PyTorch or convert TensorFlow models to ONNX with the TensorFlow-ONNX converter.

We are incredibly grateful for all the support we have received from contributors and users over the years since the initial open-source release of CNTK. CNTK has enabled both Microsoft teams and external users to execute complex and large-scale workloads in all manner of deep learning applications, such as historical breakthroughs in speech recognition achieved by Microsoft Speech researchers, the originators of the framework.

As ONNX is increasingly employed in serving models used across Microsoft products such as Bing and Office, we are dedicated to synthesizing innovations from research with the rigorous demands of production to progress the ecosystem forward.

Above all, our goal is to make innovations in deep learning across the software and hardware stacks as open and accessible as possible. We will be working hard to bring both the existing strengths of CNTK and new state-of-the-art research into other open-source projects to truly broaden the reach of such technologies.

With gratitude,

-- The CNTK Team

Microsoft Open Source Code of Conduct

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact [email protected] with any additional questions or comments.

News

You can find more news on the official project feed

2019-03-29. CNTK 2.7.0

Highlights of this release

  • Moved to CUDA 10 for both Windows and Linux.
  • Support advance RNN loop in ONNX export.
  • Export larger than 2GB models in ONNX format.
  • Support FP16 in Brain Script train action.

CNTK support for CUDA 10

CNTK now supports CUDA 10. This requires an update to build environment to Visual Studio 2017 v15.9 for Windows.

To setup build and runtime environment on Windows: * Install Visual Studio 2017. Note: going forward for CUDA 10 and beyond, it is no longer required to install and run with the specific VC Tools version 14.11. * Install Nvidia CUDA 10* From PowerShell, run: DevInstall.ps1* Start Visual Studio 2017 and open CNTK.sln.

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.

Support advance RNN loop in ONNX export

CNTK models with recursive loops can be exported to ONNX models with scan ops.

Export larger than 2GB models in ONNX format

To export models larger than 2GB in ONNX format, use cntk.Function API: save(self, filename, format=ModelFormat.CNTKv2, use_external_files_to_store_parameters=False) with 'format' set to ModelFormat.ONNX and use_external_files_to_store_parameters set to True. In this case, model parameters are saved in external files. Exported models shall be used with external parameter files when doing model evaluation with onnxruntime.

2018-11-26.
Netron now supports visualizing CNTK v1 and CNTK v2

.model

files.

NetronCNTKDark1 NetronCNTKLight1

Project changelog

2018-09-17. CNTK 2.6.0

Efficient group convolution

The implementation of group convolution in CNTK has been updated. The updated implementation moves away from creating a sub-graph for group convolution (using slicing and splicing), and instead uses cuDNN7 and MKL2017 APIs directly. This improves the experience both in terms of performance and model size.

As an example, for a single group convolution op with the following attributes:

  • Input tensor (C, H, W) = (32, 128, 128)
  • Number of output channels = 32 (channel multiplier is 1)
  • Groups = 32 (depth wise convolution)
  • Kernel size = (5, 5)

The comparison numbers for this single node are as follows:

| First Header | GPU exec. time (in millisec., 1000 run avg.) | CPU exec. time (in millisec., 1000 run avg.) | Model Size (in KB, CNTK format) | ------------- | ------------- | ------------- | ------------- | | Old implementation | 9.349 | 41.921 | 38 | | New implementation | 6.581 | 9.963 | 5 | | Speedup/savings Approx. | 30% Approx. | 65-75% Approx. | 87% |

Sequential Convolution

The implementation of sequential convolution in CNTK has been updated. The updated implementation creates a separate sequential convolution layer. Different from regular convolution layer, this operation convolves also on the dynamic axis(sequence), and filter_shape[0] is applied to that axis. The updated implementation supports broader cases, such as where stride > 1 for the sequence axis.

For example, a sequential convolution over a batch of one-channel black-and-white images. The images have the same fixed height of 640, but each with width of variable lengths. The width is then represented by sequential axis. Padding is enabled, and strides for both width and height are 2.

\>\>\> f = SequentialConvolution((3,3), reduction\_rank=0, pad=True, strides=(2,2), activation=C.relu) \>\>\> x = C.input\_variable(\*\*Sequence[Tensor[640]]) \>\>\> x.shape (640,) \>\>\> h = f(x) \>\>\> h.shape (320,) \>\>\> f.W.shape (1, 1, 3, 3)

Operators

depth_to_space and space_to_depth

There is a breaking change in the depth_to_space and space_to_depth operators. These have been updated to match ONNX specification, specifically the permutation for how the depth dimension is placed as blocks in the spatial dimensions, and vice-versa, has been changed. Please refer to the updated doc examples for these two ops to see the change.

Tan and Atan

Added support for trigonometric ops

Tan

and

Atan

.

ELU

Added support for

alpha

attribute in ELU op.

Convolution

Updated auto padding algorithms of

Convolution

to produce symmetric padding at best effort on CPU, without affecting the final convolution output values. This update increases the range of cases that could be covered by MKL API and improves the performance, E.g. ResNet50.

Default arguments order

There is a breaking change in the arguments property in CNTK python API. The default behavior has been updated to return arguments in python order instead of in C++ order. This way it will return arguments in the same order as they are fed into ops. If you wish to still get arguments in C++ order, you can simply override the global option. This change should only affect the following ops: Times, TransposeTimes, and Gemm(internal).

Bug fixes

  • Updated doc for Convolution layer to include group and dilation arguments.
  • Added improved input validation for group convolution.
  • Updated
    LogSoftMax
    to use more numerically stable implementation.
  • Fixed Gather op's incorrect gradient value.
  • Added validation for 'None' node in python clone substitution.
  • Added validation for padding channel axis in convolution.
  • Added CNTK native default lotusIR logger to fix the "Attempt to use DefaultLogger" error when loading some ONNX models.
  • Added proper initialization for ONNX TypeStrToProtoMap.
  • Updated python doctest to handle different print format for newer version numpy(version >= 1.14).
  • Fixed Pooling(CPU) to produce correct output values when kernel center is on padded input cells.

ONNX

Updates

  • Updated CNTK's ONNX import/export to use ONNX 1.2 spec.
  • Major update to how batch and sequence axes are handled in export and import. As a result, the complex scenarios and edge cases are handled accurately.
  • Updated CNTK's ONNX
    BatchNormalization
    op export/import to latest spec.
  • Added model domain to ONNX model export.
  • Improved error reporting during import and export of ONNX models.
  • Updated
    DepthToSpace
    and
    SpaceToDepth
    ops to match ONNX spec on the permutation for how the depth dimension is placed as block dimension.
  • Added support for exporting
    alpha
    attribute in
    ELU
    ONNX op.
  • Major overhaul to
    Convolution
    and
    Pooling
    export. Unlike before, these ops do not export an explicit
    Pad
    op in any situation.
  • Major overhaul to
    ConvolutionTranspose
    export and import. Attributes such as
    output\_shape
    ,
    output\_padding
    , and
    pads
    are fully supported.
  • Added support for CNTK's
    StopGradient
    as a no-op.
  • Added ONNX support for TopK op.
  • Added ONNX support for sequence ops: sequence.slice, sequence.first, sequence.last, sequence.reduce_sum, sequence.reduce_max, sequence.softmax. For these ops, there is no need to expand ONNX spec. CNTK ONNX exporter just builds computation equivalent graphs for these sequence ops.
  • Added full support for Softmax op.
  • Made CNTK broadcast ops compatible with ONNX specification.
  • Handle to_batch, to_sequence, unpack_batch, sequence.unpack ops in CNTK ONNX exporter.
  • ONNX tests to export ONNX test cases for other toolkits to run and to validate.
  • Fixed
    Hardmax
    /
    Softmax
    /
    LogSoftmax
    import/export.
  • Added support for
    Select
    op export.
  • Added import/export support for several trigonometric ops.
  • Updated CNTK support for ONNX
    MatMul
    op.
  • Updated CNTK support for ONNX
    Gemm
    op.
  • Updated CNTK's ONNX
    MeanVarianceNormalization
    op export/import to latest spec.
  • Updated CNTK's ONNX
    LayerNormalization
    op export/import to latest spec.
  • Updated CNTK's ONNX
    PRelu
    op export/import to latest spec.
  • Updated CNTK's ONNX
    Gather
    op export/import to latest spec.
  • Updated CNTK's ONNX
    ImageScaler
    op export/import to latest spec.
  • Updated CNTK's ONNX
    Reduce
    ops export/import to latest spec.
  • Updated CNTK's ONNX
    Flatten
    op export/import to latest spec.
  • Added CNTK support for ONNX
    Unsqueeze
    op.

Bug or minor fixes:

  • Updated LRN op to match ONNX 1.2 spec where the
    size
    attribute has the semantics of diameter, not radius. Added validation if LRN kernel size is larger than channel size.
  • Updated
    Min
    /
    Max
    import implementation to handle variadic inputs.
  • Fixed possible file corruption when resaving on top of existing ONNX model file.

.Net Support

The Cntk.Core.Managed library has officially been converted to .Net Standard and supports .Net Core and .Net Framework applications on both Windows and Linux. Starting from this release, .Net developers should be able to restore CNTK Nuget packages using new .Net SDK style project file with package management format set to PackageReference.

The following C# code now works on both Windows and Linux:

\>\>\> var weightParameterName = "weight"; \>\>\> var biasParameterName = "bias"; \>\>\> var inputName = "input"; \>\>\> var outputDim = 2; \>\>\> var inputDim = 3; \>\>\> Variable inputVariable = Variable.InputVariable(new int[] { inputDim }, DataType.Float, inputName); \>\>\> var weightParameter = new Parameter(new int[] { outputDim, inputDim }, DataType.Float, 1, device, weightParameterName); \>\>\> var biasParameter = new Parameter(new int[] { outputDim }, DataType.Float, 0, device, biasParameterName); \>\>\> \>\>\> Function modelFunc = CNTKLib.Times(weightParameter, inputVariable) + biasParameter;

For example, simply adding an ItemGroup clause in the .csproj file of a .Net Core application is sufficient: >>> >>> >>> >>> netcoreapp2.1 >>> x64 >>> >>> >>> >>> >>> >>> >>>

Bug or minor fixes:

  • Fixed C# string and char to native wstring and wchar UTF conversion issues on Linux.
  • Fixed multibyte and wide character conversions across the codebase.
  • Fixed Nuget package mechanism to pack for .Net Standard.
  • Fixed a memory leak issue in Value class in C# API where Dispose was not called upon object destruction.

Misc

2018-04-16. CNTK 2.5.1

Repack CNTK 2.5 with third party libraries included in the bundles (Python wheel packages)


2018-03-15. CNTK 2.5

Change profiler details output format to be

chrome://tracing

Enable per-node timing. Working example here* per-node timing creates items in profiler details when profiler is enabled. * usage in Python:

import cntk as C C.debugging.debug.set\_node\_timing(True) C.debugging.start\_profiler() # optional C.debugging.enable\_profiler() # optional #<trainer> executions
<trainer>.print_node_timing()
C.debugging.stop_profiler()
</trainer></trainer>

Example profiler details view in

chrome://tracing

ProfilerDetailWithNodeTiming

CPU inference performance improvements using MKL * Accelerates some common tensor ops in Intel CPU inference for float32, especially for fully connected networks * Can be turned on/off by

cntk.cntk\_py.enable\_cpueval\_optimization()/cntk.cntk\_py.disable\_cpueval\_optimization()

1BitSGD incorporated into CNTK *

1BitSGD

source code is now available with CNTK license (MIT license) under

Source/1BitSGD/

*

1bitsgd

build target was merged into existing gpu target

New loss function: hierarchical softmax * Thanks @yaochengji for the contribution!

Distributed Training with Multiple Learners * Trainer now accepts multiple parameter learners for distributed training. With this change, different parameters of a network can be learned by different learners in a single training session. This also facilitates distributed training for GANs. For more information, please refer to the Basic_GAN_Distributed.py and the cntk.learners.distributed_multi_learner_test.py

Operators * Added

MeanVarianceNormalization

operator.

Bug fixes * Fixed convergence issue in Tutorial 201B * Fixed pooling/unpooling to support free dimension for sequences * Fixed crash in

CNTKBinaryFormat

deserializer when crossing sweep boundary * Fixed shape inference bug in RNN step function for scalar broadcasting * Fixed a build bug when

mpi=no

* Improved distributed training aggregation speed by increasing packing threshold, and expose the knob in V2 * Fixed a memory leak in MKL layout * Fixed a bug in

cntk.convert

API in

misc.converter.py

, which prevents converting complex networks.

ONNX * Updates * CNTK exported ONNX models are now

ONNX.checker

compliant. * Added ONNX support for CNTK’s

OptimizedRNNStack

operator (LSTM only). * Added support for LSTM and GRU operators * Added support for experimental ONNX op

MeanVarianceNormalization

. * Added support for experimental ONNX op

Identity

. * Added support for exporting CNTK’s

LayerNormalization

layer using ONNX

MeanVarianceNormalization

op. * Bug or minor fixes: * Axis attribute is optional in CNTK’s ONNX

Concat

operator. * Bug fix in ONNX broadcasting for scalars. * Bug fix in ONNX ConvTranspose operator. * Backward compatibility bug fix in

LeakyReLu

(argument ‘alpha’ reverted to type double).

Misc * Added a new API

find\_by\_uid()

under

cntk.logging.graph

.


2018-02-28. CNTK supports nightly build

If you prefer to use latest CNTK bits from master, use one of the CNTK nightly package. * Nightly packages for Windows* Nightly packages for Linux

Alternatively, you can also click corresponding build badge to land to nightly build page.


2018-01-31. CNTK 2.4

Highlights: * Moved to CUDA9, cuDNN 7 and Visual Studio 2017. * Removed Python 3.4 support. * Added Volta GPU and FP16 support. * Better ONNX support. * CPU perf improvement. * More OPs.

OPs *

top\_k

operation: in the forward pass it computes the top (largest) k values and corresponding indices along the specified axis. In the backward pass the gradient is scattered to the top k elements (an element not in the top k gets a zero gradient). *

gather

operation now supports an axis argument *

squeeze

and

expand\_dims

operations for easily removing and adding singleton axes *

zeros\_like

and

ones\_like

operations. In many situations you can just rely on CNTK correctly broadcasting a simple 0 or 1 but sometimes you need the actual tensor. *

depth\_to\_space

: Rearranges elements in the input tensor from the depth dimension into spatial blocks. Typical use of this operation is for implementing sub-pixel convolution for some image super-resolution models. *

space\_to\_depth

: Rearranges elements in the input tensor from the spatial dimensions to the depth dimension. It is largely the inverse of DepthToSpace. *

sum

operation: Create a new Function instance that computes element-wise sum of input tensors. *

softsign

operation: Create a new Function instance that computes the element-wise softsign of a input tensor. *

asinh

operation: Create a new Function instance that computes the element-wise asinh of a input tensor. *

log\_softmax

operation: Create a new Function instance that computes the logsoftmax normalized values of a input tensor. *

hard\_sigmoid

operation: Create a new Function instance that computes the hard_sigmoid normalized values of a input tensor. * `element_and

,

element_not

,

element_or

,

element_xor

element-wise logic operations \*

reduce_l1

operation: Computes the L1 norm of the input tensor's element along the provided axes. \*

reduce_l2

operation: Computes the L2 norm of the input tensor's element along the provided axes. \*

reduce_sum_square

operation: Computes the sum square of the input tensor's element along the provided axes. \*

image_scaler` operation: Alteration of image by scaling its individual values.

ONNX * There have been several improvements to ONNX support in CNTK. * Updates * Updated ONNX

Reshape

op to handle

InferredDimension

. * Adding

producer\_name

and

producer\_version

fields to ONNX models. * Handling the case when neither

auto\_pad

nor

pads

atrribute is specified in ONNX

Conv

op. * Bug fixes * Fixed bug in ONNX

Pooling

op serialization * Bug fix to create ONNX

InputVariable

with only one batch axis. * Bug fixes and updates to implementation of ONNX

Transpose

op to match updated spec. * Bug fixes and updates to implementation of ONNX

Conv

,

ConvTranspose

, and

Pooling

ops to match updated spec.

Operators * Group convolution * Fixed bug in group convolution. Output of CNTK

Convolution

op will change for groups > 1. More optimized implementation of group convolution is expected in the next release. * Better error reporting for group convolution in

Convolution

layer.

Halide Binary Convolution - The CNTK build can now use optional Halide libraries to build

Cntk.BinaryConvolution.so/dll

library that can be used with the

netopt

module. The library contains optimized binary convolution operators that perform better than the python based binarized convolution operators. To enable Halide in the build, please download Halide release and set

HALIDE\_PATH

environment varibale before starting a build. In Linux, you can use

./configure --with-halide[=directory]

to enable it. For more information on how to use this feature, please refer to How_to_use_network_optimization.

See more in the Release Notes. Get the Release from the CNTK Releases page.

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