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|evaluator|function> executions
<trainer|evaluator|function>.print_node_timing()
C.debugging.stop_profiler()
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 
Distributed Training with Mulitple 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.
2018-01-22. CNTK support for CUDA 9
CNTK now supports CUDA 9/cuDNN 7. This requires an update to build environment to Ubuntu 16/GCC 5 for Linux, and Visual Studio 2017/VCTools 14.11 for Windows. With CUDA 9, CNTK also added a preview for 16-bit floating point (a.k.a FP16) computation.
Please check out the example of FP16 in ResNet50 here
Notes on FP16 preview:
FP16 implementation on CPU is not optimized, and it's not supposed to be used in CPU inference directly. User needs to convert the model to 32-bit floating point before running on CPU.
Loss/Criterion for FP16 training needs to be 32bit for accumulation without overflow, using cast function. Please check the example above.
Readers do not have FP16 output unless using numpy to feed data, cast from FP32 to FP16 is needed. Please check the example above.
FP16 gradient aggregation is currently only implemented on GPU using NCCL2. Distributed training with FP16 with MPI is not supported.
FP16 math is a subset of current FP32 implementation. Some model may get Feature Not Implemented exception using FP16.
FP16 is currently not supported in BrainScript. Please use Python for FP16.
To setup build and runtime environment on Windows:
Install Visual Studio 2017 with following workloads and components. From command line (use Community version installer as example): vs_community.exe --add Microsoft.VisualStudio.Workload.NativeDesktop --add Microsoft.VisualStudio.Workload.ManagedDesktop --add Microsoft.VisualStudio.Workload.Universal --add Microsoft.Component.PythonTools --add Microsoft.VisualStudio.Component.VC.Tools.14.11
Install NVidia CUDA 9
From PowerShell, run: DevInstall.ps1
Start VCTools 14.11 command line, run: cmd /k "%VS2017INSTALLDIR%\VC\Auxiliary\Build\vcvarsall.bat" x64 --vcvars_ver=14.11
Open CNTK.sln from the VCTools 14.11 command line. Note that starting CNTK.sln other than VCTools 14.11 command line, would causes CUDA 9 build error.
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.
2017-12-05. CNTK 2.3.1 Release of Cognitive Toolkit v.2.3.1.
CNTK support for ONNX format is now out of preview mode.
If you want to try ONNX, you can build from master or pip install one of the below wheels that matches your Python environment.
For Windows CPU-Only:
Python 2.7: https://cntk.ai/PythonWheel/CPU-Only/cntk-2.3.1-cp27-cp27m-win_amd64.whl
Python 3.4: https://cntk.ai/PythonWheel/CPU-Only/cntk-2.3.1-cp34-cp34m-win_amd64.whl
Python 3.5: https://cntk.ai/PythonWheel/CPU-Only/cntk-2.3.1-cp35-cp35m-win_amd64.whl
Python 3.6: https://cntk.ai/PythonWheel/CPU-Only/cntk-2.3.1-cp36-cp36m-win_amd64.whl
For Windows GPU:
Python 2.7: https://cntk.ai/PythonWheel/GPU/cntk-2.3.1-cp27-cp27m-win_amd64.whl
Python 3.4: https://cntk.ai/PythonWheel/GPU/cntk-2.3.1-cp34-cp34m-win_amd64.whl
Python 3.5: https://cntk.ai/PythonWheel/GPU/cntk-2.3.1-cp35-cp35m-win_amd64.whl
Python 3.6: https://cntk.ai/PythonWheel/GPU/cntk-2.3.1-cp36-cp36m-win_amd64.whl
Linux CPU-Only:
Python 2.7: https://cntk.ai/PythonWheel/CPU-Only/cntk-2.3.1-cp27-cp27mu-linux_x86_64.whl
Python 3.4: https://cntk.ai/PythonWheel/CPU-Only/cntk-2.3.1-cp34-cp34m-linux_x86_64.whl
Python 3.5: https://cntk.ai/PythonWheel/CPU-Only/cntk-2.3.1-cp35-cp35m-linux_x86_64.whl
Python 3.6: https://cntk.ai/PythonWheel/CPU-Only/cntk-2.3.1-cp36-cp36m-linux_x86_64.whl
Linux GPU:
Python 2.7: https://cntk.ai/PythonWheel/GPU/cntk-2.3.1-cp27-cp27mu-linux_x86_64.whl
Python 3.4: https://cntk.ai/PythonWheel/GPU/cntk-2.3.1-cp34-cp34m-linux_x86_64.whl
Python 3.5: https://cntk.ai/PythonWheel/GPU/cntk-2.3.1-cp35-cp35m-linux_x86_64.whl
Python 3.6: https://cntk.ai/PythonWheel/GPU/cntk-2.3.1-cp36-cp36m-linux_x86_64.whl
You can also try one of the below NuGet package.
CNTK, CPU-Only Build
CNTK, GPU Build
CNTK, UWP CPU-Only Build
CNTK CPU-only Model Evaluation Libraries (MKL based)
See all news
Introduction
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 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
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
Nightly packages
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
Learning CNTK
You may learn more about CNTK with the following resources:
General documentation
Python API documentation
BrainScript documentation
Evaluation documentation (C++, C#/.NET, Python, Java)
Manual
Tutorials
Examples
Pretrained models
Blog
Presentations
License
More information
Contribute to CNTK
FAQ
Feedback
Disclaimer
CNTK is in active use at Microsoft and constantly evolving. There will be bugs.
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.
