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Preparing Release 2.5 - update documentation

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Thiago Crepaldi 2018-03-14 22:30:53 -07:00
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README.md
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@ -6,6 +6,66 @@
## Latest news
***2018-03-15.*** CNTK 2.5
## Change profiler details output format to be chrome://tracing
## Enable per-node timing. Working example [here](../Examples/Image/Classification/MLP/Python/SimpleMNIST.py)
- 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()
```
## 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 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](../Examples/Image/GAN/Basic_GAN_Distributed.py) and the [cntk.learners.distributed_multi_learner_test.py](../bindings/python/cntk/learners/tests/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.
@ -129,90 +189,6 @@ You can also try one of the below NuGet package.
* [CNTK, UWP CPU-Only Build](http://www.nuget.org/packages/CNTK.UWP.CPUOnly/2.3.1)
* [CNTK CPU-only Model Evaluation Libraries (MKL based)](http://www.nuget.org/packages/Microsoft.Research.CNTK.CpuEval-mkl/2.3.1)
***2017-11-22.* CNTK 2.3**
Release of Cognitive Toolkit v.2.3.
Highlights:
* Better ONNX support.
* Switched to NCCL2 for better performance in distributed training.
* Improved C# API.
* OpenCV is not required to install CNTK, it is only required for Tensorboard Image feature and image reader.
* Various performance improvement.
* Added Network Optimization API.
* Faster Adadelta for sparse.
See more in the [Release Notes](https://docs.microsoft.com/en-us/cognitive-toolkit/ReleaseNotes/CNTK_2_3_Release_Notes).
Get the Release from the [CNTK Releases page](https://github.com/Microsoft/CNTK/releases).
***2017-11-10.*** Switch from CNTKCustomMKL to Intel MKLML. MKLML is released with [Intel MKL-DNN](https://github.com/01org/mkl-dnn/releases) as a trimmed version of Intel MKL for MKL-DNN. To set it up:
On Linux:
sudo mkdir /usr/local/mklml
sudo wget https://github.com/01org/mkl-dnn/releases/download/v0.11/mklml_lnx_2018.0.1.20171007.tgz
sudo tar -xzf mklml_lnx_2018.0.1.20171007.tgz -C /usr/local/mklml
On Windows:
Create a directory on your machine to hold MKLML, e.g. mkdir c:\local\mklml
Download the file [mklml_win_2018.0.1.20171007.zip](https://github.com/01org/mkl-dnn/releases/download/v0.11/mklml_win_2018.0.1.20171007.zip).
Unzip it into your MKLML path, creating a versioned sub directory within.
Set the environment variable `MKLML_PATH` to the versioned sub directory, e.g. setx MKLML_PATH c:\local\mklml\mklml_win_2018.0.1.20171007
***2017-10-10.*** Preview: CNTK ONNX Format Support
Update CNTK to support load and save ONNX format from https://github.com/onnx/onnx, please try it and provide feedback. We only support ONNX OPs. This is a preview, and we expect a breaking change in the future.
* Support loading a model saved in ONNX format.
* Support saving a model in ONNX format, not all CNTK models are currently supported. Only a subset of CNTK models are supported and no RNN. We will add more in the future.
To load an ONNX model, simply specify the format parameter for the load function.
```
import cntk as C
C.Function.load(<path of your ONNX model>, format=C.ModelFormat.ONNX)
```
To save a CNTK graph as ONNX model, simply specify the format in the save function.
```
import cntk as C
x = C.input_variable(<input shape>)
z = create_model(x)
z.save(<path of where to save your ONNX model>, format=C.ModelFormat.ONNX)
```
If you want to try ONNX, you can build from master or `pip install` one of the below wheel that matches you Python environment.
For Windows CPU-Only:
* Python 2.7: https://cntk.ai/PythonWheel/CPU-Only/cntk-2.3-Pre-cp27-cp27m-win_amd64.whl
* Python 3.4: https://cntk.ai/PythonWheel/CPU-Only/cntk-2.3-Pre-cp34-cp34m-win_amd64.whl
* Python 3.5: https://cntk.ai/PythonWheel/CPU-Only/cntk-2.3-Pre-cp35-cp35m-win_amd64.whl
* Python 3.6: https://cntk.ai/PythonWheel/CPU-Only/cntk-2.3-Pre-cp36-cp36m-win_amd64.whl
For Windows GPU:
* Python 2.7: https://cntk.ai/PythonWheel/GPU/cntk-2.3-Pre-cp27-cp27m-win_amd64.whl
* Python 3.4: https://cntk.ai/PythonWheel/GPU/cntk-2.3-Pre-cp34-cp34m-win_amd64.whl
* Python 3.5: https://cntk.ai/PythonWheel/GPU/cntk-2.3-Pre-cp35-cp35m-win_amd64.whl
* Python 3.6: https://cntk.ai/PythonWheel/GPU/cntk-2.3-Pre-cp36-cp36m-win_amd64.whl
Linux CPU-Only:
* Python 2.7: https://cntk.ai/PythonWheel/CPU-Only/cntk-2.3-Pre-cp27-cp27mu-linux_x86_64.whl
* Python 3.4: https://cntk.ai/PythonWheel/CPU-Only/cntk-2.3-Pre-cp34-cp34m-linux_x86_64.whl
* Python 3.5: https://cntk.ai/PythonWheel/CPU-Only/cntk-2.3-Pre-cp35-cp35m-linux_x86_64.whl
* Python 3.6: https://cntk.ai/PythonWheel/CPU-Only/cntk-2.3-Pre-cp36-cp36m-linux_x86_64.whl
Linux GPU:
* Python 2.7: https://cntk.ai/PythonWheel/GPU/cntk-2.3-Pre-cp27-cp27mu-linux_x86_64.whl
* Python 3.4: https://cntk.ai/PythonWheel/GPU/cntk-2.3-Pre-cp34-cp34m-linux_x86_64.whl
* Python 3.5: https://cntk.ai/PythonWheel/GPU/cntk-2.3-Pre-cp35-cp35m-linux_x86_64.whl
* Python 3.6: https://cntk.ai/PythonWheel/GPU/cntk-2.3-Pre-cp36-cp36m-linux_x86_64.whl
See more in the [Release Notes](https://docs.microsoft.com/en-us/cognitive-toolkit/ReleaseNotes/CNTK_2_2_Release_Notes).  
Get the Release from the [CNTK Releases page](https://github.com/Microsoft/CNTK/releases).
See [all news](https://docs.microsoft.com/en-us/cognitive-toolkit/news)
## Introduction