Bump version
This commit is contained in:
Mark Hillebrand
Родитель
1a197f596f
Коммит
80f70b638d
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@ -46,7 +46,7 @@ CNTK_EXAMPLES_PATH="$PWD/Examples"
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CNTK_TUTORIALS_PATH="$PWD/Tutorials"
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CNTK_BINARY="$CNTK_BIN_PATH/cntk"
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CNTK_PY_ENV_FILE="$SCRIPT_DIR/conda-linux-cntk-py$PY_VERSION-environment.yml"
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CNTK_WHEEL_PATH="cntk/python/cntk-2.0.beta5.0-cp$PY_VERSION-cp${PY_VERSION}m-linux_x86_64.whl"
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CNTK_WHEEL_PATH="cntk/python/cntk-2.0.beta6.0-cp$PY_VERSION-cp${PY_VERSION}m-linux_x86_64.whl"
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test -d "$CNTK_BIN_PATH" && test -d "$CNTK_LIB_PATH" && test -d "$CNTK_DEP_LIB_PATH" &&
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test -d "$CNTK_TUTORIALS_PATH" &&
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test -d "$CNTK_EXAMPLES_PATH" && test -x "$CNTK_BINARY" &&
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@ -645,7 +645,7 @@ int wmainWithBS(int argc, wchar_t* argv[]) // called from wmain which is a wrapp
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static void PrintBanner(int argc, wchar_t* argv[], const string& timestamp)
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{
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fprintf(stderr, "CNTK 2.0.beta5.0+ (");
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fprintf(stderr, "CNTK 2.0.beta6.0+ (");
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#ifdef _GIT_EXIST
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fprintf(stderr, "%s %.6s, ", _BUILDBRANCH_, _BUILDSHA1_);
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#endif
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@ -10,7 +10,7 @@
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"\n",
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"This tutorial is targeted to individuals who are new to CNTK and to machine learning. In this tutorial, you will train a simple yet powerful machine learning model that is widely used in industry for a variety of applications. The model trained below scales to massive data sets in the most expeditious manner by harnessing computational scalability leveraging the computational resources you may have (one or more CPU cores, one or more GPUs, a cluster of CPUs or a cluster of GPUs), transparently via the CNTK library.\n",
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"\n",
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"The following notebook users Python APIs. If you are looking for this example in BrainScript, please look [here](https://github.com/Microsoft/CNTK/tree/v2.0.beta5.0/Tutorials/HelloWorld-LogisticRegression). \n",
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"The following notebook users Python APIs. If you are looking for this example in BrainScript, please look [here](https://github.com/Microsoft/CNTK/tree/v2.0.beta6.0/Tutorials/HelloWorld-LogisticRegression). \n",
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"\n",
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"## Introduction\n",
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"\n",
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@ -768,7 +768,7 @@
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"\n",
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"If you want to try running the tutorial from python command prompt. Please run the [FeedForwardNet.py][] example.\n",
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"\n",
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"[FeedForwardNet.py]: https://github.com/Microsoft/CNTK/blob/v2.0.beta5.0/Tutorials/NumpyInterop/FeedForwardNet.py"
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"[FeedForwardNet.py]: https://github.com/Microsoft/CNTK/blob/v2.0.beta6.0/Tutorials/NumpyInterop/FeedForwardNet.py"
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]
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},
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{
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@ -12,7 +12,7 @@
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"\n",
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"CNTK 103 tutorial is divided into two parts:\n",
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"- Part A: Familiarize with the [MNIST][] database that will be used later in the tutorial\n",
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"- [Part B](https://github.com/Microsoft/CNTK/blob/v2.0.beta5.0/Tutorials/CNTK_103B_MNIST_FeedForwardNetwork.ipynb): We will use the feedforward classifier used in CNTK 102 to classify digits in MNIST data set.\n",
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"- [Part B](https://github.com/Microsoft/CNTK/blob/v2.0.beta6.0/Tutorials/CNTK_103B_MNIST_FeedForwardNetwork.ipynb): We will use the feedforward classifier used in CNTK 102 to classify digits in MNIST data set.\n",
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"\n",
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"[MNIST]: http://yann.lecun.com/exdb/mnist/\n",
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"\n"
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@ -12,7 +12,7 @@
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"\n",
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"We assume that you have successfully completed CNTK 103 Part A.\n",
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"\n",
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"In this tutorial we will train a fully connected network on MNIST data. This notebook provides the recipe using Python APIs. If you are looking for this example in BrainScript, please look [here](https://github.com/Microsoft/CNTK/tree/v2.0.beta5.0/Examples/Image/GettingStarted)\n",
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"In this tutorial we will train a fully connected network on MNIST data. This notebook provides the recipe using Python APIs. If you are looking for this example in BrainScript, please look [here](https://github.com/Microsoft/CNTK/tree/v2.0.beta6.0/Examples/Image/GettingStarted)\n",
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"\n",
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"## Introduction\n",
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"\n",
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@ -763,7 +763,7 @@
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"source": [
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"#### Code link\n",
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"\n",
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"If you want to try running the tutorial from python command prompt. Please run the [SimpleMNIST.py](https://github.com/Microsoft/CNTK/tree/v2.0.beta5.0/Examples/Image/Classification/MLP/Python) example."
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"If you want to try running the tutorial from python command prompt. Please run the [SimpleMNIST.py](https://github.com/Microsoft/CNTK/tree/v2.0.beta6.0/Examples/Image/Classification/MLP/Python) example."
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]
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},
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{
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@ -41,12 +41,12 @@
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"In this tutorial we are going to use a (lightly preprocessed) version of the ATIS dataset. You can download the data automatically by running the cells below or by executing the manual instructions.\n",
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"\n",
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"#### Fallback manual instructions\n",
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"Download the ATIS [training](https://github.com/Microsoft/CNTK/blob/v2.0.beta5.0/Tutorials/SLUHandsOn/atis.train.ctf) \n",
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"and [test](https://github.com/Microsoft/CNTK/blob/v2.0.beta5.0/Tutorials/SLUHandsOn/atis.test.ctf) \n",
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"Download the ATIS [training](https://github.com/Microsoft/CNTK/blob/v2.0.beta6.0/Tutorials/SLUHandsOn/atis.train.ctf) \n",
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"and [test](https://github.com/Microsoft/CNTK/blob/v2.0.beta6.0/Tutorials/SLUHandsOn/atis.test.ctf) \n",
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"files and put them at the same folder as this notebook. If you want to see how the model is \n",
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"predicting on new sentences you will also need the vocabulary files for \n",
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"[queries](https://github.com/Microsoft/CNTK/blob/v2.0.beta5.0/Examples/LanguageUnderstanding/ATIS/BrainScript/query.wl) and\n",
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"[slots](https://github.com/Microsoft/CNTK/blob/v2.0.beta5.0/Examples/LanguageUnderstanding/ATIS/BrainScript/slots.wl)"
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"[queries](https://github.com/Microsoft/CNTK/blob/v2.0.beta6.0/Examples/LanguageUnderstanding/ATIS/BrainScript/query.wl) and\n",
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"[slots](https://github.com/Microsoft/CNTK/blob/v2.0.beta6.0/Examples/LanguageUnderstanding/ATIS/BrainScript/slots.wl)"
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]
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},
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{
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@ -87,7 +87,7 @@
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" print(\"Reusing locally cached:\", item['file'])\n",
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" else:\n",
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" print(\"Starting download:\", item['file'])\n",
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" url = \"https://github.com/Microsoft/CNTK/blob/v2.0.beta5.0/%s/%s?raw=true\"%(location, item['file'])\n",
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" url = \"https://github.com/Microsoft/CNTK/blob/v2.0.beta6.0/%s/%s?raw=true\"%(location, item['file'])\n",
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" download(url, item['file'])\n",
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" print(\"Download completed\")\n"
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]
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@ -632,7 +632,7 @@
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"> Note: training with Batch Normalization is currently only supported on GPU.\n",
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"\n",
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"So your task will be to insert batch-normalization layers before and after the recurrent LSTM layer.\n",
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"If you have completed the [hands-on labs on image processing](https://github.com/Microsoft/CNTK/blob/v2.0.beta5.0/Tutorials/CNTK_201B_CIFAR-10_ImageHandsOn.ipynb),\n",
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"If you have completed the [hands-on labs on image processing](https://github.com/Microsoft/CNTK/blob/v2.0.beta6.0/Tutorials/CNTK_201B_CIFAR-10_ImageHandsOn.ipynb),\n",
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"you may remember that the [batch-normalization layer](https://www.cntk.ai/pythondocs/layerref.html#batchnormalization-layernormalization-stabilizer) has this form:\n",
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"```\n",
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" BatchNormalization()\n",
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@ -157,7 +157,7 @@
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" if os.path.exists(file):\n",
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" print(\"Reusing locally cached: \", file)\n",
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" else:\n",
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" url = \"https://github.com/Microsoft/CNTK/blob/v2.0.beta5.0/Examples/SequenceToSequence/CMUDict/Data/%s?raw=true\"%file\n",
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" url = \"https://github.com/Microsoft/CNTK/blob/v2.0.beta6.0/Examples/SequenceToSequence/CMUDict/Data/%s?raw=true\"%file\n",
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" print(\"Starting download:\", file)\n",
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" download(url, file)\n",
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" print(\"Download completed\")\n"
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@ -63,9 +63,9 @@ author = 'Microsoft'
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# built documents.
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#
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# The short X.Y version.
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version = '2.0.beta5.0'
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version = '2.0.beta6.0'
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# The full version, including alpha/beta/rc tags.
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release = '2.0.beta5.0'
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release = '2.0.beta6.0'
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# The language for content autogenerated by Sphinx. Refer to documentation
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# for a list of supported languages.
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@ -4,27 +4,30 @@ Examples
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The best way to learn about the APIs is to look at the
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following examples in the [CNTK clone root]/Examples directory:
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- `MNIST <https://github.com/Microsoft/CNTK/blob/v2.0.beta5.0/Examples/Image/Classification/MLP/Python/SimpleMNIST.py>`__:
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- `MNIST <https://github.com/Microsoft/CNTK/blob/v2.0.beta6.0/Examples/Image/Classification/MLP/Python/SimpleMNIST.py>`__:
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A fully connected feed-forward model for classification of MNIST
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images. (follow the instructions in
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Examples/Image/DataSets/MNIST/README.md)
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- `TrainResNet_CIFAR10 <https://github.com/Microsoft/CNTK/blob/v2.0.beta5.0/Examples/Image/Classification/ResNet/Python/TrainResNet_CIFAR10.py>`__:
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- `TrainResNet_CIFAR10 <https://github.com/Microsoft/CNTK/blob/v2.0.beta6.0/Examples/Image/Classification/ResNet/Python/TrainResNet_CIFAR10.py>`__:
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An image classification ResNet model for training on the CIFAR image
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dataset. (follow the instructions in
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Examples/Image/DataSets/CIFAR-10/README.md to get the CIFAR dataset
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and convert it to the CNTK supported format)
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- `SequenceClassification <https://github.com/Microsoft/CNTK/blob/v2.0.beta5.0/Examples/SequenceClassification/SimpleExample/Python/SequenceClassification.py>`__:
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- `SequenceClassification <https://github.com/Microsoft/CNTK/blob/v2.0.beta6.0/Examples/SequenceClassification/SimpleExample/Python/SequenceClassification.py>`__:
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An LSTM sequence classification model for text data.
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- `Sequence2Sequence <https://github.com/Microsoft/CNTK/blob/v2.0.beta5.0/Examples/SequenceToSequence/CMUDict/Python/Sequence2Sequence.py>`__:
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- `Sequence2Sequence <https://github.com/Microsoft/CNTK/blob/v2.0.beta6.0/Examples/SequenceToSequence/CMUDict/Python/Sequence2Sequence.py>`__:
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A sequence to sequence grapheme to phoneme translation model that
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trains on the CMUDict corpus.
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- `NumpyInterop <https://github.com/Microsoft/CNTK/blob/v2.0.beta5.0/Tutorials/NumpyInterop/FeedForwardNet.py>`__
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- `NumpyInterop <https://github.com/Microsoft/CNTK/blob/v2.0.beta6.0/Tutorials/NumpyInterop/FeedForwardNet.py>`__
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- NumPy interoperability example showing how to train a simple feed-forward
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network with training data fed using NumPy arrays.
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- `LanguageUnderstanding <https://github.com/Microsoft/CNTK/blob/v2.0.beta5.0/Examples/LanguageUnderstanding/ATIS/Python/LanguageUnderstanding.py>`__
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- `LanguageUnderstanding <https://github.com/Microsoft/CNTK/blob/v2.0.beta6.0/Examples/LanguageUnderstanding/ATIS/Python/LanguageUnderstanding.py>`__
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- Language Understanding.
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- `Video <https://github.com/Microsoft/CNTK/blob/v2.0.beta6.0/Examples/Video/GettingStarted/Python/Conv3D_UCF11.py>`__
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- Basic 3D convolution networks for deep learning on video tasks.
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@ -2,7 +2,7 @@
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.. some aliases
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.. _CNTK: http://cntk.ai/
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Python API for CNTK (2.0.beta5.0)
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Python API for CNTK (2.0.beta6.0)
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===============================
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CNTK_, the Microsoft Cognitive Toolkit, is a system for describing, training,
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@ -10,7 +10,7 @@ and executing computational networks. It is also a framework for describing
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arbitrary learning machines such as deep neural networks (DNNs). CNTK is an
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implementation of computational networks that supports both CPU and GPU.
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This page describes the Python API for CNTK_ version 2.0.beta5.0. This is an ongoing effort
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This page describes the Python API for CNTK_ version 2.0.beta6.0. This is an ongoing effort
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to expose such an API to the CNTK system, thus enabling the use of higher-level
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tools such as IDEs to facilitate the definition of computational networks, to execute
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them on sample data in real time.
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@ -20,13 +20,13 @@ Tutorials
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#. CNTK 204: `Sequence to sequence basics`_ with CMU pronouncing dictionary
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.. _`Logistic Regression`: https://github.com/Microsoft/CNTK/tree/v2.0.beta5.0/Tutorials/CNTK_101_LogisticRegression.ipynb
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.. _`Feed Forward Network`: https://github.com/Microsoft/CNTK/tree/v2.0.beta5.0/Tutorials/CNTK_102_FeedForward.ipynb
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.. _`MNIST data preparation`: https://github.com/Microsoft/CNTK/tree/v2.0.beta5.0/Tutorials/CNTK_103A_MNIST_DataLoader.ipynb
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.. _`Feed Forward classifier`: https://github.com/Microsoft/CNTK/tree/v2.0.beta5.0/Tutorials/CNTK_103B_MNIST_FeedForwardNetwork.ipynb
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.. _`Time Series basics`: https://github.com/Microsoft/CNTK/tree/v2.0.beta5.0/Tutorials/CNTK_104_Finance_Timeseries_Basic_with_Pandas_Numpy.ipynb
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.. _`CIFAR-10 Data preparation`: https://github.com/Microsoft/CNTK/tree/v2.0.beta5.0/Tutorials/CNTK_201A_CIFAR-10_DataLoader.ipynb
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.. _`VGG and ResNet classifiers`: https://github.com/Microsoft/CNTK/tree/v2.0.beta5.0/Tutorials/CNTK_201B_CIFAR-10_ImageHandsOn.ipynb
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.. _`Language understanding`: https://github.com/Microsoft/CNTK/blob/v2.0.beta5.0/Tutorials/CNTK_202_Language_Understanding.ipynb
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.. _`Reinforcement learning basics`: https://github.com/Microsoft/CNTK/blob/v2.0.beta5.0/Tutorials/CNTK_203_Reinforcement_Learning_Basics.ipynb
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.. _`Sequence to sequence basics`: https://github.com/Microsoft/CNTK/blob/v2.0.beta5.0/Tutorials/CNTK_204_Sequence_To_Sequence.ipynb
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.. _`Logistic Regression`: https://github.com/Microsoft/CNTK/tree/v2.0.beta6.0/Tutorials/CNTK_101_LogisticRegression.ipynb
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.. _`Feed Forward Network`: https://github.com/Microsoft/CNTK/tree/v2.0.beta6.0/Tutorials/CNTK_102_FeedForward.ipynb
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.. _`MNIST data preparation`: https://github.com/Microsoft/CNTK/tree/v2.0.beta6.0/Tutorials/CNTK_103A_MNIST_DataLoader.ipynb
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.. _`Feed Forward classifier`: https://github.com/Microsoft/CNTK/tree/v2.0.beta6.0/Tutorials/CNTK_103B_MNIST_FeedForwardNetwork.ipynb
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.. _`Time Series basics`: https://github.com/Microsoft/CNTK/tree/v2.0.beta6.0/Tutorials/CNTK_104_Finance_Timeseries_Basic_with_Pandas_Numpy.ipynb
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.. _`CIFAR-10 Data preparation`: https://github.com/Microsoft/CNTK/tree/v2.0.beta6.0/Tutorials/CNTK_201A_CIFAR-10_DataLoader.ipynb
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.. _`VGG and ResNet classifiers`: https://github.com/Microsoft/CNTK/tree/v2.0.beta6.0/Tutorials/CNTK_201B_CIFAR-10_ImageHandsOn.ipynb
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.. _`Language understanding`: https://github.com/Microsoft/CNTK/blob/v2.0.beta6.0/Tutorials/CNTK_202_Language_Understanding.ipynb
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.. _`Reinforcement learning basics`: https://github.com/Microsoft/CNTK/blob/v2.0.beta6.0/Tutorials/CNTK_203_Reinforcement_Learning_Basics.ipynb
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.. _`Sequence to sequence basics`: https://github.com/Microsoft/CNTK/blob/v2.0.beta6.0/Tutorials/CNTK_204_Sequence_To_Sequence.ipynb
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@ -163,7 +163,7 @@ else:
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kwargs = dict(package_data = package_data)
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setup(name="cntk",
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version="2.0.beta5.0",
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version="2.0.beta6.0",
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url="http://cntk.ai",
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ext_modules=[cntk_module],
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packages=packages,
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