- + This content is moved to a new MXNet install page. Redirecting...
diff --git a/docs/install/build_from_source.md b/docs/install/build_from_source.md index 4f7083a82432..5c558a9565be 100644 --- a/docs/install/build_from_source.md +++ b/docs/install/build_from_source.md @@ -1,6 +1,6 @@ # Build MXNet from Source -**NOTE:** For MXNet with Python installation, please refer to the [new install guide](http://mxnet.io/get_started/install.html). +**NOTE:** For MXNet with Python installation, please refer to the [new install guide](http://mxnet.io/install/index.html). This document explains how to build MXNet from sources. Building MXNet from sources is a 2 step process. diff --git a/docs/install/centos_setup.md b/docs/install/centos_setup.md index 054e0304e107..42a4fcb0eb89 100644 --- a/docs/install/centos_setup.md +++ b/docs/install/centos_setup.md @@ -1,8 +1,8 @@ - +- + This content is moved to a new MXNet install page. Redirecting...
diff --git a/docs/install/osx_setup.md b/docs/install/osx_setup.md index a009123fa0af..8980de552087 100644 --- a/docs/install/osx_setup.md +++ b/docs/install/osx_setup.md @@ -1,6 +1,6 @@ # Installing MXNet froum source on OS X (Mac) -**NOTE:** For prebuild MXNet with Python installation, please refer to the [new install guide](http://mxnet.io/get_started/install.html). +**NOTE:** For prebuild MXNet with Python installation, please refer to the [new install guide](http://mxnet.io/install/index.html). Installing MXNet is a two-step process: @@ -217,5 +217,5 @@ After you build the shared library, run the following command from the MXNet sou ## Next Steps * [Tutorials](http://mxnet.io/tutorials/index.html) -* [How To](http://mxnet.io/how_to/index.html) +* [How To](http://mxnet.io/faq/index.html) * [Architecture](http://mxnet.io/architecture/index.html) diff --git a/docs/install/raspbian_setup.md b/docs/install/raspbian_setup.md index 054e0304e107..42a4fcb0eb89 100644 --- a/docs/install/raspbian_setup.md +++ b/docs/install/raspbian_setup.md @@ -1,8 +1,8 @@ - +- + This content is moved to a new MXNet install page. Redirecting...
diff --git a/docs/install/tx2_setup.md b/docs/install/tx2_setup.md index 054e0304e107..42a4fcb0eb89 100644 --- a/docs/install/tx2_setup.md +++ b/docs/install/tx2_setup.md @@ -1,8 +1,8 @@ - +- + This content is moved to a new MXNet install page. Redirecting...
diff --git a/docs/install/ubuntu_setup.md b/docs/install/ubuntu_setup.md index 15d06fc6ae98..d33c04259cb7 100644 --- a/docs/install/ubuntu_setup.md +++ b/docs/install/ubuntu_setup.md @@ -1,6 +1,6 @@ # Installing MXNet on Ubuntu -**NOTE:** For MXNet with Python installation, please refer to the [new install guide](http://mxnet.io/get_started/install.html). +**NOTE:** For MXNet with Python installation, please refer to the [new install guide](http://mxnet.io/install/index.html). MXNet currently supports Python, R, Julia, Scala, and Perl. For users of R on Ubuntu operating systems, MXNet provides a set of Git Bash scripts that installs all of the required MXNet dependencies and the MXNet library. @@ -262,5 +262,5 @@ Before you build MXNet for Perl from source code, you must complete [building th ## Next Steps * [Tutorials](http://mxnet.io/tutorials/index.html) -* [How To](http://mxnet.io/how_to/index.html) +* [How To](http://mxnet.io/faq/index.html) * [Architecture](http://mxnet.io/architecture/index.html) diff --git a/docs/install/windows_setup.md b/docs/install/windows_setup.md index e5e92a7303c3..598a12fc4cc4 100755 --- a/docs/install/windows_setup.md +++ b/docs/install/windows_setup.md @@ -296,5 +296,5 @@ To install the MXNet Scala package into your local Maven repository, run the fol ## Next Steps * [Tutorials](http://mxnet.io/tutorials/index.html) -* [How To](http://mxnet.io/how_to/index.html) +* [How To](http://mxnet.io/faq/index.html) * [Architecture](http://mxnet.io/architecture/index.html) diff --git a/docs/tutorials/basic/data.md b/docs/tutorials/basic/data.md index b60626a489c3..66479d5acca4 100644 --- a/docs/tutorials/basic/data.md +++ b/docs/tutorials/basic/data.md @@ -8,7 +8,7 @@ Here we discuss the API conventions and several provided iterators. To complete this tutorial, we need: -- MXNet. See the instructions for your operating system in [Setup and Installation](http://mxnet.io/get_started/install.html). +- MXNet. See the instructions for your operating system in [Setup and Installation](http://mxnet.io/install/index.html). - [OpenCV Python library](http://opencv.org/opencv-3-2.html), [Python Requests](http://docs.python-requests.org/en/master/), [Matplotlib](https://matplotlib.org/) and [Jupyter Notebook](http://jupyter.org/index.html). @@ -31,10 +31,10 @@ Iterators provide an abstract interface for traversing various types of iterable In MXNet, data iterators return a batch of data as `DataBatch` on each call to `next`. A `DataBatch` often contains *n* training examples and their corresponding labels. Here *n* is the `batch_size` of the iterator. At the end of the data stream when there is no more data to read, the iterator raises ``StopIteration`` exception like Python `iter`. -The structure of `DataBatch` is defined [here](http://mxnet.io/api/python/io.html#mxnet.io.DataBatch). +The structure of `DataBatch` is defined [here](http://mxnet.io/api/python/io/io.html#mxnet.io.DataBatch). Information such as name, shape, type and layout on each training example and their corresponding label can be provided as `DataDesc` data descriptor objects via the `provide_data` and `provide_label` properties in `DataBatch`. -The structure of `DataDesc` is defined [here](http://mxnet.io/api/python/io.html#mxnet.io.DataDesc). +The structure of `DataDesc` is defined [here](http://mxnet.io/api/python/io/io.html#mxnet.io.DataDesc). All IO in MXNet is handled via `mx.io.DataIter` and its subclasses. In this tutorial, we'll discuss a few commonly used iterators provided by MXNet. @@ -56,7 +56,7 @@ warnings.filterwarnings("ignore", category=DeprecationWarning) ## Reading data in memory When data is stored in memory, backed by either an `NDArray` or ``numpy`` `ndarray`, -we can use the [__`NDArrayIter`__](http://mxnet.io/api/python/io.html#mxnet.io.NDArrayIter) to read data as below: +we can use the [__`NDArrayIter`__](http://mxnet.io/api/python/io/io.html#mxnet.io.NDArrayIter) to read data as below: ```python @@ -69,7 +69,7 @@ for batch in data_iter: ``` ## Reading data from CSV files -MXNet provides [`CSVIter`](http://mxnet.io/api/python/io.html#mxnet.io.CSVIter) +MXNet provides [`CSVIter`](http://mxnet.io/api/python/io/io.html#mxnet.io.CSVIter) to read from CSV files and can be used as below: ```python @@ -88,7 +88,7 @@ An iterator in _MXNet_ should 1. Implement `next()` in ``Python2`` or `__next()__` in ``Python3``, returning a `DataBatch` or raising a `StopIteration` exception if at the end of the data stream. 2. Implement the `reset()` method to restart reading from the beginning. -3. Have a `provide_data` attribute, consisting of a list of `DataDesc` objects that store the name, shape, type and layout information of the data (more info [here](http://mxnet.io/api/python/io.html#mxnet.io.DataBatch)). +3. Have a `provide_data` attribute, consisting of a list of `DataDesc` objects that store the name, shape, type and layout information of the data (more info [here](http://mxnet.io/api/python/io/io.html#mxnet.io.DataBatch)). 4. Have a `provide_label` attribute consisting of a list of `DataDesc` objects that store the name, shape, type and layout information of the label. When creating a new iterator, you can either start from scratch and define an iterator or reuse one of the existing iterators. @@ -209,8 +209,8 @@ Record IO is a file format used by MXNet for data IO. It compactly packs the data for efficient read and writes from distributed file system like Hadoop HDFS and AWS S3. You can learn more about the design of `RecordIO` [here](http://mxnet.io/architecture/note_data_loading.html). -MXNet provides [__`MXRecordIO`__](http://mxnet.io/api/python/io.html#mxnet.recordio.MXRecordIO) -and [__`MXIndexedRecordIO`__](http://mxnet.io/api/python/io.html#mxnet.recordio.MXIndexedRecordIO) +MXNet provides [__`MXRecordIO`__](http://mxnet.io/api/python/io/io.html#mxnet.recordio.MXRecordIO) +and [__`MXIndexedRecordIO`__](http://mxnet.io/api/python/io/io.html#mxnet.recordio.MXIndexedRecordIO) for sequential access of data and random access of the data. ### MXRecordIO @@ -273,7 +273,7 @@ The `mx.recordio` package provides a few utility functions for such operations, #### Packing/Unpacking Binary Data -[__`pack`__](http://mxnet.io/api/python/io.html#mxnet.recordio.pack) and [__`unpack`__](http://mxnet.io/api/python/io.html#mxnet.recordio.unpack) are used for storing float (or 1d array of float) label and binary data. The data is packed along with a header. The header structure is defined [here](http://mxnet.io/api/python/io.html#mxnet.recordio.IRHeader). +[__`pack`__](http://mxnet.io/api/python/io/io.html#mxnet.recordio.pack) and [__`unpack`__](http://mxnet.io/api/python/io/io.html#mxnet.recordio.unpack) are used for storing float (or 1d array of float) label and binary data. The data is packed along with a header. The header structure is defined [here](http://mxnet.io/api/python/io/io.html#mxnet.recordio.IRHeader). ```python @@ -296,7 +296,7 @@ print(mx.recordio.unpack(s2)) #### Packing/Unpacking Image Data -MXNet provides [__`pack_img`__](http://mxnet.io/api/python/io.html#mxnet.recordio.pack_img) and [__`unpack_img`__](http://mxnet.io/api/python/io.html#mxnet.recordio.unpack_img) to pack/unpack image data. +MXNet provides [__`pack_img`__](http://mxnet.io/api/python/io/io.html#mxnet.recordio.pack_img) and [__`unpack_img`__](http://mxnet.io/api/python/io/io.html#mxnet.recordio.unpack_img) to pack/unpack image data. Records packed by `pack_img` can be loaded by `mx.io.ImageRecordIter`. @@ -321,9 +321,9 @@ An example of how to use the script for converting to *RecordIO* format is shown In this section, we will learn how to preprocess and load image data in MXNet. There are 4 ways of loading image data in MXNet. - 1. Using [__mx.image.imdecode__](http://mxnet.io/api/python/io.html#mxnet.image.imdecode) to load raw image files. - 2. Using [__`mx.img.ImageIter`__](http://mxnet.io/api/python/io.html#mxnet.image.ImageIter) implemented in Python which is very flexible to customization. It can read from .rec(`RecordIO`) files and raw image files. - 3. Using [__`mx.io.ImageRecordIter`__](http://mxnet.io/api/python/io.html#mxnet.io.ImageRecordIter) implemented on the MXNet backend in C++. This is less flexible to customization but provides various language bindings. + 1. Using [__mx.image.imdecode__](http://mxnet.io/api/python/io/io.html#mxnet.image.imdecode) to load raw image files. + 2. Using [__`mx.img.ImageIter`__](http://mxnet.io/api/python/io/io.html#mxnet.image.ImageIter) implemented in Python which is very flexible to customization. It can read from .rec(`RecordIO`) files and raw image files. + 3. Using [__`mx.io.ImageRecordIter`__](http://mxnet.io/api/python/io/io.html#mxnet.io.ImageRecordIter) implemented on the MXNet backend in C++. This is less flexible to customization but provides various language bindings. 4. Creating a Custom iterator inheriting `mx.io.DataIter` @@ -407,7 +407,7 @@ os.system("python %s/tools/im2rec.py --num-thread=4 --pass-through=1 data/caltec The record io files are now saved at here (./data) #### Using ImageRecordIter -[__`ImageRecordIter`__](http://mxnet.io/api/python/io.html#mxnet.io.ImageRecordIter) can be used for loading image data saved in record io format. To use ImageRecordIter, simply create an instance by loading your record file: +[__`ImageRecordIter`__](http://mxnet.io/api/python/io/io.html#mxnet.io.ImageRecordIter) can be used for loading image data saved in record io format. To use ImageRecordIter, simply create an instance by loading your record file: ```python @@ -428,7 +428,7 @@ plt.show() ``` #### Using ImageIter -[__ImageIter__](http://mxnet.io/api/python/io.html#mxnet.io.ImageIter) is a flexible interface that supports loading of images in both RecordIO and Raw format. +[__ImageIter__](http://mxnet.io/api/python/io/io.html#mxnet.io.ImageIter) is a flexible interface that supports loading of images in both RecordIO and Raw format. ```python diff --git a/docs/tutorials/basic/module.md b/docs/tutorials/basic/module.md index 6141f3e2fd0a..2d4495181314 100644 --- a/docs/tutorials/basic/module.md +++ b/docs/tutorials/basic/module.md @@ -18,7 +18,7 @@ this tutorial. To complete this tutorial, we need: -- MXNet. See the instructions for your operating system in [Setup and Installation](http://mxnet.io/get_started/install.html). +- MXNet. See the instructions for your operating system in [Setup and Installation](http://mxnet.io/install/index.html). - [Jupyter Notebook](http://jupyter.org/index.html) and [Python Requests](http://docs.python-requests.org/en/master/) packages. ``` @@ -141,7 +141,7 @@ for epoch in range(5): Epoch 4, Training ('accuracy', 0.764375) -To learn more about these APIs, visit [Module API](http://mxnet.io/api/python/module.html). +To learn more about these APIs, visit [Module API](http://mxnet.io/api/python/module/module.html). ## High-level Interface @@ -149,7 +149,7 @@ To learn more about these APIs, visit [Module API](http://mxnet.io/api/python/mo Module also provides high-level APIs for training, predicting and evaluating for user convenience. Instead of doing all the steps mentioned in the above section, -one can simply call [fit API](http://mxnet.io/api/python/module.html#mxnet.module.BaseModule.fit) +one can simply call [fit API](http://mxnet.io/api/python/module/module.html#mxnet.module.BaseModule.fit) and it internally executes the same steps. To fit a module, call the `fit` function as follows: @@ -232,7 +232,7 @@ assert score[0][1] > 0.77, "Achieved accuracy (%f) is less than expected (0.77)" Some of the other metrics which can be used are `top_k_acc`(top-k-accuracy), `F1`, `RMSE`, `MSE`, `MAE`, `ce`(CrossEntropy). To learn more about the metrics, -visit [Evaluation metric](http://mxnet.io/api/python/metric.html). +visit [Evaluation metric](http://mxnet.io/api/python/metric/metric.html). One can vary number of epochs, learning_rate, optimizer parameters to change the score and tune these parameters to get best score. diff --git a/docs/tutorials/basic/ndarray.md b/docs/tutorials/basic/ndarray.md index bc5ce89c7bad..2c171f2627e8 100644 --- a/docs/tutorials/basic/ndarray.md +++ b/docs/tutorials/basic/ndarray.md @@ -42,7 +42,7 @@ Each NDArray supports some important attributes that you'll often want to query: To complete this tutorial, we need: -- MXNet. See the instructions for your operating system in [Setup and Installation](http://mxnet.io/get_started/install.html) +- MXNet. See the instructions for your operating system in [Setup and Installation](http://mxnet.io/install/index.html) - [Jupyter](http://jupyter.org/) ``` pip install jupyter diff --git a/docs/tutorials/basic/symbol.md b/docs/tutorials/basic/symbol.md index dc7daaea857e..3a40e5978464 100644 --- a/docs/tutorials/basic/symbol.md +++ b/docs/tutorials/basic/symbol.md @@ -26,7 +26,7 @@ which values will be needed later on. But with symbolic programming, we declare the required outputs in advance. This means that we can recycle memory allocated in intermediate steps, as by performing operations in place. Symbolic API also uses less memory for the -same network. Refer to [How To](http://mxnet.io/how_to/index.html) and +same network. Refer to [How To](http://mxnet.io/faq/index.html) and [Architecture](http://mxnet.io/architecture/index.html) section to know more. In our design notes, we present [a more thorough discussion on the comparative strengths @@ -40,7 +40,7 @@ can produce multiple output symbols and can maintain internal state symbols. For a visual explanation of these concepts, see -[Symbolic Configuration and Execution in Pictures](http://mxnet.io/api/python/symbol_in_pictures.html). +[Symbolic Configuration and Execution in Pictures](http://mxnet.io/api/python/symbol_in_pictures/symbol_in_pictures.html). To make things concrete, let's take a hands-on look at the Symbol API. There are a few different ways to compose a `Symbol`. @@ -49,7 +49,7 @@ There are a few different ways to compose a `Symbol`. To complete this tutorial, we need: -- MXNet. See the instructions for your operating system in [Setup and Installation](http://mxnet.io/get_started/install.html) +- MXNet. See the instructions for your operating system in [Setup and Installation](http://mxnet.io/install/index.html) - [Jupyter](http://jupyter.org/) ``` pip install jupyter @@ -383,7 +383,7 @@ Most operators such as `mx.sym.Convolution` and `mx.sym.Reshape` are implemented in C++ for better performance. MXNet also allows users to write new operators using any front-end language such as Python. It often makes the developing and debugging much easier. To implement an operator in Python, refer to -[How to create new operators](http://mxnet.io/how_to/new_op.html). +[How to create new operators](http://mxnet.io/faq/new_op.html). ## Advanced Usages diff --git a/docs/tutorials/embedded/wine_detector.md b/docs/tutorials/embedded/wine_detector.md index f2f7a4eda126..605b657f60ff 100644 --- a/docs/tutorials/embedded/wine_detector.md +++ b/docs/tutorials/embedded/wine_detector.md @@ -37,9 +37,7 @@ To complete this tutorial, you need: ## Building MXNet for The Pi -The first step will be to get MXNet with the Python bindings running on your Raspberry Pi 3. There is a tutorial for that provided on [here](http://mxnet.io/get_started/raspbian_setup.html). In short you will have to download the dependencies, and build the full MXNet library for the Pi with the ARM specific compile flags. Be sure to build the library with open CV as we will be using a model that requires it to process images. Then you will finally the Python bindings. Once this is done you should test that works by opening a python REPL on your Pi and typing the following commands: - -The first step is to get MXNet with the Python bindings running on your Raspberry Pi 3. There is a tutorial for that provided [here](http://mxnet.io/get_started/raspbian_setup.html). The linked tutorial walks you through downloading the dependencies, and building the full MXNet library for the Pi with the ARM specific compile flags. Be sure to build the library with open CV as we will be using a model that requires it to process images. Then you will register the Python bindings to MXNet. After this is done you should test that your installation works by opening a python REPL on your Pi and typing the following commands: +The first step is to get MXNet with the Python bindings running on your Raspberry Pi 3. There is a tutorial for that provided [here](http://mxnet.io/insstall/index.html). The linked tutorial walks you through downloading the dependencies, and building the full MXNet library for the Pi with the ARM specific compile flags. Be sure to build the library with open CV as we will be using a model that requires it to process images. Then you will register the Python bindings to MXNet. After this is done you should test that your installation works by opening a python REPL on your Pi and typing the following commands: ```bash diff --git a/docs/tutorials/gluon/mnist.md b/docs/tutorials/gluon/mnist.md index ce23f1f7e892..0bd616c369a7 100644 --- a/docs/tutorials/gluon/mnist.md +++ b/docs/tutorials/gluon/mnist.md @@ -16,7 +16,7 @@ This is based on the Mnist tutorial with symbolic approach. You can find it [her ## Prerequisites To complete this tutorial, we need: -- MXNet. See the instructions for your operating system in [Setup and Installation](http://mxnet.io/get_started/install.html). +- MXNet. See the instructions for your operating system in [Setup and Installation](http://mxnet.io/install/index.html). - [Python Requests](http://docs.python-requests.org/en/master/) and [Jupyter Notebook](http://jupyter.org/index.html). diff --git a/docs/tutorials/python/linear-regression.md b/docs/tutorials/python/linear-regression.md index fc3e7136c135..9dfcf07981d7 100644 --- a/docs/tutorials/python/linear-regression.md +++ b/docs/tutorials/python/linear-regression.md @@ -8,7 +8,7 @@ The function we are trying to learn is: *y = x1 + 2x2*, To complete this tutorial, we need: -- MXNet. See the instructions for your operating system in [Setup and Installation](http://mxnet.io/get_started/install.html). +- MXNet. See the instructions for your operating system in [Setup and Installation](http://mxnet.io/install/index.html). - [Jupyter Notebook](http://jupyter.org/index.html). @@ -56,7 +56,7 @@ eval_iter = mx.io.NDArrayIter(eval_data, eval_label, batch_size, shuffle=False) In the above example, we have made use of `NDArrayIter`, which is useful for iterating over both numpy ndarrays and MXNet NDArrays. In general, there are different types of iterators in MXNet and you can use one based on the type of data you are processing. -Documentation for iterators can be found [here](http://mxnet.io/api/python/io.html). +Documentation for iterators can be found [here](http://mxnet.io/api/python/io/io.html). ## MXNet Classes @@ -94,7 +94,7 @@ and make up various components of the model. Symbols are used to define: The ones described above and other symbols are chained together with the output of one symbol serving as input to the next to build the network topology. More information -about the different types of symbols can be found [here](http://mxnet.io/api/python/symbol.html). +about the different types of symbols can be found [here](http://mxnet.io/api/python/symbol/symbol.html). ```python X = mx.sym.Variable('data') diff --git a/docs/tutorials/python/mnist.md b/docs/tutorials/python/mnist.md index 8e3340950bf5..067ded96ab31 100644 --- a/docs/tutorials/python/mnist.md +++ b/docs/tutorials/python/mnist.md @@ -11,7 +11,7 @@ MNIST is a widely used dataset for the hand-written digit classification task. I ## Prerequisites To complete this tutorial, we need: -- MXNet version 0.10 or later. See the installation instructions for your operating system in [Setup and Installation](http://mxnet.io/get_started/install.html). +- MXNet version 0.10 or later. See the installation instructions for your operating system in [Setup and Installation](http://mxnet.io/install/index.html). - [Python Requests](http://docs.python-requests.org/en/master/) and [Jupyter Notebook](http://jupyter.org/index.html). @@ -57,7 +57,7 @@ data = mx.sym.flatten(data=data) ``` One might wonder if we are discarding valuable information by flattening. That is indeed true and we'll cover this more when we talk about convolutional neural networks where we preserve the input shape. For now, we'll go ahead and work with flattened images. -MLPs contains several fully connected layers. A fully connected layer or FC layer for short, is one where each neuron in the layer is connected to every neuron in its preceding layer. From a linear algebra perspective, an FC layer applies an [affine transform](https://en.wikipedia.org/wiki/Affine_transformation) to the *n x m* input matrix *X* and outputs a matrix *Y* of size *n x k*, where *k* is the number of neurons in the FC layer. *k* is also referred to as the hidden size. The output *Y* is computed according to the equation *Y = X WT + b*. The FC layer has two learnable parameters, the *k x m* weight matrix *W* and the *1 x k* bias vector *b*. The summation of bias vector follows the broadcasting rules explained in [`mxnet.sym.broadcast_to()`](https://mxnet.incubator.apache.org/api/python/symbol.html#mxnet.symbol.broadcast_to). Conceptually, broadcasting replicates row elements of the bias vector to create an *n x k* matrix before summation. +MLPs contains several fully connected layers. A fully connected layer or FC layer for short, is one where each neuron in the layer is connected to every neuron in its preceding layer. From a linear algebra perspective, an FC layer applies an [affine transform](https://en.wikipedia.org/wiki/Affine_transformation) to the *n x m* input matrix *X* and outputs a matrix *Y* of size *n x k*, where *k* is the number of neurons in the FC layer. *k* is also referred to as the hidden size. The output *Y* is computed according to the equation *Y = X WT + b*. The FC layer has two learnable parameters, the *k x m* weight matrix *W* and the *1 x k* bias vector *b*. The summation of bias vector follows the broadcasting rules explained in [`mxnet.sym.broadcast_to()`](https://mxnet.incubator.apache.org/api/python/symbol/symbol.html#mxnet.symbol.broadcast_to). Conceptually, broadcasting replicates row elements of the bias vector to create an *n x k* matrix before summation. In an MLP, the outputs of most FC layers are fed into an activation function, which applies an element-wise non-linearity. This step is critical and it gives neural networks the ability to classify inputs that are not linearly separable. Common choices for activation functions are sigmoid, tanh, and [rectified linear unit](https://en.wikipedia.org/wiki/Rectifier_%28neural_networks%29) (ReLU). In this example, we'll use the ReLU activation function which has several desirable properties and is typically considered a default choice. diff --git a/docs/tutorials/python/predict_image.md b/docs/tutorials/python/predict_image.md index 9a62e67feabc..afd2bd719356 100644 --- a/docs/tutorials/python/predict_image.md +++ b/docs/tutorials/python/predict_image.md @@ -7,7 +7,7 @@ pre-trained model, and how to perform feature extraction. To complete this tutorial, we need: -- MXNet. See the instructions for your operating system in [Setup and Installation](http://mxnet.io/get_started/install.html) +- MXNet. See the instructions for your operating system in [Setup and Installation](http://mxnet.io/install/index.html) - [Python Requests](http://docs.python-requests.org/en/master/), [Matplotlib](https://matplotlib.org/) and [Jupyter Notebook](http://jupyter.org/index.html). diff --git a/docs/tutorials/r/ndarray.md b/docs/tutorials/r/ndarray.md index e00f9470688b..cb7639a8a44d 100644 --- a/docs/tutorials/r/ndarray.md +++ b/docs/tutorials/r/ndarray.md @@ -199,7 +199,7 @@ the results. ## Next Steps * [Symbol](http://mxnet.io/tutorials/r/symbol.html) -* [Write and use callback functions](http://mxnet.io/tutorials/r/CallbackFunctionTutorial.html) +* [Write and use callback functions](http://mxnet.io/tutorials/r/CallbackFunction.html) * [Neural Networks with MXNet in Five Minutes](http://mxnet.io/tutorials/r/fiveMinutesNeuralNetwork.html) * [Classify Real-World Images with Pre-trained Model](http://mxnet.io/tutorials/r/classifyRealImageWithPretrainedModel.html) * [Handwritten Digits Classification Competition](http://mxnet.io/tutorials/r/mnistCompetition.html) diff --git a/docs/tutorials/r/symbol.md b/docs/tutorials/r/symbol.md index 6ab4dc2d3d31..4a87643b9f50 100644 --- a/docs/tutorials/r/symbol.md +++ b/docs/tutorials/r/symbol.md @@ -123,7 +123,7 @@ be more memory efficient than CXXNet and gets to the same runtime with greater flexibility. ## Next Steps -* [Write and use callback functions](http://mxnet.io/tutorials/r/CallbackFunctionTutorial.html) +* [Write and use callback functions](http://mxnet.io/tutorials/r/CallbackFunction.html) * [Neural Networks with MXNet in Five Minutes](http://mxnet.io/tutorials/r/fiveMinutesNeuralNetwork.html) * [Classify Real-World Images with Pre-trained Model](http://mxnet.io/tutorials/r/classifyRealImageWithPretrainedModel.html) * [Handwritten Digits Classification Competition](http://mxnet.io/tutorials/r/mnistCompetition.html) diff --git a/docs/tutorials/scala/char_lstm.md b/docs/tutorials/scala/char_lstm.md index 466d82726920..5ec303e7dfe8 100644 --- a/docs/tutorials/scala/char_lstm.md +++ b/docs/tutorials/scala/char_lstm.md @@ -6,7 +6,7 @@ There are many documents that explain LSTM concepts. If you aren't familiar with - Christopher Olah's [Understanding LSTM blog post](http://colah.github.io/posts/2015-08-Understanding-LSTMs/) - [Training a LSTM char-rnn in Julia to Generate Random Sentences](http://dmlc.ml/mxnet/2015/11/15/char-lstm-in-julia.html) - [Bucketing in MXNet in Python](https://github.com/dmlc/mxnet-notebooks/blob/master/python/tutorials/char_lstm.ipynb) -- [Bucketing in MXNet](http://mxnet.io/how_to/bucketing.html) +- [Bucketing in MXNet](http://mxnet.io/faq/bucketing.html) ## How to Use This Tutorial @@ -56,7 +56,7 @@ In this tutorial, you will accomplish the following: To complete this tutorial, you need: -- MXNet. See the instructions for your operating system in [Setup and Installation](http://mxnet.io/get_started/install.html) +- MXNet. See the instructions for your operating system in [Setup and Installation](http://mxnet.io/install/index.html) - [Scala 2.11.8](https://www.scala-lang.org/download/2.11.8.html) - [Maven 3](https://maven.apache.org/install.html) diff --git a/docs/tutorials/scala/mnist.md b/docs/tutorials/scala/mnist.md index ad55ee4c0257..6df9175536d4 100644 --- a/docs/tutorials/scala/mnist.md +++ b/docs/tutorials/scala/mnist.md @@ -7,7 +7,7 @@ Let's train a 3-layer network (i.e multilayer perceptron network) on the MNIST d ## Prerequisites To complete this tutorial, we need: -- to compile the latest MXNet version. See the MXNet installation instructions for your operating system in [Setup and Installation](http://mxnet.io/get_started/install.html). +- to compile the latest MXNet version. See the MXNet installation instructions for your operating system in [Setup and Installation](http://mxnet.io/install/index.html). - to compile the Scala API. See Scala API build instructions in [Build](https://github.com/dmlc/mxnet/tree/master/scala-package). ## Define the Network diff --git a/docs/tutorials/scala/mxnet_scala_on_intellij.md b/docs/tutorials/scala/mxnet_scala_on_intellij.md index eb667e979282..dd2ac630e95e 100644 --- a/docs/tutorials/scala/mxnet_scala_on_intellij.md +++ b/docs/tutorials/scala/mxnet_scala_on_intellij.md @@ -7,7 +7,7 @@ To use this tutorial, you need: - [Maven 3](https://maven.apache.org/install.html). - [Scala 2.11.8](https://www.scala-lang.org/download/2.11.8.html). -- MXNet. See the instructions for your operating system in [Setup and Installation](http://mxnet.io/get_started/install.html). +- MXNet. See the instructions for your operating system in [Setup and Installation](http://mxnet.io/install/index.html). - The MXNet package for Scala. For installation instructions, see [this procedure](http://mxnet.io/get_started/osx_setup.html#install-the-mxnet-package-for-scala). - [IntelliJ IDE](https://www.jetbrains.com/idea/). diff --git a/docs/tutorials/sparse/csr.md b/docs/tutorials/sparse/csr.md index f4d7b7ddf7f5..bbe71ff40c2b 100644 --- a/docs/tutorials/sparse/csr.md +++ b/docs/tutorials/sparse/csr.md @@ -21,7 +21,7 @@ The introduction of `CSRNDArray` also brings a new attribute, `stype` as a holde To complete this tutorial, you will need: -- MXNet. See the instructions for your operating system in [Setup and Installation](https://mxnet.io/get_started/install.html) +- MXNet. See the instructions for your operating system in [Setup and Installation](https://mxnet.io/install/index.html) - [Jupyter](http://jupyter.org/) ``` pip install jupyter diff --git a/docs/tutorials/sparse/row_sparse.md b/docs/tutorials/sparse/row_sparse.md index 70ca6b8838f4..d4f688441148 100644 --- a/docs/tutorials/sparse/row_sparse.md +++ b/docs/tutorials/sparse/row_sparse.md @@ -80,7 +80,7 @@ In this tutorial, we will describe what the row sparse format is and how to use To complete this tutorial, we need: -- MXNet. See the instructions for your operating system in [Setup and Installation](https://mxnet.io/get_started/install.html) +- MXNet. See the instructions for your operating system in [Setup and Installation](https://mxnet.io/install/index.html) - [Jupyter](http://jupyter.org/) ``` pip install jupyter diff --git a/docs/tutorials/sparse/train.md b/docs/tutorials/sparse/train.md index 6f4e8087bba3..e31f0465da93 100644 --- a/docs/tutorials/sparse/train.md +++ b/docs/tutorials/sparse/train.md @@ -10,7 +10,7 @@ then train a linear regression model using sparse symbols with the Module API. To complete this tutorial, we need: -- MXNet. See the instructions for your operating system in [Setup and Installation](http://mxnet.io/get_started/install.html). +- MXNet. See the instructions for your operating system in [Setup and Installation](http://mxnet.io/install/index.html). - [Jupyter Notebook](http://jupyter.org/index.html) and [Python Requests](http://docs.python-requests.org/en/master/) packages. ``` @@ -214,8 +214,8 @@ The function you will explore is: *y = x1 + 2x2 + ... 10 ### Preparing the Data -In MXNet, both [mx.io.LibSVMIter](https://mxnet.incubator.apache.org/versions/master/api/python/io.html#mxnet.io.LibSVMIter) -and [mx.io.NDArrayIter](https://mxnet.incubator.apache.org/versions/master/api/python/io.html#mxnet.io.NDArrayIter) +In MXNet, both [mx.io.LibSVMIter](https://mxnet.incubator.apache.org/versions/master/api/python/io/io.html#mxnet.io.LibSVMIter) +and [mx.io.NDArrayIter](https://mxnet.incubator.apache.org/versions/master/api/python/io/io.html#mxnet.io.NDArrayIter) support loading sparse data in CSR format. In this example, we'll use the `NDArrayIter`. You may see some warnings from SciPy. You don't need to worry about those for this example. diff --git a/docs/tutorials/vision/large_scale_classification.md b/docs/tutorials/vision/large_scale_classification.md index 1cf22708efde..17701e6047f4 100644 --- a/docs/tutorials/vision/large_scale_classification.md +++ b/docs/tutorials/vision/large_scale_classification.md @@ -3,7 +3,7 @@ Training a neural network with a large number of images presents several challenges. Even with the latest GPUs, it is not possible to train large networks using a large number of images in a reasonable amount of time using a single GPU. This problem can be somewhat mitigated by using multiple GPUs in a single machine. But there is a limit to the number of GPUs that can be attached to one machine (typically 8 or 16). This tutorial explains how to train large networks with terabytes of data using multiple machines each containing multiple GPUs. ## Prerequisites -- MXNet. See the instructions for your operating system in [Setup and Installation](http://mxnet.io/get_started/install.html). +- MXNet. See the instructions for your operating system in [Setup and Installation](http://mxnet.io/install/index.html). - [OpenCV Python library](http://opencv.org/opencv-3-2.html) @@ -247,7 +247,7 @@ It is often straightforward to achieve a reasonable validation accuracy, but ach - Increase --data-nthreads (default is 4) to use more threads for data preprocessing. - Data preprocessing is done by opencv. If opencv is compiled from source code, check if it is configured correctly. - Use `--benchmark 1` to use randomly generated data rather than real data to narrow down where the bottleneck is. -- Check [this](http://mxnet.io/how_to/perf.html) page for more details. +- Check [this](http://mxnet.io/faq/perf.html) page for more details. ### Memory If the batch size is too big, it can exhaust GPU memory. If this happens, you’ll see the error message “cudaMalloc failed: out of memory” or something similar. There are a couple of ways to fix this: diff --git a/example/caffe/README.md b/example/caffe/README.md index 2a28e012a53a..466305cc9b88 100644 --- a/example/caffe/README.md +++ b/example/caffe/README.md @@ -2,7 +2,7 @@ [Caffe](http://caffe.berkeleyvision.org/) has been a well-known and widely-used deep learning framework. Now MXNet has supported calling most caffe operators(layers) and loss functions directly in its symbolic graph! Using one's own customized caffe layer is also effortless. -Besides Caffe, MXNet has already embedded Torch modules and its tensor mathematical functions. ([link](https://github.com/dmlc/mxnet/blob/master/docs/how_to/torch.md)) +Besides Caffe, MXNet has already embedded Torch modules and its tensor mathematical functions. ([link](https://github.com/dmlc/mxnet/blob/master/docs/faq/torch.md)) This blog demonstrates two steps to use Caffe op in MXNet: diff --git a/example/image-classification/README.md b/example/image-classification/README.md index 8a64b5530a4e..296760590fd6 100644 --- a/example/image-classification/README.md +++ b/example/image-classification/README.md @@ -205,7 +205,7 @@ python fine-tune.py --pretrained-model imagenet11k-resnet-152 --gpus 0,1,2,3,4,5 We obtained 87.3% top-1 validation accuracy, and the training log is available [here](https://gist.github.com/mli/900b810258e2e0bc26fa606977a3b043#file-finetune-caltech265). See -the [python notebook](http://mxnet.io/how_to/finetune.html) for more +the [python notebook](http://mxnet.io/faq/finetune.html) for more explanations. ## Distributed Training @@ -242,7 +242,7 @@ For more usages: - One can use [benchmark.py](https://github.com/dmlc/mxnet/blob/master/example/image-classification/benchmark.py) to run distributed benchmarks (also for multiple GPUs with single machine) -- A how-to [tutorial](http://mxnet.io/how_to/multi_devices.html) with more +- A how-to [tutorial](http://mxnet.io/faq/multi_devices.html) with more explanation. - A [blog](https://aws.amazon.com/blogs/compute/distributed-deep-learning-made-easy/) @@ -357,7 +357,7 @@ aspects: codes, check if it is configured correctly. - Use `--benchmark 1` to use randomly generated data rather than real data. -Refer to [how_to/performance](http://mxnet.io/how_to/perf.html) for more details +Refer to [faq/performance](http://mxnet.io/faq/perf.html) for more details about CPU, GPU and multi-device performance. ### Memory diff --git a/example/recommenders/crossentropy.py b/example/recommenders/crossentropy.py index d8577ed898d7..ff4480828791 100644 --- a/example/recommenders/crossentropy.py +++ b/example/recommenders/crossentropy.py @@ -25,7 +25,7 @@ import numpy as np import mxnet as mx -# ref: http://mxnet.io/how_to/new_op.html +# ref: http://mxnet.io/faq/new_op.html class CrossEntropyLoss(mx.operator.CustomOp): """An output layer that calculates gradient for cross-entropy loss diff --git a/example/recommenders/randomproj.py b/example/recommenders/randomproj.py index ba080a07ec38..83ce3a1e7385 100644 --- a/example/recommenders/randomproj.py +++ b/example/recommenders/randomproj.py @@ -23,7 +23,7 @@ import mxnet as mx -# ref: http://mxnet.io/how_to/new_op.html +# ref: http://mxnet.io/faq/new_op.html class RandomBagOfWordsProjection(mx.operator.CustomOp): """Random projection layer for sparse bag-of-words (n-hot) inputs. diff --git a/example/rnn/bucketing/README.md b/example/rnn/bucketing/README.md index 0481609c2363..b46642bea0a3 100644 --- a/example/rnn/bucketing/README.md +++ b/example/rnn/bucketing/README.md @@ -32,5 +32,5 @@ This folder contains RNN examples using high level mxnet.rnn interface. ### Performance Note: -More ```MXNET_GPU_WORKER_NTHREADS``` may lead to better performance. For setting ```MXNET_GPU_WORKER_NTHREADS```, please refer to [Environment Variables](http://mxnet.incubator.apache.org/how_to/env_var.html). +More ```MXNET_GPU_WORKER_NTHREADS``` may lead to better performance. For setting ```MXNET_GPU_WORKER_NTHREADS```, please refer to [Environment Variables](http://mxnet.incubator.apache.org/faq/env_var.html). diff --git a/example/rnn/old/README.md b/example/rnn/old/README.md index 754048136c93..c03b36a9d846 100644 --- a/example/rnn/old/README.md +++ b/example/rnn/old/README.md @@ -15,4 +15,4 @@ Run `get_ptb_data.sh` to download PenTreeBank data. Performance Note: -More ```MXNET_GPU_WORKER_NTHREADS``` may lead to better performance. For setting ```MXNET_GPU_WORKER_NTHREADS```, please refer to [Environment Variables](https://mxnet.readthedocs.org/en/latest/how_to/env_var.html). +More ```MXNET_GPU_WORKER_NTHREADS``` may lead to better performance. For setting ```MXNET_GPU_WORKER_NTHREADS```, please refer to [Environment Variables](https://mxnet.readthedocs.org/en/latest/faq/env_var.html). diff --git a/example/sparse/linear_classification/README.md b/example/sparse/linear_classification/README.md index 7e2a7ad37f0b..926d9234269d 100644 --- a/example/sparse/linear_classification/README.md +++ b/example/sparse/linear_classification/README.md @@ -2,7 +2,7 @@ Linear Classification Using Sparse Matrix Multiplication =========== This examples trains a linear model using the sparse feature in MXNet. This is for demonstration purpose only. -The example utilizes the sparse data loader ([mx.io.LibSVMIter](https://mxnet.incubator.apache.org/versions/master/api/python/io.html#mxnet.io.LibSVMIter)), +The example utilizes the sparse data loader ([mx.io.LibSVMIter](https://mxnet.incubator.apache.org/versions/master/api/python/io/io.html#mxnet.io.LibSVMIter)), the sparse dot operator and [sparse gradient updaters](https://mxnet.incubator.apache.org/versions/master/api/python/ndarray/sparse.html#updater) to train a linear model on the [Avazu](https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary.html#avazu) click-through-prediction dataset. diff --git a/example/ssd/tools/caffe_converter/README.md b/example/ssd/tools/caffe_converter/README.md index 5d40024c0588..2e74fc56e022 100644 --- a/example/ssd/tools/caffe_converter/README.md +++ b/example/ssd/tools/caffe_converter/README.md @@ -10,7 +10,7 @@ python convert_caffe_modelzoo.py resnet-50 ``` Please refer to -[docs/how_to/caffe.md](../../docs/how_to/caffe.md) for more details. +[docs/faq/caffe.md](../../docs/faq/caffe.md) for more details. ### How to use To convert ssd caffemodels, Use: `python convert_model.py prototxt caffemodel outputprefix` diff --git a/perl-package/AI-MXNet/lib/AI/MXNet/Gluon/Trainer.pm b/perl-package/AI-MXNet/lib/AI/MXNet/Gluon/Trainer.pm index 63f521c5c699..c2e8f313482c 100644 --- a/perl-package/AI-MXNet/lib/AI/MXNet/Gluon/Trainer.pm +++ b/perl-package/AI-MXNet/lib/AI/MXNet/Gluon/Trainer.pm @@ -39,7 +39,7 @@ use Mouse; The set of parameters to optimize. optimizer : str or Optimizer The optimizer to use. See - `help