[mkldnn-v1.0]rebase with master

benchmark/python/einsum/benchmark_einsum.py

@@ -48,6 +48,15 @@ def test_np_einsum():
     cost = measure_cost(500, np.einsum, *args, optimize=True)
     print("Greedy einsum: {} ms".format(cost * 1000))
 
+    print("RNN Use Case:")
+    a = np.random.uniform(0, 1, size=(64, 128, 512))
+    b = np.random.uniform(0, 1, size=(128, 512, 2, 2))
+    args = ['bij, ijkl->bkl', a, b]
+    cost = measure_cost(2, np.einsum, *args, optimize=True)
+    print('Greedy einsum: {} ms'.format(cost * 1000))
+    cost = measure_cost(2, np.einsum, *args)
+    print('Basic einsum: {} ms'.format(cost * 1000))
+
     print('Inner Product:')
     a = np.ones(6000000)
     b = np.ones(6000000)

ci/docker/runtime_functions.sh

@@ -1482,8 +1482,9 @@ nightly_test_installation() {
 nightly_test_imagenet_inference() {
     set -ex
     echo $PWD
-    cp /work/mxnet/build/cpp-package/example/imagenet_inference .
-    /work/mxnet/cpp-package/example/inference/unit_test_imagenet_inference.sh
+    cp /work/mxnet/build/cpp-package/example/imagenet_inference /work/mxnet/cpp-package/example/inference/
+    cd /work/mxnet/cpp-package/example/inference/
+    ./unit_test_imagenet_inference.sh
 }
 
 #Runs a simple MNIST training example

docs/python_docs/environment.yml

@@ -27,13 +27,12 @@ dependencies:
 - matplotlib
 - notebook
 - pip:
-  # using nbconvert master until v5.5 comes out
-  - git+https://github.com/jupyter/nbconvert@master
-  - nbsphinx>=0.4.2
-  - recommonmark
-  - notedown
-  - pypandoc
-  - breathe
-  - mock
-  - awscli
-  - autodocsumm
+  - nbconvert==5.6.1
+  - nbsphinx==0.4.3
+  - recommonmark==0.6.0
+  - notedown==1.5.1
+  - pypandoc==1.4
+  - breathe==4.13.1
+  - mock==3.0.5
+  - awscli==1.16.266
+  - autodocsumm==0.1.11

docs/python_docs/python/tutorials/extend/custom_layer.md

@@ -57,7 +57,7 @@ The rest of methods of the `Block` class are already implemented, and majority o
 
 Looking into implementation of [existing layers](https://mxnet.apache.org/api/python/gluon/nn.html), one may find that more often a block inherits from a [HybridBlock](https://github.com/apache/incubator-mxnet/blob/master/python/mxnet/gluon/block.py#L428), instead of directly inheriting from `Block`.
 
-The reason for that is that `HybridBlock` allows to write custom layers that can be used in imperative programming as well as in symbolic programming. It is convinient to support both ways, because the imperative programming eases the debugging of the code and the symbolic one provides faster execution speed. You can learn more about the difference between symbolic vs. imperative programming from [this article](https://mxnet.apache.org/architecture/program_model.html).
+The reason for that is that `HybridBlock` allows to write custom layers that can be used in imperative programming as well as in symbolic programming. It is convinient to support both ways, because the imperative programming eases the debugging of the code and the symbolic one provides faster execution speed. You can learn more about the difference between symbolic vs. imperative programming from [this article](/api/architecture/program_model).
 
 Hybridization is a process that Apache MxNet uses to create a symbolic graph of a forward computation. This allows to increase computation performance by optimizing the computational symbolic graph. Once the symbolic graph is created, Apache MxNet caches and reuses it for subsequent computations.
 

docs/python_docs/python/tutorials/getting-started/gluon_from_experiment_to_deployment.md

@@ -99,14 +99,14 @@ ctx = [mx.gpu(i) for i in range(num_gpus)] if num_gpus > 0 else [mx.cpu()]
 batch_size = per_device_batch_size * max(num_gpus, 1)
 ```
 
-Now we will apply data augmentations on training images. This makes minor alterations on the training images, and our model will consider them as distinct images. This can be very useful for fine-tuning on a relatively small dataset, and it will help improve the model. We can use the Gluon [DataSet API](https://mxnet.apache.org/tutorials/gluon/datasets.html), [DataLoader API](https://mxnet.apache.org/tutorials/gluon/datasets.html), and [Transform API](https://mxnet.apache.org/tutorials/gluon/data_augmentation.html) to load the images and apply the following data augmentations:
+Now we will apply data augmentations on training images. This makes minor alterations on the training images, and our model will consider them as distinct images. This can be very useful for fine-tuning on a relatively small dataset, and it will help improve the model. We can use the Gluon [DataSet API](/api/python/docs/api/gluon/data/index.html#mxnet.gluon.data.Dataset), [DataLoader API](/api/python/docs/api/gluon/data/index.html#mxnet.gluon.data.DataLoader), and [Transform API](/api/python/docs/api/gluon/data/index.html#mxnet.gluon.data.Dataset.transform) to load the images and apply the following data augmentations:
 1. Randomly crop the image and resize it to 224x224
 2. Randomly flip the image horizontally
 3. Randomly jitter color and add noise
 4. Transpose the data from `[height, width, num_channels]` to `[num_channels, height, width]`, and map values from [0, 255] to [0, 1]
 5. Normalize with the mean and standard deviation from the ImageNet dataset.
 
-For validation and inference, we only need to apply step 1, 4, and 5. We also need to save the mean and standard deviation values for [inference using C++](https://mxnet.apache.org/versions/master/tutorials/c++/mxnet_cpp_inference_tutorial.html).
+For validation and inference, we only need to apply step 1, 4, and 5. We also need to save the mean and standard deviation values for [inference using C++](/api/cpp/docs/tutorials/cpp_inference).
 
 ```python
 jitter_param = 0.4

docs/python_docs/python/tutorials/packages/gluon/training/fit_api_tutorial.md

@@ -252,7 +252,7 @@ with warnings.catch_warnings():
     Epoch 2, loss 0.3229 <!--notebook-skip-line-->
 ```
 
-You can load the saved model, by using the `load_parameters` API in Gluon. For more details refer to the [Loading model parameters from file tutorial](../blocks/save_load_params.html#saving-model-parameters-to-file)
+You can load the saved model, by using the `load_parameters` API in Gluon. For more details refer to the [Loading model parameters from file tutorial](/api/python/docs/tutorials/packages/gluon/blocks/save_load_params.html#saving-model-parameters-to-file)
 
 
 ```python

docs/python_docs/python/tutorials/packages/ndarray/sparse/train.md

@@ -240,8 +240,8 @@ The function you will explore is: *y = x<sub>1</sub>  +  2x<sub>2</sub> + ... 10
 
 ### Preparing the Data
 
-In MXNet, both [mx.io.LibSVMIter](https://mxnet.apache.org/versions/master/api/python/io/io.html#mxnet.io.LibSVMIter)
-and [mx.io.NDArrayIter](https://mxnet.apache.org/versions/master/api/python/io/io.html#mxnet.io.NDArrayIter)
+In MXNet, both [mx.io.LibSVMIter](/api/python/docs/api/mxnet/io/index.html#mxnet.io.LibSVMIter)
+and [mx.io.NDArrayIter](/api/python/docs/api/mxnet/io/index.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.