You are viewing a plain text version of this content. The canonical link for it is here.
Posted to commits@mxnet.apache.org by zh...@apache.org on 2018/08/27 21:04:00 UTC
[incubator-mxnet] branch v1.3.x updated: Cherry-pick few commits to
release 1.3 branch (#12375)
This is an automated email from the ASF dual-hosted git repository.
zhasheng pushed a commit to branch v1.3.x
in repository https://gitbox.apache.org/repos/asf/incubator-mxnet.git
The following commit(s) were added to refs/heads/v1.3.x by this push:
new 05b6dc3 Cherry-pick few commits to release 1.3 branch (#12375)
05b6dc3 is described below
commit 05b6dc31689838ab8d0529b01666193fe4da2d8f
Author: Roshani Nagmote <ro...@gmail.com>
AuthorDate: Mon Aug 27 14:03:14 2018 -0700
Cherry-pick few commits to release 1.3 branch (#12375)
* Add a tutorial for control flow operators. (#12340)
* the first version.
* fix.
* add to test.
* fix.
* fix.
* fix
* fix.
* fix.
* add title.
* add link
* fix.
* Update ONNX API docs references (#12317)
* update onnx API references
* update descriptions
* [MXAPPS-581] Disable an additional long test in the SD nightly (#12343)
* Disable an additional test in the SD nightly that also takes over the
timeout.
* Documentation update related to sparse support (#12367)
* Update sparse.md
* Update sparse.md
* Update csr.md
* Update row_sparse.md
* Update train.md
---
docs/api/python/contrib/onnx.md | 22 +-
docs/api/python/ndarray/sparse.md | 10 +-
docs/api/python/symbol/sparse.md | 7 +-
docs/tutorials/control_flow/ControlFlowTutorial.md | 388 +++++++++++++++++++++
docs/tutorials/index.md | 1 +
docs/tutorials/sparse/csr.md | 4 +-
docs/tutorials/sparse/row_sparse.md | 7 +-
docs/tutorials/sparse/train.md | 2 +-
python/mxnet/contrib/onnx/mx2onnx/export_model.py | 2 +-
python/mxnet/contrib/onnx/onnx2mx/import_model.py | 3 +-
.../straight_dope/test_notebooks_single_gpu.py | 4 +-
tests/tutorials/test_tutorials.py | 3 +
12 files changed, 416 insertions(+), 37 deletions(-)
diff --git a/docs/api/python/contrib/onnx.md b/docs/api/python/contrib/onnx.md
index 4499414..f8210ad 100644
--- a/docs/api/python/contrib/onnx.md
+++ b/docs/api/python/contrib/onnx.md
@@ -22,10 +22,9 @@ This document describes all the ONNX-MXNet APIs.
.. autosummary::
:nosignatures:
- mxnet.contrib.onnx.import_model
- mxnet.contrib.onnx.get_model_metadata
- mxnet.contrib.onnx.import_to_gluon
- mxnet.contrib.onnx.export_model
+ mxnet.contrib.onnx.onnx2mx.import_model
+ mxnet.contrib.onnx.onnx2mx.import_to_gluon
+ mxnet.contrib.onnx.mx2onnx.export_model
```
## ONNX Tutorials
@@ -33,7 +32,7 @@ This document describes all the ONNX-MXNet APIs.
```eval_rst
.. toctree::
:maxdepth: 1
-
+
/tutorials/onnx/super_resolution.md
/tutorials/onnx/export_mxnet_to_onnx.md
/tutorials/onnx/inference_on_onnx_model.md
@@ -43,7 +42,7 @@ This document describes all the ONNX-MXNet APIs.
## ONNX Examples
* Face Recognition with [ArcFace](https://github.com/onnx/models/tree/master/models/face_recognition/ArcFace)
-* Image Classification with [MobileNet](https://github.com/onnx/models/tree/master/models/image_classification/mobilenet), [ResNet](https://github.com/onnx/models/tree/master/models/image_classification/resnet), [SqueezeNet](https://github.com/onnx/models/tree/master/models/image_classification/squeezenet), [VGG](https://github.com/onnx/models/tree/master/models/image_classification/vgg)
+* Image Classification with [MobileNet](https://github.com/onnx/models/tree/master/models/image_classification/mobilenet), [ResNet](https://github.com/onnx/models/tree/master/models/image_classification/resnet), [SqueezeNet](https://github.com/onnx/models/tree/master/models/image_classification/squeezenet), [VGG](https://github.com/onnx/models/tree/master/models/image_classification/vgg)
## API Reference
@@ -51,11 +50,12 @@ This document describes all the ONNX-MXNet APIs.
```eval_rst
-.. automodule:: mxnet.contrib.onnx.import_model
-.. automodule:: mxnet.contrib.onnx.get_model_metadata
-.. automodule:: mxnet.contrib.onnx.import_to_gluon
-.. automodule:: mxnet.contrib.onnx.export_model
-
+.. automodule:: mxnet.contrib.onnx.onnx2mx.import_model
+ :members: import_model, get_model_metadata
+.. automodule:: mxnet.contrib.onnx.onnx2mx.import_to_gluon
+ :members: import_to_gluon
+.. automodule:: mxnet.contrib.onnx.mx2onnx.export_model
+ :members: export_model
```
<script>auto_index("api-reference");</script>
diff --git a/docs/api/python/ndarray/sparse.md b/docs/api/python/ndarray/sparse.md
index 85d33b1..2ade059 100644
--- a/docs/api/python/ndarray/sparse.md
+++ b/docs/api/python/ndarray/sparse.md
@@ -16,7 +16,7 @@ This document lists the routines of the *n*-dimensional sparse array package:
```
The `CSRNDArray` and `RowSparseNDArray` API, defined in the `ndarray.sparse` package, provides
-imperative sparse tensor operations on **CPU**.
+imperative sparse tensor operations.
An `CSRNDArray` inherits from `NDArray`, and represents a two-dimensional, fixed-size array in compressed sparse row format.
@@ -63,16 +63,13 @@ A detailed tutorial is available at
```eval_rst
-.. note:: ``mxnet.ndarray.sparse.RowSparseNDArray`` and ``mxnet.ndarray.sparse.CSRNDArray`` DO NOT support the ``mxnet.gluon`` high-level interface yet.
-
.. note:: ``mxnet.ndarray.sparse`` is similar to ``mxnet.ndarray`` in some aspects. But the differences are not negligible. For instance:
- - Only a subset of operators in ``mxnet.ndarray`` have specialized implementations in ``mxnet.ndarray.sparse``.
- Operators such as Convolution and broadcasting do not have sparse implementations yet.
+ - Only a subset of operators in ``mxnet.ndarray`` have efficient sparse implementations in ``mxnet.ndarray.sparse``.
+ - If an operator do not occur in the ``mxnet.ndarray.sparse`` namespace, that means the operator does not have an efficient sparse implementation yet. If sparse inputs are passed to such an operator, it will convert inputs to the dense format and fallback to the already available dense implementation.
- The storage types (``stype``) of sparse operators' outputs depend on the storage types of inputs.
By default the operators not available in ``mxnet.ndarray.sparse`` infer "default" (dense) storage type for outputs.
Please refer to the [API Reference](#api-reference) section for further details on specific operators.
- - GPU support for ``mxnet.ndarray.sparse`` is experimental. Only a few sparse operators are supported on GPU such as ``sparse.dot``.
.. note:: ``mxnet.ndarray.sparse.CSRNDArray`` is similar to ``scipy.sparse.csr_matrix`` in some aspects. But they differ in a few aspects:
@@ -559,7 +556,6 @@ We summarize the interface for each class in the following sections.
sgd_update
sgd_mom_update
adam_update
- ftrl_update
adagrad_update
```
diff --git a/docs/api/python/symbol/sparse.md b/docs/api/python/symbol/sparse.md
index d26ba07..cd8272c 100644
--- a/docs/api/python/symbol/sparse.md
+++ b/docs/api/python/symbol/sparse.md
@@ -16,7 +16,7 @@ This document lists the routines of the sparse symbolic expression package:
```
The `Sparse Symbol` API, defined in the `symbol.sparse` package, provides
-sparse neural network graphs and auto-differentiation on CPU.
+sparse neural network graphs and auto-differentiation.
The storage type of a variable is speficied by the `stype` attribute of the variable.
The storage type of a symbolic expression is inferred based on the storage types of the variables and the operators.
@@ -43,12 +43,11 @@ array([ 1., 1.],
.. note:: most operators provided in ``mxnet.symbol.sparse`` are similar to those in
``mxnet.symbol`` although there are few differences:
- - Only a subset of operators in ``mxnet.symbol`` have specialized implementations in ``mxnet.symbol.sparse``.
- Operators such as reduction and broadcasting do not have sparse implementations yet.
+ - Only a subset of operators in ``mxnet.symbol`` have efficient sparse implementations in ``mxnet.symbol.sparse``.
+ - If an operator do not occur in the ``mxnet.symbol.sparse`` namespace, that means the operator does not have an efficient sparse implementation yet. If sparse inputs are passed to such an operator, it will convert inputs to the dense format and fallback to the already available dense implementation.
- The storage types (``stype``) of sparse operators' outputs depend on the storage types of inputs.
By default the operators not available in ``mxnet.symbol.sparse`` infer "default" (dense) storage type for outputs.
Please refer to the API reference section for further details on specific operators.
- - GPU support for ``mxnet.symbol.sparse`` is experimental.
```
diff --git a/docs/tutorials/control_flow/ControlFlowTutorial.md b/docs/tutorials/control_flow/ControlFlowTutorial.md
new file mode 100644
index 0000000..9e4c66f
--- /dev/null
+++ b/docs/tutorials/control_flow/ControlFlowTutorial.md
@@ -0,0 +1,388 @@
+# Hybridize Gluon models with control flows.
+
+MXNet currently provides three control flow operators: `cond`, `foreach` and `while_loop`. Like other MXNet operators, they all have a version for NDArray and a version for Symbol. These two versions have exactly the same semantics. We can take advantage of this and use them in Gluon to hybridize models.
+
+In this tutorial, we use a few examples to demonstrate the use of control flow operators in Gluon and show how a model that requires control flow is hybridized.
+
+## Prepare running the code
+
+
+```python
+import mxnet as mx
+from mxnet.gluon import HybridBlock
+```
+
+## foreach
+`foreach` is a for loop that iterates over the first dimension of the input data (it can be an array or a list of arrays). It is defined with the following signature:
+
+```python
+foreach(body, data, init_states, name) => (outputs, states)
+```
+
+It runs the Python function defined in `body` for every slice from the input arrays. The signature of the `body` function is defined as follows:
+
+```python
+body(data, states) => (outputs, states)
+```
+
+The inputs of the `body` function have two parts: `data` is a slice of an array (if there is only one input array in `foreach`) or a list of slices (if there are a list of input arrays); `states` are the arrays from the previous iteration. The outputs of the `body` function also have two parts: `outputs` is an array or a list of arrays; `states` is the computation states of the current iteration. `outputs` from all iterations are concatenated as the outputs of `foreach`.
+
+The following pseudocode illustrates the execution of `foreach`.
+
+```python
+def foreach(body, data, init_states):
+ states = init_states
+ outs = []
+
+ for i in range(data.shape[0]):
+ s = data[i]
+ out, states = body(s, states)
+ outs.append(out)
+ outs = mx.nd.stack(*outs)
+ return outs, states
+```
+
+### Example 1: `foreach` works like map
+`foreach` can work like a map function of a functional language. In this case, the states of `foreach` can be an empty list, which means the computation doesn't carry computation states across iterations.
+
+In this example, we use `foreach` to increase each element's value of an array by one.
+
+
+```python
+data = mx.nd.arange(5)
+print(data)
+```
+
+
+ [ 0. 1. 2. 3. 4.]
+ <NDArray 5 @cpu(0)>
+
+
+
+```python
+def add1(data, _):
+ return data + 1, []
+
+class Map(HybridBlock):
+ def hybrid_forward(self, F, data):
+ out, _ = F.contrib.foreach(add1, data, [])
+ return out
+
+map_layer = Map()
+out = map_layer(data)
+print(out)
+```
+
+
+ [[ 1.]
+ [ 2.]
+ [ 3.]
+ [ 4.]
+ [ 5.]]
+ <NDArray 5x1 @cpu(0)>
+
+
+We can hybridize the block and run the computation again. It should generate the same result.
+
+
+```python
+map_layer.hybridize()
+out = map_layer(data)
+print(out)
+```
+
+
+ [[ 1.]
+ [ 2.]
+ [ 3.]
+ [ 4.]
+ [ 5.]]
+ <NDArray 5x1 @cpu(0)>
+
+
+### Example 2: `foreach` works like scan
+`foreach` can work like a scan function in a functional language. In this case, the outputs of the Python function is an empty list.
+
+
+```python
+def sum(data, state):
+ return [], state + data
+
+class Scan(HybridBlock):
+ def hybrid_forward(self, F, data):
+ _, state = F.contrib.foreach(sum, data, F.zeros((1)))
+ return state
+scan_layer = Scan()
+state = scan_layer(data)
+print(data)
+print(state)
+```
+
+
+ [ 0. 1. 2. 3. 4.]
+ <NDArray 5 @cpu(0)>
+
+ [ 10.]
+ <NDArray 1 @cpu(0)>
+
+
+
+```python
+scan_layer.hybridize()
+state = scan_layer(data)
+print(state)
+```
+
+
+ [ 10.]
+ <NDArray 1 @cpu(0)>
+
+
+### Example 3: `foreach` with both outputs and states
+This is probably the most common use case of `foreach`. We extend the previous scan example and return both output and states.
+
+
+```python
+def sum(data, state):
+ return state + data, state + data
+
+class ScanV2(HybridBlock):
+ def hybrid_forward(self, F, data):
+ out, state = F.contrib.foreach(sum, data, F.zeros((1)))
+ return out, state
+scan_layer = ScanV2()
+out, state = scan_layer(data)
+print(out)
+print(state)
+```
+
+
+ [[ 0.]
+ [ 1.]
+ [ 3.]
+ [ 6.]
+ [ 10.]]
+ <NDArray 5x1 @cpu(0)>
+
+ [ 10.]
+ <NDArray 1 @cpu(0)>
+
+
+
+```python
+scan_layer.hybridize()
+out, state = scan_layer(data)
+print(out)
+print(state)
+```
+
+
+ [[ 0.]
+ [ 1.]
+ [ 3.]
+ [ 6.]
+ [ 10.]]
+ <NDArray 5x1 @cpu(0)>
+
+ [ 10.]
+ <NDArray 1 @cpu(0)>
+
+
+### Example 4: use `foreach` to run an RNN on a variable-length sequence
+Previous examples illustrate `foreach` with simple use cases. Here we show an example of processing variable-length sequences with `foreach`. The same idea is used by `dynamic_rnn` in TensorFlow for processing variable-length sequences.
+
+
+```python
+class DynamicRNNLayer(HybridBlock):
+ def __init__(self, cell, prefix=None, params=None):
+ super(DynamicRNNLayer, self).__init__(prefix=prefix, params=params)
+ self.cell = cell
+ def hybrid_forward(self, F, inputs, begin_state, valid_length):
+ states = begin_state
+ zeros = []
+ for s in states:
+ zeros.append(F.zeros_like(s))
+ # the last state is the iteration number.
+ states.append(F.zeros((1)))
+ def loop_body(inputs, states):
+ cell_states = states[:-1]
+ # Get the iteration number from the states.
+ iter_no = states[-1]
+ out, new_states = self.cell(inputs, cell_states)
+ # Copy the old state if we have reached the end of a sequence.
+ for i, state in enumerate(cell_states):
+ new_states[i] = F.where(F.broadcast_greater(valid_length, iter_no),
+ new_states[i], state)
+ new_states.append(iter_no + 1)
+ return out, new_states
+
+ outputs, states = F.contrib.foreach(loop_body, inputs, states)
+ outputs = F.SequenceMask(outputs, sequence_length=valid_length,
+ use_sequence_length=True, axis=0)
+ # the last state is the iteration number. We don't need it.
+ return outputs, states[:-1]
+
+
+seq_len = 10
+batch_size = 2
+input_size = 5
+hidden_size = 6
+
+rnn_data = mx.nd.normal(loc=0, scale=1, shape=(seq_len, batch_size, input_size))
+init_states = [mx.nd.normal(loc=0, scale=1, shape=(batch_size, hidden_size)) for i in range(2)]
+valid_length = mx.nd.round(mx.nd.random.uniform(low=1, high=10, shape=(batch_size)))
+
+lstm = DynamicRNNLayer(mx.gluon.rnn.LSTMCell(hidden_size))
+lstm.initialize()
+res, states = lstm(rnn_data, [x for x in init_states], valid_length)
+
+lstm.hybridize()
+res, states = lstm(rnn_data, [x for x in init_states], valid_length)
+```
+
+## while_loop
+`while_loop` defines a while loop. It has the following signature:
+
+```python
+while_loop(cond, body, loop_vars, max_iterations, name) => (outputs, states)
+```
+
+Instead of running over the first dimension of an array, `while_loop` checks a condition function in every iteration and runs a `body` function for computation. The signature of the `body` function is defined as follows:
+
+```python
+body(state1, state2, ...) => (outputs, states)
+```
+
+The inputs of the `body` function in `while_loop` are a little different from the one in `foreach`. It has a variable number of input arguments. Each input argument is a loop variable and the number of arguments is determined by the number of loop variables. The outputs of the `body` function also have two parts: `outputs` is an array or a list of arrays; `states` are loop variables and will be passed to the next iteration as inputs of `body`. Like `foreach`, both `outputs` and `states` [...]
+
+### Example 5: scan with while_loop
+`while_loop` is more general than `foreach`. We can also use it to iterate over an array and sum all of its values together. In this example, instead of summing over the entire array, we only sum over the first 4 elements.
+
+**Note**: the output arrays of the current implementation of `while_loop` is determined by `max_iterations`. As such, even though the while loop in this example runs 4 iterations, it still outputs an array of 5 elements. The last element in the output array is actually filled with an arbitrary value.
+
+
+```python
+class ScanV2(HybridBlock):
+ def hybrid_forward(self, F, data):
+ def sum(state, i):
+ s = state + data[i]
+ return s, [s, i + 1]
+
+ def sum_cond(state, i):
+ return i < 4
+
+ out, state = F.contrib.while_loop(sum_cond, sum,
+ [F.zeros((1)), F.zeros((1))], max_iterations=5)
+ return out, state
+scan_layer = ScanV2()
+out, state = scan_layer(data)
+print(out)
+print(state)
+```
+
+
+ [[ 0.]
+ [ 1.]
+ [ 3.]
+ [ 6.]
+ [ 0.]]
+ <NDArray 5x1 @cpu(0)>
+ [
+ [ 6.]
+ <NDArray 1 @cpu(0)>,
+ [ 4.]
+ <NDArray 1 @cpu(0)>]
+
+
+## cond
+`cond` defines an if condition. It has the following signature:
+
+```python
+cond(pred, then_func, else_func, name)
+```
+
+`cond` checks `pred`, which is a symbol or an NDArray with one element. If its value is true, it calls `then_func`. Otherwise, it calls `else_func`. The signature of `then_func` and `else_func` are as follows:
+
+```python
+func() => [outputs]
+```
+
+`cond` requires all outputs from `then_func` and `else_func` have the same number of Symbols/NDArrays with the same shapes and data types.
+
+### Example 6: skip RNN computation with cond
+Example 4 shows how to process a batch with sequences of different lengths. It performs computation for all steps but discards some of the computation results.
+
+In this example, we show how to skip computation after we have reached the end of a sequence, whose length is indicated by `length`. The code below only works for a batch with one sequence.
+
+
+```python
+class SkipRNNCell(HybridBlock):
+ def __init__(self, cell, prefix=None, params=None):
+ super(SkipRNNCell, self).__init__(prefix=prefix, params=params)
+ self.cell = cell
+ def hybrid_forward(self, F, i, length, data, states):
+ def run_rnn():
+ return self.cell(data, states)
+
+ def copy_states():
+ return F.zeros_like(data), states
+ out, state = F.contrib.cond(i < length, run_rnn, copy_states)
+ return out, state
+
+class RNNLayer(HybridBlock):
+ def __init__(self, cell, prefix=None, params=None):
+ super(RNNLayer, self).__init__(prefix=prefix, params=params)
+ self.cell = SkipRNNCell(cell)
+ def hybrid_forward(self, F, length, data, init_states):
+ def body(data, states):
+ i = states[0]
+ out, states = self.cell(i, length, data, states[1])
+ return out, [i + 1, states]
+ print()
+ out, state = F.contrib.foreach(body, data, [F.zeros((1)), init_states])
+ return out, state
+
+
+seq_len = 5
+batch_size = 1
+input_size = 3
+hidden_size = 3
+
+rnn_data = mx.nd.normal(loc=0, scale=1, shape=(seq_len, batch_size, input_size))
+init_states = [mx.nd.normal(loc=0, scale=1, shape=(batch_size, hidden_size)) for i in range(2)]
+
+cell = mx.gluon.rnn.LSTMCell(hidden_size)
+layer = RNNLayer(cell)
+layer.initialize()
+
+out, states = layer(mx.nd.array([3]), rnn_data, init_states)
+print(rnn_data)
+print(out)
+```
+
+ ()
+
+ [[[-1.25296438 0.387312 -0.41055229]]
+
+ [[ 1.28453672 0.21001032 -0.08666432]]
+
+ [[ 1.46422136 -1.30581355 0.9344402 ]]
+
+ [[ 0.5380863 -0.16038011 0.84187603]]
+
+ [[-1.00553632 3.13221502 -0.4358989 ]]]
+ <NDArray 5x1x3 @cpu(0)>
+
+ [[[-0.02620504 0.1605694 0.29636264]]
+
+ [[-0.00474182 0.08719197 0.17757624]]
+
+ [[ 0.00631597 0.04674901 0.12468992]]
+
+ [[ 0. 0. 0. ]]
+
+ [[ 0. 0. 0. ]]]
+ <NDArray 5x1x3 @cpu(0)>
+
+
+<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
diff --git a/docs/tutorials/index.md b/docs/tutorials/index.md
index ae08514..1b32333 100644
--- a/docs/tutorials/index.md
+++ b/docs/tutorials/index.md
@@ -96,6 +96,7 @@ Select API:
* [Fine-Tuning a pre-trained ImageNet model with a new dataset](/faq/finetune.html)
* [Large-Scale Multi-Host Multi-GPU Image Classification](/tutorials/vision/large_scale_classification.html)
* [Importing an ONNX model into MXNet](/tutorials/onnx/super_resolution.html)
+ * [Hybridize Gluon models with control flows](/tutorials/control_flow/ControlFlowTutorial.html)
* API Guides
* Core APIs
* NDArray
diff --git a/docs/tutorials/sparse/csr.md b/docs/tutorials/sparse/csr.md
index c2842ac..0aede1a 100644
--- a/docs/tutorials/sparse/csr.md
+++ b/docs/tutorials/sparse/csr.md
@@ -512,9 +512,7 @@ Note that in the file the column indices are expected to be sorted in ascending
### GPU Support
-By default, `CSRNDArray` operators are executed on CPU. In MXNet, GPU support for `CSRNDArray` is experimental with only a few sparse operators such as [dot](https://mxnet.incubator.apache.org/api/python/ndarray/sparse.html#mxnet.ndarray.sparse.dot).
-
-To create a `CSRNDArray` on a GPU, we need to explicitly specify the context:
+By default, `CSRNDArray` operators are executed on CPU. To create a `CSRNDArray` on a GPU, we need to explicitly specify the context:
**Note** If a GPU is not available, an error will be reported in the following section. In order to execute it a cpu, set `gpu_device` to `mx.cpu()`.
diff --git a/docs/tutorials/sparse/row_sparse.md b/docs/tutorials/sparse/row_sparse.md
index c4cab75..27cc0d3 100644
--- a/docs/tutorials/sparse/row_sparse.md
+++ b/docs/tutorials/sparse/row_sparse.md
@@ -541,12 +541,7 @@ Note that only [mxnet.optimizer.SGD](https://mxnet.incubator.apache.org/api/pyth
### GPU Support
-By default, RowSparseNDArray operators are executed on CPU. In MXNet, GPU support for RowSparseNDArray is limited
-to a few sparse operators such as [sgd_update](https://mxnet.incubator.apache.org/api/python/ndarray/sparse.html#mxnet.ndarray.sparse.sgd_update),
-[dot](https://mxnet.incubator.apache.org/api/python/ndarray/sparse.html#mxnet.ndarray.sparse.dot) and
-[Embedding](https://mxnet.incubator.apache.org/api/python/ndarray/ndarray.html#mxnet.ndarray.Embedding).
-
-To create a RowSparseNDArray on gpu, we need to explicitly specify the context:
+By default, RowSparseNDArray operators are executed on CPU. To create a RowSparseNDArray on gpu, we need to explicitly specify the context:
**Note** If a GPU is not available, an error will be reported in the following section. In order to execute it on a cpu, set gpu_device to mx.cpu().
diff --git a/docs/tutorials/sparse/train.md b/docs/tutorials/sparse/train.md
index 7472fcd..fde4c0e 100644
--- a/docs/tutorials/sparse/train.md
+++ b/docs/tutorials/sparse/train.md
@@ -314,7 +314,7 @@ assert metric.get()[1] < 1, "Achieved MSE (%f) is larger than expected (1.0)" %
### Training the model with multiple machines or multiple devices
-To train a sparse model with multiple machines, you need to call `prepare` before `forward`, or `save_checkpoint`.
+Distributed training with `row_sparse` weights and gradients are supported in MXNet, which significantly reduces communication cost for large models. To train a sparse model with multiple machines, you need to call `prepare` before `forward`, or `save_checkpoint`.
Please refer to the example in [mxnet/example/sparse/linear_classification](https://github.com/apache/incubator-mxnet/tree/master/example/sparse/linear_classification)
for more details.
diff --git a/python/mxnet/contrib/onnx/mx2onnx/export_model.py b/python/mxnet/contrib/onnx/mx2onnx/export_model.py
index 0dbfdc1..33292bf 100644
--- a/python/mxnet/contrib/onnx/mx2onnx/export_model.py
+++ b/python/mxnet/contrib/onnx/mx2onnx/export_model.py
@@ -18,7 +18,7 @@
# coding: utf-8
#pylint: disable-msg=too-many-arguments
-"""export function"""
+"""Exports an MXNet model to the ONNX model format"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
diff --git a/python/mxnet/contrib/onnx/onnx2mx/import_model.py b/python/mxnet/contrib/onnx/onnx2mx/import_model.py
index 4e4d786..e190c3b 100644
--- a/python/mxnet/contrib/onnx/onnx2mx/import_model.py
+++ b/python/mxnet/contrib/onnx/onnx2mx/import_model.py
@@ -16,7 +16,7 @@
# under the License.
# coding: utf-8
-"""import function"""
+"""Functions for importing ONNX models to MXNet and for checking metadata"""
# pylint: disable=no-member
from .import_onnx import GraphProto
@@ -72,6 +72,7 @@ def get_model_metadata(model_file):
'output_tensor_data' : <list of tuples representing the shape of the output
of the model>
}
+
"""
graph = GraphProto()
try:
diff --git a/tests/nightly/straight_dope/test_notebooks_single_gpu.py b/tests/nightly/straight_dope/test_notebooks_single_gpu.py
index a60498c..a6437cd 100644
--- a/tests/nightly/straight_dope/test_notebooks_single_gpu.py
+++ b/tests/nightly/straight_dope/test_notebooks_single_gpu.py
@@ -40,6 +40,7 @@ NOTEBOOKS_WHITELIST = [
'chapter07_distributed-learning/multiple-gpus-scratch',
'chapter07_distributed-learning/multiple-gpus-gluon',
'chapter07_distributed-learning/training-with-multiple-machines',
+ 'chapter08_computer-vision/visual-question-answer', # > 10 mins.
'chapter11_recommender-systems/intro-recommender-systems', # Early draft, non-working.
'chapter12_time-series/intro-forecasting-gluon',
'chapter12_time-series/intro-forecasting-2-gluon',
@@ -228,9 +229,6 @@ class StraightDopeSingleGpuTests(unittest.TestCase):
def test_fine_tuning(self):
assert _test_notebook('chapter08_computer-vision/fine-tuning')
- def test_visual_question_answer(self):
- assert _test_notebook('chapter08_computer-vision/visual-question-answer')
-
# Chapter 9
def test_tree_lstm(self):
diff --git a/tests/tutorials/test_tutorials.py b/tests/tutorials/test_tutorials.py
index 2c87682..503df01 100644
--- a/tests/tutorials/test_tutorials.py
+++ b/tests/tutorials/test_tutorials.py
@@ -183,3 +183,6 @@ def test_vision_large_scale_classification():
def test_vision_cnn_visualization():
assert _test_tutorial_nb('vision/cnn_visualization')
+
+def test_control_flow():
+ assert _test_tutorial_nb('control_flow/ControlFlowTutorial')