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[MXNET-1194] Reenable nightly tutorials tests for Python2 and Python3 #13099

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marcoabreu merged 9 commits into from
Nov 7, 2018
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[MXNET-1194] Reenable nightly tutorials tests for Python2 and Python3 #13099

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8d86b7e
Reenable nightly tests tutorials
ThomasDelteil Nov 3, 2018
94815ca
small fix to settings
ThomasDelteil Nov 3, 2018
a309fbe
optimize a few more tutorials
ThomasDelteil Nov 3, 2018
6f5f044
Update tests
ThomasDelteil Nov 5, 2018
8cc43f1
Update runtime_functions.sh
ThomasDelteil Nov 5, 2018
87192c7
Update fine_tuning_gluon.md
ThomasDelteil Nov 5, 2018
49ff01d
Update JenkinsfileForBinaries
ThomasDelteil Nov 6, 2018
97de6c3
Update JenkinsfileForBinaries
ThomasDelteil Nov 7, 2018
59dda48
remove coverage
ThomasDelteil Nov 7, 2018
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51 changes: 27 additions & 24 deletions ci/docker/runtime_functions.sh
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Original file line number Diff line number Diff line change
Expand Up @@ -706,30 +706,6 @@ unittest_ubuntu_python2_gpu() {
nosetests-2.7 $NOSE_COVERAGE_ARGUMENTS --with-xunit --xunit-file nosetests_gpu.xml --verbose tests/python/gpu
}

tutorialtest_ubuntu_python3_gpu() {
set -ex
cd /work/mxnet/docs
export MXNET_DOCS_BUILD_MXNET=0
make html
export MXNET_STORAGE_FALLBACK_LOG_VERBOSE=0
export PYTHONPATH=/work/mxnet/python/
export MXNET_TUTORIAL_TEST_KERNEL=python3
cd /work/mxnet/tests/tutorials
nosetests-3.4 $NOSE_COVERAGE_ARGUMENTS --with-xunit --xunit-file nosetests_tutorials.xml test_tutorials.py --nologcapture
}

tutorialtest_ubuntu_python2_gpu() {
set -ex
cd /work/mxnet/docs
export MXNET_DOCS_BUILD_MXNET=0
make html
export MXNET_STORAGE_FALLBACK_LOG_VERBOSE=0
export PYTHONPATH=/work/mxnet/python/
export MXNET_TUTORIAL_TEST_KERNEL=python2
cd /work/mxnet/tests/tutorials
nosetests-3.4 $NOSE_COVERAGE_ARGUMENTS --with-xunit --xunit-file nosetests_tutorials.xml test_tutorials.py --nologcapture
}

unittest_ubuntu_python3_gpu() {
set -ex
export PYTHONPATH=./python/
Expand Down Expand Up @@ -1124,6 +1100,33 @@ nightly_straight_dope_python3_multi_gpu_tests() {
test_notebooks_multi_gpu.py --nologcapture
}

nightly_tutorial_test_ubuntu_python3_gpu() {
set -ex
cd /work/mxnet/docs
export BUILD_VER=tutorial
export MXNET_DOCS_BUILD_MXNET=0
make html
export MXNET_STORAGE_FALLBACK_LOG_VERBOSE=0
export PYTHONPATH=/work/mxnet/python/
export MXNET_TUTORIAL_TEST_KERNEL=python3
cd /work/mxnet/tests/tutorials
nosetests-3.4 --with-xunit --xunit-file nosetests_tutorials.xml test_tutorials.py --nologcapture
}

nightly_tutorial_test_ubuntu_python2_gpu() {
set -ex
cd /work/mxnet/docs
export BUILD_VER=tutorial
export MXNET_DOCS_BUILD_MXNET=0
make html
export MXNET_STORAGE_FALLBACK_LOG_VERBOSE=0
export PYTHONPATH=/work/mxnet/python/
export MXNET_TUTORIAL_TEST_KERNEL=python2
cd /work/mxnet/tests/tutorials
nosetests-3.4 --with-xunit --xunit-file nosetests_tutorials.xml test_tutorials.py --nologcapture
}


# Deploy

deploy_docs() {
Expand Down
6 changes: 6 additions & 0 deletions docs/settings.ini
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@@ -1,6 +1,12 @@
[mxnet]
build_mxnet = 0

[document_sets_tutorial]
clojure_docs = 0
doxygen_docs = 1
r_docs = 0
scala_docs = 0

[document_sets_default]
clojure_docs = 1
doxygen_docs = 1
Expand Down
148 changes: 90 additions & 58 deletions docs/tutorials/basic/module.md
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Expand Up @@ -39,11 +39,16 @@ training examples each time. A separate iterator is also created for test data.

```python
import logging
import random
logging.getLogger().setLevel(logging.INFO)

import mxnet as mx
import numpy as np

mx.random.seed(1234)
np.random.seed(1234)
random.seed(1234)

fname = mx.test_utils.download('https://s3.us-east-2.amazonaws.com/mxnet-public/letter_recognition/letter-recognition.data')
data = np.genfromtxt(fname, delimiter=',')[:,1:]
label = np.array([ord(l.split(',')[0])-ord('A') for l in open(fname, 'r')])
Expand All @@ -64,7 +69,7 @@ net = mx.sym.FullyConnected(net, name='fc1', num_hidden=64)
net = mx.sym.Activation(net, name='relu1', act_type="relu")
net = mx.sym.FullyConnected(net, name='fc2', num_hidden=26)
net = mx.sym.SoftmaxOutput(net, name='softmax')
mx.viz.plot_network(net)
mx.viz.plot_network(net, node_attrs={"shape":"oval","fixedsize":"false"})
```


Expand Down Expand Up @@ -135,11 +140,17 @@ for epoch in range(5):
print('Epoch %d, Training %s' % (epoch, metric.get()))
```

Epoch 0, Training ('accuracy', 0.4554375)
Epoch 1, Training ('accuracy', 0.6485625)
Epoch 2, Training ('accuracy', 0.7055625)
Epoch 3, Training ('accuracy', 0.7396875)
Epoch 4, Training ('accuracy', 0.764375)

Expected output:


```
Epoch 0, Training ('accuracy', 0.434625)
Epoch 1, Training ('accuracy', 0.6516875)
Epoch 2, Training ('accuracy', 0.6968125)
Epoch 3, Training ('accuracy', 0.7273125)
Epoch 4, Training ('accuracy', 0.7575625)
```


To learn more about these APIs, visit [Module API](http://mxnet.io/api/python/module/module.html).
Expand Down Expand Up @@ -172,34 +183,36 @@ mod.fit(train_iter,
optimizer='sgd',
optimizer_params={'learning_rate':0.1},
eval_metric='acc',
num_epoch=8)
num_epoch=7)
```

INFO:root:Epoch[0] Train-accuracy=0.364625
INFO:root:Epoch[0] Time cost=0.388
INFO:root:Epoch[0] Validation-accuracy=0.557250
INFO:root:Epoch[1] Train-accuracy=0.633625
INFO:root:Epoch[1] Time cost=0.470
INFO:root:Epoch[1] Validation-accuracy=0.634750
INFO:root:Epoch[2] Train-accuracy=0.697187
INFO:root:Epoch[2] Time cost=0.402
INFO:root:Epoch[2] Validation-accuracy=0.665500
INFO:root:Epoch[3] Train-accuracy=0.735062
INFO:root:Epoch[3] Time cost=0.402
INFO:root:Epoch[3] Validation-accuracy=0.713000
INFO:root:Epoch[4] Train-accuracy=0.762563
INFO:root:Epoch[4] Time cost=0.408
INFO:root:Epoch[4] Validation-accuracy=0.742000
INFO:root:Epoch[5] Train-accuracy=0.782312
INFO:root:Epoch[5] Time cost=0.400
INFO:root:Epoch[5] Validation-accuracy=0.778500
INFO:root:Epoch[6] Train-accuracy=0.797188
INFO:root:Epoch[6] Time cost=0.392
INFO:root:Epoch[6] Validation-accuracy=0.798250
INFO:root:Epoch[7] Train-accuracy=0.807750
INFO:root:Epoch[7] Time cost=0.401
INFO:root:Epoch[7] Validation-accuracy=0.789250

Expected output:


```
INFO:root:Epoch[0] Train-accuracy=0.325437
INFO:root:Epoch[0] Time cost=0.550
INFO:root:Epoch[0] Validation-accuracy=0.568500
INFO:root:Epoch[1] Train-accuracy=0.622188
INFO:root:Epoch[1] Time cost=0.552
INFO:root:Epoch[1] Validation-accuracy=0.656500
INFO:root:Epoch[2] Train-accuracy=0.694375
INFO:root:Epoch[2] Time cost=0.566
INFO:root:Epoch[2] Validation-accuracy=0.703500
INFO:root:Epoch[3] Train-accuracy=0.732187
INFO:root:Epoch[3] Time cost=0.562
INFO:root:Epoch[3] Validation-accuracy=0.748750
INFO:root:Epoch[4] Train-accuracy=0.755375
INFO:root:Epoch[4] Time cost=0.484
INFO:root:Epoch[4] Validation-accuracy=0.761500
INFO:root:Epoch[5] Train-accuracy=0.773188
INFO:root:Epoch[5] Time cost=0.383
INFO:root:Epoch[5] Validation-accuracy=0.715000
INFO:root:Epoch[6] Train-accuracy=0.794687
INFO:root:Epoch[6] Time cost=0.378
INFO:root:Epoch[6] Validation-accuracy=0.802250
```

By default, `fit` function has `eval_metric` set to `accuracy`, `optimizer` to `sgd`
and optimizer_params to `(('learning_rate', 0.01),)`.
Expand All @@ -225,12 +238,17 @@ It can be used as follows:
```python
score = mod.score(val_iter, ['acc'])
print("Accuracy score is %f" % (score[0][1]))
assert score[0][1] > 0.77, "Achieved accuracy (%f) is less than expected (0.77)" % score[0][1]
assert score[0][1] > 0.76, "Achieved accuracy (%f) is less than expected (0.76)" % score[0][1]
```

Accuracy score is 0.789250

Expected output:


```
Accuracy score is 0.802250
```

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/metric.html).
Expand All @@ -252,22 +270,27 @@ mod = mx.mod.Module(symbol=net)
mod.fit(train_iter, num_epoch=5, epoch_end_callback=checkpoint)
```

INFO:root:Epoch[0] Train-accuracy=0.101062
INFO:root:Epoch[0] Time cost=0.422
INFO:root:Saved checkpoint to "mx_mlp-0001.params"
INFO:root:Epoch[1] Train-accuracy=0.263313
INFO:root:Epoch[1] Time cost=0.785
INFO:root:Saved checkpoint to "mx_mlp-0002.params"
INFO:root:Epoch[2] Train-accuracy=0.452188
INFO:root:Epoch[2] Time cost=0.624
INFO:root:Saved checkpoint to "mx_mlp-0003.params"
INFO:root:Epoch[3] Train-accuracy=0.544125
INFO:root:Epoch[3] Time cost=0.427
INFO:root:Saved checkpoint to "mx_mlp-0004.params"
INFO:root:Epoch[4] Train-accuracy=0.605250
INFO:root:Epoch[4] Time cost=0.399
INFO:root:Saved checkpoint to "mx_mlp-0005.params"

Expected output:


```
INFO:root:Epoch[0] Train-accuracy=0.098437
INFO:root:Epoch[0] Time cost=0.421
INFO:root:Saved checkpoint to "mx_mlp-0001.params"
INFO:root:Epoch[1] Train-accuracy=0.257437
INFO:root:Epoch[1] Time cost=0.520
INFO:root:Saved checkpoint to "mx_mlp-0002.params"
INFO:root:Epoch[2] Train-accuracy=0.457250
INFO:root:Epoch[2] Time cost=0.562
INFO:root:Saved checkpoint to "mx_mlp-0003.params"
INFO:root:Epoch[3] Train-accuracy=0.558187
INFO:root:Epoch[3] Time cost=0.434
INFO:root:Saved checkpoint to "mx_mlp-0004.params"
INFO:root:Epoch[4] Train-accuracy=0.617750
INFO:root:Epoch[4] Time cost=0.414
INFO:root:Saved checkpoint to "mx_mlp-0005.params"
```

To load the saved module parameters, call the `load_checkpoint` function. It
loads the Symbol and the associated parameters. We can then set the loaded
Expand Down Expand Up @@ -299,16 +322,25 @@ mod.fit(train_iter,
assert score[0][1] > 0.77, "Achieved accuracy (%f) is less than expected (0.77)" % score[0][1]
```

INFO:root:Epoch[3] Train-accuracy=0.544125
INFO:root:Epoch[3] Time cost=0.398
INFO:root:Epoch[4] Train-accuracy=0.605250
INFO:root:Epoch[4] Time cost=0.545
INFO:root:Epoch[5] Train-accuracy=0.644312
INFO:root:Epoch[5] Time cost=0.592
INFO:root:Epoch[6] Train-accuracy=0.675000
INFO:root:Epoch[6] Time cost=0.491
INFO:root:Epoch[7] Train-accuracy=0.695812
INFO:root:Epoch[7] Time cost=0.363

Expected output:


```
INFO:root:Epoch[3] Train-accuracy=0.555438
INFO:root:Epoch[3] Time cost=0.377
INFO:root:Epoch[4] Train-accuracy=0.616625
INFO:root:Epoch[4] Time cost=0.457
INFO:root:Epoch[5] Train-accuracy=0.658438
INFO:root:Epoch[5] Time cost=0.518
...........................................
INFO:root:Epoch[18] Train-accuracy=0.788687
INFO:root:Epoch[18] Time cost=0.532
INFO:root:Epoch[19] Train-accuracy=0.789562
INFO:root:Epoch[19] Time cost=0.531
INFO:root:Epoch[20] Train-accuracy=0.796250
INFO:root:Epoch[20] Time cost=0.531
```



Expand Down
8 changes: 4 additions & 4 deletions docs/tutorials/basic/symbol.md
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Expand Up @@ -89,7 +89,7 @@ f = mx.sym.reshape(d+e, shape=(1,4))
# broadcast
g = mx.sym.broadcast_to(f, shape=(2,4))
# plot
mx.viz.plot_network(symbol=g)
mx.viz.plot_network(symbol=g, node_attrs={"shape":"oval","fixedsize":"false"})
```

The computations declared in the above examples can be bound to the input data
Expand All @@ -108,7 +108,7 @@ net = mx.sym.FullyConnected(data=net, name='fc1', num_hidden=128)
net = mx.sym.Activation(data=net, name='relu1', act_type="relu")
net = mx.sym.FullyConnected(data=net, name='fc2', num_hidden=10)
net = mx.sym.SoftmaxOutput(data=net, name='out')
mx.viz.plot_network(net, shape={'data':(100,200)})
mx.viz.plot_network(net, shape={'data':(100,200)}, node_attrs={"shape":"oval","fixedsize":"false"})
```

Each symbol takes a (unique) string name. NDArray and Symbol both represent
Expand Down Expand Up @@ -211,7 +211,7 @@ def ConvFactory(data, num_filter, kernel, stride=(1,1), pad=(0, 0),name=None, su
prev = mx.sym.Variable(name="Previous Output")
conv_comp = ConvFactory(data=prev, num_filter=64, kernel=(7,7), stride=(2, 2))
shape = {"Previous Output" : (128, 3, 28, 28)}
mx.viz.plot_network(symbol=conv_comp, shape=shape)
mx.viz.plot_network(symbol=conv_comp, shape=shape, node_attrs={"shape":"oval","fixedsize":"false"})
```

Then we can define a function that constructs an inception module based on
Expand All @@ -237,7 +237,7 @@ def InceptionFactoryA(data, num_1x1, num_3x3red, num_3x3, num_d3x3red, num_d3x3,
return concat
prev = mx.sym.Variable(name="Previous Output")
in3a = InceptionFactoryA(prev, 64, 64, 64, 64, 96, "avg", 32, name="in3a")
mx.viz.plot_network(symbol=in3a, shape=shape)
mx.viz.plot_network(symbol=in3a, shape=shape, node_attrs={"shape":"oval","fixedsize":"false"})
```

Finally, we can obtain the whole network by chaining multiple inception
Expand Down
12 changes: 6 additions & 6 deletions docs/tutorials/control_flow/ControlFlowTutorial.md
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Expand Up @@ -15,13 +15,13 @@ 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)
```

Expand Down Expand Up @@ -243,13 +243,13 @@ 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)
```

Expand Down Expand Up @@ -297,13 +297,13 @@ print(state)
## 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]
```

Expand Down
1 change: 1 addition & 0 deletions docs/tutorials/gluon/hybrid.md
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Expand Up @@ -125,6 +125,7 @@ with other language front-ends like C, C++ and Scala. To this end, we simply
use `export` and `SymbolBlock.imports`:

```python
net(x)
net.export('model', epoch=1)
```

Expand Down
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