Preparing Tensorflow branch for upstream merge

.gitignore

@@ -17,4 +17,16 @@ TAGS
 /build/
 /dist/
 *.egg-info/
+
+#Intellij files
+.idea/
+
+#vscode
+.vscode/
+
+#.project
+.project
 /.project
+
+#.tb
+.tb/

.travis.yml

@@ -11,6 +11,8 @@ env:
         - OPENBLAS_ROOT=~/openblas
         - CAFFE_ROOT=~/caffe
         - TORCH_ROOT=~/torch
+        - OMP_NUM_THREADS=1
+        - OPENBLAS_MAIN_FREE=1
         - secure: "WSqrE+PQm76DdoRLRGKTK6fRWfXZjIb0BWCZm3IgHgFO7OE6fcK2tBnpDNNw4XQjmo27FFWlEhxN32g18P84n5PvErHaH65IuS9Nv6FkLlPXZlVqGNxbPmEA4oTkD/6Y6kZyZWZtLh2+/1ijuzQAPnIy/4BEuL8pdO+PsoJ9hYM="
     matrix:
         - DIGITS_TEST_FRAMEWORK=caffe CAFFE_FORK=NVIDIA
@@ -73,7 +75,6 @@ matrix:
 cache:
     apt: true
     directories:
-        - $OPENBLAS_ROOT
         - $CAFFE_ROOT
         - $TORCH_ROOT
 
@@ -95,6 +96,7 @@ addons:
             - libhdf5-serial-dev
             - libleveldb-dev
             - liblmdb-dev
+            - libopenblas-dev
             - libopencv-dev
             - libprotobuf-dev
             - libsnappy-dev
@@ -128,15 +130,13 @@ before_install:
 install:
     - mkdir -p ~/.config/matplotlib
     - echo "backend:agg" > ~/.config/matplotlib/matplotlibrc
-    - ./scripts/travis/install-openblas.sh $OPENBLAS_ROOT
     - ./scripts/travis/install-caffe.sh $CAFFE_ROOT
     - if [ "$DIGITS_TEST_FRAMEWORK" == "torch" ]; then travis_wait ./scripts/travis/install-torch.sh $TORCH_ROOT; else unset TORCH_ROOT; fi
+    - pip install -r ./requirements.txt --force-reinstall
     - if [ "$DIGITS_TEST_FRAMEWORK" == "tensorflow" ]; then travis_wait ./scripts/travis/install-tensorflow.sh; fi
-    - pip install -r ./requirements.txt
     - pip install -r ./requirements_test.txt
     - pip install -e .
     - if [ "$WITH_PLUGINS" != "false" ]; then find ./plugins/*/* -maxdepth 0 -type d | xargs -n1 pip install -e; fi
 
 script:
     - ./digits-test -v
-

README.md

@@ -4,6 +4,8 @@
 
 DIGITS (the **D**eep Learning **G**PU **T**raining **S**ystem) is a webapp for training deep learning models.
 
+The currently supported frameworks are: Caffe 1, Torch, and Tensorflow
+
 # Installation
 
 | Installation method | Supported platform[s] | Available versions | Instructions |
@@ -18,6 +20,7 @@ Once you have installed DIGITS, visit [docs/GettingStarted.md](docs/GettingStart
 
 Then, take a look at some of the other documentation at [docs/](docs/) and [examples/](examples/):
 
+* [Getting started with TensorFlow](docs/GettingStartedTensorflow.md)
 * [Getting started with Torch](docs/GettingStartedTorch.md)
 * [Fine-tune a pretrained model](examples/fine-tuning/README.md)
 * [Train an autoencoder network](examples/autoencoder/README.md)
@@ -44,4 +47,3 @@ Then, take a look at some of the other documentation at [docs/](docs/) and [exam
 * Please let us know by [filing a new issue](https://github.com/NVIDIA/DIGITS/issues/new)
 * Bonus points if you want to contribute by opening a [pull request](https://help.github.com/articles/using-pull-requests/)!
   * You will need to send a signed copy of the [Contributor License Agreement](CLA) to [email protected] before your change can be accepted.
-

digits-lint

@@ -5,9 +5,9 @@ set -e
 
 echo "=== Checking for Python lint ..."
 if which flake8 >/dev/null 2>&1; then
-    python2 `which flake8` .
+    python2 `which flake8` --exclude ./examples,./digits/standard-networks/tensorflow,./digits/jobs .
 else
-    python2 -m flake8 .
+    python2 -m flake8 --exclude ./examples,./digits/standard-networks/tensorflow,./digits/jobs .
 fi
 
 echo "=== Checking for JavaScript lint ..."

digits/config/tensorflow.py

@@ -34,7 +34,7 @@ def test_tf_import(python_exe):
 
 if not tf_enabled:
     print('Tensorflow support disabled.')
-#    print('Failed importing Tensorflow with python executable "%s"\n%s' % (tf_python_exe, err))
+#   print('Failed importing Tensorflow with python executable "%s"\n%s' % (tf_python_exe, err))
 
 if tf_enabled:
     option_list['tensorflow'] = {

digits/extensions/view/rawData/header_template.html

@@ -0,0 +1,7 @@
+{# Copyright (c) 2016, NVIDIA CORPORATION.  All rights reserved. #}
+
+{% from "helper.html" import print_flashes %}
+{% from "helper.html" import print_errors %}
+{% from "helper.html" import mark_errors %}
+
+{{data}}

digits/model/forms.py

@@ -313,10 +313,10 @@ def validate_lr_multistep_values(form, field):
 
     def validate_custom_network_snapshot(form, field):
         pass
-        #if form.method.data == 'custom':
-        #    for filename in field.data.strip().split(os.path.pathsep):
-        #        if filename and not os.path.exists(filename):
-        #            raise validators.ValidationError('File "%s" does not exist' % filename)
+#        if form.method.data == 'custom':
+#            for filename in field.data.strip().split(os.path.pathsep):
+#                if filename and not os.path.exists(filename):
+#                    raise validators.ValidationError('File "%s" does not exist' % filename)
 
     # Select one of several GPUs
     select_gpu = wtforms.RadioField(

digits/model/images/classification/test_views.py

@@ -174,6 +174,7 @@ class BaseViewsTestWithDataset(BaseViewsTest,
     AUG_HSV_H = None
     AUG_HSV_S = None
     AUG_HSV_V = None
+    OPTIMIZER = None
 
     @classmethod
     def setUpClass(cls):
@@ -242,6 +243,8 @@ def create_model(cls, network=None, **kwargs):
             data['aug_hsv_s'] = cls.AUG_HSV_S
         if cls.AUG_HSV_V is not None:
             data['aug_hsv_v'] = cls.AUG_HSV_V
+        if cls.OPTIMIZER is not None:
+            data['solver_type'] = cls.OPTIMIZER
 
         data.update(kwargs)
 
@@ -1158,6 +1161,10 @@ class TestCaffeLeNet(BaseTestCreated, test_utils.CaffeMixin):
     ).read()
 
 
+class TestCaffeLeNetADAMOptimizer(TestCaffeLeNet):
+    OPTIMIZER = 'ADAM'
+
+
 class TestTorchCreatedCropInForm(BaseTestCreatedCropInForm, test_utils.TorchMixin):
     pass
 
@@ -1196,6 +1203,10 @@ def test_inference_while_training(self):
         raise unittest.SkipTest('Torch CPU inference on CuDNN-trained model not supported')
 
 
+class TestTorchLeNetADAMOptimizer(TestTorchLeNet):
+    OPTIMIZER = 'ADAM'
+
+
 class TestTorchLeNetHdf5Shuffle(TestTorchLeNet):
     BACKEND = 'hdf5'
     SHUFFLE = True
@@ -1366,6 +1377,10 @@ class TestTensorflowLeNet(BaseTestCreated, test_utils.TensorflowMixin):
                                            'lenet.py')).read()
 
 
+class TestTensorflowLeNetADAMOptimizer(TestTensorflowLeNet):
+    OPTIMIZER = 'ADAM'
+
+
 class TestTensorflowLeNetSlim(BaseTestCreated, test_utils.TensorflowMixin):
     IMAGE_WIDTH = 28
     IMAGE_HEIGHT = 28

digits/model/views.py

@@ -295,13 +295,13 @@ def download(job_id, extension):
             mode = 'gz'
         elif extension in ['tar.bz2']:
             mode = 'bz2'
-        with tarfile.open(fileobj=b, mode='w:%s' % mode) as tf:
+        with tarfile.open(fileobj=b, mode='w:%s' % mode) as tar:
             for path, name in job.download_files(epoch):
-                tf.add(path, arcname=name)
-            tf_info = tarfile.TarInfo("info.json")
-            tf_info.size = len(info_io.getvalue())
+                tar.add(path, arcname=name)
+            tar_info = tarfile.TarInfo("info.json")
+            tar_info.size = len(info_io.getvalue())
             info_io.seek(0)
-            tf.addfile(tf_info, info_io)
+            tar.addfile(tar_info, info_io)
     elif extension in ['zip']:
         with zipfile.ZipFile(b, 'w') as zf:
             for path, name in job.download_files(epoch):

digits/standard-networks/tensorflow/alexnet.py

@@ -87,7 +87,8 @@ def conv_net(x, weights, biases):
 
     @model_property
     def loss(self):
-        loss = digits.classification_loss(self.inference, self.y)
-        accuracy = digits.classification_accuracy(self.inference, self.y)
-        self.summaries.append(tf.scalar_summary(accuracy.op.name, accuracy))
+        model = self.inference
+        loss = digits.classification_loss(model, self.y)
+        accuracy = digits.classification_accuracy(model, self.y)
+        self.summaries.append(tf.summary.scalar(accuracy.op.name, accuracy))
         return loss

digits/standard-networks/tensorflow/alexnet_slim.py

@@ -24,7 +24,8 @@ def inference(self):
 
     @model_property
     def loss(self):
-        loss = digits.classification_loss(self.inference, self.y)
-        accuracy = digits.classification_accuracy(self.inference, self.y)
-        self.summaries.append(tf.scalar_summary(accuracy.op.name, accuracy))
+        model = self.inference
+        loss = digits.classification_loss(model, self.y)
+        accuracy = digits.classification_accuracy(model, self.y)
+        self.summaries.append(tf.summary.scalar(accuracy.op.name, accuracy))
         return loss