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Chris Fregly edited this page May 29, 2016 · 23 revisions

Setup TensorFlow

cd $MYAPPS_HOME/tensorflow
./setup-tensorflow.sh
No GPU support will be enabled for TensorFlow
Configuration finished

Setup MNIST Classifier (Images of Numbers)

  • Requires 24 GB Minimum Docker Container
  • Errors during these build steps are likely due to not enough memory.
  • 24 GB+ is required. Please believe us! :)

Build the MNIST Inference, Client, and Export Components

cd $TENSORFLOW_SERVING_HOME

bazel build //tensorflow_serving/example:mnist_inference_2
bazel build //tensorflow_serving/example:mnist_client

# This command takes a while (10-15 mins), so please be patient.
bazel build //tensorflow_serving/example:mnist_export

Train and Deploy v1 MNIST Model to TensorFlow Serving

  • This will train and deploy the v1 model with 1,000 training iterations
  • TensorFlow Serving will automatically pick up the v1 MNIST Model
  • This step will take a while (3-5 mins), so please be patient
cd $TENSORFLOW_SERVING_HOME

$TENSORFLOW_SERVING_HOME/bazel-bin/tensorflow_serving/example/mnist_export --training_iteration=1000 --export_version=1 $DATASETS_HOME/tensorflow/serving/mnist_model

Start TensorFlow Serving with MNIST Model (Port 9090)

  • This will pick up the v1 MNIST Model that has been training using 1,000 training iterations
cd $MYAPPS_HOME/tensorflow
./start-tensorflow-mnist-serving-service.sh
  • Verify that TensorFlow Serving has found the v1 MNIST Model mnist_model/00000001 
tail -f $LOGS_HOME/tensorflow/serving/nohup-mnist.out 
tensorflow_serving/sources/storage_path/file_system_storage_path_source.cc:85] Found a servable {name: default version: 1} at path /root/pipeline/datasets/tensorflow/serving/mnist_model/00000001
tensorflow_serving/sources/storage_path/file_system_storage_path_source.cc:149] Aspiring 1 versions for servable default
  • ctrl-c to exit the tail log

Perform MNIST Classification using v1 MNIST Model (Port 9090)

  • This will run 1000 tests against the v1 MNIST Model and output an Inference error rate metric that we'll try to minimize in the next step
  • (This is kind of boring, but this will get more interesting down below... stick with us.)
cd $TENSORFLOW_SERVING_HOME

$TENSORFLOW_SERVING_HOME/bazel-bin/tensorflow_serving/example/mnist_client --num_tests=1000 --server=localhost:9090
...
Inference error rate: 13.1%

Train and Deploy v2 MNIST Model to TensorFlow Serving

  • This will train and deploy the v2 model with 10,000 training iterations - an order of magnitude more than v1
  • TensorFlow Serving will automatically pick up the new v2 MNIST Model
  • This step will take a while (3-5 mins), so please be patient
cd $TENSORFLOW_SERVING_HOME

$TENSORFLOW_SERVING_HOME/bazel-bin/tensorflow_serving/example/mnist_export --training_iteration=10000 --export_version=2 $DATASETS_HOME/tensorflow/serving/mnist_model
  • Verify that TensorFlow Serving has found the new v2 MNIST Model mnist_model/00000002 
tail -f $LOGS_HOME/tensorflow/serving/nohup-mnist.out 
tensorflow_serving/sources/storage_path/file_system_storage_path_source.cc:149] Aspiring 1 versions for servable default
tensorflow_serving/sources/storage_path/file_system_storage_path_source.cc:45] Polling the file system to look for a servable path under: /root/pipeline/datasets/tensorflow/serving/mnist_model
tensorflow_serving/sources/storage_path/file_system_storage_path_source.cc:85] Found a servable {name: default version: 2} at path /root/pipeline/datasets/tensorflow/serving/mnist_model/00000002
  • ctrl-c to exit the tail log

Perform MNIST Classification using v2 MNIST Model (Port 9090)

  • This will run the same number of tests (1000) as the prior run, but against the new v2 MNIST Model
  • Inference error rate should have decreased from the earlier run
cd $TENSORFLOW_SERVING_HOME

$TENSORFLOW_SERVING_HOME/bazel-bin/tensorflow_serving/example/mnist_client --num_tests=1000 --server=localhost:9090
...
Inference error rate: 9.5%

Setup Inception Model Classifier

Build TensorFlow Serving Inception Example

  • Requires 24 GB Minimum Docker Container
  • Errors during these build steps are likely due to not enough memory.
  • 24 GB+ is required. Please believe us! :)

Build the Inference and Client Components

cd $TENSORFLOW_SERVING_HOME

# Build Inference, Client, and Export Components
bazel build //tensorflow_serving/example:inception_inference
bazel build //tensorflow_serving/example:inception_client
bazel build //tensorflow_serving/example:inception_export

Train and Deploy the Inception Model to TensorFlow Serving

  • Because of the size of the Inception Model, we need to download a checkpoint version to start with
  • The --checkpoint_dir must be local to where we build the model or else we see an error like the following:
"ValueError: Restore called with invalid save path model.ckpt-157585"
  • Follow these instructions and you'll be fine
cd $TENSORFLOW_SERVING_HOME

# Download the Inception Model Checkpoint to Train On
wget http://download.tensorflow.org/models/image/imagenet/inception-v3-2016-03-01.tar.gz

tar -xvzf inception-v3-2016-03-01.tar.gz

$TENSORFLOW_SERVING_HOME/bazel-bin/tensorflow_serving/example/inception_export --checkpoint_dir=inception-v3 --export_dir=$DATASETS_HOME/tensorflow/serving/inception_model

Start TensorFlow Serving with Inception Model (Port 9091)

$MYAPPS_HOME/serving/tensorflow/start-tensorflow-inception-serving-service.sh
  • Verify that TensorFlow Serving found v00157585 Inception Model inception_model/00157585 
tail -f $LOGS_HOME/tensorflow/serving/nohup-inception.out
tensorflow_serving/sources/storage_path/file_system_storage_path_source.cc:149] Aspiring 1 versions for servable default
tensorflow_serving/sources/storage_path/file_system_storage_path_source.cc:45] Polling the file system to look for a servable path under: /root/pipeline/datasets/tensorflow/serving/inception_model
tensorflow_serving/sources/storage_path/file_system_storage_path_source.cc:85] Found a servable {name: default version: 157585} at path /root/pipeline/datasets/tensorflow/serving/inception_model/00157585

Perform Image Classification using Inception Model (Port 9091)

cd $TENSORFLOW_SERVING_HOME

$TENSORFLOW_SERVING_HOME/bazel-bin/tensorflow_serving/example/inception_client --server=localhost:9091 --image=$DATASETS_HOME/inception/cropped_panda.jpg

Classify Your Own Image using Inception Model (Port 9091)

wget <my-remote-image-url>

cd $TENSORFLOW_SERVING_HOME

$TENSORFLOW_SERVING_HOME/bazel-bin/tensorflow_serving/example/inception_client --server=localhost:9091 --image=<my-local-image-filename>

Classify an Image at Full Precision

bazel-bin/tensorflow/examples/label_image/label_image \
  --graph=$DATASETS_HOME/inception/classify_image_graph_def.pb \
  --input_width=299 \
  --input_height=299 \  
  --input_mean=128 \  
  --input_std=128 \  
  --input_layer="Mul:0" \
  --output_layer="softmax:0" \
  --image=$DATASETS_HOME/inception/cropped_panda.jpg

...

W tensorflow/core/framework/op_def_util.cc:332] Op BatchNormWithGlobalNormalization is deprecated. It will cease to work in GraphDef version 9. Use tf.nn.batch_normalization().
I tensorflow/examples/label_image/main.cc:207] giant panda (169): 0.89233
I tensorflow/examples/label_image/main.cc:207] indri (75): 0.00858706
I tensorflow/examples/label_image/main.cc:207] lesser panda (7): 0.00264235
I tensorflow/examples/label_image/main.cc:207] custard apple (325): 0.00140671
I tensorflow/examples/label_image/main.cc:207] earthstar (878): 0.00107063

Classify an Image after 8-bit Quantization

  • http://www.kdnuggets.com/2016/05/how-quantize-neural-networks-tensorflow.html
  • This will produce a new model that runs the same operations as the original, but with eight bit calculations internally, and all weights quantized as well.
  • If you look at the file size, you’ll see it’s about a quarter of the original (23MB versus 91MB).
  • You can still run this model using exactly the same inputs and outputs though, and you should get equivalent results.
  • Here's an example:
# Add 8-bit Quantization Libraries to this label_image example's BUILD before building

As of May 2016, you must replace the existing label_image/BUILD file

with the one from our repo in order to pick up the quantization libs

mv $TENSORFLOW_BLEEDINGEDGE_HOME/tensorflow/examples/label_image/BUILD $TENSORFLOW_BLEEDINGEDGE_HOME/tensorflow/examples/label_image/BUILD.orig

cp $MYAPPS_HOME/tensorflow/hack-add-quantization-dependencies-BUILD $TENSORFLOW_BLEEDINGEDGE_HOME/tensorflow/examples/label_image/BUILD

Build the label_image example

bazel build tensorflow/examples/label_image/...

Classify the image using the smaller (1/3rd) quantization model

and compare results to full-precision model above (equivalent)

bazel-bin/tensorflow/examples/label_image/label_image
--graph=$DATASETS_HOME/inception/quantized_classify_image_graph_def.pb
--input_width=299
--input_height=299 \ --input_mean=128
--input_std=128
--input_layer="Mul:0"
--output_layer="softmax:0"
--image=$DATASETS_HOME/inception/cropped_panda.jpg ...

I tensorflow/examples/label_image/main.cc:207] giant panda (169): 0.849015 I tensorflow/examples/label_image/main.cc:207] indri (75): 0.00533261 I tensorflow/examples/label_image/main.cc:207] lesser panda (7): 0.00349496 I tensorflow/examples/label_image/main.cc:207] custard apple (325): 0.00193439 I tensorflow/examples/label_image/main.cc:207] earthstar (878): 0.0013225


## 8-bit Quantization of Inception Model
* This has already been done for you, but if you would like to do it manually, here's how:

curl http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz -o /tmp/inceptionv3.tgz

bazel build tensorflow/contrib/quantization/tools:quantize_graph bazel build //tensorflow/contrib/quantization/tools:quantize_graph

bazel-bin/tensorflow/contrib/quantization/tools/quantize_graph
--input=$DATASETS_HOME/inception/classify_image_graph_def.pb
--mode=eightbit
--output_node_names=softmax2
--output=$DATASETS_HOME/inception/quantized_classify_image_graph_def.pb


## TODO:  SyntaxNet
* https://github.com/tensorflow/models/tree/master/syntaxnet

## TODO:  NLP Sentence Prediction with PTB
* Build Code

cd $TENSORFLOW_HOME bazel build //tensorflow/models/rnn/ptb:ptb_word_lm


* Train Model

python ptb_word_lm.py --data_path=$DATASETS_HOME/ptb/simple-examples/data --model small


### Predict Sentence

TODO


## Troubleshooting
### src/main/tools/namespace-sandbox.c:774: mount("none", "/", NULL, MS_REC | MS_PRIVATE, NULL): Permission denied
* You're likely running this inside a Docker Container that has not been run with `--privileged`

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