Deep learning has exploded over the landscape of both the popular and business media landscapes.  Current and upcoming technology capable of powering the calculations required by deep learning algorithms has enabled a rapid transition from new theories to new applications.  One of current supporting technologies that is expanding at an increasing rate is in the area of faster and more use case specific hardware accelerators for deep learning such as GPUs with tensor cores and FPGAs hosted inside of servers. Another foundational deep learning technology that has advanced very rapidly is the software that enables implementations of complex deep learning networks. New frameworks, tools and applications are entering the landscape quickly to accomplish this, some compatible with existing infrastructure and others that require workflow overhauls.


As organizations begin to develop more complex strategies for incorporating deep learning they are likely to start to leverage multiple frameworks and application stacks for specific use cases and to compare performance and accuracy. But training models is time consuming and ties up expensive compute resources. In addition, adjustments and tuning can vary between frameworks, creating a large number of framework knobs and levers to remember how to operate. What if there was a framework that could just consume these models right out the box?


 

BigDL is a distributed deep learning framework with native Spark integration, allowing it to leverage Spark during model training, prediction, and tuning. One of the things that I really like about Intel BigDL is how easy it is to work with models built and/or trained in Tensorflow, Caffe and Torch. This rich interop support for deep learning models allows BigDL applications to leverage the plethora of models that currently exist with little or no additional effort. Here are just a few ways this might be used in your applications:


  • Efficient Scale Out - Using BigDL you can scale out a model that was trained on a single node or workstation and leverage it at scale with Apache Spark. This can be useful for training on a large distributed dataset that already exists in your HDFS environment or for performing inferencing such as prediction and classification on a very large and often changing dataset.


  • Transfer Learning - Load a pretrained model with weights and then freeze some layers, append new layers and train / retrain layers. Transfer learning can improve accuracy or reduce training time by allowing you to start with a model that is used to do one thing, such as classify a different objects, and use it to accelerate development of a model to classify something else, such as specific car models.

  • High Performance on CPU - GPUs get all of the hype when it comes to deep learning. By leveraging Intel MKL and multi threading Spark tasks you can achieve better CPU driven performance leveraging BigDL than you would see with Tensorflow, Caffe or Torch when using Xeon processors.

  • Dataset Access - Designed to run in Hadoop, BigDL can compute where your data already exists. This can save time and effort since data does not need to be transferred to a seperate GPU environment to be used with the deep learning model. This means that your entire pipeline from ingest to model training and inference can all happen in one environment, Hadoop.


Real Data + Real Problem


Recently I had a chance to take advantage of the model portability feature of BigDL. After learning of an internal project here at Dell EMC, leveraging deep learning and telemetry data to predict component failures, my team decided we wanted to take our Ready Solution for AI - Machine Learning with Hadoop and see how it did with the problem.


The team conducting the project for our support organization was using Tensorflow with GPU accelerators to train an LSTM model. The dataset was sensor readings from internal components collected at 15 minute intervals showing all kinds of metrics like temperature, fan speeds, runtimes, faults etc.


Initially my team wanted to focus on testing out two use cases for BigDL:


  • Using BigDL model portability to perform inference using the existing tensorflow model
  • Implement an LSTM model in BigDL and train it with this dataset


As always, there were some preprocessing and data cleaning steps that had happened before we could get to modeling and inference. Luckily for us though we received the clean output of those steps from our support team to get started quickly. We received the data in the form of multiple csv files, already balanced with records of devices that did fail and those that did not. We got over 200,000 rows of data that looked something like this:


device_id,timestamp,model,firmware,sensor1,sensor2,sensor3,sensorN,label

String,string,string,string,float,float,float,float,int


Converting the data to a tfrecord format used by Tensorflow was being done with Python and pandas dataframes. Moving this process to be done in Spark is another area we knew we wanted to dig in to, but to start we wanted to focus on our above mentioned goals. When we started the pipeline looked like this:


From Tensorflow to BigDL

 

For BigDL, instead of creating tfrecords we needed to end up with an RDD of Sample(s). Each Sample is one record of your dataset in the form of feature, label. Feature and label are in the form of one or more tensors and we create the sample from ndarray. Looking at the current pipeline we were able to simple take the objects created before writing to tfrecord and instead wrote a function that took these arrays and formed our RDD of Sample for BigDL.


def convert_to(x, y):
  sequences = x
  labels = y

  record = zip(x,y)
  record_rdd = sc.parallelize(record)

  sample_rdd = record_rdd.map(lambda x:Sample.from_ndarray(x[0], x[1]))
  return sample_rdd

train = convert_to(x_train,y_train)
val = convert_to(x_val,y_val)
test = convert_to(x_test,y_test)




After that we took the pb and bin files representing the pretrained models definition and weights and loaded it using the BigDL Model.load_tensorflow function. It requires knowing the input and output names for the model, but the tensorflow graph summary tool can help out with that. It also requires a pb and bin file specifically, but if what you have is a ckpt file from tensorflow that can be converted with tools provided by BigDL.


model_def = "tf_modell/model.pb"
model_variable = "tf_model/model.bin"
inputs = ["Placeholder"]
outputs = ["prediction/Softmax"]
trained_tf_model = Model.load_tensorflow(model_def, inputs, outputs, byte_order = "little_endian", bigdl_type="float", bin_file=model_variable)




Now with our data already in the correct format we can go ahead and inference against our test dataset. BigDL provides Model.evaluate and we can pass it our RDD as well as the validation method to use, in this case Top1Accuracy.


results = trained_tf_model.evaluate(test,128,[Top1Accuracy()])



 

Defining a Model with BigDL

 

After testing out loading the pretrained tensorflow model the next experiment we wanted to conduct was to train an LSTM model defined with BigDL. BigDL provides a Sequential API and a Functional API for defining models. The Sequential API is for simpler models, with the Functional API being better for complex models. The Functional API describes the model as a graph. Since our model is LSTM we will use the Sequential API.


Defining an LSTM model is as simple as:


def build_model(input_size, hidden_size, output_size):
    model = Sequential()
    recurrent = Recurrent()
    recurrent.add(LSTM(input_size, hidden_size))
    model.add(InferReshape([-1, input_size], True))
    model.add(recurrent)
    model.add(Select(2, -1))
    model.add(Linear(hidden_size, output_size))
    return model

lstm_model = build_model(n_input, n_hidden, n_classes)



 

After creating our model the next step is the optimizer and validation logic that our model will use to train and learn.


Create the optimizer:


optimizer = Optimizer(
    model=lstm_model,
    training_rdd=train,
    criterion=CrossEntropyCriterion(),
    optim_method=Adam(),
    end_trigger=MaxEpoch(50),
    batch_size=batch_size)



 

Set the validation logic:

 

optimizer.set_validation(
    batch_size=batch_size,
    val_rdd=val,
    trigger=EveryEpoch(),
    val_method=[Top1Accuracy()])



 

Now we can do trained_model = optimizer.optimize() to train our model, in this case for 50 epochs. We also set our TrainSummary folder so that the data was logged. This allowed us to also get visualizations in Tensorboard, something that BigDL supports.


 

At this point we had completed the two initial tasks we had set out to do, load a pretrained Tensorflow model using BigDL and train a new model with BigDL. Hopefully you found some of this process interesting, and also got an idea for how easy BigDL is for this use case. The ability to leverage deep learning models inside Hadoop with no specialized hardware like Infiniband, GPU accelerators etc provides a great tool that is sure to change up the way you currently view your existing analytics.