Hi Joseph, Thank your for the tips. I understand what should I do when my data are represented as a RDD. The thing that I can't figure out is how to do the same thing when the data is view as a DataFrame and I need to add the result of my pretrained model as a new column in the DataFrame. Preciselly, I want to implement the following transformer :
class DeepCNNFeature extends Transformer ... { } On Sun, Mar 1, 2015 at 1:32 AM, Joseph Bradley <jos...@databricks.com> wrote: > Hi Jao, > > You can use external tools and libraries if they can be called from your > Spark program or script (with appropriate conversion of data types, etc.). > The best way to apply a pre-trained model to a dataset would be to call the > model from within a closure, e.g.: > > myRDD.map { myDatum => preTrainedModel.predict(myDatum) } > > If your data is distributed in an RDD (myRDD), then the above call will > distribute the computation of prediction using the pre-trained model. It > will require that all of your Spark workers be able to run the > preTrainedModel; that may mean installing Caffe and dependencies on all > nodes in the compute cluster. > > For the second question, I would modify the above call as follows: > > myRDD.mapPartitions { myDataOnPartition => > val myModel = // instantiate neural network on this partition > myDataOnPartition.map { myDatum => myModel.predict(myDatum) } > } > > I hope this helps! > Joseph > > On Fri, Feb 27, 2015 at 10:27 PM, Jaonary Rabarisoa <jaon...@gmail.com> > wrote: > >> Dear all, >> >> >> We mainly do large scale computer vision task (image classification, >> retrieval, ...). The pipeline is really great stuff for that. We're trying >> to reproduce the tutorial given on that topic during the latest spark >> summit ( >> http://ampcamp.berkeley.edu/5/exercises/image-classification-with-pipelines.html >> ) >> using the master version of spark pipeline and dataframe. The tutorial >> shows different examples of feature extraction stages before running >> machine learning algorithms. Even the tutorial is straightforward to >> reproduce with this new API, we still have some questions : >> >> - Can one use external tools (e.g via pipe) as a pipeline stage ? An >> example of use case is to extract feature learned with convolutional >> neural >> network. In our case, this corresponds to a pre-trained neural network >> with >> Caffe library (http://caffe.berkeleyvision.org/) . >> >> >> - The second question is about the performance of the pipeline. >> Library such as Caffe processes the data in batch and instancing one Caffe >> network can be time consuming when this network is very deep. So, we can >> gain performance if we minimize the number of Caffe network creation and >> give data in batch to the network. In the pipeline, this corresponds to >> run >> transformers that work on a partition basis and give the whole partition >> to >> a single caffe network. How can we create such a transformer ? >> >> >> >> Best, >> >> Jao >> > >