Dear all,
I'm still struggling to make a pre-trained caffe model transformer for
dataframe works. The main problem is that creating a caffe model inside the
UDF is very slow and consumes memories.
Some of you suggest to broadcast the model. The problem with broadcasting
is that I use a JNI interface to caffe C++ with javacpp-preset and it is
not serializable.
Another possible approach is to use a UDF that can handle a whole
partitions instead of just a row in order to minimize the caffe model
instantiation.
Is there any ideas to solve one of these two issues ?
Best,
Jao
On Tue, Mar 3, 2015 at 10:04 PM, Joseph Bradley <jos...@databricks.com>
wrote:
> I see. I think your best bet is to create the cnnModel on the master and
> then serialize it to send to the workers. If it's big (1M or so), then you
> can broadcast it and use the broadcast variable in the UDF. There is not a
> great way to do something equivalent to mapPartitions with UDFs right now.
>
> On Tue, Mar 3, 2015 at 4:36 AM, Jaonary Rabarisoa <jaon...@gmail.com>
> wrote:
>
>> Here is my current implementation with current master version of spark
>>
>>
>>
>>
>> *class DeepCNNFeature extends Transformer with HasInputCol with
>> HasOutputCol ... { override def transformSchema(...) { ... }*
>> * override def transform(dataSet: DataFrame, paramMap: ParamMap):
>> DataFrame = {*
>>
>> * transformSchema(dataSet.schema, paramMap, logging = true)*
>>
>>
>>
>> * val map = this.paramMap ++ paramMap val
>> deepCNNFeature = udf((v: Vector)=> {*
>>
>> * val cnnModel = new CaffeModel *
>>
>> * cnnModel.transform(v)*
>>
>>
>>
>>
>> * } : Vector )
>> dataSet.withColumn(map(outputCol), deepCNNFeature(col(map(inputCol))))*
>>
>>
>> * }*
>> *}*
>>
>> where CaffeModel is a java api to Caffe C++ model.
>>
>> The problem here is that for every row it will create a new instance of
>> CaffeModel which is inefficient since creating a new model
>> means loading a large model file. And it will transform only a single row
>> at a time. Or a Caffe network can process a batch of rows efficiently.
>> In other words, is it possible to create an UDF that can operatats on a
>> partition in order to minimize the creation of a CaffeModel and
>> to take advantage of the Caffe network batch processing ?
>>
>>
>>
>> On Tue, Mar 3, 2015 at 7:26 AM, Joseph Bradley <jos...@databricks.com>
>> wrote:
>>
>>> I see, thanks for clarifying!
>>>
>>> I'd recommend following existing implementations in spark.ml
>>> transformers. You'll need to define a UDF which operates on a single Row
>>> to compute the value for the new column. You can then use the DataFrame
>>> DSL to create the new column; the DSL provides a nice syntax for what would
>>> otherwise be a SQL statement like "select ... from ...". I'm recommending
>>> looking at the existing implementation (rather than stating it here)
>>> because it changes between Spark 1.2 and 1.3. In 1.3, the DSL is much
>>> improved and makes it easier to create a new column.
>>>
>>> Joseph
>>>
>>> On Sun, Mar 1, 2015 at 1:26 AM, Jaonary Rabarisoa <jaon...@gmail.com>
>>> wrote:
>>>
>>>> class DeepCNNFeature extends Transformer ... {
>>>>
>>>> override def transform(data: DataFrame, paramMap: ParamMap):
>>>> DataFrame = {
>>>>
>>>>
>>>> // How can I do a map partition on the underlying RDD
>>>> and then add the column ?
>>>>
>>>> }
>>>> }
>>>>
>>>> On Sun, Mar 1, 2015 at 10:23 AM, Jaonary Rabarisoa <jaon...@gmail.com>
>>>> wrote:
>>>>
>>>>> 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
>>>>>>>
>>>>>>
>>>>>>
>>>>>
>>>>
>>>
>>
>
