Dask DataFrame - Prediction of Keras Model

Question

I am working for the first time with dask and trying to run predict() from a trained keras model.

If I dont use dask, the function works fine (i.e. pd.DataFrame() versus dd.DataFrame () ). With Dask the error is below. Is this not a common use case (aside from scoring a groupby perhaps)

def calc_HR_ind_dsk(grp):
    model=keras.models.load_model('/home/embedding_model.h5')
    topk=10

    x=[grp['user'].values,grp['item'].values]
    pred_act=list(zip(model.predict(x)[:,0],grp['respond'].values))
    top=sorted(pred_act, key=lambda x: -x[0])[0:topk]
    hit=sum([x[1] for x in top])
    return(hit)



import dask.dataframe as dd

#step 1 - read in data as a dask df. We could reference more than 1 files using '*' wildcard
df = dd.read_csv('/home/test_coded_final.csv',dtype='int64')
results=df.groupby('user').apply(calc_HR_ind_dsk).compute()

TypeError: Cannot interpret feed_dict key as Tensor: Tensor Tensor("Placeholder_30:0", shape=(55188, 32), dtype=float32) is not an element of this graph.

Answer 1

I found the answer. It is an issue with keras or tensorflow: https://github.com/keras-team/keras/issues/2397

Below code worked and using dask shaved 50% from the time versus standard pandas groupby.

#dask
model=keras.models.load_model('/home/embedding_model.h5')

#this part
import tensorflow as tf
global graph
graph = tf.get_default_graph()


def calc_HR_ind_dsk(grp):
    topk=10
    x=[grp['user'].values,grp['item'].values]

    with graph.as_default(): #and this part from https://github.com/keras-team/keras/issues/2397
        pred_act=list(zip(model.predict(x)[:,0],grp['respond'].values))
    top=sorted(pred_act, key=lambda x: -x[0])[0:topk]
    hit=sum([x[1] for x in top])

    return(hit)



import dask.dataframe as dd


df = dd.read_csv('/home/test_coded_final.csv',dtype='int64')
results=df.groupby('user').apply(calc_HR_ind_dsk).compute()