How can I run SVM on 500k rows with 81 columns? I have about 500k rows of data. I'm new to data science and I'm trying to train a model utilizing support vector machines as part of my analysis. On my little macbook pro, it seems endless. Right now I'm using an RBF kernel and am not seeing an end to this computation.
I do however have access to 100+ compute nodes with 16 cores a piece. My problem is, I don't know how to utilize this to my extent as I am lacking knowledge on how I should approach this SVM. Right now I am using scipy.
SciPy Code
def makeModelandPrediction(trainData, trainLabel, testData):
    model = svm.SVC(
        C=1.0,
        cache_size=200,
        class_weight=None,
        coef0=0.0,
        decision_function_shape=None,
        degree=3,
        gamma='auto',
        kernel='rbf',
        max_iter=-1,
        probability=False,
        random_state=None,
        shrinking=True,
        tol=0.001,
        verbose=False,
        )
    model.fit(trainData, trainLabel)
    prediction = model.predict(testData)
    return prediction

I've already preprocessed the data and done a 70/30 train/test split on the data. Can someone point a beginner in the right direction?
If you want to stick to SVC(sklearn) , you can use Ensembling
we have many ways of ensembling 
good guide is here : http://mlwave.com/kaggle-ensembling-guide/
for your problem you can train multiple SVC models on different subset of your data,and as quick solution you can combine them whit BaggingClassifier like this : 

clf = BaggingClassifier(SVC(C=1.0,
        cache_size=200,
        class_weight=None,
        coef0=0.0,
        decision_function_shape=None,
        degree=3,
        gamma='auto',
        kernel='rbf',
        max_iter=-1,
        probability=False,
        random_state=None,
        shrinking=True,
        tol=0.001,
        verbose=False,
        ))


If you want to use each record is used only once for training in the BaggingClassifier, set the bootstrap parameter to False.
now you can parallel each of this models in on core of your cluster.