Data Science, Machine Learning.
indptr), shape=mat.shape) return bcast_mat def parallelize_matrix(scipy_mat, rows_per_chunk=100): [rows, cols] = scipy_mat.shape i = 0 submatrices = [] while i < rows: current_chunk_size = min(rows_per_chunk, rows - i) submat = scipy_mat[i:i + current_chunk_size] submatrices.append((i,