Where all your earlier computation actually being carried out, including operations likeĭd.read_csv(). The DaskDMatrix constructor forces lazy computations to be evaluated, which means it’s See the previous link for details in dask, and this wiki for information on the general concept of lazy evaluation. Means that computation is not carried out until you explicitly ask for a result by, for example,Ĭalling compute(). By default most dask computations are lazily evaluated, which You might have observed that DaskDMatrix construction can take large amounts of time,Īnd sometimes throws errors that don’t seem to be relevant to DaskDMatrix. Interface, DaskDMatrix is implicitly constructed for all input data during the fit or
The dask API in XGBoost requires construction of DaskDMatrix. DaskXGBRegressor () Why is the initialization of DaskDMatrix so slow and throws weird errors DaskDMatrix ( client, X, y ) output = xgb. random ( size = ( num_obs, 1 ), chunks = ( 1000, 1 )) dtrain = xgb. random ( size = ( num_obs, num_features ), chunks = ( 1000, num_features )) y = da. Client ( cluster ) # X and y must be Dask dataframes or arrays num_obs = 1e5 num_features = 20 X = da.
Import xgboost as xgb import dask.array as da import dask.distributed if _name_ = "_main_" : cluster = dask.
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