问题
I am trying to compute the linear trend over time on a large high resolution ocean model dataset using dask.
I have followed this example (Applying a function along an axis of a dask array) and found the syntax of apply_along_axis easier.
I am currently using dask.array.apply_along_axis to wrap a numpy function on 1 dimensional arrays and then package the resulting dask array into an xarray Dataarray. Using top -u <username> suggest that the computation is not executed in parallel (~100% cpu use).
Should I expect a better performance from map_blocks? Or are there any suggestions on how to improve the performance of apply_along_axis?
Any tips are highly appreciated.
import numpy as np
from scipy import optimize
import xarray as xr
import dask.array as dsa
def _lin_trend(y):
x = np.arange(len(y))
return np.polyfit(x, y, 1)
def linear_trend(da, dim, name='parameter'):
da = da.copy()
axis_num = da.get_axis_num(dim)
dims = list(da.dims)
dims[axis_num] = name
coords = da.rename({dim:name}).coords
coords[name] = ['slope', 'intercept']
dsk = da.data
dsk_trend = dsa.apply_along_axis(_lin_trend,0,dsk)
out = xr.DataArray(dsk_trend, dims=dims, coords=coords)
return out
回答1:
I have been doing something similar using xarray's apply_ufunc (requires xarray v0.10 or later). This is likely to be a bit easier to manage than using the apply_along_axis function in dask.
import xarray as xr
import numpy as np
from scipy import stats
def _calc_slope(x, y):
'''wrapper that returns the slop from a linear regression fit of x and y'''
slope = stats.linregress(x, y)[0] # extract slope only
return slope
def linear_trend(obj):
time_nums = xr.DataArray(obj['time'].values.astype(np.float),
dims='time',
coords={'time': obj['time']},
name='time_nums')
trend = xr.apply_ufunc(_calc_slope, time_nums, obj,
vectorize=True,
input_core_dims=[['time'], ['time']],
output_core_dims=[[]],
output_dtypes=[np.float],
dask='parallelized')
return trend
Addressing your question about why the performance isn't as expected. This could be from a number of reasons. How is your dask array chunked? Which dask scheduler are you using? I'll update the second part of my answer after I get a better idea what your configuration is?
回答2:
I think that ultimately the performance is limited by the filesystem I am working on. To answer your question though, my dataset has the following shape:
<xarray.Dataset>
Dimensions: (st_edges_ocean: 51, st_ocean: 50, time: 101, xt_ocean: 3600, yt_ocean: 2700)
Coordinates:
* xt_ocean (xt_ocean) float64 -279.9 -279.8 -279.7 -279.6 -279.5 ...
* yt_ocean (yt_ocean) float64 -81.11 -81.07 -81.02 -80.98 -80.94 ...
* st_ocean (st_ocean) float64 5.034 15.1 25.22 35.36 45.58 55.85 ...
* st_edges_ocean (st_edges_ocean) float64 0.0 10.07 20.16 30.29 40.47 ...
* time (time) float64 3.634e+04 3.671e+04 3.707e+04 3.744e+04 ...
So it is rather big and needs a long time to read from disk. I have rechunked it so that the time dimension is a single chunk
dask.array<concatenate, shape=(101, 50, 2700, 3600), dtype=float64,
chunksize=(101, 1, 270, 3600)>
That did not make a big difference for the performance (it still takes about 20 hrs for the function to finish (that is including reading and writing to disk). I am currently only chunking in time, e.g.
dask.array<concatenate, shape=(101, 50, 2700, 3600), dtype=float64,
chunksize=(1, 1, 2700, 3600)>
I was interested in the relative performance of both methods and ran a test on my laptop.
import xarray as xr
import numpy as np
from scipy import stats
import dask.array as dsa
slope = 10
intercept = 5
t = np.arange(250)
x = np.arange(10)
y = np.arange(500)
z = np.arange(200)
chunks = {'x':10, 'y':10}
noise = np.random.random([len(x), len(y), len(z), len(t)])
ones = np.ones_like(noise)
time = ones*t
data = (time*slope+intercept)+noise
da = xr.DataArray(data, dims=['x', 'y', 'z', 't'],
coords={'x':('x', x),
'y':('y', y),
'z':('z', z),
't':('t', t)})
da = da.chunk(chunks)
da
I now defined a set of private functions (using both linregress and polyfit to calculate the slope of a timeseries), as well as different implementations using dask.apply_along and xarray.apply_ufunc.
def _calc_slope_poly(y):
"""ufunc to be used by linear_trend"""
x = np.arange(len(y))
return np.polyfit(x, y, 1)[0]
def _calc_slope(y):
'''returns the slop from a linear regression fit of x and y'''
x = np.arange(len(y))
return stats.linregress(x, y)[0]
def linear_trend_along(da, dim):
"""computes linear trend over 'dim' from the da.
Slope and intercept of the least square fit are added to a new
DataArray which has the dimension 'name' instead of 'dim', containing
slope and intercept for each gridpoint
"""
da = da.copy()
axis_num = da.get_axis_num(dim)
trend = dsa.apply_along_axis(_calc_slope, axis_num, da.data)
return trend
def linear_trend_ufunc(obj, dim):
trend = xr.apply_ufunc(_calc_slope, obj,
vectorize=True,
input_core_dims=[[dim]],
output_core_dims=[[]],
output_dtypes=[np.float],
dask='parallelized')
return trend
def linear_trend_ufunc_poly(obj, dim):
trend = xr.apply_ufunc(_calc_slope_poly, obj,
vectorize=True,
input_core_dims=[[dim]],
output_core_dims=[[]],
output_dtypes=[np.float],
dask='parallelized')
return trend
def linear_trend_along_poly(da, dim):
"""computes linear trend over 'dim' from the da.
Slope and intercept of the least square fit are added to a new
DataArray which has the dimension 'name' instead of 'dim', containing
slope and intercept for each gridpoint
"""
da = da.copy()
axis_num = da.get_axis_num(dim)
trend = dsa.apply_along_axis(_calc_slope_poly, axis_num, da.data)
return trend
trend_ufunc = linear_trend_ufunc(da, 't')
trend_ufunc_poly = linear_trend_ufunc_poly(da, 't')
trend_along = linear_trend_along(da, 't')
trend_along_poly = linear_trend_along_poly(da, 't')
Timing the computation seems to indicate that the apply_along method might be marginally faster. Using polyfit instead of linregress seems to have quite a big influences though. I am not sure why this is much faster but perhaps this is of interest to you.
%%timeit
print(trend_ufunc[1,1,1].data.compute())
4.89 s ± 180 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
%%timeit
trend_ufunc_poly[1,1,1].compute()
2.74 s ± 182 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
%%timeit
trend_along[1,1,1].compute()
4.58 s ± 193 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
%%timeit
trend_along_poly[1,1,1].compute()
2.64 s ± 65 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
来源：https://stackoverflow.com/questions/47314800/dask-performance-apply-along-axis