I have this function that I would like to apply to a large dataframe in parallel:
from rdkit import Chem
from rdkit.Chem.MolStandardize import rdMolStandardize
from rdkit import RDLogger
RDLogger.DisableLog('rdApp.*')
def standardize_smiles(smiles):
if smiles is None:
return None
try:
mol = Chem.MolFromSmiles(smiles)
# removeHs, disconnect metal atoms, normalize the molecule, reionize the molecule
clean_mol = rdMolStandardize.Cleanup(mol)
# if many fragments, get the "parent" (the actual mol we are interested in)
parent_clean_mol = rdMolStandardize.FragmentParent(clean_mol)
# try to neutralize molecule
uncharger = rdMolStandardize.Uncharger() # annoying, but necessary as no convenience method exists
uncharged_parent_clean_mol = uncharger.uncharge(parent_clean_mol)
# note that no attempt is made at reionization at this step
# nor at ionization at some pH (rdkit has no pKa caculator)
# the main aim to to represent all molecules from different sources
# in a (single) standard way, for use in ML, catalogue, etc.
te = rdMolStandardize.TautomerEnumerator() # idem
taut_uncharged_parent_clean_mol = te.Canonicalize(uncharged_parent_clean_mol)
return Chem.MolToSmiles(taut_uncharged_parent_clean_mol)
#except:
# return False
standardize_smiles('CCC')
'CCC'
However, neither Dask, nor Swifter, nor Ray can do the job. All frameworks use a single CPU for some reason.
Native Pandas
import pandas as pd
N = 1000
smilest_test = pd.DataFrame({'smiles': ['CCC']*N})
smilest_test
CPU times: user 3.58 s, sys: 0 ns, total: 3.58 s
Wall time: 3.58 s
Swifter 1.3.4
smiles_test['standardized_siles'] = smiles_test.smiles.swifter.allow_dask_on_strings(True).apply(standardize_smiles)
CPU times: user 892 ms, sys: 31.4 ms, total: 923 ms
Wall time: 5.14 s
While this WORKS with the dummy data, it does not with the real data, which looks like this:
The strings are a bit more complicated than the ones in the dummy data.
it seems first swifter needs some time to prepare the parallel execution and only uses one core, but then uses more cores. However, for the real data, it only uses 3 out of 8 cores.
I have the same issue with other frameworks such as dask, ray, modin, swifter.
Is there something that I miss here? Is there a problem when the dataframe contains stings? Why does the parallel execution take so much time even on a single computer (with multiple cores)? Or is there an issue with the RDKit library that I am using that makes it difficult to parallelize the above function?
Related
I tried to perform with Dask and xarray some analysis (e.g. avg) over two datasets, then compute a difference between the two results.
This is my code
cluster = LocalCluster(n_workers=5, threads_per_worker=3, **worker_kwargs)
def calc_avg(path):
mean = xr.open_mfdataset( path,combine='nested', concat_dim="time", parallel=True, decode_times=False, decode_cf=False)['var'].sel(lat=slice(south,north), lon=slice(west,east)).mean(dim='time')
return mean
def diff_(x,y):
return x-y
p1 = "/path/to/first/multi-file/dataset"
p2 = "/path/to/second/multi-file/dataset"
a = dask.delayed(calc_avg)(p1)
b = dask.delayed(calc_avg)(p2)
total = dask.delayed(diff_)(a,b)
result = total.compute()
The executiuon time here is 17s.
However, plotting the result (result.plot()) takes more than 1 min, so it seems that the calculation actually happens when trying to plot the result.
Is this the proper way to use Dask delayed?
You’re wrapping a call to xr.open_mfdataset, which is itself a dask operation, in a delayed function. So when you call result.compute, you’re executing the functions calc_avg and mean. However, calc_avg returns a dask-backed DataArray. So yep, the 17s task converts the scheduled delayed dask graph of calc_avg and mean into a scheduled dask.array dask graph of open_mfdataset and array ops.
To resolve this, drop the delayed wrappers and simply use the dask.array xarray workflow:
a = calc_avg(p1) # this is already a dask array because
# calc_avg calls open_mfdataset
b = calc_avg(p2) # so is this
total = a - b # dask understands array math, so this "just works"
result = total.compute() # execute the scheduled job
See the xarray guide to parallel computing with dask for an introduction.
My problem is as follow:
I have several datasets (900K, 1M7 and 1M7 entries) in csv format which I load into multiple Dask Dataframe.
Then I concatenate them all in one Dask Dataframe that I can feed to my Snorkel Applier, which applies a bunch of Labeling Function to each row of my Dataframe and return a numpy array with as many rows as there are in the Dataframe and as many columns as there are Labeling Functions.
The call to Snorkel Applier seems to take forever when I do that with 3 datasets (more than 2 days...). However if I just run the code with only the first dataset, the call takes around 2 hours. Of course I don't do the concatenation step.
So I was wondering how can this be ? Should I change the number of partitions in the concatenated Dataframe ? Or maybe I'm using Dask badly in the first place ?
Here is the code I'm using:
from snorkel.labeling.apply.dask import DaskLFApplier
import dask.dataframe as dd
import numpy as np
import os
start = time.time()
applier = DaskLFApplier(lfs) # lfs are the function that are going to be applied, one of them featurize one of the column of my Dataframe and apply a sklearn classifier (I put n_jobs to None when loading the model)
# If I have only one CSV to read
if isinstance(PATH_TO_CSV, str):
training_data = dd.read_csv(PATH_TO_CSV, lineterminator=os.linesep, na_filter=False, dtype={'size': 'int32'})
slices = None
# If I have several CSV
elif isinstance(PATH_TO_CSV, list):
training_data_list = [dd.read_csv(path, lineterminator=os.linesep, na_filter=False, dtype={'size': 'int32'}) for path in PATH_TO_CSV]
training_data = dd.concat(training_data_list, axis=0)
# some useful things I do to know where to slice my final result and be sure I can assign each part to each dataset
df_sizes = [len(df) for df in training_data_list]
cut_idx = np.insert(np.cumsum(df_sizes), 0, 0)
slices = list(zip(cut_idx[:-1], cut_idx[1:]))
# The call that lasts forever: I tested all the code above without that line on my 3 datasets and it runs perfectly fine
L_train = applier.apply(training_data)
end = time.time()
print('Time elapsed: {}'.format(timedelta(seconds=end-start)))
If you need more info I will try to get them to you as much as I can.
Thank in you advance for your help :)
It seems that by default applier function is using processes, so does not benefit from additional workers you might have available:
# add this to the beginning of your code
from dask.distributed import Client
client = Client()
# you can see the address of the client by typing `client` and opening the dashboard
# skipping your other code
# you need to pass the client explicitly to the applier
# after launching this open the dashboard and watch the workers work :)
L_train = applier.apply(training_data, scheduler=client)
I am using Dask/Xarray with a ~150 GB dataset on a distributed cluster on a HPC system. I have the computation component complete, which takes about ~30 minutes. I want to save the final result to a NETCDF4 file, but writing the data to a NETCDF file is quite slow (~3hrs) and seems to not run in parallel. It is unclear to me if the "to_netcdf" function in Xarray is supposed to support parallel writes. Currently my approach is to write an empty netcdf file with NetCDF4 and then append the data from the Xarray:
f_mosaic = 't1.nc'
meta = {'width': dat_f.shape[1],
'height': dat_f.shape[2],
'crs': rasterio.crs.CRS(init='epsg:'+fi['CPER']['Reflectance']['Metadata']['Coordinate_System']['EPSG Code'].value.decode("utf-8")),
'transform': aff_final,
'count': dat_f.shape[0]}
with netCDF4.Dataset(f_mosaic, mode='w', format="NETCDF4") as t1:
# Create spatial dimensions
y = t1.createDimension('y', meta['width'])
x = t1.createDimension('x', meta['height'])
wl_dim = t1.createDimension('wl',meta['count'])
reflectance = t1.createVariable("reflectance","int16",("wl","y","x",),fill_value=null_val,zlib=True)
reflectance.setncattr('grid_mapping', 'crs')
crs = t1.createVariable('crs', 'c')
crs.spatial_ref = meta['crs'].wkt
crs.epsg_code = meta['crs'].to_string()
crs.GeoTransform = " ".join(str(x) for x in meta['transform'].to_gdal())
dat_f.to_netcdf(path=f_mosaic,mode='a',format='NETCDF4',encoding={'reflectance':{'zlib':True}})
Overall, the question is, how can I write this data to a NETCDF4 file quickly? Does dask/Xarray support parallel writes with NETCDF4? If so, what am I doing incorrectly?
Thanks!
I'm trying to use dask bag for wordcount 30GB of json files, I strict according to the tutoral from offical web: http://dask.pydata.org/en/latest/examples/bag-word-count-hdfs.html
But still not work, my single machine is 32GB memory and 8 cores CPU.
My code below, I used to processing 10GB file even not work, the error is running couple of hours without any notification the jupyter was collapsed, i tried on Ubuntu and Windows both system is the same problem. So i suspect if dask bag can processing data out of memory? or is that my code incorrect?
The test data from http://files.pushshift.io/reddit/comments/
import dask.bag as db
import json
b = db.read_text('D:\RC_2015-01\RC_2012-04')
records = b.map(json.loads)
result = b.str.split().concat().frequencies().topk(10, lambda x: x[1])
%time f = result.compute()
f
Try setting a blocksize in the 10MB range when reading from the single file to break it up a bit.
In [1]: import dask.bag as db
In [2]: b = db.read_text('RC_2012-04', blocksize=10000000)
In [3]: %time b.count().compute()
CPU times: user 1.22 s, sys: 56 ms, total: 1.27 s
Wall time: 20.4 s
Out[3]: 19044534
Also, as a warning, you create a bag records but then don't do anything with it. You might want to remove that line.
I'm writing code to find local alignments between two sequences. Here is a minimal, working example I've been working on:
from Bio import pairwise2
from Bio.pairwise2 import format_alignment
seq1 = "GTGGTCCTAGGC"
seq2 = "GCCTAGGACCAC"
# scores for the alignment
match =1
mismatch = -2
gapopen = -2
gapext = 0
# see: http://biopython.org/DIST/docs/api/Bio.pairwise2-module.html
# 'localms' takes <seq1,seq2, match,mismatch,open,extend>
for a in pairwise2.align.localms(seq1,seq2,match,mismatch,gapopen,gapext):
print(format_alignment(*a))
The following code runs with the output
GTGGTCCTAGGC----
|||||
----GCCTAGGACCAC
Score=5
But a score of '6' should be possible, finding the 'C-C' next to the 5 alignments, like so:
GTGGTCCTAGGC----
||||||
----GCCTAGGACCAC
Score=6
Any ideas on what's going on?
This seems to be a bug in the current implementation of local alignments in Biopython's pairwise2 module. There is a recent pull request (#782) on Biopython's GitHub, which should solve your problem:
>>> from Bio import pairwise2 # This is the version from the pull request
>>> seq1 = 'GTGGTCCTAGGC'
>>> seq2 = 'GCCTAGGACCAC'
>>> for a in pairwise2.align.localms(seq1, seq2, 1, -2, -2, 0):
print pairwise2.format_alignment(*a)
GTGGTCCTAGGC----
||||||
----GCCTAGGACCAC
Score=6
If you are working with short sequences only, you can just download
the code for pairwise2.py from the pull request
mentioned above. In addition you need to 'inactivate' the respective
C module (cpairwise2.pyd or
cpairwise2.so), e.g. by renaming it or by removing the
import of the C functions at the end of
pairwise2.py(from .cpairwise import ...).
If your are working with longer sequences, you will need the speed enhancement of the C module. Thus you also have to download
cpairwise2module.c from the pull request and compile it
into cpairwise2.pyd (for Windows systems) or
cpairwise2.so (Unix, Linux).
EDIT: In Biopython 1.68 the problem is solved.