Comments (6)
--filter_sTF 1 --filter_sStart 3 --filter_sEnd 4 --suffix_label 0
--genus (should not affect)
--corr (should not affect)
Those should be the less memory using settings
if there is still a memory problem it might be because of the memory it needs to create the huge images. So input should not been affecting memory but image creation is affecting it.
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Thanks, I am trying the options now and will keep you up to date
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How much memory did you have allocated in slurm?
rcug_lw@hpc05:/working2/rcug_lw/konstantinos/fabian_test$ srun python3 heat5.py --batch_files marie.txt.filt.sort.csv.forpython.txt --filter_sTF 1 --filter_sStart 3 --filter_s End 4 --suffix_label 0 --where ../cov_test_mariep/
srun: job 3367604 queued and waiting for resources
srun: error: Lookup failed: Unknown host
srun: job 3367604 has been allocated resources
/working2/rcug_lw/miniconda3/lib/python3.7/site-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.
import pandas.util.testing as tm
####Current directory is : /working2/rcug_lw/konstantinos/fabian_test
####Current file.txt with filenames to open is:marie.txt.filt.sort.csv.forpython.txt
KGCF01_S4_R1 dataset
Successfully created the directory /working2/rcug_lw/konstantinos/fabian_test/KGCF01_S4_R1
Successfully created the directory /working2/rcug_lw/konstantinos/fabian_test/KGCF01_S4_R1/heatmap
Traceback (most recent call last):
File "heat5.py", line 211, in <module>
result = pd.merge(leftPANDA, rightPANDA, how='outer', on=['colA'])
File "/working2/rcug_lw/miniconda3/lib/python3.7/site-packages/pandas/core/reshape/merge.py", line 88, in merge
return op.get_result()
File "/working2/rcug_lw/miniconda3/lib/python3.7/site-packages/pandas/core/reshape/merge.py", line 668, in get_result
self._maybe_add_join_keys(result, left_indexer, right_indexer)
File "/working2/rcug_lw/miniconda3/lib/python3.7/site-packages/pandas/core/reshape/merge.py", line 821, in _maybe_add_join_keys
result[name] = key_col
File "/working2/rcug_lw/miniconda3/lib/python3.7/site-packages/pandas/core/frame.py", line 2938, in __setitem__
self._set_item(key, value)
File "/working2/rcug_lw/miniconda3/lib/python3.7/site-packages/pandas/core/frame.py", line 3000, in _set_item
value = self._sanitize_column(key, value)
File "/working2/rcug_lw/miniconda3/lib/python3.7/site-packages/pandas/core/frame.py", line 3645, in _sanitize_column
value = value.copy(deep=True)
File "/working2/rcug_lw/miniconda3/lib/python3.7/site-packages/pandas/core/indexes/base.py", line 861, in copy
new_index = self._shallow_copy(self._data.copy())
MemoryError: Unable to allocate 41.4 GiB for an array with shape (5556669383,) and data type object
srun: error: hpc-rc05: task 0: Exited with exit code 1
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@Colorstorm I haven't tried the script out, but I saw uses up to 480 GB RAM on one server this morning.
from heatmaps.
Konstantinos' working command:
command i used on Ubuntu * ubuntu on windows 10..! on a 8 GB deskop on toy dataset (Github) :
python3 heat5.py --batch_files sample.txt --filter_sTF 1 --filter_sStart 0 --filter_sEnd 4 --suffix_label A --genus 1 --corr 0
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Huge RAM use was due to the coverage window, and not the bam.txt files, being used as input.
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Related Issues (6)
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