Comments (7)
Hey :),
Thanks for finding our work interesting. The first thing that comes into my mind, is that maybe the arguments might not be set ideally. For example, with a large dataset, func_eval_time
needs to cover the time for a full function evaluation.
WellTunedSimpleNets/cocktails/main_experiment.py
Lines 61 to 65 in 494c978
I would suggest running for one epoch initially and not for 105, to see if the problem persists and then to adjust accordingly.
WellTunedSimpleNets/cocktails/main_experiment.py
Lines 66 to 70 in 494c978
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I will consider the issue resolved, if it still does not work, you can reopen it and we can discuss it further :).
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Sorry for the delayed response.
I tested you proposal with a highly reduced dataset = 7500 samples and a much longer func_eval_time = 86400 / epoch = 1. This setup achieved a result within ~ 45 minutes.
The moment I increased the dataset to 15000 samples with same func_eval_time and epoch it fails again.
By the way: I increased the number of workers to 8.
Any further suggestions? Any help highly appreciated :)
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HI @ArlindKadra, by any chance some additional ideas?
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Hey @DonIvanCorleone,
We do have an experimental branch that you could try out, it is reg_cocktails-pytorch_embedding
. Theoretically, it should perform better in your case if there are multiple features and possibly some of them are categorical with a high dimensionality.
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Hey @ArlindKadra,
thanks for your support. I tried yesterday and today to get your new approach running but at the moment something broke "entirely".
I am trying to dockerize the entire project and integrated all the required steps from this repository and the image seems to be properly setup but unfortunately in the very end I always receive the following new error after starting the process:
[ERROR] [2022-10-28 10:30:17,276:Client-AutoPyTorch:734d2973-56ab-11ed-815f-0242ac110002:1] get_smac_object() got an unexpected keyword argument 'initial_configurations'
Its unclear to me, whats happening and more specifically Why? :)
It seems to me, that the setup of autoPyTorch and this repository is more complicated than expected and I don`t want to steal more support time from you. If you ever find the time to create a wokring dockerized version of your solution it would be awesome, but at the moment I don't see whats not working properly.
Many thanks again
Cheers
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For the sake of completion these are the installed modules
Package Version
absl-py 1.3.0
autoPyTorch 0.2
cachetools 5.2.0
catboost 1.1
certifi 2022.9.24
charset-normalizer 2.1.1
click 8.1.3
cloudpickle 2.2.0
ConfigSpace 0.6.0
contourpy 1.0.5
cycler 0.11.0
Cython 0.29.32
dask 2022.10.0
distributed 2022.10.0
emcee 3.1.3
flaky 3.7.0
fonttools 4.38.0
fsspec 2022.10.0
google-auth 2.13.0
google-auth-oauthlib 0.4.6
graphviz 0.20.1
grpcio 1.50.0
HeapDict 1.0.1
idna 3.4
imageio 2.22.2
imgaug 0.4.0
importlib-metadata 5.0.0
Jinja2 3.1.2
joblib 1.2.0
kiwisolver 1.4.4
liac-arff 2.5.0
lightgbm 3.3.3
locket 1.0.0
lockfile 0.12.2
Markdown 3.4.1
MarkupSafe 2.1.1
matplotlib 3.6.1
minio 7.1.12
msgpack 1.0.4
networkx 2.8.7
numpy 1.23.4
oauthlib 3.2.2
opencv-python 4.6.0.66
openml 0.12.2
packaging 21.3
pandas 1.5.1
partd 1.3.0
Pillow 9.2.0
pip 22.2.2
plotly 5.11.0
protobuf 3.19.6
psutil 5.9.3
pyarrow 10.0.0
pyasn1 0.4.8
pyasn1-modules 0.2.8
pynisher 0.6.4
pyparsing 3.0.9
pyrfr 0.8.3
python-dateutil 2.8.2
pytz 2022.5
PyWavelets 1.4.1
PyYAML 6.0
regex 2022.9.13
requests 2.28.1
requests-oauthlib 1.3.1
rsa 4.9
scikit-image 0.19.3
scikit-learn 0.24.2
scipy 1.9.3
setuptools 63.4.1
Shapely 1.8.5.post1
six 1.16.0
smac 1.4.0
sortedcontainers 2.4.0
tabulate 0.9.0
tblib 1.7.0
tenacity 8.1.0
tensorboard 2.10.1
tensorboard-data-server 0.6.1
tensorboard-plugin-wit 1.8.1
threadpoolctl 3.1.0
tifffile 2022.10.10
toolz 0.12.0
torch 1.12.1
torchvision 0.13.1
tornado 6.1
typing_extensions 4.4.0
urllib3 1.26.12
Werkzeug 2.2.2
wheel 0.37.1
xmltodict 0.13.0
zict 2.2.0
zipp 3.10.0
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