alfabet
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NREL
Please update install info, can't replicate a working version
The installation info could be better. I didn't get it to run at all as described using pip install, because then upon import it complains that tensorflow is missing. If I use the setup.py instead, then the scikit module complains about min. py3.8. Finally, after all that (and a lot of wasted time), the tests_model.py fails due to uncompiled tensorflow model:
2023-01-04 15:48:50.354177: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: SSE4.1 SSE4.2 AVX AVX2 FMA To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags. 2023-01-04 15:48:52.097245: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: SSE4.1 SSE4.2 AVX AVX2 FMA To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags. 2023-01-04 15:48:52.744007: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1616] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 3849 MB memory: -> device: 0, name: NVIDIA GeForce GTX 1080 Ti, pci bus id: 0000:82:00.0, compute capability: 6.1 WARNING:tensorflow:No training configuration found in save file, so the model was not compiled. Compile it manually.
How did you complete the installation at the end?
I didn't, that's why I wrote here. But by now, I am sorry to say, I have put this on ice.
How did you complete the installation at the end?
I didn't, that's why I wrote here. But by now, I am sorry to say, I have put this on ice.
I couldn't install it on an M1 mac but on a linux no problem. Hope that helps
I managed a version which runs under Linux, unfortunately not with GPU (some library errors), but it works with CPU at least (tested on two entirely different machines) and works at least for regular py files (jupyter not included). create a "environment.yml" file (text to paste in the end of this post) in the alfabet folder, then run
conda env create -f environment.yml
if you want to test to see if it works at all without the jupyter stuff, here is a tiny script:
from alfabet import model
mm = model.predict(['CC', 'NCCO', "C1=CC=C(C=C1)[N+](=O)[O-]", 'CC(=O)O'])
mm.rename(columns={"bde_pred": "bde_pred(ML)", "bde": "bde(dft)"}, inplace=True)
mm.drop(columns=["bdfe_pred", "bdfe"], inplace=True)
print(mm)
in the file, paste this:
name: alfabetpy3.7 channels:
- conda-forge
- defaults dependencies:
- _libgcc_mutex=0.1
- _openmp_mutex=4.5
- boost=1.78.0
- boost-cpp=1.78.0
- brotli=1.1.0
- brotli-bin=1.1.0
- bzip2=1.0.8
- ca-certificates=2024.2.2
- cairo=1.16.0
- certifi=2024.2.2
- cycler=0.11.0
- expat=2.5.0
- font-ttf-dejavu-sans-mono=2.37
- font-ttf-inconsolata=3.000
- font-ttf-source-code-pro=2.038
- font-ttf-ubuntu=0.83
- fontconfig=2.14.2
- fonts-conda-ecosystem=1
- fonts-conda-forge=1
- fonttools=4.38.0
- freetype=2.12.1
- gettext=0.21.1
- greenlet=1.1.3
- icu=72.1
- importlib-metadata=4.11.4
- jpeg=9e
- kiwisolver=1.4.4
- lcms2=2.14
- ld_impl_linux-64=2.40
- lerc=4.0.0
- libblas=3.9.0
- libbrotlicommon=1.1.0
- libbrotlidec=1.1.0
- libbrotlienc=1.1.0
- libcblas=3.9.0
- libdeflate=1.14
- libexpat=2.5.0
- libffi=3.4.2
- libgcc-ng=13.2.0
- libgfortran-ng=13.2.0
- libgfortran5=13.2.0
- libglib=2.78.4
- libgomp=13.2.0
- libiconv=1.17
- liblapack=3.9.0
- libnsl=2.0.1
- libopenblas=0.3.26
- libpng=1.6.42
- libsqlite=3.45.1
- libstdcxx-ng=13.2.0
- libtiff=4.4.0
- libuuid=2.38.1
- libwebp-base=1.3.2
- libxcb=1.13
- libzlib=1.2.13
- matplotlib-base=3.5.3
- munkres=1.1.4
- ncurses=6.4
- numpy=1.21.6
- openjpeg=2.5.0
- openssl=3.2.1
- packaging=23.2
- pandas=1.3.5
- pcre2=10.42
- pillow=9.2.0
- pip=24.0
- pixman=0.43.2
- pthread-stubs=0.4
- pycairo=1.21.0
- pyparsing=3.1.1
- python=3.7.12
- python-dateutil=2.8.2
- python_abi=3.7
- pytz=2024.1
- rdkit=2022.09.1
- readline=8.2
- reportlab=3.5.68
- setuptools=59.8.0
- six=1.16.0
- sqlalchemy=1.4.42
- sqlite=3.45.1
- tk=8.6.13
- typing-extensions=4.7.1
- typing_extensions=4.7.1
- unicodedata2=14.0.0
- wheel=0.42.0
- xorg-kbproto=1.0.7
- xorg-libice=1.1.1
- xorg-libsm=1.2.4
- xorg-libx11=1.8.4
- xorg-libxau=1.0.11
- xorg-libxdmcp=1.1.3
- xorg-libxext=1.3.4
- xorg-libxrender=0.9.10
- xorg-renderproto=0.11.1
- xorg-xextproto=7.3.0
- xorg-xproto=7.0.31
- xz=5.2.6
- zipp=3.15.0
- zlib=1.2.13
- zstd=1.5.5
- pip:
- absl-py==2.1.0
- astunparse==1.6.3
- backcall==0.2.0
- cachetools==5.3.2
- charset-normalizer==3.3.2
- debugpy==1.7.0
- decorator==5.1.1
- entrypoints==0.4
- exceptiongroup==1.2.0
- flatbuffers==23.5.26
- gast==0.4.0
- google-auth==2.28.1
- google-auth-oauthlib==0.4.6
- google-pasta==0.2.0
- grpcio==1.62.0
- h5py==3.8.0
- idna==3.6
- iniconfig==2.0.0
- ipykernel==6.16.2
- ipython==7.34.0
- jedi==0.19.1
- joblib==1.3.2
- jupyter-client==7.4.9
- jupyter-core==4.12.0
- keras==2.11.0
- libclang==16.0.6
- markdown==3.4.4
- markupsafe==2.1.5
- matplotlib-inline==0.1.6
- nest-asyncio==1.6.0
- networkx==2.6.3
- nfp==0.3.12
- oauthlib==3.2.2
- opt-einsum==3.3.0
- parso==0.8.3
- pexpect==4.9.0
- pickleshare==0.7.5
- platformdirs==4.0.0
- pluggy==1.2.0
- pooch==1.8.1
- prompt-toolkit==3.0.43
- protobuf==3.19.6
- psutil==5.9.8
- ptyprocess==0.7.0
- pyasn1==0.5.1
- pyasn1-modules==0.3.0
- pygments==2.17.2
- pytest==7.4.4
- pyzmq==25.1.2
- requests==2.31.0
- requests-oauthlib==1.3.1
- rsa==4.9
- scikit-learn==0.24.2
- scipy==1.7.3
- tensorboard==2.11.2
- tensorboard-data-server==0.6.1
- tensorboard-plugin-wit==1.8.1
- tensorflow==2.11.0
- tensorflow-estimator==2.11.0
- tensorflow-io-gcs-filesystem==0.34.0
- termcolor==2.3.0
- threadpoolctl==3.1.0
- tomli==2.0.1
- tornado==6.2
- tqdm==4.66.2
- traitlets==5.9.0
- urllib3==2.0.7
- wcwidth==0.2.13
- werkzeug==2.2.3
- wrapt==1.16.0
For folks still waiting for a working version, it appears that the original authors have created some new/updated models while neglecting this version (including their web-based version). The paper providing updates and a link to a new repository is below. While not as easy as a pip install, I was able to get a working version going by downloading their code/models directly, following their example as provided.
https://doi.org/10.1039/D3DD00169E
https://github.com/patonlab/BDE-db2
For folks still waiting for a working version, it appears that the original authors have created some new/updated models while neglecting this version (including their web-based version). The paper providing updates and a link to a new repository is below. While not as easy as a pip install, I was able to get a working version going by downloading their code/models directly, following their example as provided.
https://doi.org/10.1039/D3DD00169E
https://github.com/patonlab/BDE-db2
Thanks!