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‎CITATION.cff‎

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# This CITATION.cff file was generated with cffinit.
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# Visit https://bit.ly/cffinit to generate yours today!
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cff-version: 1.2.0
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title: >-
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Learned regularizations for multi-parameter elastic full
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waveform inversion using diffusion models
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message: >-
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If you use this software, please cite it using the
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metadata from this file.
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type: software
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authors:
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- given-names: Mohammad Hasyim
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family-names: Taufik
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email: mohammad.taufik@kaust.edu.sa
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name-particle: Mohammad
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affiliation: KAUST
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- given-names: Fu
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family-names: Wang
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email: fu.wang@kaust.edu.sa
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affiliation: KAUST
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- given-names: Tariq
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family-names: Alkhalifah
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email: tariq.alkhalifah@kaust.edu.sa
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affiliation: KAUST
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repository-code: 'https://github.com/DeepWave-KAUST/diffefwi'

‎README.md‎

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![LOGO](https://github.com/DeepWave-KAUST/diffefwi/blob/main/asset/diffefwi.png)
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Reproducible material for **DW0028 - Mohammad H. Taufik, Fu Wang, Tariq Alkhalifah.**
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# Project structure
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This repository is organized as follows:
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* :open_file_folder: **asset**: folder containing logo.
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* :open_file_folder: **data**: a folder containing the subsampled velocity models used to train the diffusion model.
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* :open_file_folder: **notebooks**: reproducible notebook for the third synthetic test of the paper (near-surface SEAM Arid model).
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* :open_file_folder: **saves**: a folder containing the trained diffusion model (using the combined dataset) and results from the EFWI.
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* :open_file_folder: **scripts**: a set of Python scripts used to run diffusion training, diffusion sampling, and EFWI.
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* :open_file_folder: **src**: a folder containing routines for the `diffefwi` source file.
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## Notebooks
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The following notebooks are provided:
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- :orange_book: ``Example-2-efwi.ipynb``: notebook reproducing the results of the near-surface synthetic test in the paper.
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## Scripts
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The following scripts are provided:
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- 📝: ``Example-0-unconditional-sampling.py``: drawing unconditional samples from a trained diffusion model.
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- 📝: ``Example-1-diffusion-training.py``: diffusion model training using the `combined` dataset of the paper.
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- 📝: ``Example-2-efwi.py``: simple multi-parameter checkerboard test with an acquisition setting mimicking the land field data application of the paper.
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## Getting started :space_invader: :robot:
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To ensure the reproducibility of the results, we suggest using the `environment.yml` file when creating an environment.
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To install the environment, run the following command:
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```
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./install_env.sh
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```
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It will take some time, but if, in the end, you see the word `Done!` on your terminal, you are ready to go.
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Remember to always activate the environment by typing:
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```
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conda activate diffefwi
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```
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**Disclaimer:** All experiments have been carried on a Intel(R) Xeon(R) Silver 4316 CPU @ 2.30GHz equipped with a single NVIDIA A100 GPU. Different environment
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configurations may be required for different combinations of workstation and GPU.
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## Cite us
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```bibtex
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@article{taufik2024learned,
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title={Learned regularizations for multi-parameter elastic full waveform inversion using diffusion models},
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doi={10.1029/2024JH000125},
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author={Taufik, Mohammad Hasyim and Wang, Fu and Alkhalifah, Tariq},
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journal={Journal of Geophysical Research: Machine Learning and Computation},
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year={2024},
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publisher={Wiley Online Library}
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}
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‎asset/diffefwi.png‎

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‎asset/placeholder‎

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‎environment.yml‎

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name: diffefwi
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channels:
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- conda-forge
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- defaults
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dependencies:
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- _libgcc_mutex=0.1=conda_forge
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- _openmp_mutex=4.5=2_gnu
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- bzip2=1.0.8=hd590300_5
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- ca-certificates=2023.11.17=hbcca054_0
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- cudatoolkit-dev=11.7.0=h1de0b5d_6
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- ld_impl_linux-64=2.40=h41732ed_0
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- libffi=3.4.2=h7f98852_5
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- libgcc-ng=13.2.0=h807b86a_3
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- libgomp=13.2.0=h807b86a_3
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- libnsl=2.0.1=hd590300_0
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- libsqlite=3.44.2=h2797004_0
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- libuuid=2.38.1=h0b41bf4_0
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- libzlib=1.2.13=hd590300_5
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- ncurses=6.4=h59595ed_2
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- openssl=3.2.0=hd590300_1
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- pip=23.3.1=pyhd8ed1ab_0
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- python=3.9.18=h0755675_0_cpython
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- readline=8.2=h8228510_1
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- setuptools=68.2.2=pyhd8ed1ab_0
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- tk=8.6.13=noxft_h4845f30_101
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- wheel=0.42.0=pyhd8ed1ab_0
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- xz=5.2.6=h166bdaf_0
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- pip:
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- absl-py==2.0.0
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- anyio==4.1.0
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- appdirs==1.4.4
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- argon2-cffi==23.1.0
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- argon2-cffi-bindings==21.2.0
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- arrow==1.3.0
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- asttokens==2.4.1
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- async-lru==2.0.4
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- attrs==23.1.0
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- babel==2.13.1
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- beautifulsoup4==4.12.2
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- bleach==6.1.0
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- cachetools==5.3.2
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- certifi==2022.12.7
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- cffi==1.16.0
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- charset-normalizer==2.1.1
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- click==8.1.7
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- cmake==3.25.0
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- comm==0.2.0
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- contourpy==1.2.0
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- cycler==0.12.1
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- debugpy==1.8.0
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- decorator==5.1.1
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- deepwave==0.0.20
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- defusedxml==0.7.1
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- docker-pycreds==0.4.0
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- einops==0.7.0
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- exceptiongroup==1.2.0
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- executing==2.0.1
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- fastjsonschema==2.19.0
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- filelock==3.9.0
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- fonttools==4.45.1
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- fqdn==1.5.1
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- gitdb==4.0.11
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- gitpython==3.1.40
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- google-auth==2.23.4
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- google-auth-oauthlib==1.1.0
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- grpcio==1.59.3
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- h5py==3.10.0
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- idna==3.4
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- imageio==2.33.0
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- importlib-metadata==6.8.0
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- importlib-resources==6.1.1
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- ipykernel==6.27.1
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- ipython==8.18.1
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- ipywidgets==8.1.1
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- isoduration==20.11.0
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- jedi==0.19.1
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- jinja2==3.1.2
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- json5==0.9.14
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- jsonpointer==2.4
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- jsonschema==4.20.0
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- jsonschema-specifications==2023.11.1
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- jupyter==1.0.0
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- jupyter-client==8.6.0
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- jupyter-console==6.6.3
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- jupyter-core==5.5.0
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- jupyter-events==0.9.0
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- jupyter-lsp==2.2.1
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- jupyter-server==2.11.1
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- jupyter-server-terminals==0.4.4
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- jupyterlab==4.0.9
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- jupyterlab-pygments==0.3.0
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- jupyterlab-server==2.25.2
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- jupyterlab-widgets==3.0.9
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- kiwisolver==1.4.5
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- lazy-loader==0.3
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- lit==15.0.7
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- markdown==3.5.1
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- markupsafe==2.1.3
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- matplotlib==3.8.2
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- matplotlib-inline==0.1.6
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- mistune==3.0.2
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- mpmath==1.3.0
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- nbclient==0.9.0
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- nbconvert==7.11.0
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- nbformat==5.9.2
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- nest-asyncio==1.5.8
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- networkx==3.0
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- ninja==1.11.1.1
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- notebook==7.0.6
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- notebook-shim==0.2.3
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- numpy==1.24.1
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- nvidia-cublas-cu11==11.10.3.66
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- nvidia-cuda-cupti-cu11==11.7.101
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- nvidia-cuda-nvrtc-cu11==11.7.99
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- nvidia-cuda-runtime-cu11==11.7.99
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- nvidia-cudnn-cu11==8.5.0.96
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- nvidia-cufft-cu11==10.9.0.58
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- nvidia-curand-cu11==10.2.10.91
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- nvidia-cusolver-cu11==11.4.0.1
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- nvidia-cusparse-cu11==11.7.4.91
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- nvidia-nccl-cu11==2.14.3
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- nvidia-nvtx-cu11==11.7.91
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- oauthlib==3.2.2
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- overrides==7.4.0
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- pandas==2.1.3
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- pandocfilters==1.5.0
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- parso==0.8.3
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- pexpect==4.9.0
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- pillow==9.3.0
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- platformdirs==4.0.0
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- prometheus-client==0.19.0
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- prompt-toolkit==3.0.41
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- protobuf==4.23.4
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- psutil==5.9.6
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- ptyprocess==0.7.0
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- pure-eval==0.2.2
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- pyasn1==0.5.1
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- pyasn1-modules==0.3.0
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- pycparser==2.21
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- pygments==2.17.2
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- pyparsing==3.1.1
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- python-dateutil==2.8.2
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- python-json-logger==2.0.7
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- pytz==2023.3.post1
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- pyyaml==6.0.1
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- pyzmq==25.1.1
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- qtconsole==5.5.1
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- qtpy==2.4.1
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- referencing==0.31.1
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- requests==2.31.0
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- requests-oauthlib==1.3.1
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- rfc3339-validator==0.1.4
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- rfc3986-validator==0.1.1
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- rpds-py==0.13.2
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- rsa==4.9
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- scikit-image==0.22.0
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- scipy==1.11.4
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- send2trash==1.8.2
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- sentry-sdk==1.38.0
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- setproctitle==1.3.3
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- six==1.16.0
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- smmap==5.0.1
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- sniffio==1.3.0
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- soupsieve==2.5
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- stack-data==0.6.3
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- sympy==1.12
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- tensorboard==2.15.1
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- tensorboard-data-server==0.7.2
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- terminado==0.18.0
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- tifffile==2023.9.26
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- tinycss2==1.2.1
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- tomli==2.0.1
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- tornado==6.4
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- tqdm==4.66.1
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- traitlets==5.14.0
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- triton==2.0.0
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- types-python-dateutil==2.8.19.14
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- typing-extensions==4.4.0
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- tzdata==2023.3
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- uri-template==1.3.0
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- urllib3==1.26.13
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- wandb==0.16.0
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- wcwidth==0.2.12
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- webcolors==1.13
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- webencodings==0.5.1
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- websocket-client==1.6.4
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- werkzeug==3.0.1
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- widgetsnbextension==4.0.9
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- zipp==3.17.0

‎hatsyim.mplstyle‎

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# Matplotlib style for scientific plotting
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# This is the base style for "SciencePlots"
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# see: https://github.com/garrettj403/SciencePlots
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# Set color cycle: blue, green, yellow, red, violet, gray
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axes.prop_cycle : cycler('color', ['0C5DA5', '00B945', 'FF9500', 'FF2C00', '845B97', '474747', '9e9e9e'])
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axes.linewidth: 2.
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# label size
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axes.labelsize : large
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# Set default figure size
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figure.figsize : 3.5, 2.625
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figure.dpi : 200
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# Set x axis
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xtick.direction : in
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xtick.major.size : 3
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xtick.major.width : 0.5
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xtick.minor.size : 1.5
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xtick.minor.width : 0.5
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xtick.minor.visible : True
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xtick.top : True
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xtick.labelsize : large
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# Set y axis
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ytick.direction : in
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ytick.major.size : 3
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ytick.major.width : 0.5
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ytick.minor.size : 1.5
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ytick.minor.width : 0.5
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ytick.minor.visible : True
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ytick.right : True
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ytick.labelsize : large
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# dpi
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savefig.dpi : 100
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# Set line widths
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grid.linewidth : 1.
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lines.linewidth : 1.5
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# Remove legend frame
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legend.frameon : False
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# Always save as 'tight'
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savefig.bbox : tight
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savefig.pad_inches : 0.05
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savefig.transparent : True
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# Use serif fonts
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# font.serif : Times
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font.family : serif
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mathtext.fontset : dejavuserif
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# Use LaTeX for math formatting
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text.usetex : True
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text.latex.preamble : \usepackage{amsmath} \usepackage{amssymb}

‎install_env.sh‎

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#!/bin/bash
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#
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# Installer for package
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#
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# Run: ./install_env.sh
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# Create conda env
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source ~/miniconda3/etc/profile.d/conda.sh
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conda env create -f environment.yml
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conda activate diffefwi
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conda env list
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pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu117
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pip3 install --use-pep517 -e .
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pip3 install packaging
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pip3 install flash-attn --no-build-isolation
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echo 'Created and activated environment:' $(which python)
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# Check cupy works as expected
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echo 'Checking torch version and GPU'
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conda activate diffefwi
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python -c 'import torch; print(torch.__version__); print(torch.cuda.get_device_name(torch.cuda.current_device())); print(torch.ones(10).to("cuda:0"))'
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echo 'Done!'

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