RosettaCommons / RoseTTAFold

This package contains deep learning models and related scripts for RoseTTAFold
MIT License
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[Question] Is Linux ARM64 supported ? #127

Open gancho-ivanov opened 2 years ago

gancho-ivanov commented 2 years ago

Hello Rosetta community!

I'd like to ask whether it is safe to run RoseTTAFold on Linux ARM64 machines ? Does anyone have positive experience with it ?

I am talking about cloud friendly environments like AWS Graviton, Oracle/Azure/Google Ampere and Huawei's TaiShan. I am not asking about RasberryPi!

Thank you! Gancho

markjens commented 2 years ago

I am also interested in this! Linux ARM64 is used more and more for HPC. +1 for better support!

julien-faye commented 1 year ago

In my experience Linux ARM64 is not supported at the moment.

Creating conda environment on my Ubuntu 20.04.5 ARM64 fails with:

$ conda env create -f RoseTTAFold-linux.yml
Collecting package metadata (repodata.json): done
Solving environment: failed

ResolvePackageNotFound: 
  - gnutls==3.6.15=he1e5248_0
  - torchaudio==0.9.0=py38
  - intel-openmp==2021.2.0=h06a4308_610
  - libgfortran-ng==7.5.0=h14aa051_19
  - libunistring==0.9.10=h27cfd23_0
  - mkl-service==2.3.0=py38h27cfd23_1
  - pytorch-cluster==1.5.9=py38_torch_1.9.0_cu111
  - xz==5.2.5=h7b6447c_0
  - libpng==1.6.37=hbc83047_0
  - ffmpeg==4.3=hf484d3e_0
  - libidn2==2.3.1=h27cfd23_0
  - bzip2==1.0.8=h7b6447c_0
  - pytorch-spline-conv==1.2.1=py38_torch_1.9.0_cu111
  - biocore::blast-legacy=2.2.26
  - libffi==3.3=he6710b0_2
  - libiconv==1.15=h63c8f33_5
  - hhsuite
  - pytorch-geometric==1.7.2=py38_torch_1.9.0_cu111
  - zstd==1.4.9=haebb681_0
  - libstdcxx-ng==9.3.0=hd4cf53a_17
  - certifi==2021.5.30=py38h06a4308_0
  - mkl==2021.2.0=h06a4308_296
  - libgomp==9.3.0=h5101ec6_17
  - sqlite==3.36.0=hc218d9a_0
  - cffi==1.14.5=py38ha65f79e_0
  - ncurses==6.2=he6710b0_1
  - libtasn1==4.16.0=h27cfd23_0
  - torchvision==0.10.0=py38_cu111
  - brotlipy==0.7.0=py38h497a2fe_1001
  - psipred=4.01
  - libuv==1.40.0=h7b6447c_0
  - freetype==2.10.4=h5ab3b9f_0
  - ninja==1.10.2=hff7bd54_1
  - zlib==1.2.11=h7b6447c_3
  - pysocks==1.7.1=py38h578d9bd_3
  - openh264==2.1.0=hd408876_0
  - cudatoolkit==11.1.74=h6bb024c_0
  - lame==3.100=h7b6447c_0
  - lcms2==2.12=h3be6417_0
  - ld_impl_linux-64==2.35.1=h7274673_9
  - numpy==1.20.2=py38h2d18471_0
  - pytorch==1.9.0=py3.8_cuda11.1_cudnn8.0.5_0
  - nettle==3.7.3=hbbd107a_1
  - libgcc-ng==9.3.0=h5101ec6_17
  - pytorch-sparse==0.6.10=py38_torch_1.9.0_cu111
  - ca-certificates==2021.5.25=h06a4308_1
  - readline==8.1=h27cfd23_0
  - pillow==8.2.0=py38he98fc37_0
  - gmp==6.2.1=h2531618_2
  - python==3.8.10=h12debd9_8
  - pandas==1.2.5=py38h1abd341_0
  - libtiff==4.2.0=h85742a9_0
  - jpeg==9b=h024ee3a_2
  - scikit-learn==0.24.2=py38ha9443f7_0
  - numpy-base==1.20.2=py38hfae3a4d_0
  - cryptography==3.4.7=py38ha5dfef3_0
  - chardet==4.0.0=py38h578d9bd_1
  - libgfortran4==7.5.0=h14aa051_19
  - libwebp-base==1.2.0=h27cfd23_0
  - markupsafe==2.0.1=py38h497a2fe_0
  - setuptools==52.0.0=py38h06a4308_0
  - mkl_random==1.2.1=py38ha9443f7_2
  - pytorch-scatter==2.0.7=py38_torch_1.9.0_cu111
  - mkl_fft==1.3.0=py38h42c9631_2
  - tk==8.6.10=hbc83047_0
  - openssl==1.1.1k=h27cfd23_0
  - pip==21.1.3=py38h06a4308_0
  - lz4-c==1.9.3=h2531618_0
  - blas==1.0=mkl