conda_install_scripts

Usage

Polaris

qsub -A datascience -q preemptable -l select=1:ncpus=64:ngpus=4,filesystems=swift,walltime=02:30:00 -joe -- install_datascience_conda_fromsource.sh /soft/datascience/conda/2023-01-10

ThetaGPU

qsub -t 170 -n 1 -q full-node -A datascience -M <email> -o build.out -e build.out ./install_datascience_conda.sh /lus/theta-fs0/software/thetagpu/conda/2023-01-11

Dependencies

How to download the CUDA Toolkit from NVIDIA and extract just the toolkit, not installing the driver, etc. without sudo permissions:

E.g. on Polaris, download from NVIDIA, selecting Target Platform:

Linux > x86_64 > SLES > Version 15 > runfile (local) =
https://developer.nvidia.com/cuda-downloads?target_os=Linux&target_arch=x86_64&Distribution=SLES&target_version=15&target_type=runfile_local

cd /soft/compilers/cudatoolkit
wget https://developer.download.nvidia.com/compute/cuda/11.7.1/local_installers/cuda_11.7.1_515.65.01_linux.run
sh cuda_11.7.1_515.65.01_linux.run --silent --toolkit --toolkitpath=$PWD/cuda-11.7.1

To-do and notes, 2023

Create bash scripts for testing environments based on notes in https://anl.app.box.com/notes/1001252052445 (WIP: https://anl.app.box.com/notes/1124584874420)
Revisit potential cloning issues from around 2023-01-13, namely Romit vs. Taylor's error logs
ThetaGPU script is not setup to install parallel h5py like in Polaris script
Add Mxnet
Move future conda environments from Python 3.8 to 3.9 (requirement for HPE Dragon e.g.), or even 3.10 for better Python error messages. Done. Switched to 3.10 starting in January 2023
conda-forge just has numpy, non-metapackage? No numpy-base, unlike defaults? https://stackoverflow.com/questions/50699252/anaconda-environment-installing-packages-numpy-base
Why does ThetaGPU seem to demand an OpenMPI/UCX module built against CUDA 11.8 and not 11.4 when TF/Torch/etc. built with 11.8, yet Cray MPICH on Polaris doesnt seem to care about the minor version of CUDA loaded at runtime and used to build the deep learning libraries?
Double check that rpath solution to DeepSpeed dynamic linking to libaio is working. Kinda. Still need to do this at runtime: CFLAGS="-I${CONDA_PREFIX}/include/" LDFLAGS="-L${CONDA_PREFIX}/lib/" ds_report, presumably because of the JITing? How will this work for users in practice?
Why does pip install sdv>=0.17.1 reinstall numpy everywhere, and also breaks torch, installs other junk on Polaris? numpy 1.24.1 ---> 1.22.4 on ThetaGPU, even though the existing version seems to match??? I was skipping installing sdv, but switching from Python 3.8 to 3.10 somehow avoided the problems on Polaris. Doesn't make much sense, as even the conditional python_version<'3.10' requirements seemed sufficiently loose to not cause problems. Maybe it was the CTGAN dependency, since it is the only one which direclty lists torch in setup.py? But those version requirements are also loose; working theory was that some dep was picky about numpy version, which forced a re-install of PyTorch
- https://github.com/sdv-dev/SDV/blob/master/setup.py
- https://github.com/sdv-dev/CTGAN/blob/master/setup.py
- cd /soft/datascience/conda/2023-01-10/deephyper pip install --dry-run ".[analytics,hvd,nas,popt,autodeuq,sdv]"
- Binary search the deps of sdv in the future if it causes problems again.

Collecting numpy<2,>=1.20.0
  Downloading numpy-1.22.4-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (16.9 MB)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 16.9/16.9 MB 228.4 MB/s eta 0:00:00
       Attempting uninstall: numpy
    Found existing installation: numpy 1.24.1
    Uninstalling numpy-1.24.1:
      Successfully uninstalled numpy-1.24.1

Consider fixes for problems arising from mixing conda-forge and defaults packages. Edit: now trying conda install -c defaults -c conda-forge ... on the one line.
- Check that "conda install" after the environment is made doesnt return a bunch of inconsistent pkg warnings

https://conda.io/projects/conda/en/latest/user-guide/tasks/manage-channels.html

https://conda-forge.org/docs/user/tipsandtricks.html

conda config --set channel_priority strict

https://stackoverflow.com/questions/65483245/how-to-avoid-using-conda-forge-packages-unless-necessary

conda install -c defaults -c conda-forge somepackage

which puts defaults with top priority. Or:

conda install conda-forge::somepackage

and this will not change the channel priority.

Also, interestingly:

To solve these issues, conda-forge has created special dummy builds of the mpich and openmpi libraries that are simply shell packages with no contents. These packages allow the conda solver to produce correct environments while avoiding installing MPI binaries from conda-forge. You can install the dummy package with the following command

$ conda install "mpich=x.y.z=external_*"
$ conda install "openmpi=x.y.z=external_*"