Installed Software
Delta software is provisioned, when possible, using Spack to produce modules for use via the Lmod based module system. Select NVIDIA NGC containers are made available (see Containers) and are periodically updated from the NVIDIA NGC site. An automated list of available software can be found on the ACCESS website.
Modules/Lmod
Delta provides a set of modules and a variety of compilers. The default environment loads gcc and openmpi for CPU programs (no GPU-direct).
Modules supporting NVIDIA GPUs will contain “cuda” in the name of the module, for example, openmpi/4.1.5+cuda.
Unload cuda when building CPU-only packages to avoid accidentally linking cuda libraries.
Use module spider package_name to search for software in Lmod and see the steps to load it in your environment.
Module (Lmod) Command |
Example |
|---|---|
(display the currently loaded modules) |
$ module list
Currently Loaded Modules:
1) gcc/11.2.0 3) openmpi/4.1.2 5) modtree/gpu
2) ucx/1.11.2 4) cuda/11.6.1
|
(loads a package or metamodule such as modtree/gpu or netcdf-c) |
$ module load modtree/cpu
Due to MODULEPATH changes, the following have been reloaded:
1) gcc/11.2.0 2) openmpi/4.1.2 3) ucx/1.11.2
The following have been reloaded with a version change:
1) modtree/gpu => modtree/cpu
|
(finds modules and displays the ways to load them)
|
$ module spider openblas
---------------------------------------------------------------------------
openblas: openblas/0.3.20
----------------------------------------------------------------------------
You will need to load all module(s) on any one of the lines below before the
"openblas/0.3.20" module is available to load.
aocc/3.2.0
gcc/11.2.0
Help:
OpenBLAS: An optimized BLAS library
$ module -r spider "^r$"
----------------------------------------------------------------------------
r:
----------------------------------------------------------------------------
Versions:
r/4.1.
...
|
See also: User Guide for Lmod.
Please submit a support request for help with software not currently installed on the Delta system.
For single user or single project use cases the preference is for the user to use the Spack software package manager to install software locally against the system Spack installation. Delta support staff are available to provide limited assistance.
For general installation requests, the Delta project office will review requests for broad use and installation effort.
Python
Note
When submitting support requests for python, please provide the following and understand that Delta support staff time is a finite resource while python developments (new software and modules) are growing at nearly infinite velocity:
Python version or environment used (describe fully, with the commands needed to reproduce)
Error output or log from what went wrong (screenshots are more difficult to work with than text data)
Pertinent URLs describing what you were following/attempting (if applicable), note that URL recipes specific to vendors may be difficult to reproduce when not using their cloud resources (Google Colab, for example)
On Delta, you may install your own python software stacks, as needed.
There are choices when customizing your python setup. If you anticipate maintaining multiple python environments or installing many packages, you may want to target a filesystem with more quota space (not $HOME) for your environments. /scratch or /projects may be more appropriate in that case.
You may use any of these methods with any of the python versions or instances described below (or you may install your own python versions):
venv (python virtual environment)
Can name environments (metadata) and have multiple environments per python version or instance. pip installs are local to the environment. You specify the path when using venv:
python -m venv /path/to/env.conda (or miniconda) environments
Similar to venv but with more flexibility, see this comparison table. See also the miniconda environment option: anaconda or miniconda. pip and conda installs are local to the environment and the location defaults to
$HOME/.conda. You can override the default location in$HOMEby using the--prefixsyntax:conda create --prefix /path/to/env.pip3:
pip3 install --user <python_package>(installs to$HOME/.local/unless you are using a venv or conda environment).Useful when you only need one python environment per python version. CAUTION: Python modules installed this way into your
$HOME/.local/will match on python versions. This can create incompatibilities between containers or python venv or conda environments when they have a common python version number.conda-env-mod: conda-env-mod lmod module generator from Purdue
The conda-env-mod script will generate a python module you can load or share with your team. This makes it simpler to manage multiple python scenarios that you can activate and deactivate with module commands.
Examples using all the above are shown in the Intel scikit-learn-intelex repository (an Intel accelerated scikit learn subset library for x86_64 architecture).
pyenv: pyenv python version management
Pyenv helps you manage multiple python versions. You can also use more than one python version at once in a project using pyenv.
Note
The NVIDIA NGC Containers on Delta provide optimized python frameworks built for Delta’s A100 and A40 GPUs. Delta staff recommend using an NGC container when possible with the GPU nodes (or use the anaconda3_gpu module).
The default GCC (latest version) programming environment for either modtree/cpu or modtree/gpu contains:
Python (a recent or latest version)
If you don’t need all the extra modules provided by Anaconda, use the basic python installation under the gcc module.
You can add modules via pip3 install --user <modulename>, setup virtual environments, and customize, as needed, for your workflow starting from a smaller installed base of python than Anaconda.
$ module load gcc python
$ which python
/sw/spack/delta-2022-03/apps/python/3.10.4-gcc-11.2.0-3cjjp6w/bin/python
$ module list
Currently Loaded Modules:
1) modtree/gpu 3) gcc/11.2.0 5) ucx/1.11.2 7) python/3.10.4
2) default 4) cuda/11.6.1 6) openmpi/4.1.2
List of modules available in python from pip3 list:
Package Version
------------------ ---------
certifi 2021.10.8
cffi 1.15.0
charset-normalizer 2.0.12
click 8.1.2
cryptography 36.0.2
globus-cli 3.4.0
globus-sdk 3.5.0
idna 3.3
jmespath 0.10.0
pip 22.0.4
pycparser 2.21
PyJWT 2.3.0
requests 2.27.1
setuptools 58.1.0
urllib3 1.26.9
Anaconda
anaconda3_cpu
Use python from the anaconda3_cpu module if you need some of the modules provided by Anaconda in your python workflow. See the Managing Environments section of the conda getting started guide to learn how to customize conda for your workflow and add extra python modules to your environment. NCSA staff recommend starting with anaconda3_cpu for modtree/cpu and the CPU nodes. Do not use this module with GPUs, use anaconda3_gpu instead (anaconda3_gpu (for CUDA) , anaconda3_mi100 (for ROCm)). The Delta team frequently updates anaconda3_* to track the latest packages.
Note
If you use anaconda with NGC containers, take care to use python from the container and not python from Anaconda or one of its environments.
The container’s python should be first in $PATH.
You may --bind the Anaconda directory or other paths into the container so that you can start your conda environments with the container’s python (/usr/bin/python).
The Anaconda archive contains previous Anaconda versions. The bundles are not small, but using one from Anaconda will ensure that you get software that was built to work together. If you require an older version of a python lib/module, NCSA staff suggest looking back in time at the Anaconda site.
$ module load modtree/cpu
$ module load gcc anaconda3_cpu
$ which conda
/sw/external/python/anaconda3_cpu/conda
$ module list Currently Loaded Modules:
1) cue-login-env/1.0 6) libfabric/1.14.0 11) ucx/1.11.2
2) default 7) lustre/2.14.0_ddn23 12) openmpi/4.1.2
3) gcc/11.2.0 8) openssh/8.0p1 13) modtree/cpu
4) knem/1.1.4 9) pmix/3.2.3 14) anaconda3_cpu/4.13.0
5) libevent/2.1.8 10) rdma-core/32.0
List of modules in anaconda3_cpu
The current list of modules available in anaconda3_cpu is shown via conda list, including TensorFlow and PyTorch:
anaconda3_cpu modules: conda list (click to expand/collapse)
# packages in environment at /sw/external/python/anaconda3_cpu: # Name Version Build Channel
_ipyw_jlab_nb_ext_conf 0.1.0 py39h06a4308_1
_libgcc_mutex 0.1 main
_openmp_mutex 4.5 1_gnu
absl-py 1.1.0 pypi_0 pypi
aiobotocore 2.3.3 pypi_0 pypi
aiohttp 3.8.1 py39h7f8727e_1
aioitertools 0.10.0 pypi_0 pypi
aiosignal 1.2.0 pyhd3eb1b0_0
alabaster 0.7.12 pyhd3eb1b0_0
anaconda 2022.05 py39_0
anaconda-client 1.9.0 py39h06a4308_0
anaconda-navigator 2.1.4 py39h06a4308_0
anaconda-project 0.10.2 pyhd3eb1b0_0
anyio 3.5.0 py39h06a4308_0
appdirs 1.4.4 pyhd3eb1b0_0
argon2-cffi 21.3.0 pyhd3eb1b0_0
argon2-cffi-bindings 21.2.0 py39h7f8727e_0
arrow 1.2.2 pyhd3eb1b0_0
astroid 2.6.6 py39h06a4308_0
astropy 5.0.4 py39hce1f21e_0
asttokens 2.0.5 pyhd3eb1b0_0
astunparse 1.6.3 pypi_0 pypi
async-timeout 4.0.1 pyhd3eb1b0_0
atomicwrites 1.4.0 py_0
attrs 21.4.0 pyhd3eb1b0_0
automat 20.2.0 py_0
autopep8 1.6.0 pyhd3eb1b0_0
awscli 1.25.14 pypi_0 pypi
babel 2.9.1 pyhd3eb1b0_0
backcall 0.2.0 pyhd3eb1b0_0
backports 1.1 pyhd3eb1b0_0
backports.functools_lru_cache 1.6.4 pyhd3eb1b0_0
backports.tempfile 1.0 pyhd3eb1b0_1
backports.weakref 1.0.post1 py_1
bcrypt 3.2.0 py39he8ac12f_0
beautifulsoup4 4.11.1 py39h06a4308_0
binaryornot 0.4.4 pyhd3eb1b0_1
bitarray 2.4.1 py39h7f8727e_0
bkcharts 0.2 py39h06a4308_0
black 19.10b0 py_0
blas 1.0 mkl
bleach 4.1.0 pyhd3eb1b0_0
blosc 1.21.0 h8c45485_0
bokeh 2.4.2 py39h06a4308_0
boto3 1.21.32 pyhd3eb1b0_0
botocore 1.24.21 pypi_0 pypi
bottleneck 1.3.4 py39hce1f21e_0
brotli 1.0.9 he6710b0_2
brotlipy 0.7.0 py39h27cfd23_1003
brunsli 0.1 h2531618_0
bzip2 1.0.8 h7b6447c_0
c-ares 1.18.1 h7f8727e_0
ca-certificates 2022.3.29 h06a4308_1
cachetools 4.2.2 pyhd3eb1b0_0
certifi 2021.10.8 py39h06a4308_2
cffi 1.15.0 py39hd667e15_1
cfitsio 3.470 hf0d0db6_6
chardet 4.0.0 py39h06a4308_1003
charls 2.2.0 h2531618_0
charset-normalizer 2.0.4 pyhd3eb1b0_0
click 8.0.4 py39h06a4308_0
cloudpickle 2.0.0 pyhd3eb1b0_0
clyent 1.2.2 py39h06a4308_1
colorama 0.4.4 pyhd3eb1b0_0
colorcet 2.0.6 pyhd3eb1b0_0
conda 4.13.0 py39h06a4308_0
conda-build 3.21.8 py39h06a4308_2
conda-content-trust 0.1.1 pyhd3eb1b0_0
conda-env 2.6.0 1
conda-pack 0.6.0 pyhd3eb1b0_0
conda-package-handling 1.8.1 py39h7f8727e_0
conda-repo-cli 1.0.4 pyhd3eb1b0_0
conda-token 0.3.0 pyhd3eb1b0_0
conda-verify 3.4.2 py_1
constantly 15.1.0 pyh2b92418_0
cookiecutter 1.7.3 pyhd3eb1b0_0
cpuonly 2.0 0 pytorch-nightly
cryptography 3.4.8 py39hd23ed53_0
cssselect 1.1.0 pyhd3eb1b0_0
curl 7.82.0 h7f8727e_0
cycler 0.11.0 pyhd3eb1b0_0
cython 0.29.28 py39h295c915_0
cytoolz 0.11.0 py39h27cfd23_0
daal4py 2021.5.0 py39h78b71dc_0
dal 2021.5.1 h06a4308_803
dask 2022.2.1 pyhd3eb1b0_0
dask-core 2022.2.1 pyhd3eb1b0_0
dataclasses 0.8 pyh6d0b6a4_7
datashader 0.13.0 pyhd3eb1b0_1
datashape 0.5.4 py39h06a4308_1
dbus 1.13.18 hb2f20db_0
debugpy 1.5.1 py39h295c915_0
decorator 5.1.1 pyhd3eb1b0_0
defusedxml 0.7.1 pyhd3eb1b0_0
diff-match-patch 20200713 pyhd3eb1b0_0
dill 0.3.5.1 pypi_0 pypi
distributed 2022.2.1 pyhd3eb1b0_0
docutils 0.16 pypi_0 pypi
entrypoints 0.4 py39h06a4308_0
et_xmlfile 1.1.0 py39h06a4308_0
etils 0.7.1 pypi_0 pypi
executing 0.8.3 pyhd3eb1b0_0
expat 2.4.4 h295c915_0
ffmpeg 4.2.2 h20bf706_0
filelock 3.6.0 pyhd3eb1b0_0
flake8 3.9.2 pyhd3eb1b0_0
flask 1.1.2 pyhd3eb1b0_0
flatbuffers 1.12 pypi_0 pypi
fontconfig 2.13.1 h6c09931_0
fonttools 4.25.0 pyhd3eb1b0_0
freetype 2.11.0 h70c0345_0
frozenlist 1.2.0 py39h7f8727e_0
fsspec 2022.5.0 pypi_0 pypi
funcx 1.0.2 pypi_0 pypi
funcx-common 0.0.15 pypi_0 pypi
future 0.18.2 py39h06a4308_1
gast 0.4.0 pypi_0 pypi
gensim 4.1.2 py39h295c915_0
giflib 5.2.1 h7b6447c_0
glib 2.69.1 h4ff587b_1
glob2 0.7 pyhd3eb1b0_0
globus-cli 3.8.0 pypi_0 pypi
globus-sdk 3.11.0 pypi_0 pypi
gmp 6.2.1 h2531618_2
gmpy2 2.1.2 py39heeb90bb_0
gnutls 3.6.15 he1e5248_0
google-api-core 1.25.1 pyhd3eb1b0_0
google-auth 1.33.0 pyhd3eb1b0_0
google-auth-oauthlib 0.4.6 pypi_0 pypi
google-cloud-core 1.7.1 pyhd3eb1b0_0
google-cloud-storage 1.31.0 py_0
google-crc32c 1.1.2 py39h27cfd23_0
google-pasta 0.2.0 pypi_0 pypi
google-resumable-media 1.3.1 pyhd3eb1b0_1
googleapis-common-protos 1.53.0 py39h06a4308_0
greenlet 1.1.1 py39h295c915_0
grpcio 1.42.0 py39hce63b2e_0
gst-plugins-base 1.14.0 h8213a91_2
gstreamer 1.14.0 h28cd5cc_2
gviz-api 1.10.0 pypi_0 pypi
h5py 3.6.0 py39ha0f2276_0
hdf5 1.10.6 hb1b8bf9_0
heapdict 1.0.1 pyhd3eb1b0_0
holoviews 1.14.8 pyhd3eb1b0_0
hvplot 0.7.3 pyhd3eb1b0_1
hyperlink 21.0.0 pyhd3eb1b0_0
icu 58.2 he6710b0_3
idna 3.3 pyhd3eb1b0_0
imagecodecs 2021.8.26 py39h4cda21f_0
imageio 2.9.0 pyhd3eb1b0_0
imagesize 1.3.0 pyhd3eb1b0_0
importlib-metadata 4.11.3 py39h06a4308_0
importlib-resources 5.9.0 pypi_0 pypi
importlib_metadata 4.11.3 hd3eb1b0_0
incremental 21.3.0 pyhd3eb1b0_0
inflection 0.5.1 py39h06a4308_0
iniconfig 1.1.1 pyhd3eb1b0_0
intake 0.6.5 pyhd3eb1b0_0
intel-openmp 2021.4.0 h06a4308_3561
intervaltree 3.1.0 pyhd3eb1b0_0
ipykernel 6.9.1 py39h06a4308_0
ipython 8.2.0 py39h06a4308_0
ipython_genutils 0.2.0 pyhd3eb1b0_1
ipywidgets 7.6.5 pyhd3eb1b0_1
isort 5.9.3 pyhd3eb1b0_0
itemadapter 0.3.0 pyhd3eb1b0_0
itemloaders 1.0.4 pyhd3eb1b0_1
itsdangerous 2.0.1 pyhd3eb1b0_0
jax 0.3.16 pypi_0 pypi
jaxlib 0.3.15 pypi_0 pypi
jdcal 1.4.1 pyhd3eb1b0_0
jedi 0.18.1 py39h06a4308_1
jeepney 0.7.1 pyhd3eb1b0_0
jinja2 2.11.3 pyhd3eb1b0_0
jinja2-time 0.2.0 pyhd3eb1b0_3
jmespath 0.10.0 pyhd3eb1b0_0
joblib 1.1.0 pyhd3eb1b0_0
jpeg 9e h7f8727e_0
jq 1.6 h27cfd23_1000
json5 0.9.6 pyhd3eb1b0_0
jsonschema 4.4.0 py39h06a4308_0
jupyter 1.0.0 py39h06a4308_7
jupyter_client 6.1.12 pyhd3eb1b0_0
jupyter_console 6.4.0 pyhd3eb1b0_0
jupyter_core 4.9.2 py39h06a4308_0
jupyter_server 1.13.5 pyhd3eb1b0_0
jupyterlab 3.3.2 pyhd3eb1b0_0
jupyterlab_pygments 0.1.2 py_0
jupyterlab_server 2.10.3 pyhd3eb1b0_1
jupyterlab_widgets 1.0.0 pyhd3eb1b0_1
jxrlib 1.1 h7b6447c_2
keras 2.9.0 pypi_0 pypi
keras-preprocessing 1.1.2 pypi_0 pypi
keyring 23.4.0 py39h06a4308_0
kiwisolver 1.3.2 py39h295c915_0
krb5 1.19.2 hac12032_0
lame 3.100 h7b6447c_0
lazy-object-proxy 1.6.0 py39h27cfd23_0
lcms2 2.12 h3be6417_0
ld_impl_linux-64 2.35.1 h7274673_9
lerc 3.0 h295c915_0
libaec 1.0.4 he6710b0_1
libarchive 3.4.2 h62408e4_0
libclang 14.0.1 pypi_0 pypi
libcrc32c 1.1.1 he6710b0_2
libcurl 7.82.0 h0b77cf5_0
libdeflate 1.8 h7f8727e_5
libedit 3.1.20210910 h7f8727e_0
libev 4.33 h7f8727e_1
libffi 3.3 he6710b0_2
libgcc-ng 9.3.0 h5101ec6_17
libgfortran-ng 7.5.0 ha8ba4b0_17
libgfortran4 7.5.0 ha8ba4b0_17
libgomp 9.3.0 h5101ec6_17
libidn2 2.3.2 h7f8727e_0
liblief 0.11.5 h295c915_1
libllvm11 11.1.0 h3826bc1_1
libnghttp2 1.46.0 hce63b2e_0
libopus 1.3.1 h7b6447c_0
libpng 1.6.37 hbc83047_0
libprotobuf 3.19.1 h4ff587b_0
libsodium 1.0.18 h7b6447c_0
libspatialindex 1.9.3 h2531618_0
libssh2 1.10.0 h8f2d780_0
libstdcxx-ng 9.3.0 hd4cf53a_17
libtasn1 4.16.0 h27cfd23_0
libtiff 4.2.0 h85742a9_0
libunistring 0.9.10 h27cfd23_0
libuuid 1.0.3 h7f8727e_2
libvpx 1.7.0 h439df22_0
libwebp 1.2.2 h55f646e_0
libwebp-base 1.2.2 h7f8727e_0
libxcb 1.14 h7b6447c_0
libxml2 2.9.12 h03d6c58_0
libxslt 1.1.34 hc22bd24_0
libzopfli 1.0.3 he6710b0_0
llvmlite 0.38.0 py39h4ff587b_0
locket 0.2.1 py39h06a4308_2
lxml 4.8.0 py39h1f438cf_0
lz4-c 1.9.3 h295c915_1
lzo 2.10 h7b6447c_2
markdown 3.3.4 py39h06a4308_0
markupsafe 2.0.1 py39h27cfd23_0
matplotlib 3.5.1 py39h06a4308_1
matplotlib-base 3.5.1 py39ha18d171_1
matplotlib-inline 0.1.2 pyhd3eb1b0_2
mccabe 0.6.1 py39h06a4308_1
mistune 0.8.4 py39h27cfd23_1000
mkl 2021.4.0 h06a4308_640
mkl-service 2.4.0 py39h7f8727e_0
mkl_fft 1.3.1 py39hd3c417c_0
mkl_random 1.2.2 py39h51133e4_0
mock 4.0.3 pyhd3eb1b0_0
mpc 1.1.0 h10f8cd9_1
mpfr 4.0.2 hb69a4c5_1
mpi 1.0 mpich
mpich 3.3.2 hc856adb_0
mpmath 1.2.1 py39h06a4308_0
msgpack-python 1.0.2 py39hff7bd54_1
multidict 5.2.0 py39h7f8727e_2
multipledispatch 0.6.0 py39h06a4308_0
munkres 1.1.4 py_0
mypy_extensions 0.4.3 py39h06a4308_1
navigator-updater 0.2.1 py39_1
nbclassic 0.3.5 pyhd3eb1b0_0
nbclient 0.5.13 py39h06a4308_0
nbconvert 6.4.4 py39h06a4308_0
nbformat 5.3.0 py39h06a4308_0
ncurses 6.3 h7f8727e_2
nest-asyncio 1.5.5 py39h06a4308_0
nettle 3.7.3 hbbd107a_1
networkx 2.7.1 pyhd3eb1b0_0
nltk 3.7 pyhd3eb1b0_0
nose 1.3.7 pyhd3eb1b0_1008
notebook 6.4.8 py39h06a4308_0
numba 0.55.1 py39h51133e4_0
numexpr 2.8.1 py39h6abb31d_0
numpy 1.21.5 py39he7a7128_1
numpy-base 1.21.5 py39hf524024_1
numpydoc 1.2 pyhd3eb1b0_0
oauthlib 3.2.0 pypi_0 pypi
olefile 0.46 pyhd3eb1b0_0
oniguruma 6.9.7.1 h27cfd23_0
openh264 2.1.1 h4ff587b_0
openjpeg 2.4.0 h3ad879b_0
openpyxl 3.0.9 pyhd3eb1b0_0
openssl 1.1.1n h7f8727e_0
opt-einsum 3.3.0 pypi_0 pypi
packaging 21.3 pyhd3eb1b0_0
pandas 1.4.2 py39h295c915_0
pandocfilters 1.5.0 pyhd3eb1b0_0
panel 0.13.0 py39h06a4308_0
param 1.12.0 pyhd3eb1b0_0
parsel 1.6.0 py39h06a4308_0
parso 0.8.3 pyhd3eb1b0_0
partd 1.2.0 pyhd3eb1b0_1
patchelf 0.13 h295c915_0
pathspec 0.7.0 py_0
patsy 0.5.2 py39h06a4308_1
pcre 8.45 h295c915_0
pep8 1.7.1 py39h06a4308_0
pexpect 4.8.0 pyhd3eb1b0_3
pickleshare 0.7.5 pyhd3eb1b0_1003
pillow 9.0.1 py39h22f2fdc_0
pip 21.2.4 py39h06a4308_0
pkginfo 1.8.2 pyhd3eb1b0_0
plotly 5.6.0 pyhd3eb1b0_0
pluggy 1.0.0 py39h06a4308_1
poyo 0.5.0 pyhd3eb1b0_0
prometheus_client 0.13.1 pyhd3eb1b0_0
prompt-toolkit 3.0.20 pyhd3eb1b0_0
prompt_toolkit 3.0.20 hd3eb1b0_0
protego 0.1.16 py_0
protobuf 3.19.1 py39h295c915_0
psutil 5.8.0 py39h27cfd23_1
ptyprocess 0.7.0 pyhd3eb1b0_2
pure_eval 0.2.2 pyhd3eb1b0_0
py 1.11.0 pyhd3eb1b0_0
py-lief 0.11.5 py39h295c915_1
pyasn1 0.4.8 pyhd3eb1b0_0
pyasn1-modules 0.2.8 py_0
pycodestyle 2.7.0 pyhd3eb1b0_0
pycosat 0.6.3 py39h27cfd23_0
pycparser 2.21 pyhd3eb1b0_0
pyct 0.4.6 py39h06a4308_0
pycurl 7.44.1 py39h8f2d780_1
pydantic 1.10.2 pypi_0 pypi
pydispatcher 2.0.5 py39h06a4308_2
pydocstyle 6.1.1 pyhd3eb1b0_0
pyerfa 2.0.0 py39h27cfd23_0
pyflakes 2.3.1 pyhd3eb1b0_0
pygments 2.11.2 pyhd3eb1b0_0
pyhamcrest 2.0.2 pyhd3eb1b0_2
pyjwt 2.1.0 py39h06a4308_0
pylint 2.9.6 py39h06a4308_1
pyls-spyder 0.4.0 pyhd3eb1b0_0
pyodbc 4.0.32 py39h295c915_1
pyopenssl 21.0.0 pyhd3eb1b0_1
pyparsing 3.0.4 pyhd3eb1b0_0
pyqt 5.9.2 py39h2531618_6
pyrsistent 0.18.0 py39heee7806_0
pysocks 1.7.1 py39h06a4308_0
pytables 3.6.1 py39h77479fe_1
pytest 7.1.1 py39h06a4308_0
python 3.9.12 h12debd9_0
python-dateutil 2.8.2 pyhd3eb1b0_0
python-fastjsonschema 2.15.1 pyhd3eb1b0_0
python-libarchive-c 2.9 pyhd3eb1b0_1
python-lsp-black 1.0.0 pyhd3eb1b0_0
python-lsp-jsonrpc 1.0.0 pyhd3eb1b0_0
python-lsp-server 1.2.4 pyhd3eb1b0_0
python-slugify 5.0.2 pyhd3eb1b0_0
python-snappy 0.6.0 py39h2531618_3
pytorch 1.13.0.dev20220620 py3.9_cpu_0 pytorch-nightly
pytorch-mutex 1.0 cpu pytorch-nightly
pytz 2021.3 pyhd3eb1b0_0
pyviz_comms 2.0.2 pyhd3eb1b0_0
pywavelets 1.3.0 py39h7f8727e_0
pyxdg 0.27 pyhd3eb1b0_0
pyyaml 5.4.1 pypi_0 pypi
pyzmq 22.3.0 py39h295c915_2
qdarkstyle 3.0.2 pyhd3eb1b0_0
qstylizer 0.1.10 pyhd3eb1b0_0
qt 5.9.7 h5867ecd_1
qtawesome 1.0.3 pyhd3eb1b0_0
qtconsole 5.3.0 pyhd3eb1b0_0
qtpy 2.0.1 pyhd3eb1b0_0
queuelib 1.5.0 py39h06a4308_0
readline 8.1.2 h7f8727e_1
regex 2022.3.15 py39h7f8727e_0
requests 2.27.1 pyhd3eb1b0_0
requests-file 1.5.1 pyhd3eb1b0_0
requests-oauthlib 1.3.1 pypi_0 pypi
ripgrep 12.1.1 0
rope 0.22.0 pyhd3eb1b0_0
rsa 4.7.2 pyhd3eb1b0_1
rtree 0.9.7 py39h06a4308_1
ruamel_yaml 0.15.100 py39h27cfd23_0
s3fs 2022.5.0 pypi_0 pypi
s3transfer 0.6.0 pypi_0 pypi
scikit-image 0.19.2 py39h51133e4_0
scikit-learn 1.0.2 py39h51133e4_1
scikit-learn-intelex 2021.5.0 py39h06a4308_0
scipy 1.7.3 py39hc147768_0
scrapy 2.6.1 py39h06a4308_0
seaborn 0.11.2 pyhd3eb1b0_0
secretstorage 3.3.1 py39h06a4308_0
send2trash 1.8.0 pyhd3eb1b0_1
service_identity 18.1.0 pyhd3eb1b0_1
setuptools 61.2.0 py39h06a4308_0
sip 4.19.13 py39h295c915_0
six 1.16.0 pyhd3eb1b0_1
smart_open 5.1.0 pyhd3eb1b0_0
snappy 1.1.9 h295c915_0
sniffio 1.2.0 py39h06a4308_1
snowballstemmer 2.2.0 pyhd3eb1b0_0
sortedcollections 2.1.0 pyhd3eb1b0_0
sortedcontainers 2.4.0 pyhd3eb1b0_0
soupsieve 2.3.1 pyhd3eb1b0_0
sphinx 4.4.0 pyhd3eb1b0_0
sphinxcontrib-applehelp 1.0.2 pyhd3eb1b0_0
sphinxcontrib-devhelp 1.0.2 pyhd3eb1b0_0
sphinxcontrib-htmlhelp 2.0.0 pyhd3eb1b0_0
sphinxcontrib-jsmath 1.0.1 pyhd3eb1b0_0
sphinxcontrib-qthelp 1.0.3 pyhd3eb1b0_0
sphinxcontrib-serializinghtml 1.1.5 pyhd3eb1b0_0
spyder 5.1.5 py39h06a4308_1
spyder-kernels 2.1.3 py39h06a4308_0
sqlalchemy 1.4.32 py39h7f8727e_0
sqlite 3.38.2 hc218d9a_0
stack_data 0.2.0 pyhd3eb1b0_0
statsmodels 0.13.2 py39h7f8727e_0
sympy 1.10.1 py39h06a4308_0
tabulate 0.8.9 py39h06a4308_0
tbb 2021.5.0 hd09550d_0
tbb4py 2021.5.0 py39hd09550d_0
tblib 1.7.0 pyhd3eb1b0_0
tenacity 8.0.1 py39h06a4308_0
tensorboard 2.9.1 pypi_0 pypi
tensorboard-data-server 0.6.1 pypi_0 pypi
tensorboard-plugin-profile 2.8.0 pypi_0 pypi
tensorboard-plugin-wit 1.8.1 pypi_0 pypi
tensorflow 2.9.1 pypi_0 pypi
tensorflow-estimator 2.9.0 pypi_0 pypi
tensorflow-io-gcs-filesystem 0.26.0 pypi_0 pypi
termcolor 1.1.0 pypi_0 pypi
terminado 0.13.1 py39h06a4308_0
testpath 0.5.0 pyhd3eb1b0_0
text-unidecode 1.3 pyhd3eb1b0_0
textdistance 4.2.1 pyhd3eb1b0_0
threadpoolctl 2.2.0 pyh0d69192_0
three-merge 0.1.1 pyhd3eb1b0_0
tifffile 2021.7.2 pyhd3eb1b0_2
tinycss 0.4 pyhd3eb1b0_1002
tk 8.6.11 h1ccaba5_0
tldextract 3.2.0 pyhd3eb1b0_0
toml 0.10.2 pyhd3eb1b0_0
tomli 1.2.2 pyhd3eb1b0_0
toolz 0.11.2 pyhd3eb1b0_0
torchaudio 0.13.0.dev20220621 py39_cpu pytorch-nightly
torchvision 0.14.0.dev20220621 py39_cpu pytorch-nightly
tornado 6.1 py39h27cfd23_0
tqdm 4.64.0 py39h06a4308_0
traitlets 5.1.1 pyhd3eb1b0_0
twisted 22.2.0 py39h7f8727e_0
typed-ast 1.4.3 py39h7f8727e_1
typing-extensions 4.1.1 hd3eb1b0_0
typing_extensions 4.1.1 pyh06a4308_0
tzdata 2022a hda174b7_0
ujson 5.1.0 py39h295c915_0
unidecode 1.2.0 pyhd3eb1b0_0
unixodbc 2.3.9 h7b6447c_0
urllib3 1.26.9 py39h06a4308_0
w3lib 1.21.0 pyhd3eb1b0_0
watchdog 2.1.6 py39h06a4308_0
wcwidth 0.2.5 pyhd3eb1b0_0
webencodings 0.5.1 py39h06a4308_1
websocket-client 0.58.0 py39h06a4308_4
websockets 10.3 pypi_0 pypi
werkzeug 2.0.3 pyhd3eb1b0_0
wget 1.21.3 h0b77cf5_0
wheel 0.37.1 pyhd3eb1b0_0
widgetsnbextension 3.5.2 py39h06a4308_0
wrapt 1.12.1 py39he8ac12f_1
wurlitzer 3.0.2 py39h06a4308_0
x264 1!157.20191217 h7b6447c_0
xarray 0.20.1 pyhd3eb1b0_1
xlrd 2.0.1 pyhd3eb1b0_0
xlsxwriter 3.0.3 pyhd3eb1b0_0
xz 5.2.5 h7b6447c_0
yaml 0.2.5 h7b6447c_0
yapf 0.31.0 pyhd3eb1b0_0
yarl 1.6.3 py39h27cfd23_0
zeromq 4.3.4 h2531618_0
zfp 0.5.5 h295c915_6
zict 2.0.0 pyhd3eb1b0_0
zipp 3.7.0 pyhd3eb1b0_0
zlib 1.2.12 h7f8727e_2
zope 1.0 py39h06a4308_1
zope.interface 5.4.0 py39h7f8727e_0
zstd 1.4.9 haebb681_0
anaconda3_gpu (for CUDA) , anaconda3_mi100 (for ROCm)
Similar to the setup for anaconda_cpu, Delta has GPU versions of anaconda3 (module load anaconda3_gpu) and installed PyTorch and TensorFlow CUDA aware python modules into these versions.
You may use these modules when working with the GPU nodes.
See conda list after loading the module to review what is already installed.
As with anaconda3_cpu, submit a support request if there are generally useful modules you would like installed for the broader community.
A sample TensorFlow test script:
#!/bin/bash
#SBATCH --mem=64g
#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --cpus-per-task=16 # <- match to OMP_NUM_THREADS
#SBATCH --partition=gpuA100x4-interactive
#SBATCH --time=00:10:00
#SBATCH --account=account_name # <- match to a "Project" returned by the "accounts" command
#SBATCH --job-name=tf_anaconda
### GPU options ###
#SBATCH --gpus-per-node=1
#SBATCH --gpus-per-task=1
#SBATCH --gpu-bind=verbose,per_task:1
###SBATCH --gpu-bind=none # <- or closest
module purge # drop modules and explicitly load the ones needed
# (good job metadata and reproducibility)
module load anaconda3_gpu
module list # job documentation and metadata
echo "job is starting on `hostname`"
which python3
conda list tensorflow
srun python3 \
tf_gpu.py
exit
Python Environments with anaconda3
Recent Changes
To address a problem with PATH ordering when using anaconda3 modules, a warning in the module was put in place to caution loading an anaconda3 module while in a virtual environment, or if conda init has been used to modify one’s environment.
(base) [arnoldg@dt-login03 ~]$ module load anaconda3_gpu
A conda environment has been detected CONDA_PREFIX=
/sw/external/python/anaconda3
anaconda3_gpu is loaded. Consider running conda deactivate and reloading it.
See the Conda configuration documentation if you want to disable automatic conda environment activation.
Note
When using your own custom conda environment with a batch job, submit the batch job from within the environment and do not add conda activate commands to the job script; the job inherits your environment.
Batch Jobs
Batch jobs will honor the commands you execute within them. Purge/unload/load modules as needed for that job.
A clean slate might resemble (user has a conda init clause in bashrc for a custom environment):
conda deactivate
conda deactivate # just making sure
module purge
module reset # load the default Delta modules
conda activate base
# commands to load modules and activate environs such that your environment is active before
# you use slurm ( no conda activate commands in the slurm script )
sbatch myjob.slurm # or srun or salloc
Non-python/conda HPC users would see per-job stderr from the conda deactivate above (user has never run conda init bash):
[arnoldg@dt-login03 ~]$ conda deactivate
bash: conda: command not found
[arnoldg@dt-login03 ~]$
# or
[arnoldg@dt-login03 ~]$ conda deactivate
CommandNotFoundError: Your shell has not been properly configured to use 'conda deactivate'.
To initialize your shell, run
$ conda init <SHELL_NAME>
Currently supported shells are:
- bash
- fish
- tcsh
- xonsh
- zsh
- powershell
See 'conda init --help' for more information and options.
IMPORTANT: You may need to close and restart your shell after running 'conda init'.
Intel AI Analytics Toolkit
The Intel AI Analytics Toolkit (AI Kit) module contains a subset of what you will find in anaconda_cpu.
It contains conda environments optimized for CPU execution: PyTorch & TensorFlow.
We have seen up to 2x speedup when using the AI Kit compared to the stock anaconda_cpu.
For best results, set OMP_NUM_THREADS to the number of cores you’d like to use (--cpus-per-task in Slurm).
Containers
See Containers.
Jupyter Notebooks
Note
The Delta Open OnDemand (OOD) portal provides an easy method to start a Jupyter notebook; this is the recommended method.
Go to OOD Jupyter interactive app for instructions on how to start an OOD JupyterLab session.
You can also customize your OOD JupyterLab environment:
Do not run Jupyter on the shared login nodes. Instead, follow these steps to attach a Jupyter notebook running on a compute node to your local web browser:
The Jupyter notebook executables are in your $PATH after loading the anaconda3 module. If you run into problems from a previously saved Jupyter session (for example, you see paths where you do not have write permission), you may remove this file to get a fresh start: $HOME/.jupyter/lab/workspaces/default-*.
Follow these steps to run Jupyter on a compute node (CPU or GPU):
On your local machine/laptop, open a terminal.
SSH into Delta. (Replace
<my_delta_username>with your Delta login username).ssh <my_delta_username>@login.delta.ncsa.illinois.edu
Enter your NCSA password and complete the Duo MFA. Note, the terminal will not show your password (or placeholder symbols such as asterisks [*]) as you type.
Warning
If there is a conda environment active when you log into Delta, deactivate it before you continue. You will know you have an active conda environment if your terminal prompt has an environment name in parentheses prepended to it, like these examples:
(base) [<delta_username>@dt-login01 ~]$ (mynewenv) [<delta_username>@dt-login01 ~]$
Run
conda deactivateuntil there is no longer a name in parentheses prepended to your terminal prompt. When you don’t have any conda environment active, your prompt will look like this:[<delta_username>@dt-login01 ~]$
Load the appropriate anaconda module. To see all of the available anaconda modules, run
module avail anaconda. This example usesanaconda3_cpu.module load anaconda3_cpu
Verify the module is loaded.
module list
Verify a jupyter-notebook is in your
$PATH.which jupyter-notebook
Generate a
MYPORTnumber and copy it to a notepad (you will use it in steps 9 and 12).MYPORT=$(($(($RANDOM % 10000))+49152)); echo $MYPORT
Find the the
account_namethat you are going to use and copy it to a notepad (you will use it in step 9); your accounts are listed underProjectwhen you run theaccountscommand.Note
To use a GPU node, you must pick a GPU account (the account name will end in “…-gpu”).
accountsRun the following
sruncommand, with these replacements:Replace
<account_name>with the account you are going to use, which you found and copied in step 8.Replace
<$MYPORT>with the$MYPORTnumber you generated in step 7.Modify the
--partition,--time, and--memoptions and/or add other options to meet your needs.
srun --account=<account_name> --partition=cpu-interactive --time=00:30:00 --mem=32g jupyter-notebook --no-browser --port=<$MYPORT> --ip=0.0.0.0
Copy the last 5 lines returned beginning with: “To access the notebook, open this file in a browser…” to a notepad (you will use this information steps 12 and 14). (It may take a few minutes for these lines to be returned.)
Note these two things about the URLs you copied:
The first URL begins with
http://<cnXXX>.delta...,<cnXXX>is the internal hostname and will be used in step 12.The second URL begins with
http://127.0..., you will use this entire URL in step 14.
Open a second terminal on your local machine/laptop.
Run the following
sshcommand, with these replacements:Replace
<my_delta_username>with your Delta login username.Replace
<$MYPORT>with the$MYPORTnumber you generated in step 7.Replace
<cn0XX>with internal hostname you copied in step 10.
ssh -l <my_delta_username> -L 127.0.0.1:<$MYPORT>:<cn0XX>.delta.ncsa.illinois.edu:<$MYPORT> dt-login.delta.ncsa.illinois.edu
Enter your NCSA password and complete the Duo MFA. Note, the terminal will not show your password (or placeholder symbols such as asterisks [*]) as you type.
Copy and paste the entire second URL from step 10 (begins with
https://127.0...) into your browser. You will be connected to the Jupyter instance running on your compute node of Delta.
Follow these steps to run Jupyter on a compute node, in an NGC container:
On your local machine/laptop, open a terminal.
SSH into Delta. (Replace
<my_delta_username>with your Delta login username.)ssh <my_delta_username>@login.delta.ncsa.illinois.edu
Enter your NCSA password and complete the Duo MFA. Note, the terminal will not show your password (or placeholder symbols such as asterisks [*]) as you type.
Generate a
$MYPORTnumber and copy it to a notepad (you will use it in steps 6, 8, and 14).MYPORT=$(($(($RANDOM % 10000))+49152)); echo $MYPORT
Find the the
account_namethat you are going to use and copy it to a notepad (you will use it in step 6); your accounts are listed underProjectwhen you run theaccountscommand.Note
To use a GPU node, you must pick a GPU account (the account name will end in “…-gpu”).
accountsRun the following
sruncommand, with these replacements:Replace
<account_name>with the account you are going to use, which you found and copied in step #5.Replace
<project_path>with the name of your projects folder (in two places).Replace
<$MYPORT>with theMYPORTnumber you generated in step 4.Modify the
--partition,--time,--mem, and--gpus-per-nodeoptions and/or add other options to meet your needs.
srun --account=<account_name> --partition=gpuA100x4-interactive --time=00:30:00 --mem=64g --gpus-per-node=1 apptainer run --nv --bind /projects/<project_path> /sw/external/NGC/pytorch:22.02-py3 jupyter-notebook --notebook-dir /projects/<project_path> --no-browser --port=<$MYPORT> --ip=0.0.0.0
Copy the last 2 lines returned (beginning with “Or copy and paste this URL…”) to a notepad. (It may take a few minutes for these lines to be returned.)
Modify the URL you copied in step 7 by changing
hostname:8888to127.0.0.1:<$MYPORT>. You will use the modified URL in step 16. (Replace<$MYPORT>with the$MYPORTnumber you generated in step 4.)Open a second terminal.
SSH into Delta. (Replace
<my_delta_username>with your Delta login username.)ssh <my_delta_username>@login.delta.ncsa.illinois.edu
Enter your NCSA password and complete the Duo MFA. Note, the terminal will not show your password (or placeholder symbols such as asterisks [*]) as you type.
Find the internal hostname for your job and copy it to a notepad (you will use it in step 14).
squeue -u $USER
The value returned under
NODELISTis the internal hostname for your GPU job (gpuaXXX). You can now close this terminal.Open a third terminal.
Run the following
sshcommand, with these replacements:Replace
<my_delta_username>with your Delta login username.Replace
<$MYPORT>with the$MYPORTnumber you generated in step 4.Replace
<gpuaXXX>with internal hostname you copied in step 12.
ssh -l <my_delta_username> -L 127.0.0.1:<$MYPORT>:<gpuaXXX>.delta.internal.ncsa.edu:<$MYPORT> dt-login.delta.ncsa.illinois.edu
Enter your NCSA password and complete the Duo MFA. Note, the terminal will not show your password (or placeholder symbols such as asterisks [*]) as you type.
Copy and paste the entire modified URL (beginning with
https://127.0...) from step 8 into your browser. You will be connected to the Jupyter instance running on your gpu node of Delta.
List of Installed Software (CPU & GPU)
Delta software module list (click to expand/collapse)
The following modules are installed on Delta CPUs, GPUs, or both, as indicated.
Module |
CPU, GPU, or both |
|---|---|
armadillo |
both |
charmpp |
CPU |
darshan-runtime |
CPU |
fftw |
both |
gromacs |
both |
hdf5 |
both |
kokkos |
GPU |
lammps |
CPU |
namd |
CPU |
netcdf-c |
both |
netcdf-fortran |
both |
osu-micro-benchmarks |
GPU |
netlib-scalapack |
CPU |
parallel-netcdf |
both |
parmetis |
GPU |
petsc |
both |
plumed |
both |
anaconda3 |
both |
automake |
CPU |
binutils |
CPU |
boost |
both |
cuda |
GPU |
cmake |
CPU |
compositeproto |
CPU |
cutensor |
GPU |
darshan-util |
CPU |
dyninst |
CPU |
elfutils |
CPU |
flex |
CPU |
freeglut |
GPU |
gdal |
CPU |
gettext |
CPU |
glew |
GPU |
gmake |
CPU |
gnuplot |
both |
gsl |
both |
hdf5 |
both |
hpctoolkit |
both |
hpcviewer |
CPU |
intel-tbb |
CPU |
intel-xed |
CPU |
knem |
CPU |
libaio |
both |
libdwarf |
CPU |
libevent |
CPU |
libfabric |
CPU |
libiberty |
CPU |
libjpeg |
GPU |
libmonitor |
CPU |
libnsl |
GPU |
libsndfile |
both |
libunwind |
CPU |
libxcb |
GPU |
libxcomposite |
CPU |
libxcrypt |
CPU |
libxkbcommon |
GPU |
libxml2 |
CPU |
libxshmfence |
GPU |
libxxf86vm |
GPU |
linux-headers |
CPU |
llvm |
CPU |
lustre |
CPU |
lzma |
CPU |
magma |
GPU |
memkind |
CPU |
mesa |
GPU |
metis |
both |
mpich |
CPU |
muparser |
CPU |
nccl |
GPU |
ncurses |
both |
openblas |
both |
openexr |
GPU |
openjdk |
both |
openmpi |
both |
openssh |
both |
p7zip |
GPU |
papi |
CPU |
perl |
both |
pmix |
CPU |
qt |
CPU |
r |
CPU |
rdma-core |
CPU |
readline |
both |
subversion |
both |
tcl |
CPU |
time |
both |
tk |
CPU |
ucx |
both |
wayland-protocols |
GPU |
wayland |
GPU |
xbitmaps |
CPU |
xcb-util-image |
GPU |
xcb-util-keysyms |
GPU |
xcb-util-renderutil |
GPU |
xcb-util-wm |
GPU |
xcb-util |
GPU |
xerces-c |
CPU |
xz |
CPU |
yaml-cpp |
CPU |
AMDuProf |
both |
ImageMagick |
both |
Intel_AI_toolkit |
both |
anaconda3_Rcpu |
both |
anaconda3_cpu |
both |
anaconda3_gpu |
both |
anaconda3_mi100 |
both |
aws-cli |
both |
cudnn |
both |
cue-login-env |
both |
gurobi |
both |
julia |
both |
lammps |
both |
llvm |
both |
matlab_unlicensed |
both |
namd3 |
both |
nvhpc_latest |
both |
openmpi-5.0_beta |
both |
openmpi-v5.0.x-202305240344_s11 |
both |
paraview |
both |
posix2ime |
both |
slurm-env |
both |
visit |
both |
westpa |
both |
accessusage |
both |
aocc |
both |
banner |
GPU |
cmake |
both |
cuda |
GPU |
dos2unix |
both |
gcc |
both |
git |
GPU |
htop |
both |
intel-oneapi-advisor |
both |
intel-oneapi-compilers |
both |
intel-oneapi-mkl |
both |
libfabric |
GPU |
libffi |
GPU |
libtirpc |
GPU |
modtree |
both |
modtree/cpu |
both |
modtree/gpu |
both |
mpich |
GPU |
ndiff |
both |
nvhpc |
GPU |
nvtop |
GPU |
parallel |
GPU |
subversion |
GPU |
xclock |
both |
zip |
GPU |
lmod |
both |
settarg |
both |