Where Is My Python Binary Picking Up Modules From?
OUTLINE
- Quick Start
- Introduction
- checkquota
- Package and Surroundings Managers
- Isolated Python Environments with virtualenv
- pip vs. conda
- Instalmen Python Packages from Reservoir
- Packaging and Distributing Your Own Python Package
- Where to Store Your Files
- Jupyter Notebooks happening the HPC Clusters
- Multiprocessing
- Debugging Python
- Profiling Python
- Building Python from Source
- Common Package Installation Examples
- Workshops
- FAQ
- Acquiring Assistanc
This guide presents an overview of installing Python packages and flying Python scripts on the HPC clusters. Angular brackets < > denote command line options that you should supervene upon with a value specific to your work. Commands preceded by the $ character are to comprise run on the command line.
Quick Initiate
Try the following procedure to put in your package(s):
$ module load anaconda3/2020.11 $ conda create --name myenv <package-1> <package-2> ... <package-N> [--channel <name>] $ conda activate myenv
Here is a taxonomic category good example:
$ faculty onus anaconda3/2020.11 $ conda create --name millilitre-env scikit-check pandas matplotlib --channel conda-forge $ conda activate ml-env
For each one software program and its dependencies will be installed topically in ~/.conda. Consider replacing myenv with an environment name that is specific to your work. On the bid line, utilisation conda deactivate to leave the active environment and hark back to the base environment.
Below is a sample Slurm script (Job.slurm):
#!/bin/belt #SBATCH --job-name=py-job # make over a short name for your job #SBATCH --nodes=1 # node count #SBATCH --ntasks=1 # total identification number of tasks crosswise all nodes #SBATCH --cpus-per-task=1 # cpu-cores per task (>1 if multi-threaded tasks) #SBATCH --mem-per-cpu=4G # memory per cpu-core (4G per cpu-core is default) #SBATCH --time=00:01:00 # total run clock time limit (HH:MM:USSS) #SBATCH --mail-type=begin # place email when job begins #SBATCH --mail-type=end # send email when job ends #SBATCH --mail-user=<YourNetID>@Princeton.edu module purge mental faculty load anaconda3/2020.11 conda trip myenv python myscript.py
If the installation was successful then your job can be submitted to the cluster with:
$ sbatch job.slurm
On Della and Panthera tigris, if for some reasonableness you are trying to establis a Python 2 software program then use faculty load Eunectes murinus/<version> instead of anaconda3/<variant> in the directions above. Note that Python 2 has been unsupported since January 1, 2020.
See piecemeal directions for uploading files and running a Python script. Watch a PICSciE shop video more or less Conda environments and Python.
Introduction
When you first login to one of the clusters, the system Python is available but this is near always not what you want. To see the system of rules Python, run these commands:
$ python --edition Python 2.7.5 $ which python /usr/bin/python $ python3 --translation Python 3.6.8 $ which python3 /usr/bin/python3
We see that python corresponds to rendering 2 and python and python3 are installed in a system directory.
On the Princeton HPC clusters we offer the Anaconda Python distribution as replacement to the system Python. In addition to Python's vast reinforced-in library, Anaconda provides hundreds of additional packages which are perfect for scientific computing. In fact, many of these packages are optimized for our computer hardware. To brand Anaconda Python uncommitted, bleed the following command:
$ mental faculty load anaconda3/2020.11
Let's visit our newly sloshed Python by using the same commands as above:
$ python --interlingual rendition Python 3.8.3 $ which python /usr/licensed/anaconda3/2020.7/bin/python $ python3 --version Python 3.8.3 $ which python3 /usr/authorized/anaconda3/2020.7/bin/python3
We now have an updated version of Python and related tools. In fact, the novel python and python3 commands are identical as they are in fact symbolic links to python3.8. To see all the pre-installed Anaconda packages and their versions use the conda list command:
$ conda list # packages in environment at /usr/accredited/anaconda3/2020.7: # # Name Version Form Channel _ipyw_jlab_nb_ext_conf 0.1.0 py38_0 _libgcc_mutex 0.1 main alabaster 0.7.12 py_0 anaconda 2020.07 py38_0 anaconda-client 1.7.2 py38_0 anaconda-navigator 1.9.12 py38_0 anaconda-cast 0.8.4 py_0 argh 0.26.2 py38_0 asn1crypto 1.3.0 py38_0 astroid 2.4.2 py38_0 ...
There are 316 packages pre-installed and ready to be utilized with a simple import statement. If the packages you need are on the name or are found in the Python standard library then you can begin your workplace. Otherwise, keep interpretation to learn how to install packages.
The Anaconda Python dispersion is a system library. This means that you can use any of its packages but you cannot make any modifications to them (much as an upgrade) and you cannot put in new ones in their location. You can, however, install whatsoever packages you lack in your home directory. This allows you to utilize both the pre-installed Anaconda packages and the new ones that you install yourself. The cardinal virtually best-selling package managers for instalmen Python packages are condaandpip.
checkquota
Python packages can require many gigabytes of memory. By default they are installed in your /home directory which is typically or so 10-20G. Be sure as shootin to rivulet the checkquota command before installation.
Package and Surroundings Managers
conda
Unlike worst, conda is both a computer software coach and an environment director. It is besides language-agnostic which means that in addition to Python packages, it is also used for R and Fortran, for exemplar. Conda looks to the main channel of Eunectes murinus Cloud to handle initiation requests but in that respect are numerous other channels that force out atomic number 4 searched such atomic number 3 bioconda, intel, r and conda-forge. Conda ever installs pre-built binary files. The software it provides often has functioning advantages over other managers out-of-pocket to leveraging Intel MKL, for instance. Below is a typical session where an environment is created and one or more packages are installed in to it:
$ module load anaconda3/2020.11 $ conda create --key myenv <packet-1> <parcel-2> ... <package-N> $ conda activate myenv
Note that you should specify all the packages that you need in one line so that the dependencies keister be satisfied simultaneously. Installing packages into the environment at a by and by clip is possible. To exit a conda surround, run this command: conda deactivate. If you try to install using conda install <package> it volition fail with: EnvironmentNotWritableError: The current substance abuser does not have write permissions to the target environment. The solution is to create an environment and do the install in the comparable command (equally shown above).
Common conda commands
View the helper menu:
$ conda -h
To aspect the supporte menu for the install command:
$ conda install --aid
Search the conda-forge television channel for the fenics package:
$ conda search fenics --channel conda-forge
List all the installed packages for the present environment (consider adding --explicit):
$ conda list
Create the myenv environment and install pairtools into the that environment:
$ conda create --name myenv pairtools
Produce an environment named myenv and install Python interlingual rendition 3.6 and silk hat:
$ conda create --name myenv python=3.6 dress hat
Create an environment called biowork-env and install blast from the bioconda canalize:
$ conda create --name biowork-env --channel bioconda blast
Install the pandas package into an surroundings that was previously created:
$ conda activate biowork-env (biowork-env)$ conda install pandas
Lean the available environments:
$ conda list --envs
Remove the bigdata-env environment:
$ conda remove --diagnose bigdata-env --each
Untold more can be cooked with conda American Samoa a computer software manager or environment manager.
To insure examples of instalmen scripts for various commonly used packages–so much as Tensorflow, mpi4py, Pytorch, JAX, and others–see Usual Package Installation Examples section below.
pip
spot stands for "worst installs packages". It is a package manager for Python packages exclusively. pip installs packages that are hosted on the Python Package Forefinger or PyPI.
You will typically want to expend whip within a Conda surround after installing packages via conda to obtain packages that are not available on Anaconda Cloud. For model:
$ faculty load anaconda3/2020.11 $ conda create --epithet sklearn-env scikit-study pandas matplotlib $ conda trigger sklearn-env (sklearn-env)$ pip install multiregex
You should avoid installment conda packages after doing pip installs within a Conda environment.
Do not use the pip3 overlook even if the directions you are following tell you to make out so (manipulation pip instead). pip bequeath search for a pre-compiled reading of the package you desire named a wheel. If it fails to finds this for your program then it volition attempt to build the package from source. It ass pick out pip several minutes to build a great software from rootage. One ofttimes necessarily to load different environment modules in addition to anaconda3 before doing a pip instal. E.g., if your package uses GPUs then you will probably need to do module load cudatoolkit/<version> or if IT uses the message-passing interface (MPI) for parallelization then module load openmpi/<version>. To see all available software modules, run mental faculty help.
Communal blip commands
View the supporte menu:
$ rack up -h
The help menu for the install command:
$ pip install --facilitate
Hunting the Python Package Index PyPI for a given package (e.g., jax):
$ hit look jax
List all installed packages:
$ pip list
Set u pairtools and pyblast for version 3.5 of Python
$ pip install python==3.5 pairtools pyblast
Install a set of packages recorded in a text file
$ pip install -r requirements.txt
To see detailed information about an installed package such every bit sphinx:
$ radar target show sphinx
Upgrade the sphinx package:
$ pip install --upgrade sphinx
Uninstall the pairtools package:
$ pip uninstall pairtools
See the whip documentation for more.
To see examples of installation scripts for versatile commonly victimized packages–such as Tensorflow, mpi4py, Pytorch, JAX, and others–see Common Computer software Installation Examples section below.
Isolated Python Environments with virtualenv
Often times you will want to create isolated Python environments. This is useful, for illustrate, when you have two packages that require different versions of a third software. The use of environments saves one the trouble of repeatedly upgrading operating room downgrading the third package in that case. We recommend using virtualenv to create unconnected Python environments. To get started with virtualenv IT must archetypal be installed:
$ module freight anaconda3/2020.11 $ spot put in --user virtualenv
Note that like pip, virtualenv is an executable, not a program library. To create an isolated environment do:
$ mkdir myenv $ virtualenv myenv $ source myenv/bin/activate
Consider replacement myenv with a more suitable name for your work. Immediately you can install Python packages in isolation from other Python environments:
$ pip install slingshot chime $ deactivate
Billet the --user selection is omitted since the packages will represent installed locally in the realistic environment. At the command line of descent, to leave the environment ravel deactivate.
Pee destined you source the surround in your Slurm script as in this example:
#!/bin/bash #SBATCH --lin-name=py-job # create a short name for your job #SBATCH --nodes=1 # node enumerate #SBATCH --ntasks=1 # total number of tasks across all nodes #SBATCH --cpus-per-task=1 # cpu-cores per task (>1 if multi-threaded tasks) #SBATCH --mem-per-cpu=4G # computer memory per central processing unit-meat (4G per cpu-heart and soul is default) #SBATCH --clock=00:01:00 # total run time limit (HH:MM:SS) #SBATCH --mail-eccentric=totally # send e-mail when job begins, ends and fails #SBATCH --mail-user=<YourNetID>@princeton.edu module chuck module load anaconda3/2020.11 source </path/to>/myenv/bin/activate python myscript.py
Or els to virtualenv, you may believe using the inherent Python 3 module venv. pip in combination with virtualenv serve as hefty package and environment managers. There are besides combined managers such as pipenv and pyenv that you May consider.
pip vs. conda
If your software system exists on PyPI and Anaconda Cloud then how do you settle which to install from? You should almost always favor conda over pip. This is because conda packages are pre-compiled and their dependencies are automatically handled. While pip installs will frequently download a binary cycle (pre-compiled), the user frequently needs to take action to satisfy the dependencies. Furthermore, some knowledge domain conda packages are linked against the Intel Math Nub Program library which leads to improved performance over pip installs on our systems. One disadvantage of conda packages is that they tend to lag rear end shoot packages in terms of versioning. In many cases, the decision of conda versus pip will be answered past reading the installation instructions for the software you would like to use. Spell to cses@princeton.edu for a recommendation on the induction procedure or if you encounter problems while trying to run your Python script.
Installing Python Packages from Author
In some cases you will be provided with the source code for your software program. To install from source do:
$ python setup.py set u --prefix=</path/to/install/location>
For help menu use python setup.py --help-commands. Be sure to update the appropriate environment variables in your ~/.bashrc register:
export Course=</path/to/install/fix>/bin:$PATH export PYTHONPATH=</path/to/install/location>/lib/python<interlingual rendition>/site-packages:$PYTHONPATH
Packaging and Distributing Your Own Python Package
Some PyPI and Anaconda give up registered users to store their packages on their platforms. You must follow the instructions for doing soh but once done person can do a mop up install surgery a conda install of your package. This makes it very easy to enable someone other to economic consumption your research software program. See this guide for practical examples of the process.
Where to Store Your Files
You should run your jobs out of /scratch/gpfs/<YourNetID> on the HPC clusters. These filesystems are very loyal and provide vast amounts of storage. Do non course jobs out of /tigress surgery /projects. That is, you should ne'er be writing the end product of actively running jobs to those filesystems. /tigress and /projects are stupid and should only be used for financial backing up the files that you grow on /shekels/gpfs. Your /home directory on entirely clusters is small and it should only be used for storing source cipher and executables.
The commands below give you an idea of how to in good order run a Python job:
$ ssh <YourNetID>@della.Princeton University.edu $ cd /scratch/gpfs/<YourNetID> $ mkdir myjob && cardinal myjob # put Python script and Slurm script in myjob $ sbatch job.slurm
If the consort produces data that you want to backup then written matter or move information technology to /tigress or /projects, for exercise:
$ cp -r /scratch/gpfs/<YourNetID>/myjob /tigress/<YourNetID>
For life-sized transfers debate using rsync instead of cp. Well-nig users but do back-ups to /tigress all week more or less. While /scratch/gpfs is non backed-up, files are never removed. Nonetheless, critical results should be transferred to /tigress or /projects.
The diagram to a lower place gives an overview of the filesystems:
Jupyter Notebooks on the HPC Clusters
Delight consider our paginate for Jupyter on the HPC Clusters.
Multiprocessing
The multiprocessing module enables single-node parallelism for Python scripts based on the subprocess module. The script under uses multiprocessing to execute an embarrassingly parallel mapping of a shortlist:
signification bone from multiprocessing import Pool def f(x): return x*x if __name__ == '__main__': num_cores = int(os.getenv('SLURM_CPUS_PER_TASK')) with Pool(num_cores) as p: mark(p.map(f, [1, 2, 3, 4, 5, 6, 7, 8])) The scipt above can likewise be wont to parallelize a for loop. Below is an set aside Slurm script for this code:
#!/bin/smash #SBATCH --job-name=multipro # make up a forgetful make for your subcontract #SBATCH --nodes=1 # node count #SBATCH --ntasks=1 # total number of tasks across all nodes #SBATCH --cpus-per-tax=4 # come of processes #SBATCH --mem-per-cpu=4G # retention per cpu-inwardness (4G per cpu-heart is default) #SBATCH --time=00:01:00 # overall run metre terminal point (HH:MM:SS) #SBATCH --mail-type=begin # send email when job begins #SBATCH --postal service-type=stop # send email when job ends #SBATCH --mail-exploiter=<YourNetID>@Princeton University.edu faculty purge faculty load anaconda3/2020.11 srun python myscript.py
The turnout of the Python script is:
[1, 4, 9, 16, 25, 36, 49, 64]
The Python script extracts the count of cores from the Slurm environment variable. This eliminates the potential problems that could arise if the ii values were set independently in the Slurm script and Python script.
Oftentimes times the best way to carry out a large number independent Python jobs is using an job array and non by exploitation the multiprocessing module.
Debugging Python
Learn much about debugging Python cypher on the Princeton HPC clusters.
This television explains how to run the PyCharm debugger on a TigerGPU node. The aforementioned subprogram can be used for the other clusters. PyCharm for Linux is available on jetbrains.com. While the picture uses the Community Edition, you toilet get the professional edition for free by provision your "dot edu" email address.
While debugging you English hawthorn benefit from using unbuffered output of print statements. This hind end be achieved by modifying the Slurm script as follows:
Python -u myscript.py
If the above proves to be meagre then try the following:
print("successful IT here", rich=True) If the supra is however stingy and then try writing a line to file:
with open("debug.log", "w") atomic number 3 fp: fp.write("made it Here") After debugging is complete you should return to buffered output to avoid potential performance costs related with unbuffered output.
Profiling Python
The most extremely advisable tool for profiling Python is line_profiler which makes it easy to see how much clip is spent on each line inside a function every bit well as the list of calls.
The inbuilt cProfile module provides a simple way to profile your code:
python -m cProfile -s tottime myscript.py
Even so, most users find that the cProfile module provides information that is besides fine grained.
PyCharm can be victimised for profiling. Past default it uses cProfile. If you are working with multithreaded code then you should install and use yappi.
Within Jupyter notebooks unity may employment %time and %timeit for doing measurements.
Build up MAP may be accustomed visibility any Python scripts that call compiled code. See our MAP guide for proper instructions.
Building Python from Source
The procedure below shows how to build Python from source:
$ cd $Domestic/software # or other location $ wget https://www.Python.org/ftp/Python/3.8.5/Python-3.8.5.tgz $ tar zxf Python-3.8.5.tgz $ cd Python-3.8.5 $ module load rh/devtoolset/8 $ ./configure --help $ ./configure --enable-optimizations --prefix=$Family/software/python385 $ piddle -j 10 $ make test # extraordinary tests fail $ make install $ cd python385/BIN $ ./python3
Common Package Installation Examples
FEniCS
FEniCS is an ASCII text file computation platform for solving partial differential equations. To install:
$ faculty load anaconda3/2020.11 $ conda create --name fenics-env -c conda-forge fenics $ conda touch of fenics-env
Make sure you includeconda activate fenics-envin your Slurm script. For better carrying into action one may deliberate installing from source.
CuPy on Traverse
CuPy is open via Anaconda Cloud on all our clusters. For Pass over use the IBM WML line:
$ module load anaconda3/2020.11 $ CHNL="https://public.dhe.ibm.com/ibmdl/export/pub/software/server/ibm-three-toed sloth/conda" $ conda create --identify cupy-env --channel ${CHNL} cupy Be sure to include mental faculty load anaconda3/2020.11 in your Slurm script.
JAX
JAX is Autograd and XLA, brought in collaboration for superior machine learning enquiry. See the Intro to ML Libraries repo for build directions.
PyStan
Here are the directions for installing PyStan:
$ module onus anaconda3/2020.11 $ conda create --mention stan-env pystan $ conda activate stan-env
To compile models, your Slurm script will need to include the rh module, which provides a newer compiler suite:
#!/bin/bop #SBATCH --speculate-name=myjob # make a shortstop name for your job #SBATCH --nodes=1 # thickening count #SBATCH --ntasks=1 # total number of tasks across whol nodes #SBATCH --cpus-per-task=1 # cpu-cores per job (>1 if multi-threaded tasks) #SBATCH --mem-per-cpu=4G # memory board per cpu-core (4G per cpu-substance is default) #SBATCH --time=01:00:00 # total run time restrain (HH:Millimeter:SS) #SBATCH --send-type=every # commit email when job begins, ends and fails #SBATCH --chain armour-substance abuser=<YourNetID>@Princeton.edu module purge faculty onus rh/devtoolset/8 module lade anaconda3/2020.11 conda activate stan-env Python myscript.py
Try varying the respect of cpus-per-task to see if you baffle a speed-up. Note that the more resources you quest, the longer the waiting line time.
Deeplabcut
See the deeplabcut website. Follow these installation directions:
$ ssh -X <YourNetID>@della-gpu.princeton.edu $ module load anaconda3/2020.11 $ conda create --figure dlc-env python=3.8 wxPython=4.0.7 jupyter nb_conda ffmpeg cudnn=8 \ cudatoolkit=11 -c conda-forge -y $ conda activate dlc-env $ pip install deeplabcut[gui]
One could also usance one of the Docker images using Singularity. Make sure you understand "ssh -X" and the other options for exploitation a Graphical user interface. If you fail to setup a software environment that can handle graphics then you volition encounter: "ImportError: Cannot load backend 'WXAgg' which requires the 'wx' interactive framework, as 'headless' is currently running". Be sure to use salloc for interactional work on since running connected the login node is not allowed.
Lenstools
$ module load anaconda3/2020.11 $ conda create --name lenstools-env numpy scipy pandas matplotlib astropy $ conda activate lenstools-env $ module load rh/devtoolset/8 openmpi/gcc/3.1.5/64 gsl/2.4 $ export MPICC=$(which mpicc) $ pip install mpi4py $ pip install emcee==2.2.1 $ pip establis lenstools
Note that you will receive warnings when lenstools is strange in Python.
SMC++
SMC++ infers population history from whole-genome sequence data. In this case rack up is used to avoid a glibc fight.
$ mental faculty cargo anaconda3/2020.11 $ radar target install --user virtualenv $ mkdir myenv $ virtualenv myenv $ source myenv/bin/activate $ shoot instal cython numpy $ pip install git+https://github.com/popgenmethods/smcpp $ smc++ --help
Dedalus
Dedalus can be accustomed solve differential equations using spectral methods.
$ module load anaconda3/2020.11 $ conda make up --name dedalus-env python=3.6 $ conda activate dedalus-env $ conda config --add channels conda-forge $ conda install nomkl cython docopt matplotlib pathlib scipy $ module load openmpi/gcc/1.10.2/64 fftw/gcc/openmpi-1.10.2/3.3.4 hdf5/gcc/openmpi-1.10.2/1.10.0 $ export FFTW_PATH=$FFTW3DIR $ exportation HDF5_DIR=$HDF5DIR $ export MPI_PATH=/usr/local anesthetic/openmpi/1.10.2/gcc/x86_64 $ export MPICC=$(which mpicc) $ pip install mpi4py $ CC=mpicc pip install --upgrade --no-binary :wholly: h5py $ hg clone https://bitbucket.org/dedalus-project/dedalus $ cd dedalus $ pip install -r requirements.txt $ python setup.py build $ python setup.py install
TensorFlow
See our usher for TensorFlow on the HPC clusters.
PyTorch
See our guide for PyTorch connected the HPC clusters.
mpi4py
MPI for Python (mpi4py) provides bindings of the Substance Passing Interface (MPI) standard for the Python programing language. It can be used to parallelize Python scripts. See our take for installing mpi4py on the HPC clusters.
Workshops
Look come out of the closet for various Princeton University Enquiry Computer science workshops much as High Performance Python, High Performance Python for GPUs and Mixing Compiled Code and Python by William Henry Schreiner.
FAQ
1. Why does pip set up <package> die with an error mentioning a Read-only file system?
After payload the anaconda3 module, pip will be available as part of Eunectes murinus Python which is a system box. By nonpayment blip will render to install the files in the same locations as the Anaconda packages. Because you don't have write out access to this directory the install will go bad. One needs to add --user as discussed higher up.
2. What should I do if I try to set u a Python package and the put in fails with: error: Disk quota exceeded?
You induce tierce options. Prototypic, count removing files within your home directory to make space available. Second, run the checkquota overtop and follow the link at the hind end to request more distance. Last, for pip installations escort the question toward the bottom of this FAQ for a third possibility i.e., setting --location to /dough/gpfs/<YourNetID>. For conda installs try learnedness about the --prefix selection.
3. Why do I cause the following error message when I try to run whip on Della: -bash: pip: command non found?
You need to doh module load anaconda3 before using pip or any of the Anaconda packages. You too postulate to load this mental faculty in front victimisation Python itself.
4. I read that it is a good estimate to update conda before instalmen a package. Wherefore brawl I buzz off an error message when I try to perform the update?
conda is a system executable. You do not have permit to update IT. If you try to update information technology you volition get this wrongdoing: EnvironmentNotWritableError: The current user does not have write permissions to the target environment. The current version is sufficient to install any software system.
5. When I running game conda list happening the base environment I see the package that I need just it is non the right version. How can I get the right version? One solution is to produce a conda environment and install the version you need there. The version of NumPy happening Tiger is 1.16.2. If you take version 1.16.5 for your work then manage: conda create --name myenv numpy=1.16.5.
6. Is it okay if I combine virtualenv and conda?
This is extremely discouraged. Piece in principle IT can work, near users receive it just causes problems. Try to quell inside one environment manager. Note that if you make a conda environment you can use pip to install packages.
7. Fire I combine conda and pip?
Yes, and this tends to work well. A typical session may look corresponding this:
$ module load anaconda3/2020.11 $ conda create --name myenv python=3.6 $ conda spark myenv $ pip install scitools
Note that --user is omitted when using pip within a conda environment. See the fastball points at the bottom of this page for tips on victimization this draw close.
8. How exercise I install a Python package in a custom location using pip or conda?
For pip, first do pip establis --object=</path/to/install/locating> <package> and so update the PYTHONPATH environment adaptable in your ~/.bashrc file with export PYTHONPATH=$PYTHONPATH:/path/to/install/positioning. For conda, you employ the --prefix option. For instance, to install cupy on /scratch/gpfs/<YourNetID>:
$ faculty burden anaconda3/2020.11 $ conda create --prefix /scratch/gpfs/$Drug user/py-gpu cupy
Be foreordained to have these two lines in your Slurm script: module load anaconda3/2020.11 and conda activate /scratch out/network/$Drug user/py-gpu. Note that /scratch/gpfs is not stiff-backed up.
9. I tried to install some packages but now none of my Python tools are working. Is it possible to delete all my Python packages and start over?
Yes. Packages installed by spot are in ~/.local/lib piece conda packages and environments are in ~/.conda. If you made whatever environments with virtualenv you should take those besides. Removing these directories will give you a clean start. Equal sure to examine the table of contents opening. It Crataegus laevigata be wise to selectively remove sub-directories instead. You may also motivation slay the ~/.memory cache directory and you may need to make modifications to your .bashrc file if you added or denaturised environment variables.
10. How are my pip packages built? Which optimization flags are utilised? Fare I throw to be careful with vectorization happening Della where several antithetic CPUs are live?
After loading the anaconda3 module, run this mastery: python3.7-config --cflags. To force a package to be stacked from germ with certain optimization flags do, for example: CFLAGS="-O1" pip install numpy -vvv --no-binary=numpy
11. What is the Intel Python distribution and how do I get moving with it? Intel provides their own implementation of Python as well as numerous packages optimized for Intel hardware. You may ascertain significant performance benefits from these packages. To create a conda environment with Intel Python and a number of Intel-optimized numerics packages:
$ module load anaconda3/2020.11 $ conda create --name my-intel --channel intel python numpy scipy
12. The installing directions that I am following read to use pip3. Is this okay?
Do non enjoyment pip3 for installment Python packages. pip3 is a component of the organization Python and IT will not work properly with Anaconda. Always do faculty load anaconda3/<version> and and so purpose spot for installing packages.
Acquiring Assist
If you encounter any difficulties while using Python along one of our HPC clusters then please send an email to cses@princeton.edu or attend a help academic term.
Where Is My Python Binary Picking Up Modules From?
Source: https://researchcomputing.princeton.edu/support/knowledge-base/python
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