Python

Description

Python is an interpreted programming language with a design philosophy stressing the readability of code. Its syntax is simple and expressive. Python has an extensive, easy-to-use standard library.

The capabilities of Python can be extended with packages developed by third parties. In general, to simplify operations, it is left up to individual users and groups to install these third-party packages in their own directories. However, most systems offer several versions of Python as well as tools to help you install the third-party packages that you need.

The following sections discuss the Python interpreter, and how to install and use packages.

Loading an interpreter

Default Python version

When you log into our clusters, a default Python version will be available, but that is generally not the one that you should use, especially if you need to install any Python packages. You should try to find out which version of Python is required to run your Python programs and load the appropriate module. If you are not sure which version you need, then it is reasonable to use the latest version available.

Loading a Python module

To discover the versions of Python available:

[name@server ~]$ module avail python

You can then load the version of your choice using module load. For example, to load Python 3.10 you can use the command

[name@server ~]$ module load python/3.10

Python version supported

In general in the Python ecosystem, the transition to more modern versions of python is accelerating, with many packages only supporting the latest few versions of Python 3.x. In our case, we provide prebuilt Python packages in our wheelhouse only for the 3 most recent Python versions available on the systems. This will result in dependencies issues when trying to install those packages with older versions of Python. See Troubleshooting.

Below is a table indicating when we stopped building wheels for each version of Python.

Python version	Date
3.10
3.9
3.8
3.7	2022-02
3.6	2021-02
3.5	2020-02
2.7	2020-01

SciPy stack

In addition to the base Python module, the SciPy package is also available as an environment module. The scipy-stack module includes:

• NumPy
• SciPy
• Matplotlib
  • dateutil
  • pytz
• IPython
  • pyzmq
  • tornado
• pandas
• Sympy
• nose

If you want to use any of these Python packages, load a Python version of your choice and then module load scipy-stack.

To get a complete list of the packages contained in scipy-stack, along with their version numbers, run module spider scipy-stack/2020a (replacing 2020a with whichever version you want to find out about).

Creating and using a virtual environment

With each version of Python, we provide the tool virtualenv. This tool allows users to create virtual environments within which you can easily install Python packages. These environments allow one to install many versions of the same package, for example, or to compartmentalize a Python installation according to the needs of a specific project. Usually you should create your Python virtual environment(s) in your /home directory or in one of your /project directories. (See "Creating virtual environments inside of your jobs" below for a third alternative.)

To create a virtual environment, make sure you have selected a Python version with module load python as shown above in section Loading a Python module. If you expect to use any of the packages listed in section SciPy stack above, also run module load scipy-stack. Then enter the following command, where ENV is the name of the directory for your new environment:

[name@server ~]$ virtualenv --no-download ENV

Once the virtual environment has been created, it must be activated:

[name@server ~]$ source ENV/bin/activate

You should also upgrade pip in the environment:

[name@server ~]$ pip install --no-index --upgrade pip

To exit the virtual environment, simply enter the command deactivate:

(ENV) [name@server ~] deactivate

You can now use the same virtual environment over and over again. Each time:

1. Load the same environment modules that you loaded when you created the virtual environment, e.g. module load python scipy-stack
2. Activate the environment, source ENV/bin/activate

Installing packages

Once you have a virtual environment loaded, you will be able to run the pip command. This command takes care of compiling and installing most of Python packages and their dependencies. A comprehensive index of Python packages can be found at PyPI.

All of pip's commands are explained in detail in the user guide. We will cover only the most important commands and use the Numpy package as an example.

We first load the Python interpreter:

[name@server ~]$ module load python/3.10

We then activate the virtual environment, previously created using the virtualenv command:

[name@server ~]$ source ENV/bin/activate

Finally, we install the latest stable version of Numpy:

(ENV) [name@server ~] pip install numpy --no-index

The pip command can install packages from a variety of sources, including PyPI and prebuilt distribution packages called Python wheels. We provide Python wheels for a number of packages. In the above example, the --no-index option tells pip to not install from PyPI, but instead to install only from locally-available packages, i.e. our wheels.

Whenever we provide a wheel for a given package, we strongly recommend to use it by way of the --no-index option. Compared to using packages from PyPI, wheels that have been compiled by our staff can prevent issues with missing or conflicting dependencies, and were optimised for our clusters hardware and libraries. See Available wheels.

If you omit the --no-index option, pip will search both PyPI and local packages, and use the latest version available. If PyPI has a newer version, it will be installed instead of our wheel, possibly causing issues. If you are certain that you prefer to download a package from PyPI rather than use a wheel, you can use the --no-binary option, which tells pip to ignore prebuilt packages entirely. Note that this will also ignore wheels that are distributed through PyPI, and will always compile the package from source.

To see where the pip command is installing a python package from, diagnosing installation issues, you can tell it to be more verbose with the -vvv option. It also worth mentioning that when installing multiple packages it is advisable to install them with one command as it helps pip resolve dependencies.

Installing dependent packages

In some cases, such as TensorFlow, we provide wheels for a specific host (cpu or gpu), suffixed with _cpu or _gpu. Packages dependent on tensorflow will then fail to install. If my_package depend on numpy and tensorflow, then the following will allow us to install it:

(ENV) [name@server ~] pip install numpy tensorflow_cpu --no-index
(ENV) [name@server ~] pip install my_package --no-deps

The --no-deps options tells pip to ignore dependencies.

Creating virtual environments inside of your jobs

Parallel filesystems such as the ones used on our clusters are very good at reading or writing large chunks of data, but can be bad for intensive use of small files. Launching a software and loading libraries, such as starting python and loading a virtual environment, can be slow for this reason.

As a workaround for this kind of slowdown, and especially for single-node Python jobs, you can create your virtual environment inside of your job, using the compute node's local disk. It may seem counter-intuitive to recreate your environment for every job, but it can be faster than running from the parallel filesystem, and will give you some protection against some filesystem performance issues. This approach, of creating a node-local virtualenv, has to be done for each node in the job, since the virtualenv is only accessible on one node. Following job submission script demonstrates how to do this for a single-node job:

File : submit_venv.sh

#!/bin/bash
#SBATCH --account=def-someuser
#SBATCH --mem-per-cpu=1.5G      # increase as needed
#SBATCH --time=1:00:00

module load python/3.10
virtualenv --no-download $SLURM_TMPDIR/env
source $SLURM_TMPDIR/env/bin/activate
pip install --no-index --upgrade pip

pip install --no-index -r requirements.txt
python ...

where the requirements.txt file will have been created from a test environment. For example, if you want to create an environment for TensorFlow, you would do the following on a login node :

[name@server ~]$ module load python/3.10
[name@server ~]$ ENVDIR=/tmp/$RANDOM
[name@server ~]$ virtualenv --no-download $ENVDIR
[name@server ~]$ source $ENVDIR/bin/activate
[name@server ~]$ pip install --no-index --upgrade pip
[name@server ~]$ pip install --no-index tensorflow
[name@server ~]$ pip freeze --local > requirements.txt
[name@server ~]$ deactivate
[name@server ~]$ rm -rf $ENVDIR

This will yield a file called requirements.txt, with content such as the following

File : requirements.txt

absl_py==1.2.0+computecanada
astunparse==1.6.3+computecanada
cachetools==5.2.0+computecanada
certifi==2022.6.15+computecanada
charset_normalizer==2.1.0+computecanada
flatbuffers==1.12+computecanada
gast==0.4.0+computecanada
google-pasta==0.2.0+computecanada
google_auth==2.9.1+computecanada
google_auth_oauthlib==0.4.6+computecanada
grpcio==1.47.0+computecanada
h5py==3.6.0+computecanada
idna==3.3+computecanada
keras==2.9.0+computecanada
Keras-Preprocessing==1.1.2+computecanada
libclang==14.0.1+computecanada
Markdown==3.4.1+computecanada
numpy==1.23.0+computecanada
oauthlib==3.2.0+computecanada
opt-einsum==3.3.0+computecanada
packaging==21.3+computecanada
protobuf==3.19.4+computecanada
pyasn1==0.4.8+computecanada
pyasn1-modules==0.2.8+computecanada
pyparsing==3.0.9+computecanada
requests==2.28.1+computecanada
requests_oauthlib==1.3.1+computecanada
rsa==4.8+computecanada
six==1.16.0+computecanada
tensorboard==2.9.1+computecanada
tensorboard-data-server==0.6.1+computecanada
tensorboard_plugin_wit==1.8.1+computecanada
tensorflow==2.9.0+computecanada
tensorflow_estimator==2.9.0+computecanada
tensorflow_io_gcs_filesystem==0.23.1+computecanada
termcolor==1.1.0+computecanada
typing_extensions==4.3.0+computecanada
urllib3==1.26.11+computecanada
Werkzeug==2.1.2+computecanada
wrapt==1.13.3+computecanada

This file will ensure that your environment is reproducible between jobs.

Note that the above instructions require all of the packages you need to be available in the python wheels that we provide (see "Available wheels" below). If the wheel is not available in our wheelhouse, you can pre-download it (see "Pre-downloading packages" section below). If you think that the missing wheel should be included in our wheelhouse, please contact Technical support to make a request.

Available wheels

Currently available wheels are listed on the Available Python wheels page. You can also run the command avail_wheels on the cluster. By default, it will:

• only show you the latest version of a specific package (unless versions are given);
• only show you versions that are compatible with the python module (if one loaded) or virtual environment (if activated), otherwise all versions will be shown;
• only show you versions that are compatible with the CPU architecture that you are currently running on.

Names

To list wheels containing cdf (case insensitive) in its name:

[name@server ~]$ avail_wheels "*cdf*"
name      version    python    arch
--------  ---------  --------  -------
h5netcdf  0.7.4      py2,py3   generic
netCDF4   1.5.8      cp39      avx2
netCDF4   1.5.8      cp38      avx2
netCDF4   1.5.8      cp310     avx2

Or an exact name:

[name@server ~]$ avail_wheels numpy
name    version    python    arch
------  ---------  --------  -------
numpy   1.23.0     cp39      generic
numpy   1.23.0     cp38      generic
numpy   1.23.0     cp310     generic

Version

To list a specific version, you can use the same format as with `pip`:

[name@server ~]$ avail_wheels numpy==1.23
name    version    python    arch
------  ---------  --------  -------
numpy   1.23.0     cp39      generic
numpy   1.23.0     cp38      generic
numpy   1.23.0     cp310     generic

Or use the long option:

[name@server ~]$ avail_wheels numpy --version 1.23
name    version    python    arch
------  ---------  --------  -------
numpy   1.23.0     cp39      generic
numpy   1.23.0     cp38      generic
numpy   1.23.0     cp310     generic

With the pip format, you can use different operators : ==, <, >, ~=, <=,>=, !=. For instance, to list inferior versions:

[name@server ~]$ avail_wheels 'numpy<1.23'
name    version    python    arch
------  ---------  --------  -------
numpy   1.22.2     cp39      generic
numpy   1.22.2     cp38      generic
numpy   1.22.2     cp310     generic

And to list all available versions:

[name@server ~]$ avail_wheels "*cdf*" --all-version
name      version    python    arch
--------  ---------  --------  -------
h5netcdf  0.7.4      py2,py3   generic
netCDF4   1.5.8      cp39      avx2
netCDF4   1.5.8      cp38      avx2
netCDF4   1.5.8      cp310     avx2
netCDF4   1.5.6      cp38      avx2
netCDF4   1.5.6      cp37      avx2
netCDF4   1.5.4      cp38      avx2
netCDF4   1.5.4      cp37      avx2
netCDF4   1.5.4      cp36      avx2

Python

You can list a specific version of Python:

[name@server ~]$ avail_wheels 'numpy<1.23' --python 3.9
name    version    python    arch
------  ---------  --------  -------
numpy   1.22.2     cp39      generic

The python column tell us for which version the wheel is available, where cp39 stands for cpython 3.9.

Requirements file

One can list available wheels based on a requirements.txt file with:

[name@server ~]$ avail_wheels -r requirements.txt 
name       version    python    arch
---------  ---------  --------  -------
packaging  21.3       py3       generic
tabulate   0.8.10     py3       generic

And display wheels that are not available:

[name@server ~]$ avail_wheels -r requirements.txt --not-available
name       version    python    arch
---------  ---------  --------  -------
packaging  21.3       py3       generic
pip
tabulate   0.8.10     py3       generic

Pre-downloading packages

Here is how to pre-download a package called tensorboardX on a login node, and install it on a compute node:

1. Run pip download --no-deps tensorboardX. This will download the package as tensorboardX-1.9-py2.py3-none-any.whl (or similar) in the working directory. The syntax of pip download is the same as pip install.
2. If the filename does not end with none-any, and ends with something like linux_x86_64 or manylinux*_x86_64, the wheel might not function correctly. You should contact Technical support so that we compile the wheel and make it available on our systems.
3. Then, when installing, use the path for file pip install tensorboardX-1.9-py2.py3-none-any.whl.

Parallel programming with the Python multiprocessing module

Doing parallel programming with Python can be an easy way to get results faster. An usual way of doing so is to use the multiprocessing module. Of particular interest is the Pool class of this module, since it allows one to control the number of processes started in parallel, and apply the same calculation to multiple data. As an example, suppose we want to calculate the cube of a list of numbers. The serial code would look like this :

Using a loopUsing a map

File : cubes_sequential.py

def cube(x):
    return x**3

data = [1, 2, 3, 4, 5, 6]
cubes = [cube(x) for x in data]
print(cubes)

File : cubes_sequential.py

def cube(x):
    return x**3

data = [1, 2, 3, 4, 5, 6]
cubes = list(map(cube,data))
print(cubes)

Using the Pool class, running in parallel, the above codes become :

Using a loopUsing a map

File : cubes_parallel.py

import multiprocessing as mp

def cube(x):
    return x**3

pool = mp.Pool(processes=4)
data = [1, 2, 3, 4, 5, 6]
results = [pool.apply_async(cube, args=(x,)) for x in data]
cubes = [p.get() for p in results]
print(cubes)

File : cubes_parallel.py

import multiprocessing as mp

def cube(x):
    return x**3

pool = mp.Pool(processes=4)
data = [1, 2, 3, 4, 5, 6]
cubes = pool.map(cube, data)
print(cubes)

The above examples will however be limited to using 4 processes. On a cluster, it is very important to use the cores that are allocated to your job. Launching more processes than you have cores requested will slow down your calculation and possibly overload the compute node. Launching fewer processes than you have cores will result in wasted resources and cores remaining idle. The correct number of cores to use in your code is determined by the amount of resources you requested to the scheduler. For example, if you have the same computation to perform on many tens of data or more, it would make sense to use all of the cores of a node. In this case, you can write your job submission script with the following header :

File : submit.sh

#SBATCH --ntasks-per-node=1
#SBATCH --cpus-per-task=32

python cubes_parallel.py

and then, your code would become the following :

Using a loopUsing a map

File : cubes_parallel.py

import multiprocessing as mp
import os

def cube(x):
    return x**3

ncpus = int(os.environ.get('SLURM_CPUS_PER_TASK',default=1))
pool = mp.Pool(processes=ncpus)
data = [1, 2, 3, 4, 5, 6]
results = [pool.apply_async(cube, args=(x,)) for x in data]
cubes = [p.get() for p in results]
print(cubes)

File : cubes_parallel.py

import multiprocessing as mp
import os

def cube(x):
    return x**3

ncpus = int(os.environ.get('SLURM_CPUS_PER_TASK',default=1))
pool = mp.Pool(processes=ncpus)
data = [1, 2, 3, 4, 5, 6]
cubes = pool.map(cube, data)
print(cubes)

Note that in the above example, the function cube itself is sequential. If you are calling some external library, such as numpy, it is possible that the functions called by your code are themselves parallel. If you want to distribute processes with the technique above, you should verify whether the functions you call are themselves parallel, and if they are, you need to control how many threads they will take themselves. If, for example, they take all the cores available (32 in the above example), and you are yourself starting 32 processes, this will slow down your code and possibly overload the node as well.

Note that the multiprocessing module is restricted to using a single compute node, so the speedup achievable by your program is usually limited to the total number of CPU cores in that node. If you want to go beyond this limit and use multiple nodes, consider using mpi4py or PySpark. Other methods of parallelizing Python (not all of them necessarily supported on our clusters) are listed here. Also note that you can greatly improve the performance of your Python program by ensuring it is written efficiently, so that should be done first before parallelizing. If you are not sure if your Python code is efficient, please contact technical support and have them look at your code.

Anaconda

Please see Anaconda.

Jupyter

Please see Jupyter.

Troubleshooting

Python script is hanging

By using the faulthandler module, you can edit your script to allow dumping a traceback after a timeout. See faulthandler.dump_traceback_later().

You can also inspect a python process while the job is running, without modifying it beforehand, using py-spy:

1. Install py-spy in a virtualenv in your home
2. Attach to the running job, using srun --pty --jobid JOBID bash
3. Use htop -u $USER to find the process ID of your python script
4. Activate the virtualenv where py-spy is installed
5. Run py-spy top --pid PID to see live feedback about where your code is spending time
6. Run py-spy dump --pid PID to get a traceback of where your code is currently at.

Package 'X' requires a different Python: X.Y.Z not in '>=X.Y'

When installing packages, you may encounter an error similar to: ERROR: Package 'X' requires a different Python: 3.6.10 not in '>=3.7'.

The current python module loaded (3.6.10 in this case) is not supported by that package. You can update to a more recent version, such as the latest available module. Or install an older version of package 'X'.

Package has requirement X, but you'll have Y which is incompatible

When installing packages, you may encounter an error similar to: ERROR: Package has requirement X, but you'll have Y which is incompatible..

Upgrade pip to the latest version or higher than [21.3] to use the new dependency resolver:

(ENV) [name@server ~] pip install --no-index --upgrade pip

Then re-run your install command.

No matching distribution found for X

When installing packages, you may encounter an error similar to:

(ENV) [name@server ~] pip install X
ERROR: Could not find a version that satisfies the requirement X (from versions: none)
ERROR: No matching distribution found for X

pip did not find a package to install that satisfies the requirements (name, version or tags). Verify that the name and version are correct. Note also that manylinux_x_y wheels are discarded.

You can also verify that the package is available from the wheelhouse with the avail_wheels command or by searching on Available Python wheels page.

Installing many packages

When installing multiple packages, it is best to install them in one command when possible:

(ENV) [name@server ~] pip install --upgrade pip
(ENV) [name@server ~] pip install package1 package2 package3 package4

as this helps pip resolve dependencies issues.

My virtual environment was working yesterday but not anymore

Packages are often updated and this leads to a non-reproducible virtual environment. To remedy that, one should pin the specific packages and their versions with

(ENV) [name@server ~] pip install --upgrade pip
(ENV) [name@server ~] pip install --no-index package1==X.Y package2==X.Y.Z package3<X.Y package4>X.Y

and then create a requirements file that will be used to install the required packages in your job.

X is not a supported wheel on this platform

When installing a package you may encounter the following error: ERROR: package-3.8.1-cp311-cp311-manylinux_2_28_x86_64.whl is not a supported wheel on this platform.

Some packages may be incompatible or not supported on the systems. Two common cases are:

• trying to install a manylinux package
• or a python package built for a different Python version (e.g. installing a package built for python 3.11 when you have python 3.9).

Some manylinux package can be made available through the wheelhouse.

AttributeError: module ‘numpy’ has no attribute ‘X’.

When installing a wheel that requests any numpy version, the latest available version will be installed. In numpy v1.20, many attributes were set for deprecation and are now expired in v1.24.

This may result in an error, depending on the attribute accessed. For example, AttributeError: module ‘numpy’ has no attribute ‘bool’.

This can be solved by installing a previous version of numpy: pip install --no-index 'numpy<1.24'