Installing mpi4py and h5py

Warning

This guide has been adapted for Frontier only for a conda workflow. Using the default cray-python module on Frontier does not work with parallel h5py (because Python 3.9 is incompatible). Thus, this guide assumes that you are using a personal Miniconda distribution on Frontier.

Note

For venv users only interested in installing mpi4py, the pip command in this guide is still accurate.

This guide has been adapted from a challenge in OLCF’s Hands-On with Summit GitHub repository (Python: Parallel HDF5).

Note

The guide is designed to be followed from start to finish, as certain steps must be completed in the correct order before some commands work properly.

Overview

This guide teaches you how to build a personal, parallel-enabled version of h5py and how to write an HDF5 file in parallel using mpi4py and h5py.

In this guide, you will:

• Learn how to install mpi4py
• Learn how to install parallel h5py
• Test your build with Python scripts

OLCF Systems this guide applies to:

• Summit
• Andes
• Frontier

Parallel HDF5

Scientific simulations generate large amounts of data on Summit (about 100 Terabytes per day for some applications). Because of how large some datafiles may be, it is important that writing and reading these files is done as fast as possible. Less time spent doing input/output (I/O) leaves more time for advancing a simulation or analyzing data.

One of the most utilized file types is the Hierarchical Data Format (HDF), specifically the HDF5 format. HDF5 is designed to manage large amounts of data and is built for fast I/O processing and storage. An HDF5 file is a container for two kinds of objects: “datasets”, which are array-like collections of data, and “groups”, which are folder-like containers that hold datasets and other groups.

There are various tools that allow users to interact with HDF5 data, but we will be focusing on h5py – a Python interface to the HDF5 library. h5py provides a simple interface to exploring and manipulating HDF5 data as if they were Python dictionaries or NumPy arrays. For example, you can extract specific variables through slicing, manipulate the shapes of datasets, and even write completely new datasets from external NumPy arrays.

Both HDF5 and h5py can be compiled with MPI support, which allows you to optimize your HDF5 I/O in parallel. MPI support in Python is accomplished through the mpi4py package, which provides complete Python bindings for MPI. Building h5py against mpi4py allows you to write to an HDF5 file using multiple parallel processes, which can be helpful for users handling large datasets in Python. h5Py is available after loading the default Python module on either Summit or Andes, but it has not been built with parallel support.

Setting up the environment

Warning

Before setting up your environment, you must exit and log back in so that you have a fresh login shell. This is to ensure that no previously activated environments exist in your $PATH environment variable. Additionally, you should execute module reset.

Building h5py from source is highly sensitive to the current environment variables set in your profile. Because of this, it is extremely important that all the modules and environments you plan to load are done in the correct order, so that all the environment variables are set correctly.

First, load the gnu compiler module (most Python packages assume GCC), hdf5 module (necessary for h5py), and the python module (allows you to create a new environment):

Summit

$ module load gcc
$ module load hdf5
$ module load python

Andes

$ module load gcc
$ module load hdf5
$ module load python

Frontier

$ module load PrgEnv-gnu
$ module load hdf5

# Make sure your personal miniconda installation is in your path
$ export PATH="/path/to/your/miniconda/bin:$PATH"

Loading a python module puts you in a “base” environment, but you need to create a new environment using the conda create command:

Summit

$ conda create -p /ccs/proj/<project_id>/<user_id>/envs/summit/h5pympi-summit python=3.8 numpy

Andes

$ conda create -p /ccs/proj/<project_id>/<user_id>/envs/andes/h5pympi-andes python=3.8 numpy

Frontier

$ conda create -p /ccs/proj/<project_id>/<user_id>/envs/frontier/h5pympi-frontier python=3.8 libssh numpy -c conda-forge

Note

As noted in the Python on OLCF Systems page, it is highly recommended to create new environments in the “Project Home” directory.

NumPy is installed ahead of time because h5py depends on it.

After following the prompts for creating your new environment, you can now activate it:

Summit

$ source activate /ccs/proj/<project_id>/<user_id>/envs/summit/h5pympi-summit

Andes

$ source activate /ccs/proj/<project_id>/<user_id>/envs/andes/h5pympi-andes

Frontier

$ source activate /ccs/proj/<project_id>/<user_id>/envs/frontier/h5pympi-frontier

Installing mpi4py

Now that you have a fresh environment, you will next install mpi4py from source into your new environment. To make sure that you are building from source, and not a pre-compiled binary, use pip:

Summit

$ MPICC="mpicc -shared" pip install --no-cache-dir --no-binary=mpi4py mpi4py

Andes

$ MPICC="mpicc -shared" pip install --no-cache-dir --no-binary=mpi4py mpi4py

Frontier

$ MPICC="cc -shared" pip install --no-cache-dir --no-binary=mpi4py mpi4py

The MPICC flag ensures that you are using the correct C wrapper for MPI on the system. Building from source typically takes longer than a simple conda install, so the download and installation may take a couple minutes. If everything goes well, you should see a “Successfully installed mpi4py” message.

Installing h5py

Next, install h5py from source.

Summit

$ HDF5_MPI="ON" CC=mpicc pip install --no-cache-dir --no-binary=h5py h5py

Andes

$ HDF5_MPI="ON" CC=mpicc pip install --no-cache-dir --no-binary=h5py h5py

Frontier

$ HDF5_MPI="ON" CC=cc HDF5_DIR=${OLCF_HDF5_ROOT} pip install --no-cache-dir --no-binary=h5py h5py

The HDF5_MPI flag is the key to telling pip to build h5py with parallel support, while the CC flag makes sure that you are using the correct C wrapper for MPI. This installation will take much longer than both the mpi4py and NumPy installations (5+ minutes if the system is slow). When the installation finishes, you will see a “Successfully installed h5py” message.

Testing parallel h5py

Test your build by trying to write an HDF5 file in parallel using 42 MPI tasks.

First, change directories to your GPFS scratch area:

$ cd $MEMBERWORK/<YOUR_PROJECT_ID>
$ mkdir h5py_test
$ cd h5py_test

Let’s test that mpi4py is working properly first by executing the example Python script “hello_mpi.py”:

# hello_mpi.py
from mpi4py import MPI

comm = MPI.COMM_WORLD      # Use the world communicator
mpi_rank = comm.Get_rank() # The process ID (integer 0-41 for a 42-process job)

print('Hello from MPI rank %s !' %(mpi_rank))

To do so, submit a job to the batch queue:

Summit

$ bsub -L $SHELL submit_hello.lsf

Andes

$ sbatch --export=NONE submit_hello.sl

Frontier

$ sbatch --export=NONE submit_hello.sl

Example “submit_hello” batch script:

Summit

#!/bin/bash
#BSUB -P <PROJECT_ID>
#BSUB -W 00:05
#BSUB -nnodes 1
#BSUB -J mpi4py
#BSUB -o mpi4py.%J.out
#BSUB -e mpi4py.%J.err

cd $LSB_OUTDIR
date

module load gcc
module load hdf5
module load python

source activate /ccs/proj/<project_id>/<user_id>/envs/summit/h5pympi-summit

jsrun -n1 -r1 -a42 -c42 python3 hello_mpi.py

Andes

#!/bin/bash
#SBATCH -A <PROJECT_ID>
#SBATCH -J mpi4py
#SBATCH -N 1
#SBATCH -p gpu
#SBATCH -t 0:05:00

unset SLURM_EXPORT_ENV

cd $SLURM_SUBMIT_DIR
date

module load gcc
module load hdf5
module load python

source activate /ccs/proj/<project_id>/<user_id>/envs/andes/h5pympi-andes

srun -n42 python3 hello_mpi.py

Frontier

#!/bin/bash
#SBATCH -A <PROJECT_ID>
#SBATCH -J mpi4py
#SBATCH -N 1
#SBATCH -p batch
#SBATCH -t 0:05:00

unset SLURM_EXPORT_ENV

cd $SLURM_SUBMIT_DIR
date

module load PrgEnv-gnu
module load hdf5
export PATH="/path/to/your/miniconda/bin:$PATH"

source activate /ccs/proj/<project_id>/<user_id>/envs/frontier/h5pympi-frontier

srun -n42 python3 hello_mpi.py

If mpi4py is working properly, in mpi4py.<JOB_ID>.out you should see output similar to:

Hello from MPI rank 21 !
Hello from MPI rank 23 !
Hello from MPI rank 28 !
Hello from MPI rank 40 !
Hello from MPI rank 0 !
Hello from MPI rank 1 !
Hello from MPI rank 32 !
.
.
.

If you see this, great, it means that mpi4py was built successfully in your environment.

Finally, let’s see if you can get these tasks to write to an HDF5 file in parallel using the “hdf5_parallel.py” script:

# hdf5_parallel.py
from mpi4py import MPI
import h5py

comm = MPI.COMM_WORLD      # Use the world communicator
mpi_rank = comm.Get_rank() # The process ID (integer 0-41 for a 42-process job)
mpi_size = comm.Get_size() # Total amount of ranks

with h5py.File('output.h5', 'w', driver='mpio', comm=MPI.COMM_WORLD) as f:
    dset = f.create_dataset('test', (42,), dtype='i')
    dset[mpi_rank] = mpi_rank

comm.Barrier()

if (mpi_rank == 0):
    print('42 MPI ranks have finished writing!')

The MPI tasks are going to write to a file named “output.h5”, which contains a dataset called “test” that is of size 42 (assigned to the “dset” variable in Python). Each MPI task is going to assign their rank value to the “dset” array in Python, so you should end up with a dataset that contains 0-41 in ascending order.

Time to execute “hdf5_parallel.py” by submitting “submit_h5py” to the batch queue:

Summit

$ bsub -L $SHELL submit_h5py.lsf

Andes

$ sbatch --export=NONE submit_h5py.sl

Frontier

$ sbatch --export=NONE submit_h5py.sl

Example “submit_h5py” batch script:

Summit

#!/bin/bash
#BSUB -P <PROJECT_ID>
#BSUB -W 00:05
#BSUB -nnodes 1
#BSUB -J h5py
#BSUB -o h5py.%J.out
#BSUB -e h5py.%J.err

cd $LSB_OUTDIR
date

module load gcc
module load hdf5
module load python

source activate /ccs/proj/<project_id>/<user_id>/envs/summit/h5pympi-summit

jsrun -n1 -r1 -a42 -c42 python3 hdf5_parallel.py

Andes

#!/bin/bash
#SBATCH -A <PROJECT_ID>
#SBATCH -J h5py
#SBATCH -N 1
#SBATCH -p gpu
#SBATCH -t 0:05:00

unset SLURM_EXPORT_ENV

cd $SLURM_SUBMIT_DIR
date

module load gcc
module load hdf5
module load python

source activate /ccs/proj/<project_id>/<user_id>/envs/andes/h5pympi-andes

srun -n42 python3 hdf5_parallel.py

Frontier

#!/bin/bash
#SBATCH -A <PROJECT_ID>
#SBATCH -J h5py
#SBATCH -N 1
#SBATCH -p batch
#SBATCH -t 0:05:00

unset SLURM_EXPORT_ENV

cd $SLURM_SUBMIT_DIR
date

module load PrgEnv-gnu
module load hdf5
export PATH="/path/to/your/miniconda/bin:$PATH"

source activate /ccs/proj/<project_id>/<user_id>/envs/frontier/h5pympi-frontier

srun -n42 python3 hdf5_parallel.py

Provided there are no errors, you should see “42 MPI ranks have finished writing!” in your output file, and there should be a new file called “output.h5” in your directory. To see explicitly that the MPI tasks did their job, you can use the h5dump command to view the dataset named “test” in output.h5:

$ h5dump output.h5

HDF5 "output.h5" {
GROUP "/" {
   DATASET "test" {
      DATATYPE  H5T_STD_I32LE
      DATASPACE  SIMPLE { ( 42 ) / ( 42 ) }
      DATA {
      (0): 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18,
      (19): 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34,
      (35): 35, 36, 37, 38, 39, 40, 41
      }
   }
}
}

If you see the above output, then the build was a success!

Additional Resources

• h5py Documentation
• mpi4py Documentation
• HDF5 Support Page