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(Created page with "= YT rendering on clusters = To install [http://yt-project.org YT] for CPU rendering on a cluster in your own directory, please do $ module load python $ virtualenv astro...")
 
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= YT rendering on clusters =
<languages />
[[Category:Software]]


<translate>
= YT rendering on clusters = <!--T:1-->
<!--T:2-->
To install [http://yt-project.org YT] for CPU rendering on a cluster in your own directory, please do
To install [http://yt-project.org YT] for CPU rendering on a cluster in your own directory, please do


  $ module load python
  <!--T:3-->
$ module load python mpi4py
  $ virtualenv astro    # install Python tools in your $HOME/astro
  $ virtualenv astro    # install Python tools in your $HOME/astro
  $ source ~/astro/bin/activate
  $ source ~/astro/bin/activate
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  $ pip install numpy
  $ pip install numpy
  $ pip install yt
  $ pip install yt
$ pip install mpi4py


<!--T:4-->
Then, in normal use, simply load the environment and start python
Then, in normal use, simply load the environment and start python


  $ source ~/astro/bin/activate  # load the environment
  <!--T:5-->
$ source ~/astro/bin/activate  # load the environment
  $ python
  $ python
  ...
  ...
  $ deactivate
  $ deactivate


<!--T:6-->
We assume that you have downloaded the sample dataset Enzo_64 from http://yt-project.org/data. Start with the following script `grids.py` to render 90 frames rotating the dataset around the vertical axis
We assume that you have downloaded the sample dataset Enzo_64 from http://yt-project.org/data. Start with the following script `grids.py` to render 90 frames rotating the dataset around the vertical axis


  import yt
  <!--T:7-->
import yt
  from numpy import pi
  from numpy import pi
  yt.enable_parallelism()  # turn on MPI parallelism via mpi4py
  yt.enable_parallelism()  # turn on MPI parallelism via mpi4py
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     sc.save('frame%04d.png' % (i+1), sigma_clip=4)
     sc.save('frame%04d.png' % (i+1), sigma_clip=4)


<!--T:8-->
and the job submission script `yt-mpi.sh`
and the job submission script `yt-mpi.sh`


  #!/bin/bash
  <!--T:9-->
#!/bin/bash
  #SBATCH --time=0:30:00  # walltime in d-hh:mm or hh:mm:ss format
  #SBATCH --time=0:30:00  # walltime in d-hh:mm or hh:mm:ss format
  #SBATCH --ntasks=4      # number of MPI processes
  #SBATCH --ntasks=4      # number of MPI processes
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  srun python grids.py
  srun python grids.py


<!--T:10-->
Then submit the job with `sbatch yt-mpi.sh`, wait for it to finish, and then create a movie at 30fps
Then submit the job with `sbatch yt-mpi.sh`, wait for it to finish, and then create a movie at 30fps


  $ ffmpeg -r 30 -i frame%04d.png -c:v libx264 -pix_fmt yuv420p -vf "scale=trunc(iw/2)*2:trunc(ih/2)*2" grids.mp4
  <!--T:11-->
$ ffmpeg -r 30 -i frame%04d.png -c:v libx264 -pix_fmt yuv420p -vf "scale=trunc(iw/2)*2:trunc(ih/2)*2" grids.mp4
</translate>

Latest revision as of 15:45, 16 December 2019

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YT rendering on clusters[edit]

To install YT for CPU rendering on a cluster in your own directory, please do

$ module load python mpi4py
$ virtualenv astro    # install Python tools in your $HOME/astro
$ source ~/astro/bin/activate
$ pip install cython
$ pip install numpy
$ pip install yt

Then, in normal use, simply load the environment and start python

$ source ~/astro/bin/activate   # load the environment
$ python
...
$ deactivate

We assume that you have downloaded the sample dataset Enzo_64 from http://yt-project.org/data. Start with the following script `grids.py` to render 90 frames rotating the dataset around the vertical axis

import yt
from numpy import pi
yt.enable_parallelism()   # turn on MPI parallelism via mpi4py
ds = yt.load("Enzo_64/DD0043/data0043")
sc = yt.create_scene(ds, ('gas', 'density'))
cam = sc.camera
cam.resolution = (1024, 1024)   # resolution of each frame
sc.annotate_domain(ds, color=[1, 1, 1, 0.005])   # draw the domain boundary [r,g,b,alpha]
sc.annotate_grids(ds, alpha=0.005)   # draw the grid boundaries
sc.save('frame0000.png', sigma_clip=4)
nspin = 90
for i in cam.iter_rotate(pi, nspin):   # rotate by 180 degrees over nspin frames
    sc.save('frame%04d.png' % (i+1), sigma_clip=4)

and the job submission script `yt-mpi.sh`

#!/bin/bash
#SBATCH --time=0:30:00   # walltime in d-hh:mm or hh:mm:ss format
#SBATCH --ntasks=4       # number of MPI processes
#SBATCH --mem-per-cpu=3800
#SBATCH --account=...
source $HOME/astro/bin/activate
srun python grids.py

Then submit the job with `sbatch yt-mpi.sh`, wait for it to finish, and then create a movie at 30fps

$ ffmpeg -r 30 -i frame%04d.png -c:v libx264 -pix_fmt yuv420p -vf "scale=trunc(iw/2)*2:trunc(ih/2)*2" grids.mp4