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Don't use jaas as if offers not enough configuration possibilities.

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Patrick Jentsch
2019-07-19 13:28:57 +02:00
parent 7b185d929e
commit cd79061579
1 changed files with 76 additions and 86 deletions
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148
app/swarm.py
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@@ -1,98 +1,88 @@
import docker
import subprocess
import time
class Swarm:
def __init__(self):
self.docker = docker.from_env()
self.checkout()
def checkout(self):
cpus = 0
memory = 0
for node in self.docker.nodes.list(filters={'role': 'worker'}):
if node.attrs.get('Status').get('State') == 'ready':
cpus += 0 or node.attrs \
.get('Description') \
.get('Resources') \
.get('NanoCPUs')
memory += 0 or node.attrs \
.get('Description') \
.get('Resources') \
.get('MemoryBytes')
'''
' For whatever reason the Python Docker SDK provides a CPU count in
' nano (10^-6), whilst this is not that handy, it gets converted.
' Swarm mode is intendet to run containers which serve a non terminating
' service like a webserver. For processing an occuring job it is necessary
' to use a one-shot container, which stops after the wrapped job process is
' completly executed. In order to run these one-shot containers in Swarm
' mode, the following run method is implemented analog to the presented
' implementation in Alex Ellis' blog post "One-shot containers on Docker
' Swarm"¹.
'
' ¹ https://blog.alexellis.io/containers-on-swarm/
'''
cpus *= 10 ** -9
'''
' For a more natural handling the memory information
' gets converted from bytes to megabytes.
'''
memory *= 10 ** -6
self.cpus = int(cpus)
self.memory = int(memory)
self.available_cpus = self.cpus
self.available_memory = self.memory
def run(self, job):
if self.available_cpus < job['requested_cpus'] or \
self.available_memory < job['requested_memory']:
print('Not enough ressources available.')
'''
' TODO: At this point the scheduler thinks that the job gets
' processed, which apparently is not the case. So the job
' needs to get rescheduled and gain a new chance to get
' processed (next).
'
' Note: Maybe it is a good idea to create a method that checks if
' enough ressources are available before the run method gets
' executed. This would replace the need of the TODO mentioned
' above.
'''
return
job['status'] = 'running'
# TODO: Push job changes to the database
self.available_cpus -= job['requested_cpus']
self.available_memory -= job['requested_memory']
container_command = 'ocr' \
# Prepare argument values needed for the service creation.
_command = 'ocr' \
+ ' -i /input/{}'.format(job['id']) \
+ ' -l {}'.format(job['service_args']['lang']) \
+ ' -o /output' \
+ ' --keep-intermediates' \
+ ' --nCores {}'.format(job['requested_cpus'])
container_image = 'gitlab.ub.uni-bielefeld.de:4567/sfb1288inf/ocr'
container_mount = '/media/sf_files/=/input/'
'''
' Swarm mode is intendet to run containers which are meant to serve a
' non terminating service like a webserver. In order to process the
' occuring jobs it is necessary to use one-shot (terminating)
' containers. These one-shot containers are spawned with a programm
' called JaaS¹ (Jobs as a Service), which is described in Alex Ellis'
' short article "One-shot containers on Docker Swarm"².
'
' ¹ https://github.com/alexellis/jaas
' ² https://blog.alexellis.io/containers-on-swarm/
'''
cmd = ['jaas', 'run'] \
+ ['--command', container_command] \
+ ['--image', container_image] \
+ ['--mount', container_mount] \
+ ['--timeout', '86400s']
completed_process = subprocess.run(
cmd,
stderr=subprocess.DEVNULL,
stdout=subprocess.DEVNULL
_constraints = ['node.role==worker']
_image = 'gitlab.ub.uni-bielefeld.de:4567/sfb1288inf/{}:{}'.format(
job['service'],
job['service_args']['version']
)
self.available_cpus += job['requested_cpus']
self.available_memory += job['requested_memory']
if (completed_process.returncode == 0):
job['status'] = 'finished'
else:
job['status'] = 'failed'
# TODO: Push job changes to the database
_labels = {'service': 'ocr'}
_mounts = ['/media/sf_files:/input:rw']
_name = job['id']
'''
' The Docker SDK for Python expects the cpu_reservation value to be
' scaled to nanos (10^9). Because the job object contains unscaled
' (10^0) values, it must be conveted.
'
' While the cpu_reservation value has to be in nanos, the
' mem_reservation value must be presented in bytes (intuitive right?).
' Bacause the job object provides the memory value in megabytes, it is
' also necessary to convert the value.
'''
_resources = docker.types.Resources(
cpu_reservation=job['requested_cpus'] * (10 ** 9),
mem_reservation=job['requested_memory'] * (10 ** 6)
)
_restart_policy = docker.types.RestartPolicy(condition='none')
'''
' Create the service with the prepared values.
'
' Note: A service reserves hardware ressources. In case no worker node
' has the required ressources available (not reserved), the
' service gets queued by the Docker engine until a node is able
' to meet the requirements.
'
' TODO: The name argument should be used with the prepared value
' (name=_name). Because there is no id generator for now, it is
' not set, so that the Docker engine assigns a random name.
'''
service = self.docker.services.create(
_image,
command=_command,
constraints=_constraints,
labels=_labels,
mounts=_mounts,
resources=_resources,
restart_policy=_restart_policy
)
'''
' Poll the service until the job is completly executed.
'
' Note: Because it takes some time until all data in the service object
' is initialized (especcially the task list returned by the tasks
' method) the poll loop also checks if the task list is empy (The
' not service.tasks() condition implements this).
'''
while not service.tasks() or \
service.tasks()[0].get('Status').get('State') != 'complete':
time.sleep(1)
service.reload()
# Remove the service from the swarm.
service.remove()
return