def test_update_job_containers(self):
update_job_containers(event=status_experiment_job_event_with_conditions['object'],
status=JobLifeCycle.BUILDING,
job_container_name=settings.CONTAINER_NAME_EXPERIMENT_JOB)
# Assert it's still 0 because no job was created with that job_uuid
assert len(RedisJobContainers.get_containers()) == 0 # pylint:disable=len-as-condition
# Create a job with a specific uuid
labels = status_experiment_job_event_with_conditions['object']['metadata']['labels']
ExperimentJobFactory(uuid=labels['job_uuid'])
job = ExperimentJob.objects.get(uuid=labels['job_uuid'])
update_job_containers(event=status_experiment_job_event_with_conditions['object'],
status=JobLifeCycle.BUILDING,
job_container_name=settings.CONTAINER_NAME_EXPERIMENT_JOB)
# Assert now it has started monitoring the container
assert len(RedisJobContainers.get_containers()) == 1
container_id = '539e6a6f4209997094802b0657f90576fe129b7f81697120172836073d9bbd75'
assert RedisJobContainers.get_containers() == [container_id]
job_uuid, experiment_uuid = RedisJobContainers.get_job(container_id)
assert job.uuid.hex == job_uuid
assert job.experiment.uuid.hex == experiment_uuid
def get_container_resources(node, container, gpu_resources):
# Check if the container is running
if container.status != ContainerStatuses.RUNNING:
logger.debug("`%s` container is not running", container.name)
RedisJobContainers.remove_container(container.id)
return
job_uuid, experiment_uuid = RedisJobContainers.get_job(container.id)
if not job_uuid:
logger.debug("`%s` container is not recognised", container.name)
return
logger.debug(
"Streaming resources for container %s in (job, experiment) (`%s`, `%s`) ",
container.id, job_uuid, experiment_uuid)
try:
stats = container.stats(decode=True, stream=False)
except json.decoder.JSONDecodeError:
logger.info("Error streaming states for `%s`", container.name)
except NotFound:
logger.debug("`%s` was not found", container.name)
RedisJobContainers.remove_container(container.id)
return
except requests.ReadTimeout:
return
precpu_stats = stats['precpu_stats']
cpu_stats = stats['cpu_stats']
pre_total_usage = float(precpu_stats['cpu_usage']['total_usage'])
total_usage = float(cpu_stats['cpu_usage']['total_usage'])
delta_total_usage = total_usage - pre_total_usage
pre_system_cpu_usage = float(precpu_stats['system_cpu_usage'])
system_cpu_usage = float(cpu_stats['system_cpu_usage'])
delta_system_cpu_usage = system_cpu_usage - pre_system_cpu_usage
percpu_usage = cpu_stats['cpu_usage']['percpu_usage']
num_cpu_cores = len(percpu_usage)
if num_cpu_cores >= node.cpu * 1.5:
logger.warning('Docker reporting num cpus `%s` and kubernetes reporting `%s`',
num_cpu_cores, node.cpu)
num_cpu_cores = node.cpu
cpu_percentage = 0.
percpu_percentage = [0.] * num_cpu_cores
if delta_total_usage > 0 and delta_system_cpu_usage > 0:
cpu_percentage = (delta_total_usage / delta_system_cpu_usage) * num_cpu_cores * 100.0
percpu_percentage = [cpu_usage / total_usage * cpu_percentage for cpu_usage in percpu_usage]
memory_used = int(stats['memory_stats']['usage'])
memory_limit = int(stats['memory_stats']['limit'])
container_gpu_resources = None
if gpu_resources:
gpu_indices = get_container_gpu_indices(container)
container_gpu_resources = [gpu_resources[gpu_indice] for gpu_indice in gpu_indices]
return ContainerResourcesConfig.from_dict({
'job_uuid': job_uuid,
'job_name': job_uuid, # it will be updated during the streaming
'experiment_uuid': experiment_uuid,
'container_id': container.id,
'cpu_percentage': cpu_percentage,
'n_cpus': num_cpu_cores,
'percpu_percentage': percpu_percentage,
'memory_used': memory_used,
'memory_limit': memory_limit,
'gpu_resources': container_gpu_resources
})