# The spawner is based on PRP's https://gitlab.nrp-nautilus.io/vpeddu/jupyterlab-west.git
# This file is managed by [youngsu.kim|dung.vu]@csusb.edu
from kubespawner import KubeSpawner
class MySpawner(KubeSpawner):
#Read custom HTML for spawner from my_spanwer.html
with open('/etc/jupyterhub/custom/my_spawner.html', 'r') as f:
profile_form_template = f.readlines()
profile_form_template = "".join(profile_form_template)
def options_from_form(self, formdata):
# Uncomment these lines to add pvc to a class
# course_pvc_test = [
# "000065181@csusb.edu",
# "dung.vu@csusb.edu",
# "dvu@csusb.edu",
# "youngsu.kim@csusb.edu",
# ]
# cephfs_pvc_users = {
# "000065181@csusb.edu":"csusb-dungvu-share",
# "dung.vu@csusb.edu":"csusb-dungvu-share",
# "dvu@csusb.edu":"csusb-dungvu-share",
# "youngsu.kim@csusb.edu":"csusb-dungvu-share",
# }
if not self.profile_list or not hasattr(self, '_profile_list'):
return formdata
selected_profile = int(formdata.get('profile', [0])[0])
options = self._profile_list[selected_profile]
self.log.debug("Applying KubeSpawner override for profile '%s'", options['display_name'])
kubespawner_override = options.get('kubespawner_override', {})
for k, v in kubespawner_override.items():
if callable(v):
v = v(self)
self.log.debug(".. overriding KubeSpawner value %s=%s (callable result)", k, v)
else:
self.log.debug(".. overriding KubeSpawner value %s=%s", k, v)
setattr(self, k, v)
gpus = int(formdata.get('gpus', [0])[0])
#Add at least one gpu for certain profiles
# if "Datascience" in options['display_name'] and gpus == int(0):
# gpus = int(1)
#These two lines to avoid overriding the GPU assigned to some stacks by default
if "nvidia.com/gpu" in options["kubespawner_override"]["extra_resource_limits"] and gpus == 0:
pass
else:
setattr(self, "extra_resource_limits", {"nvidia.com/gpu": gpus})
# setattr(self, "extra_resource_limits", {"nvidia.com/gpu": gpus})
setattr(self, "mem_guarantee", formdata.get('ram', [0])[0]+"G")
setattr(self, "cpu_guarantee", float(formdata.get('cores', [0])[0]))
setattr(self, "mem_limit", formdata.get('ram', [0])[0]+"G")
setattr(self, "cpu_limit", float(formdata.get('cores', [0])[0]))
nodeSelectorTermsExpressions = [{
'key': 'topology.kubernetes.io/region',
'operator': 'In',
'values': ["us-west"]
}]
if formdata.get('gputype', [0])[0]:
nodeSelectorTermsExpressions.append({
'key': 'nvidia.com/gpu.product',
'operator': 'In',
'values': formdata.get('gputype', [0])
})
setattr(self, 'extra_pod_config', {
'affinity': {
'nodeAffinity': {
'requiredDuringSchedulingIgnoredDuringExecution': {
'nodeSelectorTerms': [{
'matchExpressions': nodeSelectorTermsExpressions,
}],
},
},
}
})
self.volume_mounts = [
{
'name': 'volume-{username}',
'mountPath': '/home/jovyan',
}
]
self.volumes = [
{
'name': 'volume-{username}',
'persistentVolumeClaim': {
'claimName': 'claim-{username}'
}
}
]
# if formdata.get('shm', [0])[0]:
# self.volume_mounts.append({
# 'name': 'dshm',
# 'mountPath': '/dev/shm',
# })
# self.volumes.append({
# 'name': 'dshm',
# 'emptyDir': {'medium': 'Memory'}
# })
# The following is based on PRP's JupyterHub West
# We use course based shared folders
# if formdata.get('cephfs', [0])[0] and self.user.name in cephfs_pvc_users:
# self.volume_mounts.append({
# 'name': 'cephfs',
# 'mountPath': '/home/jovyan/cephfs',
# })
# self.volumes.append({
# 'name': 'cephfs',
# 'persistentVolumeClaim': {
# 'claimName': cephfs_pvc_users[self.user.name]
# }
# })
# Uncomment these lines to add pvc to a class
# if formdata.get('cephfscourse', [0])[0] and self.user.name in course_pvc_test:
# self.volume_mounts.append({
# 'name': 'volumn-course-pvc-test',
# 'mountPath': '/home/jovyan/course-pvc-test',
# })
# self.volumes.append({
# 'name': 'volumn-course-pvc-test',
# 'persistentVolumeClaim': {
# 'claimName': 'course-pvc-test',
# }
# })
return options
# profile_list = [
# {
# "display_name": "Stack Minimal",
# "default": true,
# "kubespawner_override": {
# "image_spec": "localhost:30081/prp/jupyter-stack/minimal"
# }
# },
# {
# "display_name": "Stack Minimal + Desktop GUI + Relion",
# "kubespawner_override": {
# "image_spec": "localhost:30081/prp/jupyter-stack/relion-desktop"
# }
# },
# {
# "display_name": "Stack Datascience",
# "kubespawner_override": {
# "image_spec": "localhost:30081/prp/jupyter-stack/datascience",
# "extra_resource_limits": {
# "nvidia.com/gpu": "2"
# }
# }
# },
# {
# "display_name": "Stack Tensorflow [2GPUs]",
# "kubespawner_override": {
# "image_spec": "localhost:30081/prp/jupyter-stack/prp",
# "extra_resource_limits": {
# "nvidia.com/gpu": "2"
# }
# }
# },
# {
# "display_name": "Stack Tensorflow [4GPUs]",
# "kubespawner_override": {
# "image_spec": "localhost:30081/prp/jupyter-stack/prp",
# "extra_resource_limits": {
# "nvidia.com/gpu": "4"
# }
# }
# },
# {
# "display_name": "Stack R-Studio",
# "kubespawner_override": {
# "image_spec": "localhost:30081/prp/jupyter-stack/r-studio"
# }
# },
# {
# "display_name": "Stack Pyspark [2GPUs]",
# "kubespawner_override": {
# "image_spec": "localhost:30081/prp/jupyter-stack/pyspark",
# "extra_resource_limits": {
# "nvidia.com/gpu": "2"
# }
# }
# },
# {
# "display_name": "Stack Pyspark [4GPUs]",
# "kubespawner_override": {
# "image_spec": "localhost:30081/prp/jupyter-stack/pyspark",
# "extra_resource_limits": {
# "nvidia.com/gpu": "4"
# }
# }
# },
# {
# "display_name": "Stack SageMath",
# "kubespawner_override": {
# "image_spec": "gitlab-registry.nrp-nautilus.io/youngsu_kim/csusb-jupyter-stack/stack-sagemath:b353d66b"
# }
# }
# ]
profile_list = [
{
'display_name': 'Stack Minimal',
'kubespawner_override': {
'image_spec': 'localhost:30081/prp/jupyter-stack/minimal',
}
},
{
'display_name': 'Stack Minimal + Desktop GUI + Relion',
'kubespawner_override': {
'image_spec': 'localhost:30081/prp/jupyter-stack/relion-desktop',
}
},
{
"display_name": "Stack Datascience",
"kubespawner_override": {
"image_spec": "localhost:30081/prp/jupyter-stack/datascience",
"extra_resource_limits": {
"nvidia.com/gpu": "2"
}
}
},
]
c.JupyterHub.spawner_class = MySpawner
# c.JupyterHub.template_vars = {'announcement_login': 'announcements here'}