def test_get_pod_manifest_tolerates_mixed_input():
"""
Test that the get_pod_manifest function can handle a either a dictionary or
an object both representing V1Container objects and that the function
returns a V1Pod object containing V1Container objects.
"""
c = Config()
dict_model = {
'name': 'mock_name_1',
'image': 'mock_image_1',
'command': ['mock_command_1']
}
object_model = V1Container(
name="mock_name_2",
image="mock_image_2",
command=['mock_command_2'],
security_context=V1SecurityContext(
privileged=True,
run_as_user=0,
capabilities=V1Capabilities(add=['NET_ADMIN'])
)
)
c.KubeSpawner.init_containers = [dict_model, object_model]
spawner = KubeSpawner(config=c, _mock=True)
# this test ensures the following line doesn't raise an error
manifest = sync_wait(spawner.get_pod_manifest())
# and tests the return value's types
assert isinstance(manifest, V1Pod)
assert isinstance(manifest.spec.init_containers[0], V1Container)
assert isinstance(manifest.spec.init_containers[1], V1Container)
def _create_multus_init_container(image="lsstit/ddsnet4u:latest"):
"""Create the privileged container to allow CNI bridging for DDS
multicast.
"""
return V1Container(
name="multus-init",
security_context=V1SecurityContext(privileged=True),
image=image,
)
def test_update_k8s_model():
"""Ensure update_k8s_model does what it should. The test is first updating
attributes using the function and then and manually verifies that the
correct changes have been made."""
manually_updated_target = V1Container(
name="mock_name",
image="mock_image",
command=['iptables'],
security_context=V1SecurityContext(
privileged=True,
run_as_user=0,
capabilities=V1Capabilities(add=['NET_ADMIN'])))
target = copy.deepcopy(manually_updated_target)
source = {"name": "new_mock_name"}
update_k8s_model(target, source)
manually_updated_target.name = "new_mock_name"
assert target == manually_updated_target
def make_pod(
name,
cmd,
port,
image,
image_pull_policy,
image_pull_secrets=None,
node_selector=None,
run_as_uid=None,
run_as_gid=None,
fs_gid=None,
supplemental_gids=None,
run_privileged=False,
allow_privilege_escalation=True,
env=None,
working_dir=None,
volumes=None,
volume_mounts=None,
labels=None,
annotations=None,
cpu_limit=None,
cpu_guarantee=None,
mem_limit=None,
mem_guarantee=None,
extra_resource_limits=None,
extra_resource_guarantees=None,
lifecycle_hooks=None,
init_containers=None,
service_account=None,
extra_container_config=None,
extra_pod_config=None,
extra_containers=None,
scheduler_name=None,
tolerations=None,
node_affinity_preferred=None,
node_affinity_required=None,
pod_affinity_preferred=None,
pod_affinity_required=None,
pod_anti_affinity_preferred=None,
pod_anti_affinity_required=None,
priority_class_name=None,
logger=None,
):
"""
Make a k8s pod specification for running a user notebook.
Parameters
----------
name:
Name of pod. Must be unique within the namespace the object is
going to be created in. Must be a valid DNS label.
image:
Image specification - usually a image name and tag in the form
of image_name:tag. Same thing you would use with docker commandline
arguments
image_pull_policy:
Image pull policy - one of 'Always', 'IfNotPresent' or 'Never'. Decides
when kubernetes will check for a newer version of image and pull it when
running a pod.
image_pull_secrets:
Image pull secrets - a list of references to Kubernetes Secret resources
with credentials to pull images from image registries. This list can
either have strings in it or objects with the string value nested under
a name field.
port:
Port the notebook server is going to be listening on
cmd:
The command used to execute the singleuser server.
node_selector:
Dictionary Selector to match nodes where to launch the Pods
run_as_uid:
The UID used to run single-user pods. The default is to run as the user
specified in the Dockerfile, if this is set to None.
run_as_gid:
The GID used to run single-user pods. The default is to run as the primary
group of the user specified in the Dockerfile, if this is set to None.
Setting this parameter requires that *feature-gate* **RunAsGroup** be enabled,
otherwise the effective GID of the pod will be 0 (root). In addition, not
setting `run_as_gid` once feature-gate RunAsGroup is enabled will also
result in an effective GID of 0 (root).
fs_gid
The gid that will own any fresh volumes mounted into this pod, if using
volume types that support this (such as GCE). This should be a group that
the uid the process is running as should be a member of, so that it can
read / write to the volumes mounted.
supplemental_gids:
A list of GIDs that should be set as additional supplemental groups to
the user that the container runs as. You may have to set this if you are
deploying to an environment with RBAC/SCC enforced and pods run with a
'restricted' SCC which results in the image being run as an assigned
user ID. The supplemental group IDs would need to include the
corresponding group ID of the user ID the image normally would run as.
The image must setup all directories/files any application needs access
to, as group writable.
run_privileged:
Whether the container should be run in privileged mode.
allow_privilege_escalation:
Controls whether a process can gain more privileges than its parent process.
env:
Dictionary of environment variables.
volumes:
List of dictionaries containing the volumes of various types this pod
will be using. See k8s documentation about volumes on how to specify
these
volume_mounts:
List of dictionaries mapping paths in the container and the volume(
specified in volumes) that should be mounted on them. See the k8s
documentaiton for more details
working_dir:
String specifying the working directory for the notebook container
labels:
Labels to add to the spawned pod.
annotations:
Annotations to add to the spawned pod.
cpu_limit:
Float specifying the max number of CPU cores the user's pod is
allowed to use.
cpu_guarentee:
Float specifying the max number of CPU cores the user's pod is
guaranteed to have access to, by the scheduler.
mem_limit:
String specifying the max amount of RAM the user's pod is allowed
to use. String instead of float/int since common suffixes are allowed
mem_guarantee:
String specifying the max amount of RAM the user's pod is guaranteed
to have access to. String ins loat/int since common suffixes
are allowed
lifecycle_hooks:
Dictionary of lifecycle hooks
init_containers:
List of initialization containers belonging to the pod.
service_account:
Service account to mount on the pod. None disables mounting
extra_container_config:
Extra configuration (e.g. envFrom) for notebook container which is not covered by parameters above.
extra_pod_config:
Extra configuration (e.g. tolerations) for pod which is not covered by parameters above.
extra_containers:
Extra containers besides notebook container. Used for some housekeeping jobs (e.g. crontab).
scheduler_name:
The pod's scheduler explicitly named.
tolerations:
Tolerations can allow a pod to schedule or execute on a tainted node. To
learn more about pod tolerations, see
https://kubernetes.io/docs/concepts/configuration/taint-and-toleration/.
Pass this field an array of "Toleration" objects.*
* https://kubernetes.io/docs/reference/generated/kubernetes-api/v1.10/#nodeselectorterm-v1-core
node_affinity_preferred:
Affinities describe where pods prefer or require to be scheduled, they
may prefer or require a node to have a certain label or be in proximity
/ remoteness to another pod. To learn more visit
https://kubernetes.io/docs/concepts/configuration/assign-pod-node/
Pass this field an array of "PreferredSchedulingTerm" objects.*
* https://kubernetes.io/docs/reference/generated/kubernetes-api/v1.10/#preferredschedulingterm-v1-core
node_affinity_required:
Affinities describe where pods prefer or require to be scheduled, they
may prefer or require a node to have a certain label or be in proximity
/ remoteness to another pod. To learn more visit
https://kubernetes.io/docs/concepts/configuration/assign-pod-node/
Pass this field an array of "NodeSelectorTerm" objects.*
* https://kubernetes.io/docs/reference/generated/kubernetes-api/v1.10/#nodeselectorterm-v1-core
pod_affinity_preferred:
Affinities describe where pods prefer or require to be scheduled, they
may prefer or require a node to have a certain label or be in proximity
/ remoteness to another pod. To learn more visit
https://kubernetes.io/docs/concepts/configuration/assign-pod-node/
Pass this field an array of "WeightedPodAffinityTerm" objects.*
* https://kubernetes.io/docs/reference/generated/kubernetes-api/v1.10/#weightedpodaffinityterm-v1-core
pod_affinity_required:
Affinities describe where pods prefer or require to be scheduled, they
may prefer or require a node to have a certain label or be in proximity
/ remoteness to another pod. To learn more visit
https://kubernetes.io/docs/concepts/configuration/assign-pod-node/
Pass this field an array of "PodAffinityTerm" objects.*
* https://kubernetes.io/docs/reference/generated/kubernetes-api/v1.10/#podaffinityterm-v1-core
pod_anti_affinity_preferred:
Affinities describe where pods prefer or require to be scheduled, they
may prefer or require a node to have a certain label or be in proximity
/ remoteness to another pod. To learn more visit
https://kubernetes.io/docs/concepts/configuration/assign-pod-node/
Pass this field an array of "WeightedPodAffinityTerm" objects.*
* https://kubernetes.io/docs/reference/generated/kubernetes-api/v1.10/#weightedpodaffinityterm-v1-core
pod_anti_affinity_required:
Affinities describe where pods prefer or require to be scheduled, they
may prefer or require a node to have a certain label or be in proximity
/ remoteness to another pod. To learn more visit
https://kubernetes.io/docs/concepts/configuration/assign-pod-node/
Pass this field an array of "PodAffinityTerm" objects.*
* https://kubernetes.io/docs/reference/generated/kubernetes-api/v1.10/#podaffinityterm-v1-core
priority_class_name:
The name of the PriorityClass to be assigned the pod. This feature is Beta available in K8s 1.11.
"""
pod = V1Pod()
pod.kind = "Pod"
pod.api_version = "v1"
pod.metadata = V1ObjectMeta(name=name,
labels=(labels or {}).copy(),
annotations=(annotations or {}).copy())
pod.spec = V1PodSpec(containers=[])
pod.spec.restart_policy = 'OnFailure'
if image_pull_secrets is not None:
# image_pull_secrets as received by the make_pod function should always
# be a list, but it is allowed to have "a-string" elements or {"name":
# "a-string"} elements.
pod.spec.image_pull_secrets = [
V1LocalObjectReference(name=secret_ref) if type(secret_ref) == str
else get_k8s_model(V1LocalObjectReference, secret_ref)
for secret_ref in image_pull_secrets
]
if node_selector:
pod.spec.node_selector = node_selector
if lifecycle_hooks:
lifecycle_hooks = get_k8s_model(V1Lifecycle, lifecycle_hooks)
# There are security contexts both on the Pod level or the Container level.
# The security settings that you specify for a Pod apply to all Containers
# in the Pod, but settings on the container level can override them.
#
# We configure the pod to be spawned on the container level unless the
# option is only available on the pod level, such as for those relating to
# the volumes as compared to the running user of the container. Volumes
# belong to the pod and are only mounted by containers after all.
#
# ref: https://kubernetes.io/docs/tasks/configure-pod-container/security-context/
# ref: https://kubernetes.io/docs/reference/generated/kubernetes-api/v1.16/#securitycontext-v1-core (container)
# ref: https://kubernetes.io/docs/reference/generated/kubernetes-api/v1.16/#podsecuritycontext-v1-core (pod)
pod_security_context = V1PodSecurityContext()
if fs_gid is not None:
pod_security_context.fs_group = int(fs_gid)
if supplemental_gids is not None and supplemental_gids:
pod_security_context.supplemental_groups = [
int(gid) for gid in supplemental_gids
]
# Only clutter pod spec with actual content
if not all([e is None for e in pod_security_context.to_dict().values()]):
pod.spec.security_context = pod_security_context
container_security_context = V1SecurityContext()
if run_as_uid is not None:
container_security_context.run_as_user = int(run_as_uid)
if run_as_gid is not None:
container_security_context.run_as_group = int(run_as_gid)
if run_privileged:
container_security_context.privileged = True
if not allow_privilege_escalation:
container_security_context.allow_privilege_escalation = False
# Only clutter container spec with actual content
if all([e is None for e in container_security_context.to_dict().values()]):
container_security_context = None
# Transform a dict into valid Kubernetes EnvVar Python representations. This
# representation shall always have a "name" field as well as either a
# "value" field or "value_from" field. For examples see the
# test_make_pod_with_env function.
prepared_env = []
for k, v in (env or {}).items():
if type(v) == dict:
if not "name" in v:
v["name"] = k
prepared_env.append(get_k8s_model(V1EnvVar, v))
else:
prepared_env.append(V1EnvVar(name=k, value=v))
notebook_container = V1Container(
name='notebook',
image=image,
working_dir=working_dir,
ports=[V1ContainerPort(name='notebook-port', container_port=port)],
env=prepared_env,
args=cmd,
image_pull_policy=image_pull_policy,
lifecycle=lifecycle_hooks,
resources=V1ResourceRequirements(),
volume_mounts=[
get_k8s_model(V1VolumeMount, obj) for obj in (volume_mounts or [])
],
security_context=container_security_context,
)
if service_account is None:
# This makes sure that we don't accidentally give access to the whole
# kubernetes API to the users in the spawned pods.
pod.spec.automount_service_account_token = False
else:
pod.spec.service_account_name = service_account
notebook_container.resources.requests = {}
if cpu_guarantee:
notebook_container.resources.requests['cpu'] = cpu_guarantee
if mem_guarantee:
notebook_container.resources.requests['memory'] = mem_guarantee
if extra_resource_guarantees:
notebook_container.resources.requests.update(extra_resource_guarantees)
notebook_container.resources.limits = {}
if cpu_limit:
notebook_container.resources.limits['cpu'] = cpu_limit
if mem_limit:
notebook_container.resources.limits['memory'] = mem_limit
if extra_resource_limits:
notebook_container.resources.limits.update(extra_resource_limits)
if extra_container_config:
notebook_container = update_k8s_model(
target=notebook_container,
changes=extra_container_config,
logger=logger,
target_name="notebook_container",
changes_name="extra_container_config",
)
pod.spec.containers.append(notebook_container)
if extra_containers:
pod.spec.containers.extend(
[get_k8s_model(V1Container, obj) for obj in extra_containers])
if tolerations:
pod.spec.tolerations = [
get_k8s_model(V1Toleration, obj) for obj in tolerations
]
if init_containers:
pod.spec.init_containers = [
get_k8s_model(V1Container, obj) for obj in init_containers
]
if volumes:
pod.spec.volumes = [get_k8s_model(V1Volume, obj) for obj in volumes]
else:
# Keep behaving exactly like before by not cleaning up generated pod
# spec by setting the volumes field even though it is an empty list.
pod.spec.volumes = []
if scheduler_name:
pod.spec.scheduler_name = scheduler_name
node_affinity = None
if node_affinity_preferred or node_affinity_required:
node_selector = None
if node_affinity_required:
node_selector = V1NodeSelector(node_selector_terms=[
get_k8s_model(V1NodeSelectorTerm, obj)
for obj in node_affinity_required
], )
preferred_scheduling_terms = None
if node_affinity_preferred:
preferred_scheduling_terms = [
get_k8s_model(V1PreferredSchedulingTerm, obj)
for obj in node_affinity_preferred
]
node_affinity = V1NodeAffinity(
preferred_during_scheduling_ignored_during_execution=
preferred_scheduling_terms,
required_during_scheduling_ignored_during_execution=node_selector,
)
pod_affinity = None
if pod_affinity_preferred or pod_affinity_required:
weighted_pod_affinity_terms = None
if pod_affinity_preferred:
weighted_pod_affinity_terms = [
get_k8s_model(V1WeightedPodAffinityTerm, obj)
for obj in pod_affinity_preferred
]
pod_affinity_terms = None
if pod_affinity_required:
pod_affinity_terms = [
get_k8s_model(V1PodAffinityTerm, obj)
for obj in pod_affinity_required
]
pod_affinity = V1PodAffinity(
preferred_during_scheduling_ignored_during_execution=
weighted_pod_affinity_terms,
required_during_scheduling_ignored_during_execution=
pod_affinity_terms,
)
pod_anti_affinity = None
if pod_anti_affinity_preferred or pod_anti_affinity_required:
weighted_pod_affinity_terms = None
if pod_anti_affinity_preferred:
weighted_pod_affinity_terms = [
get_k8s_model(V1WeightedPodAffinityTerm, obj)
for obj in pod_anti_affinity_preferred
]
pod_affinity_terms = None
if pod_anti_affinity_required:
pod_affinity_terms = [
get_k8s_model(V1PodAffinityTerm, obj)
for obj in pod_anti_affinity_required
]
pod_anti_affinity = V1PodAffinity(
preferred_during_scheduling_ignored_during_execution=
weighted_pod_affinity_terms,
required_during_scheduling_ignored_during_execution=
pod_affinity_terms,
)
affinity = None
if (node_affinity or pod_affinity or pod_anti_affinity):
affinity = V1Affinity(
node_affinity=node_affinity,
pod_affinity=pod_affinity,
pod_anti_affinity=pod_anti_affinity,
)
if affinity:
pod.spec.affinity = affinity
if priority_class_name:
pod.spec.priority_class_name = priority_class_name
if extra_pod_config:
pod.spec = update_k8s_model(
target=pod.spec,
changes=extra_pod_config,
logger=logger,
target_name="pod.spec",
changes_name="extra_pod_config",
)
return pod
def make_pod(
name,
cmd,
port,
image_spec,
image_pull_policy,
image_pull_secret=None,
node_selector=None,
run_as_uid=None,
run_as_gid=None,
fs_gid=None,
supplemental_gids=None,
run_privileged=False,
env=None,
working_dir=None,
volumes=None,
volume_mounts=None,
labels=None,
annotations=None,
cpu_limit=None,
cpu_guarantee=None,
mem_limit=None,
mem_guarantee=None,
extra_resource_limits=None,
extra_resource_guarantees=None,
lifecycle_hooks=None,
init_containers=None,
service_account=None,
extra_container_config=None,
extra_pod_config=None,
extra_containers=None,
scheduler_name=None,
tolerations=None,
node_affinity_preferred=None,
node_affinity_required=None,
pod_affinity_preferred=None,
pod_affinity_required=None,
pod_anti_affinity_preferred=None,
pod_anti_affinity_required=None,
priority_class_name=None,
logger=None,
userdir={},
):
"""
Make a k8s pod specification for running a user notebook.
Parameters
----------
name:
Name of pod. Must be unique within the namespace the object is
going to be created in. Must be a valid DNS label.
image_spec:
Image specification - usually a image name and tag in the form
of image_name:tag. Same thing you would use with docker commandline
arguments
image_pull_policy:
Image pull policy - one of 'Always', 'IfNotPresent' or 'Never'. Decides
when kubernetes will check for a newer version of image and pull it when
running a pod.
image_pull_secret:
Image pull secret - Default is None -- set to your secret name to pull
from private docker registry.
port:
Port the notebook server is going to be listening on
cmd:
The command used to execute the singleuser server.
node_selector:
Dictionary Selector to match nodes where to launch the Pods
run_as_uid:
The UID used to run single-user pods. The default is to run as the user
specified in the Dockerfile, if this is set to None.
run_as_gid:
The GID used to run single-user pods. The default is to run as the primary
group of the user specified in the Dockerfile, if this is set to None.
fs_gid
The gid that will own any fresh volumes mounted into this pod, if using
volume types that support this (such as GCE). This should be a group that
the uid the process is running as should be a member of, so that it can
read / write to the volumes mounted.
supplemental_gids:
A list of GIDs that should be set as additional supplemental groups to
the user that the container runs as. You may have to set this if you are
deploying to an environment with RBAC/SCC enforced and pods run with a
'restricted' SCC which results in the image being run as an assigned
user ID. The supplemental group IDs would need to include the
corresponding group ID of the user ID the image normally would run as.
The image must setup all directories/files any application needs access
to, as group writable.
run_privileged:
Whether the container should be run in privileged mode.
env:
Dictionary of environment variables.
volumes:
List of dictionaries containing the volumes of various types this pod
will be using. See k8s documentation about volumes on how to specify
these
volume_mounts:
List of dictionaries mapping paths in the container and the volume(
specified in volumes) that should be mounted on them. See the k8s
documentaiton for more details
working_dir:
String specifying the working directory for the notebook container
labels:
Labels to add to the spawned pod.
annotations:
Annotations to add to the spawned pod.
cpu_limit:
Float specifying the max number of CPU cores the user's pod is
allowed to use.
cpu_guarentee:
Float specifying the max number of CPU cores the user's pod is
guaranteed to have access to, by the scheduler.
mem_limit:
String specifying the max amount of RAM the user's pod is allowed
to use. String instead of float/int since common suffixes are allowed
mem_guarantee:
String specifying the max amount of RAM the user's pod is guaranteed
to have access to. String ins loat/int since common suffixes
are allowed
lifecycle_hooks:
Dictionary of lifecycle hooks
init_containers:
List of initialization containers belonging to the pod.
service_account:
Service account to mount on the pod. None disables mounting
extra_container_config:
Extra configuration (e.g. envFrom) for notebook container which is not covered by parameters above.
extra_pod_config:
Extra configuration (e.g. tolerations) for pod which is not covered by parameters above.
extra_containers:
Extra containers besides notebook container. Used for some housekeeping jobs (e.g. crontab).
scheduler_name:
The pod's scheduler explicitly named.
tolerations:
Tolerations can allow a pod to schedule or execute on a tainted node. To
learn more about pod tolerations, see
https://kubernetes.io/docs/concepts/configuration/taint-and-toleration/.
Pass this field an array of "Toleration" objects.*
* https://kubernetes.io/docs/reference/generated/kubernetes-api/v1.10/#nodeselectorterm-v1-core
node_affinity_preferred:
Affinities describe where pods prefer or require to be scheduled, they
may prefer or require a node to have a certain label or be in proximity
/ remoteness to another pod. To learn more visit
https://kubernetes.io/docs/concepts/configuration/assign-pod-node/
Pass this field an array of "PreferredSchedulingTerm" objects.*
* https://kubernetes.io/docs/reference/generated/kubernetes-api/v1.10/#preferredschedulingterm-v1-core
node_affinity_required:
Affinities describe where pods prefer or require to be scheduled, they
may prefer or require a node to have a certain label or be in proximity
/ remoteness to another pod. To learn more visit
https://kubernetes.io/docs/concepts/configuration/assign-pod-node/
Pass this field an array of "NodeSelectorTerm" objects.*
* https://kubernetes.io/docs/reference/generated/kubernetes-api/v1.10/#nodeselectorterm-v1-core
pod_affinity_preferred:
Affinities describe where pods prefer or require to be scheduled, they
may prefer or require a node to have a certain label or be in proximity
/ remoteness to another pod. To learn more visit
https://kubernetes.io/docs/concepts/configuration/assign-pod-node/
Pass this field an array of "WeightedPodAffinityTerm" objects.*
* https://kubernetes.io/docs/reference/generated/kubernetes-api/v1.10/#weightedpodaffinityterm-v1-core
pod_affinity_required:
Affinities describe where pods prefer or require to be scheduled, they
may prefer or require a node to have a certain label or be in proximity
/ remoteness to another pod. To learn more visit
https://kubernetes.io/docs/concepts/configuration/assign-pod-node/
Pass this field an array of "PodAffinityTerm" objects.*
* https://kubernetes.io/docs/reference/generated/kubernetes-api/v1.10/#podaffinityterm-v1-core
pod_anti_affinity_preferred:
Affinities describe where pods prefer or require to be scheduled, they
may prefer or require a node to have a certain label or be in proximity
/ remoteness to another pod. To learn more visit
https://kubernetes.io/docs/concepts/configuration/assign-pod-node/
Pass this field an array of "WeightedPodAffinityTerm" objects.*
* https://kubernetes.io/docs/reference/generated/kubernetes-api/v1.10/#weightedpodaffinityterm-v1-core
pod_anti_affinity_required:
Affinities describe where pods prefer or require to be scheduled, they
may prefer or require a node to have a certain label or be in proximity
/ remoteness to another pod. To learn more visit
https://kubernetes.io/docs/concepts/configuration/assign-pod-node/
Pass this field an array of "PodAffinityTerm" objects.*
* https://kubernetes.io/docs/reference/generated/kubernetes-api/v1.10/#podaffinityterm-v1-core
priority_class_name:
The name of the PriorityClass to be assigned the pod. This feature is Beta available in K8s 1.11.
"""
pod = V1Pod()
pod.kind = "Pod"
pod.api_version = "v1"
pod.metadata = V1ObjectMeta(name=name,
labels=(labels or {}).copy(),
annotations=(annotations or {}).copy())
pod.spec = V1PodSpec(containers=[])
pod.spec.restartPolicy = 'Never'
security_context = V1PodSecurityContext()
if fs_gid is not None:
security_context.fs_group = int(fs_gid)
if supplemental_gids is not None and supplemental_gids:
security_context.supplemental_groups = [
int(gid) for gid in supplemental_gids
]
if run_as_uid is not None:
security_context.run_as_user = int(run_as_uid)
if run_as_gid is not None:
security_context.run_as_group = int(run_as_gid)
pod.spec.security_context = security_context
if image_pull_secret is not None:
pod.spec.image_pull_secrets = []
image_secret = V1LocalObjectReference()
image_secret.name = image_pull_secret
pod.spec.image_pull_secrets.append(image_secret)
if node_selector:
pod.spec.node_selector = node_selector
if lifecycle_hooks:
lifecycle_hooks = get_k8s_model(V1Lifecycle, lifecycle_hooks)
notebook_container = V1Container(
name='notebook',
image=image_spec,
working_dir=working_dir,
ports=[V1ContainerPort(name='notebook-port', container_port=port)],
env=[V1EnvVar(k, v) for k, v in (env or {}).items()],
args=cmd,
image_pull_policy=image_pull_policy,
lifecycle=lifecycle_hooks,
resources=V1ResourceRequirements(),
volume_mounts=[
get_k8s_model(V1VolumeMount, obj) for obj in (volume_mounts or [])
],
)
if service_account is None:
# This makes sure that we don't accidentally give access to the whole
# kubernetes API to the users in the spawned pods.
pod.spec.automount_service_account_token = False
else:
pod.spec.service_account_name = service_account
if run_privileged:
notebook_container.security_context = V1SecurityContext(
privileged=True)
notebook_container.resources.requests = {}
if userdir.get('jp_cpu_request'):
notebook_container.resources.requests['cpu'] = userdir.get(
'jp_cpu_request')
elif cpu_guarantee:
notebook_container.resources.requests['cpu'] = cpu_guarantee
if userdir.get('jp_mem_request'):
notebook_container.resources.requests['memory'] = userdir.get(
'jp_mem_request')
elif mem_guarantee:
notebook_container.resources.requests['memory'] = mem_guarantee
if extra_resource_guarantees:
notebook_container.resources.requests.update(extra_resource_guarantees)
notebook_container.resources.limits = {}
if userdir.get('jp_cpu_limit'):
notebook_container.resources.limits['cpu'] = userdir.get(
'jp_cpu_limit')
elif cpu_limit:
notebook_container.resources.limits['cpu'] = cpu_limit
if userdir.get('jp_mem_limit'):
notebook_container.resources.limits['memory'] = userdir.get(
'jp_mem_limit')
elif mem_limit:
notebook_container.resources.limits['memory'] = mem_limit
if extra_resource_limits:
notebook_container.resources.limits.update(extra_resource_limits)
if userdir.get("jp_gpu_enable", False):
notebook_container.resources.limits[r'nvidia.com/gpu'] = userdir.get(
'jp_gpu_number', 0)
if extra_container_config:
notebook_container = update_k8s_model(
target=notebook_container,
changes=extra_container_config,
logger=logger,
target_name="notebook_container",
changes_name="extra_container_config",
)
pod.spec.containers.append(notebook_container)
if extra_containers:
pod.spec.containers.extend(
[get_k8s_model(V1Container, obj) for obj in extra_containers])
if tolerations:
pod.spec.tolerations = [
get_k8s_model(V1Toleration, obj) for obj in tolerations
]
if init_containers:
pod.spec.init_containers = [
get_k8s_model(V1Container, obj) for obj in init_containers
]
if volumes:
pod.spec.volumes = [get_k8s_model(V1Volume, obj) for obj in volumes]
else:
# Keep behaving exactly like before by not cleaning up generated pod
# spec by setting the volumes field even though it is an empty list.
pod.spec.volumes = []
if scheduler_name:
pod.spec.scheduler_name = scheduler_name
node_affinity = None
if node_affinity_preferred or node_affinity_required:
node_selector = None
if node_affinity_required:
node_selector = V1NodeSelector(node_selector_terms=[
get_k8s_model(V1NodeSelectorTerm, obj)
for obj in node_affinity_required
], )
preferred_scheduling_terms = None
if node_affinity_preferred:
preferred_scheduling_terms = [
get_k8s_model(V1PreferredSchedulingTerm, obj)
for obj in node_affinity_preferred
]
node_affinity = V1NodeAffinity(
preferred_during_scheduling_ignored_during_execution=
preferred_scheduling_terms,
required_during_scheduling_ignored_during_execution=node_selector,
)
pod_affinity = None
if pod_affinity_preferred or pod_affinity_required:
weighted_pod_affinity_terms = None
if pod_affinity_preferred:
weighted_pod_affinity_terms = [
get_k8s_model(V1WeightedPodAffinityTerm, obj)
for obj in pod_affinity_preferred
]
pod_affinity_terms = None
if pod_affinity_required:
pod_affinity_terms = [
get_k8s_model(V1PodAffinityTerm, obj)
for obj in pod_affinity_required
]
pod_affinity = V1PodAffinity(
preferred_during_scheduling_ignored_during_execution=
weighted_pod_affinity_terms,
required_during_scheduling_ignored_during_execution=
pod_affinity_terms,
)
pod_anti_affinity = None
if pod_anti_affinity_preferred or pod_anti_affinity_required:
weighted_pod_affinity_terms = None
if pod_anti_affinity_preferred:
weighted_pod_affinity_terms = [
get_k8s_model(V1WeightedPodAffinityTerm, obj)
for obj in pod_anti_affinity_preferred
]
pod_affinity_terms = None
if pod_anti_affinity_required:
pod_affinity_terms = [
get_k8s_model(V1PodAffinityTerm, obj)
for obj in pod_anti_affinity_required
]
pod_anti_affinity = V1PodAffinity(
preferred_during_scheduling_ignored_during_execution=
weighted_pod_affinity_terms,
required_during_scheduling_ignored_during_execution=
pod_affinity_terms,
)
affinity = None
if (node_affinity or pod_affinity or pod_anti_affinity):
affinity = V1Affinity(
node_affinity=node_affinity,
pod_affinity=pod_affinity,
pod_anti_affinity=pod_anti_affinity,
)
if affinity:
pod.spec.affinity = affinity
if priority_class_name:
pod.spec.priority_class_name = priority_class_name
if extra_pod_config:
pod.spec = update_k8s_model(
target=pod.spec,
changes=extra_pod_config,
logger=logger,
target_name="pod.spec",
changes_name="extra_pod_config",
)
return pod
def make_pod(name,
cmd,
port,
image_spec,
image_pull_policy,
image_pull_secret=None,
node_selector=None,
run_as_uid=None,
run_as_gid=None,
fs_gid=None,
supplemental_gids=None,
run_privileged=False,
env={},
working_dir=None,
volumes=[],
volume_mounts=[],
labels={},
annotations={},
cpu_limit=None,
cpu_guarantee=None,
mem_limit=None,
mem_guarantee=None,
extra_resource_limits=None,
extra_resource_guarantees=None,
lifecycle_hooks=None,
init_containers=None,
service_account=None,
extra_container_config=None,
extra_pod_config=None,
extra_containers=None,
scheduler_name=None):
"""
Make a k8s pod specification for running a user notebook.
Parameters
----------
name:
Name of pod. Must be unique within the namespace the object is
going to be created in. Must be a valid DNS label.
image_spec:
Image specification - usually a image name and tag in the form
of image_name:tag. Same thing you would use with docker commandline
arguments
image_pull_policy:
Image pull policy - one of 'Always', 'IfNotPresent' or 'Never'. Decides
when kubernetes will check for a newer version of image and pull it when
running a pod.
image_pull_secret:
Image pull secret - Default is None -- set to your secret name to pull
from private docker registry.
port:
Port the notebook server is going to be listening on
cmd:
The command used to execute the singleuser server.
node_selector:
Dictionary Selector to match nodes where to launch the Pods
run_as_uid:
The UID used to run single-user pods. The default is to run as the user
specified in the Dockerfile, if this is set to None.
run_as_gid:
The GID used to run single-user pods. The default is to run as the primary
group of the user specified in the Dockerfile, if this is set to None.
fs_gid
The gid that will own any fresh volumes mounted into this pod, if using
volume types that support this (such as GCE). This should be a group that
the uid the process is running as should be a member of, so that it can
read / write to the volumes mounted.
supplemental_gids:
A list of GIDs that should be set as additional supplemental groups to
the user that the container runs as. You may have to set this if you are
deploying to an environment with RBAC/SCC enforced and pods run with a
'restricted' SCC which results in the image being run as an assigned
user ID. The supplemental group IDs would need to include the
corresponding group ID of the user ID the image normally would run as.
The image must setup all directories/files any application needs access
to, as group writable.
run_privileged:
Whether the container should be run in privileged mode.
env:
Dictionary of environment variables.
volumes:
List of dictionaries containing the volumes of various types this pod
will be using. See k8s documentation about volumes on how to specify
these
volume_mounts:
List of dictionaries mapping paths in the container and the volume(
specified in volumes) that should be mounted on them. See the k8s
documentaiton for more details
working_dir:
String specifying the working directory for the notebook container
labels:
Labels to add to the spawned pod.
annotations:
Annotations to add to the spawned pod.
cpu_limit:
Float specifying the max number of CPU cores the user's pod is
allowed to use.
cpu_guarentee:
Float specifying the max number of CPU cores the user's pod is
guaranteed to have access to, by the scheduler.
mem_limit:
String specifying the max amount of RAM the user's pod is allowed
to use. String instead of float/int since common suffixes are allowed
mem_guarantee:
String specifying the max amount of RAM the user's pod is guaranteed
to have access to. String ins loat/int since common suffixes
are allowed
lifecycle_hooks:
Dictionary of lifecycle hooks
init_containers:
List of initialization containers belonging to the pod.
service_account:
Service account to mount on the pod. None disables mounting
extra_container_config:
Extra configuration (e.g. envFrom) for notebook container which is not covered by parameters above.
extra_pod_config:
Extra configuration (e.g. tolerations) for pod which is not covered by parameters above.
extra_containers:
Extra containers besides notebook container. Used for some housekeeping jobs (e.g. crontab).
scheduler_name:
A custom scheduler's name.
"""
pod = V1Pod()
pod.kind = "Pod"
pod.api_version = "v1"
pod.metadata = V1ObjectMeta(name=name,
labels=labels.copy(),
annotations=annotations.copy())
pod.spec = V1PodSpec(containers=[])
pod.spec.restartPolicy = 'Never'
security_context = V1PodSecurityContext()
if fs_gid is not None:
security_context.fs_group = int(fs_gid)
if supplemental_gids is not None and supplemental_gids:
security_context.supplemental_groups = [
int(gid) for gid in supplemental_gids
]
if run_as_uid is not None:
security_context.run_as_user = int(run_as_uid)
if run_as_gid is not None:
security_context.run_as_group = int(run_as_gid)
pod.spec.security_context = security_context
if image_pull_secret is not None:
pod.spec.image_pull_secrets = []
image_secret = V1LocalObjectReference()
image_secret.name = image_pull_secret
pod.spec.image_pull_secrets.append(image_secret)
if node_selector:
pod.spec.node_selector = node_selector
notebook_container = V1Container(
name='notebook',
image=image_spec,
working_dir=working_dir,
ports=[V1ContainerPort(name='notebook-port', container_port=port)],
env=[V1EnvVar(k, v) for k, v in env.items()],
args=cmd,
image_pull_policy=image_pull_policy,
lifecycle=lifecycle_hooks,
resources=V1ResourceRequirements())
if service_account is None:
# Add a hack to ensure that no service accounts are mounted in spawned pods
# This makes sure that we don"t accidentally give access to the whole
# kubernetes API to the users in the spawned pods.
# Note: We don't simply use `automountServiceAccountToken` here since we wanna be compatible
# with older kubernetes versions too for now.
hack_volume = V1Volume(name='no-api-access-please', empty_dir={})
hack_volumes = [hack_volume]
hack_volume_mount = V1VolumeMount(
name='no-api-access-please',
mount_path="/var/run/secrets/kubernetes.io/serviceaccount",
read_only=True)
hack_volume_mounts = [hack_volume_mount]
# Non-hacky way of not mounting service accounts
pod.spec.automount_service_account_token = False
else:
hack_volumes = []
hack_volume_mounts = []
pod.spec.service_account_name = service_account
if run_privileged:
notebook_container.security_context = V1SecurityContext(
privileged=True)
notebook_container.resources.requests = {}
if cpu_guarantee:
notebook_container.resources.requests['cpu'] = cpu_guarantee
if mem_guarantee:
notebook_container.resources.requests['memory'] = mem_guarantee
if extra_resource_guarantees:
for k in extra_resource_guarantees:
notebook_container.resources.requests[
k] = extra_resource_guarantees[k]
notebook_container.resources.limits = {}
if cpu_limit:
notebook_container.resources.limits['cpu'] = cpu_limit
if mem_limit:
notebook_container.resources.limits['memory'] = mem_limit
if extra_resource_limits:
for k in extra_resource_limits:
notebook_container.resources.limits[k] = extra_resource_limits[k]
notebook_container.volume_mounts = volume_mounts + hack_volume_mounts
pod.spec.containers.append(notebook_container)
if extra_container_config:
for key, value in extra_container_config.items():
setattr(notebook_container,
_map_attribute(notebook_container.attribute_map, key),
value)
if extra_pod_config:
for key, value in extra_pod_config.items():
setattr(pod.spec, _map_attribute(pod.spec.attribute_map, key),
value)
if extra_containers:
pod.spec.containers.extend(extra_containers)
pod.spec.init_containers = init_containers
pod.spec.volumes = volumes + hack_volumes
if scheduler_name:
pod.spec.scheduler_name = scheduler_name
return pod
