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Add ability to allocate gpu, cpu, mem for jupyterlab workspace
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@mboglesby
mboglesby committed Mar 19, 2021
1 parent 350b1bc commit 1f088f5
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22 changes: 17 additions & 5 deletions Kubernetes/README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -109,18 +109,21 @@ The following options/arguments are optional:

```
-c, --volume-snapshot-class= Kubernetes VolumeSnapshotClass to use when creating clone. If not specified, "csi-snapclass" will be used. Note: VolumeSnapshotClass must be configured to use Trident.
-g, --nvidia-gpu= Number of NVIDIA GPUs to allocate to new JupyterLab workspace. Format: '1', '4', etc. If not specified, no GPUs will be allocated.
-h, --help Print help text.
-j, --source-workspace-name= Name of JupyterLab workspace to use as source for clone. Either -s/--source-snapshot-name or -j/--source-workspace-name must be specified.
-m, --memory= Amount of memory to reserve for new JupyterLab workspace. Format: '1024Mi', '100Gi', '10Ti', etc. If not specified, no memory will be reserved.
-n, --namespace= Kubernetes namespace that source workspace is located in. If not specified, namespace "default" will be used.
-p, --cpu= Number of CPUs to reserve for new JupyterLab workspace. Format: '0.5', '1', etc. If not specified, no CPUs will be reserved.
-s, --source-snapshot-name= Name of Kubernetes VolumeSnapshot to use as source for clone. Either -s/--source-snapshot-name or -j/--source-workspace-name must be specified.
-j, --source-workspace-name= Name of JupyterLab workspace to use as source for clone. Either -s/--source-snapshot-name or -j/--source-workspace-name must be specified.
```

##### Example Usage

Near-instantaneously create a new JupyterLab workspace, named 'project1-experiment3', that is an exact copy of the current contents of existing JupyterLab workspace 'project1' in namespace 'default'.
Near-instantaneously create a new JupyterLab workspace, named 'project1-experiment3', that is an exact copy of the current contents of existing JupyterLab workspace 'project1' in namespace 'default'. Allocate 2 NVIDIA GPUs to the new workspace.

```sh
./ntap_dsutil_k8s.py clone jupyterlab --new-workspace-name=project1-experiment3 --source-workspace-name=project1
./ntap_dsutil_k8s.py clone jupyterlab --new-workspace-name=project1-experiment3 --source-workspace-name=project1 --nvidia-gpu=2
Creating new JupyterLab workspace 'project1-experiment3' from source workspace 'project1' in namespace 'default'...
Creating new VolumeSnapshot 'ntap-dsutil.for-clone.20210315185504' for source PVC 'ntap-dsutil-jupyterlab-project1' in namespace 'default' to use as source for clone...
Expand Down Expand Up @@ -196,17 +199,20 @@ The following options/arguments are optional:

```
-c, --storage-class= Kubernetes StorageClass to use when provisioning backing volume for new workspace. If not specified, default StorageClass will be used. Note: StorageClass must be configured to use Trident.
-g, --nvidia-gpu= Number of NVIDIA GPUs to allocate to JupyterLab workspace. Format: '1', '4', etc. If not specified, no GPUs will be allocated.
-h, --help Print help text.
-i, --image= Container image to use when creating workspace. If not specified, "jupyter/tensorflow-notebook" will be used.
-m, --memory= Amount of memory to reserve for JupyterLab workspace. Format: '1024Mi', '100Gi', '10Ti', etc. If not specified, no memory will be reserved.
-n, --namespace= Kubernetes namespace to create new workspace in. If not specified, workspace will be created in namespace "default".
-p, --cpu= Number of CPUs to reserve for JupyterLab workspace. Format: '0.5', '1', etc. If not specified, no CPUs will be reserved.
```

##### Example Usage

Provision a new JupyterLab workspace named 'mike' of size 10GB in namespace 'default'.
Provision a new JupyterLab workspace named 'mike' of size 10GB in namespace 'default'. Allocate 1 NVIDIA GPU to the new workspace.

```sh
./ntap_dsutil_k8s.py create jupyterlab --workspace-name=mike --size=10Gi
./ntap_dsutil_k8s.py create jupyterlab --workspace-name=mike --size=10Gi --nvidia-gpu=1
Set workspace password (this password will be required in order to access the workspace):
Re-enter password:
Expand Down Expand Up @@ -813,6 +819,9 @@ def cloneJupyterLab(
newWorkspacePassword: str = None, # Workspace password (this password will be required in order to access the workspace). If not specified, you will be prompted to enter a password via the console.
volumeSnapshotClass: str = "csi-snapclass", # Kubernetes VolumeSnapshotClass to use when creating clone. If not specified, "csi-snapclass" will be used. Note: VolumeSnapshotClass must be configured to use Trident.
namespace: str = "default", # Kubernetes namespace that source workspace is located in. If not specified, namespace "default" will be used.
requestCpu: str = None, # Number of CPUs to reserve for new JupyterLab workspace. Format: '0.5', '1', etc. If not specified, no CPUs will be reserved.
requestMemory: str = None, # Amount of memory to reserve for newe JupyterLab workspace. Format: '1024Mi', '100Gi', '10Ti', etc. If not specified, no memory will be reserved.
requestNvidiaGpu: str = None, # Number of NVIDIA GPUs to allocate to new JupyterLab workspace. Format: '1', '4', etc. If not specified, no GPUs will be allocated.
printOutput: bool = False # Denotes whether or not to print messages to the console during execution.
) :
```
Expand Down Expand Up @@ -848,6 +857,9 @@ def createJupyterLab(
namespace: str = "default", # Kubernetes namespace to create new workspace in. If not specified, workspace will be created in namespace "default".
workspacePassword: str = None, # Workspace password (this password will be required in order to access the workspace). If not specified, you will be prompted to enter a password via the console.
workspaceImage: str = "jupyter/tensorflow-notebook", # Container image to use when creating workspace. If not specified, "jupyter/tensorflow-notebook" will be used.
requestCpu: str = None, # Number of CPUs to reserve for JupyterLab workspace. Format: '0.5', '1', etc. If not specified, no CPUs will be reserved.
requestMemory: str = None, # Amount of memory to reserve for JupyterLab workspace. Format: '1024Mi', '100Gi', '10Ti', etc. If not specified, no memory will be reserved.
requestNvidiaGpu: str = None, # Number of NVIDIA GPUs to allocate to JupyterLab workspace. Format: '1', '4', etc. If not specified, no GPUs will be allocated.
printOutput: bool = False # Denotes whether or not to print messages to the console during execution.
) -> str :
```
Expand Down
69 changes: 53 additions & 16 deletions Kubernetes/ntap_dsutil_k8s.py
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Original file line number Diff line number Diff line change
Expand Up @@ -598,7 +598,7 @@ def jupyterLabDeployment(workspaceName: str) -> str :
## Functions relating to JupyterLab workspaces

# Function for retrieving JupyterLab access url
def retrieveJupyterLabURL(workspaceName: str, printOutput: bool = False) -> str :
def retrieveJupyterLabURL(workspaceName: str, namespace: str = "default", printOutput: bool = False) -> str :
# Retrieve kubeconfig
try :
loadKubeConfig()
Expand Down Expand Up @@ -707,7 +707,7 @@ def retrieveImageForJupyterLabDeployment(workspaceName: str, namespace: str = "d


## Function for creating a new JupyterLab workspace
def createJupyterLab(workspaceName: str, workspaceSize: str, storageClass: str = None, namespace: str = "default", workspacePassword: str = None, workspaceImage: str = "jupyter/tensorflow-notebook", printOutput: bool = False, pvcAlreadyExists: bool = False, labels: dict = None) -> str :
def createJupyterLab(workspaceName: str, workspaceSize: str, storageClass: str = None, namespace: str = "default", workspacePassword: str = None, workspaceImage: str = "jupyter/tensorflow-notebook", requestCpu: str = None, requestMemory: str = None, requestNvidiaGpu: str = None, printOutput: bool = False, pvcAlreadyExists: bool = False, labels: dict = None) -> str :
# Retrieve kubeconfig
try :
loadKubeConfig()
Expand Down Expand Up @@ -838,14 +838,27 @@ def createJupyterLab(workspaceName: str, workspaceSize: str, storageClass: str =
name = "workspace",
mount_path = "/home/jovyan"
)
]
],
resources = {
"limits": dict(),
"requests": dict()
}
)
]
)
)
)
)

# Apply resource requests
if requestCpu :
deployment.spec.template.spec.containers[0].resources["requests"]["cpu"] = requestCpu
if requestMemory :
deployment.spec.template.spec.containers[0].resources["requests"]["memory"] = requestMemory
if requestNvidiaGpu :
deployment.spec.template.spec.containers[0].resources["requests"]["nvidia.com/gpu"] = requestNvidiaGpu
deployment.spec.template.spec.containers[0].resources["limits"]["nvidia.com/gpu"] = requestNvidiaGpu

# Create deployment
if printOutput :
print("\nCreating Deployment '" + jupyterLabDeployment(workspaceName=workspaceName) + "' in namespace '" + namespace + "'.")
Expand All @@ -868,7 +881,7 @@ def createJupyterLab(workspaceName: str, workspaceSize: str, storageClass: str =

# Step 4 - Retrieve access URL
try :
url = retrieveJupyterLabURL(workspaceName=workspaceName, printOutput=printOutput)
url = retrieveJupyterLabURL(workspaceName=workspaceName, namespace=namespace, printOutput=printOutput)
except APIConnectionError as err :
if printOutput :
print("Aborting workspace creation...")
Expand Down Expand Up @@ -967,7 +980,7 @@ def listJupyterLabs(namespace: str = "default", printOutput: bool = False) -> li
workspaceDict["StorageClass"] = ""

# Retrieve access URL
workspaceDict["Access URL"] = retrieveJupyterLabURL(workspaceName=workspaceName, printOutput=printOutput)
workspaceDict["Access URL"] = retrieveJupyterLabURL(workspaceName=workspaceName, namespace=namespace, printOutput=printOutput)

# Retrieve clone details
try :
Expand Down Expand Up @@ -1062,7 +1075,7 @@ def restoreJupyterLabSnapshot(snapshotName: str = None, namespace: str = "defaul


## Function for cloning a JupyterLab workspace
def cloneJupyterLab(newWorkspaceName: str, sourceWorkspaceName: str, sourceSnapshotName: str = None, newWorkspacePassword: str = None, volumeSnapshotClass: str = "csi-snapclass", namespace: str = "default", printOutput: bool = False) :
def cloneJupyterLab(newWorkspaceName: str, sourceWorkspaceName: str, sourceSnapshotName: str = None, newWorkspacePassword: str = None, volumeSnapshotClass: str = "csi-snapclass", namespace: str = "default", requestCpu: str = None, requestMemory: str = None, requestNvidiaGpu: str = None, printOutput: bool = False) :
# Determine source PVC details
if sourceSnapshotName :
sourcePvcName, workspaceSize = retrieveSourceVolumeDetailsForVolumeSnapshot(snapshotName=sourceSnapshotName, namespace=namespace, printOutput=printOutput)
Expand Down Expand Up @@ -1093,7 +1106,7 @@ def cloneJupyterLab(newWorkspaceName: str, sourceWorkspaceName: str, sourceSnaps

# Create new workspace
print()
createJupyterLab(workspaceName=newWorkspaceName, workspaceSize=workspaceSize, namespace=namespace, workspacePassword=newWorkspacePassword, workspaceImage=sourceWorkspaceImage, printOutput=printOutput, pvcAlreadyExists=True, labels=labels)
createJupyterLab(workspaceName=newWorkspaceName, workspaceSize=workspaceSize, namespace=namespace, workspacePassword=newWorkspacePassword, workspaceImage=sourceWorkspaceImage, requestCpu=requestCpu, requestMemory=requestMemory, requestNvidiaGpu=requestNvidiaGpu, printOutput=printOutput, pvcAlreadyExists=True, labels=labels)

if printOutput :
print("JupyterLab workspace successfully cloned.")
Expand Down Expand Up @@ -1143,14 +1156,17 @@ def cloneJupyterLab(newWorkspaceName: str, sourceWorkspaceName: str, sourceSnaps
Optional Options/Arguments:
\t-c, --volume-snapshot-class=\tKubernetes VolumeSnapshotClass to use when creating clone. If not specified, "csi-snapclass" will be used. Note: VolumeSnapshotClass must be configured to use Trident.
\t-g, --nvidia-gpu=\t\tNumber of NVIDIA GPUs to allocate to new JupyterLab workspace. Format: '1', '4', etc. If not specified, no GPUs will be allocated.
\t-h, --help\t\t\tPrint help text.
\t-j, --source-workspace-name=\tName of JupyterLab workspace to use as source for clone. Either -s/--source-snapshot-name or -j/--source-workspace-name must be specified.
\t-m, --memory=\t\t\tAmount of memory to reserve for new JupyterLab workspace. Format: '1024Mi', '100Gi', '10Ti', etc. If not specified, no memory will be reserved.
\t-n, --namespace=\t\tKubernetes namespace that source workspace is located in. If not specified, namespace "default" will be used.
\t-p, --cpu=\t\t\tNumber of CPUs to reserve for new JupyterLab workspace. Format: '0.5', '1', etc. If not specified, no CPUs will be reserved.
\t-s, --source-snapshot-name=\tName of Kubernetes VolumeSnapshot to use as source for clone. Either -s/--source-snapshot-name or -j/--source-workspace-name must be specified.
\t-j, --source-workspace-name=\tName of JupyterLab workspace to use as source for clone. Either -s/--source-snapshot-name or -j/--source-workspace-name must be specified.
Examples:
\t./ntap_dsutil.py clone jupyterlab --new-workspace-name=project1-experiment1 --source-workspace-name=project1
\t./ntap_dsutil.py clone jupyterlab -w project2-mike -s project2-snap1 -n team1
\t./ntap_dsutil.py clone jupyterlab --new-workspace-name=project1-experiment1 --source-workspace-name=project1 --nvidia-gpu=1
\t./ntap_dsutil.py clone jupyterlab -w project2-mike -s project2-snap1 -n team1 -g 1 -p 0.5 -m 1Gi
'''
helpTextCloneVolume = '''
Command: clone volume
Expand Down Expand Up @@ -1184,13 +1200,16 @@ def cloneJupyterLab(newWorkspaceName: str, sourceWorkspaceName: str, sourceSnaps
Optional Options/Arguments:
\t-c, --storage-class=\tKubernetes StorageClass to use when provisioning backing volume for new workspace. If not specified, default StorageClass will be used. Note: StorageClass must be configured to use Trident.
\t-g, --nvidia-gpu=\tNumber of NVIDIA GPUs to allocate to JupyterLab workspace. Format: '1', '4', etc. If not specified, no GPUs will be allocated.
\t-h, --help\t\tPrint help text.
\t-i, --image=\t\tContainer image to use when creating workspace. If not specified, "jupyter/tensorflow-notebook" will be used.
\t-m, --memory=\t\tAmount of memory to reserve for JupyterLab workspace. Format: '1024Mi', '100Gi', '10Ti', etc. If not specified, no memory will be reserved.
\t-n, --namespace=\tKubernetes namespace to create new workspace in. If not specified, workspace will be created in namespace "default".
\t-p, --cpu=\t\tNumber of CPUs to reserve for JupyterLab workspace. Format: '0.5', '1', etc. If not specified, no CPUs will be reserved.
Examples:
\t./ntap_dsutil_k8s.py create jupyterlab --workspace-name=mike --size=10Gi
\t./ntap_dsutil_k8s.py create jupyterlab -n dst-test -w dave -i jupyter/scipy-notebook:latest -s 2Ti -c ontap-flexgroup
\t./ntap_dsutil_k8s.py create jupyterlab --workspace-name=mike --size=10Gi --nvidia-gpu=2
\t./ntap_dsutil_k8s.py create jupyterlab -n dst-test -w dave -i jupyter/scipy-notebook:latest -s 2Ti -c ontap-flexgroup -g 1 -p 0.5 -m 1Gi
'''
helpTextCreateJupyterLabSnapshot = '''
Command: create jupyterlab-snapshot
Expand Down Expand Up @@ -1500,10 +1519,13 @@ def getTarget(args: list) -> str:
sourceSnapshotName = None
volumeSnapshotClass = "csi-snapclass"
namespace = "default"
requestNvidiaGpu = None
requestMemory = None
requestCpu = None

# Get command line options
try :
opts, args = getopt.getopt(sys.argv[3:], "hw:c:n:s:j:", ["help", "new-workspace-name=", "volume-snapshot-class=", "namespace=", "source-snapshot-name=", "source-workspace-name="])
opts, args = getopt.getopt(sys.argv[3:], "hw:c:n:s:j:g:m:p:", ["help", "new-workspace-name=", "volume-snapshot-class=", "namespace=", "source-snapshot-name=", "source-workspace-name=", "nvidia-gpu=", "memory=", "cpu="])
except :
handleInvalidCommand(helpText=helpTextCloneJupyterLab, invalidOptArg=True)

Expand All @@ -1522,6 +1544,12 @@ def getTarget(args: list) -> str:
sourceSnapshotName = arg
elif opt in ("-j", "--source-workspace-name") :
sourceWorkspaceName = arg
elif opt in ("-g", "--nvidia-gpu") :
requestNvidiaGpu = arg
elif opt in ("-m", "--memory") :
requestMemory = arg
elif opt in ("-p", "--cpu") :
requestCpu = arg

# Check for required options
if not newWorkspaceName or (not sourceSnapshotName and not sourceWorkspaceName) :
Expand All @@ -1532,7 +1560,7 @@ def getTarget(args: list) -> str:

# Clone volume
try :
cloneJupyterLab(newWorkspaceName=newWorkspaceName, sourceWorkspaceName=sourceWorkspaceName, sourceSnapshotName=sourceSnapshotName, volumeSnapshotClass=volumeSnapshotClass, namespace=namespace, printOutput=True)
cloneJupyterLab(newWorkspaceName=newWorkspaceName, sourceWorkspaceName=sourceWorkspaceName, sourceSnapshotName=sourceSnapshotName, volumeSnapshotClass=volumeSnapshotClass, namespace=namespace, requestCpu=requestCpu, requestMemory=requestMemory, requestNvidiaGpu=requestNvidiaGpu, printOutput=True)
except (InvalidConfigError, APIConnectionError) :
sys.exit(1)

Expand Down Expand Up @@ -1622,10 +1650,13 @@ def getTarget(args: list) -> str:
namespace = "default"
storageClass = None
workspaceImage = "jupyter/scipy-notebook:latest"
requestNvidiaGpu = None
requestMemory = None
requestCpu = None

# Get command line options
try :
opts, args = getopt.getopt(sys.argv[3:], "hw:s:n:c:i:", ["help", "workspace-name=", "size=", "namespace=", "storage-class=", "image="])
opts, args = getopt.getopt(sys.argv[3:], "hw:s:n:c:i:g:m:p:", ["help", "workspace-name=", "size=", "namespace=", "storage-class=", "image=", "nvidia-gpu=", "memory=", "cpu="])
except :
handleInvalidCommand(helpText=helpTextCreateJupyterLab, invalidOptArg=True)

Expand All @@ -1644,14 +1675,20 @@ def getTarget(args: list) -> str:
storageClass = arg
elif opt in ("-i", "--image") :
workspaceImage = arg
elif opt in ("-g", "--nvidia-gpu") :
requestNvidiaGpu = arg
elif opt in ("-m", "--memory") :
requestMemory = arg
elif opt in ("-p", "--cpu") :
requestCpu = arg

# Check for required options
if not workspaceName or not workspaceSize :
handleInvalidCommand(helpText=helpTextCreateJupyterLab, invalidOptArg=True)

# Create JupyterLab workspace
try :
createJupyterLab(workspaceName=workspaceName, workspaceSize=workspaceSize, storageClass=storageClass, namespace=namespace, workspaceImage=workspaceImage, printOutput=True)
createJupyterLab(workspaceName=workspaceName, workspaceSize=workspaceSize, storageClass=storageClass, namespace=namespace, workspaceImage=workspaceImage, requestCpu=requestCpu, requestMemory=requestMemory, requestNvidiaGpu=requestNvidiaGpu, printOutput=True)
except (InvalidConfigError, APIConnectionError) :
sys.exit(1)

Expand Down

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