Currently the Jupyter Compute Tier management is set on an individual user level. Ideally we'd like this to at least be configured on either a role and/or group level to make this as scalable as possible.
Thank you!
This would be a massive unlock for us.
@Oleksii @OleksiiZakrevskyi i'm pretty sure this is built into domojupyter already you should be able to write a MONIT dataset, then just scrape for the PUT command to edit the tier
Stub: GET Jupyter workspace configuration via Domo API Since PUT isn't supported, this is read-only monitoring.
Two API endpoints do the heavy lifting:
Compute tier and python_version are NOT direct fields on the workspace object. They're derived by cross-referencing workspace.cpu/memory against settings.instanceTypes, and workspace kernel info against settings.jupyterKernels. """
import requests import pandas as pd from typing import Optional
def get_jupyter_workspaces(instance: str, token: str) -> list[dict]: """GET /api/content/v1/jupyter/workspaces — paginated list.""" url = f"https://{instance}.domo.com/api/content/v1/jupyter/workspaces" headers = {"X-DOMO-Developer-Token": token} all_workspaces = [] offset = 0 limit = 50
while True: params = {"limit": limit, "offset": offset} resp = requests.get(url, headers=headers, params=params) resp.raise_for_status() data = resp.json() workspaces = data.get("workspaces", []) all_workspaces.extend(workspaces) if len(workspaces) < limit: break offset += limit return all_workspaces
def get_jupyter_settings(instance: str, token: str) -> dict: """GET /api/content/v1/jupyter/settings — instanceTypes, jupyterKernels, etc.""" url = f"https://{instance}.domo.com/api/content/v1/jupyter/settings" headers = {"X-DOMO-Developer-Token": token} resp = requests.get(url, headers=headers) resp.raise_for_status() return resp.json()
def resolve_compute_tier(cpu: str, memory: int, instance_types: list[dict]) -> Optional[str]: """ Match workspace cpu/memory against settings.instanceTypes to find the named compute tier.
instance_types is a list like: [{"name": "Small", "cpu": "1", "memory": 4}, {"name": "Medium", "cpu": "2", "memory": 8}, ...] """ for tier in instance_types: if str(tier.get("cpu")) == str(cpu) and int(tier.get("memory", 0)) == int(memory): return tier.get("name") return None
def resolve_python_version(workspace: dict, jupyter_kernels: list[dict]) -> Optional[str]: """ Try to extract python version from workspace's kernel/instance data. Fall back to matching against settings.jupyterKernels.
jupyter_kernels is a list like: [{"name": "python3", "displayName": "Python 3.11", "language": "python"}, ...] The workspace raw response may include kernel info in: - workspace["kernel"] or workspace["instances"][0]["kernel"] """ # Check if kernel info is in the raw workspace response raw = workspace.get("raw", workspace) kernel_name = raw.get("kernel") or raw.get("kernelName") # Check instances (only populated when workspace is running) if not kernel_name and raw.get("instances"): kernel_name = raw["instances"][0].get("kernel") # Match against known kernels if kernel_name: for k in jupyter_kernels: if k.get("name") == kernel_name: return k.get("displayName") or k.get("language") return kernel_name # return raw value if no match
def generate_monit_jupyter_workspaces(instance: str, token: str) -> pd.DataFrame: """ Fetch all workspace configs and return a DataFrame suitable for upload as MONIT_jupyter_workspaces dataset. """ # Fetch data workspaces = get_jupyter_workspaces(instance, token) settings = get_jupyter_settings(instance, token)
instance_types = settings.get("instanceTypes", []) jupyter_kernels = settings.get("jupyterKernels", []) # Build rows rows = [] for ws in workspaces: cpu = ws.get("cpu") memory = ws.get("memory") rows.append({ "workspace_id": ws.get("id"), "name": ws.get("name"), "description": ws.get("description"), "owner": ws.get("owner"), "cpu": cpu, "memory": memory, "compute_tier": resolve_compute_tier(cpu, memory, instance_types), "python_version": resolve_python_version(ws, jupyter_kernels), "status": ws.get("status"), "created_dt": ws.get("createdDt") or ws.get("created_dt"), "updated_dt": ws.get("updatedDt") or ws.get("updated_dt"), "last_run_dt": ws.get("lastRunDt") or ws.get("last_run_dt"), "timeout": ws.get("timeout"), "input_datasets": ws.get("inputDataSets", []), "output_datasets": ws.get("outputDataSets", []), }) return pd.DataFrame(rows)
if name == "main": INSTANCE = "your-instance" # e.g. "domo-community" TOKEN = "your-developer-token"
df = generate_monit_jupyter_workspaces(INSTANCE, TOKEN) print(df.to_string(index=False)) # Upload as MONIT_ dataset: # domo.dataframe_to_dataset(df, "MONIT_jupyter_workspaces")
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