Workspaces

Workspaces are the fundamental organizational and authorization boundary in NeMo Platform. All platform resources—models, datasets, customization jobs, evaluation results, and more—must belong to a workspace.

When authentication is enabled, users are granted roles (Viewer, Editor, or Admin) within specific workspaces, and all resources within a workspace inherit its access controls.

Create separate workspaces when you need true isolation—for example, to separate teams (team-ml-research, team-nlp), individual users (user-jsmith), environments (dev, staging, production), or clients (client-acme). For lighter-weight organization within a workspace that does not require access separation, use projects.

Built-in Workspaces

The platform automatically creates two built-in workspaces during initialization:

• default — General-purpose workspace for experimentation, editable by all authenticated users.
• system — Reserved for platform-provided resources (for example, evaluation targets, customization templates). Read-only for regular users; only platform administrators can modify.

Create a Workspace

To create a workspace, provide a name and optionally a description. The name must be unique within your deployment.

Resource names must follow these rules:

• Must start with a lowercase letter (a-z)
• 2-63 characters long
• Allowed characters: lowercase letters, digits, hyphens, and temporarily @, ., +, _
• No consecutive hyphens (--)
• Cannot end with a hyphen

Example valid names: my-model, llama-3.2-3b, test-config-v1

Once created, a workspace cannot be renamed—choose the name carefully. The description is a free-form text field to help identify the workspace’s purpose.

The system also generates a unique id (UUID) for internal use, but the name is the primary identifier used in API paths and SDK calls.

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import os
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from nemo_platform import NeMoPlatform
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4
client = NeMoPlatform(
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    base_url=os.environ.get("NMP_BASE_URL", "http://localhost:8080"),
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    workspace="default",
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)
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workspace = client.workspaces.create(
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    name="ml-team", description="Machine Learning Team workspace"
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)

List Workspaces

To list workspaces, call the list endpoint. When authentication is enabled, only workspaces the user has access to are returned. The response includes pagination metadata.

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import os
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from nemo_platform import NeMoPlatform
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4
client = NeMoPlatform(
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    base_url=os.environ.get("NMP_BASE_URL", "http://localhost:8080"),
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    workspace="default",
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)
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response = client.workspaces.list()
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for workspace in response.data:
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    print(f"{workspace.name}: {workspace.description}")

Get a Workspace

To retrieve a specific workspace by its name:

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import os
2
from nemo_platform import NeMoPlatform
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4
client = NeMoPlatform(
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    base_url=os.environ.get("NMP_BASE_URL", "http://localhost:8080"),
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    workspace="default",
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)
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workspace = client.workspaces.retrieve("ml-team")

Update a Workspace

To update a workspace, only the description field can be modified. The name cannot be changed after creation.

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import os
2
from nemo_platform import NeMoPlatform
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4
client = NeMoPlatform(
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    base_url=os.environ.get("NMP_BASE_URL", "http://localhost:8080"),
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    workspace="default",
7
)
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9
workspace = client.workspaces.update(
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    "ml-team", description="Updated: ML Team workspace for model development"
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)

Delete a Workspace

Deleting a workspace removes the workspace and all of its resources (jobs, deployments, filesets, role bindings, and other entities). The API returns immediately after revoking access, and an asynchronous cleanup controller handles deletion of the remaining resources in the background.

During cleanup, the workspace is no longer visible to users. If cleanup fails, the workspace enters a FAILED state that requires administrator attention.

1
import os
2
from nemo_platform import NeMoPlatform
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4
client = NeMoPlatform(
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    base_url=os.environ.get("NMP_BASE_URL", "http://localhost:8080"),
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    workspace="default",
7
)
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client.workspaces.delete("ml-team")