data-science-pack/Tiltfile at main · nebari-dev/data-science-pack · GitHub

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# -*- mode: Python -*-
# Tiltfile for nebari-data-science-pack local development
#
# References:
# - Tilt helm integration: https://docs.tilt.dev/helm.html
# - allow_k8s_contexts: https://docs.tilt.dev/api.html#api.allow_k8s_contexts

# Increase apply timeout for slow operations like image pulls
# Reference: https://docs.tilt.dev/api.html#api.update_settings
update_settings(k8s_upsert_timeout_secs=600)

# Safety: Only allow deployment to local k3d cluster
# Reference: https://github.com/tilt-dev/tilt/blob/main/internal/tiltfile/k8scontext/k8scontext.go
# k3d clusters use context name format: k3d-<cluster-name>
allow_k8s_contexts('k3d-nebari-dev')

# Build images locally from images/Dockerfile
# Reference: https://docs.tilt.dev/api.html#api.docker_build
docker_build(
    'nebari-data-science-pack-jupyterhub',
    context='./images',
    dockerfile='./images/Dockerfile',
    target='jupyterhub',
)

# JupyterLab image: Use quay.io/nebari/nebari-data-science-pack-jupyterlab from values.yaml
# (no local build needed - singleuser pods pull directly from quay.io)

# Deploy the Helm chart using helm() for templating
# Reference: https://docs.tilt.dev/helm.html
# Using helm() instead of helm_resource() because:
# - Better integration with Tilt's resource tracking
# - Automatic port-forward support via k8s_resource
# - Individual pod logs in Tilt UI
# Note: helm() skips chart hooks (like image-puller), which is fine for local dev
k8s_yaml(helm(
    '.',
    name='data-science-pack',
    namespace='default',
    set=[
        # Disable NebariApp CRD for local dev (not running on Nebari)
        'nebariapp.enabled=false',
        # Override hub image with local build (singleuser uses quay.io from values.yaml)
        'jupyterhub.hub.image.name=nebari-data-science-pack-jupyterhub',
        'jupyterhub.hub.image.tag=latest',
    ],
))

# Configure the proxy resource for port forwarding
# Reference: https://docs.tilt.dev/api.html#api.k8s_resource
k8s_resource(
    workload='proxy',
    port_forwards=['8000:8000'],
    labels=['jupyterhub'],
)

# Configure the hub resource
k8s_resource(
    workload='hub',
    labels=['jupyterhub'],
)