Routes 3D ResNets PyTorch video action-recognition workflows across training, inference, and data preparation.
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Operate and extend GeoNode geospatial content-management deployments, resource APIs, uploads, metadata/catalogues, GeoServer security, harvesting, and administration with explicit service and safety gates.
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Review the instructions the agent will receive before you install.
Use this skill when a task names GeoNode, asks about its Django/REST APIs, geospatial resource catalogues, dataset/document/map uploads, GeoServer publication, metadata/CSW, harvesting, or GeoNode deployment operations. This graph covers the GeoNode 5.1-style package and service topology. It is operating guidance for a later Researcher; it is not a claim that external services are running.
For a released distribution, install the package in an isolated environment
using the project-supported Python version and geospatial prerequisites. A
source/development checkout commonly needs PostgreSQL client headers for
psycopg2 and a matching GDAL native library for the Python GDAL binding.
Do not install into a shared production environment just to inspect APIs.
python -m pip install GeoNode
python -c "import geonode; print(geonode.__version_str__)"
For a repository checkout, use the repository's documented editable install only in a disposable development environment, then confirm both the Python package and native geospatial imports. A successful import does not validate PostGIS, GeoServer, Redis/Celery, Nginx, remote OGC endpoints, credentials, or browser behavior.
--help, schema discovery, bounded API reads,
and tiny local validators before mutation or network work.No reviews yet. Tell others what you thought.
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Routes 3D ResNets PyTorch video action-recognition workflows across training, inference, and data preparation.
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Guide 3DDFA Python inference, geometry rendering, training/evaluation, and optional C++ ONNX workflows for 3D dense face alignment.
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Routes 3DDFA_V2 face-alignment setup, still-image demos, video tracking, and ONNX benchmarking workflows.
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Operate AB3DMOT 3D multi-object tracking workflows for KITTI and nuScenes data, tracking, evaluation, and visualization.
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Use Hugging Face Accelerate for PyTorch training-loop migration, distributed launch/configuration, DeepSpeed/FSDP/TPU backend setup, big-model inference/offload, checkpointing, tracking, and troubleshooting.
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Route Acme reinforcement-learning framework tasks across core loops, replay/data, JAX agents, and TensorFlow/Sonnet agents.
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