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experiments/gnn_architecture_comparison.yaml

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+# Fleet Spec: Controlled GNN Architecture Comparison
+#
+# Direct comparison of 5 GNN architectures (GCN, SAGE, GAT, GIN, GraphConv)
+# with controlled hyperparameters for Ruby code complexity prediction.
+#
+# Launch:
+#   ratiocinator fleet run experiments/gnn_architecture_comparison.yaml
+#
+# Each arm varies ONLY the conv_type. All other hyperparameters are identical.
+
+name: gnn-architecture-comparison
+description: "5-way GNN architecture comparison on Ruby AST complexity prediction"
+
+hardware:
+  gpu: "RTX 4090"
+  num_gpus: 1
+  min_cpu_ram_gb: 32
+  min_inet_down: 1000.0
+  min_cuda_version: 12.0
+  max_dph: 0.40
+  disk_gb: 50.0
+  image: pytorch/pytorch:2.7.0-cuda12.8-cudnn9-runtime
+
+repo:
+  url: https://github.com/timlawrenz/jubilant-palm-tree.git
+  branch: experiment/ratiocinator-gnn-study
+  clone_depth: 1
+
+data:
+  source: none
+
+deps:
+  pre_install:
+    - "apt-get update -qq && apt-get install -y -qq git-lfs > /dev/null 2>&1 || true"
+    - "cd /workspace/experiment && git lfs install && git lfs pull"
+    - "pip install torch-geometric torch-scatter torch-sparse -f https://data.pyg.org/whl/torch-2.7.0+cu128.html"
+    - "pip install pandas tqdm sentence-transformers nltk scikit-learn numpy"
+  requirements: requirements.txt
+  exclude_from_requirements:
+    - torch
+    - torchvision
+    - torch_geometric
+  verify: "python -c \"import torch_geometric; print(f'PyG {torch_geometric.__version__}')\""
+
+arms:
+  # ── Architecture comparison (same hyperparams, different conv) ──
+  - name: sage-baseline
+    description: "GraphSAGE baseline (original architecture)"
+    command: "bash scripts/run_complexity_arm.sh"
+    env:
+      CONV_TYPE: "SAGE"
+      HIDDEN_DIM: "64"
+      NUM_LAYERS: "3"
+      DROPOUT: "0.1"
+      LEARNING_RATE: "0.001"
+      EPOCHS: "50"
+
+  - name: gcn
+    description: "Graph Convolutional Network"
+    command: "bash scripts/run_complexity_arm.sh"
+    env:
+      CONV_TYPE: "GCN"
+      HIDDEN_DIM: "64"
+      NUM_LAYERS: "3"
+      DROPOUT: "0.1"
+      LEARNING_RATE: "0.001"
+      EPOCHS: "50"
+
+  - name: gat
+    description: "Graph Attention Network"
+    command: "bash scripts/run_complexity_arm.sh"
+    env:
+      CONV_TYPE: "GAT"
+      HIDDEN_DIM: "64"
+      NUM_LAYERS: "3"
+      DROPOUT: "0.1"
+      LEARNING_RATE: "0.001"
+      EPOCHS: "50"
+
+  - name: gin
+    description: "Graph Isomorphism Network"
+    command: "bash scripts/run_complexity_arm.sh"
+    env:
+      CONV_TYPE: "GIN"
+      HIDDEN_DIM: "64"
+      NUM_LAYERS: "3"
+      DROPOUT: "0.1"
+      LEARNING_RATE: "0.001"
+      EPOCHS: "50"
+
+  - name: graphconv
+    description: "GraphConv (Morris et al.)"
+    command: "bash scripts/run_complexity_arm.sh"
+    env:
+      CONV_TYPE: "GraphConv"
+      HIDDEN_DIM: "64"
+      NUM_LAYERS: "3"
+      DROPOUT: "0.1"
+      LEARNING_RATE: "0.001"
+      EPOCHS: "50"
+
+  # ── Hyperparameter variants on best-expected architectures ──
+  - name: sage-wide
+    description: "SAGE with 128 hidden dim"
+    command: "bash scripts/run_complexity_arm.sh"
+    env:
+      CONV_TYPE: "SAGE"
+      HIDDEN_DIM: "128"
+      NUM_LAYERS: "3"
+      DROPOUT: "0.1"
+      LEARNING_RATE: "0.001"
+      EPOCHS: "50"
+
+  - name: gat-wide
+    description: "GAT with 128 hidden dim"
+    command: "bash scripts/run_complexity_arm.sh"
+    env:
+      CONV_TYPE: "GAT"
+      HIDDEN_DIM: "128"
+      NUM_LAYERS: "3"
+      DROPOUT: "0.1"
+      LEARNING_RATE: "0.001"
+      EPOCHS: "50"
+
+  - name: sage-deep
+    description: "SAGE with 5 layers"
+    command: "bash scripts/run_complexity_arm.sh"
+    env:
+      CONV_TYPE: "SAGE"
+      HIDDEN_DIM: "64"
+      NUM_LAYERS: "5"
+      DROPOUT: "0.1"
+      LEARNING_RATE: "0.001"
+      EPOCHS: "50"
+
+metrics:
+  protocol: json_line
+  json_prefix: "METRICS:"
+
+budget:
+  max_dollars: 10.00
+  train_timeout_s: 1200
+  download_timeout_s: 600