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Getting Started

Requirements

  • Python 3.10+
  • PyTorch 2.0+ (GPU: pip install torch --index-url https://download.pytorch.org/whl/cu124)
  • CPU sufficient for Phases 1-24; GPU (6 GB+ VRAM) recommended for Phase 25+

Local Setup

pip install -r requirements.txt

Running Experiments

Phase 1-15: Core Validation

python -m experiments.phase1_edge_attention       # Edge vs node attention
python -m experiments.phase2_dual_attention        # Sequential vs dual parallel
python -m experiments.phase3_router                # Sparsity vs accuracy
python -m experiments.phase4_memory                # Tiered memory recall
python -m experiments.phase5_construction          # Graph construction
python -m experiments.phase6_full_model            # End-to-end integration
python -m experiments.phase7_gumbel_routing        # Differentiable routing
python -m experiments.phase8_scaling               # Scaling analysis
python -m experiments.phase9_multi_hop             # Multi-hop reasoning
python -m experiments.phase10_analogy              # Analogical reasoning
python -m experiments.phase11_multi_hop_edges      # Multi-hop edge adjacency
python -m experiments.phase12_curriculum_routing    # Gumbel curriculum
python -m experiments.phase13_harder_benchmarks     # Compositional reasoning
python -m experiments.phase14_contrastive_analogy   # Contrastive training
python -m experiments.phase15_kg_benchmark          # Synthetic KG benchmark

Phase 16-24: Fixes & Scale Validation

python -m experiments.phase16_post_attention_pruning   # Post-attn vs pre-attn routing
python -m experiments.phase17_sparse_multi_hop         # Sparse COO scaling
python -m experiments.phase18_variational_memory       # Variational compression
python -m experiments.phase19_per_layer_constructor    # Per-layer edge projections
python -m experiments.phase20_bfs_partition            # BFS partition scaling
python -m experiments.phase21_learned_attention_dropout # Learned dropout
python -m experiments.phase22_scale_stress_test        # N=1000, 15% noise
python -m experiments.phase23_realistic_kg_benchmark   # vs TransE/RotatE/CompGCN
python -m experiments.phase24_combined_integration     # All fixes at scale

Phase 25-37: Real Data & GPU

python experiments/phase25_fb15k237_gpu.py             # Real FB15k-237 on GPU
python experiments/phase26_adaptive_hop_depth.py       # Adaptive multi-hop depth
python experiments/phase27b_bootstrap_batched.py       # Bootstrap relational (corrected)
python experiments/phase28_hard_ablation.py            # Hard ablation benchmark
python experiments/phase29_multi_seed.py               # Multi-seed evaluation (5 seeds)
python experiments/phase30_edge_adj_sampling.py        # GPU edge adj sampling
python experiments/phase31_mini_batching.py            # Full FB15k-237 mini-batching
python experiments/phase32_cross_graph_transfer.py     # FB15k-237 -> WN18RR
python experiments/phase33_task_aware_construction.py  # Hybrid constructor
python experiments/phase34_graphgps_grit_comparison.py # DELTA vs GraphGPS vs GRIT
python experiments/phase35_relational_transfer.py      # Domain-agnostic transfer
python experiments/phase36_task_aware_at_scale.py      # Constructor at scale
python experiments/phase37_real_comparison.py           # Parameter-matched comparison

Phase 38–49: Construction & Temperature

python experiments/phase46_differentiable_constructor.py  # Phase 38: Differentiable constructor
python experiments/phase46b_self_bootstrapped.py          # Phase 39: Self-bootstrapped DELTA
python experiments/phase46c_link_prediction.py            # Phase 40: Correct LP evaluation
python experiments/phase41_generalization_gap.py          # Phase 41: Weight decay investigation
python experiments/phase42_multihop.py                    # Phase 42: Multi-hop 1p/2p/3p
python experiments/phase43_regularization.py              # Phase 43: DropEdge robustness
python experiments/phase44_depth.py                       # Phase 44: Extended depth 4p/5p
python experiments/phase45_inference_timing.py            # Phase 45: Inference + multi-seed
python experiments/phase46_capacity_signal.py             # Phase 46: Learnable temperature
python experiments/phase47_layer_specific_temp.py         # Phase 47: Layer-specific temp
python experiments/phase48_asymmetric_temp.py             # Phase 48: Asymmetric node/edge temp
python experiments/phase49_l0_temp.py --epochs 500        # Phase 49: L0 temp + asymmetric L1+L2

Phase 50–54: Temperature Annealing & Validation

python experiments/phase50_temp_anneal.py                 # Phase 50: Temperature annealing (breaks 3p ceiling)
python experiments/phase51_static_vs_trajectory.py        # Phase 51: Static init vs annealing trajectory
python experiments/phase52_closing_lp_gap.py              # Phase 52: Edge sharpness + LP gap
python experiments/phase53_multiseed_validation.py        # Phase 53: Multi-seed statistical validation
python experiments/phase54_highpower_multihop.py          # Phase 54: 10k-query multi-hop evaluation

Phase 55–57: Brain Architecture (Differentiable Graph Construction)

python experiments/phase55_brain_port.py                  # Phase 55: BrainEncoder LP validation
python experiments/phase56_density_ablation.py            # Phase 56: Constructor density ablation
python experiments/phase57_brain_temp_anneal.py           # Phase 57: Brain temperature annealing

Running Tests

# Run all tests (44/44 should pass)
python -m pytest tests/ -q

Cloud GPU Setup (Colab / RunPod)

Google Colab Pro+

For Phases 25+ and full-scale experiments. Colab Pro+ ($49.99/month) gives access to H100 (80GB) and A100 (40GB) GPUs.

  1. Subscribe at colab.research.google.com
  2. Runtime -> Change runtime type -> GPU -> H100 (first choice) or A100
  3. Verify: !nvidia-smi
# Clone and install
!git clone https://github.com/bdbrown4/DELTA.git
%cd DELTA
!pip install torch>=2.0.0 numpy>=1.24.0

# Verify
!python -c "from delta import DELTAModel; print('DELTA ready')"

# Run tests
!python -m pytest tests/ -q

# Run experiments
!python experiments/phase31_mini_batching.py --full --epochs 50

RunPod / vast.ai

For longer runtimes or 80GB A100/H100:

  • RunPod (runpod.io): ~$1.50/hr for A100 80GB. Deploy GPU Pod -> Select PyTorch template -> SSH in.
  • vast.ai (vast.ai): ~$0.80-2/hr for A100. Cheapest option for burst compute.

Estimated GPU Times

Experiment Est. Time
Phase 34 (synthetic, 5 seeds) 5–8 min (H100)
Phase 31 (full FB15k-237, 50 epochs) ~3.7 hours (H100)
Phase 42–45 (multi-hop + timing) ~2–3 hours (H100)
Phase 55 (brain port, 1 seed, 150 epochs) ~30–40 min (T4/Colab)
Phase 56 (density ablation, 2 conditions, 300 epochs) ~2 hours (RTX 6000)
Phase 57 (brain temp annealing, 3 conditions, 200 epochs) ~2 hours (RTX 6000)