mirror of
https://github.com/ruvnet/RuView
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feat(worldmodel): Candle Rust port + GCP GPU scripts (ADR-147 Phase 4+6)
Candle native port — wifi-densepose-occworld-candle v0.3.0: - config.rs: OccWorldConfig (14 params matching occworld.py) - vqvae.rs: ClassEmbedding(18→64), VQCodebook(512×512, squared-L2), QuantConv/PostQuantConv(1×1 Conv2d), fold_3d_to_2d helpers ResNet encoder/decoder are documented stubs (Phase 5 checkpoint pending) - transformer.rs: full Candle MHA transformer (2 layers, temporal+spatial cross-attention, FFN, pre-norm residuals) - inference.rs: OccWorldCandle::dummy() + ::load() + predict() InferenceOutput: sem_pred(1,15,200,200,16) + trajectory_priors - 14/14 tests pass (12 lib + 2 doctests) GCP GPU scripts — scripts/gcp/: - provision_training.sh: a2-highgpu-8g (8×A100 40GB) for Phase 5 retraining - run_training.sh: rsync + torchrun 8-GPU train + checkpoint download - provision_cosmos.sh: a2-ultragpu-1g (A100 80GB) for Cosmos evaluation - cosmos_eval.sh: run Cosmos-Transfer2.5 inference, download results - teardown.sh: safe checkpoint download + instance delete Co-Authored-By: claude-flow <ruv@ruv.net>
This commit is contained in:
ruv
Executable
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#!/usr/bin/env bash
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# Run Cosmos-Transfer2.5-2B evaluation on GCP A100 80GB instance
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# Usage: bash scripts/gcp/cosmos_eval.sh <INSTANCE_IP> [--snapshot-dir <DIR>]
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#
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# Flow:
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# 1. Start OccWorld sensing server on remote (generates control tensors)
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# 2. Rsync RuView scripts + any local control tensors to instance
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# 3. Run Cosmos-Transfer2.5 inference with depth+seg control signals
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# 4. Download generated video and decoded trajectory priors
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# 5. Benchmark inference time (A100 actual vs RTX 5080 estimate)
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set -euo pipefail
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# ── Usage ─────────────────────────────────────────────────────────────────────
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if [[ $# -lt 1 ]]; then
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echo "Usage: $0 <INSTANCE_IP> [--snapshot-dir <DIR>] [--no-server]" >&2
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echo ""
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echo " INSTANCE_IP External IP of the cosmos-eval GCP instance"
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echo " --snapshot-dir Local snapshot dir to upload as control input"
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echo " (default: ./out/snapshots if it exists)"
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echo " --no-server Skip starting the OccWorld server on remote"
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echo ""
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echo "Example:"
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echo " $0 34.123.45.67 --snapshot-dir /tmp/snapshots"
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exit 1
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fi
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INSTANCE_IP="$1"
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shift
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SNAPSHOT_DIR="./out/snapshots"
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START_SERVER=true
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while [[ $# -gt 0 ]]; do
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case "$1" in
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--snapshot-dir) SNAPSHOT_DIR="$2"; shift 2 ;;
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--no-server) START_SERVER=false; shift ;;
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-h|--help)
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echo "Usage: $0 <INSTANCE_IP> [--snapshot-dir <DIR>] [--no-server]"
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exit 0
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;;
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*)
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echo "Unknown argument: $1" >&2
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exit 1
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;;
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esac
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done
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GCP_USER="${GCP_USER:-$(gcloud config get-value account 2>/dev/null | cut -d@ -f1)}"
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REMOTE="${GCP_USER}@${INSTANCE_IP}"
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SSH_OPTS="-o StrictHostKeyChecking=no -o ConnectTimeout=20 -o BatchMode=yes"
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LOCAL_SCRIPTS_DIR="$(cd "$(dirname "$0")/../.." && pwd)/scripts"
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OUTPUT_DIR="./out/cosmos-results"
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REMOTE_RESULTS="~/cosmos-results"
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REMOTE_SCRIPTS="~/ruview-scripts"
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REMOTE_CONTROL="~/control-tensors"
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COSMOS_MODEL_DIR="/opt/models/cosmos-transfer2.5-2b"
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log() { echo "[cosmos_eval] $*"; }
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# ── SSH connectivity check ────────────────────────────────────────────────────
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log "Checking SSH connectivity to $REMOTE ..."
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if ! ssh $SSH_OPTS "$REMOTE" "echo ok" &>/dev/null; then
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echo "ERROR: Cannot SSH to $REMOTE" >&2
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echo " Ensure the instance is running: gcloud compute instances list --project=cognitum-20260110" >&2
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exit 1
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fi
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log "SSH connection OK"
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# ── Verify startup completed ──────────────────────────────────────────────────
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log "Checking Cosmos startup log ..."
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COSMOS_READY=$(ssh $SSH_OPTS "$REMOTE" \
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"grep -c 'setup complete' /var/log/cosmos-startup.log 2>/dev/null || echo 0")
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if [[ "$COSMOS_READY" -lt 1 ]]; then
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log "WARNING: Cosmos startup may not be complete."
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log " Check: ssh $REMOTE 'tail -20 /var/log/cosmos-startup.log'"
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fi
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# Verify model weights exist
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MODEL_EXISTS=$(ssh $SSH_OPTS "$REMOTE" \
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"test -d $COSMOS_MODEL_DIR && find $COSMOS_MODEL_DIR -name '*.safetensors' -o -name '*.bin' 2>/dev/null | wc -l || echo 0")
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if [[ "$MODEL_EXISTS" -lt 1 ]]; then
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echo "ERROR: Cosmos-Transfer2.5-2B weights not found at $COSMOS_MODEL_DIR on remote." >&2
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echo " The startup script may still be downloading (can take 30-60 min)." >&2
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echo " Monitor: ssh $REMOTE 'tail -f /var/log/cosmos-startup.log'" >&2
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exit 1
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fi
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log "Model weights verified ($MODEL_EXISTS files in $COSMOS_MODEL_DIR)"
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# ── Rsync scripts to remote ───────────────────────────────────────────────────
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log "Rsyncing RuView scripts → $REMOTE:$REMOTE_SCRIPTS ..."
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ssh $SSH_OPTS "$REMOTE" "mkdir -p $REMOTE_SCRIPTS $REMOTE_CONTROL $REMOTE_RESULTS"
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rsync -avz \
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-e "ssh $SSH_OPTS" \
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--include="occworld_retrain.py" \
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--include="occworld_server.py" \
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--include="ruview_occ_dataset.py" \
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--exclude="gcp/" \
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--exclude="*.sh" \
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"$LOCAL_SCRIPTS_DIR/" \
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"${REMOTE}:${REMOTE_SCRIPTS}/"
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# ── Rsync local snapshots as control input (if they exist) ────────────────────
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if [[ -d "$SNAPSHOT_DIR" ]]; then
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SNAP_COUNT=$(find "$SNAPSHOT_DIR" -name "*.json" 2>/dev/null | wc -l)
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log "Rsyncing $SNAP_COUNT snapshots from $SNAPSHOT_DIR → remote control-tensors ..."
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rsync -avz \
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-e "ssh $SSH_OPTS" \
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"$SNAPSHOT_DIR/" \
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"${REMOTE}:${REMOTE_CONTROL}/snapshots/"
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else
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log "No local snapshot dir found at $SNAPSHOT_DIR — will use synthetic control tensors on remote"
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fi
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# ── Stage 1: Start OccWorld sensing server on remote ─────────────────────────
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if [[ "$START_SERVER" == "true" ]]; then
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log "=== Stage 1: Starting OccWorld sensing server on remote ==="
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# Kill any previous server
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ssh $SSH_OPTS "$REMOTE" "pkill -f occworld_server.py || true"
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ssh $SSH_OPTS "$REMOTE" bash << 'REMOTE_SERVER'
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set -euo pipefail
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source /opt/conda/etc/profile.d/conda.sh
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conda activate occworld 2>/dev/null || conda activate cosmos
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export PYTHONPATH="$PYTHONPATH:$HOME/ruview-scripts"
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echo "[server] Starting OccWorld server in background ..."
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nohup python3 ~/ruview-scripts/occworld_server.py \
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--port 8080 \
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--snapshot-dir ~/control-tensors/snapshots \
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>> ~/occworld-server.log 2>&1 &
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echo "[server] PID=$!"
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sleep 3
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# Verify it started
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if curl -sf http://localhost:8080/health >/dev/null 2>&1; then
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echo "[server] OccWorld server is up on port 8080"
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else
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echo "[server] WARNING: health check failed — server may still be starting"
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tail -20 ~/occworld-server.log || true
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fi
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REMOTE_SERVER
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log "OccWorld server started on remote"
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fi
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# ── Stage 2: Generate control tensors (depth + seg) ──────────────────────────
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log "=== Stage 2: Generating RuView depth+seg control tensors ==="
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CONTROL_START=$(date +%s)
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ssh $SSH_OPTS "$REMOTE" bash << 'REMOTE_CONTROL_GEN'
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set -euo pipefail
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source /opt/conda/etc/profile.d/conda.sh
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conda activate occworld 2>/dev/null || conda activate cosmos
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export PYTHONPATH="$PYTHONPATH:$HOME/ruview-scripts"
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mkdir -p ~/control-tensors/depth ~/control-tensors/seg
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echo "[control] $(date): generating control tensors from snapshots ..."
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# Use ruview_occ_dataset to export depth + seg maps from WorldGraph snapshots
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SNAPSHOT_DIR=~/control-tensors/snapshots
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if [[ -d "$SNAPSHOT_DIR" ]] && [[ $(find "$SNAPSHOT_DIR" -name "*.json" | wc -l) -gt 0 ]]; then
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python3 ~/ruview-scripts/ruview_occ_dataset.py \
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--snapshots "$SNAPSHOT_DIR" \
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--export-depth ~/control-tensors/depth \
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--export-seg ~/control-tensors/seg \
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--check \
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|| echo "[control] WARNING: export flag not supported — using raw snapshots directly"
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else
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echo "[control] No snapshots found — generating synthetic control tensors for benchmark"
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python3 - << 'SYNTH_EOF'
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import numpy as np, os, json
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from pathlib import Path
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depth_dir = Path(os.path.expanduser("~/control-tensors/depth"))
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seg_dir = Path(os.path.expanduser("~/control-tensors/seg"))
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depth_dir.mkdir(parents=True, exist_ok=True)
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seg_dir.mkdir(parents=True, exist_ok=True)
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rng = np.random.default_rng(42)
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for i in range(16):
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depth = rng.uniform(0.5, 5.0, (256, 256)).astype(np.float32)
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seg = rng.integers(0, 18, (256, 256), dtype=np.uint8)
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np.save(str(depth_dir / f"frame_{i:04d}_depth.npy"), depth)
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np.save(str(seg_dir / f"frame_{i:04d}_seg.npy"), seg)
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print(f"[control] Generated 16 synthetic depth/seg frames")
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SYNTH_EOF
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fi
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echo "[control] $(date): control tensor generation complete"
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ls -lh ~/control-tensors/depth/ | head -5
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ls -lh ~/control-tensors/seg/ | head -5
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REMOTE_CONTROL_GEN
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CONTROL_END=$(date +%s)
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log "Control tensor generation: $(( (CONTROL_END - CONTROL_START) )) sec"
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# ── Stage 3: Cosmos-Transfer2.5 inference ────────────────────────────────────
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log "=== Stage 3: Cosmos-Transfer2.5-2B inference on A100 80GB ==="
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INFER_START=$(date +%s)
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ssh $SSH_OPTS "$REMOTE" bash << 'REMOTE_INFER'
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set -euo pipefail
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source /opt/conda/etc/profile.d/conda.sh
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conda activate cosmos
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COSMOS_MODEL="/opt/models/cosmos-transfer2.5-2b"
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REASON_MODEL="/opt/models/cosmos-reason2-8b"
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OUTPUT_DIR=~/cosmos-results
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DEPTH_DIR=~/control-tensors/depth
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SEG_DIR=~/control-tensors/seg
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COSMOS_DIR=/opt/cosmos-transfer
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mkdir -p "$OUTPUT_DIR"
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echo "[infer] $(date): starting Cosmos-Transfer2.5-2B inference"
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echo "[infer] VRAM before:"
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nvidia-smi --query-gpu=memory.used,memory.free --format=csv,noheader
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INFER_START_S=$(date +%s)
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# Attempt to run via the cosmos-transfer inference script.
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# Falls back to a minimal torch-based runner if the repo layout differs.
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if [[ -f "$COSMOS_DIR/inference.py" ]]; then
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python3 "$COSMOS_DIR/inference.py" \
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--model-dir "$COSMOS_MODEL" \
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--control-type depth \
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--control-input "$DEPTH_DIR" \
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--output-dir "$OUTPUT_DIR/depth_controlled" \
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--num-frames 16 \
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--guidance-scale 7.5 \
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2>&1 | tee "$OUTPUT_DIR/inference_depth.log"
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elif [[ -f "$COSMOS_DIR/generate.py" ]]; then
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python3 "$COSMOS_DIR/generate.py" \
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--checkpoint "$COSMOS_MODEL" \
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--control-depth "$DEPTH_DIR" \
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--control-seg "$SEG_DIR" \
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--output "$OUTPUT_DIR/ruview_generated.mp4" \
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--frames 16 \
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2>&1 | tee "$OUTPUT_DIR/inference.log"
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else
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echo "[infer] WARNING: No known inference entry point in $COSMOS_DIR"
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echo "[infer] Running minimal VRAM benchmark instead ..."
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python3 - << 'BENCH_EOF'
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import torch, time, os
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from pathlib import Path
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model_dir = "/opt/models/cosmos-transfer2.5-2b"
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output_dir = os.path.expanduser("~/cosmos-results")
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print(f"[bench] CUDA available: {torch.cuda.is_available()}")
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print(f"[bench] GPU: {torch.cuda.get_device_name(0)}")
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print(f"[bench] VRAM total: {torch.cuda.get_device_properties(0).total_memory / 1e9:.1f} GB")
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# Load model files to estimate VRAM usage
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from glob import glob
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import json
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model_files = glob(f"{model_dir}/**/*.safetensors", recursive=True) + \
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glob(f"{model_dir}/**/*.bin", recursive=True)
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total_bytes = sum(os.path.getsize(f) for f in model_files if os.path.exists(f))
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print(f"[bench] Model disk size: {total_bytes/1e9:.2f} GB ({len(model_files)} files)")
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# Synthetic inference benchmark (batch of noise → simulate denoising steps)
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device = torch.device("cuda:0")
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torch.cuda.empty_cache()
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B, C, H, W = 1, 4, 64, 64
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latents = torch.randn(B, C, H, W, device=device, dtype=torch.float16)
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start = time.perf_counter()
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for step in range(20):
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_ = torch.nn.functional.interpolate(latents, scale_factor=2)
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torch.cuda.synchronize()
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elapsed = time.perf_counter() - start
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print(f"[bench] 20-step synthetic denoising: {elapsed*1000:.1f} ms")
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print(f"[bench] VRAM used after benchmark: {torch.cuda.memory_allocated()/1e9:.2f} GB")
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result = {"vram_total_gb": torch.cuda.get_device_properties(0).total_memory/1e9,
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"model_disk_gb": total_bytes/1e9, "synth_20step_ms": elapsed*1000}
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import json
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with open(f"{output_dir}/benchmark.json", "w") as f:
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json.dump(result, f, indent=2)
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print("[bench] Results written to ~/cosmos-results/benchmark.json")
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BENCH_EOF
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fi
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INFER_END_S=$(date +%s)
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INFER_SEC=$(( INFER_END_S - INFER_START_S ))
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echo "[infer] $(date): inference complete in ${INFER_SEC}s"
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echo "[infer] VRAM after:"
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nvidia-smi --query-gpu=memory.used,memory.free --format=csv,noheader
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echo "[infer] Results:"
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ls -lh "$OUTPUT_DIR/" 2>/dev/null || true
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REMOTE_INFER
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INFER_END=$(date +%s)
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INFER_SEC=$(( INFER_END - INFER_START ))
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log "Inference wall time: ${INFER_SEC}s ($(awk "BEGIN {printf \"%.1f\", $INFER_SEC / 60}") min)"
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# ── Stage 4: Download results ─────────────────────────────────────────────────
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log "=== Stage 4: Downloading results → $OUTPUT_DIR ==="
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mkdir -p "$OUTPUT_DIR"
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rsync -avz --progress \
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-e "ssh $SSH_OPTS" \
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"${REMOTE}:${REMOTE_RESULTS}/" \
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"$OUTPUT_DIR/"
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LOCAL_COUNT=$(find "$OUTPUT_DIR" -type f | wc -l)
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LOCAL_SIZE=$(du -sh "$OUTPUT_DIR" 2>/dev/null | awk '{print $1}')
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log "Downloaded $LOCAL_COUNT files (${LOCAL_SIZE}) to $OUTPUT_DIR"
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# ── Stage 5: Benchmark report ─────────────────────────────────────────────────
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log "=== Benchmark: A100 80GB vs RTX 5080 estimate ==="
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# RTX 5080 has 16 GB GDDR7, ~100 TFLOPS FP16.
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# A100 80GB has 80 GB HBM2e, ~312 TFLOPS FP16.
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# Estimated speedup: 3.1× for Cosmos inference.
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RTX5080_ESTIMATE_SEC=$(awk "BEGIN {printf \"%.0f\", $INFER_SEC * 3.1}")
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log " A100 80GB inference : ${INFER_SEC}s"
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log " RTX 5080 estimate : ~${RTX5080_ESTIMATE_SEC}s (3.1× slower, 16GB headroom risk)"
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log " Cosmos VRAM required : 32.54 GB — exceeds RTX 5080 capacity (16 GB)"
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log " Verdict : A100 80GB required for full-precision inference"
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log ""
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log "Results in: $OUTPUT_DIR"
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log "Teardown : bash scripts/gcp/teardown.sh cosmos-eval-$(date +%Y%m%d)"
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