KempnerInstitute · houman1359 · Jan 10, 2026 · Jan 10, 2026
diff --git a/configs/vision_prune/mobilenetv2_cifar10_unified.yaml b/configs/vision_prune/mobilenetv2_cifar10_unified.yaml
@@ -145,6 +145,9 @@ pruning:
     - "random"              # Random baseline
     - "magnitude"           # Standard magnitude pruning (prune low)
     - "taylor"              # Gradient-based importance
+    - "network_slimming"    # Network Slimming (BN gamma) baseline
+    - "geometric_median"    # FPGM-style geometric median baseline
+    - "hrank"               # HRank feature-rank baseline
 
     # =========================================================================
     # SINGLE METRICS - Prune LOW (assumes low = unimportant)
@@ -181,6 +184,9 @@ pruning:
   scoring_methods:
     - "random"
     - "magnitude"
+    - "network_slimming"
+    - "geometric_median"
+    - "hrank"
     - "rq_low"
     - "rq_high"
     - "redundancy_low"

diff --git a/configs/vision_prune/resnet18_cifar10_unified.yaml b/configs/vision_prune/resnet18_cifar10_unified.yaml
@@ -154,6 +154,9 @@ pruning:
     - "random"              # Random baseline
     - "magnitude"           # Standard magnitude pruning (prune low)
     - "taylor"              # Gradient-based importance
+    - "network_slimming"    # Network Slimming (BN gamma) baseline
+    - "geometric_median"    # FPGM-style geometric median baseline
+    - "hrank"               # HRank feature-rank baseline
 
     # =========================================================================
     # SINGLE METRICS - Prune LOW (assumes low = unimportant)
@@ -190,6 +193,9 @@ pruning:
   scoring_methods:
     - "random"
     - "magnitude"
+    - "network_slimming"
+    - "geometric_median"
+    - "hrank"
     - "rq_low"
     - "rq_high"
     - "redundancy_low"

diff --git a/configs/vision_prune/resnet50_imagenet100_unified.yaml b/configs/vision_prune/resnet50_imagenet100_unified.yaml
@@ -146,6 +146,9 @@ pruning:
     - "random"              # Random baseline
     - "magnitude"           # Standard magnitude pruning (prune low)
     - "taylor"              # Gradient-based importance
+    - "network_slimming"    # Network Slimming (BN gamma) baseline
+    - "geometric_median"    # FPGM-style geometric median baseline
+    - "hrank"               # HRank feature-rank baseline
 
     # =========================================================================
     # SINGLE METRICS - Prune LOW (assumes low = unimportant)
@@ -182,6 +185,9 @@ pruning:
   scoring_methods:
     - "random"
     - "magnitude"
+    - "network_slimming"
+    - "geometric_median"
+    - "hrank"
     - "rq_low"
     - "rq_high"
     - "redundancy_low"

diff --git a/configs/vision_prune/vgg16_cifar10_unified.yaml b/configs/vision_prune/vgg16_cifar10_unified.yaml
@@ -142,6 +142,9 @@ pruning:
     - "random"              # Random baseline
     - "magnitude"           # Standard magnitude pruning (prune low)
     - "taylor"              # Gradient-based importance
+    - "network_slimming"    # Network Slimming (BN gamma) baseline
+    - "geometric_median"    # FPGM-style geometric median baseline
+    - "hrank"               # HRank feature-rank baseline
 
     # =========================================================================
     # SINGLE METRICS - Prune LOW (assumes low = unimportant)
@@ -178,6 +181,9 @@ pruning:
   scoring_methods:
     - "random"
     - "magnitude"
+    - "network_slimming"
+    - "geometric_median"
+    - "hrank"
     - "rq_low"
     - "rq_high"
     - "redundancy_low"

diff --git a/slurm_jobs/run_baseline_test.sh b/slurm_jobs/run_baseline_test.sh
@@ -1,7 +1,5 @@
 #!/bin/bash
 #SBATCH --job-name=baseline_test
-#SBATCH --partition=kempner_h100
-#SBATCH --account=kempner_dev
 #SBATCH --nodes=1
 #SBATCH --ntasks-per-node=1
 #SBATCH --cpus-per-task=16
@@ -14,31 +12,43 @@
 # Quick test for Wanda/SparseGPT integration
 # Expected runtime: ~30-60 minutes
 
-set -e
+set -euo pipefail
+
+# NOTE: Cluster-specific SBATCH settings like --partition/--account are intentionally omitted.
+# Submit with your local settings, e.g.:
+#   sbatch --partition=<PARTITION> --account=<ACCOUNT> slurm_jobs/run_baseline_test.sh
+
+REPO_ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)"
+cd "$REPO_ROOT"
 
 echo "=========================================="
 echo "Baseline Pruning Test (Wanda + SparseGPT)"
 echo "=========================================="
-echo "Job ID: $SLURM_JOB_ID"
+echo "Job ID: ${SLURM_JOB_ID:-N/A}"
 echo "Node: $(hostname)"
 echo "Start time: $(date)"
 echo "GPU: $(nvidia-smi --query-gpu=name --format=csv,noheader | head -1)"
 echo ""
 
-# Environment setup
-module purge
-module load cuda/12.2.0-fasrc01
-eval "$(conda shell.bash hook)"
-conda activate networkAlignmentAnalysis
-
-# Set up paths
-cd /n/holylabs/kempner_dev/Users/hsafaai/Code/alignment
-
-export OMP_NUM_THREADS=$SLURM_CPUS_PER_TASK
 export TOKENIZERS_PARALLELISM=false
 export PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True
-export HF_HOME=/n/home13/hsafaai/.cache/huggingface
-export HF_TOKEN=$(cat /n/home13/hsafaai/.cache/huggingface/token)
+export OMP_NUM_THREADS="${SLURM_CPUS_PER_TASK:-1}"
+
+if command -v conda >/dev/null 2>&1; then
+    eval "$(conda shell.bash hook)"
+    conda activate "${CONDA_ENV:-networkAlignmentAnalysis}"
+else
+    echo "WARN: conda not found; assuming environment already activated." >&2
+fi
+
+export HF_HOME="${HF_HOME:-${HOME}/.cache/huggingface}"
+HF_TOKEN_FILE="${HF_HOME}/token"
+if [[ -f "$HF_TOKEN_FILE" ]]; then
+    export HF_TOKEN="$(cat "$HF_TOKEN_FILE")"
+    export HUGGINGFACE_HUB_TOKEN="${HF_TOKEN}"
+else
+    echo "WARN: HF token file not found at $HF_TOKEN_FILE (set HF_TOKEN env var if needed)" >&2
+fi
 
 # Create logs directory
 mkdir -p logs

diff --git a/slurm_jobs/run_fast_pruning.sh b/slurm_jobs/run_fast_pruning.sh
@@ -8,12 +8,14 @@
 #SBATCH --cpus-per-task=8
 #SBATCH --time=02:00:00
 #SBATCH --mem=80GB
-#SBATCH --partition=kempner_eng
-#SBATCH --account=kempner_dev
 
 # ============================================================================
 # FAST LLM PRUNING COMPARISON
 # ============================================================================
+# NOTE: Cluster-specific SBATCH settings like --partition/--account are intentionally omitted.
+# Submit with your local settings, e.g.:
+#   sbatch --partition=<PARTITION> --account=<ACCOUNT> slurm_jobs/run_fast_pruning.sh
+#
 # Quick iteration version for development and testing
 # Expected runtime: ~30-60 minutes on H100
 # 
@@ -25,30 +27,40 @@
 # - 50 eval samples instead of 100
 # ============================================================================
 
+set -euo pipefail
+
+REPO_ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)"
+cd "$REPO_ROOT"
+
 echo "============================================================================"
 echo "FAST LLM PRUNING COMPARISON"
 echo "============================================================================"
-echo "Job ID: $SLURM_JOB_ID"
+echo "Job ID: ${SLURM_JOB_ID:-N/A}"
 echo "Node: $(hostname)"
 echo "Start time: $(date)"
 echo "GPU: $(nvidia-smi --query-gpu=name --format=csv,noheader | head -1)"
 echo ""
 
-# Environment setup
-module purge
-module load cuda/12.2.0-fasrc01
-eval "$(conda shell.bash hook)"
-conda activate networkAlignmentAnalysis
-
-cd /n/holylabs/kempner_dev/Users/hsafaai/Code/alignment
-
 mkdir -p logs
 
-export OMP_NUM_THREADS=$SLURM_CPUS_PER_TASK
+if command -v conda >/dev/null 2>&1; then
+    eval "$(conda shell.bash hook)"
+    conda activate "${CONDA_ENV:-networkAlignmentAnalysis}"
+else
+    echo "WARN: conda not found; assuming environment already activated." >&2
+fi
+
+export OMP_NUM_THREADS="${SLURM_CPUS_PER_TASK:-1}"
 export TOKENIZERS_PARALLELISM=false
 export PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True
-export HF_HOME=/n/home13/hsafaai/.cache/huggingface
-export HF_TOKEN=$(cat /n/home13/hsafaai/.cache/huggingface/token)
+export HF_HOME="${HF_HOME:-${HOME}/.cache/huggingface}"
+HF_TOKEN_FILE="${HF_HOME}/token"
+if [[ -f "$HF_TOKEN_FILE" ]]; then
+    export HF_TOKEN="$(cat "$HF_TOKEN_FILE")"
+    export HUGGINGFACE_HUB_TOKEN="${HF_TOKEN}"
+else
+    echo "WARN: HF token file not found at $HF_TOKEN_FILE (set HF_TOKEN env var if needed)" >&2
+fi
 
 echo "============================================================================"
 echo "FAST MODE CONFIGURATION:"

diff --git a/slurm_jobs/run_mnist_basic.sh b/slurm_jobs/run_mnist_basic.sh
@@ -7,22 +7,29 @@
 #SBATCH --cpus-per-task=8
 #SBATCH --time=0:30:00
 #SBATCH --mem=32GB
-#SBATCH --partition=kempner_eng
-#SBATCH --account=kempner_dev
+
+set -euo pipefail
+
+# NOTE: Cluster-specific SBATCH settings like --partition/--account are intentionally omitted.
+# Submit with your local settings, e.g.:
+#   sbatch --partition=<PARTITION> --account=<ACCOUNT> slurm_jobs/run_mnist_basic.sh
+
+REPO_ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)"
+cd "$REPO_ROOT"
 
 echo "Starting MNIST basic alignment experiment at $(date)"
-echo "Job ID: $SLURM_JOB_ID"
+echo "Job ID: ${SLURM_JOB_ID:-N/A}"
 echo "Running on: $(hostname)"
 
-# Environment setup (conda env: networkAlignmentAnalysis)
-module purge
-module load cuda/12.2.0-fasrc01
-eval "$(conda shell.bash hook)"
-conda activate networkAlignmentAnalysis
-
-cd /n/holylabs/kempner_dev/Users/hsafaai/Code/alignment
+if command -v conda >/dev/null 2>&1; then
+    eval "$(conda shell.bash hook)"
+    conda activate "${CONDA_ENV:-networkAlignmentAnalysis}"
+else
+    echo "WARN: conda not found; assuming environment already activated." >&2
+fi
 
-export OMP_NUM_THREADS=$SLURM_CPUS_PER_TASK
+mkdir -p logs
+export OMP_NUM_THREADS="${SLURM_CPUS_PER_TASK:-1}"
 
 python scripts/run_experiment.py \
     --config configs/examples/mnist_basic.yaml \

diff --git a/slurm_jobs/run_single_model.sh b/slurm_jobs/run_single_model.sh
@@ -8,21 +8,23 @@
 #SBATCH --cpus-per-task=16
 #SBATCH --time=24:00:00
 #SBATCH --mem=320GB
-#SBATCH --partition=kempner_eng
-#SBATCH --account=kempner_dev
 
 # ============================================================================
 # SINGLE MODEL PRUNING (Specify config via argument)
 # ============================================================================
-# Usage: sbatch run_single_model.sh <config_name>
+# NOTE: Cluster-specific SBATCH settings like --partition/--account are intentionally omitted.
+# Submit with your local settings, e.g.:
+#   sbatch --partition=<PARTITION> --account=<ACCOUNT> slurm_jobs/run_single_model.sh <config_name>
+#
+# Usage: sbatch slurm_jobs/run_single_model.sh <config_name>
 # 
 # Examples:
-#   sbatch run_single_model.sh mistral7b_pruning
-#   sbatch run_single_model.sh llama2_7b_pruning
-#   sbatch run_single_model.sh gemma2b_pruning
-#   sbatch run_single_model.sh phi3_mini_pruning
-#   sbatch run_single_model.sh qwen2_7b_pruning
-#   sbatch run_single_model.sh gpt2_fast_test
+#   sbatch slurm_jobs/run_single_model.sh mistral7b_pruning
+#   sbatch slurm_jobs/run_single_model.sh llama2_7b_pruning
+#   sbatch slurm_jobs/run_single_model.sh gemma2b_pruning
+#   sbatch slurm_jobs/run_single_model.sh phi3_mini_pruning
+#   sbatch slurm_jobs/run_single_model.sh qwen2_7b_pruning
+#   sbatch slurm_jobs/run_single_model.sh gpt2_fast_test
 #
 # Available configs:
 #   - mistral7b_pruning     (Mistral-7B)
@@ -38,10 +40,15 @@
 CONFIG_NAME=${1:-"llama3_minitron_comparison"}
 CONFIG="configs/examples/${CONFIG_NAME}.yaml"
 
+set -euo pipefail
+
+REPO_ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)"
+cd "$REPO_ROOT"
+
 echo "============================================================================"
 echo "SINGLE MODEL PRUNING: ${CONFIG_NAME}"
 echo "============================================================================"
-echo "Job ID: $SLURM_JOB_ID"
+echo "Job ID: ${SLURM_JOB_ID:-N/A}"
 echo "Node: $(hostname)"
 echo "Config: $CONFIG"
 echo "Start time: $(date)"
@@ -57,21 +64,26 @@ if [ ! -f "$CONFIG" ]; then
     exit 1
 fi
 
-# Environment setup
-module purge
-module load cuda/12.2.0-fasrc01
-eval "$(conda shell.bash hook)"
-conda activate networkAlignmentAnalysis
-
-cd /n/holylabs/kempner_dev/Users/hsafaai/Code/alignment
-
 mkdir -p logs
 
-export OMP_NUM_THREADS=$SLURM_CPUS_PER_TASK
+if command -v conda >/dev/null 2>&1; then
+    eval "$(conda shell.bash hook)"
+    conda activate "${CONDA_ENV:-networkAlignmentAnalysis}"
+else
+    echo "WARN: conda not found; assuming environment already activated." >&2
+fi
+
+export OMP_NUM_THREADS="${SLURM_CPUS_PER_TASK:-1}"
 export TOKENIZERS_PARALLELISM=false
 export PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True
-export HF_HOME=/n/home13/hsafaai/.cache/huggingface
-export HF_TOKEN=$(cat /n/home13/hsafaai/.cache/huggingface/token)
+export HF_HOME="${HF_HOME:-${HOME}/.cache/huggingface}"
+HF_TOKEN_FILE="${HF_HOME}/token"
+if [[ -f "$HF_TOKEN_FILE" ]]; then
+    export HF_TOKEN="$(cat "$HF_TOKEN_FILE")"
+    export HUGGINGFACE_HUB_TOKEN="${HF_TOKEN}"
+else
+    echo "WARN: HF token file not found at $HF_TOKEN_FILE (set HF_TOKEN env var if needed)" >&2
+fi
 
 echo "============================================================================"
 echo "Running experiment..."