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Merged
CompRhys merged 7 commits into from
Mar 2, 2026
Merged

Configure Basic Logging. #484

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ce7bfa7
fea: add telemetry that configures basic logger. consider swapping to…
CompRhys Mar 2, 2026
9cffdcd
Update torch_sim/runners.py
CompRhys Mar 2, 2026
3064753
Merge remote-tracking branch 'origin/main' into telemetry
CompRhys Mar 2, 2026
f6b7238
Merge branch 'telemetry' of https://github.com/Radical-AI/torch-sim i…
CompRhys Mar 2, 2026
aa95019
lint
CompRhys Mar 2, 2026
e877954
maint: log all relevant warnings
CompRhys Mar 2, 2026
ae0349e
Merge branch 'main' into telemetry
CompRhys Mar 2, 2026
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52 changes: 28 additions & 24 deletions examples/scripts/1_introduction.py
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Original file line number Diff line number Diff line change
Expand Up @@ -18,8 +18,12 @@

from torch_sim.models.lennard_jones import LennardJonesModel
from torch_sim.models.mace import MaceModel, MaceUrls
from torch_sim.telemetry import configure_logging, get_logger


configure_logging(log_file="1_introduction.log")
log = get_logger(name="1_introduction")

# Set up the device and data type
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
dtype = torch.float32
Expand All @@ -28,9 +32,9 @@
# ============================================================================
# SECTION 1: Lennard-Jones Model - Simple Classical Potential
# ============================================================================
print("\n" + "=" * 70)
print("SECTION 1: Lennard-Jones Model")
print("=" * 70)
log.info("=" * 70)
log.info("SECTION 1: Lennard-Jones Model")
log.info("=" * 70)

# Create face-centered cubic (FCC) Argon
# 5.26 Å is a typical lattice constant for Ar
Expand Down Expand Up @@ -103,19 +107,19 @@
results = lj_model(state)

# Print the results
print(f"Energy: {results['energy']}")
print(f"Forces shape: {results['forces'].shape}")
print(f"Stress shape: {results['stress'].shape}")
print(f"Per-atom energies shape: {results['energies'].shape}")
print(f"Per-atom stresses shape: {results['stresses'].shape}")
log.info(f"Energy: {results['energy']}")
log.info(f"Forces shape: {results['forces'].shape}")
log.info(f"Stress shape: {results['stress'].shape}")
log.info(f"Per-atom energies shape: {results['energies'].shape}")
log.info(f"Per-atom stresses shape: {results['stresses'].shape}")


# ============================================================================
# SECTION 2: MACE Model - Machine Learning Potential (Batched)
# ============================================================================
print("\n" + "=" * 70)
print("SECTION 2: MACE Model with Batched Input")
print("=" * 70)
log.info("=" * 70)
log.info("SECTION 2: MACE Model with Batched Input")
log.info("=" * 70)

# Load the raw model from the downloaded model
loaded_model = mace_mp(
Expand Down Expand Up @@ -164,10 +168,10 @@
)

# You can see their shapes are as expected
print(f"Positions: {positions.shape}")
print(f"Cell: {cell.shape}")
print(f"Atomic numbers: {atomic_numbers.shape}")
print(f"System indices: {system_idx.shape}")
log.info(f"Positions: {positions.shape}")
log.info(f"Cell: {cell.shape}")
log.info(f"Atomic numbers: {atomic_numbers.shape}")
log.info(f"System indices: {system_idx.shape}")

# Masses for Silicon (28.085 amu)
masses_si = torch.full((positions.shape[0],), 28.085, device=device, dtype=dtype)
Expand All @@ -185,13 +189,13 @@
)

# The energy has shape (n_systems,) as the structures in a batch
print(f"Energy shape: {results['energy'].shape}")
log.info(f"Energy shape: {results['energy'].shape}")

# The forces have shape (n_atoms, 3) same as positions
print(f"Forces shape: {results['forces'].shape}")
log.info(f"Forces shape: {results['forces'].shape}")

# The stress has shape (n_systems, 3, 3) same as cell
print(f"Stress shape: {results['stress'].shape}")
log.info(f"Stress shape: {results['stress'].shape}")

# Check if the energy, forces, and stress are the same for the Si system across batches
# Each system has 64 atoms (2x2x2 supercell of 8-atom Si diamond)
Expand All @@ -204,10 +208,10 @@
)
stress_diff = torch.max(torch.abs(results["stress"][0] - results["stress"][1]))

print(f"\nMax energy difference: {energy_diff}")
print(f"Max forces difference: {forces_diff}")
print(f"Max stress difference: {stress_diff}")
log.info(f"Max energy difference: {energy_diff}")
log.info(f"Max forces difference: {forces_diff}")
log.info(f"Max stress difference: {stress_diff}")

print("\n" + "=" * 70)
print("Introduction examples completed!")
print("=" * 70)
log.info("=" * 70)
log.info("Introduction examples completed!")
log.info("=" * 70)
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