Performance Tuning

GPU detection and TF32 settings, choosing per_core_batch_size for the memory you have, and memory management strategies for large series counts.

⚙️ Performance Tuning

GPU Acceleration

python

import torch

# Check GPU availability
if torch.cuda.is_available():
    print(f"GPU: {torch.cuda.get_device_name(0)}")
    print(f"VRAM: {torch.cuda.get_device_properties(0).total_mem / 1e9:.1f} GB")
elif hasattr(torch.backends, "mps") and torch.backends.mps.is_available():
    print("Apple Silicon MPS available")
else:
    print("CPU only — inference will be slower but still works")

# Always set this for Ampere+ GPUs (A100, RTX 3090, etc.)
torch.set_float32_matmul_precision("high")

Batch Size Tuning

python

# Start conservative, increase until OOM
# GPU with 8 GB VRAM:  per_core_batch_size=64
# GPU with 16 GB VRAM: per_core_batch_size=128
# GPU with 24 GB VRAM: per_core_batch_size=256
# CPU with 8 GB RAM:   per_core_batch_size=8
# CPU with 16 GB RAM:  per_core_batch_size=32
# CPU with 32 GB RAM:  per_core_batch_size=64

model.compile(timesfm.ForecastConfig(
    max_context=1024,
    max_horizon=256,
    per_core_batch_size=32,  # <-- tune this
    normalize_inputs=True,
    use_continuous_quantile_head=True,
    fix_quantile_crossing=True,
))

Memory-Constrained Environments

python

import gc, torch

# Force garbage collection before loading
gc.collect()
if torch.cuda.is_available():
    torch.cuda.empty_cache()

# Load model
model = timesfm.TimesFM_2p5_200M_torch.from_pretrained(
    "google/timesfm-2.5-200m-pytorch"
)

# Use small batch size on low-memory machines
model.compile(timesfm.ForecastConfig(
    max_context=512,        # Reduce context if needed
    max_horizon=128,        # Reduce horizon if needed
    per_core_batch_size=4,  # Small batches
    normalize_inputs=True,
    use_continuous_quantile_head=True,
    fix_quantile_crossing=True,
))

# Process series in chunks to avoid OOM
CHUNK = 50
all_results = []
for i in range(0, len(inputs), CHUNK):
    chunk = inputs[i:i+CHUNK]
    p, q = model.forecast(horizon=H, inputs=chunk)
    all_results.append((p, q))
    gc.collect()  # Clean up between chunks