Background and Symptoms

Why these issues are hard to catch

Caffe's design emphasizes static computation graphs and layer-by-layer execution, but its reliance on underlying BLAS libraries, CUDA/cuDNN kernels, and protobuf-based model definitions introduces variability. In multi-GPU or hybrid CPU/GPU modes, numerical differences can accumulate, especially with mixed precision or when switching between deterministic and non-deterministic algorithms. Memory fragmentation arises from repeated blob allocation and deallocation during training/testing cycles, particularly in custom layers or online inference services.

  • Model outputs differ slightly between runs, even with fixed seeds.
  • GPU memory usage grows over time in a service, eventually causing OOM errors.
  • Training loss occasionally spikes without obvious data anomalies.

Architectural Context

Caffe operates with a Net object containing Layers, each managing its own Blob buffers. Memory allocation can happen on CPU, GPU, or both (for synchronization). Performance and stability heavily depend on:

  • BLAS backend (OpenBLAS, MKL, cuBLAS)
  • CUDA/cuDNN versions and deterministic settings
  • Protobuf model parsing and parameter initialization
  • Layer implementations (especially custom or third-party layers)

Root Causes

1. Non-deterministic cuDNN kernels

Some cuDNN algorithms (e.g., convolution backward) are non-deterministic by default for performance. This leads to minor output variation between runs.

2. Mixed precision without proper scaling

Using half precision (FP16) for memory savings without loss scaling can cause underflow/overflow in gradients.

3. Memory fragmentation from blob churn

Repeated creation/destruction of large blobs in custom layers or repeated Net instantiation can fragment GPU memory, reducing available contiguous blocks.

4. Inconsistent parameter initialization

If model prototxt files rely on default filler parameters, switching BLAS/cuDNN versions can change initialization order and minor numeric results.

5. Data layer bottlenecks

Slow or variable data feeding from LMDB/LevelDB or Python layers can cause stalls, impacting GPU utilization and stability.

Diagnostics

Reproducing the problem

  • Run caffe time on the same model multiple times with --gpu and --cpu to detect output drift.
  • Monitor GPU memory with nvidia-smi --query-gpu=memory.used --loop=1 to identify fragmentation patterns.
  • Enable verbose logging (--log_level=2) to track layer allocations and initializations.

Code snippet for deterministic mode

caffe.set_mode_gpu()
caffe.set_device(0)
# Force deterministic cuDNN algorithms
os.environ["CUDNN_DETERMINISTIC"] = "1"
# Fix seeds
caffe.set_random_seed(42)
np.random.seed(42)
random.seed(42)

Common Pitfalls

  • Assuming fixed seeds guarantee identical results without forcing deterministic algorithms.
  • Ignoring gradual GPU memory increase in inference services until OOM occurs.
  • Upgrading CUDA/cuDNN without re-benchmarking layer performance and stability.

Step-by-Step Resolution

1. Enforce Determinism

Set CUDNN_DETERMINISTIC=1 and avoid algorithms that trade determinism for speed. Validate outputs against CPU mode to quantify drift.

2. Manage Precision Properly

When using FP16, enable dynamic loss scaling in custom training loops or switch to FP32 for sensitive layers.

3. Reduce Memory Fragmentation

Reuse Net instances where possible. In long-running services, pre-allocate blobs and avoid frequent creation/destruction. Use caffe.set_mode_cpu() periodically in tests to confirm it's GPU fragmentation, not a leak.

4. Lock Initialization

Explicitly set all filler parameters in prototxt files to avoid backend-dependent defaults.

5. Optimize Data Layers

Batch and prefetch aggressively. For LMDB/LevelDB, increase prefetch count and use SSDs. For Python data layers, offload heavy preprocessing to separate processes.

Best Practices

  • Pin BLAS and cuDNN versions for reproducibility.
  • Benchmark each cuDNN algorithm for performance vs. determinism trade-offs.
  • Modularize custom layers to minimize allocation churn.
  • Separate training and inference environments to avoid long-lived allocation states.
  • Integrate GPU memory monitoring into service health checks.

Conclusion

Advanced Caffe troubleshooting demands a holistic view of GPU memory management, numerical determinism, and data pipeline performance. By controlling cuDNN behavior, managing blob lifecycles, and locking initialization parameters, teams can eliminate subtle drift and instability. Continuous monitoring and careful environment pinning ensure Caffe remains a stable and high-performance choice for deep learning workloads.

FAQs

1. Why do results differ between GPU and CPU in Caffe?

GPU layers may use non-deterministic algorithms or different numeric precision, causing slight deviations. Enforcing deterministic cuDNN algorithms minimizes the gap.

2. How can I prevent GPU memory fragmentation?

Pre-allocate and reuse blobs, avoid frequent Net re-instantiation, and design custom layers to minimize temporary allocations.

3. Does upgrading cuDNN always improve stability?

No. Newer versions may change algorithms or defaults, impacting determinism and memory usage. Always re-benchmark and validate after upgrades.

4. What's the safest precision mode for Caffe?

FP32 remains the safest for stability. FP16 can be used for inference with caution and proper scaling to avoid numerical issues.

5. Can data layer performance affect model stability?

Yes. Slow or inconsistent data feeding can cause GPU underutilization and timing-dependent numerical variation in certain training setups.