frugally-deep

Several key layer types, such as LSTM, GRU, and Conv3D, are not supported, restricting its use for models that rely on these advanced architectures, as noted in the README's 'Currently not supported' list.

The library ignores GPUs and uses only one CPU core per prediction, which can be a performance bottleneck for compute-intensive models that typically benefit from GPU acceleration, despite throughput gains via parallelism.

Requires specific versions of Python, TensorFlow, and Keras (e.g., TensorFlow 2.18.0, Keras 3.8.0), which can complicate integration in environments with different or custom setups, potentially leading to compatibility issues.

The disclaimer explicitly states that the API might change in the future, posing a risk for long-term maintenance and production systems that rely on stable interfaces.