Understanding Why MirroredStrategy Doesn’t Use GPUs

While developing machine learning models using distributed systems, TensorFlow offers several strategies to enable parallel training workflows. One such approach is the `MirroredStrategy`, a data parallelism method where each replica on different devices, often on different GPUs, processes a part of the input data. However, the phrase "MirroredStrategy doesn't use GPUs" can be misleading. Let’s delve into why the MirroredStrategy might be perceived as such, unpacking both its intended use and common misunderstandings.

What is MirroredStrategy?

`MirroredStrategy` is TensorFlow’s distribution strategy designed for synchronous training across multiple devices. TensorFlow uses graph replication to perform operations on each available device, merging results across devices at specified intervals.

Example:

• If GPUs are not correctly configured in the environment or the necessary CUDA and cuDNN libraries are missing, TensorFlow may fall back to using CPUs.
• If the `devices` parameter is not specified or improperly configured, TensorFlow defaults to using CPU.
• If only `/cpu:0` is specified within `MirroredStrategy`, naturally only CPUs will be utilized.
• Resource limitations like insufficient GPU memory might cause TensorFlow operations to revert to the CPU.
• When the operations within the model are not suitable for GPU acceleration, resulting in execution on CPU.
• Logical Device Creation:
  • TensorFlow creates logical devices that allow an operation to be replicated as needed. If GPUs are present, logical devices are mapped across them.
• Data Synchronization:
  • The arithmetic operations are synchronized across devices by strategy via an all-reduce algorithm, commonly using NVIDIA’s NCCL for GPU environments.
  • Not every operation is optimized for GPUs. Pre-processing or tensor manipulation may remain on CPU based on operation type.
  • Setup may ignore TensorFlow logs indicating device placement, which helps in diagnosing whether an operation defaults to the CPU.
• Checking Device Placement Logs:
  • Enable device placement logs through TensorFlow's configuration:
• Validate Environment Configuration:
  • Ensure that the environment variables and paths to GPU libraries are set up properly.
• Specify GPUs Explicitly:
  • Use the `devices` parameter in `MirroredStrategy` to explicitly list GPU devices.