Introduction
This article explains in Spanish how to use TensorFlow's tf.distribute.Strategy with custom training loops, an ideal alternative when Keras' model.fit does not offer the necessary flexibility. You will learn how to distribute datasets, calculate gradients, handle loss scaling, and configure training on multiple GPUs, TPUs, or machines.
Why use custom training loops
Custom loops allow total control over each training step, useful for complex architectures, RL algorithms, non-standard losses, or integration with advanced data pipelines. Combined with tf.distribute.Strategy, they scale easily to high-performance infrastructures.
Overview of tf.distribute.Strategy
tf.distribute.Strategy provides an abstraction to run the same training step on multiple replicas. Common strategies are MirroredStrategy for multiple GPUs on a single machine, TPUStrategy for TPU, and MultiWorkerMirroredStrategy for distributed training across several machines. The general pattern is to create the strategy, enter strategy.scope to instantiate the model and optimizer, prepare the distributed dataset, and execute training steps with strategy.run and metric reduction with strategy.reduce.
Distributing datasets
Convert a tf.data.Dataset to a distributed version using strategy.experimental_distribute_dataset or adapt the pipeline for sharding per worker. Ensure proper batching and prefetching. Avoid order-dependent operations outside the distributed scope and use batch per replica calculated as global_batch divided by num_replicas_in_sync.
Calculating gradients and applying updates
Within a training step, use tf.GradientTape to calculate the loss per replica. Execute the step function with strategy.run to replicate execution across all replicas. Obtain gradients per replica and combine them automatically when using optimizer.apply_gradients outside replication, or use strategy.reduce to sum or average losses. Note the difference between loss per example and loss per replica when calculating the final metric.
Loss scaling and mixed precision
With mixed precision, it is common to use a LossScale to prevent gradient underflow. Use an optimizer with loss scaling such as LossScaleOptimizer or TensorFlow's mixed precision API. In the workflow, scale the loss before calculating gradients and descale before applying the gradients. When distributing, keep the scaling synchronized between replicas and ensure gradients are reduced correctly.
Multi-GPU and multi-machine training
With MirroredStrategy, the model is replicated on each GPU and gradients are synchronized with all-reduce at each step. With TPUStrategy, follow the specific pattern of TPU environment initialization and dataset creation adapted to the hardware. For MultiWorkerMirroredStrategy, configure communication between workers and adjust batch size and dataset sharding for each worker.
Configuring TF_CONFIG for distributed setups
For distributed training across machines, define the TF_CONFIG environment variable with a JSON object describing the topology. That object must include a cluster field containing lists of addresses for roles such as workers and chief, and a task field indicating type with the current role and index with the worker index. Each machine must receive its own version of TF_CONFIG pointing to the same cluster and with task adjusted to its role and position. Also ensure proper network communication and permissions between machines.
Training loop step details
1 Prepare strategy and initialize with strategy.scope to create model and optimizer 2 Build and distribute the dataset with experimental_distribute_dataset and calculate batch per replica 3 Define a step function that calculates loss per replica using GradientTape and returns metrics 4 Run strategy.run on the step function and reduce metrics with strategy.reduce 5 Handle checkpointing and callbacks manually if necessary 6 Adjust learning rate, loss scaling, and synchronization according to the global batch size.
Best practices and points to watch
Ensure that shared state initializations occur within the strategy scope, avoid operations with global order dependencies outside replication, and test first on one replica or a single machine before scaling. Monitor memory usage and all-reduce efficiency. Adjust the global batch to maintain numerical stability and performance.
Conceptual examples
MirroredStrategy is typically used on a single machine with multiple GPUs. TPUStrategy requires initializing the TPU resolver and converting the dataset to the format required by TPU. MultiWorkerMirroredStrategy requires TF_CONFIG and a data pipeline with sharding per worker. In all cases, the core pattern is strategy.scope for building, strategy.experimental_distribute_dataset for data, and strategy.run to execute the replicated step.
Integration with production tools
In production environments, combine periodic checkpoints, metric logging to telemetry systems, and distributed validation tests. Encrypt sensitive communications and use private networks for traffic between workers in multi-machine setups. For cloud deployments, consider managed services and load balancing.
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Conclusion
Using tf.distribute.Strategy with custom training loops provides control and scalability to train complex models on GPUs, TPUs, or multi-worker clusters. By following the practices described and with adequate infrastructure support, you can achieve performance and stability. If you need help implementing or scaling distributed training, Q2BSTUDIO can support you with custom development, cloud integration, and security and monitoring solutions.
