Performance Autotuning

Bagua comes with several adjustable hyperparameters for communication that can affect runtime performance. For example tensor fusion bucket size.

Determining the best combination of these hyperparameters to maximize system performance can be a process of a lot of trial-and-error, as many factors including model complexity, network bandwidth, and GPU hardware can all affect the best parameter to choose.

Bagua provides a mechanism to automate this process of choosing the best values for these hyperparameters. The Bagua autotuning system uses Bayesian optimization to efficiently search through the space of hyperparameters. This feature can be enabled by providing the --autotune_level 1 flag to

python -m --nproc_per_node ... --autotune_level 1 python

The main process of autotune is simple. The autotune system finds groups of hyperparameters through Bayesian optimization, and the hyperparameters are brought into the training to verify the performance, each group of hyperparameters takes seconds to verify.

Generally speaking, the larger the is, the larger the search space, and the more likely it is to find the best hyperparameters. The larger the , the more accurate the measurement of the group of hyperparameters' performance.

In addition, the autotune system skips the first seconds to warmup.

You can adjust with the --autotune_max_samples flag and adjust with the --autotune_sampling_confidence_time flag, adjust with the --autotune_warmup_time.


With --is_output_autotune_log argument, Bagua will write autotuning log in /tmp/bagua_autotune_${RANDOM_STR}.log

The file is in csv format, each row is the hyperparameters and score of a single tuning step:


bucket_size_2p is the power of 2 of the bucket size, for example bucket_size_2p=23 means bucket_size is 8388608 bytes ().

Case study

For example, on a popular speech recognition task (aishell2), training with autotune increased the throughput by 8.26%.

Training performance improvement during the hyperparameter tuning process.

This figure shows the gradual increase in training performance during tuning. In this experiment, the hyperparameters are changed approximately every 100 iterations. The x-axis is the number of iterations. The y-axis is the data throughput.