Hi folks, here is some context of the limitation we encountered. 1. There is a check from "mirrored_strategy_runner.py" that "task_gpu_amount" cannot be less than 1. https://github.com/tensorflow/ecosystem/blob/master/spark/spark-tensorflow-distributor/spark_tensorflow_distributor/mirrored_strategy_runner.py#L161-L164 3. "spark.task.resource.gpu.amount" can by default be set to a decimal amount per Nvidia's docs, https://www.nvidia.com/en-us/ai-data-science/spark-ebook/getting-started-spark-3/. 4. There is an option in TensorFlow to set a fractional GPU amount to limit the memory usage: https://www.tensorflow.org/api_docs/python/tf/compat/v1/GPUOptions, https://github.com/tensorflow/tensorflow/issues/25138 In this case, does it make sense for Spark TensorFlow distributer to allow the GPU per task to be less than 1?
Hi folks, here is some context of the limitation we encountered.
https://github.com/tensorflow/ecosystem/blob/master/spark/spark-tensorflow-distributor/spark_tensorflow_distributor/mirrored_strategy_runner.py#L161-L164
In this case, does it make sense for Spark TensorFlow distributer to allow the GPU per task to be less than 1?