I’m not sure, if you are also hitting an out of memory issue or what the “busy” status indicates?
In case you are indeed seeing an OOM, you could e.g. reduce the batch size if you are running OOM quickly. On the other hand, if the OOM error is raised later during the training, check if the input samples have a static shape and if not, check what e.g. the max. sequence length is. A specific batch might be using longer sequences and could thus yield this error.