# ValueError: integer division or modulo by zero, in tower 0 **URL:** \ **Category:** RLlib\ **Created:** [October 23, 2021, 6:58pm UTC](https://discuss.ray.io/t/valueerror-integer-division-or-modulo-by-zero-in-tower-0/3912 "2021-10-23T18:58:37Z")\ **Posts on this page:** 7\ **Page:** 1
**Author:** ![LukasNothhelfer](https://sea2.discourse-cdn.com/flex020/user_avatar/discuss.ray.io/lukasnothhelfer/32/1062_2.png) [@LukasNothhelfer](https://discuss.ray.io/u/LukasNothhelfer)\ **Post date:** [October 23, 2021, 6:58pm UTC](https://discuss.ray.io/t/valueerror-integer-division-or-modulo-by-zero-in-tower-0/3912/1 "2021-10-23T18:58:37Z")
I have a very strange problem with Ray RLlib. Unfortunately the code is too large for me to reduce it to a simple script. On top of that I don’t know how the problem is caused. I am using Ray 2.0.0. On my local laptop the training of my PPO agent runs through, without any problems. However, when I run the code in a container on my university’s Kubernetes cluster, I always get the following error message. I have absolutely no idea what is happening. I have made sure that the code is running on my local machine in the exact same context as it is running on the cluster. The code I run locally is 1:1 the same as the code I run remotely. Can anyone give me a hint? ```auto > ray::PPO.train_buffered() (pid=133, ip=10.1.11.26, repr=PPO) > File "/opt/conda/lib/python3.8/site-packages/ray/tune/trainable.py", line 189, in train_buffered > result = self.train() > File "/opt/conda/lib/python3.8/site-packages/ray/rllib/agents/trainer.py", line 681, in train > raise e > File "/opt/conda/lib/python3.8/site-packages/ray/rllib/agents/trainer.py", line 667, in train > result = Trainable.train(self) > File "/opt/conda/lib/python3.8/site-packages/ray/tune/trainable.py", line 248, in train > result = self.step() > File "/opt/conda/lib/python3.8/site-packages/ray/rllib/agents/trainer_template.py", line 206, in step > step_results = next(self.train_exec_impl) > File "/opt/conda/lib/python3.8/site-packages/ray/util/iter.py", line 756, in __next__ > return next(self.built_iterator) > File "/opt/conda/lib/python3.8/site-packages/ray/util/iter.py", line 783, in apply_foreach > for item in it: > File "/opt/conda/lib/python3.8/site-packages/ray/util/iter.py", line 783, in apply_foreach > for item in it: > File "/opt/conda/lib/python3.8/site-packages/ray/util/iter.py", line 843, in apply_filter > for item in it: > File "/opt/conda/lib/python3.8/site-packages/ray/util/iter.py", line 843, in apply_filter > for item in it: > File "/opt/conda/lib/python3.8/site-packages/ray/util/iter.py", line 783, in apply_foreach > for item in it: > File "/opt/conda/lib/python3.8/site-packages/ray/util/iter.py", line 783, in apply_foreach > for item in it: > File "/opt/conda/lib/python3.8/site-packages/ray/util/iter.py", line 791, in apply_foreach > result = fn(item) > File "/opt/conda/lib/python3.8/site-packages/ray/rllib/execution/train_ops.py", line 197, in __call__ > results = policy.learn_on_loaded_batch( > File "/opt/conda/lib/python3.8/site-packages/ray/rllib/policy/torch_policy.py", line 607, in learn_on_loaded_batch > return self.learn_on_batch(batch) > File "/opt/conda/lib/python3.8/site-packages/ray/rllib/utils/threading.py", line 21, in wrapper > return func(self, *a, **k) > File "/opt/conda/lib/python3.8/site-packages/ray/rllib/policy/torch_policy.py", line 507, in learn_on_batch > grads, fetches = self.compute_gradients(postprocessed_batch) > File "/opt/conda/lib/python3.8/site-packages/ray/rllib/policy/policy_template.py", line 336, in compute_gradients > return parent_cls.compute_gradients(self, batch) > File "/opt/conda/lib/python3.8/site-packages/ray/rllib/utils/threading.py", line 21, in wrapper > return func(self, *a, **k) > File "/opt/conda/lib/python3.8/site-packages/ray/rllib/policy/torch_policy.py", line 678, in compute_gradients > tower_outputs = self._multi_gpu_parallel_grad_calc( > File "/opt/conda/lib/python3.8/site-packages/ray/rllib/policy/torch_policy.py", line 1052, in _multi_gpu_parallel_grad_calc > raise last_result[0] from last_result[1] > ValueError: integer division or modulo by zero > In tower 0 on device cpu ``` ---
**Author:** ![mannyv](https://sea2.discourse-cdn.com/flex020/user_avatar/discuss.ray.io/mannyv/32/606_2.png) [@mannyv](https://discuss.ray.io/u/mannyv)\ **Post date:** [October 23, 2021, 8:13pm UTC](https://discuss.ray.io/t/valueerror-integer-division-or-modulo-by-zero-in-tower-0/3912/2 "2021-10-23T20:13:34Z")
Hi @LukasNothhelfer, Are you intending to train with a cpu or gpu and if gpu how many? ---
**Author:** ![LukasNothhelfer](https://sea2.discourse-cdn.com/flex020/user_avatar/discuss.ray.io/lukasnothhelfer/32/1062_2.png) [@LukasNothhelfer](https://discuss.ray.io/u/LukasNothhelfer)\ **Post date:** [October 23, 2021, 8:44pm UTC](https://discuss.ray.io/t/valueerror-integer-division-or-modulo-by-zero-in-tower-0/3912/3 "2021-10-23T20:44:54Z")
I am intending to train with a cpu: ```auto ("framework", "torch"), ("log_level", "WARN"), ("num_gpus", 0), ("num_workers", 0), ("num_envs_per_worker", 1), ("num_cpus_per_worker", 1), ("num_gpus_per_worker", 0), ("custom_resources_per_worker", {}), ("evaluation_num_workers", 1), ("num_cpus_for_driver", 1), ("create_env_on_driver", True), ``` @mannyv But wait a second. I didnt specify the resources per trial. The kubernetes cluster has gpus which i dont want to use. Let me check if this problem is solved when i specify the `resources_per_trial` argument. ---
**Author:** ![mannyv](https://sea2.discourse-cdn.com/flex020/user_avatar/discuss.ray.io/mannyv/32/606_2.png) [@mannyv](https://discuss.ray.io/u/mannyv)\ **Post date:** [October 23, 2021, 8:56pm UTC](https://discuss.ray.io/t/valueerror-integer-division-or-modulo-by-zero-in-tower-0/3912/4 "2021-10-23T20:56:09Z")
@LukasNothhelfer, Sometimes when I want to use a cpu but the system has gpus and torch is installed with gpu support enabled then I have to manually set `export CUDA_VISIBLE_DEVICES=""` before running. You could also make an environment that has the cpu only version. Whatever the equivalent of this is for your setup: `conda install pytorch torchvision torchaudio cpuonly -c pytorch` ---
**Author:** ![LukasNothhelfer](https://sea2.discourse-cdn.com/flex020/user_avatar/discuss.ray.io/lukasnothhelfer/32/1062_2.png) [@LukasNothhelfer](https://discuss.ray.io/u/LukasNothhelfer)\ **Post date:** [October 23, 2021, 9:11pm UTC](https://discuss.ray.io/t/valueerror-integer-division-or-modulo-by-zero-in-tower-0/3912/5 "2021-10-23T21:11:08Z")
@mannyv I can’t specify `resources_per_trial` because I use Ray RLLib’s own PPO algorithm and the resources are controlled via config. Unfortunately, it doesn’t work when I empty the environment variable `CUDA_VISIBLE_DEVICES` either. ---
**Author:** ![mannyv](https://sea2.discourse-cdn.com/flex020/user_avatar/discuss.ray.io/mannyv/32/606_2.png) [@mannyv](https://discuss.ray.io/u/mannyv)\ **Post date:** [October 23, 2021, 9:29pm UTC](https://discuss.ray.io/t/valueerror-integer-division-or-modulo-by-zero-in-tower-0/3912/6 "2021-10-23T21:29:28Z")
@LukasNothhelfer You could try setting config[“simple\_optimizer”]=True That would avoid the multi\_gpu branch of the code but I think it will still try and use gpu for parts. ---
**Author:** ![LukasNothhelfer](https://sea2.discourse-cdn.com/flex020/user_avatar/discuss.ray.io/lukasnothhelfer/32/1062_2.png) [@LukasNothhelfer](https://discuss.ray.io/u/LukasNothhelfer)\ **Post date:** [October 23, 2021, 9:58pm UTC](https://discuss.ray.io/t/valueerror-integer-division-or-modulo-by-zero-in-tower-0/3912/7 "2021-10-23T21:58:02Z")
@mannyv Thanks, that solved my problem. Since I train everything on the CPU(s) anyway, I was able to solve the problem with this. I am using Ray2.0.0, which is currently still in development. I suspect that this problem is in the bugs category.