NeMo Llama 2 70B Pre-Training 20TB Dataset OOM
Increasing num_workers can increase the host memory usage. Is my understanding correct that these A3 mega nodes have ~1.8TB of host memory? Do you have data points of how the process mem increase as you increase num_workers?

Does increasing the shared memory like in [this summary comment](https://github.com/pytorch/pytorch/issues/13246#issuecomment-905703662) help in your case? Also, if you're not using [Nemo 2.0](https://docs.nvidia.com/nemo-framework/user-guide/latest/nemo-2.0/index.html), I think that would be worth a try. I have seen anecdotes of host OOMs going away after switching > Is my understanding correct that these A3 mega nodes have ~1.8TB of host memory?

Correct, our two-node cluster has a total of 3,760 GB

>  Do you have data points of how the process mem increase as you increase num_workers?

What would be the most helpful format to share this data?

Reducing num_nodes or reducing the dataset size will reduce the total amount of CPU memory used. We will experiment increasing shared memory. > Also, if you're not using [Nemo 2.0](https://docs.nvidia.com/nemo-framework/user-guide/latest/nemo-2.0/index.html), I think that would be worth a try. I have seen anecdotes of host OOMs going away after switching

Hi Terry, thank you for your help. We did try NeMo 2.0, and found that it does indeed resolve the OOM issue.

However, it appears that NeMo 2.0 does not have Kubernetes support for it yet. Do you have any ideas on when that might happen? > Hi Terry, thank you for your help. We did try NeMo 2.0, and found that it does indeed resolve the OOM issue.

Actually there is k8s support via skypilot: https://github.com/NVIDIA/NeMo-Run/blob/main/docs/source/guides/execution.md#execute-nemo-run

Please give that a try and feel free to leave feedback for the nemo-run team if something is missing. Closing this issue as the original OOM has a resolution. Please open another issue for any issues/requests for k8s.
**Describe the bug**

NeMo Llama 2 70B Pre-Training 20TB Dataset out-of-memory (CPU memory)

**Steps/Code to reproduce bug**

Using the default YAML file here: https://github.com/NVIDIA/NeMo-Framework-Launcher/blob/main/launcher_scripts/conf/training/llama/llama2_70b.yaml

The dataset is the pile dataset replicated 35 times such that its size is 20TB

We run this on a GKE cluster with 2 A3 Mega nodes

**Expected behavior**

We expect the training to run successfully with num_workers=4. But this runs into OOM error. Reducing num_workers to 2 worked. But this is problematic because we are using GCSFuse and the latency is high, so num_workers=2 is not able to saturate the training.

So we want to know if this is a known problem on Nvidia's end.

**Environment overview (please complete the following information)**

 - Environment location: GCP GKE

**Environment details**

NeMo 24.07

