Comments (1)
young-geng
commented on September 24, 2024
As described in this issue, the configurations would be similar to the pretraining on TPU pods, with the additional jax distributed configurations. However, you'll have to tune the mesh shape and batch size yourself according to the configuration of your own cluster in order to obtain the best throughput. Unfortunately I don't have access to a few hundred A100s so I cannot provide a good example for that.
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Related Issues (20)
- Conflicting dependencies for jax[cuda11-pip]==0.4.14
- why 'LLaMATokenizer' object has no attribute 'sp_model'? HOT 3
- TPU specific flags produce errors HOT 2
- Serving errors: deprecated dependencies and structure error HOT 2
- Mistral HOT 5
- Multi host TPU training
- How does `accumulate_gradient_steps` work?
- ERROR: Accessing retired flag 'jax_enable_async_collective_offload' HOT 1
- Llama 7b Pretraining Dtype
- [Bug] Error in Evaluation
- Dataset for the paper: The False Promise of Imitating Proprietary LLMs (https://arxiv.org/pdf/2305.15717)
- Unusual Initializer Range default HOT 3
- how to train llama2-7b in a100 80G gpu? HOT 3
- Converting the Koala Weights to HF Transformers HOT 1
- pip conflict when run `conda env create -f scripts/gpu_environment.yml`
- Are you guys planning to implement GQA? HOT 1
- What is the full batch size if mesh_dim is set to 1,1,-1, on TPUv3-8? HOT 3
- Missing config for Open LLaMA 3B HOT 1
- OOM trying to pretrain llama 7b on v4-256 HOT 7
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