Comments (2)
I think the current streaming checkpointer is quite good and I'm not planning to switch to orbax. Specifically, the streaming checkpointer can save and load checkpoints with minimal memory and temporary disk usage, which means that you can checkpoint models that does not fit in the memory or local disk of a single machine. Also as a personally preference, I want to stay away from complicated Google libraries as much as possible.
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obrax do not support load streaming and sharding data or array across devices with pjit so I think the current checkpointing method that is being used right now is a smart move :)
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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'
- 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)
- Questions about inference mode HOT 2
- 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 6
- Does EasyLM work for LLama-2?
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