Comments (2)
Something is up with the finetune code. Even 2x40GB with base model and code size 1, GPU mem hits 25GB then tries to allocate 25GB more and OOM.
File "/home/amicus/atlas/src/index.py", line 111, in load_index
self.embeddings = torch.concat(embeddings, dim=1)
RuntimeError: CUDA out of memory. Tried to allocate 22.99 GiB (GPU 1; 47.54 GiB total capacity; 23.00 GiB already allocated; 22.92 GiB free; 23.00 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
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is it possible to train and test a model using the free version of Google Colab without the need for high-end GPU?
I'm a student and I want to train and test this model.
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Related Issues (18)
- what is srun? HOT 1
- "AttributeError: module 'torch.optim.adamw' has no attribute 'F'" in AdamWFP32Copy.py HOT 2
- RuntimeError: ProcessGroupNCCL does not support gather HOT 1
- Small ATLAS HOT 8
- "RuntimeError: einsum(): operands do not broadcast with remapped shapes [original->remapped]" during reproducing mlm pre-training HOT 5
- Poor quality of outputs from large model on 1/10th of wikipedia
- Access to the models [fully and 64-shot] fine-tuned on different datasets HOT 1
- Possibility of releasing closed-book Atlas models? HOT 1
- Not able to download the NQ data HOT 1
- How do you conduct distributed training? HOT 1
- Save and load compressed index HOT 2
- Example training script for the KILT task HOT 2
- [Fix] Retriever tokenization function in atlas.py needs correction HOT 1
- How can run this code on a normal personal machine? We don't have slurm configured, can we run it by simply modifying the run script? HOT 1
- Why is the index size so big?
- What's the difference between train.py and finetune_qa.py
- Are the pretrained models available somewhere?
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