Comments (2)
Hey @1106944911
it should be the names of your GPU devices as a list of strings separated by spaces
i.e
python3 main.py --model <your_config_name> --gpu_ids device:GPU:0 device:GPU:1
this is what @lucidrains used in testing, but your device names may differ. Let us know how training goes, we've mostly been testing on TPUs so far and have had mixed success with GPU training.
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@lucidrains Hi Can you tell me how to train on multi nodes and multi gpu ?
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Related Issues (20)
- Exception: stream did not contain valid UTF-8
- Dataset preparation HOT 1
- Inferencing HOT 2
- Freeze Transformer Weight HOT 1
- Incosistent inference TPU vs GPU (huggingface) HOT 1
- Colab: Download of pre trained dataset not possible. the-eye.eu is offline HOT 1
- GPT3_1_3B configuration for a v3-32 TPU HOT 3
- the-eye.eu is down again, is there a mirror? HOT 6
- GPT-neo 350M weights? HOT 3
- Links in the readme to the-eye.eu don't work HOT 1
- Argument not a list with same length as devices
- The locally ran gpt-neo-2.7B is using CPU instead of GPU HOT 1
- Generation should allow user to specify max length of generated portion, rather than total HOT 5
- Not able to generate predicted text after `Done with copy master to slices.` with 1.3B pre-trained model
- The temperature at 0.0001 (or other arbitrarily small float) is still too high HOT 5
- The model should return just the generated text, not the prompt text + generated text. HOT 2
- TPU device does not support heartbeats.
- IndexError: index out of range in self HOT 1
- FYI:Japanese pre-trained gpt-neo implementation showcase by using PyTorch, Transformers, and Rust HOT 1
- Cannot Connect To Local TPU-VM HOT 1
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