Comments (4)
会的 用 ZeRO3 即可
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会的 用 ZeRO3 即可
感谢!所以开zero2的时候,每张卡上num_gpus_per_actor无论设置为几显存都一样大是正常现象吗?这个值是否不应该大于1?
from openrlhf.
会的 用 ZeRO3 即可
感谢!所以开zero2的时候,每张卡上num_gpus_per_actor无论设置为几显存都一样大是正常现象吗?这个值是否不应该大于1?
开zero2 optimizer会切片 num_gpus_per_actor越大 占用内存越小。
from openrlhf.
感谢!不好意思还有最后一个问题。openrlhf/trainer/ray/launcher.py中的写法是
for rank in range(1, world_size):
local_rank = rank % self._num_gpus_per_node
if pg:
worker_actor = self.ray_actor_type.options(
num_cpus=num_gpus_per_actor,
num_gpus=num_gpus_per_actor,
resources=self._resources,
scheduling_strategy=PlacementGroupSchedulingStrategy(
placement_group=pg,
placement_group_bundle_index=rank // self._num_gpus_per_node,
),
).remote(world_size, rank, local_rank, master_addr, master_port)
也就是说创建的Actor的数量不少于Actor占用的总GPU数量?我是否可以只创建一个Actor而让其占用多个显卡,还是因为某些原因必须在每张卡上都创建一个Actor?存在更加节省显存的方式吗?十分感谢您耐心的解答!
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Related Issues (20)
- DPO Finetuning constantly gives preference loss as 0.6931 HOT 8
- Difference between `DeepSpeedEngine.save_checkpoint()` and `DeepSpeedStrategy.save_model()` HOT 2
- DPO后的模型推理出的结果都是无序符号 HOT 1
- Support training from breakpoint HOT 3
- llama3 70B DPO example script
- where is gradient_accumulation HOT 1
- Support RLOO HOT 1
- ConnectionRefusedError: [Errno 111] Connection refused HOT 5
- packing的问题 HOT 2
- "right" padding hardcoded HOT 3
- Error while saving the model under 4bit lora HOT 2
- multinode ppo training extremely slow HOT 15
- 使用ray的时候Request Entity Too Large HOT 3
- dpo 训练显存 OOM HOT 1
- Online DPO 支持 HOT 4
- Feature: add DPO-P
- Zero stage 3 error HOT 1
- Performance of Iterative DPO? HOT 1
- Why multiplying rstd instead of dividing by rstd? HOT 1
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