Comments (2)
I recommend this project provide a docker image. It will be helpful if you are also considering making it a cloud-native platform.
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As mentioned in this issue, PyTorch seems to have no distribution for python 3.10, so downgrading to python 3.9 or below is required. A dockerfile is added in PR #126
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Related Issues (20)
- [BUG]: with Phi2 + torch_fsdp, got RecursionError: maximum recursion depth exceeded
- [shardformer] enable padding vocabulary size.
- [BUG]: Size mismatch is ignored when loading checkpoint
- [FEATURE]: support dit in Shardformer
- [FEATURE]: Upgrade the transformers version from 4.33.0 to 4.36.0 for Shardformer. HOT 1
- [BUG]: Coati Lora incompatible with Gemini & HybridParallel(pp=1), but runs well with HybridParallel(tp>=2) HOT 1
- [shardformer, pipeline]: add `gradient_checkpointing_ratio` and heterogenous shard policy
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- [Zero] optimize offload mode and add sub dp_group
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- [FEATURE]: Running Colossal AI in H800 with torch 2.0, Cuda 12.0 HOT 2
- [FEATURE]: Distributed computing HOT 1
- [BUG]: HybridParallelOptimizer holds unsharded model parameters after sharding HOT 9
- When I execute `colossalai run --nproc_per_node 2 --master_addr localhost --master_port 29500 train.py`, I encounter an error. HOT 2
- [FEATURE]: pretrain data example
- Pedido pra voltar (1) HOT 1
- [BUG]: OOM during llama2 pretraining with flashattention and PP HOT 3
- [BUG]: Cannot find module 'colossalai.kernel.cuda_native' when run sequence_parallel example HOT 4
- TypeError: LlamaRotaryEmbedding.forward() got an unexpected keyword argument 'seq_len' HOT 4
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