Comments (1)
Hi, we currently do not support TensorFlow even though it is in our future plan. Currently, you need to implement your model in PyTorch and create an example.
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Related Issues (20)
- [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
- [BUG], please delete this item.
- [FEATURE]: cuda 12 support HOT 2
- [BUG]: ValueError: mutable default <class 'colossalai.legacy.tensor.distspec._DistSpec'> for field dist_attr is not allowed: use default_factory HOT 1
- [BUG]: AttributeError: type object 'ColoParameter' has no attribute 'from_torch_tensor' when run hybrid_parallel example HOT 3
- [FEATURE]: Support qwen2 model
- [BUG]: OOM when saving 70B model HOT 2
- [DOC]: What is the datasetset used to train the Colossal-Llama-2? HOT 1
- [BUG]: Running ColossalAI in H800 with torch 2.0 HOT 28
- [BUG]: pretraing llama2 using "gemini" plugin, can not resume from saved checkpoints HOT 1
- [BUG] [Shardformer]: Error in blip2 testing with half precision HOT 1
- [FEATURE]: support multiple (partial) backward passes for zero
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