Describe the feature

if the network is poor, sync_grad may be the main performance bottleneck, and the pipeline bubble could be huge if we overlap sync_grad with computation, since pp0 must wait the communication of other stages in current implementation

Will you implement it?

• I would like to implement this feature and create a PR!

描述问题

请问目前支持哪些并行训练方式，和训练框架和deepspeed，megatron，fsdp这些的区别和联系？谢谢

Describe the question.

目前在internLM2的教程中只有XTuner版本的，InternEvo的版本尚未发布吗，请问有计划什么时候发布吗？请问InternEVO相对于XTuner的区别在哪？

Describe the bug

Details：https://github.com/InternLM/InternEvo/actions/runs/8301063319/job/22722755612

Environment

python 3.10

Other information

No response

Describe the feature

currently we use several ways to get device

• internlm_accelerator.device()
• internlm_accelerator.current_device()
• from internlm.utils.common import get_current_device

we should the consist way to perform the get_device operation.

In addition, the interface " internlm_accelerator.device()" is necessary?

Will you implement it?

• I would like to implement this feature and create a PR!

Describe the question.

A typo is found when loading and saving scheduler states:

Maybe it's a better option to aggregate all constant variables into a single module and modify the file directly for global change?

https://github.com/huggingface/transformers/blob/efdd436663436e78d8ad3213d11325d86578db95/src/transformers/trainer.py#L246-L253

Describe the feature

internlm2-1.8b finetuning config is missing

Will you implement it?

• I would like to implement this feature and create a PR!

Describe the feature

they should not in separated parameter group

Will you implement it?

• I would like to implement this feature and create a PR!

Describe the feature

Will you implement it?

• I would like to implement this feature and create a PR!

描述该错误

环境信息

no flashattention case

其他信息

No response

描述该错误

非常感谢您的工作！
我在使用代码进行sft时遇到了一个问题。在不使用moe的config时能够很好的运行，在使用moe的config文件后报错。
运行代码：

torchrun --nnodes=1 --nproc_per_node=8 train.py --config ./configs/7B_MoE4_sft.py --launcher "torch"

报错信息：

Traceback (most recent call last):
  File "train.py", line 324, in <module>
    main(args)
  File "train.py", line 105, in main
    model = initialize_model()
  File "/root/wbq/internlm_moe/InternEvo/internlm/utils/timeout.py", line 102, in wrapper
    result = func(*args, **kwargs)
  File "/root/wbq/internlm_moe/InternEvo/internlm/train/pipeline.py", line 167, in initialize_model
    model = MODEL_INITIALIZER.get_module(module_name=gpc.config.model_type)(**(gpc.config.model))
  File "/root/wbq/internlm_moe/InternEvo/internlm/model/modeling_moe.py", line 584, in build_model_with_moe_cfg
    return _build_generic_model_1d(num_layers=num_layers, num_chunks=num_chunks, **cfg)
  File "/root/wbq/internlm_moe/InternEvo/internlm/model/modeling_moe.py", line 482, in _build_generic_model_1d
    chunk = PackedFlashInternLm1D(**filter_kwargs(PackedFlashInternLm1D.__init__, kwargs)).to(device)
  File "/root/wbq/internlm_moe/InternEvo/internlm/model/modeling_moe.py", line 356, in __init__
    [
  File "/root/wbq/internlm_moe/InternEvo/internlm/model/modeling_moe.py", line 357, in <listcomp>
    PackedFlashBaseLayer1D(
  File "/root/wbq/internlm_moe/InternEvo/internlm/model/modeling_moe.py", line 94, in __init__
    self.mixer = MHA(
  File "/root/wbq/internlm_moe/InternEvo/internlm/model/modules/multi_head_attention.py", line 364, in __init__
    self.rotary_emb = RotaryEmbedding(
  File "/root/wbq/internlm_moe/InternEvo/internlm/model/modules/embedding.py", line 287, in __init__
    self.inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2, device=device, dtype=torch.float32) / dim))
TypeError: arange() received an invalid combination of arguments - got (int, int, int, dtype=torch.dtype, device=device), but expected one of:
 * (Number end, *, Tensor out, torch.dtype dtype, torch.layout layout, torch.device device, bool pin_memory, bool requires_grad)
 * (Number start, Number end, *, torch.dtype dtype, torch.layout layout, torch.device device, bool pin_memory, bool requires_grad)
 * (Number start, Number end, Number step, *, Tensor out, torch.dtype dtype, torch.layout layout, torch.device device, bool pin_memory, bool requires_grad)

环境信息

torch==2.1.0+cu118
transformers<4.30.0
sentencepiece
numpy
tqdm
psutil
packaging
pre-commit
ninja
gputil
pytest
packaging
boto3
botocore
torch-scatter
pyecharts
py-libnuma
pynvml
tensorboard

其他信息

1、我只修改了./configs/7B_MoE4_sft.py中训练集和测试集的地址

Describe the bug

Traceback (most recent call last):
File "/root/miniconda3/envs/internLM/lib/python3.8/multiprocessing/pool.py", line 131, in worker
put((job, i, result))
File "/root/miniconda3/envs/internLM/lib/python3.8/multiprocessing/queues.py", line 368, in put
self._writer.send_bytes(obj)
File "/root/miniconda3/envs/internLM/lib/python3.8/multiprocessing/connection.py", line 200, in send_bytes
self._send_bytes(m[offset:offset + size])
File "/root/miniconda3/envs/internLM/lib/python3.8/multiprocessing/connection.py", line 411, in _send_bytes
self._send(header + buf)
File "/root/miniconda3/envs/internLM/lib/python3.8/multiprocessing/pool.py", line 131, in worker
put((job, i, result))
File "/root/miniconda3/envs/internLM/lib/python3.8/multiprocessing/connection.py", line 368, in _send
n = write(self._handle, buf)
File "/root/miniconda3/envs/internLM/lib/python3.8/multiprocessing/queues.py", line 368, in put
self._writer.send_bytes(obj)
File "/root/miniconda3/envs/internLM/lib/python3.8/multiprocessing/connection.py", line 200, in send_bytes
self._send_bytes(m[offset:offset + size])
BrokenPipeError: [Errno 32] Broken pipe
File "/root/miniconda3/envs/internLM/lib/python3.8/multiprocessing/connection.py", line 411, in _send_bytes
self._send(header + buf)

During handling of the above exception, another exception occurred:

File "/root/miniconda3/envs/internLM/lib/python3.8/multiprocessing/connection.py", line 368, in _send
n = write(self._handle, buf)
Traceback (most recent call last):
BrokenPipeError: [Errno 32] Broken pipe

Environment

python==3.8
torch==2.0.1

Other information

No response

描述问题

Describe the feature

***/evo_runner/_work/InternEvo/InternEvo/internlm/solver/optimizer/utils.py:389: UserWarning: The torch.cuda.DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=, device='cuda') to create tensors. (Triggered internally at ../torch/csrc/tensor/python_tensor.cpp:83.)

Will you implement it?

• I would like to implement this feature and create a PR!

Describe the bug

Environment

Torch2.1

Other information

No response

Describe the feature

no need to do two stage norm calc

Will you implement it?

• I would like to implement this feature and create a PR!

Describe the feature

update readme with new version of dependency.

Will you implement it?

• I would like to implement this feature and create a PR!

Describe the feature

Currently, we only use "use_flash_attention" to define that the dataset is packed. In the future, we need to extend this ability to multiple chips. Thus we need to define a new configuration named use_packed_dataset to control the logic in the training system in stead of always using "use_flash_attention". The default value would be true.

Will you implement it?

• I would like to implement this feature and create a PR!

Describe the bug

I recommend to define class RMSNormTorch(torch.nn.Module) just as RMSNorm

Environment

PyTorch 2.1

Other information

No response

Describe the feature

在目前的情形下，pipeline parallel会在第一份数据进入时固定logits等的形状，但事实上，下游任务中很多时候最好能支持变长以节省算力，而不是进行过多的padding。

Will you implement it?

• I would like to implement this feature and create a PR!

描述问题

请问基于一个书生格式的ckpt希望做张量并行或流水线并行，需要自己切分模型以适配对应的并行度吗？
直接加载会报错，官方有提供相应的脚本吗？

Describe the bug

There is an error in acc tests

https://github.com/InternLM/InternEvo/actions/runs/8630477081/job/23663676249

Environment

python3.10
torch2.1

Other information

No response

描述该功能

是否希望自己实现该功能？

• 我希望自己来实现这一功能，并向 InternLM 贡献代码！

Describe the feature

目前 rotary_embedding类的实现有大量历史遗留的代码，建议和https://github.com/Dao-AILab/flash-attention/blob/v2.2.1/flash_attn/layers/rotary.py 对齐，并且支持triton算子

Will you implement it?

• I would like to implement this feature and create a PR!

Describe the bug

weekly定时测例中，tests/test_training/test_loss.py loss验证失败

详情查看：https://github.com/InternLM/InternEvo/actions/runs/7588007470/job/20710589255

Environment

python3.10
T ： llm-flash2.0

Other information

No response

描述该错误

make -f docker.Makefile BASE_OS=ubuntu20.04 时，总是会出一个错误，无法解决。发生在[intrenlm-dev 3/3] RUN git submodule update --init --recursive 这一步

环境信息

ERROR: failed to solve: process "/bin/sh -c git submodule update --init --recursive && /opt/conda/bin/pip --no-cache-dir install -r requirements/torch.txt && /opt/conda/bin/pip --no-cache-dir install -r requirements/runtime.txt && cd /InternLM/third_party/flash-attention && /opt/conda/bin/python setup.py install && cd ./csrc && cd fused_dense_lib && /opt/conda/bin/pip install -v . && cd ../xentropy && /opt/conda/bin/pip install -v . && cd ../rotary && /opt/conda/bin/pip install -v . && cd ../layer_norm && /opt/conda/bin/pip install -v . && cd ../../../../ && cd ./third_party/apex && /opt/conda/bin/pip --no-cache-dir install -v --disable-pip-version-check --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./ && /opt/conda/bin/pip cache purge && rm -rf ~/.cache/pip" did not complete successfully: exit code: 1
make: *** [docker.Makefile:103: devel-image] Error 1

其他信息

有什么方法可以解决这问题。
所给的docker创建文件是否正确
RUN git submodule update --init --recursive 想知道这一步在docker文件的哪一部分，想要先注释掉，后续再安装

Describe the feature

Should we remove other dependency of flash-attention, and only keep the core attention related ops?

If possible, we can only use pip to install flash-attention, avoiding a lot of compiling operations.

To seek whether it is possible, we need to check whether it would reduce the training performance a lot.

Will you implement it?

• I would like to implement this feature and create a PR!

Describe the bug

Environment

chrome

Other information

No response

描述该功能

hi there,

could you give some suggestions for training small model size, such as 1B or 3B, and related configurations?

thanks a ton!

是否希望自己实现该功能？

• 我希望自己来实现这一功能，并向 InternLM 贡献代码！

描述该错误

在我的数据不足以跑完整个totalstep的时候，会出现StopIteration的报错。原因是虽然在外面使用了try-except，但是在第512行的next(train_state.batch_sampler_iter仍然会出现越界，原因是train_state.batch_sampler也跟着迭代，所以即使train_state.batch_sampler_iter重新赋值，仍然会越界。

环境信息

其他信息

做了如下修改之后，可以跑通。

描述该功能

目前仓库对pip安装的依赖库版本有严格的指定，对于广泛的使用会有一定的不便。需要逐步放松对应的限制

是否希望自己实现该功能？

• 我希望自己来实现这一功能，并向 InternLM 贡献代码！

Describe the feature

CI should have a true no flashattention env

Will you implement it?

• I would like to implement this feature and create a PR!

Describe the bug

we have a lot of cases like following:

data = torch.empty(partition_size, dtype=tensor.dtype, device=torch.cuda.current_device(), requires_grad=False)

where we directly use device=torch.cuda.current_device(). However, it is not recommended to do like it, since torch.cuda.current_device() only returns device id. It is OK to run such codes on GPUs. However, maybe there are some problems when running on NPU

Environment

python3.8 + torch2.1

Other information

No response

Describe the bug

Details:
https://github.com/InternLM/InternEvo/actions/runs/8758331037/job/24131265916

Environment

python3.10
torch2.1

Other information

No response

Describe the question.

v100支持张量并行吗？（我理解张量并行为模型并行）因为模型比较大

Describe the feature

In a lot of cases, we need full seq_length for performance test with the random_dataset. Currently, we should use with pack_sample_into_one to achieve this goal. However, it requires us to use flash_attention since it actually generates packed dataset. Thus, we need the random_dataset has the ability to generate full seq_length samples

Will you implement it?

• I would like to implement this feature and create a PR!

Describe the bug

we need to automatically switch the profiling, current we force to use activities=[torch.profiler.ProfilerActivity.CPU, torch.profiler.ProfilerActivity.CUDA],

Environment

PyTorch2.1

Other information

No response

Describe the feature

Some CPU synchronizations block the GPU kernel, leading to bubbles between GPU kernels. It should be optimized in the future.

1. item() in rotary embedding.
2. moe_loss construction.

Will you implement it?

• I would like to implement this feature and create a PR!

Describe the feature

num_processes_on_current_node 如果不使用，建议删除

Will you implement it?

• I would like to implement this feature and create a PR!

Describe the feature

Current implementation does not support partially training, i.e., part of the model parameters are frozen.
If I understand it correctly, the assertion around line 584 in hybrid_zero_optim.py suggests that all parameters should be involved in the training.

Very looking forward this feature being implemented as there are many scenarios where users only want to finetune parts of the model.

Is there any plan on this feature?

Will you implement it?

• I would like to implement this feature and create a PR!

Describe the bug

The container version of internlm has not been built successfully. After pulling the image and entering the container, it was found that it is not possible to directly use the container for training and inference. The environment inside the container also differs significantly from the actual runtime environment needed.

Environment

The container version of internlm has not been built successfully. After pulling the image and entering the container, it was found that it is not possible to directly use the container for training and inference. The environment inside the container also differs significantly from the actual runtime environment needed.

Other information

No response

Describe the feature

supporting hugging-face modeling python file

Will you implement it?

• I would like to implement this feature and create a PR!

Describe the bug

执行如下代码，报错

>>> import InternEvo
>>> print(InternEvo.__version__)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
AttributeError: module 'InternEvo' has no attribute '__version__'

Environment

python3.10

Other information

No response

描述该功能

目前能编译出来的windows版本的flash-attention是依赖cu121+py310+torch2.1
而InternEvo又只依赖cu118，导致两个库冲突了，无法在windows上训练
未来会有计划升级到cu121吗？谢谢！

是否希望自己实现该功能？

• 我希望自己来实现这一功能，并向 InternLM 贡献代码！

Describe the feature

since this is the public repo, there is no need to keep the norm value per layer feature

Will you implement it?

• I would like to implement this feature and create a PR!

📚 The doc issue

Conversely, if a = 1,InternEvo utilizes 34bSH bytes for activation storage. -> Conversely, if a = 0,InternEvo utilizes 34bSH bytes for activation storage.

Suggest a potential alternative/fix

No response

描述该错误

2024-04-19 06:06:37,071 INFO writer.py:60 in init_tb_writer -- Login tensorboard logs to: RUN/7b_internlm2_train/04-19-06.06.02/tensorboards 2024-04-19 06:06:37,761 ERROR train.py:307 in <module> -- Raise exception from c394df8c9997 with rank id: 0 Traceback (most recent call last): File "/data/InternEvo/train.py", line 305, in <module> main(args) File "/data/InternEvo/train.py", line 215, in main _, _, loss = trainer.execute_schedule( File "/data/InternEvo/internlm/core/trainer.py", line 213, in execute_schedule return self._schedule.forward_backward_step(self._engine, /data_iter, **kwargs) File "/data/InternEvo/internlm/utils/timeout.py", line 102, in wrapper result = func(*args, **kwargs) File "/data/InternEvo/internlm/core/scheduler/no_pipeline_scheduler.py", line 220, in forward_backward_step _output, _loss, _moe_loss = self._train_one_batch( File "/data/InternEvo/internlm/core/scheduler/no_pipeline_scheduler.py", line 125, in _train_one_batch output = self._call_engine(engine, /data) File "/data/InternEvo/internlm/core/scheduler/base_scheduler.py", line 86, in _call_engine return engine(**inputs) File "/data/InternEvo/internlm/core/engine.py", line 164, in __call__ return self.model(*args, **kwargs) File "/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl return self._call_impl(*args, **kwargs) File "/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1527, in _call_impl return forward_call(*args, **kwargs) File "/data/InternEvo/internlm/core/naive_amp.py", line 155, in forward out = self.model(*args, **kwargs) File "/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl return self._call_impl(*args, **kwargs) File "/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1527, in _call_impl return forward_call(*args, **kwargs) File "/data/InternEvo/internlm/model/modeling_internlm2.py", line 934, in forward hidden_states = self.tok_embeddings(input_ids) File "/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl return self._call_impl(*args, **kwargs) File "/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1527, in _call_impl return forward_call(*args, **kwargs) File "/data/InternEvo/internlm/model/modules/embedding.py", line 66, in forward output = F.embedding(input_, self.weight, self.padding_idx, *self.embed_args, **self.embed_kwargs) File "/opt/conda/lib/python3.10/site-packages/torch/nn/functional.py", line 2233, in embedding return torch.embedding(weight, input, padding_idx, scale_grad_by_freq, sparse) RuntimeError: CUDA error: device-side assert triggered Compile with TORCH_USE_CUDA_DSA` to enable device-side assertions.

terminate called after throwing an instance of 'std::runtime_error' what(): [Rank 2] NCCL watchdog thread terminated with exception: CUDA error: device-side assert triggered Compile withTORCH_USE_CUDA_DSA` to enable device-side assertions.

Exception raised from c10_cuda_check_implementation at ../c10/cuda/CUDAException.cpp:44 (most recent call first):
frame #0: c10::Error::Error(c10::SourceLocation, std::string) + 0x57 (0x7f97371d5617 in /opt/conda/lib/python3.10/site-packages/torch/lib/libc10.so)
frame #1: c10::detail::torchCheckFail(char const*, char const*, unsigned int, std::string const&) + 0x64 (0x7f973719098d in /opt/conda/lib/python3.10/site-packages/torch/lib/libc10.so)
frame #2: c10::cuda::c10_cuda_check_implementation(int, char const*, char const*, int, bool) + 0x118 (0x7f9737286518 in /opt/conda/lib/python3.10/site-packages/torch/lib/libc10_cuda.so)
frame #3: c10d::ProcessGroupNCCL::WorkNCCL::finishedGPUExecutionInternal() const + 0x80 (0x7f973868a150 in /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_cuda.so)
frame #4: c10d::ProcessGroupNCCL::WorkNCCL::isCompleted() + 0x58 (0x7f973868df78 in /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_cuda.so)
frame #5: c10d::ProcessGroupNCCL::workCleanupLoop() + 0x24b (0x7f97386a47bb in /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_cuda.so)
frame #6: c10d::ProcessGroupNCCL::ncclCommWatchdog() + 0x78 (0x7f97386a4ac8 in /opt/conda/lib/python3.10/site-packages/torch/lib/libtorch_cuda.so)
frame #7: + 0xd6bf0 (0x7f97a7af3bf0 in /usr/local/gcc-10.2.0/lib64/libstdc++.so.6)
frame #8: + 0x8609 (0x7f97d2bff609 in /usr/lib/x86_64-linux-gnu/libpthread.so.0)
frame #9: clone + 0x43 (0x7f97d29ca133 in /usr/lib/x86_64-linux-gnu/libc.so.6)

../aten/src/ATen/native/cuda/Indexing.cu:1292: indexSelectLargeIndex: block: [604,0,0], thread: [32,0,0] Assertion srcIndex < srcSelectDimSize failed.
../aten/src/ATen/native/cuda/Indexing.cu:1292: indexSelectLargeIndex: block: [604,0,0], thread: [33,0,0] Assertion srcIndex < srcSelectDimSize failed.
../aten/src/ATen/native/cuda/Indexing.cu:1292: indexSelectLargeIndex: block: [604,0,0], thread: [34,0,0] Assertion srcIndex < srcSelectDimSize failed.
../aten/src/ATen/native/cuda/Indexing.cu:1292: indexSelectLargeIndex: block: [604,0,0], thread: [35,0,0] Assertion srcIndex < srcSelectDimSize failed.
../aten/src/ATen/native/cuda/Indexing.cu:1292: indexSelectLargeIndex: block: [604,0,0], thread: [36,0,0] Assertion srcIndex < srcSelectDimSize failed.
../aten/src/ATen/native/cuda/Indexing.cu:1292: indexSelectLargeIndex: block: [604,0,0], thread: [37,0,0] Assertion srcIndex < srcSelectDimSize failed.
../aten/src/ATen/native/cuda/Indexing.cu:1292: indexSelectLargeIndex: block: [604,0,0], thread: [38,0,0] Assertion srcIndex < srcSelectDimSize failed.
../aten/src/ATen/native/cuda/Indexing.cu:1292: indexSelectLargeIndex: block: [604,0,0], thread: [39,0,0] Assertion srcIndex < srcSelectDimSize failed.
../aten/src/ATen/native/cuda/Indexing.cu:1292: indexSelectLargeIndex: block: [604,0,0], thread: [40,0,0] Assertion srcIndex < srcSelectDimSize failed.
../aten/src/ATen/native/cuda/Indexing.cu:1292: indexSelectLargeIndex: block: [604,0,0], thread: [41,0,0] Assertion srcIndex < srcSelectDimSize failed.
../aten/src/ATen/native/cuda/Indexing.cu:1292: indexSelectLargeIndex: block: [604,0,0], thread: [42,0,0] Assertion srcIndex < srcSelectDimSize failed.
../aten/src/ATen/native/cuda/Indexing.cu:1292: indexSelectLargeIndex: block: [604,0,0], thread: [43,0,0] Assertion srcIndex < srcSelectDimSize failed.
../aten/src/ATen/native/cuda/Indexing.cu:1292: indexSelectLargeIndex: block: [604,0,0], thread: [44,0,0] Assertion srcIndex < srcSelectDimSize failed.
../aten/src/ATen/native/cuda/Indexing.cu:1292: indexSelectLargeIndex: block: [604,0,0], thread: [45,0,0] Assertion srcIndex < srcSelectDimSize failed.
../aten/src/ATen/native/cuda/Indexing.cu:1292: indexSelectLargeIndex: block: [604,0,0], thread: [46,0,0] Assertion srcIndex < srcSelectDimSize failed.
../aten/src/ATen/native/cuda/Indexing.cu:1292: indexSelectLargeIndex: block: [604,0,0], thread: [47,0,0] Assertion srcIndex < srcSelectDimSize failed.
../aten/src/ATen/native/cuda/Indexing.cu:1292: indexSelectLargeIndex: block: [604,0,0], thread: [48,0,0] Assertion srcIndex < srcSelectDimSize failed.
../aten/src/ATen/native/cuda/Indexing.cu:1292: indexSelectLargeIndex: block: [604,0,0], thread: [49,0,0] Assertion srcIndex < srcSelectDimSize failed.
../aten/src/ATen/native/cuda/Indexing.cu:1292: indexSelectLargeIndex: block: [604,0,0], thread: [50,0,0] Assertion srcIndex < srcSelectDimSize failed.
../aten/src/ATen/native/cuda/Indexing.cu:1292: indexSelectLargeIndex: block: [604,0,0], thread: [51,0,0] Assertion `srcIndex < srcSelect

环境信息

软件环境: ubuntu 官方镜像
硬件环境：A800

其他信息

No response

Describe the bug

Deatials:
https://github.com/InternLM/InternEvo/actions/runs/8395156088

Environment

python3.10

Other information

No response

Describe the feature

a very simple on-the-fly dataloader is needed to support most pubic dataset

Will you implement it?

• I would like to implement this feature and create a PR!

Describe the bug

Failed to import module uniscale_monitoring. Use default python logger.

no need to uniscale_monitoring in public repo

Environment

public conda

Other information

No response