common_init_from_params: setting dry_penalty_last_n to ctx_size = 4096
common_init_from_params: warming up the model with an empty run - please wait ... (--no-warmup to disable)
ggml_cuda_compute_forward: ADD failed
CUDA error: the provided PTX was compiled with an unsupported toolchain.
current device: 0, in function ggml_cuda_compute_forward at D:\a\llama.cpp\llama.cpp\ggml\src\ggml-cuda\ggml-cuda.cu:2230
err
D:\a\llama.cpp\llama.cpp\ggml\src\ggml-cuda\ggml-cuda.cu:71: CUDA error
sh: ./llama-server.exe: このアプリケーションで、スタック ベースのバッファーのオーバーランが検出されました。このオーバーラン により、悪質なユーザーがこのアプリケーションを制御できるようになる可能性があります。 Error 0xc0000409
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ModuleNotFoundError: No module named 'pydantic_core._pydantic_core'
Traceback:
File "C:\Dev\llm\softwaredesigne\softwaredesign-llm-application\14\.venv\Lib\site-packages\streamlit\runtime\scriptrunner\exec_code.py", line 88, in exec_func_with_error_handling
result = func()
^^^^^^
File "C:\Dev\llm\softwaredesigne\softwaredesign-llm-application\14\.venv\Lib\site-packages\streamlit\runtime\scriptrunner\script_runner.py", line 590, in code_to_exec
exec(code, module.__dict__)
File "C:\Dev\llm\softwaredesigne\softwaredesign-llm-application\14\app.py", line 5, in <module>
from agent import HumanInTheLoopAgent
...
C:\Dev\llm\softwaredesigne\softwaredesign-llm-application\14> pip install fastapi==0.99.0
...
Using cached starlette-0.27.0-py3-none-any.whl (66 kB)
Installing collected packages: pydantic, starlette, fastapi
Attempting uninstall: pydantic
Found existing installation: pydantic 2.9.2
Uninstalling pydantic-2.9.2:
Successfully uninstalled pydantic-2.9.2
Attempting uninstall: starlette
Found existing installation: starlette 0.41.2
Uninstalling starlette-0.41.2:
Successfully uninstalled starlette-0.41.2
Attempting uninstall: fastapi
Found existing installation: fastapi 0.115.4
Uninstalling fastapi-0.115.4:
Successfully uninstalled fastapi-0.115.4
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
composio-core 0.5.40 requires pydantic<3,>=2.6.4, but you have pydantic 1.10.19 which is incompatible.
Successfully installed fastapi-0.99.0 pydantic-1.10.19 starlette-0.27.0
T4 GPUs include Tensor Cores, which can accelerate certain deep learning operations, particularly when working with mixed-precision training
GPU P100
NVIDIA GPU model from the Tesla series.
Pascal
3,584 CUDA cores
16 GB or 12 GB of HBM2 memory.
In general, GPU P100 is considered more powerful than GPU T4, primarily due to the higher number of CUDA cores and better memory bandwidth. However, the actual performance depends on the specific workload you’re running.
GPU Model: T4 is an NVIDIA GPU model from the Tesla series. Architecture: T4 is based on the Turing architecture. CUDA Cores: T4 has 2,560 CUDA cores. Memory: T4 typically has 16 GB of GDDR6 memory. Performance: T4 offers good performance for a range of tasks, including machine learning, deep learning, and general GPU-accelerated computations. Tensor Cores: T4 GPUs include Tensor Cores, which can accelerate certain deep learning operations, particularly when working with mixed-precision training.
