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  1. 2026-01-14/runs/26786-21002369414/ci_results_run_models_gpu/model_results.json +116 -0
2026-01-14/runs/26786-21002369414/ci_results_run_models_gpu/model_results.json ADDED
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+ "line": "tests/models/deepseek_v2/test_modeling_deepseek_v2.py::DeepseekV2ModelTest::test_cpu_offload",
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+ "trace": "(line 2847) AssertionError: Tensor-likes are not close!"
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+ "line": "tests/models/deepseek_v2/test_modeling_deepseek_v2.py::DeepseekV2ModelTest::test_disk_offload_bin",
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+ "trace": "(line 2765) AssertionError: Tensor-likes are not close!"
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+ "line": "tests/models/deepseek_v2/test_modeling_deepseek_v2.py::DeepseekV2ModelTest::test_disk_offload_safetensors",
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+ "trace": "(line 2805) AssertionError: Tensor-likes are not close!"
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+ "line": "tests/models/deepseek_v2/test_modeling_deepseek_v2.py::DeepseekV2IntegrationTest::test_batch_fa2",
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+ "trace": "(line 5113) torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 704.00 MiB. GPU 0 has a total capacity of 22.30 GiB of which 182.69 MiB is free. Process 47930 has 22.12 GiB memory in use. Of the allocated memory 20.58 GiB is allocated by PyTorch, with 22.00 MiB allocated in private pools (e.g., CUDA Graphs), and 982.75 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)"
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+ "line": "tests/models/deepseek_v2/test_modeling_deepseek_v2.py::DeepseekV2IntegrationTest::test_deepseek_v2_lite",
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+ "trace": "(line 692) torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 400.00 MiB. GPU 0 has a total capacity of 22.30 GiB of which 182.69 MiB is free. Process 47930 has 22.12 GiB memory in use. Of the allocated memory 20.58 GiB is allocated by PyTorch, with 22.00 MiB allocated in private pools (e.g., CUDA Graphs), and 982.75 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)"
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+ "trace": "(line 692) torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 400.00 MiB. GPU 0 has a total capacity of 22.30 GiB of which 182.69 MiB is free. Process 47930 has 22.12 GiB memory in use. Of the allocated memory 21.18 GiB is allocated by PyTorch, with 22.00 MiB allocated in private pools (e.g., CUDA Graphs), and 369.74 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)"
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+ "line": "tests/models/deepseek_v2/test_modeling_deepseek_v2.py::DeepseekV2ModelTest::test_cpu_offload",
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+ "line": "tests/models/deepseek_v2/test_modeling_deepseek_v2.py::DeepseekV2ModelTest::test_disk_offload_bin",
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+ "trace": "(line 5113) torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 704.00 MiB. GPU 0 has a total capacity of 22.30 GiB of which 224.69 MiB is free. Process 694893 has 22.08 GiB memory in use. Of the allocated memory 20.24 GiB is allocated by PyTorch, with 22.00 MiB allocated in private pools (e.g., CUDA Graphs), and 1.18 GiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)"
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+ "trace": "(line 692) torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 400.00 MiB. GPU 0 has a total capacity of 22.30 GiB of which 224.69 MiB is free. Process 694893 has 22.08 GiB memory in use. Of the allocated memory 20.24 GiB is allocated by PyTorch, with 22.00 MiB allocated in private pools (e.g., CUDA Graphs), and 1.18 GiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)"
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+ "trace": "(line 692) torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 400.00 MiB. GPU 0 has a total capacity of 22.30 GiB of which 224.69 MiB is free. Process 694893 has 22.08 GiB memory in use. Of the allocated memory 20.72 GiB is allocated by PyTorch, with 22.00 MiB allocated in private pools (e.g., CUDA Graphs), and 714.86 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)"
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