remove notes for better format

Signed-off-by: youkaichao <[email protected]>

Author: youkaichao

_posts/2025-08-11-cuda-debugging.md

@@ -45,11 +45,11 @@ Enabling CUDA core dump is very straightforward; you just need to set the `CUDA_
 2. Many tasks run inside containers, and when a task fails, the container is destroyed, making it impossible to retain the coredump file. In such cases, you can use the `CUDA_COREDUMP_FILE` environment variable to specify a file path template for the coredump file. For example, you can store the coredump file in a persistent storage directory: `CUDA_COREDUMP_FILE="/persistent_dir/cuda_coredump_%h.%p.%t"`, where `%h` is the hostname, `%p` is the process ID, and `%t` is the timestamp of the coredump.
 3. By default, the coredump procedure saves the entire GPU context. For programs like large model inference that occupy almost all GPU memory, a full coredump is impractical (hundreds of GiB of data). You can use the `CUDA_COREDUMP_GENERATION_FLAGS='skip_nonrelocated_elf_images,skip_global_memory,skip_shared_memory,skip_local_memory'` environment variable to skip saving GPU memory, shared memory, and local memory, thereby reducing the size of the coredump file.
 
-> The documentation also mentions that adding `skip_abort` to `CUDA_COREDUMP_GENERATION_FLAGS` prevents the CPU process from aborting after the coredump is complete. This allows the CPU process to add its own error trace, providing more debugging information. However, experiments have shown that this feature has a significant [bug](https://forums.developer.nvidia.com/t/cuda-core-dump-with-skip-abort-will-ignore-an-illegal-memory-access-error/341802/3), which may cause illegal memory access errors on the GPU to be ignored. In such cases, subsequent code may continue to run normally, but the program's memory data might already be corrupted. This is unacceptable for training tasks and undesirable for inference tasks. Therefore, this feature is generally unreliable and not recommended.
+The documentation also mentions that adding `skip_abort` to `CUDA_COREDUMP_GENERATION_FLAGS` prevents the CPU process from aborting after the coredump is complete. This allows the CPU process to add its own error trace, providing more debugging information. However, experiments have shown that this feature has a significant [bug](https://forums.developer.nvidia.com/t/cuda-core-dump-with-skip-abort-will-ignore-an-illegal-memory-access-error/341802/3), which may cause illegal memory access errors on the GPU to be ignored. In such cases, subsequent code may continue to run normally, but the program's memory data might already be corrupted. This is unacceptable for training tasks and undesirable for inference tasks. Therefore, this feature is generally unreliable and not recommended.
 
-> Additionally, the documentation states that enabling `CUDA_ENABLE_COREDUMP_ON_EXCEPTION=1` not only enables CUDA core dump but also generates a CPU coredump by default. However, in practice, we find that the CPU coredump contains little useful information and is difficult to analyze.
+Additionally, the documentation states that enabling `CUDA_ENABLE_COREDUMP_ON_EXCEPTION=1` not only enables CUDA core dump but also generates a CPU coredump by default. However, in practice, we find that the CPU coredump contains little useful information and is difficult to analyze.
 
-> If you want live data for debugging, you can also enable `CUDA_DEVICE_WAITS_ON_EXCEPTION=1` environment variable, which does not use CUDA core dump, but stops GPU execution immediately when an exception occurs, and hangs there, waiting for users to attach a debugger (like cuda-gdb) to inspect the GPU state, where the full GPU memory is still intact. However, this approach is less automatic and requires more manual intervention.
+If you want live data for debugging, you can also enable `CUDA_DEVICE_WAITS_ON_EXCEPTION=1` environment variable, which does not use CUDA core dump, but stops GPU execution immediately when an exception occurs, and hangs there, waiting for users to attach a debugger (like cuda-gdb) to inspect the GPU state, where the full GPU memory is still intact. However, this approach is less automatic and requires more manual intervention.
 
 In summary, when using the CUDA core dump feature, it is recommended to use the following combination of environment variables: