Christopher-Dreamwalker
commented
3 months ago LLaMA says: Chat 🦙 I'm an open-source chatbot. I can explain concepts, write poems and code, solve logic puzzles, or even name your pets. What do you want to chat about? 🥸 Hi any idea how to fix: Root Cause (first observed failure): [0]: time : 2024-07-25_12:48:53 host : ip-119-181-1-31.ec2.internal rank : 0 (local_rank: 0) exitcode : -9 (pid: 6774) error_file: <N/A> traceback : Signal 9 (SIGKILL) received by PID 6774 🦙 The error message you're seeing typically indicates that your process was terminated by the operating system, usually due to resource constraints such as running out of memory. Here are some steps you can take to try and resolve the issue:
- Check system logs: Look into system logs (e.g.,
/var/log/syslogor/var/log/messages) around the time of the error (2024-07-25_12:48:53) to see if there are any messages indicating why the process was killed. - Monitor system resources: Use tools like
top,htop, oratopto monitor your system's resource usage (CPU, memory, disk, etc.) while running your application. This can help you identify if you're running low on any resources. - Increase resources: If you're running your application on an AWS EC2 instance (as indicated by the hostname
ip-119-181-1-31.ec2.internal), you might need to upgrade to a larger instance type with more memory or adjust your application's configuration to use fewer resources. - Configure OOM Killer: If your process is being killed by the Out-of-Memory (OOM) Killer, you might want to consider configuring it to spare your critical processes or to start killing other, less important processes first.
- Check your application's memory usage: Use profiling tools to analyze your application's memory usage patterns. For example, if you're running a Python application, you could use tools like
mproforline_profilerto identify memory leaks. - Implement retries or checkpoints: If your application is performing a long-running task, consider implementing retries or checkpoints so that if it's terminated, it can resume from where it left off.
- Check for any resource-intensive tasks: Review your code for any resource-intensive tasks that might be causing the system to run low on resources.
- System configuration: Ensure your system's configuration is optimal for your workload. This includes adjusting ulimits, kernel parameters, or other system settings. If none of these suggestions help, please provide more details about your application, its environment, and any relevant configurations, and I'll do my best to provide a more specific solution.
llama inference start /opt/LLama_Agentic_System/llama3_1venv/lib/python3.11/site-packages/llama_toolchain/utils.py:43: UserWarning: The version_base parameter is not specified. Please specify a compatability version level, or None. Will assume defaults for version 1.1 initialize(config_path=relative_path) Loading config from : /root/.llama/configs/inference.yaml Yaml config:
inference_config: impl_config: impl_type: inline checkpoint_config: checkpoint: checkpoint_type: pytorch checkpoint_dir: /root/.llama/checkpoints/Meta-Llama-3.1-8B-Instruct/original/ tokenizer_path: /root/.llama/checkpoints/Meta-Llama-3.1-8B-Instruct/original/tokenizer.model model_parallel_size: 1 quantization_format: bf16 quantization: null torch_seed: null max_seq_len: 16384 max_batch_size: 1
Listening on :::5000 INFO: Started server process [6765] INFO: Waiting for application startup.