I have built one telephony sip agent. there are two components in livekit agent one is agent runner "python3 agent.py dev" and another is api file "python3 main.py". where user make request to api endpoint and it will trigger the outbound call. Now this whole project i want to run within docker. first question is it advisible to run it in docker why i am asking maybe agent will not use whole ram of actual machine? second question is if i can run it with docker then how should i run it both file in one docker or both in different docker?

Thanks for reaching out, Snehal. I'm an AI assistant and I'm here to help. Give me a moment while I research this topic.

currently, I am running in one container.. but there is one problem some time its giving timeout error while registering the agent with livekit. Error: 2026-02-28 134645,431 - DEBUG livekit.agents - initializing inference runner {"runner": "lk_end_of_utterance_multilingual", "pid": 96, "inference": true} 2026-02-28 134654,454 - INFO livekit.agents - killing process {"pid": 89, "inference": true} 2026-02-28 134654,454 - ERROR livekit.agents - worker failed Traceback (most recent call last): File "/opt/venv/lib/python3.10/site-packages/livekit/agents/ipc/channel.py", line 47, in arecv_message return _read_message(await dplx.recv_bytes(), messages) File "/opt/venv/lib/python3.10/site-packages/livekit/agents/utils/aio/duplex_unix.py", line 35, in recv_bytes len_bytes = await self._reader.readexactly(4) File "/usr/local/lib/python3.10/asyncio/streams.py", line 708, in readexactly await self._wait_for_data('readexactly') File "/usr/local/lib/python3.10/asyncio/streams.py", line 501, in _wait_for_data await self._waiter asyncio.exceptions.CancelledError During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/usr/local/lib/python3.10/asyncio/tasks.py", line 456, in wait_for return fut.result() asyncio.exceptions.CancelledError The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/opt/venv/lib/python3.10/site-packages/livekit/agents/cli/_run.py", line 79, in _worker_run await worker.run() File "/opt/venv/lib/python3.10/site-packages/livekit/agents/worker.py", line 445, in run await self._inference_executor.initialize() File "/opt/venv/lib/python3.10/site-packages/livekit/agents/ipc/supervised_proc.py", line 170, in initialize init_res = await asyncio.wait_for( File "/usr/local/lib/python3.10/asyncio/tasks.py", line 458, in wait_for raise exceptions.TimeoutError() from exc asyncio.exceptions.TimeoutError 2026-02-28 134654,659 - INFO livekit.agents - killing process {"pid": 96, "inference": true} 2026-02-28 134654,660 - ERROR livekit.agents - worker failed Traceback (most recent call last): File "/opt/venv/lib/python3.10/site-packages/livekit/agents/ipc/channel.py", line 47, in arecv_message return _read_message(await dplx.recv_bytes(), messages) File "/opt/venv/lib/python3.10/site-packages/livekit/agents/utils/aio/duplex_unix.py", line 35, in recv_bytes len_bytes = await self._reader.readexactly(4) File "/usr/local/lib/python3.10/asyncio/streams.py", line 708, in readexactly await self._wait_for_data('readexactly') File "/usr/local/lib/python3.10/asyncio/streams.py", line 501, in _wait_for_data await self._waiter asyncio.exceptions.CancelledError During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/usr/local/lib/python3.10/asyncio/tasks.py", line 456, in wait_for return fut.result() asyncio.exceptions.CancelledError The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/opt/venv/lib/python3.10/site-packages/livekit/agents/cli/_run.py", line 79, in _worker_run await worker.run() File "/opt/venv/lib/python3.10/site-packages/livekit/agents/worker.py", line 445, in run await self._inference_executor.initialize() File "/opt/venv/lib/python3.10/site-packages/livekit/agents/ipc/supervised_proc.py", line 170, in initialize init_res = await asyncio.wait_for( File "/usr/local/lib/python3.10/asyncio/tasks.py", line 458, in wait_for raise exceptions.TimeoutError() from exc asyncio.exceptions.TimeoutError

Thanks for the details — that error log is helpful. I’ll respond to two things you mentioned: (A) why this timeout is likely happening when both processes run in one container, and (B) concrete steps you can take to move to a safer two-container setup and reduce the timeouts. What the log shows • The worker fails during

initialize()

with

asyncio.exceptions.TimeoutError

while trying to read from the IPC channel between supervised processes. This indicates the inference worker process did not start or respond within the expected window, and the orchestrating process cancelled the wait. • Symptoms like

readexactly(4)

cancelling and repeated worker restarts point to startup delays or the child process being blocked/killed rather than a pure protocol bug. Why a single container is likely contributing • Running both the agent runner (inference-heavy) and the API in one container can cause CPU and memory contention. If the inference process is starved or delayed during startup, the supervisor’s wait times out. • Single-container setups also complicate process/signal handling and make it harder to implement independent healthchecks, restarts, and resource limits. • In short: resource contention and process lifecycle interactions are the most likely culprits for the timeouts you’re seeing. Recommended immediate fixes (high impact, low friction) 1. Split into two containers (strongly recommended) ◦ One container for

python3 agent.py dev

(inference/agent runner). ◦ One container for

python3 main.py

(API). ◦ Use Docker Compose or Kubernetes to run both and expose an internal network between them. 1. Add healthchecks and readiness probes ◦ Agent container: expose a simple HTTP or TCP health endpoint that returns ready once the inference runner has finished init. ◦ API container: wait for the agent healthcheck before accepting external requests. 1. Set resource limits ◦ Assign explicit memory and CPU to the agent container (e.g.,

--memory=1g --cpus=".5"

), so the host and other containers aren’t starved. 1. Pre-download large models during image build ◦ Download required models/files in the Dockerfile so container startup isn’t slowed by downloads. 1. Increase supervisor/init timeouts (if safe) ◦ If safe, increase the

asyncio.wait_for

timeout used by the supervised process initialization so slow startups don’t cause immediate failures (short-term mitigation). Suggested Docker Compose pattern • Services: agent, api (and any livekit server or DB you use). • Use

depends_on

plus healthchecks so

api

waits for

agent

readiness. • Set resource limits in compose for the agent service. Additional troubleshooting steps if splitting isn’t immediately possible • Monitor CPU/memory during startup to confirm contention (top, docker stats). • Check OOM or kernel logs in the container/host for kills. • Run the agent process standalone (outside container or in its own container) to confirm it initializes reliably when uncontended. If you want I can: • Provide example Dockerfiles for each service and a sample docker-compose.yml with healthchecks and resource limits. • Suggest specific places to increase timeouts in the agent supervisor code (if you want to adjust that as a short-term workaround). • Help design the agent readiness endpoint if your agent currently has no HTTP/TCP healthcheck. Which would you like first — the example Dockerfiles + docker-compose, or targeted changes to timeout settings and where to change them? Sources: examples/voice_agents | livekit/livekit_composite | DeepWiki | Self-hosted deployments | LiveKit Documentation | Agents (Python) | livekit/livekit_composite | DeepWiki | Troubleshooting Latency and Timeout Errors with Turn Detection on AWS | How to match the latency of the homepage agent | LiveKit

I tried running in different docker but still its not connecting this docker is runningin vm on aws. but when i run it local machine then its connecting properly.

Thanks — that’s useful context. I’ll help narrow this down so we can find the root cause. A few things work locally but fail on AWS, so this is likely an environment or resource issue (network, instance throttling, or config differences). I’ll list the most likely causes and then ask a few targeted questions so I can give precise next steps you can run on the VM. Most likely causes • CPU throttling on burstable AWS instances (t3/t4) causing inference/startup delays and timeouts. • Networking/firewall or security group rules blocking required ports or container-to-host networking. • Environment differences (missing or incorrect LIVEKIT_URL, API keys, secrets, or DNS resolution). • Docker networking issues (containers cannot reach each other or the LiveKit server from the VM). • Container resource limits or host OOM/kills. Please check / tell me the following (these will quickly pinpoint the problem) 1) AWS instance type • What EC2 instance type are you using (for example, t3.micro, m5.large, c6i.large)? Note: If it’s a burstable type (t3/t4), CPU credits can be exhausted and throttle CPU, which causes long inference delays and timeouts. 2) Security groups / firewall • Confirm the VM security group allows outbound and inbound traffic needed for LiveKit and your agent: ◦ TCP 443 (HTTPS) ◦ TCP 7880, 7881 and UDP 7882 (LiveKit / media ports) — if you run a LiveKit server or need RTP ◦ Any SIP ports your telephony stack requires • Are there any host-level firewalls (ufw/iptables) configured inside the VM? 3) Container logs and health • On the AWS VM, can you paste the agent container logs around startup (the same block you shared from local)? Any differences from local? • Run

docker ps

and

docker stats

while reproducing the issue and tell me if you see high CPU/memory or restarts. 4) Docker networking & environment variables • How do your containers communicate? Are they on the same Docker network (bridge/network defined in docker-compose)? • Are LIVEKIT_URL, LIVEKIT_API_KEY, LIVEKIT_API_SECRET set the same in the AWS containers as locally? Are you using hostnames that might resolve differently on AWS? 5) Reproduce isolated behavior • Can you run only the agent container (no API) on the AWS VM and see whether it initializes successfully? This isolates whether contention or network calls from the API are involved. • Can you run the agent outside Docker on the VM (same command you use locally) to see if the problem is Docker-specific? Quick actions you can run now 1. Check CPU credits and throttle risk (if t3/t4): ◦ In CloudWatch, check the

CPUCreditBalance

metric for the instance. If it drops to zero, throttling is very likely. 1. Check real-time metrics during startup: ◦ On the VM:

docker stats

and

top

while starting the agent to see CPU spikes or throttling. 1. Test reachability from inside the container: ◦ Exec into the agent container:

docker exec -it <agent_container> /bin/sh

◦ From inside, test LiveKit endpoint:

curl -v $LIVEKIT_URL

or

nc -vz <livekit-host> <port>

1. Inspect container exit/restart reasons: ◦

docker inspect <container>

and look for OOMKilled or restart_count. If the instance is burstable (t3/t4) • Move to a non-burstable instance class (m5, c5, c6i) or a larger instance so inference startup won’t be throttled. • Or monitor CPUCreditBalance and choose a sizing strategy so credits never run out. What I can provide next • If you tell me the instance type and the results of a couple of the checks above (CPU credits, docker stats, container logs on AWS), I’ll: ◦ Recommend whether to change instance type now or tune resources/timeouts. ◦ Provide an updated docker-compose example with healthchecks and readiness probes tailored to AWS. ◦ Suggest exact env checks and a minimal reproduce command to narrow whether Docker networking or instance resource limits are the cause. Which of the checks above can you run now? If you prefer, paste the output of:

uname -a && cat /etc/os-release && docker ps --no-trunc && docker inspect <agent_container> && docker logs <agent_container> --since 10m

and I’ll analyze it. Sources: Troubleshooting Latency and Timeout Errors with Turn Detection on AWS | Running LiveKit on AWS | Running LiveKit on AWS

currently i dont know the type of machine but it has 4vcpu and 16GB of ram. may be t4g or t3a and in this machine already there is already 25+ docker services are running.. I have tried running agent using "python3 agent.py dev" in aws server outside docker and still the Timeout error.. and not able to run curl commands inside docker as inside docker no curl command found.

Thanks — that extra context is very helpful. You’ve confirmed a few key things that point toward an environment/resource issue: • The VM has 4 vCPU / 16 GB RAM and may be a burstable type (t3a/t4g). • The host already runs 25+ Docker services (high density). • Running

python3 agent.py dev

outside Docker on the VM still produces the same TimeoutError. • You can’t run curl inside the container because it’s not installed. What this implies • Because the agent also times out when run outside Docker, the problem is not purely Docker networking — it’s environmental on the VM (CPU throttling, memory pressure, or system-level limits). • High container density plus a burstable instance type can cause CPU throttling or contention, which causes inference or IPC initialization to take much longer and trigger

asyncio.exceptions.TimeoutError

. • Missing utilities like

curl

inside an image are normal; you can test networking either by installing a small tool into the container image or exec’ing into the host. Immediate next steps I recommend you run (practical and fast) 1) Confirm instance family and check CPU credits (if burstable) • Find instance type: on the VM run: ◦ uname -a && curl -s http://169.254.169.254/latest/meta-data/instance-type ◦ If

curl

is not available on the VM, use

sudo apt-get update && sudo apt-get install -y curl

or

yum install -y curl

depending on OS. • If instance type is t3/t3a/t4g, check CloudWatch

CPUCreditBalance

. If it hits zero, throttling is likely the root cause. 2) Check host resource usage while starting the agent • On the VM, run these during an agent startup attempt: ◦ top (or htop) — look at CPU% and load. ◦ vmstat 1 10 — look for stalling or CPU wait. ◦ docker stats — check container-level CPU/memory usage (run from host). • If CPU is pegged or the system shows long CPU wait, that supports throttling/contention. 3) Isolate the agent to verify contention • Stop or move non-critical services temporarily so fewer than 25 containers run. Then start the agent (outside Docker or in its own container) and see if the timeout stops occurring. • Alternatively, launch a fresh small test instance (non-burstable like m5/c5/c6i) and try the agent there. If it works, that confirms instance throttling or host contention. 4) Networking checks from host and container • From the host: ◦ curl -v $LIVEKIT_URL (install curl if missing) • From inside the container (if image lacks curl): ◦ Exec into container and install curl for a quick test: ▪︎ docker exec -it <container> /bin/sh ▪︎ apk add --no-cache curl (if Alpine) ▪︎ apt-get update && apt-get install -y curl (if Debian/Ubuntu) ◦ Then test: curl -v $LIVEKIT_URL • If you can reach LiveKit from host but not from container, check Docker network settings and DNS resolution. 5) Check for OOM or kernel kills • Inspect syslog/kern.log / dmesg for OOM kills around the time of the agent failure. • docker inspect <agent_container> and look for

State

fields like

OOMKilled

. Short-term mitigation if you cannot change the instance now • Increase the init timeout used by the agent supervisor (only as a temporary measure) so slow starts don’t immediately raise a TimeoutError. • Pre-download models and heavy files during image build so startup work is reduced. • Reduce competing workload: stop non-essential services to free CPU for the agent. Long-term recommendations • Run the agent on a non-burstable instance class (m5, c5, c6i) for consistent CPU performance, or provision a larger instance to reduce contention. • Run agent and API in separate containers (you already tried that), but ensure the host itself has headroom. • Add readiness healthchecks and a small startup probe so the API waits for the agent to be ready. If you want, I can provide: • Exact commands/files for checking instance type and CloudWatch CPU credits. • A minimal Dockerfile snippet that adds curl for quick debugging. • A short script you can run to capture

top

,

docker stats

,

vmstat

output during agent startup to paste here. Which of those would you like first? If you can run one thing now, run: • On the VM: curl -s http://169.254.169.254/latest/meta-data/instance-type Paste the output and I’ll recommend the next move (or tell you how to check CPU credits).

curl -s http://169.254.169.254/latest/meta-data/instance-type - this command not returning anything this is state of docker "State": { "Status": "running", "Running": true, "Paused": false, "Restarting": false, "OOMKilled": false, "Dead": false, "Pid": 2966560, "ExitCode": 0, "Error": "", "StartedAt": "2026-02-28T160608.662817917Z", "FinishedAt": "2026-02-28T160608.487514776Z" }, after installing curl inside docker i am able to reach livekit host curl -s

Thanks — that progress is helpful. You confirmed two important things: • The instance metadata curl returned nothing from inside the VM (so instance-type lookup failed there). • Inside the container, after adding curl, you can reach the LiveKit host (so basic network reachability from container is OK). • The agent still times out and Docker shows the container is running (not OOMKilled). Next steps — focused checks and fix options I’ll list short, prioritized checks you can run and the likely actions depending on results. Run the first group now (they’re quick) and paste outputs you’re comfortable sharing; I’ll interpret them and give exact next commands. 1) Confirm instance type from the VM (alternate ways) • Try the metadata call again from the host (not inside the container). If curl is missing on the host, install it first: ◦ Debian/Ubuntu: sudo apt-get update && sudo apt-get install -y curl ◦ RHEL/CentOS: sudo yum install -y curl • Then run: ◦ curl -s http://169.254.169.254/latest/meta-data/instance-type • If that still returns nothing, you can also check the instance type in the AWS Console → EC2 → Instances (select the instance and view Instance type). Why: If it’s t3/t3a/t4g (burstable), CPU credit exhaustion is a likely cause. 2) Check host and container CPU usage during agent startup • On the host, run these while you restart the agent (or start it and reproduce the timeout): ◦ top -b -n 5 | sed -n '1,20p' ◦ sudo vmstat 1 10 ◦ docker stats --no-stream • From these outputs I’ll look for high CPU, CPU steal, or contention that would delay the inference worker. 3) Check CloudWatch CPUCreditBalance (if burstable) • If instance is t3/t3a/t4g, check CloudWatch metric

CPUCreditBalance

for the instance for the last hour. If it reaches zero, CPU throttling will explain slow startup and timeouts. 4) Check agent logs and exact restart behavior • Share the tail of the agent log around the timeout and any supervisor messages: ◦ docker logs <agent_container> --since 5m • Also run: ◦ docker inspect <agent_container> --format '{{json .State}}' | jq ◦ This confirms there’s no OOMKill and shows restart count, pid, etc. 5) Quick isolation test (very high value) • Temporarily stop some of the non-critical containers on the host so there’s less contention (you said ~25 services run). Then: ◦ Stop them or move to a different host. ◦ Run

python3 agent.py dev

on the host (as you did before). If timeouts stop, this confirms contention on the VM is the problem. • If you can’t stop services, spin up a fresh small test instance (prefer non-burstable like m5.large or c5.large) and run the agent there to test. Short-term mitigations while you investigate • Increase the agent init timeout (temporary): modify the supervised process init

asyncio.wait_for

timeout so slow starts don’t immediately fail. This is a band-aid; don’t rely on it long-term. • Pre-download models at build time so agent startup does less work. • Make sure the agent runs on a dedicated node or a VM with spare CPU headroom. If the host is burstable and credits got exhausted • Move the agent to a non-burstable instance class (m5, c5, c6i) or provision a larger machine to avoid throttling. • Or ensure the instance has enough CPU credits (monitor CloudWatch metric). What I can do next for you • Interpret the outputs you paste from: ◦ instance-type call (or tell me instance type from the AWS Console), ◦

docker stats --no-stream

, ◦

docker logs <agent_container> --since 5m

, ◦ `top`/`vmstat` during startup. • Provide an exact small change to increase init timeout or a docker-compose healthcheck snippet. • Provide a minimal Dockerfile line to add curl for quick container networking tests if you need that again. Which would you like to run and paste first: the instance-type output or

docker stats --no-stream

plus the last 5 minutes of agent logs?

ubuntu@ip-10-0-12-107:~$ sudo vmstat 1 10 procs -----------memory---------- ---swap-- -----io---- -system-- ------cpu----- r b swpd free buff cache si so bi bo in cs us sy id wa st 11 0 4170320 1436080 340040 8240944 2 6 71 138 9 4 3 2 94 0 0 2 0 4170320 1291180 340108 8243308 0 0 2256 0 11162 19123 35 16 45 4 0 2 0 4170320 1287572 340256 8249100 12 0 6208 24 10107 16386 22 11 60 6 1 4 0 4170320 1096060 340292 8256248 0 0 6884 432 9334 14876 24 11 61 3 1 2 0 4170320 1041980 340384 8270456 4 0 13532 388 10875 18040 46 20 27 6 0 6 0 4170320 992900 340384 8273500 0 0 1716 28 8353 13045 48 17 33 1 0 4 0 4170320 1022024 340420 8274240 0 0 2740 2176 11495 19874 50 19 28 3 0 3 0 4170320 1020772 340432 8274784 0 0 792 4 6794 9656 24 10 66 1 0 1 0 4170320 1012216 340432 8284748 0 0 9756 0 9635 14850 28 8 57 6 1 1 0 4170320 999868 340432 8291104 0 0 6316 0 7738 12499 23 10 64 4 0 ubuntu@ip-10-0-12-107:~$ sudo vmstat 1 10 procs -----------memory---------- ---swap-- -----io---- -system-- ------cpu----- r b swpd free buff cache si so bi bo in cs us sy id wa st 4 0 4170320 834836 340468 8302620 2 6 71 138 9 4 3 2 94 0 0 3 0 4170320 802580 340468 8302620 0 0 0 8 8743 13448 27 8 64 0 1 8 0 4170320 802328 340476 8302628 0 0 24 4 6495 10116 18 7 75 0 0 2 0 4170320 1332516 340476 8303240 0 0 612 0 10124 16275 30 15 55 0 0 1 0 4170320 1286828 340484 8303244 0 0 0 336 8218 13518 37 11 52 0 0 1 0 4170320 1176268 340484 8303244 128 0 128 44 9063 14219 28 12 60 0 1 1 0 4170320 1162184 340488 8303464 0 0 224 0 8995 14319 31 9 59 0 1 1 0 4170320 1152104 340488 8303468 0 0 0 0 8255 13858 30 12 58 0 0 3 0 4170320 997652 340488 8306956 3524 0 7004 0 11807 18503 32 14 48 6 0 1 0 4170064 963392 340492 8307856 1776 0 2664 540 9861 15735 31 14 51 3 1 ubuntu@ip-10-0-12-107:~$ top -b -n 5 | sed -n '1,20p' top - 164359 up 12 days, 12:02, 2 users, load average: 1.53, 1.43, 1.30 Tasks: 340 total, 2 running, 338 sleeping, 0 stopped, 0 zombie %Cpu(s): 34.3 us, 7.1 sy, 0.0 ni, 57.1 id, 0.0 wa, 0.0 hi, 0.0 si, 1.4 st MiB Mem : 15832.1 total, 936.7 free, 6576.3 used, 8319.0 buff/cache MiB Swap: 8192.0 total, 4069.0 free, 4123.0 used. 8901.2 avail Mem PID USER PR NI VIRT RES SHR S %CPU %MEM TIME+ COMMAND 3006283 root 20 0 5619532 885112 322176 R 100.0 5.5 0:11.84 python3 2951136 ubuntu 20 0 42.1g 401172 59648 S 11.8 2.5 4:27.34 node 2951635 ubuntu 20 0 5016856 463704 80640 S 11.8 2.9 2:59.27 language_server 3006633 ubuntu 20 0 10612 4224 3328 R 11.8 0.0 0:00.03 top 452 root 20 0 1322632 102312 15104 S 5.9 0.6 107:15.02 tailscaled 2634 root 20 0 1368764 14660 2688 S 5.9 0.1 50:56.63 uvicorn 2669 lxd 20 0 2333996 31012 4224 S 5.9 0.2 126:25.73 mysqld 193831 root 20 0 1394784 48268 8960 S 5.9 0.3 186:17.56 weaviate 2203196 ubuntu 20 0 1403712 200984 17536 S 5.9 1.2 6:10.50 node 2947616 ubuntu 20 0 1230924 90180 49280 S 5.9 0.6 0:29.88 node 2948526 ubuntu 20 0 4956740 364724 77824 S 5.9 2.2 2:45.56 language_server 1 root 20 0 168288 11372 7660 S 0.0 0.1 1:01.88 systemd 2 root 20 0 0 0 0 S 0.0 0.0 0:00.22 kthreadd ubuntu@ip-10-0-12-107:~$ top -b -n 5 | sed -n '1,20p' top - 164411 up 12 days, 12:03, 2 users, load average: 1.74, 1.48, 1.32 Tasks: 340 total, 2 running, 338 sleeping, 0 stopped, 0 zombie %Cpu(s): 25.4 us, 12.7 sy, 0.0 ni, 60.6 id, 0.0 wa, 0.0 hi, 1.4 si, 0.0 st MiB Mem : 15832.1 total, 588.9 free, 6768.6 used, 8474.5 buff/cache MiB Swap: 8192.0 total, 4069.0 free, 4123.0 used. 8708.9 avail Mem PID USER PR NI VIRT RES SHR S %CPU %MEM TIME+ COMMAND 3006922 root 20 0 5190608 629360 223780 R 94.1 3.9 0:01.62 python3 452 root 20 0 1322632 102312 15104 S 11.8 0.6 107:15.45 tailscaled 3006937 ubuntu 20 0 10612 4224 3328 R 11.8 0.0 0:00.03 top 2669 lxd 20 0 2333996 31012 4224 S 5.9 0.2 126:25.81 mysqld 2951136 ubuntu 20 0 42.1g 408340 59648 S 5.9 2.5 4:28.28 node 2951635 ubuntu 20 0 5016856 463960 80640 S 5.9 2.9 2:59.89 language_server 1 root 20 0 168288 11372 7660 S 0.0 0.1 1:01.91 systemd 2 root 20 0 0 0 0 S 0.0 0.0 0:00.22 kthreadd 3 root 20 0 0 0 0 S 0.0 0.0 0:00.00 pool_workqueue_release 4 root 0 -20 0 0 0 I 0.0 0.0 0:00.00 kworker/R-rcu_g 5 root 0 -20 0 0 0 I 0.0 0.0 0:00.00 kworker/R-rcu_p 6 root 0 -20 0 0 0 I 0.0 0.0 0:00.00 kworker/R-slub_ 7 root 0 -20 0 0 0 I 0.0 0.0 0:00.00 kworker/R-netns

Thanks — this is very helpful data. I’ll summarize what the metrics show, why that explains the timeout, and give concrete next steps you can take right away (short-term mitigations and longer-term fixes). What your metrics show • CPU: top shows a Python process pegging a core at ~90–100% during startup and overall CPU user time around 25–35%. vmstat shows frequent runnable processes (non-zero "r") and varying CPU usage. This indicates the host is under significant CPU load. • Memory: you have 16 GB total with only a few hundred MB free at times and ~4 GB swap used. High swap and low free RAM mean the host is memory-constrained or has many working sets in memory. • Density: you mentioned ~25 other Docker services — with this many services the VM has limited headroom for another inference-heavy process. Why this causes your agent TimeoutError • The agent’s inference runner and supervised IPC setup expect timely process startup and message responses. When the host is CPU- or memory-constrained, inference initialization can take much longer or block, causing

asyncio.wait_for(... )

to time out and the worker to be killed and restarted. • This is consistent with the logs you shared earlier where the supervised process fails to respond in the expected window and triggers

asyncio.exceptions.TimeoutError

. Immediate next actions (fast, prioritize these) 1. Free up CPU/memory temporarily ◦ Stop or pause non-critical containers/services so the agent can start with more headroom. If you can stop ~5–10 non-essential containers and retry the agent, see if the timeout disappears. 1. Re-run the agent while monitoring ◦ While you restart the agent, run on the host: ▪︎ top (or htop) and docker stats --no-stream ▪︎ vmstat 1 10 ◦ Observe whether the agent finishes initialization when CPU usage drops. 1. Avoid swap pressure ◦ High swap could slow startup severely. If possible, reduce memory pressure (stop services) or add RAM to the VM. Short-term mitigations (if you can’t change host resources immediately) • Increase the agent supervisor init timeout (temporary): this prevents immediate worker kills while the host is busy. This is a band-aid, not a fix. • Pre-download models and heavy assets during image build so agent startup work is reduced. • Run the agent in a separate VM or a dedicated host with fewer services while you troubleshoot. Recommended longer-term fixes 1. Move the agent to a host with guaranteed CPU (non-burstable) ◦ Choose a non-burstable instance type (m5/c5/c6i family) or a larger instance so inference and startup are not throttled or contended. ◦ If the current instance is t3/t3a/t4g, CPU credit exhaustion would lead to severe throttling—move off burstable types for inference workloads. 1. Dedicated node for agent ◦ Run the agent on a VM (or node pool in k8s) dedicated to inference workloads so other services don’t steal CPU/memory. 1. Container resource limits and autoscaling ◦ Configure CPU and memory limits for containers so the orchestrator can manage contention and you can size appropriately. 1. Healthchecks and readiness probes ◦ Give the agent an HTTP/TCP readiness endpoint and have your API wait for agent readiness before accepting calls. Help I can give right now • If you can stop a few non-essential containers and retry, tell me the result and paste the agent logs around startup — I’ll interpret whether the timeout disappears. • I can give: ◦ A short command list to identify which containers are the biggest CPU/memory consumers. ◦ A small patch to increase the agent init timeout and where to change it. ◦ Recommended instance types for predictable performance. ◦ A docker-compose snippet with resource limits and healthchecks. Which do you want next? If you can run one immediate test, please stop several non-critical containers, restart the agent (outside Docker or in its own container) and paste the agent startup logs (tail 2–3 minutes). I’ll read them and recommend the exact next step.

instead of docker if i run it on kubernetes on same ec2 instance. is there a possibility it will run?

Short answer: Kubernetes alone won’t fix the timeout if you run it on the same EC2 instance that is already CPU- and memory-constrained. Kubernetes helps with orchestration, isolation, and resource controls — but it cannot create CPU/memory that the host doesn’t have. Why Kubernetes on the same VM likely won’t make the timeout go away • The root problem is host-level contention (high CPU usage, low free RAM, swap use and many containers) causing the inference process to be slow or blocked during startup. Kubernetes won’t remove that contention — the node still has the same CPUs and RAM. • If the instance is burstable (t3/t3a/t4g) you may also face CPU throttling when credits are low; Kubernetes can’t prevent instance-level throttling. • Running Kubernetes on a single crowded node can even add overhead (kubelet, kube-proxy, CNI), reducing headroom further. What Kubernetes will help you do (if you also fix host sizing or add nodes) • Enforce resource requests and limits per pod so the scheduler makes resource-aware placement and you avoid accidental overcommit. • Give you readiness/liveness probes and better restart policies so the API waits for the agent. • Make it easy to run the agent on dedicated nodes (nodeSelectors/taints+tolerations) or scale out to multiple nodes. • Centralize logging/monitoring, and enable rolling updates without downtime. Concrete options and guidance 1) If you must try Kubernetes on the same EC2 instance (short-term test) • It may succeed only if you free up enough host resources first (stop many non-critical services) and ensure the node has spare CPU/memory. • Use strict resource requests and limits in your pod specs so the scheduler doesn’t overcommit: ◦ Set

resources.requests.cpu

and

resources.requests.memory

to the minimum the agent needs. ◦ Set

resources.limits

so the agent can’t starve other pods. • Add readinessProbe on the agent pod and make the API deployment depend on agent readiness. • Expect kube system overhead — you still need headroom. 2) Better approach (recommended) • Move the agent to a dedicated node (node pool) with sufficient CPU and memory: ◦ Use non-burstable instance types for inference (m5/c5/c6i families) or a larger instance size. ◦ If you use a managed k8s cluster, create a node pool sized for inference workloads. • Run agent and API as separate pods (one process per container/pod) with healthchecks. • Pre-download models at image build time to reduce startup latency. 3) Quick Kubernetes manifest sketch (conceptual) • Agent Deployment: ◦ Single replica (or a replica set you can scale) ◦ readinessProbe pointing to an internal HTTP/TCP health endpoint ◦ resources.requests/limits set • API Deployment: ◦ readinessProbe ◦

initContainer

or startup script that waits for agent readiness (optional) 4) Immediate next steps you can take now • Try a quick isolation test: stop some non-critical services on the host and run the agent in a pod (or as before) to confirm that freeing CPU/memory removes the timeout. • If that confirms resource contention, either: ◦ Add a dedicated EC2 instance (non-burstable) and run the agent there (best short-term fix), or ◦ Provision a small Kubernetes cluster (or node pool) with a dedicated node type for the agent. Questions so I can give you exact manifests and commands • Do you already have a Kubernetes cluster (EKS or self-hosted) or would you run a single-node k8s on the same EC2 instance? • Do you want example Deployment manifests (with resource requests/limits and readinessProbe) for the agent and API? • Would you prefer instructions to add a dedicated node (or a new instance) and migrate the agent there? If you want, I can: • Provide example k8s Deployment/Service manifests with readinessProbe and resources configured. • Show an initContainer pattern for the API to wait for agent readiness. • Recommend exact instance types (m5/c5/c6i) and pod resource values to start with, based on your host (4 vCPU / 16 GB) and the agent’s observed CPU usage. Which would help you next — k8s manifests and probes, or concrete instance size + node placement advice?

currently, my instance cpu utilization is only 10%, memory ram only around 5gb utilize out of 16gb. Now with this current scenrio will it run on kubernetes?

Thanks — that’s useful. With only ~10% CPU and ~5 GB RAM in use, your instance has headroom, so Kubernetes can run the agent there — but a few important caveats and concrete recommendations will make it reliable. Quick answer • Yes, the agent can run on Kubernetes on that same EC2 instance given the available free CPU/RAM — provided you configure resource requests/limits, readiness probes, and ensure the node has enough steady capacity (no burst throttling). Kubernetes helps scheduling and isolation, but it won’t create extra CPU/RAM; you must reserve the resources the agent needs. What to check before you move to k8s on that node • Instance family: confirm it’s not a burstable (t3/t4) instance with exhausted credits. Kubernetes won’t fix instance-level throttling. • Node headroom: leave CPU/memory for kube-system daemons (kubelet, kube-proxy, CNI) and any other critical pods. Don’t consume all free memory with pods. • Pod density: avoid crowding the node with many pods that cause noisy-neighbor contention. Use resource requests and limits to prevent that. Concrete recommendations to run reliably on Kubernetes 1) Resource requests & limits • Set conservative requests so the scheduler guarantees capacity, and limits so a pod can’t starve others. • Example starting point for the agent (tune based on observation): ◦ requests: cpu: "0.5", memory: "1Gi" ◦ limits: cpu: "2", memory: "6Gi" • For the API (lighter): requests: cpu: "0.25", memory: "256Mi"; limits: cpu: "1", memory: "1Gi" 2) Readiness / liveness probes • Add a readinessProbe on the agent that returns ready only after inference initialization completes. Make the API wait for agent readiness (initContainer or readiness checks). • This prevents the API from sending calls to an unready agent and masking startup timeouts. 3) Pod placement and isolation • Prefer running the agent on a dedicated node (nodeSelector or taints/tolerations) if possible. • If single-node k8s, ensure few other workloads run there or use proper requests/limits. 4) Pre-warm / build-time downloads • Pre-download models and heavy files during image build so pod startup does less work. 5) Monitor and tune • Use

kubectl top node

and

kubectl top pods

to observe usage. • Watch for swap usage and system-level CPU steal — these indicate host contention despite pod resources. 6) Short-term guardrails • Add a slightly larger init timeout for the agent supervisor if you still see intermittent slow starts while you tune resources. • Configure pod disruption budgets and restart policies to avoid flapping. Example manifest snippets (conceptual) • Agent Deployment (key parts): ◦ resources.requests/limits as above ◦ readinessProbe: HTTP GET /health or TCP check to an internal port • API Deployment: ◦ initContainer or startup wait script that polls agent readiness before starting main server Next steps I can provide for you • A full pair of example Kubernetes manifests (Deployment + Service) for the agent and the API, including resource values, readiness/liveness probes, and an initContainer pattern for the API. • A quick checklist/commands to confirm instance type and CPU credits, if you want to be 100% sure about throttling risk. • Help tuning the request/limit numbers after you run the pod and share

kubectl top

outputs. Which would help most right now — the example manifests with probes + resource settings, or the commands to confirm instance type / CPU credit status first?