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Tune two independent controls: the ECS container health check for application readiness, and the ECS instance-health signal for GPU device impairment when you run on Amazon ECS Managed Instances. Then size minimumHealthyPercent and maximumPercent against both desired task count and real, compatible GPU capacity. A percentage that permits another task mathematically does not guarantee that a suitable GPU slot exists.
AWS documents the mechanisms and supported ranges, but it does not publish one universal threshold for every GPU workload. Model loading, CUDA context creation, inference latency, placement constraints, and spare GPU capacity should determine your values.
Understand the two health signals before changing settings
ECS health monitoring has two different scopes. Mixing them leads to false confidence during a GPU failure.
Container health checks measure application readiness
The healthCheck object in the ECS task definition runs a command inside the container. It is intended to show whether the application is available to serve useful work. ECS only monitors the check declared in the task definition. A Docker HEALTHCHECK baked into an image is not monitored by the ECS agent when the task definition omits its own health-check configuration.
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The command must exist in the runtime image and must be executable with the container’s user, filesystem, network, and environment. A missing curl, shell, or GPU utility causes a failed check; it does not produce a meaningful signal about your service.
GPU instance health measures the device or host
On Amazon ECS Managed Instances, accelerated-compute health monitoring can report an impaired GPU. Container-instance health and health-change events can identify the ACCELERATED_COMPUTE signal, and an impaired NVIDIA device may include an Xid code in statusReason. This is an instance/device signal, not proof that your application endpoint is ready.
The cited ECS documentation describes this accelerated-compute check for Managed Instances. Confirm your launch type before depending on it; do not assume the same signal is available for every ECS capacity model.
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|---|---|---|---|
| Task-definition container health check | Is the application inside this task ready and responding? | Hung server, failed model load, unusable endpoint, or process that is alive but not serving | That the underlying GPU or instance is healthy |
| Managed Instance accelerated-compute health | Is the host’s accelerated-compute device reporting impairment? | GPU or host condition associated with an NVIDIA Xid reason | That the application has completed initialization or can answer requests |
Configure an ECS container health check for a GPU workload
1. Put the check in the container definition
Add healthCheck to the relevant container definition in the task definition. Use CMD for a direct executable and argument list. Use CMD-SHELL only when shell operators, pipes, variable expansion, or compound logic are required.
{
"healthCheck": {
"command": ["CMD", "/app/healthcheck", "--ready"],
"interval": 30,
"timeout": 5,
"retries": 3,
"startPeriod": 120
}
}The executable in this example is illustrative: use the readiness command that is actually present in your image. If you choose a shell form, make the shell and every utility available in the image:
{
"healthCheck": {
"command": ["CMD-SHELL", "curl -fsS http://127.0.0.1:8080/ready || exit 1"],
"interval": 30,
"timeout": 5,
"retries": 3,
"startPeriod": 120
}
}Do not copy the second command into a minimal image that does not contain curl. A direct application health endpoint or an image-native probe is usually less fragile than depending on an extra shell tool.
2. Make the probe represent useful service
A process-existence test can pass while the model is still loading, the CUDA context is unusable, or the request queue is rejecting work. Prefer a readiness condition that matches what callers need: for example, a local endpoint that returns success only after model initialization and required GPU setup complete.
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3. Set timing from observed startup and failure behavior
The supported Managed Instances parameter ranges and listed defaults are product settings documented by Amazon Web Services, not performance recommendations for a particular model:
| Parameter | Supported values | Listed default | Operational meaning |
|---|---|---|---|
interval |
5–300 seconds | 30 seconds | Time between probes |
timeout |
2–60 seconds | 5 seconds | How long one probe may run before it fails |
retries |
1–10 | 3 | Consecutive failed probes required after startup before the task is unhealthy |
startPeriod |
0–300 seconds | Disabled by default | Startup grace period during which early failures do not count in the normal way |
These values are ranges and defaults from the AWS parameter documentation; the reviewed page does not state a universal production threshold.
4. Give model initialization enough startup grace
Set startPeriod to cover the normal path for image startup, model download or mount, weight loading, CUDA context creation, and any warm-up your service performs. A task that normally needs 90 seconds should not be judged by a probe that starts counting failures immediately.
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The grace period is not a permanent shield. A successful check during startPeriod makes the container healthy; later failures are then evaluated against retries. If the application reports ready too early and fails while loading the model, ECS can quite reasonably replace it.
5. Balance detection speed against transient tolerance
Short intervals and low retry counts detect hard failures quickly but can turn brief CPU contention, a temporary event-loop stall, or a slow GPU operation into replacement churn. Long intervals, generous timeouts, and many retries reduce false positives but extend the time an actually broken task remains registered.
Choose the combination as a single detection budget. For example, a probe every 30 seconds with three failed attempts and a five-second timeout has a different operational consequence from a five-second interval with one retry, even though both use the same command. Validate the behavior under the application’s normal load and startup variance rather than treating the AWS defaults as an SLO.
Use Managed Instance GPU health as a complementary signal
Monitor container-instance health and health-change events for ACCELERATED_COMPUTE when the service runs on ECS Managed Instances. An impaired GPU can expose an NVIDIA Xid code in statusReason, which gives operators a device-oriented clue that a container probe alone cannot provide.
Route this signal into the same incident workflow as task health, but keep the meanings separate:
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- A failed container check says the task is not serving correctly; it does not identify whether the cause is the application, model, network, or GPU.
- An accelerated-compute impairment says the instance’s GPU health is suspect; it does not guarantee that every task on the instance is already failing.
- When both appear together, preserve the device reason and task-level evidence so replacement and instance remediation can be correlated.
Because the documented GPU signal is tied to Managed Instances, verify launch type and current ECS feature availability before writing automation that assumes it will exist.
Tune service replacement with both percentages and capacity
minimumHealthyPercent: the service floor
Set minimumHealthyPercent to the lowest healthy service capacity you can tolerate while ECS deploys or replaces tasks. The replica-service API reference lists 100% as the default. A value of 100% asks ECS to retain the full desired healthy count during replacement when capacity permits; a lower value allows a temporary reduction.
For GPU services, lowering this floor can make recovery possible when no spare GPU exists, but it also reduces serving capacity during the event. Decide whether losing one replica is acceptable for your workload, latency target, and queue depth.
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maximumPercent limits how many tasks ECS may run relative to desiredCount during deployment or replacement. Set it high enough to run a replacement beside the current tasks when your cluster has a compatible GPU slot. The percentage alone is not a reservation: ECS still needs an instance that satisfies the task’s GPU requirement, architecture, networking, and placement constraints.
If the maximum prevents ECS from launching a replacement first, the scheduler can stop an unhealthy task to release room and then launch the replacement. This is a deliberate fallback, not evidence that the task was healthy. If the replacement is also unhealthy while ECS reconciles toward desiredCount, ECS may stop either unhealthy task.
Why task-count headroom is not GPU headroom
Suppose a service has a desired count of four and a maximum that permits a fifth task. You still need one free, compatible GPU allocation. An instance can have spare CPU and memory while having no unallocated GPU, the wrong GPU model, an incompatible architecture, or a placement constraint that excludes it.
Before increasing maximumPercent, inspect actual free GPU resources and the task definition’s GPU requirements. ECS placement and GPU allocation determine whether the nominal replacement slot is usable.
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|---|---|---|
Healthy replacement can fit under maximumPercent and compatible GPU capacity exists |
Launch the replacement before stopping the unhealthy task in the normal replacement-first path | Best chance of preserving capacity |
| Maximum is restrictive, so an additional task cannot start | Stop an unhealthy task to free room, then attempt replacement | Temporary loss of one task is possible |
| Percentage allows another task, but no compatible GPU slot exists | Placement remains blocked until capacity or constraints change | Higher percentage alone does not recover the service |
| New replacement becomes unhealthy | ECS can stop either unhealthy task while reconciling to desired count | Investigate the common failure rather than repeatedly raising percentages |
Account for load-balancer health and rollout timing
If the service uses a load balancer, ECS may require both container health and target-group health before counting a task as healthy for deployment progress. A task can pass its local probe while still failing registration, port reachability, or target-group checks.
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Include image pull time, model initialization, GPU warm-up, container probe timing, and load-balancer health timing in rollout expectations. Making the probe aggressively fast does not make the model initialize faster; it can instead create a loop in which normally slow-starting tasks are killed and relaunched.
A practical tuning workflow for SREs
- Define readiness. Write down the condition that means the task can accept production traffic, including model and GPU initialization.
- Verify the command. Run the exact probe inside the built image. Confirm the executable, shell, endpoint, permissions, and required environment are present.
- Measure normal startup. Record startup times across cold image pulls, model loads, and GPU context creation. Choose
startPeriodabove the normal path with room for ordinary variance. - Set the failure budget. Select
interval,timeout, andretriesso a real outage is detected within your recovery objective without reacting to expected transient work. - Inventory GPU capacity. Check free compatible GPU resources, instance types, task GPU requirements, and placement constraints. Do this before relying on a larger
maximumPercent. - Choose the healthy floor. Set
minimumHealthyPercentfrom the traffic level you must retain during replacement, not from a generic deployment template. - Test replacement behavior. Exercise an application failure and, where supported, review the Managed Instance accelerated-compute signal. Observe whether ECS launches first, stops an unhealthy task to make room, or remains blocked on GPU placement.
- Alert on causes, not only counts. Capture task health reasons, container-instance health changes, Xid details when present, placement failures, and target-group health.
Failure patterns and corrective actions
Tasks fail immediately after launch
Check whether the command exists in the image and whether its exit status is inverted by shell syntax. If the application is still loading a model, increase startPeriod to cover the measured startup path. Do not hide a genuine initialization failure by making the grace period indefinitely long.
Tasks flap healthy and unhealthy under load
Inspect probe cost, endpoint latency, and timeout. A probe that competes with inference for the same constrained resources may time out during legitimate load. Increase timeout or retries only when measurements show transient saturation; otherwise fix the readiness endpoint or resource allocation.
Replacement never starts despite a permissive maximum
Look beyond percentages. Confirm that a compatible GPU is free, the task’s GPU requirement can be allocated, and placement constraints, capacity providers, networking, and architecture permit scheduling. A task-count ceiling cannot manufacture an eligible GPU instance.
A GPU impairment is reported but the application probe passes
Treat the events as different scopes. Preserve the accelerated-compute reason and investigate the instance while deciding whether to drain or replace affected tasks according to your operational runbook. Passing application health does not disprove a device-level warning.
Deployment stalls with healthy task counts
Compare container health with load-balancer target health and inspect registration and connectivity. ECS deployment accounting can wait for required health statuses; a local readiness response alone may not satisfy all of them.
Example service settings to review together
The following is a review shape, not a universal recommendation. Replace the probe and timing with values derived from your service’s measurements:
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{
"deploymentConfiguration": {
"minimumHealthyPercent": 100,
"maximumPercent": 200
},
"containerDefinitions": [
{
"name": "inference",
"healthCheck": {
"command": ["CMD", "/app/healthcheck", "--ready"],
"interval": 30,
"timeout": 5,
"retries": 3,
"startPeriod": 120
}
}
]
}In this shape, the healthy floor and ceiling describe desired task-count behavior, while the container settings describe readiness detection. Neither setting guarantees GPU placement; capacity and task resource requirements remain a separate constraint.
What to standardize in an SRE runbook
- Record the ECS launch type and whether Managed Instance accelerated-compute health is available.
- Document the exact container probe command and the image version that contains it.
- Record measured cold-start and model-load times alongside
startPeriod. - Document the intended healthy-task floor and the GPU capacity required for one replacement.
- Define the escalation path for an NVIDIA Xid or other accelerated-compute impairment.
- During incidents, distinguish application failure, device impairment, placement failure, and load-balancer failure before changing percentages.
The operational decision is therefore two-dimensional: make the health signal meaningful for the failure you need to detect, then make replacement headroom large enough to recover without violating the GPU capacity and availability limits your service can actually sustain.
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