What does this failure look like in production?

In production this failure typically appears during the first tens-to-hundreds of training steps: loss curves become non-monotonic, validation metrics plateau above the baseline, and downstream checkpoints either diverge or fail to improve. Engineers frequently misread it as data drift when the underlying mechanism is ray data operator resource requirement (gpu:1, cpu:1) does not match any available node shape in the cluster or cloud provider. Validating against the symptom list in this entry is the fastest way to confirm the diagnosis.

Is this a hardware or a software issue?

Infrastructure failures can look like either layer depending on the symptom. The discriminator is the resolution of the symptom to the recommended fix: if applying the steps under "How to fix it" eliminates the symptom, this is a software/configuration class issue regardless of the hardware it surfaced on. Repeat the fix on a fresh node to confirm.

How long does the fix typically take?

Most resolutions from this entry take 10 to 30 minutes of engineer time once the diagnosis is confirmed. Long fixes come from skipping the diagnosis step and chasing apparent symptoms (validation drift, memory growth, slow throughput) instead of the actual root cause listed above. Apply the fix in a small experiment first, then roll it forward.

Does this affect inference or only training?

Primarily this is a infrastructure failure observed during training, but the same misconfiguration can leak into downstream evaluation and serving. Treat the prevention checklist as a guardrail: any pipeline that touches a trained checkpoint should re-validate the failure mode before promoting the model.

What's the cheapest way to confirm this is the problem?

Cheapest signal: reproduce the symptom on a 1-GPU toy run with the same configuration. If the toy run exhibits the same behavior, the cause is the configuration rather than the cluster. If only the multi-GPU run is affected, the failure is a distributed interaction, so Ray Data pipelines with MapBatches operators specifying non-standard resource requirements is the most likely layer to inspect.

Can the Denpex platform detect this automatically?

Yes. Denpex correlates the symptoms in this entry against your training logs in real time and surfaces the failure taxonomy, root cause, and exact remediation command on the console. Three full diagnoses are included free during signup, and the encyclopedia entry you are reading is generated from the same signal library that powers the platform.

What related failures should I check next?

Closely related failures cluster around #anyscale, #ray, #autoscaling. After applying this fix, validate against the related entries below to confirm no overlapping cause is masking as this one. Engineers routinely discover that the dominant failure mode is actually a second, sibling failure compounded on top.

Will this recur after the fix?

Applied once with the prevention checklist, this failure does not recur under normal operation. The recurring variant most engineers encounter is the re-emergence of the same symptom on a different model or stack after anyscale is upgraded. Pin compatible versions and rerun the prevention smoke test on every dependency change.

Is there a fingerprint I can monitor?

Yes, Error: `ray.exceptions.ActorUnschedulableError: The actor is not schedulable` with resource shape {GPU: 1, CPU: 1} is the highest-signal fingerprint. Wire a metric or alert on that signal (or its absence after the fix) so you can detect the recurrence automatically. The Denpex platform encodes this exact fingerprint and surfaces it directly in the dashboard.

What is the official documentation that covers this?

Reference documentation is listed in the References section at the bottom of this entry. The most authoritative URLs are the upstream framework docs and the issue trackers cited; the Denpex entry synthesizes both into the executive, engineer, and on-call perspectives so each audience can act without reading every linked source.

Does this entry include working code?

Yes. The "Code Examples" section contains copy-paste-ready snippets that target the most common stacks (PyTorch, Hugging Face Trainer, DeepSpeed, Megatron-LM, vLLM/Transformers). Each snippet is annotated with why it works so you can adapt the pattern to your own framework without losing fidelity.

Ray Data Auto-Scaling Failure from Resource Shape Mismatch Between Requested and Available

Q: How does this scale from 1 GPU to 1000 GPUs?

At single-GPU scale the symptom usually looks like slow convergence or a single anomalous loss value. At 64+ GPU scale, distributed interactions amplify ray data operator resource requirement (gpu:1, cpu:1) does not match any available node shape in the cluster or cloud provider through collectives, and the failure can manifest as a full job abort. Always reproduce the fix at the smallest cluster configuration that still exhibits the symptom before promoting to production scale.

Ray Data auto-scaling fails when operator resource requirements exceed available cluster resources, throwing ActorUnschedulableError. The specific case documented by Anyscale involved a MapBatches operator with concurrency=2, each task requesting 1 CPU and 1 GPU, totaling 2 CPUs and 2 GPUs, but the cluster did not have enough resources to satisfy the combined GPU+CPU placement constraint.

Quick answer

Ray Data auto-scaling fails when operator resource requirements exceed available cluster resources, throwing ActorUnschedulableError.

Symptom: Cluster auto-scaler logs show: "There are insufficient compute resources to run this workload" with specific resource bundle
Root cause: Ray Data operator resource requirement (GPU:1, CPU:1) does not match any available node shape in the cluster or cloud provider. Each MapBatches actor needs 1 GPU + 1 CPU on the same node, but the smallest GPU node has more CPUs than GPUs, making the per-task CPU reservation too small to fit the GPU+CPU constraint efficiently. The combined resource bundle of 2 CPUs + 2 GPUs exceeds any single node's capacity or the cluster cannot add a node with that exact CPU:GPU ratio.
Recommended fix: ray list nodes
How Denpex helps: Denpex investigates Ray Data Auto-Scaling Failure from Resource Shape Mismatch Between Requested and Available using the evidence you provide or your connected workload collects. Earlier rank, host or application evidence is needed to distinguish an initiating failure from a downstream report.

Infrastructure#anyscale#ray#autoscaling#resource-shape#actoreunschedulable#cluster-sizing

What this failure is

Ray Data Auto-Scaling Failure from Resource Shape Mismatch Between Requested and Available is a Infrastructure failure seen during ML training runs. Ray Data auto-scaling fails when operator resource requirements exceed available cluster resources, throwing ActorUnschedulableError. The specific case documented by Anyscale involved a MapBatches operator with concurrency=2, each task requesting 1 CPU and 1 GPU, totaling 2 CPUs and 2 GPUs, but the cluster did not have enough resources to satisfy the combined GPU+CPU placement constraint. Common tags: Anyscale, Ray, Autoscaling, Resource Shape.

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Why it happens (the mechanism)

Ray Data operator resource requirement (GPU:1, CPU:1) does not match any available node shape in the cluster or cloud provider. Each MapBatches actor needs 1 GPU + 1 CPU on the same node, but the smallest GPU node has more CPUs than GPUs, making the per-task CPU reservation too small to fit the GPU+CPU constraint efficiently. The combined resource bundle of 2 CPUs + 2 GPUs exceeds any single node's capacity or the cluster cannot add a node with that exact CPU:GPU ratio. Taken together, these mechanisms explain why the failure is reproducible, why it tends to surface on specific workloads or scales, and why generic mitigation attempts often fall short without addressing the underlying cause.

What you'll observe

Ray Data pipeline fails with ActorUnschedulableError: The actor is not schedulable
Auto-scaler reports insufficient compute resources for requested resource bundles
Job fails 5 minutes after starting as auto-scaler cannot provision nodes matching resource shape

Common symptoms and what they mean

Symptom	Why it happens
Error: `ray.exceptions.ActorUnschedulableError: The actor is not schedulable` with resource shape {GPU: 1, CPU: 1}	Ray Data operator resource requirement (GPU:1, CPU:1) does not match any available node shape in the cluster or cloud provider
Cluster auto-scaler logs show: "There are insufficient compute resources to run this workload" with specific resource bundle	Each MapBatches actor needs 1 GPU + 1 CPU on the same node, but the smallest GPU node has more CPUs than GPUs, making the per-task CPU reservation too small to fit the GPU+CPU constraint efficiently
Ray Dashboard shows pending actors with resource requirements that don't match available node shapes	The combined resource bundle of 2 CPUs + 2 GPUs exceeds any single node's capacity or the cluster cannot add a node with that exact CPU:GPU ratio

Which systems are affected

Ray Data pipelines with MapBatches operators specifying non-standard resource requirements
GPU clusters where node shapes have specific CPU:GPU ratios (e.g., 4:8 ratio)
Auto-scaling Ray clusters where new nodes cannot be provisioned with the exact resource bundle requested

How to confirm this is the problem

Use this checklist to test the hypothesis against a small reproduction. No single line proves the root cause, so preserve the preceding events and compare one variable at a time.

✓Reproduce the failure from a clean checkpoint/seed: the symptom must appear without warm-up state from a previous run.
✓Verified signal present: Error: `ray.exceptions.ActorUnschedulableError: The actor is not schedulable` with resource shape {GPU: 1, CPU: 1}
✓Verified signal present: Cluster auto-scaler logs show: "There are insufficient compute resources to run this workload" with specific resource bundle
✓Verified signal present: Ray Dashboard shows pending actors with resource requirements that don't match available node shapes
✓A targeted fix from the "How to fix it" section eliminates or substantially reduces the symptom within one validation pass.

Root cause

Ray Data operator resource requirement (GPU:1, CPU:1) does not match any available node shape in the cluster or cloud provider
Each MapBatches actor needs 1 GPU + 1 CPU on the same node, but the smallest GPU node has more CPUs than GPUs, making the per-task CPU reservation too small to fit the GPU+CPU constraint efficiently
The combined resource bundle of 2 CPUs + 2 GPUs exceeds any single node's capacity or the cluster cannot add a node with that exact CPU:GPU ratio

The fix and how to prevent it

Searchable error signature

search key

Cluster auto-scaler logs show: "There are insufficient compute resources to run this workload" with specific resource bundle

Use this text as a lookup key in logs and upstream issue trackers. It is not presented as a captured customer log. Confirm the cause from your own preceding events, versions, configuration and the cited references.

The fix and the prevention pattern

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