Kubernetes vs. Docker for ML workloads
Understand how Docker containers and Kubernetes orchestration solve different parts of the ML delivery path, from reproducible images to scheduled production workloads.
Kubernetes and Docker are often presented as competing choices, but they address different layers. Docker tools build and run containers. Kubernetes schedules and manages containerized workloads across a cluster through a standard container runtime interface.
For ML teams, the common path is to build a container image with Docker-compatible tooling and run that image through Kubernetes. The real decision is when a local container is enough and when a shared orchestration platform is worth its operational cost.
Separate packaging from orchestration
An image packages an application, libraries, system dependencies, and startup command. A container is an isolated process created from that image. Docker's container overview explains this process-level isolation and portability model.
Kubernetes manages desired state around containers: scheduling, restarts, networking, service discovery, configuration, secrets, storage attachments, rollouts, quotas, and access controls. It does not replace the need for a well-built image.
| Need | Container tooling | Kubernetes |
|---|---|---|
| Package Python and native libraries | Primary responsibility | Consumes the image |
| Reproduce a local command | Direct and simple | Possible but more infrastructure |
| Schedule across many machines | Not the main scope | Primary responsibility |
| Manage services and batch Jobs | Limited single-host patterns | Controllers and workload APIs |
| Allocate shared GPUs | One host at a time | Cluster scheduling and policy |
| Roll out replicas | Manual or separate tooling | Deployment reconciliation |
Use Docker for the image contract
Build a small, deterministic image with pinned dependencies, a non-root runtime user, declared entry point, and no embedded credentials. Separate build-time tools from the final runtime image. Scan packages and record the source revision and digest.
Use the same image in development, CI, training, evaluation, and deployment where the workload allows it. Parameterize behavior through arguments and configuration rather than building a new image for every experiment.
Container portability has limits. GPU drivers, CPU architecture, kernel features, mounted data, identity, network policy, and available accelerators still depend on the target environment. Test the image under the same runtime constraints used in production.
Add Kubernetes when coordination matters
Kubernetes becomes useful when several teams share capacity, workloads need rescheduling, services require rollouts, batch operations must run repeatedly, or policy has to apply consistently across nodes. Its concepts overview describes the APIs for clusters, containers, workloads, networking, storage, configuration, and resource management.
ML platforms add scheduling concerns that a single container cannot solve:
- assigning scarce GPU types to eligible jobs;
- enforcing team quotas and priority;
- coordinating distributed workers;
- mounting data and artifact connections;
- separating interactive, batch, and serving workloads;
- recording retries and termination reasons;
- scaling worker pools with demand.
These capabilities require controllers, policies, and operations. If one researcher only needs to reproduce a script on one machine, a local container may be the better starting point.
Avoid assuming Kubernetes makes images safe
Kubernetes will run the specification it accepts. It does not automatically remove vulnerabilities, prevent root execution, block network egress, or hide secrets from a process. Apply image scanning, admission rules, Pod Security settings, service accounts, resource limits, NetworkPolicies, and secret-management practices.
Use immutable image references for controlled releases and restrict who can push to trusted registries. Preserve build provenance and promotion decisions. A mutable tag can otherwise point the same workload definition at different code.
Also test shutdown and retry behavior. Training processes should handle termination signals and save checkpoints safely. Batch actions should be idempotent where possible because a restarted pod may repeat work.
Plan data outside the container
Container filesystems are not a durable artifact store. Mount only the data a workload needs, use ephemeral storage for replaceable scratch, and copy declared outputs to durable storage. Keep dataset and model versions independent from the image so code and data can evolve with traceable lineage.
For large datasets, avoid rebuilding images or pulling the same archive into every container. Stage or stream data according to access patterns and measure accelerator idle time while data loads.
Compare operational cost
Local Docker workflows minimize platform overhead but rely more heavily on individual machines and manual coordination. Kubernetes introduces control-plane, networking, storage, security, upgrade, and observability responsibilities. Managed services reduce some work but do not remove workload design and governance.
Measure queue time, startup time, successful runs, utilization, failure recovery, and operator effort. A cluster that improves sharing and repeatability may justify its cost. A lightly used cluster operated for one simple job may not.
Use both with Polyaxon
Build the workload as a container image, then use Polyaxon to run it on Kubernetes with declared resources, parameters, connections, and execution context. Record experiments through tracking, persist outputs with artifacts, and coordinate multi-step work through pipelines.
Docker provides the portable workload package. Kubernetes provides the shared execution and reconciliation layer. Polyaxon connects that infrastructure to the ML run, lineage, scheduling, and collaboration workflow.