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dfb775d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 | # Ops
Container, compose, Kubernetes, vmm, and Gensyn manifests for mindxtrain. Per
mindxtrain2.md §Part 4 (`ops/`).
## Layout
```
ops/
├── containerfiles/
│ ├── containerfile_train # FROM rocm/primus:v26.2 (training image)
│ ├── containerfile_serve # FROM rocm/vllm-dev:rocm7.2.1 (inference image)
│ └── digest.lock # SHA256 pins, populated post-pull
├── compose/
│ └── compose_dev.yaml # full stack: vLLM + operator FastAPI for the live demo
├── k8s/
│ └── train_job.yaml # single-MI300X training Job
├── vmm/ # OpenBSD vmm vm definitions (post-hackathon)
└── gensyn/ # Gensyn distributed-training configs (post-hackathon)
```
## Build the train image (on the MI300X droplet)
```bash
podman build -f ops/containerfiles/containerfile_train -t mindxtrain/train:latest .
podman inspect --format '{{index .RepoDigests 0}}' mindxtrain/train:latest \
| tee -a ops/containerfiles/digest.lock
```
## Build the serve image
```bash
podman build -f ops/containerfiles/containerfile_serve -t mindxtrain/serve:latest .
```
## Run the demo stack
```bash
podman-compose -f ops/compose/compose_dev.yaml up -d
# vLLM-ROCm at :8000, mindxtrain operator FastAPI at :8080
```
## Submit the K8s training job
```bash
kubectl create configmap mindxtrain-run-config --from-file=run.yaml=run.yaml
kubectl apply -f ops/k8s/train_job.yaml
kubectl logs -f job/mindxtrain-job
```
The Job spec assumes a node labeled `accelerator: mi300x` and the AMD ROCm
device plugin (`amd.com/gpu` resource).
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