Deployment guide

August 12, 2026 · View on GitHub

This guide describes how to deploy Primus training across container, direct (bare metal), and Slurm environments using the unified primus-cli launcher. For environment variable semantics, see Environment variables. For YAML hierarchy and precedence, see Configuration system.

1. Deployment overview

Primus supports three deployment modes:

Mode	Description	Typical use
Container	Docker/Podman with ROCm-capable GPU devices and capabilities	Recommended default; reproducible images
Direct	Runs on the current host (or inside an existing container)	Local debugging, single-node, clusters with ROCm on nodes
Slurm	Wraps `srun`/`sbatch` and launches per-node entry scripts	Multi-node clusters with Slurm

Container image: docker.io/rocm/primus:v26.5 (default in runner/.primus.yaml). For clusters using AINIC, use runner/use_ainic.yaml and tune the image and NCCL-related variables (for example USING_AINIC, NCCL_IB_GID_INDEX) to match your fabric.

Prerequisites (baseline):

AMD ROCm >= 7.0 on the host (or in the image when using containers)
Docker or Podman >= 24.0 when using container mode
AMD Instinct GPUs and working ROCm stack (rocm-smi should report devices)

2. Container deployment

2.1 Pull the image

docker pull docker.io/rocm/primus:v26.5

The default container.options.image in runner/.primus.yaml is rocm/primus:v26.5 (equivalent to docker.io/rocm/primus:v26.5 when the registry is omitted).

2.2 Required device mounts

System defaults (runner/.primus.yaml, container.options.device) pass each path as --device to the runtime:

Device	Purpose
`/dev/kfd`	Kernel Fusion Driver (ROCm core)
`/dev/dri`	Direct Rendering Infrastructure (GPU access)
`/dev/infiniband`	InfiniBand character devices (multi-node / RDMA)

2.3 Required capabilities

Defaults (container.options.cap-add):

Capability	Purpose
`SYS_PTRACE`	Debugging and profiling tools
`CAP_SYS_ADMIN`	Administrative operations required by some ROCm/GPU workflows

2.4 Container runtime options

Defaults in runner/.primus.yaml include:

Option	Value
`ipc`	`host`
`network`	`host`
`privileged`	`true`
`security-opt`	`seccomp=unconfined`
`group-add`	`video`

primus-cli-container.sh always mounts the Primus repository root into the container at the same path (-v $PRIMUS_PATH:$PRIMUS_PATH). Mount additional paths for datasets, model weights, and outputs with --volume (or container.options.volume in YAML).

2.5 Environment passthrough

container.options.env lists names that are forwarded into the inner primus-cli invocation as --env when set on the host (see runner/.primus.yaml). Examples include:

MASTER_ADDR, MASTER_PORT, NNODES, NODE_RANK, GPUS_PER_NODE, DOCKER_IMAGE, HF_TOKEN, WANDB_API_KEY, ENABLE_NUMA_BINDING, USING_AINIC, and NCCL/GLOO socket and IB-related variables (NCCL_IB_HCA, NCCL_SOCKET_IFNAME, GLOO_SOCKET_IFNAME, NCCL_IB_GID_INDEX, and others).

Additionally, primus-cli-container.sh auto-forwards any environment variable whose name starts with PRIMUS_, NCCL_, RCCL_, GLOO_, IONIC_, or HIPBLASLT_ when present on the host.

2.6 Single-node example

./primus-cli container --volume /data:/data -- train pretrain --config /data/exp.yaml

2.7 Multi-node container deployment

Set MASTER_ADDR, MASTER_PORT, NNODES, NODE_RANK, and GPUS_PER_NODE on each node (Slurm or your orchestrator sets these; see runner/primus-cli-slurm-entry.sh). Example pattern when launching manually:

export MASTER_ADDR=<head-node-hostname>
export MASTER_PORT=1234
export NNODES=4
export NODE_RANK=<0-based index for this node>
export GPUS_PER_NODE=8

./primus-cli container -- train pretrain --config /path/to/config.yaml

Use --clean before launch to remove existing containers (primus-cli-container.sh).

3. Slurm deployment

3.1 `srun` (interactive or blocking)

./primus-cli slurm srun -N <nodes> -p <partition> -- train pretrain --config <yaml>

The Slurm entry script invokes the container launcher on each allocated node. Set the image through runner/.primus.yaml, a custom launcher config file, or site policy; the default is rocm/primus:v26.5.

3.2 `sbatch` (batch jobs)

./primus-cli slurm sbatch -N <nodes> -p <partition> --time <HH:MM:SS> --job-name <name> -o <logfile> -- \
  train pretrain --config <yaml>

Add -e <errfile> if you want separate stderr.

3.3 Slurm-to-Primus environment mapping

runner/primus-cli-slurm-entry.sh sets:

Variable	Source (typical)
`NNODES`	`SLURM_NNODES`, or `SLURM_JOB_NUM_NODES`, or existing `NNODES`
`NODE_RANK`	`SLURM_NODEID`, or `SLURM_PROCID`, or existing `NODE_RANK`
`GPUS_PER_NODE`	Default `8` if unset
`MASTER_ADDR`	First host in `SLURM_NODELIST` if unset
`MASTER_PORT`	Default `1234` if unset

The entry script then exports MASTER_ADDR, MASTER_PORT, NNODES, NODE_RANK, and GPUS_PER_NODE into the container launcher.

3.4 Slurm YAML defaults (`runner/.primus.yaml`)

Key	Default
`slurm.nodes`	`1`
`slurm.gpus_per_node`	`8`
`slurm.time`	`"4:00:00"`
`slurm.partition`	(commented; set per site)

CLI Slurm flags override YAML when both are specified (see runner/primus-cli-slurm.sh).

3.5 Entry after the first `--`

Production examples pass the Primus Python command after the Slurm -- separator, for example:

./primus-cli slurm srun -N 4 -p gpu -- train pretrain --config exp.yaml

The shipped primus-cli-slurm-entry.sh invokes primus-cli-container.sh with distributed variables set from Slurm. Container options should come from launcher configuration instead of a literal container token in the inner command. For bare-metal nodes without Docker, run primus-cli direct under your allocation and ensure the same distributed variables and ROCm layout as in Multi-node configuration.

4. Kubernetes deployment

Kubernetes integration is not shipped as a Helm chart or operator in this repository. The repo includes examples/run_k8s_pretrain.sh, a client script that talks to a Kubernetes API to create and manage training workloads (image default docker.io/rocm/primus:v26.5).

Use that script as a reference for your platform; adapt networking, storage, and scheduling to your cluster policies.

5. Multi-node configuration

Required variables for distributed training:

Variable	Role
`MASTER_ADDR`	Hostname or IP of rank-0 process
`MASTER_PORT`	TCP port for the process group rendezvous
`NNODES`	Number of nodes
`NODE_RANK`	Zero-based index of this node
`GPUS_PER_NODE`	GPUs per node used by `torchrun`

Flow: User or Slurm sets the environment → primus-cli and primus-cli-direct.sh load GPU and comm settings → torchrun launches primus/cli/main.py with the distributed topology.

Defaults from runner/.primus.yaml (direct section):

Key	Default
`direct.master_port`	`1234`
`direct.gpus_per_node`	`8`
`direct.nnodes`	`1`
`direct.master_addr`	`"localhost"`

6. Startup and shutdown

Lifecycle (high level):

Parse CLI and load YAML (--config chain: see Configuration system).
Load environment (GPU detection, hooks, patches in primus-cli-direct.sh).
Launch training via torchrun into the Python CLI.

Verification:

--dry-run prints the command that would run without executing (supported in container and Slurm scripts).
--debug sets PRIMUS_LOG_LEVEL=DEBUG for verbose launcher and shell logging.

Shutdown:

Normal completion or Ctrl+C terminates the training process.
In container mode, --clean removes existing containers before launch (primus-cli-container.sh).

Timeouts (config):

Backend	Parameter	Location
Megatron	`distributed_timeout_minutes`	`primus/configs/modules/megatron/trainer_base.yaml` (default `10`)
TorchTitan	`comm.init_timeout_seconds`	`primus/configs/modules/torchtitan/pre_trainer.yaml` (default `300`)

7. Production checklist

Item	Action
ROCm drivers	Install and verify with `rocm-smi`
Container image	Pulled and aligned with host ROCm expectations
Network	Run `preflight --network` (see Preflight)
Shared data	Paths visible and consistent on all nodes
Hugging Face	Set `HF_TOKEN` if using gated models
Checkpoints	Save directory on shared or replicated storage with sufficient space
Monitoring	Configure Weights & Biases or TensorBoard (see Monitoring and Logging)
Resources	Slurm time limits, partitions, and GPU counts match your YAML and hardware