configs

YAML Config Examples (--config)

configs/
├── templates/   # Full reference — every field shown and annotated
│   ├── local.yaml
│   ├── slurm.yaml
│   └── k8s.yaml
└── demo/        # Minimal ready-to-run examples organised by target
    ├── local/
    ├── slurm/
    └── k8s/

Workflow

Starting from scratch — copy a template, fill in your model tag and cluster settings, then delete the sections you don't need:

cp examples/configs/templates/slurm.yaml my_job.yaml
# edit my_job.yaml …
madengine run --config my_job.yaml

Starting from an example — find the demo closest to your use case and adapt it:

cp examples/configs/demo/slurm/multi-node-torchrun.yaml my_job.yaml
# tweak partition, node count, tags …
madengine run --config my_job.yaml

Inline overrides — any field can be overridden without editing the file:

madengine run --config my_job.yaml --config distributed.nnodes=4
madengine run --config my_job.yaml --config +env=nccl_debug
madengine run --config my_job.yaml --config +tools=rocprofv3_lightweight

--config is mutually exclusive with --additional-context / --additional-context-file. Job YAML uses the same top-level keys as JSON additional-context (slurm, distributed, env_vars, …), not a Hydra defaults: list. See docs/configuration.md.

---

templates/

File	Target	Contents
local.yaml	Local Docker	All docker, model, tools, scripts, log-error, output fields
slurm.yaml	SLURM	All slurm, distributed, env_vars, tools, scripts fields
k8s.yaml	Kubernetes	All k8s, distributed, env_vars, tools, secrets, storage fields

demo/local/

File	Model	Description
single-gpu.yaml	dummy	Single GPU, no distribution
multi-gpu-torchrun.yaml	dummy_torchrun	Single node, 4 GPUs, torchrun
deepspeed.yaml	dummy_deepspeed	DeepSpeed ZeRO, single node
vllm-inference.yaml	dummy_vllm	vLLM tensor parallelism, 4 GPUs
profiling.yaml	dummy	ROCprofv3 + power + VRAM profiling

demo/slurm/

File	Model	Description
single-node-single-gpu.yaml	dummy	Single GPU job
multi-node-torchrun.yaml	dummy_torchrun	2 nodes × 8 GPUs, Ethernet
multi-node-torchrun-infiniband.yaml	dummy_torchrun	4 nodes × 8 GPUs, InfiniBand, account/QoS
deepspeed.yaml	dummy_deepspeed	DeepSpeed, single node
megatron-lm.yaml	dummy_megatron_lm	Megatron-LM, 4 nodes × 8 GPUs
torchtitan.yaml	dummy_torchtitan	TorchTitan TP+PP+FSDP2, 4 nodes × 8 GPUs
vllm-inference.yaml	dummy_vllm	vLLM data parallelism, 2 nodes × 4 GPUs
sglang-inference.yaml	dummy_sglang	SGLang, 2 nodes × 4 GPUs
sglang-disagg.yaml	dummy_sglang_disagg	SGLang disaggregated prefill/decode, 5 nodes
profiling-multi-gpu.yaml	dummy_torchrun	torchrun + RCCL + power + VRAM profiling

demo/k8s/

File	Model	Description
single-gpu.yaml	dummy	Single GPU pod
multi-gpu-torchrun.yaml	dummy_torchrun	1 pod × 8 GPUs, torchrun
multi-node-torchrun.yaml	dummy_torchrun	2 pods × 8 GPUs, node selector
nvidia-gpu.yaml	dummy_torchrun	NVIDIA A100/H100, nvidia.com/gpu
deepspeed.yaml	dummy_deepspeed	DeepSpeed, single pod
megatron-lm.yaml	dummy_megatron_lm	Megatron-LM, 4 pods × 8 GPUs
torchtitan.yaml	dummy_torchtitan	TorchTitan TP+PP+FSDP2, 4 pods × 8 GPUs
vllm-inference.yaml	dummy_vllm	vLLM data parallelism, 2 pods × 4 GPUs
sglang-inference.yaml	dummy_sglang	SGLang, 2 pods × 4 GPUs
sglang-disagg.yaml	dummy_sglang_disagg	SGLang disaggregated, 5 pods