Megatron training configuration files for Cluster Validation Suite (CVS)

2026-09-24

20 min read time

Applies to Linux

JSON configs and sibling *_threshold.json files for megatron_single and megatron_distributed. MI300X and MI325X share one config per model and mode (mi3xx_megatron_{model}_{single|distributed}.json); set gpu_name and threshold_json to the SKU. MI355X ships Llama 3.1 8B and Llama 3.3 70B single-node configs only (mi355x_megatron_llama-3.1-8b_single.json, mi355x_megatron_llama-3.3-70b_single.json). Use a *_single.json file with megatron_single and a *_distributed.json file with megatron_distributed. threshold_json is resolved relative to the config file.

See Run Megatron Llama and DeepSeek training benchmarks for more information on running these tests.

Use cvs config list training/megatron to list available templates, or cvs config copy training/megatron/<name> to copy one to your working directory. Copy the matching SKU *_threshold.json into the same directory as the suite config (mi300x_* or mi325x_* for mi3xx_ templates; mi355x_* for MI355X).

Backends

The suite selects the training backend from container.image (substring primus, case-insensitive):

• Megatron-LM — image name does not contain primus. Training scripts live under /workspace/Megatron-LM inside the image. Log files use <log_dir>/megatron-logs/<combo_id>/out-node<N>/training.log.

• Primus — image name contains primus. In-image YAML lives under examples/megatron/configs/{gpu_arch}/. Log files use <log_dir>/primus-logs/<combo_id>/out-node<N>/training.log.

Llama 3.1 8B, Llama 3.3 70B, and DeepSeek V2 Lite run on both Megatron-LM and Primus (same JSON; pick the backend with container.image). Llama 3.1 405B is Primus only (distributed).

<combo_id> on disk is the sweep combination key with non-filename characters replaced (= and , become _). Pytest still uses the unsanitized key (for example MBS=4,GBS=128,PRECISION=FP8).

Note

• Parameters with the <changeme> value must have that value modified to your specifications. Unresolved placeholders cause a hard exit at load time.

• {user-id} will be resolved to the cluster username (or the local OS user as fallback). You can also set this value yourself.

• Keys prefixed with _ (for example _checkpoint_comment) are inline comments and are ignored by the loader.

• sweep.runs is required when sweep.combinations is non-empty. It must be a non-empty subset of (or equal to) those keys; an empty runs list is a load error. Omitting sweep entirely (or using empty combinations) runs one implicit default cell; train_params.micro_batch_size, global_batch_size, and precision are then required.

• Each key in sweep.combinations must be MBS=<micro_batch_size>,GBS=<global_batch_size>,PRECISION=<precision>. The suite parses those three values from the key; do not repeat them in the combination body. The body may overlay extras such as training_iterations, tensor_parallelism, and pipeline_parallelism.

Available configurations

MI300X and MI325X share mi3xx_megatron_<model>_<mode>.json. MI355X ships mi355x_megatron_llama-3.1-8b_single.json and mi355x_megatron_llama-3.3-70b_single.json. Thresholds stay SKU-specific (mi300x_*_threshold.json, mi325x_*_threshold.json, mi355x_*_threshold.json) and are selected with threshold_json.

The table below maps each supported model to its configuration file and available execution modes.

Model	MI300X / MI325X config	MI355X config	Mode
Llama 3.1 8B	mi3xx_…	mi355x_… (single only)	single, distributed (Megatron-LM or Primus); MI355X packaged as single-node
Llama 3.3 70B	mi3xx_…	mi355x_… (single only)	single, distributed (Megatron-LM or Primus); MI355X packaged as single-node
DeepSeek V2 Lite	mi3xx_…	—	single, distributed (Megatron-LM or Primus)
Llama 3.1 405B	mi3xx_…	—	distributed (Primus only)

Single-node configs set container.env.MASTER_ADDR to 127.0.0.1. NNODES is not in the JSON: the suite sets it from the cluster host count into docker run -e. Distributed configs require MASTER_ADDR and NCCL_IB_HCA and add a scaling_baseline section and checkpoint_dir to the checkpoint block. NIC type is not in the JSON: Megatron-LM defaults to thor2 for MI300X/MI325X and ainic for MI355X from gpu_name.

Leftover files mi3xx_megatron_llama_*.json and mi35x_megatron_llama_single.json are nested-schema configs for the legacy megatron_llama3_1_* suites only. Do not pass them to megatron_single or megatron_distributed.

Required edits

Set these before a run (full field tables are under Common parameters):

The following fields must be customized to match your cluster and hardware before invoking any Megatron suite.

• gpu_name / threshold_json — on mi3xx_ templates, set MI300X or MI325X and the matching SKU threshold filename. gpu_name must be exactly MI300X, MI325X, or MI355X after uppercase.

• container.image — Megatron-LM or Primus ROCm image on all nodes.

• train_params.training_iterations — default training steps (for example "30"). Each sweep.combinations body sets training_iterations (packaged value "20"); that overlay wins for that cell.

• train_params.micro_batch_size / global_batch_size / precision — required when sweep is omitted. Packaged files already set them; with a declared sweep the combination key supplies those values for each cell.

• paths.hf_token_file — Hugging Face token path on the nodes.

• container.env.NCCL_SOCKET_IFNAME / GLOO_SOCKET_IFNAME / NCCL_IB_GID_INDEX / NCCL_DEBUG — templates include example values plus <changeme> (for example enp193s0f1np1 <changeme>, 3 <changeme>, ERROR <changeme>). Remove <changeme> and keep or edit the example. Required on single-node and distributed.

• sweep.runs — combination keys to execute (same MBS=…,GBS=…,PRECISION=… strings as in sweep.combinations). Must be non-empty when combinations is present.

• Do not set NNODES in JSON.

• Distributed only: container.env.MASTER_ADDR, container.env.NCCL_IB_HCA (example HCA list plus <changeme>). paths.data_cache_dir must be a shared filesystem. When checkpoint.enforce is true, also set checkpoint.checkpoint_dir and replace the last <changeme>:<changeme> volume with that shared path.

Top-level fields

These fields appear at the root of every config file.

Field	Example	Description
gpu_name	MI300X / <changeme>	GPU architecture string used for logging and Primus YAML path resolution (examples/megatron/configs/{gpu_name}/). Loaded as uppercase (mi300x becomes MI300X). Allowed values: MI300X, MI325X, MI355X. Extra tokens fail at load. On mi3xx_ templates this is <changeme> until you set MI300X or MI325X.
paths	see Common parameters	Host paths: hf_token_file, log_dir, scripts_dir, data_cache_dir.
container	see Common parameters	Image, runtime mounts, and container.env. Packaged files keep this block immediately after paths.
train_params	see per-model tables	Model knobs plus training_iterations. Packaged files also set micro_batch_size, global_batch_size, and precision (used when sweep is omitted). With a declared sweep, those three values for each cell come from the combination key. With no sweep, those three train_params keys are required for the implicit default cell.
verify_network_errors	omitted (single) / True (distributed)	Compare RDMA and ethtool error counters before and after training. Single-node templates omit it (schema/lib default False).
enforce_thresholds	true	If false, threshold checks in test_metric log results but do not fail the test.
threshold_json	mi300x_megatron_llama-3.1-8b_single_threshold.json / <changeme>	Filename of the companion threshold file, looked up in the same directory as the config file. On mi3xx_ templates this is <changeme> until you point it at the mi300x_* or mi325x_* threshold for the SKU.

Model configurations

Each model section below shows only the train_params and sweep blocks, which are the parts that differ between models. All other sections (paths, container, smoke, loss_curve, convergence, checkpoint, scaling_baseline) are identical in structure across models and are documented in Common parameters.

Llama 3.1 8B

Available as mi3xx_megatron_llama-3.1-8b_{single,distributed}.json (MI300X/MI325X) and mi355x_megatron_llama-3.1-8b_single.json (MI355X).

mi3xx_megatron_llama-3.1-8b_single.json (representative)

{
  "gpu_name": "<changeme>",
  "enforce_thresholds": true,
  "threshold_json": "<changeme>",
  "train_params": {
    "model_name": "llama3.1_8B",
    "tokenizer_model": "meta-llama/Llama-3.1-8B",
    "model_size": "8",
    "sequence_length": "8192",
    "recompute": "0",
    "fsdp": "0",
    "tensor_parallelism": "1",
    "pipeline_parallelism": "1",
    "micro_batch_size": "4",
    "global_batch_size": "128",
    "precision": "BF16",
    "training_iterations": "<changeme>"
  },
  "sweep": {
    "combinations": {
      "MBS=4,GBS=128,PRECISION=FP8": {
        "training_iterations": "20"
      },
      "MBS=4,GBS=128,PRECISION=BF16": {
        "training_iterations": "20"
      },
      "MBS=4,GBS=128,PRECISION=MXFP4": {
        "training_iterations": "20"
      },
      "MBS=4,GBS=128,PRECISION=MXFP8": {
        "training_iterations": "20"
      }
    },
    "runs": [
      "MBS=4,GBS=128,PRECISION=FP8",
      "MBS=4,GBS=128,PRECISION=BF16",
      "MBS=4,GBS=128,PRECISION=MXFP4",
      "MBS=4,GBS=128,PRECISION=MXFP8"
    ]
  }
}

train_params

Parameter	Value	Description
model_name	llama3.1_8B	Used in log labels and report filenames.
tokenizer_model	meta-llama/Llama-3.1-8B	HuggingFace repo ID for the tokenizer and model weights.
model_size	8	Model size in billions of parameters.
sequence_length	8192	Maximum context length.
tensor_parallelism	1	Fits on a single GPU; no tensor splitting needed.
pipeline_parallelism	1	Single pipeline stage.
micro_batch_size	4	Required when sweep is omitted. With a declared sweep, the combination key supplies this for each cell.
global_batch_size	128	Required when sweep is omitted. With a declared sweep, the combination key supplies this for each cell.
precision	BF16	Required when sweep is omitted. With a declared sweep, the combination key supplies this for each cell.
training_iterations	<changeme>	Training steps (same field on every model).

Llama 3.3 70B

Available as mi3xx_megatron_llama-3.3-70b_{single,distributed}.json (MI300X/MI325X) and mi355x_megatron_llama-3.3-70b_single.json (MI355X).

mi3xx_megatron_llama-3.3-70b_single.json (representative)

{
  "gpu_name": "<changeme>",
  "enforce_thresholds": true,
  "threshold_json": "<changeme>",
  "train_params": {
    "model_name": "llama3.3_70B",
    "tokenizer_model": "meta-llama/Llama-3.3-70B-Instruct",
    "model_size": "70",
    "sequence_length": "8192",
    "recompute": "0",
    "fsdp": "0",
    "tensor_parallelism": "8",
    "pipeline_parallelism": "1",
    "micro_batch_size": "3",
    "global_batch_size": "96",
    "precision": "BF16",
    "training_iterations": "<changeme>"
  },
  "sweep": {
    "combinations": {
      "MBS=3,GBS=96,PRECISION=FP8": {
        "training_iterations": "20"
      },
      "MBS=3,GBS=96,PRECISION=BF16": {
        "training_iterations": "20"
      }
    },
    "runs": [
      "MBS=3,GBS=96,PRECISION=FP8",
      "MBS=3,GBS=96,PRECISION=BF16"
    ]
  }
}

train_params

Parameter	Value	Description
model_name	llama3.3_70B	Used in log labels and report filenames.
tokenizer_model	meta-llama/Llama-3.3-70B-Instruct	HuggingFace repo ID for the tokenizer and model weights.
model_size	70	Model size in billions of parameters.
sequence_length	8192	Maximum context length.
tensor_parallelism	8	Splits the model across all 8 GPUs on the node.
pipeline_parallelism	1	Single pipeline stage.
micro_batch_size	3	Required when sweep is omitted. With a declared sweep, the combination key supplies this for each cell.
global_batch_size	96	Required when sweep is omitted. With a declared sweep, the combination key supplies this for each cell.
precision	BF16	Required when sweep is omitted. With a declared sweep, the combination key supplies this for each cell.
training_iterations	<changeme>	Training steps (same field on every model).

DeepSeek V2 Lite

Available as mi3xx_megatron_deepseek-v2-lite_{single,distributed}.json (MI300X/MI325X).

mi3xx_megatron_deepseek-v2-lite_single.json (representative)

{
  "gpu_name": "<changeme>",
  "enforce_thresholds": true,
  "threshold_json": "<changeme>",
  "train_params": {
    "model_name": "deepseek_v2_lite",
    "tokenizer_model": "deepseek-ai/DeepSeek-V2-Lite",
    "model_size": "16",
    "sequence_length": "4096",
    "recompute": "0",
    "fsdp": "0",
    "tensor_parallelism": "1",
    "pipeline_parallelism": "1",
    "micro_batch_size": "4",
    "global_batch_size": "128",
    "precision": "BF16",
    "training_iterations": "<changeme>"
  },
  "sweep": {
    "combinations": {
      "MBS=4,GBS=128,PRECISION=BF16": {
        "training_iterations": "20"
      },
      "MBS=4,GBS=128,PRECISION=FP8": {
        "training_iterations": "20"
      }
    },
    "runs": [
      "MBS=4,GBS=128,PRECISION=BF16",
      "MBS=4,GBS=128,PRECISION=FP8"
    ]
  }
}

train_params

Parameter	Value	Description
model_name	deepseek_v2_lite	Used in log labels and report filenames.
tokenizer_model	deepseek-ai/DeepSeek-V2-Lite	HuggingFace repo ID. CVS downloads the tokenizer.model file locally before launch (test_download_tokenizer).
model_size	16	Model size in billions of parameters.
sequence_length	4096	Shorter context than Llama due to DeepSeek V2’s attention architecture.
tensor_parallelism	1	Fits on a single GPU.
pipeline_parallelism	1	Single pipeline stage.
micro_batch_size	4	Required when sweep is omitted. With a declared sweep, the combination key supplies this for each cell.
global_batch_size	128	Required when sweep is omitted. With a declared sweep, the combination key supplies this for each cell.
precision	BF16	Required when sweep is omitted. With a declared sweep, the combination key supplies this for each cell.
training_iterations	<changeme>	Training steps (same field on every model).

Llama 3.1 405B

Available as mi3xx_megatron_llama-3.1-405b_distributed.json (MI300X/MI325X; distributed only). Unlike the other models, 405B runs on Primus only: container.image must contain primus. Megatron-LM does not support 405B.

mi3xx_megatron_llama-3.1-405b_distributed.json (representative)

{
  "gpu_name": "<changeme>",
  "enforce_thresholds": true,
  "threshold_json": "<changeme>",
  "train_params": {
    "model_name": "llama3.1_405B",
    "tokenizer_model": "meta-llama/Llama-3.1-405B",
    "model_size": "405",
    "sequence_length": "8192",
    "recompute": "0",
    "fsdp": "0",
    "tensor_parallelism": "8",
    "pipeline_parallelism": "4",
    "micro_batch_size": "1",
    "global_batch_size": "64",
    "precision": "BF16",
    "training_iterations": "<changeme>"
  },
  "sweep": {
    "combinations": {
      "MBS=1,GBS=64,PRECISION=FP8": {
        "training_iterations": "20"
      },
      "MBS=1,GBS=64,PRECISION=BF16": {
        "training_iterations": "20"
      }
    },
    "runs": [
      "MBS=1,GBS=64,PRECISION=FP8",
      "MBS=1,GBS=64,PRECISION=BF16"
    ]
  }
}

train_params

Parameter	Value	Description
model_name	llama3.1_405B	Used in log labels and report filenames.
tokenizer_model	meta-llama/Llama-3.1-405B	HuggingFace repo ID for the tokenizer and model weights.
model_size	405	Model size in billions of parameters.
sequence_length	8192	Maximum context length.
tensor_parallelism	8	Splits across all 8 GPUs per node.
pipeline_parallelism	4	Splits the model across 4 pipeline stages (requires at least 4 nodes).
micro_batch_size	1	Required when sweep is omitted. With a declared sweep, the combination key supplies this for each cell.
global_batch_size	64	Required when sweep is omitted. With a declared sweep, the combination key supplies this for each cell.
precision	BF16	Required when sweep is omitted. With a declared sweep, the combination key supplies this for each cell.
training_iterations	<changeme>	Training steps.

Common parameters

These sections appear in all config files. The parameter names and semantics are identical across models and GPU variants.

paths

Host paths used by the job. They must be volume-mounted into the container (typically via the home-directory bind mount).

Parameter	Default	Description
hf_token_file	/home/{user-id}/.hf_token	Path to a Hugging Face token file for gated models and datasets.
log_dir	/home/{user-id}/LOGS/megatron	Host path where per-node training logs are written. Megatron-LM writes <log_dir>/megatron-logs/<combo_id>/out-node<N>/training.log; Primus writes <log_dir>/primus-logs/<combo_id>/out-node<N>/training.log. On disk <combo_id> is the sanitized combination key (see Backends).
scripts_dir	/home/{user-id}/SCRIPTS/megatron	Host path where the lib writes per-rank wrapper scripts.
data_cache_dir	/home/{user-id}/cache	Dataset and tokenizer cache directory. On distributed runs this path must be a shared filesystem.
rocm_dir	""	ROCm installation path inside the container. Leave empty for auto-detection.

container.env

These values are passed into the container as docker run -e flags and also flattened into the training job dict. NNODES is injected at launch from the cluster host count (not a JSON field). Packaged templates put example NIC strings in the values and keep <changeme> in the same string so load fails until you edit them. Megatron-LM and Primus wrappers do not re-export NCCL_*, GLOO_SOCKET_IFNAME, MASTER_ADDR, or NNODES. Megatron-LM still re-exports NCCL_IB_GID_INDEX after Broadcom NIC setup.

Parameter	Default	Description
MASTER_ADDR	127.0.0.1 (single) / <changeme> (distributed)	Rank-0 address (loopback on single-node).
NCCL_SOCKET_IFNAME	enp193s0f1np1 <changeme>	Network interface for NCCL control channels. Replace <changeme>; the enp193s0f1np1 token is an example.
GLOO_SOCKET_IFNAME	enp193s0f1np1 <changeme>	Network interface for Gloo control channels. Same example pattern as NCCL_SOCKET_IFNAME.
NCCL_IB_GID_INDEX	3 <changeme>	GID index for InfiniBand addressing. Replace <changeme> (typical value 3).
NCCL_DEBUG	ERROR <changeme>	NCCL log verbosity. Replace <changeme> (typical value ERROR).
NCCL_IB_HCA	bnxt_re0,...,bnxt_re8 <changeme>	(Distributed) Comma-separated InfiniBand HCA device names. The packaged list is an example; replace <changeme>.

container

Parameter	Default	Description
lifetime	per_run	When to create and destroy the container. per_run launches a fresh container for each test session.
name	(model-specific)	Container instance name, e.g. megatron_llama3_1_8b_single.
image	<changeme>	Docker image to run. If the image name contains primus (case-insensitive), the suite uses the Primus backend; otherwise it uses Megatron-LM. Set this to the image available in your environment.
runtime.name	docker	Container runtime. Currently only docker is supported.
runtime.args.network	host	Use host networking so NCCL and Gloo can reach other nodes directly.
runtime.args.ipc	host	Share the host IPC namespace for GPU shared memory.
runtime.args.privileged	true	Required for ROCm GPU and InfiniBand device access.
runtime.args.volumes	(see below)	List of host:container bind mounts. At minimum, mount the user home directory (/home/{user-id}:/home/{user-id}) so log_dir, scripts_dir, and data_cache_dir are accessible inside the container. Distributed configs also require /dev/infiniband:/dev/infiniband and the Broadcom driver library mount. The last entry (<changeme>:<changeme>) is the shared filesystem bind mount for checkpoint.checkpoint_dir — replace both sides with the same shared path (e.g. /mnt/shared/ckpt:/mnt/shared/ckpt). Required only when checkpoint.enforce is true; the loader skips this entry when enforce is false.
runtime.args.devices	/dev/kfd, /dev/dri	GPU device nodes to expose. Distributed configs also add /dev/infiniband/rdma_cm.

checkpoint

Controls the checkpoint save and resume test (test_checkpoint). The test is Primus-only: it is skipped when enforce is false, and also skipped when the container image name does not contain primus. On Primus it runs in two phases: a save phase that trains for save_iters steps writing a checkpoint every save_interval steps, followed by a resume phase that loads the last checkpoint and trains to resume_iters steps. Continuity is checked at the first resume step (last_ckpt_step + 1), which must not exceed the checkpoint-step loss by more than loss_rtol. Micro-batch size, global batch size, and precision are the same as test_smoke (the smoke block).

checkpoint_dir is only present in distributed configs. On Primus distributed runs it must be a shared filesystem path visible on every node. Single-node Primus ignores that field and writes under {log_dir}/ckpt_primus. Megatron-LM never runs test_checkpoint.

Load I/O timing is taken from the node-0 Primus log. Single-node resume lines say loading checkpoint from; distributed resume lines say loading distributed checkpoint from. Both end with successfully loaded checkpoint from. A missing load-start line yields a warning, not a test failure.

Parameter	Default	Description
enforce	false	If false, test_checkpoint is skipped. Set to true to enable checkpoint save/resume verification.
save_interval	20	How often (in steps) to write a checkpoint during the save phase. The last checkpoint lands at floor(save_iters / save_interval) * save_interval.
save_iters	21	Steps to train in the save phase. Must not be an exact multiple of save_interval so the final checkpoint is not the last step.
resume_iters	25	Steps to train in the resume phase, continuing from the last checkpoint.
loss_rtol	0.05	Relative tolerance for the loss continuity check. The first step of the resume phase must not exceed the checkpoint-step loss by more than loss_rtol * max(abs(save_loss), 1e-9).
checkpoint_dir	<changeme>	(Distributed only) Shared filesystem path for checkpoints. Must be volume-mounted into the container at the same path on all nodes. Required only when checkpoint.enforce is true; exempted from the placeholder check when enforce is false.

smoke

Controls test_smoke: a small fixed cell (not a sweep.runs entry) that loads the model and trains a few steps with no metric gating. test_checkpoint uses the same micro_batch_size, global_batch_size, and precision (it still uses checkpoint.* for step counts). Packaged configs set this explicitly; if the block is omitted the loader defaults to enabled with iters 10, MBS 1, precision BF16, and an empty global_batch_size (the suite then uses 8 on single-node and 16 on distributed).

Parameter	Default	Description
enabled	true	If false, test_smoke is skipped.
iters	10	Training steps for the smoke cell.
micro_batch_size	"1"	Micro-batch size for the smoke cell and for test_checkpoint.
global_batch_size	"8" (single) / "16" (distributed) in packaged files; empty in schema default	Global batch size for smoke and test_checkpoint. Empty string lets the suite pick the topology default above.
precision	BF16	Precision tag for the smoke cell and for test_checkpoint.

loss_curve

Parameter	Default	Description
sample_every	10	Sample a loss point every N steps for the slope check.
milestone_steps	[100, 500, 1000, 5000]	Additional steps always included in the sampled loss curve regardless of sample_every.
max_slope	0.0	Maximum allowed least-squares slope of the sampled loss curve. A positive slope (loss increasing) fails the check.
enforce	true	If false, the loss curve check is record-only and does not fail the test.

convergence

Parameter	Default	Description
target_metric	auto	Metric tracked for convergence. auto uses eval loss when --eval-interval is set in the training script, otherwise falls back to training loss.
target_value	0.0	Loss value at which the model is considered converged. 0.0 or negative disables convergence checking (record-only).

scaling_baseline

(Distributed configs only.)

Parameter	Default	Description
tokens_per_sec_total	0.0	Total tokens/sec from a prior single-node run (tokens/GPU/s × GPUs_per_node). Used to compute scaling efficiency as nodes increase. 0.0 disables the metric (record-only).
num_nodes	1	Number of nodes used to produce tokens_per_sec_total. Must be 1 for a single-node baseline.

sweep

Parameter	Default	Description
combinations	N/A	Dict of sweep cells. The key must be MBS=<micro_batch_size>,GBS=<global_batch_size>,PRECISION=<precision> (the sweep_name pytest ID and threshold cell).
combinations.*.training_iterations	"20"	Per-cell step count. Overrides train_params.training_iterations for that run. Other train_params keys may also appear in the body.
runs	N/A	Ordered list of combination keys to execute. Required and non-empty when combinations is non-empty. Omit sweep (or leave combinations empty) to run the implicit default cell.

Pytest parametrizes sweep_name from sweep.runs. The suite parses micro_batch_size, global_batch_size, and precision from each combination key. Additional fields in the combination body override the corresponding train_params values for that run, including training_iterations, tensor_parallelism, and pipeline_parallelism. If sweep is omitted or combinations is empty, one cell named default trains with train_params; micro_batch_size, global_batch_size, and precision must be set there. The matching threshold cell is default and is required when enforce_thresholds is true.

Threshold files

Each suite JSON names a sibling file in threshold_json. With a declared sweep, top-level cell keys must be the same MBS=<mbs>,GBS=<gbs>,PRECISION=<precision> strings as sweep.runs (unused combinations keys are not required). With no sweep (generic / implicit default run), the threshold cell is the literal key default — not an MBS=… string built from train_params. A missing or extra cell fails load when enforce_thresholds is true. When it is false, the default cell may be omitted: the loader warns and test_metric is record-only.

A metric is gated only when enforce_thresholds is true and the cell has a numeric spec:

Kind	Passes when
min	actual ≥ value
max	actual ≤ value
info	always; recorded only
min_ratio	actual / reference ≥ value
optional (boolean on a spec, not a kind)	if true, Megatron test_metric skips the metric when it is missing or None; a present value is still gated by kind.

Tracked metrics (namespace training.*): throughput_per_gpu, tokens_per_gpu, elapsed_time_per_iteration, mem_usage (Megatron-LM mem usages:; Primus does not emit it; packaged specs set optional: true), and on distributed configs scaling_efficiency_pct (kind: info and optional: true in packaged files).