Console commands¶

Every command the interactive console accepts, once you’re attached (see Starting and connecting for starting the server and connecting). Type help (or h / ?) inside the console at any time for this same reference with extra examples drawn from the live experiment.

Discovery and help¶

help / h / ? — show all command syntaxes and examples.
status — compact snapshot: registered models, dataloaders, optimizers, hyperparameters, and the current model age.
ledger / ledgers / snapshot — same registry snapshot as status, without the model-age lookup.
dump / d — sanitized dump of dataloaders, optimizers, and hyperparameters (models are omitted to avoid printing huge weight dumps).
ledger_dump / dump_ledger / dump_ledger_all — like dump, but includes models too. Can be large.

Training control¶

pause / p — pause training and set is_training=False.
resume / r — resume training and set is_training=True.

Registry inspection¶

list_models — registered model names.
list_optimizers — registered optimizer names.
list_loaders / loaders / list_dataloaders — registered dataloader names.
plot_model [model_name] (aliases: plot_arch, plot) — ASCII tree of the model’s architecture. Omit model_name to use the default registered model.

Sample-level dataset operations¶

Syntax: list_uids [loader_name] [--discarded] [--limit N] (aliases: uids, samples)

List sample UIDs (with tags and discard status). Omit loader_name to check every registered loader; --discarded restricts to currently discarded samples; --limit N caps the count per loader.

Syntax: discard <uid> [uid2 ...] [--loader loader_name] / undiscard <uid> [uid2 ...] [--loader loader_name]

Mark one or more samples (by sample/UID) as discarded or restore them. Tries the dataframe-backed path first (equivalent to discard_samples()); without --loader, falls back to every registered loader whose dataset exposes a discard method.

Syntax: add_tag <sample_id> <tag> [sample_id2 ...] [--loader loader_name] (alias: tag)

Add a boolean tag to one or more samples. Same dataframe-first, all-loaders-fallback behavior as discard.

Examples

list_uids
list_uids train_loader --discarded
list_uids --limit 20
discard sample_001 sample_002
undiscard sample_001
add_tag sample_001 difficult sample_002 sample_003

Hyperparameter operations¶

hp (alias: hyperparams) — list registered hyperparameter set names.
hp <name> — show one set’s values. hp show <name> also works.
set_hp [hp_name] <key.path> <value> (aliases: sethp, set-hp) — update one key path. hp_name may be omitted only when exactly one hyperparameter set is registered. value is parsed as JSON first (so 32, 0.5, true, "a string" all work), falling back to bool/int/float/string coercion.

Examples

hp
hp fashion_mnist
set_hp fashion_mnist data.train_loader.batch_size 32
set_hp optimizer.lr 0.0005    # hp_name omitted — only valid with one hp set

Evaluation¶

evaluate [split_name] [--steps N] [--tags tag1,tag2] (aliases: eval, ev) — pause training and trigger a background evaluation pass. Default split: the first registered dataloader. --tags restricts evaluation to samples carrying any of the given tags (and implies not using the full set); --steps caps the number of batches evaluated.
eval_status (aliases: es, evaluation_status) — poll progress of the current evaluation.
cancel_eval (aliases: ce, cancel_evaluation) — cancel a running or pending evaluation.

Examples

evaluate                                  # default split, full set
evaluate val_loader
evaluate test_loader --steps 50
evaluate train_loader --tags difficult,outlier
eval_status
cancel_eval

See the “Evaluation mode” section of User Functions Reference for how this integrates with (or without) your own training loop.

Audit mode¶

Syntax: audit [on|off]

Toggles auditor mode: while on, the optimizer’s step() is skipped (the training loop keeps running and forward/backward still happen) so you can inspect gradients/activations without modifying weights. With no argument, prints the current state.

Examples

audit on
audit off
audit          # show current state

AI Agent¶

Initializes and drives the same natural-language agent used by Weights Studio (discard/tag/sort/analyze via a prompt) from the console. Full sub-verb reference, examples, and setup: see Experiment Agent Assistant.

Examples

agent status
agent init --model openrouter/anthropic/claude-opus-4.6
agent models
agent model openrouter/openai/gpt-5
ask tag train samples with loss > 1.2 as goldset

Experiment report¶

Syntax: report [signal ...] [--signals a,b] [--output PATH] [--no-agent] [--distributions a,b] (alias: reports)

Generates the HTML experiment report — signal trajectory plots, a health label per signal, per-sample outliers, loss-shape tag counts, dataset stats, and an analysis written by the agent’s LLM — under <root_log_dir>/reports/, and replies with the path, how many signals went in, and whether the analysis was included. Same artifact and same code path as the Weights Studio report button and ai_report_generation(); see Experiment Reports.

With no arguments it covers every signal with at least 2 logged points. Name signals positionally (or with --signals) to restrict it, --distributions to add a histogram section for the named signals, --output to choose the file, and --no-agent to skip the LLM call entirely. If no LLM provider is configured the report is still written, just without the analysis ("analysis": false in the reply).

Examples

report
report train_loss val_loss
report --signals train_loss,val_loss
report --output /tmp/run_42.html
report --no-agent

Session control¶

exit / quit — close the client connection (handled server-side; the server replies then closes the socket).
clear / cls — clear the local terminal screen. Handled entirely by the client, not sent to the server.

What’s missing on purpose¶

Editing hyperparameters (set_hp) is the only supported mutation path for architecture-level state. There is no console command to freeze/unfreeze layers or resize a model — that lives in Experiment Agent Assistant (agent query freeze layer 3) and Weights Studio, and in the Python API (Model Interaction).