swanlab-skill

name	swanlab-skill
metadata	{"version":"0.2.0"}
description	Interact with SwanLab — both writing tracking code (init/log/finish/multimedia) and querying experiment data via CLI (`swanlab api`). Use this skill when the user wants to write training tracking code, log metrics or media, manage experiments, inspect metrics/logs/keys, list projects/runs, filter experiments, manage self-hosted users, automate queries via CLI, or mentions "swanlab", "experiment tracking", "log metrics", "swanlab api", "swanlab cli".

SwanLab Skill

SwanLab is an AI training experiment tracking platform. This skill covers two usage patterns:

• Writing tracking code — use the Python SDK (swanlab.init, swanlab.log, swanlab.finish, media helpers)
• Reading experiment data — use the swanlab api CLI to query metrics, logs, summaries, media, etc.

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Reference Routing

If the user wants to...	Read this reference
Write tracking code (init/log/finish/media)	references/SDK_QUICKSTART.md
Query data via CLI (metrics/summary/logs/filter/etc.)	references/CLI_REFERENCE.md
Understand data model / terminology / filter syntax	references/SWANLAB_CONCEPTS.md
Plot metrics or compare experiments visually	See Scripts below

Version note (SDK ≥ 0.9.0): use swanlab api run series to discover an experiment's metric keys. run column / run columns are deprecated since 0.9.0 and do not apply to multi-view experiments — only fall back to them when the installed SDK is < 0.9.0. See CLI_REFERENCE.md > Version Applicability.

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Run Modes

swanlab.init(mode=...) controls where data goes:

Mode	Local Storage	Cloud Upload	Use Case
online	Yes (protobuf)	Yes (Transport → HTTP)	Normal cloud usage. Requires login.
local	Yes (protobuf)	No	Air-gapped / no account needed.
offline	Yes (protobuf)	No (syncable later via swanlab sync)	Save locally, upload to cloud later.
disabled	No	No	Completely disable all logging.

Default is online if logged in, otherwise the user is prompted interactively (or falls back to offline).

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Scripts

Two helper scripts are available for visualizing experiment data:

scripts/plot_metrics.py — Single Experiment Line Chart

Trigger when the user wants to visualize scalar metrics from one experiment (e.g. "plot my loss curve", "show training metrics chart").

python scripts/plot_metrics.py username/project_name/run_id --keys loss,acc
python scripts/plot_metrics.py user/proj/run1 -k loss -o loss_chart.png -s 500
python scripts/plot_metrics.py --data metrics.json -k loss,acc -o chart.png   # from saved JSON

scripts/runs_benchmark.py — Cross-Experiment Comparison

Trigger when the user wants to compare the same metric across multiple experiments (e.g. "compare loss across runs", "benchmark these experiments").

python scripts/runs_benchmark.py user/proj/run1 user/proj/run2 user/proj/run3 -k loss
python scripts/runs_benchmark.py user/proj/run1 user/proj/run2 -k loss --direction lower
python scripts/runs_benchmark.py user/proj/run1 user/proj/run2 -k loss,acc --normalize
python scripts/runs_benchmark.py --data benchmark_data.json -k loss              # from saved JSON

Both scripts require swanlab login (or --api-key / --host flags).

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Path Convention

CLI commands use username/project_name (project) or username/project_name/run_id (experiment). See SWANLAB_CONCEPTS.md > Path Convention for details.

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Quick Disambiguation

User says...	They probably mean...	Route
"track my training" / "log metrics"	Write tracking code	SDK_QUICKSTART.md
"log images/audio/text"	Log media data	SDK_QUICKSTART.md
"my loss curve" / "experiment metrics"	Query scalar data	CLI_REFERENCE.md > run metrics
"filter experiments"	Query by conditions	CLI_REFERENCE.md > run filter
"my experiments" / "list runs"	List experiments	CLI_REFERENCE.md > run list
"compare runs visually"	Cross-experiment chart	scripts/runs_benchmark.py
"plot metric chart"	Single-experiment chart	scripts/plot_metrics.py
"experiment config"	Hyperparameters	CLI_REFERENCE.md > run info
"console output"	Captured logs	CLI_REFERENCE.md > run logs
"what metrics are tracked"	Metric keys	CLI_REFERENCE.md > run series
"check connectivity" / "can I reach swanlab"	Environment check	swanlab ping
"check login status" / "am I logged in"	Verify credentials	swanlab verify

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Environment Connectivity

Before writing tracking code or running CLI queries, especially in online mode, run these two checks to confirm the environment is ready:

1. swanlab ping — Test network reachability

The fastest way to diagnose connectivity issues. Run it first when a user reports upload failures, login problems, or unknown mode fallbacks.

swanlab ping
# Reports: API host, web host, latency, and login status.
# If ping fails, check SWANLAB_API_HOST / network proxy / firewall settings.

2. swanlab verify — Validate login credentials

After confirming the server is reachable, use swanlab verify to check that stored credentials are valid and have not expired. This reads the API key and host from the local .netrc file (created by swanlab login).

swanlab verify
# Validates stored API key against the server.
# Reports: which host (default https://swanlab.cn) and the logged-in username.
# Fails if: not logged in, API key is invalid, or key has expired.

swanlab verify --local
# Check local login status (.swanlab in current directory) instead of the global one.

Recommended pre-flight sequence:

1. swanlab ping → confirm the server is reachable
2. swanlab verify → confirm credentials are valid
3. Proceed with swanlab api queries or SDK code

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Behavioral Constraints

See CLI_REFERENCE.md > Behavioral Constraints for the full list. Key rules:

• Use --all only when the user explicitly asks for it (e.g. "fetch all", "get everything", "complete list"). For paginated list commands, always use default pagination (--page_num / --page_size).
• Always ask for specific metric keys before running run metrics or run medias. If the user doesn't know the key names, first run run series PATH to discover them (run columns PATH only as a fallback on SDK < 0.9.0).
• Always persist large metric data to file via --save, then visualize with scripts/plot_metrics.py --data file.json or use run summary for aggregate stats.

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Further Reading

This skill covers the most common SwanLab workflows for AI coding agents. For details that are out of scope here (full SDK surface, advanced CLI flags, self-hosted deployment, integrations, etc.), consult the official SwanLab documentation:

• Cloud guide (what is SwanLab, concepts, usage): https://docs.swanlab.cn/guide_cloud/general/what-is-swanlab.md
• Python SDK & OpenAPI reference: https://docs.swanlab.cn/api/api-index.md