Trackio Experiment Tracking

Track and visualize ML training experiments with Trackio, including metric logging, alerts and dashboard visualization.

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Buy it · $45 Read it before you buy $45 Written by huggingface · unverified publisher
Context cost
6.3k tokensestimated from the bundle, loaded when it triggers
Bundle
4 files · 25.4 kBtext throughout, nothing executable
Licence
Apache-2.0paid listing
Last change
no release on file
Servers it uses
Noneruns standalone

What it does

Track and visualize ML training experiments with Trackio. Use when logging metrics during training (Python API), firing alerts for training diagnostics, or retrieving/analyzing logged metrics (CLI). Supports real-time dashboard visualization, alerts with webhooks, HF Space syncing, and JSON output for automation.

Installed, it changes the agent in these ways.

What this skill changes about the agent is not written down here yet. The listing was collected from its source, and the description is in its own SKILL.md.

Workflow

Runs a procedure end to end.

experiment-trackingmetricstraining

The skill itself

This is the whole product. A skill is instructions the model reads, so there is nothing behind the listing you cannot see first — the front matter loads with every session, and the body below it loads when the skill triggers.

SKILL.md5.1 kB · 118 lines
--- name: huggingface-trackio description: Track and visualize ML training experiments with Trackio. Use when logging metrics during training (Python API), firing alerts for training diagnostics, or retrieving/analyzing logged metrics (CLI). Supports real-time dashboard visualization, alerts with webhooks, HF Space syncing, and JSON output for automation. ---
6# Trackio - Experiment Tracking for ML Training
7
8Trackio is an experiment tracking library for logging and visualizing ML training metrics. It syncs to Hugging Face Spaces for real-time monitoring dashboards.
9
10## Three Interfaces
11
12| Task | Interface | Reference |
13|------|-----------|-----------|
14| **Logging metrics** during training | Python API | [references/logging_metrics.md](references/logging_metrics.md) |
15| **Firing alerts** for training diagnostics | Python API | [references/alerts.md](references/alerts.md) |
16| **Retrieving metrics & alerts** after/during training | CLI | [references/retrieving_metrics.md](references/retrieving_metrics.md) |
17
18## When to Use Each
19
20### Python API → Logging
21
22Use import trackio in your training scripts to log metrics:
23
24- Initialize tracking with trackio.init()
25- Log metrics with trackio.log() or use TRL's report_to="trackio"
26- Finalize with trackio.finish()
27
28**Key concept**: For remote/cloud training, pass space_id — metrics sync to a Space dashboard so they persist after the instance terminates. Auto-created Spaces are **public by default** — pass private=True if the metrics should not be public.
29
30→ See [references/logging_metrics.md](references/logging_metrics.md) for setup, TRL integration, and configuration options.
31
32### Python API → Alerts
33
34Insert trackio.alert() calls in training code to flag important events — like inserting print statements for debugging, but structured and queryable:
35
36- trackio.alert(title="...", level=trackio.AlertLevel.WARN) — fire an alert
37- Three severity levels: INFO, WARN, ERROR
38- Alerts are printed to terminal, stored in the database, shown in the dashboard, and optionally sent to webhooks (Slack/Discord)
39
40**Key concept for LLM agents**: Alerts are the primary mechanism for autonomous experiment iteration. An agent should insert alerts into training code for diagnostic conditions (loss spikes, NaN gradients, low accuracy, training stalls). Since alerts are printed to the terminal, an agent that is watching the training script's output will see them automatically. For background or detached runs, the agent can poll via CLI instead.
41
42→ See [references/alerts.md](references/alerts.md) for the full alerts API, webhook setup, and autonomous agent workflows.
43
44### CLI → Retrieving
45
46Use the trackio command to query logged metrics and alerts:
47
48- trackio list projects/runs/metrics — discover what's available
49- trackio get project/run/metric — retrieve summaries and values
50- trackio list alerts --project <name> --json — retrieve alerts
51- trackio show — launch the dashboard
52- trackio sync — sync to HF Space
53
54**Key concept**: Add --json for programmatic output suitable for automation and LLM agents.
55
56→ See [references/retrieving_metrics.md](references/retrieving_metrics.md) for all commands, workflows, and JSON output formats.
57
58## Minimal Logging Setup
59
60```python
61import trackio
62
63# Spaces are PUBLIC by default (good for shareable dashboards);
64# pass private=True if the metrics should not be public
65trackio.init(project="my-project", space_id="username/trackio", private=True)
66trackio.log({"loss": 0.1, "accuracy": 0.9})
67trackio.log({"loss": 0.09, "accuracy": 0.91})
68trackio.finish()
69```
70
71### Minimal Retrieval
72
73```bash
74trackio list projects --json
75trackio get metric --project my-project --run my-run --metric loss --json
76```
77
78## Autonomous ML Experiment Workflow
79
80When running experiments autonomously as an LLM agent, the recommended workflow is:
81
821. **Set up training with alerts** — insert trackio.alert() calls for diagnostic conditions
832. **Launch training** — run the script in the background
843. **Poll for alerts** — use trackio list alerts --project <name> --json --since <timestamp> to check for new alerts
854. **Read metrics** — use trackio get metric ... to inspect specific values
865. **Iterate** — based on alerts and metrics, stop the run, adjust hyperparameters, and launch a new run
87
88```python
89import trackio
90
91trackio.init(project="my-project", config={"lr": 1e-4})
92
93for step in range(num_steps):
94 loss = train_step()
95 trackio.log({"loss": loss, "step": step})
96
97 if step > 100 and loss > 5.0:
98 trackio.alert(
99 title="Loss divergence",
100 text=f"Loss {loss:.4f} still high after {step} steps",
101 level=trackio.AlertLevel.ERROR,
102 )
103 if step > 0 and abs(loss) < 1e-8:
104 trackio.alert(
105 title="Vanishing loss",
106 text="Loss near zero — possible gradient collapse",
107 level=trackio.AlertLevel.WARN,
108 )
109
110trackio.finish()
111```
112
113Then poll from a separate terminal/process:
114
115```bash
116trackio list alerts --project my-project --json --since "2025-01-01T00:00:00"
117```
118
In the file
SKILL.md651 words
Files4
LicenceApache-2.0
Why you can read it

Nothing in a skill executes. The client loads the text and the model follows it, so a skill can be audited the way a runbook is — by reading it.

What it costs in context

Skills are not billed by the call. They are paid for in context: every token the instructions occupy is a token your code, your diff and your conversation cannot use. Here is what this one takes and when it takes it.

≈90
always loaded
The name and description, so the model knows the skill exists and when to reach for it.
6,260
on trigger
The instruction body and 3 supporting files, read only when the skill fires.
3.2%
of a 200k window
Ten skills this size would take about 32% of the window before you open a file.
050k100k150k200k context window

6.3k tokens, estimated from the bundle at four bytes to the token, held for the rest of the session once it triggers. Heavy. Teams tend to install this one per project rather than globally, and load it only when the job comes up.

Servers bill, skills cost

A server charges by the month. A skill charges once per session, in context, and then keeps charging it for as long as the session lives.

Before and after

The same question, put to the same model twice: once as it comes, and once with these instructions loaded.

No worked example has been published for this skill yet.

Adoption
Installsnone yet
Ratingno reviews yet

The procedure it runs

The procedure has not been published here. It is in the skill’s own SKILL.md, which its author has not sent to the marketplace yet.

Prose, not code

These steps are written for a model to follow, not executed by a runtime. It can still be told to skip one, and it will say so when it does.

Servers it uses

None. This skill calls no MCP servers at all.

Everything it needs is in the instructions, so it works in a project with nothing connected — the model reads the file and changes how it works with what it can already reach.

It writes no files and reaches no network. All it changes is how the model reasons and writes.

What it asks for
Writes filesno
Network accessno

Read from the allowed-tools line of this skill’s own SKILL.md. A skill grants no permissions of its own — it can only ask for tools your client already has.

What it will not do

Every skill is narrow, and the useful ones say where they stop. These are the jobs this one is the wrong tool for.

What this skill is not for has not been published here. Nothing is implied by that: it is a section the author has not filled in.

What is in the bundle

4 files, 25.4 kB on disk. A bundle is text throughout: the instructions the model reads, plus the templates it fills in.

  • SKILL.md5.1 kB
  • references/alerts.md6.0 kB
  • references/logging_metrics.md5.6 kB
  • references/retrieving_metrics.md8.7 kB
What is not in it

No dependencies and nothing executable: a skill is text the agent reads, so the bundle is 4 files you can review in full before installing. The Apache-2.0 licence covers the templates and examples as well as the instructions.

Install

Installing copies the bundle into your project. Nothing runs at install time — the files sit on disk until the model reads them.

$45 once
Trackio Experiment Tracking · Apache-2.0 · huggingface
one-time
Price$45 once
LicenceApache-2.0 — the author’s, unchanged by this purchase
Paid throughStripe, once, on the card you add at the checkout
Keeps workingfor good — the files are yours once they are on disk
Updatesevery update its author ships, delivered through this account

You can read the whole bundle before paying — the SKILL.md above is the product, not a preview of it. What the money buys is the delivery: the folder packaged and handed to your machine by key, every update its author ships, and our support if it does not do what this listing says. The terms of use are Apache-2.0, set by the author and unchanged by buying it here.

Payment runs through Stripe, on a page like this one rather than a redirect. Once there is an account it joins the same mcprush invoice as everything else you run, so there is never a second card to enter.

Which clients pick it up on their own

A skill is a folder of text. A client with a skills folder reads it without being told; everywhere else the same text works, it is just handed to the model rather than found.

Claude Code.claude/skills/
Claude Desktop
ChatGPT
Cursor.cursor/skills/
VS Code.github/skills/
Codex CLI.agents/skills/
Gemini CLI.gemini/skills/
Grok.grok/skills/
Zed.agents/skills/
Windsurf.windsurf/skills/
Agent SDK.claude/skills/
HTTP / API
This release
Versionnot versioned
Publishedno release date on file
Price$45
Referencehuggingface/trackio-experiment-tracking

Versions

Its author publishes no version number, so there is nothing here to pin to: what you install is the folder as it stands today. Instructions change more often than APIs do — a skill can be rewritten entirely without anything it depends on moving.

v
  • No earlier releases have been published to the marketplace.
Pinning

Nothing to pin to: this skill carries no version number of its own. What you install is what the folder holds on the day you install it.

Reviews

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Who can post

Only accounts that have had the skill installed for fourteen days, so a review is written after living with it rather than after reading it. Publishers may reply once.

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