---
title: Telemetry
description: A description of the opt-out telemetry implementation in Axolotl.
---

# Telemetry in Axolotl

Axolotl implements anonymous telemetry to help maintainers understand how the library
is used and where users encounter issues. This data helps prioritize features, optimize
performance, and fix bugs.

## Data Collection

We collect:

- System info: OS, Python version, Axolotl version, PyTorch version, Transformers
version, etc.
- Hardware info: CPU count, memory, GPU count and models
- Runtime metrics: Training progress, memory usage, timing information
- Usage patterns: Models (from a whitelist) and configurations used
- Error tracking: Stack traces and error messages (sanitized to remove personal
information)

No personally identifiable information (PII) is collected.

## Implementation

Telemetry is implemented using PostHog and consists of:

- `axolotl.telemetry.TelemetryManager`: A singleton class that initializes the
telemetry system and provides methods for tracking events.
- `axolotl.telemetry.errors.send_errors`: A decorator that captures exceptions and
sends sanitized stack traces.
- `axolotl.telemetry.runtime_metrics.RuntimeMetrics`: A dataclass that tracks runtime
metrics during training.
- `axolotl.telemetry.callbacks.TelemetryCallback`: A Trainer callback that sends
runtime metrics telemetry.

The telemetry system will block training startup for 15 seconds to ensure users are
aware of data collection, unless telemetry is explicitly enabled or disabled.

## Opt-Out Mechanism

Telemetry is **enabled by default** on an opt-out basis. To disable it, set either:

- `AXOLOTL_DO_NOT_TRACK=1` (Axolotl-specific)
- `DO_NOT_TRACK=1` (Global standard)

To acknowledge and explicitly enable telemetry (and remove the warning message), set:
`AXOLOTL_DO_NOT_TRACK=0`.

## Privacy

- All path-like config information is automatically redacted from telemetry data
- Model information is only collected for whitelisted organizations
    - See `axolotl/telemetry/whitelist.yaml` for the set of whitelisted organizations
- Each run generates a unique anonymous ID
    - This allows us to link different telemetry events in a single same training run
- Telemetry is only sent from the main process to avoid duplicate events