TorchInstruments

Record activation and output-gradient statistics from a PyTorch model without changing its training loop. Keep the sampled history in Parquet, inspect per-layer summaries in JSON, and investigate selected layers with TensorBoard histograms.

from torchinstruments import inject_observer, remove_observer

inject_observer(model, output_dir="stats")
try:
    train(model)
finally:
    remove_observer(model)

Every run produces:

stats/
    history.parquet    # All successfully collected scalar measurements
    result.json        # History aggregated by layer, call, signal, and tensor path
    index.md           # Schema, sampling information, and queries for an LLM
    tensorboard/       # Histograms for the configured focus layers

During training, history is available as completed files in history.parts/. Removal combines these into history.parquet using Polars streaming and writes the final result.json. Before removal, result.json contains the selected layer catalog with empty measurements. If the process is interrupted, completed history chunks and flushed TensorBoard events remain readable. There is no automatic interpretation, ranking, problem classification, or health score.

What is measured

The default profile is mean, population std, min, max, p25, p50, p75, zero fraction, and nonfinite fraction. Statistics describe all finite entries of each sampled output tensor. Fractions use the original tensor size. Empty or entirely invalid tensors retain explicit unavailability reasons. Output gradients are measured separately and matched to their forward.

By default, leaf modules are observed at the first forward after each 60-second interval. Sampling units are root forwards, not optimizer steps. Inputs, parameter gradients, losses, and optimizer updates are not collected.

Histograms are always enabled. By default they cover the first eight observed modules in traversal order, on every sampled forward and its observed backward. Scalar history covers every selected module. Focus the dashboard on layers you want to investigate:

from torchinstruments import EveryNForwardsSampler, HistoryConfig, inject_observer

inject_observer(
    model,
    output_dir="stats",
    sampler=EveryNForwardsSampler(100),
    histogram_selector=lambda name, module: name.startswith("encoder.blocks.7."),
    max_histogram_modules=8,
    history_config=HistoryConfig(buffer_rows=8192, window=20),
)

The histogram selector must match at least one observed module. Its limit applies to modules; shared calls and multiple tensor outputs have separate tags. Selection occurs before histogram reduction, so unselected modules pay no histogram cost. Histograms contain finite entries; inspect nonfinite_fraction to detect excluded invalid values.

Read the history with Polars

Parquet uses a long table: one row per scalar measurement. A tensor's metrics share the same layer, call index, signal, tensor path, and sample ID. A nullable value always has an unavailable_reason; it is never silently replaced with zero.

import polars as pl

trend = (
    pl.scan_parquet("stats/history.parquet")
    .filter(
        (pl.col("layer") == "encoder.blocks.7.proj")
        & (pl.col("signal") == "output_gradient")
        & (pl.col("metric") == "std")
    )
    .select("sample_id", "timestamp", "call_index", "tensor_path", "value")
    .sort("sample_id")
    .collect()
)

While training, scan stats/history.parts/*.parquet instead. Always order trends by sample_id: backward events may arrive in a different order from their forwards.

result.json is an array of layer records. Each tensor contains metric summaries with observation counts, whole-history mean, population std, min/max, and linearly interpolated quartiles. statistics is keyed by metric name. Each metric also has previous/recent window means. Aggregates give each available sample statistic equal weight; they are not pooled tensor distributions. For example, statistics.mean.p50 is the median of sampled tensor means. Unavailable values are counted and excluded from numeric aggregates; null aggregates carry an explanation. Exact samples, timestamps, and missing-value reasons stay in Parquet. All selected layers are listed, including unexecuted layers; there is no byte-budget selection.

Find a problem, then test a fix

The debugging examples reproduce inactive ReLUs, sigmoid saturation, growing scale, invalid arithmetic, and an accidentally detached branch. Each uses matched baseline, broken, and fixed runs, prints a Polars evidence table, and creates comparison plots. The fault starts after four healthy samples so its onset is visible.

uv run examples/find_problems.py

These examples demonstrate known interventions, not universal thresholds for unfamiliar models. See research workflows for evidence and interpretation limits.

Integration and ownership

rank_policy="rank0" is the default and attaches nothing on nonzero ranks. With rank_policy="all", each rank writes all four artifacts under its own rank-NNN/ directory. Compare ranks explicitly; summaries never silently average measurements across ranks.

Use capture_direct_forwards=True for model code that calls module.forward(...) directly. Removal restores observer-owned overrides. Capture does not replace model outputs or gradients, and the observer never registers a parameter, buffer, or child module.

A supplied TensorBoardSink(logger) additionally writes histograms into an existing logger. The logger remains caller-owned. The ordinary directory artifacts are still produced. The Lightning example demonstrates this with a real MNIST model.

Runtime dependencies are PyTorch, Polars, and TensorBoard. The ordinary training loop and telemetry remain trainer-independent. CPU behavior is tested; CUDA performance and torch.compile compatibility are not claimed.

Migration from 0.6

report.json, ranked findings, ReportConfig, live indicator aggregation, scalar logger exports, and rank-report merging have been removed. Use history.parquet, result.json, and HistoryConfig. DirectorySink always produces all four artifacts; write_full_details is no longer supported. Custom scalar reducers and additional sinks remain supported.

Development

uv sync --dev
uv run ruff check src tests examples
uv run ruff format --check src tests examples
uv run pyrefly check src tests examples
uv run pytest
uv build --wheel

MIT licensed. Author: Vadym Stupakov vadim.stupakov@gmail.com.