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Documentation Index

Fetch the complete documentation index at: https://docs.cirron.com/llms.txt

Use this file to discover all available pages before exploring further.

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Requirements

Python 3.11 or newer. The SDK uses tomllib from the standard library.

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Core install

pip install cirron-sdk
The core install is minimal: no pandas, no polars, no ML framework dependency. It gives you the profiler, scope tree, marks, loop wrappers, flush thread + local spool, @ci.inference, ci.env, ci.secret, and the Cirron configuration class.

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Extras

Framework hooks and data-loader backends are opt-in extras so you only pull what you use.
ExtraAdds
pandaspandas backend for ci.load() (the default return type)
polarspolars backend for ci.load(as_="polars")
arrowPyArrow, faster Parquet reads for any filesystem source
hfdatasets.Dataset return type for ci.load(as_="hf")
torchPyTorch profiling hooks (forward / backward / optimizer / DataLoader)
tensorflowTensorFlow / Keras profiling hooks (Callback-based)
transformersHuggingFace Trainer callback hooks + LLM detectors
sklearnVersion floor for ci.wrap() (scikit-learn >= 1.3)
imagePillow, image decode for ci.load() image sources
s3ci.load("s3://...") via boto3
gcsci.load("gs://...") via google-cloud-storage
azureci.load("azure://...") via azure-storage-blob
postgresci.load("postgres://...")
mysqlci.load("mysql://...")
databricksci.load("databricks://...")
snowflakeci.load("snowflake://...")
sqlAll four SQL drivers
dotenvpython-dotenv: .env loading in ci.env()
safetensorssafetensors reader, inspect snapshot blobs locally
allEvery extra above
pip install "cirron-sdk[torch,pandas]"
pip install "cirron-sdk[transformers]"
pip install "cirron-sdk[sql]"
pip install "cirron-sdk[all]"
Install multiple extras at once by comma-separating them inside the brackets.

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Authentication

Authentication is optional; the SDK runs standalone without it. To sync traces to the Cirron platform, set an API key: 
export CIRRON_API_KEY=...
export CIRRON_API_ENDPOINT=https://app.cirron.com   # optional; default shown
Inside a Cirron pipeline or deployment, the CIRRON_RUN_ID / CIRRON_PIPELINE_ID / CIRRON_DEPLOYMENT_ID / CIRRON_WORKSPACE_ID context is injected by the runner. You do not set these yourself. See Configuration for the full resolution order (explicit args → env vars → ~/.cirron/config.toml → defaults) and self-hosted endpoints.

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Verify

import cirron as ci

p = ci.profile()
print(p.health())
ci.shutdown()
You should see a dict roughly like this (exact keys and values depend on what’s installed and whether you have a CIRRON_API_KEY set): 
{
    "enabled": True,
    "frameworks_installed": [],            # ["torch"] / ["transformers", "torch"] / ...
    "transport": "file_only",              # or "http" / "event_stream"
    "scope_depth": 1,                      # the cirron.session root scope
    "mark_drop_count": 0,
    "spool_drop_count": 0,
    "spool_bytes": 0,
    "flush_latency_ns": 0,                 # 0 on the first call is expected; filled after the first flush
}
A file will also appear under ./.cirron/spool/ containing the cirron.session root span.

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Check which extras are installed

ci.deps() reports which optional extras the current environment has. Call it with no arguments for a full report, or with required names to fail fast at script startup if any are missing: 
import cirron as ci

# Report everything
ci.deps()
# {'pandas': '2.3.3', 'polars': None, 'torch': '2.6.0', ...}

# Require specific extras. Raises CirronDependencyError with a
# combined pip install command if any are missing.
ci.deps("torch", "pandas")
See ci.deps for the full surface.

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Next step

Quickstart

Three 5-minute paths: zero-touch training, custom PyTorch loop, and inference.