pontem 0.1.0

Pontem edge SDK — logs, metrics, and config for edge devices

Pontem Python SDK

Pontem Python SDK for logs, metrics, and config on Pontem-managed edge devices. Zero runtime dependencies.

Installation

pip install pontem

Quick Start

import pontem

pontem.init(service_name="my-service")

pontem.logger.info("model loaded", model="scoring_v3")

pontem.metrics.count("detections", class_name="apple")

with pontem.metrics.timer("model.inference"):
    result = model.predict(frame)

pontem.shutdown()

Stdout emit (Docker / compose)

For containerized deployments where a sidecar log collector tails the container's stdout, switch the SDK from file mode to stdout mode:

import pontem

pontem.init(service_name="my-service", emit_target="stdout")

Equivalently, set the env var (useful when the same image runs on both edge devices and compose hosts):

PONTEM_EMIT_TARGET=stdout

Selection precedence: init(emit_target=...) kwarg > PONTEM_EMIT_TARGET env var > default "file". In stdout mode emit_dir, file rotation, and gzip compression are no-ops — the docker daemon's json-file driver handles container log rotation.

The JSONL line shape is identical in both modes. See SCHEMA.md for the full record schema — that file is the contract between this SDK and any log collector that ingests its output.

Logging

Structured logs with OTel-aligned severity levels. Each log is written to logs.jsonl in the emit directory (or to stdout, one record per line, when emit_target="stdout" — see Stdout emit).

pontem.logger.info("model loaded", model="scoring_v3")
pontem.logger.warn("high latency", latency_ms=120)
pontem.logger.error("inference failed", error=str(e))

Available levels: trace, debug, info, warn, error, fatal.

Metrics

Metrics are aggregated in memory and flushed periodically to metrics.jsonl. No I/O on the caller's thread.

# Counters — incremented, flushed as a single sum
pontem.metrics.count("detections", class_name="apple")
pontem.metrics.count("bytes_sent", len(payload))

# Histograms — records min/max/sum/count summary
pontem.metrics.record("payload_size", len(data), unit="bytes")

# Gauges — last value wins
pontem.metrics.set_gauge("gpu_temp", 72.0, unit="celsius", gpu="0")

# Timers — context manager or decorator, records to histogram
with pontem.metrics.timer("model.inference"):
    result = model.predict(frame)

@pontem.metrics.timer("preprocessing")
def preprocess(frame):
    ...

Config

Reads agent-managed config from /opt/pontem/config/config.json (or $PONTEM_CONFIG_DIR).

threshold = pontem.config("my_namespace", "model_threshold", default=0.85)

# Reload after agent signals a config update
pontem.config.reload()

Shutdown

Flushes remaining metrics and logs. Also registered via atexit.

pontem.shutdown()

Documentation

See docs/getting-started.md for a comprehensive guide including full API reference, configuration options, and troubleshooting.

License

Apache 2.0 — see LICENSE.