astra-ai-sdk 0.3.0

Serve Astra-compressed models anywhere - hosted or local ONNX serving with built-in telemetry that feeds the Astra dashboard

astra-ai-sdk

Serve Astra-compressed models on your own hardware — Astra never runs your model server-side — and keep the Astra dashboard monitoring them while they run.

pip install 'astra-ai-sdk[serve]'         # on-device ONNX serving (onnxruntime, numpy)
pip install 'astra-ai-sdk[serve,system]'  # + precise CPU/RSS metrics (psutil)

Exports: LocalRunner, RunnerError, pull_artifact, TelemetryReporter, ApiError.

On-device serving (the whole product)

LocalRunner.from_deployment pulls the deployed, compressed artifact once (sha256-cached under ~/.cache/astra) and serves it with onnxruntime inside your own process — no server to stand up, no hosted inference:

from astra_sdk import LocalRunner

# base_url defaults to the hosted Astra origin (override with ASTRA_BASE_URL).
runner = LocalRunner.from_deployment("dep_ab12cd34ef", "astra_sk_live_...")
out = runner.run({"input": my_numpy_array})   # bare ndarray per input name
print(out["latencyMs"], out["outputs"], out["preMs"], out["postMs"])
runner.close()                                 # final telemetry flush

run() returns {latencyMs, outputs, preMs, postMs, raw}.

Run a file you already have

Downloaded the artifact (SDK Hub → Download Artifact) or have an .onnx on disk? Skip the deployment — serve the file directly:

from astra_sdk import LocalRunner

runner = LocalRunner.from_file("compressed.onnx")
out = runner.run({"input": my_numpy_array})
runner.close()

Telemetry is off for a bare file; pass deployment_id= + api_key= to still report local runs to that deployment.

Closed-loop telemetry (offline-durable)

Every on-device inference is measured and shipped back to Astra through a durable closed loop that can never block or break your serving path:

• buffers events in memory and spools them to disk when offline;
• flushes the buffered batches automatically on reconnect;
• deletes each batch only after the server acks it, with per-batch idempotency so a retry is never double-counted.

Stream	Cadence	Fields
Request events	per inference	timestamp, latency breakdown (preprocess / inference / postprocess ms), success / error code, batch size, region tag, input shape signature
System snapshots	~30 s	CPU %, RSS MB, throughput req/min, dropped-event count, SDK / Python / onnxruntime versions, OS, arch, execution provider, hostname
Window stats	~60 s or 200 requests	per-input tensor mean/std/min/max/NaN%, output class distribution (top-10), 16-bin confidence histogram, mean entropy, mean top-1 confidence

Window stats power the dashboard's prediction drift (PSI vs the deployment's reference distribution) and input distribution shift alerts.

Opt out any time: LocalRunner.from_deployment(..., report_telemetry=False) or ASTRA_SDK_TELEMETRY=0. Disable the on-disk spool with ASTRA_SDK_SPOOL=0 (telemetry then buffers in memory only).

CLI

astra pull                  # pull the compressed artifact
astra serve --port 8765     # on-device HTTP endpoint: POST /infer
astra bench -n 200          # on-device p50/p95, reported as telemetry

Options can come from ASTRA_BASE_URL, ASTRA_DEPLOYMENT_ID, ASTRA_API_KEY (or --deployment / --api-key / --base-url flags).