ucin 0.1.0

Native Python SDK for the UCIN Network Factory — import ucin, wrap a function, pick a tier.

UCIN SDK — import ucin

Native Python integration for the UCIN Network Factory. Wrap a function, pick a tier, get a deterministic SLA quote, accept the receipt, and the function runs on the network — its return value comes back inline.

import ucin

@ucin.workload(tier="flex")
def fine_tune_llama(dataset_path):
    import torch
    # ... standard PyTorch training loop ...
    return {"final_loss": 0.21}

result = fine_tune_llama("s3://my-bucket/data")   # quote → [Y/n] → run remotely
print(result["final_loss"])

Running python train.py does not execute the function locally. The SDK packages the call, asks the control plane for a quote, and pauses with the interactive receipt:

╔════════════════════════════════════════════════════════════╗
║ UCIN SLA QUOTE: Flex Tier                                   ║
╠════════════════════════════════════════════════════════════╣
║ Workload classified        : training (14.00 SCU)           ║
║ Profiler confidence        : 91%                            ║
║ Estimated completion       : 3h 15m                         ║
║ SLA guarantee (≤)          : 4h 00m                         ║
║ Target hardware            : H100                           ║
║ Total guaranteed cost      : ₹540.00                        ║
╚════════════════════════════════════════════════════════════╝

Accept this quote and deploy to Network Factory? [Y/n]:

Why this maps cleanly onto the existing platform

Every number on the receipt is the control plane's, not the SDK's. The SDK is a thin client — all pricing, profiling, SLA and billing logic already exists server-side:

Receipt field	Source in the codebase
Workload + SCU	auto_profiler.WorkloadProfiler.profile_workload
Confidence	auto_profiler._reconcile
Estimated completion	customer_api._build_response_fields (etc_hrs)
SLA guarantee (≤)	customer_api._apply_churn (etc_max_hrs)
Total guaranteed cost	EstimateResponse.quoted_price_inr — a hard wallet pre-auth
Budget hard-stop	gateway_api.py NF-7: billing clamps at ceiling_scu, complete_at_ceiling
"never billed over quote"	main.py heartbeat loop: force-fail removed; UCIN absorbs overruns

The flow uses three real endpoints, all Bearer-authenticated under /customer:

1. POST /jobs/audit — the pre-acceptance gauntlet (security scan, profile, capacity, balance). Returns the quote and the audit_token deploy requires in production.
2. POST /jobs/deploy — pre-authorises quoted_price_inr against the wallet and queues the Job. The SOR (sor.dispatch_loop) places it.
3. GET /jobs/{id} / GET /jobs/{id}/results — poll to completion, then pull the result artifact.

The one real architectural gap (and how this bridges it)

The platform runs container images, and auto_profiler classifies a job by substring-matching the image URL and run_command ("torchrun", "vllm", ":latest") — there is no AST analysis today, despite the original vision.

So @ucin.workload cannot "just send the function." The bridge (packaging.py

• runner.py):

• Client side: cloudpickle(func, args, kwargs) + a captured pip freeze → uploaded via POST /storage/presign. A synthetic run_command carries both the payload key and keywords (torchrun train.py …) so the existing heuristic profiler classifies the workload with no server change.
• Container side: a generic runner image (ucin/pyrunner) whose CMD is ucin-run. It fetches the payload, tops up deps, calls the function, and writes the return value to UCIN_CHECKPOINT_DIR/ucin-result.pkl — which entrypoint_wrapper.py's existing checkpoint upload pipeline already ships to object storage (AWS S3 Mumbai or E2E — S3-compatible, India-resident).

This reuses the preemption shield, checkpoint/resume, cooperative ceiling, and billing untouched.

Budget & time guarantee — tasks can never overrun

Set a hard ceiling and the SDK guarantees you never pay above it:

@ucin.workload(tier="flex", max_budget_inr=500)      # ₹ cap
def train(path): ...

@ucin.workload(tier="flex", max_runtime_hrs=4)       # wall-clock cap
def train(path): ...

How it's enforced, end to end:

1. SDK converts max_budget_inr → the exact runtime ceiling using the tier rates from GET /customer/config, audits, and re-audits until the server's own quoted_price_inr ≤ your budget (the quote is linear in runtime, so one Newton step lands it). It refuses to deploy otherwise.
2. Control plane pins that as estimated_scu at 0.95 confidence (_apply_runtime_ceiling) → quoted_price_inr is exact, and the wallet is pre-authorised for exactly that.
3. Gateway computes ceiling_scu = (quoted − base_fee) / rate and, on every billing tick, clamps scu_consumed at the ceiling and returns complete_at_ceiling: true — the job is finalised at that boundary (gateway_api.py NF-7). actual_cost ≤ quoted_price is an invariant.
4. Container receives UCIN_MAX_SCU; entrypoint_wrapper.py's ceiling watcher SIGTERMs the workload for a graceful checkpoint at 90%, before the hard clamp — so the stop happens at a checkpoint, not mid-step.

Completion vs. truncation. The cap guarantees spend, not that the work finishes. The SDK fetches the platform's uncapped estimate too; if your cap is below it, the receipt warns the job will stop at the ceiling unfinished, and in non-interactive mode run() raises BudgetError unless you pass allow_truncation=True. To guarantee completion within budget, set a cap at or above the estimate (the receipt shows both numbers).

Install & authenticate

pip install ucin
ucin login                 # prompts for email + password; token saved to ~/.ucin
ucin whoami                # confirm the signed-in account

After ucin login, every import ucin script picks up the token automatically — no env vars to manage. Programmatic alternative:

import ucin
ucin.login("[e-mail]", "hunter2")   # or totp_code=... if MFA is on

Token precedence: configure() / UCIN_API_TOKEN → persisted ucin login (~/.ucin/credentials.json, keyed by control-plane URL, written 0600).

Configuration

ucin.configure(base_url="https://api.ucin.network", api_token="ey...")

or via env: UCIN_BASE_URL, UCIN_API_TOKEN (from POST /auth/login), UCIN_AUTO_YES=true (skip the prompt in CI), UCIN_RUNNER_IMAGE, UCIN_CONFIG_DIR (override the ~/.ucin credential dir).

Escape hatches

fine_tune_llama.local(path)      # run in-process, bypass UCIN (dev/debug)
fine_tune_llama.estimate(path)   # quote only — no deploy, no prompt
fine_tune_llama.submit(path)     # deploy after receipt, return job handle (non-blocking)

Decorator options

@ucin.workload(
    tier="flex",                 # priority | core | flex
    workload_hint="training",    # training | inference | batch | auto (profiler signal)
    state_lock="Maharashtra",    # required for core tier (data residency)
    express=True,                # CPTV express routing → queue_priority=0
    gpu_count=2,                  # parallelism width (e.g. DDP), NOT a GPU model
                                 # — the Smart Order Router picks the hardware
    max_budget_inr=500,          # hard ₹ ceiling — never billed above this
    max_runtime_hrs=4,           # OR a hard wall-clock ceiling
    allow_truncation=False,      # non-interactive: forbid caps below the estimate
    requirements=["torch==2.3.0"],
)
def job(...): ...

Server-side work still required to make this fully live

The three integration points are now wired end to end (control plane + data plane + image):

1. ✅ Result artifacts in /jobs/{id}/results. On clean completion the wrapper (entrypoint_wrapper._persist_outputs_on_success) uploads CHECKPOINT_DIR and reports the key; the CP status handler persists it to Job.checkpoint_id (gateway_api.py, JobStatusUpdateRequest.checkpoint_r2_key); /customer/jobs/{id}/results labels it ucin-result.pkl with source_path="checkpoint/ucin-result.pkl", and the SDK extracts the return value from the tarball. Verified by a round-trip test.
2. ✅ Publish & pin the ucin/pyrunner image. See sdk/runner-image/ (Dockerfile + build_and_push.sh). The script prints the immutable @sha256 digest to set as UCIN_RUNNER_IMAGE / _DEFAULT_RUNNER_IMAGE. (Operator action: run the script against your registry to publish.)
3. ✅ Inject UCIN_PAYLOAD_URL at dispatch. The SDK stages the payload via /customer/storage/presign (user_id derived from the JWT) and carries the object key as UCIN_PAYLOAD_KEY; gateway_api._build_job_payload mints a fresh presigned GET into UCIN_PAYLOAD_URL at poll time (so it can't expire in the queue).

Still optional / future:

4. Real AST profiling. To honour the original vision, add an ast-based classifier path to auto_profiler keyed off an SDK-supplied source digest, instead of the synthetic run_command keywords. The viral loops (FinOps-in-CI quote-on-PR, quote sharing) then build on /jobs/estimate, which is already side-effect-free and safe to call in CI.

Layout

sdk/
  pyproject.toml          # `ucin` dist; `ucin` + `ucin-run` console scripts
  README.md
  runner-image/           # the ucin/pyrunner image (Dockerfile + build_and_push.sh)
  examples/quickstart.py  # copy-paste first run
  tests/test_flow.py      # mocked round-trip regression suite
  ucin/
    __init__.py           # public surface: workload, login, configure, UcinClient
    workload.py           # @ucin.workload decorator + escape hatches
    client.py             # control-plane HTTP client + quote→execute flow
    auth.py               # login / whoami / logout (POST /auth/login)
    credentials.py        # ~/.ucin token store (per base_url)
    cli.py                # `ucin login|whoami|logout|config`
    packaging.py          # function → (cloudpickle payload, profiler run_command)
    receipt.py            # the interactive SLA-quote box + [Y/n] prompt
    config.py             # env / configure() resolution
    errors.py             # QuoteDeclined, InsufficientBalance, JobFailed, ...
    runner.py             # container-side `ucin-run` entrypoint