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This guide shows how to put that knowledge into the weights of an open model with Cortex Training (public preview), so the model follows your policy without the policy in the prompt.\u003C/p\u003E\n","\u003Cp\u003EThe example is Northwind Supply Co., a fictional distributor. Its planning system computes the numbers for every SKU: days of cover, supplier lead time, suggested quantity, PO cap, supplier status, and flags for the two comparisons. What it needs from an agent is the decision and a planner note in Northwind's house style. You will post-train Qwen3-8B with LoRA on 200 of Northwind's decisions, score the base and tuned models side by side on 20 held-out SKUs, save a checkpoint, and deploy it as an OpenAI-compatible endpoint.\u003C/p\u003E\n","\u003Cp\u003EEverything runs in one Snowflake notebook. Your training loop runs on a CPU notebook, and Cortex Training runs the GPU work: forward and backward passes, optimizer steps, weight sync, and generation.\u003C/p\u003E\n","\u003Ch3\u003EWhat You'll Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to set up a role, secret, PAT, and external access integrations for Cortex Training from a notebook\u003C/li\u003E\u003Cli\u003EHow to write a training loop with the Cortex Training primitives \u003Ccode\u003Eforward_backward\u003C/code\u003E, \u003Ccode\u003Estep\u003C/code\u003E, \u003Ccode\u003Eweight_sync\u003C/code\u003E, and \u003Ccode\u003Egenerate\u003C/code\u003E\u003C/li\u003E\u003Cli\u003EHow to measure a base model and a tuned model on the same held-out examples\u003C/li\u003E\u003Cli\u003EHow to save a weights-only checkpoint and deploy it as an OpenAI-compatible endpoint\u003C/li\u003E\u003Cli\u003EHow to call the endpoint with the OpenAI Python SDK, including streaming\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Build\u003C/h3\u003E\n","\u003Cp\u003EA Snowflake notebook that teaches Qwen3-8B Northwind's reorder decision policy and house style. It shows the base and tuned scores, a per-SKU results table, and a chart, then calls the deployed model with only a SKU's inputs.\u003C/p\u003E\n","\u003Ch3\u003EPrerequisites\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EAccess to a \u003Ca href=\"https://signup.snowflake.com/?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_cta=developer-guides\"\u003ESnowflake account\u003C/a\u003E\u003C/li\u003E\u003Cli\u003ECortex Training enabled for the account and the tutorial role\u003C/li\u003E\u003Cli\u003EModel deployments (the OpenAI-compatible endpoint) enabled. This is a separate enablement. Without it, deployment fails with \u003Ccode\u003EHTTP 403 (517600): ... model deployments are not enabled for this account\u003C/code\u003E.\u003C/li\u003E\u003Cli\u003EEight H200 GPUs for training and sampling, then four for the endpoint, used one stage at a time. Expect about 20 to 30 minutes of GPU time.\u003C/li\u003E\u003Cli\u003EA role that can create the tutorial objects (the notebook uses \u003Ccode\u003EACCOUNTADMIN\u003C/code\u003E for setup and teardown only), and an organization policy that allows a role-restricted programmatic access token (PAT)\u003C/li\u003E\u003Cli\u003EA Snowflake Notebook on Container Runtime with Python 3.10 or later and a CPU compute pool such as \u003Ccode\u003ESYSTEM_COMPUTE_POOL_CPU\u003C/code\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003ENo OpenAI account, local Python, Docker, or repository checkout is needed.\u003C/p\u003E\n","\u003Ch2\u003ESetup\u003C/h2\u003E\n","\u003Cp\u003EDownload the \u003Ca href=\"https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/own-your-intelligence-cortex-training/assets/own-your-intelligence-cortex-training.ipynb\"\u003Enotebook\u003C/a\u003E. In \u003Cstrong\u003EProjects > Workspaces\u003C/strong\u003E, select \u003Cstrong\u003E+ Add new > Upload files\u003C/strong\u003E and upload it, then open it on a CPU Container Runtime with Python 3.10 or later. Give its service a 15-minute idle timeout. The notebook contains every code block below, in order, each with its explanation.\u003C/p\u003E\n","\u003Cp\u003EOne restart is required. Run the configuration, object, and PAT cells first. Then open \u003Cstrong\u003EConnected > Manage service\u003C/strong\u003E, select the secret \u003Ccode\u003ECT_QUICKSTART_DB.LAB.CT_PAT\u003C/code\u003E, and enable both \u003Ccode\u003ECT_QUICKSTART_API_EAI\u003C/code\u003E and \u003Ccode\u003ECT_QUICKSTART_DOWNLOAD_EAI\u003C/code\u003E. Save so the service restarts, rerun the configuration cell, and continue with the install cell.\u003C/p\u003E\n","\u003Ch3\u003EConfigure the Notebook\u003C/h3\u003E\n","\u003Cp\u003ESet \u003Ccode\u003EACCOUNT_HOST\u003C/code\u003E to this account's hostname, without \u003Ccode\u003Ehttps://\u003C/code\u003E. You can find it under your user menu > Account > View account details. Rerun this cell after every service restart.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003E# Edit these values. Rerun this cell after every service restart.\nACCOUNT_HOST = "YOUR_ORG-YOUR_ACCOUNT.snowflakecomputing.com"  # exact hostname, no https://\nADMIN_ROLE = "ACCOUNTADMIN"\nNOTEBOOK_COMPUTE_POOL = "SYSTEM_COMPUTE_POOL_CPU"\nDATABASE = "CT_QUICKSTART_DB"\nSCHEMA = "LAB"\nROLE = "CT_QUICKSTART_USER"\nWAREHOUSE = "CT_QUICKSTART_WH"\nSECRET_NAME = "CT_PAT"\nPAT_NAME = "CT_QUICKSTART_NOTEBOOK"\nPAT_DAYS = 1\n\nMODEL_NAME = "Qwen/Qwen3-8B"\nTOKENIZER_REVISION = "b968826d9c46dd6066d109eabc6255188de91218"\nHARDWARE = "H200"\nTRAINING_GPUS = 4\nSAMPLING_GPUS = 4\nSERVING_GPUS = 4\nMAX_SEQ_LEN = 1024\nSTEPS = 60\nBATCH_SIZE = 4\nLEARNING_RATE = 1e-4\n\nif "YOUR_" in ACCOUNT_HOST:\n    raise ValueError("Set ACCOUNT_HOST to your account hostname, for example myorg-myaccount.snowflakecomputing.com")\n\nimport os\n\nos.environ.setdefault("HF_HUB_DISABLE_XET", "1")\nos.environ.setdefault("HF_HUB_ETAG_TIMEOUT", "30")\nos.environ.setdefault("HF_HUB_DOWNLOAD_TIMEOUT", "60")\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ECreate the Objects\u003C/h3\u003E\n","\u003Cp\u003EThis cell creates the role, database, warehouse, grants, and network rules. Every statement uses \u003Ccode\u003EIF NOT EXISTS\u003C/code\u003E or is a grant, so rerunning it is safe.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Efrom snowflake.snowpark.context import get_active_session\n\nsession = get_active_session()\nUSER = session.sql("SELECT CURRENT_USER()").collect()[0][0]\nsession.use_role(ADMIN_ROLE)\n\nfor statement in [\n    f"CREATE ROLE IF NOT EXISTS {ROLE}",\n    f"GRANT ROLE {ROLE} TO ROLE SYSADMIN",\n    f'GRANT ROLE {ROLE} TO USER "{USER}"',\n    f"CREATE DATABASE IF NOT EXISTS {DATABASE}",\n    f"CREATE SCHEMA IF NOT EXISTS {DATABASE}.{SCHEMA}",\n    (f"CREATE WAREHOUSE IF NOT EXISTS {WAREHOUSE} WAREHOUSE_SIZE = 'XSMALL' "\n     "AUTO_SUSPEND = 60 AUTO_RESUME = TRUE INITIALLY_SUSPENDED = TRUE"),\n    f"GRANT USAGE ON DATABASE {DATABASE} TO ROLE {ROLE}",\n    f"GRANT USAGE ON SCHEMA {DATABASE}.{SCHEMA} TO ROLE {ROLE}",\n    f"GRANT CREATE EXPERIMENT ON SCHEMA {DATABASE}.{SCHEMA} TO ROLE {ROLE}",\n    f"GRANT CREATE SECRET ON SCHEMA {DATABASE}.{SCHEMA} TO ROLE {ROLE}",\n    f"GRANT USAGE ON WAREHOUSE {WAREHOUSE} TO ROLE {ROLE}",\n    f"GRANT USAGE ON COMPUTE POOL {NOTEBOOK_COMPUTE_POOL} TO ROLE {ROLE}",\n    (f"CREATE NETWORK RULE IF NOT EXISTS {DATABASE}.{SCHEMA}.CT_ACCOUNT_RULE MODE = EGRESS "\n     f"TYPE = HOST_PORT VALUE_LIST = ('{ACCOUNT_HOST.lower()}:443')"),\n    (f"CREATE NETWORK RULE IF NOT EXISTS {DATABASE}.{SCHEMA}.CT_DOWNLOAD_RULE MODE = EGRESS "\n     "TYPE = HOST_PORT VALUE_LIST = ('pypi.org:443', 'files.pythonhosted.org:443', "\n     "'github.com:443', 'codeload.github.com:443', 'huggingface.co:443', "\n     "'us.aws.cdn.hf.co:443', 'cdn-lfs.hf.co:443', 'cdn-lfs-us-1.hf.co:443', "\n     "'cas-bridge.xethub.hf.co:443', 'cas-server.xethub.hf.co:443', 'transfer.xethub.hf.co:443')"),\n]:\n    session.sql(statement).collect()\nprint(f"Objects ready for {USER}: {DATABASE}.{SCHEMA}, {WAREHOUSE}, role {ROLE}.")\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ECreate the PAT and Integrations\u003C/h3\u003E\n","\u003Cp\u003EThis cell issues a one-day PAT restricted to the tutorial role and writes it straight into the secret. The token is never printed. An active PAT is reused. It then creates the two external access integrations: one for the account API and one for package and tokenizer downloads.\u003C/p\u003E\n","\u003Cp\u003EIf your organization blocks PAT creation from a notebook session, create one in Snowsight under \u003Cstrong\u003ESettings > Authentication > Programmatic access tokens\u003C/strong\u003E, restrict it to \u003Ccode\u003ECT_QUICKSTART_USER\u003C/code\u003E, and store it with \u003Ccode\u003ECREATE SECRET\u003C/code\u003E.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003ESECRET_FQN = f"{DATABASE}.{SCHEMA}.{SECRET_NAME}"\nsession.use_role(ADMIN_ROLE)\ntokens = {row.as_dict()["name"]: row.as_dict()\n          for row in session.sql(f'SHOW USER PROGRAMMATIC ACCESS TOKENS FOR USER "{USER}"').collect()}\nsecret_exists = bool(session.sql(f"SHOW SECRETS LIKE '{SECRET_NAME}' IN SCHEMA {DATABASE}.{SCHEMA}").collect())\nactive = str(tokens.get(PAT_NAME, {}).get("status", "")).upper() == "ACTIVE"\n\nif active and secret_exists:\n    print(f"Reusing active PAT {PAT_NAME}.")\nelse:\n    if PAT_NAME in tokens:\n        session.sql(f'ALTER USER "{USER}" REMOVE PROGRAMMATIC ACCESS TOKEN {PAT_NAME}').collect()\n    created = session.sql(\n        f'ALTER USER "{USER}" ADD PROGRAMMATIC ACCESS TOKEN {PAT_NAME} '\n        f"ROLE_RESTRICTION = '{ROLE}' DAYS_TO_EXPIRY = {int(PAT_DAYS)} "\n        "COMMENT = 'Cortex Training quickstart notebook'"\n    ).collect()[0].as_dict()\n    token = {key.lower(): value for key, value in created.items()}["token_secret"]\n    session.use_role(ROLE)\n    verb = "ALTER SECRET" if secret_exists else "CREATE SECRET"\n    clause = "SET SECRET_STRING" if secret_exists else "TYPE = GENERIC_STRING SECRET_STRING"\n    session.sql(f"{verb} {SECRET_FQN} {clause} = '{token}'").collect()\n    del token, created\n    print(f"Stored a new {PAT_DAYS}-day PAT in {SECRET_FQN}.")\n\nsession.use_role(ADMIN_ROLE)\nfor statement in [\n    (f"CREATE EXTERNAL ACCESS INTEGRATION IF NOT EXISTS CT_QUICKSTART_API_EAI "\n     f"ALLOWED_NETWORK_RULES = ({DATABASE}.{SCHEMA}.CT_ACCOUNT_RULE) "\n     f"ALLOWED_AUTHENTICATION_SECRETS = ({SECRET_FQN}) ENABLED = TRUE"),\n    (f"CREATE EXTERNAL ACCESS INTEGRATION IF NOT EXISTS CT_QUICKSTART_DOWNLOAD_EAI "\n     f"ALLOWED_NETWORK_RULES = ({DATABASE}.{SCHEMA}.CT_DOWNLOAD_RULE) "\n     "ALLOWED_AUTHENTICATION_SECRETS = NONE ENABLED = TRUE"),\n    f"GRANT USAGE ON INTEGRATION CT_QUICKSTART_API_EAI TO ROLE {ROLE}",\n    f"GRANT USAGE ON INTEGRATION CT_QUICKSTART_DOWNLOAD_EAI TO ROLE {ROLE}",\n]:\n    session.sql(statement).collect()\nprint("Integrations ready. Attach CT_QUICKSTART_DB.LAB.CT_PAT and both CT_QUICKSTART integrations, save, restart, then rerun configuration.")\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch2\u003EInstall and Connect\u003C/h2\u003E\n","\u003Ch3\u003EInstall Packages\u003C/h3\u003E\n","\u003Cp\u003EAfter the restart, rerun the configuration cell, then run this cell. It installs the pinned Cortex Training SDK from a checksum-verified public archive and keeps the runtime's PyTorch, Snowpark, and connector versions in place.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eimport subprocess\nimport sys\nimport tempfile\nfrom importlib import metadata\nfrom pathlib import Path\n\nif sys.version_info < (3, 10):\n    raise RuntimeError("Use a Container Runtime with Python 3.10 or later (the SDK requires >= 3.10).")\n\nSDK_REVISION = "b4c26d57bcd741dce4cd63a6ed8f51cff6ba8829"\nSDK_SHA256 = "a7860f487ad542204cc0f2c39137ac1be0ef97d9845942d11021e991d839b4df"\nSDK_URL = f"https://github.com/snowflakedb/cortex-training/archive/{SDK_REVISION}.zip"\n\nconstraints = [f"{package}=={metadata.version(package)}" for package in\n               ("torch", "snowflake-snowpark-python", "snowflake-connector-python", "numpy")]\nrequirements = [\n    f"cortex-training @ {SDK_URL}#sha256={SDK_SHA256}",\n    "transformers==4.57.6",\n    "openai==2.6.1",\n    f"snowflake-ml-python=={metadata.version('snowflake-ml-python')}",\n    f"matplotlib=={metadata.version('matplotlib')}",\n]\n\nwith tempfile.TemporaryDirectory(prefix="ct-install-") as directory:\n    constraint_path = Path(directory) / "constraints.txt"\n    constraint_path.write_text("\\n".join(constraints) + "\\n", encoding="utf-8")\n    subprocess.run(\n        [sys.executable, "-m", "pip", "install", "--disable-pip-version-check",\n         "--constraint", str(constraint_path), *requirements],\n        check=True,\n    )\nprint("Packages installed.")\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EConnect to Cortex Training\u003C/h3\u003E\n","\u003Cp\u003EThis cell reads the PAT from the attached secret and opens the Cortex Training client. The token is kept in memory only.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eimport math\nimport random\nimport time\nfrom copy import deepcopy\nfrom pathlib import Path\n\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport torch\nfrom cortex_training import CortexTrainingClient, wire\nfrom IPython.display import Markdown, display\nfrom openai import OpenAI\nfrom snowflake.ml.experiment import ExperimentTracking\nfrom snowflake.snowpark.context import get_active_session\nfrom snowflake.snowpark.secrets import get_generic_secret_string\nfrom transformers import AutoTokenizer\n\nsession = get_active_session()\nsession.use_role(ROLE)\nsession.use_warehouse(WAREHOUSE)\nsession.use_database(DATABASE)\nsession.use_schema(SCHEMA)\n\npat = None\nfor reference in (f"{DATABASE}/{SCHEMA}/{SECRET_NAME}".lower(), f"{DATABASE}.{SCHEMA}.{SECRET_NAME}"):\n    try:\n        pat = get_generic_secret_string(reference)\n        break\n    except Exception:\n        pass\nif pat is None:\n    # Not attached: the secret is mounted only after it is selected in the service settings.\n    pat = (Path("/secrets") / f"{DATABASE}/{SCHEMA}/{SECRET_NAME}".lower() / "secret_string").read_text().strip()\n\nclient = CortexTrainingClient.from_pat(\n    host=ACCOUNT_HOST, pat=pat, database=DATABASE, schema=SCHEMA,\n    poll_timeout=1800,\n    poll_interval=1.0, request_timeout=(10.0, 120.0), max_retries=0,\n)\nprint(f"Connected as {session.get_current_role()} in {DATABASE}.{SCHEMA}.")\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch2\u003EDefine the Policy\u003C/h2\u003E\n","\u003Cp\u003EThe planning system supplies every number and flags both comparisons, so the model never calculates. It only decides and writes. Northwind's policy is checked in this order:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ECondition\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EDecision\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EPolicy\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESuggested quantity is 0\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EHOLD\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ENW-1\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ESupplier status is WATCH\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EBACKUP SUPPLIER\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ENW-4\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ECover vs lead time is SHORT\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EEXPEDITE\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ENW-2\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EQuantity vs PO cap is OVER\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003ESPLIT PO\u003C/code\u003E (first PO at the cap)\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ENW-3\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EOtherwise\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EORDER\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ENW-5\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Cp\u003EEvery answer uses the house format \u003Ccode\u003EDECISION: ... | QTY: ... | POLICY: NW-n | NOTE: ...\u003C/code\u003E, with a one-line note for the site planner. The quantity is copied from the input or the cap.\u003C/p\u003E\n","\u003Cp\u003EThe cell below generates 200 training decisions, 40 per decision, with half of them right at a threshold. It also creates 20 held-out SKUs, including five showcase SKUs with one per decision. NW-1005 has two triggers, a supplier on watch and short cover, so it tests the priority order. The model never sees the policy in a prompt.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eimport random\nimport re\n\nWAREHOUSES = ["Dallas", "Chicago", "Atlanta", "Denver", "Seattle", "Newark"]\nHOUSE_STYLE = re.compile(\n    r"DECISION: (HOLD|BACKUP SUPPLIER|EXPEDITE|SPLIT PO|ORDER) \\| QTY: \\d+ \\| POLICY: NW-[1-5] \\| NOTE: .+"\n)\n\n\ndef sku_prompt(sku, s):\n    return (\n        f"Reorder check for {sku} at {s['warehouse']}. Days of cover: {s['cover_days']}. "\n        f"Supplier lead time: {s['lead_days']} days. Suggested quantity: {s['suggested_qty']}. "\n        f"PO cap: {s['po_cap']}. Supplier status: {s['supplier']}. "\n        # The planning system flags both comparisons, so the model never compares numbers.\n        f"Cover vs lead time: {'SHORT' if s['cover_days'] < s['lead_days'] else 'OK'}. "\n        f"Quantity vs PO cap: {'OVER' if s['suggested_qty'] > s['po_cap'] else 'WITHIN'}."\n    )\n\n\ndef expected_decision(s):\n    site = s["warehouse"]\n    if s["suggested_qty"] == 0:\n        return ("HOLD", 0, "NW-1", f"Stock covers the plan. No PO; recheck {site} next cycle.")\n    if s["supplier"] == "WATCH":\n        return ("BACKUP SUPPLIER", s["suggested_qty"], "NW-4",\n                f"Primary supplier is on watch. Place with the backup supplier and copy {site} procurement.")\n    if s["cover_days"] < s["lead_days"]:\n        return ("EXPEDITE", s["suggested_qty"], "NW-2",\n                f"Cover runs out before the next delivery. Request air freight and alert the {site} planner.")\n    if s["suggested_qty"] > s["po_cap"]:\n        return ("SPLIT PO", s["po_cap"], "NW-3",\n                f"Above the PO cap. Release the first PO at the cap and queue the remainder for {site}.")\n    return ("ORDER", s["suggested_qty"], "NW-5", f"Standard replenishment for {site}. Release on the normal cycle.")\n\n\ndef expected_order(s):\n    decision_name, qty, policy, note = expected_decision(s)\n    return f"DECISION: {decision_name} | QTY: {qty} | POLICY: {policy} | NOTE: {note}"\n\n\ndef decision(answer):\n    found = re.search(r"DECISION:\\s*([A-Z ]+?)\\s*\\|.*?POLICY:\\s*(NW-\\d)", answer)\n    return (found.group(1), found.group(2)) if found else None\n\n\ndef is_correct(answer, s):\n    return decision(answer) == decision(expected_order(s))\n\n\ndef in_house_style(answer):\n    return bool(HOUSE_STYLE.fullmatch(answer.strip()))\n\n\nSHOWCASE = [\n    ("NW-1001", {"warehouse": "Dallas", "cover_days": 30, "lead_days": 14, "suggested_qty": 144, "po_cap": 500, "supplier": "OK"}),\n    ("NW-1002", {"warehouse": "Chicago", "cover_days": 5, "lead_days": 14, "suggested_qty": 240, "po_cap": 500, "supplier": "OK"}),\n    ("NW-1003", {"warehouse": "Atlanta", "cover_days": 21, "lead_days": 7, "suggested_qty": 768, "po_cap": 500, "supplier": "OK"}),\n    ("NW-1004", {"warehouse": "Denver", "cover_days": 60, "lead_days": 14, "suggested_qty": 0, "po_cap": 500, "supplier": "OK"}),\n    ("NW-1005", {"warehouse": "Seattle", "cover_days": 9, "lead_days": 21, "suggested_qty": 360, "po_cap": 500, "supplier": "WATCH"}),\n]\nassert [expected_decision(s)[0] for _, s in SHOWCASE] == ["ORDER", "EXPEDITE", "SPLIT PO", "HOLD", "BACKUP SUPPLIER"]\n\nrng = random.Random(7)\nDECISIONS = ["HOLD", "BACKUP SUPPLIER", "EXPEDITE", "SPLIT PO", "ORDER"]\n\n\ndef random_sku(target):\n    # Half of the examples sit right at a threshold, so the model learns where each rule flips.\n    lead_days = rng.choice([7, 14, 21])\n    edge = rng.random() < 0.5\n    sku = {"warehouse": rng.choice(WAREHOUSES), "lead_days": lead_days, "po_cap": 500, "supplier": "OK",\n           "cover_days": lead_days if edge else rng.randint(lead_days, 60),\n           "suggested_qty": 492 if edge else 12 * rng.randint(1, 41)}\n    if target == "HOLD":\n        sku.update(suggested_qty=0, supplier=rng.choice(["OK", "WATCH"]), cover_days=rng.randint(0, 60))\n    elif target == "BACKUP SUPPLIER":\n        sku.update(supplier="WATCH", suggested_qty=12 * rng.randint(1, 75),\n                   cover_days=rng.randint(0, lead_days - 1) if edge else rng.randint(0, 60))\n    elif target == "EXPEDITE":\n        sku.update(cover_days=lead_days - 1 if edge else rng.randint(0, lead_days - 1),\n                   suggested_qty=12 * rng.randint(1, 75))\n    elif target == "SPLIT PO":\n        sku.update(suggested_qty=504 if edge else 12 * rng.randint(42, 80))\n    assert expected_decision(sku)[0] == target\n    return sku\n\n\nTRAIN = [(f"NW-{2000 + i}", random_sku(DECISIONS[i % 5])) for i in range(200)]\nrng.shuffle(TRAIN)\nHELD_OUT = SHOWCASE + [(f"NW-{3000 + i}", random_sku(DECISIONS[i % 5])) for i in range(15)]\nTRAIN_CHATS = [\n    [{"role": "user", "content": sku_prompt(sku, s)}, {"role": "assistant", "content": expected_order(s)}]\n    for sku, s in TRAIN\n]\nHELD_PROMPTS = [sku_prompt(sku, s) for sku, s in HELD_OUT]\nassert len(TRAIN_CHATS) == 200 and len(HELD_OUT) == 20\nprint(f"{len(TRAIN_CHATS)} training decisions and {len(HELD_OUT)} held-out SKUs (5 showcase, 15 random).")\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch2\u003EPrepare Training\u003C/h2\u003E\n","\u003Ch3\u003ETokenize the Examples\u003C/h3\u003E\n","\u003Cp\u003EOnly the assistant's decision is trained; prompt tokens are masked from the loss. The check at the end stops the run before any GPU work if the tokenizer's chat template differs from this formatter.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Etokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, revision=TOKENIZER_REVISION, trust_remote_code=False)\n\n\ndef prompt_token_ids(prompt):\n    return tokenizer.apply_chat_template(\n        [{"role": "user", "content": prompt}], tokenize=True,\n        add_generation_prompt=True, enable_thinking=False,\n    )\n\n\ndef encode_example(messages):\n    prompt, answer = messages[0]["content"], messages[1]["content"]\n    prefix = prompt_token_ids(prompt)\n    answer_ids = tokenizer.encode(answer, add_special_tokens=False)\n    end_id = tokenizer.convert_tokens_to_ids("<|im_end|>")\n    ids = prefix + answer_ids + [end_id]\n    if not answer_ids or len(ids) > MAX_SEQ_LEN:\n        raise ValueError("Empty answer or example exceeds MAX_SEQ_LEN.")\n    weights = [0.0] * len(prefix) + [1.0] * (len(answer_ids) + 1)\n    return ids, weights[1:] + [0.0]\n\n\ndef to_batch(conversations):\n    sequences = [encode_example(messages) for messages in conversations]\n    width = max(len(ids) for ids, _ in sequences)\n    pad_id = tokenizer.pad_token_id if tokenizer.pad_token_id is not None else tokenizer.eos_token_id\n    ids_batch = [ids + [pad_id] * (width - len(ids)) for ids, _ in sequences]\n    attention = [[1] * len(ids) + [0] * (width - len(ids)) for ids, _ in sequences]\n    masks = [mask + [0.0] * (width - len(mask)) for _, mask in sequences]\n    input_ids = torch.tensor(ids_batch, dtype=torch.long)\n    kwargs = {\n        "input_ids": input_ids,\n        "attention_mask": torch.tensor(attention, dtype=torch.long),\n        "position_ids": torch.arange(width).expand(len(sequences), -1).contiguous(),\n        "use_cache": False,\n    }\n    context = {"input_ids": input_ids, "loss_mask": torch.tensor(masks, dtype=torch.float32)}\n    return kwargs, context\n\n\nfor messages in TRAIN_CHATS:\n    ids, mask = encode_example(messages)\n    expected = tokenizer.apply_chat_template(messages, tokenize=False, enable_thinking=False)\n    # The HF template adds a trailing newline after end-of-turn; it is not part of the target.\n    if tokenizer.decode(ids, skip_special_tokens=False) + "\\n" != expected:\n        raise RuntimeError("Pinned tokenizer template differs from the formatter; stop before allocating GPUs.")\nprint(f"Tokenizer template matches for all {len(TRAIN_CHATS)} examples.")\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EConfigure the Job\u003C/h3\u003E\n","\u003Cp\u003EThe job uses LoRA on the attention and MLP projections, with four GPUs for sampling and four for training.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003ELORA_CONFIG = {\n    "peft_type": "Lora", "r": 32, "lora_alpha": 32, "lora_dropout": 0.0, "bias": "none",\n    "target_modules": ["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"],\n}\nJOB_BODY = {\n    "hardware": HARDWARE,\n    "idle_timeout_seconds": 1800,\n    "sub_job_configs": [\n        {\n            "job_type": "sampling", "model_name": MODEL_NAME, "dtype": "bfloat16", "seed": 42,\n            "inference_config": {\n                "max_seq_len": MAX_SEQ_LEN, "n_gpus": SAMPLING_GPUS,\n                "vllm_config": {"tensor_parallel_size": SAMPLING_GPUS, "gpu_memory_utilization": 0.8},\n                "peft_config": deepcopy(LORA_CONFIG),\n            },\n        },\n        {\n            "job_type": "training", "model_name": MODEL_NAME, "dtype": "bfloat16", "seed": 42,\n            "training_config": {\n                "n_gpus": TRAINING_GPUS, "max_seq_len": MAX_SEQ_LEN,\n                "train_batch_size": BATCH_SIZE, "gradient_clipping": 1.0,\n                "model_provider": "huggingface", "attn_implementation": "flash_attention_3",\n                "optimizer": {"name": "AdamW", "lr": LEARNING_RATE, "weight_decay": 0.0,\n                              "betas": [0.9, 0.95], "eps": 1e-8},\n                "ds_config": {"zero_optimization": {"stage": 2, "reduce_scatter": True},\n                              "bf16": {"enabled": True}},\n                "peft_config": deepcopy(LORA_CONFIG),\n            },\n        },\n    ],\n}\nprint(f"Training job: {TRAINING_GPUS + SAMPLING_GPUS} {HARDWARE} GPUs, {STEPS} steps.")\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EDefine the Primitives\u003C/h3\u003E\n","\u003Cp\u003ECortex Training runs the GPU work. Your loop sends each batch to \u003Ccode\u003Eforward_backward\u003C/code\u003E, applies an optimizer step, and syncs the LoRA weights to the sampler before generating answers. \u003Ccode\u003Erelease_job\u003C/code\u003E cancels the training job and waits until its GPUs are free.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eimport math\n\nCAUSAL_CROSS_ENTROPY = {"loss_fn": "causal_cross_entropy", "post": ["compute_logprobs"], "config": {}}\nTERMINAL_JOB = {"cancelled", "failed", "done", "terminated"}\n\n\ndef job_status(job_id):\n    return str(client.get_job(job_id).get("status", "")).lower().removeprefix("job_state_")\n\n\ndef forward_backward(job_id, kwargs, context):\n    payload = wire.dumps(\n        {"args": (), "kwargs": kwargs, "context": context, "processing": CAUSAL_CROSS_ENTROPY},\n        metadata={"response_options": {"format": "dssst1", "delivery": "chunked"}},\n    )\n    request_id = client.forward_backward(job_id, payload)\n    loss = float(client.poll_request(job_id, request_id)["avg_loss"])\n    if not math.isfinite(loss):\n        raise RuntimeError("Non-finite loss; stop training and inspect the job.")\n    return loss\n\n\ndef optim_step(job_id):\n    client.poll_request(job_id, client.step(job_id, learning_rate=LEARNING_RATE))\n\n\ndef sync_weights(job_id):\n    request_id = client.weight_sync(\n        job_id, source_sub_job_id=f"{job_id}:training:0",\n        target_sub_job_ids=[f"{job_id}:sampling:0"], weight_format="lora",\n    )\n    client.poll_request(job_id, request_id)\n\n\ndef ask(job_id, prompts):\n    request_id = client.generate(\n        job_id, prompts=[prompt_token_ids(prompt) for prompt in prompts],\n        # max_tokens is the Cortex Training generate parameter, not the OpenAI endpoint's.\n        sampling_params={"max_tokens": 96, "temperature": 0.0,\n                         "stop_token_ids": [tokenizer.convert_tokens_to_ids("<|im_end|>")]},\n    )\n    results = client.poll_request(job_id, request_id).get("results") or []\n    if len(results) != len(prompts):\n        raise RuntimeError("Sampling returned an unexpected number of results.")\n    return [str(result.get("text", "")).strip() if isinstance(result, dict) else str(result)\n            for result in results]\n\n\ndef release_job(job_id, experiment_status):\n    global run_open\n    # Free the GPUs first, so an experiment-tracking error cannot leave the job running.\n    if job_status(job_id) not in TERMINAL_JOB:\n        client.cancel_job(job_id)\n    deadline = time.monotonic() + 300\n    while job_status(job_id) not in TERMINAL_JOB:\n        if time.monotonic() > deadline:\n            raise TimeoutError(f"Training job {job_id} has not stopped.")\n        time.sleep(5)\n    print(f"Training job {job_id} released.")\n    if run_open:\n        try:\n            tracker.end_run(run_name, status=experiment_status)\n        except TypeError:  # snowflake-ml-python before 1.54 has no status argument\n            tracker.end_run(run_name)\n        run_open = False\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch2\u003ETrain and Compare\u003C/h2\u003E\n","\u003Ch3\u003ERun the Training Loop\u003C/h3\u003E\n","\u003Cp\u003EThis cell creates the job, scores the untouched base model on the 20 held-out SKUs, runs 60 optimizer steps on batches of the 200 training decisions, and scores the tuned model on the same SKUs. If anything fails, it releases the training job before raising the error.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Erun_open, run_name, tracker = False, None, None\njob_id = None\ntry:\n    job_id = client.create_job_from_body(deepcopy(JOB_BODY))["job_id"]\n    client.wait_for_job(job_id)\n    info = client.get_experiment_run(job_id)\n    run_name = info["experiment_run_name"]\n    tracker = ExperimentTracking(session=session)\n    tracker.set_experiment(info["experiment_name"].split(".")[-1].strip('"'))\n    tracker.start_run(run_name)\n    run_open = True\n    tracker.log_params({"model": MODEL_NAME, "steps": STEPS, "batch_size": BATCH_SIZE,\n                        "learning_rate": LEARNING_RATE, "tokenizer_revision": TOKENIZER_REVISION})\n\n    base_answers = ask(job_id, HELD_PROMPTS)\n\n    losses = []\n    rng = random.Random(0)\n    for step in range(1, STEPS + 1):\n        kwargs, context = to_batch(rng.sample(TRAIN_CHATS, BATCH_SIZE))\n        loss = forward_backward(job_id, kwargs, context)\n        optim_step(job_id)\n        losses.append(loss)\n        tracker.log_metrics({"train_loss": loss}, step=step)\n        print(f"Step {step:02d}/{STEPS}: loss {loss:.3f}")\n    sync_weights(job_id)\n    tuned_answers = ask(job_id, HELD_PROMPTS)\nexcept BaseException:\n    if job_id:\n        release_job(job_id, "FAILED")\n    raise\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ESee the Effect\u003C/h3\u003E\n","\u003Cp\u003EEach row is a held-out SKU the model never trained on. The cell prints two scores for each model: whether the decision and policy code match Northwind's policy, and whether the answer follows the house format. It then shows a table with green and red results, and a chart of the training loss and both scores.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Ebase_ok = [is_correct(answer, s) for answer, (_, s) in zip(base_answers, HELD_OUT, strict=True)]\ntuned_ok = [is_correct(answer, s) for answer, (_, s) in zip(tuned_answers, HELD_OUT, strict=True)]\nbase_style = [in_house_style(answer) for answer in base_answers]\ntuned_style = [in_house_style(answer) for answer in tuned_answers]\n\nheadline = (f"Correct decisions: base {sum(base_ok)}/20, tuned {sum(tuned_ok)}/20. "\n            f"House style: base {sum(base_style)}/20, tuned {sum(tuned_style)}/20.")\nprint(headline)\nfor (sku, s), tuned in zip(HELD_OUT, tuned_answers, strict=True):\n    print(f"{sku}: expected {' '.join(decision(expected_order(s)))}; tuned {decision(tuned)}")\ndisplay(Markdown(f"## {headline}"))\n\nrows = []\nfor (sku, s), base, tuned, b_ok, t_ok in zip(HELD_OUT, base_answers, tuned_answers, base_ok, tuned_ok, strict=True):\n    expected_name, _, expected_policy, _ = expected_decision(s)\n    rows.append({\n        "SKU": sku,\n        "Expected": f"{expected_name} ({expected_policy})",\n        "Base model": base[:70],\n        "Base": "correct" if b_ok else "wrong",\n        "Tuned model": tuned[:70],\n        "Tuned": "correct" if t_ok else "wrong",\n    })\nframe = pd.DataFrame(rows)\n\n\ndef colour(value):\n    return {"correct": "background-color: #d4edda", "wrong": "background-color: #f8d7da"}.get(value, "")\n\n\ndisplay(frame.style.map(colour, subset=["Base", "Tuned"]))\n\nfig, (loss_ax, score_ax) = plt.subplots(1, 2, figsize=(12, 3.6))\nloss_ax.plot(range(1, STEPS + 1), losses, marker="o", color="#29B5E8")\nloss_ax.set(title="Training loss", xlabel="Optimizer step", ylabel="Loss")\nlabels = ["Correct decision", "House style"]\npositions = range(len(labels))\nbase_scores = [sum(base_ok), sum(base_style)]\ntuned_scores = [sum(tuned_ok), sum(tuned_style)]\nbase_bars = score_ax.bar([p - 0.2 for p in positions], base_scores, width=0.4, color="#9AA0A6", label="Base Qwen3-8B")\ntuned_bars = score_ax.bar([p + 0.2 for p in positions], tuned_scores, width=0.4, color="#1F77B4", label="Northwind-tuned")\nscore_ax.set(ylim=(0, 22), ylabel="Held-out SKUs (of 20)", title="Base vs tuned on 20 held-out SKUs")\nscore_ax.set_xticks(list(positions), labels)\nfor bar in [*base_bars, *tuned_bars]:\n    score_ax.text(bar.get_x() + bar.get_width() / 2, bar.get_height() + 0.4,\n                  f"{int(bar.get_height())}", ha="center", fontsize=11, weight="bold")\nscore_ax.legend(loc="upper left")\nplt.tight_layout()\nplt.show()\nplt.close(fig)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EHere is one held-out SKU, NW-1005, sent with the same prompt to both models. The base model restates the inputs without making a decision. The tuned model applies policy NW-4, because a supplier on watch outranks short cover.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/own-your-intelligence-cortex-training/before-after-nw-1005.png?v=5155e91e\" alt=\"Before and after for NW-1005. Base Qwen3-8B restates the inputs and is cut off at the sampling limit without a decision. The Northwind-tuned model answers DECISION: BACKUP SUPPLIER, QTY: 360, POLICY: NW-4, with a planner note; its single-line answer is shown one field per line\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/own-your-intelligence-cortex-training/training-loss-and-scores.png?v=5155e91e\" alt=\"Training loss falling over 60 steps, and base vs tuned scores on 20 held-out SKUs: base 0 of 20, tuned 20 of 20 for both correct decisions and house style\"\u003E\u003C/p\u003E\n","\u003Cp\u003EIn the run shown above, the base model scored 0 of 20 on both measures, and the tuned model scored 20 of 20 on both. Results can vary between runs; another run scored 19 of 20 on decisions, with one wrong policy code.\u003C/p\u003E\n","\u003Ch2\u003ESave a Checkpoint\u003C/h2\u003E\n","\u003Cp\u003EThis cell saves a weights-only checkpoint, then releases the training GPUs. The checkpoint stays in Cortex Training after the job ends, and the deployment uses it.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Echeckpoint_id = None\nif job_status(job_id) in TERMINAL_JOB:\n    # Rerun after the job was released: a released job cannot save, so reuse its checkpoint.\n    saved = [item["checkpoint_id"] for item in client.list_checkpoints(job_id)\n             if item.get("checkpoint_type") == "weights-only"]\n    if len(saved) != 1:\n        raise RuntimeError(f"Expected one saved checkpoint for {job_id}, found {len(saved)}. Retrain to save one.")\n    checkpoint_id = saved[0]\n    print("Training job already released; reusing checkpoint", checkpoint_id)\ntry:\n    if checkpoint_id is None:\n        previous = {item["checkpoint_id"] for item in client.list_checkpoints(job_id)}\n        save_result = client.poll_request(job_id, client.save(job_id, checkpoint_type="weights-only"))\n        deadline = time.monotonic() + 300\n        while checkpoint_id is None:\n            checkpoint_id = save_result.get("checkpoint_id")\n            if checkpoint_id is None:\n                new = [item["checkpoint_id"] for item in client.list_checkpoints(job_id)\n                       if item.get("checkpoint_type") == "weights-only" and item["checkpoint_id"] not in previous]\n                checkpoint_id = new[0] if len(new) == 1 else None\n            if checkpoint_id is None:\n                if time.monotonic() > deadline:\n                    raise TimeoutError("Save finished, but its checkpoint could not be identified.")\n                time.sleep(5)\nexcept BaseException:\n    release_job(job_id, "FAILED")\n    raise\nrelease_job(job_id, "FINISHED")\nprint("Checkpoint retained:", checkpoint_id)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch2\u003EDeploy the Model\u003C/h2\u003E\n","\u003Ch3\u003ECreate the Deployment\u003C/h3\u003E\n","\u003Cp\u003EThis cell creates a four-GPU evaluation deployment from the checkpoint and exposes an OpenAI-compatible endpoint. Training and serving share the account's GPU cap, so it first makes sure the training job is released. If the deployment fails to start, it is dropped.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Efrom urllib.parse import urlparse\n\nrelease_job(job_id, "FINISHED")  # Training and serving share the GPU cap; free training first. No-op if released.\ndeployment_id = client.create_model_deployment({\n    "deployment": {"class": "EVALUATION", "min_replicas": 1,\n                   "gpus_per_replica": SERVING_GPUS, "gpu_type": HARDWARE},\n    "source": {"type": "checkpoint", "job_id": job_id, "checkpoint_id": checkpoint_id},\n    "inference": {"max_seq_len": MAX_SEQ_LEN},\n})["deployment"]\ntry:\n    base_url = client.wait_for_model_deployment(deployment_id)["base_url"].rstrip("/")\n    # Accounts have more than one valid hostname, so accept any HTTPS Snowflake host.\n    endpoint = urlparse(base_url)\n    if endpoint.scheme != "https" or not (endpoint.hostname or "").endswith(".snowflakecomputing.com"):\n        raise RuntimeError("Unexpected inference URL; refusing to send the PAT there.")\nexcept BaseException:\n    client.drop_model_deployment(deployment_id)\n    raise\nprint("base_url:", base_url)\nprint("model:", deployment_id)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003ECall the Model\u003C/h3\u003E\n","\u003Cp\u003EThat's the whole client. Any OpenAI-compatible SDK works: point it at your \u003Ccode\u003Ebase_url\u003C/code\u003E, use the deployment ID as the model, and send your PAT with the \u003Ccode\u003EX-Snowflake-Authorization-Token-Type\u003C/code\u003E header. The prompt carries only the SKU's facts, no policy, so the decision comes from the tuned weights. For token-by-token output, add \u003Ccode\u003Estream=True\u003C/code\u003E.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Efrom openai import OpenAI\n\nnorthwind = OpenAI(\n    base_url=base_url,\n    api_key=pat,\n    default_headers={"X-Snowflake-Authorization-Token-Type": "PROGRAMMATIC_ACCESS_TOKEN"},\n)\nresponse = northwind.chat.completions.create(\n    model=deployment_id,\n    messages=[{"role": "user", "content": "Reorder check for NW-1005 at Seattle. Days of cover: 9. Supplier lead time: 21 days. Suggested quantity: 360. PO cap: 500. Supplier status: WATCH. Cover vs lead time: SHORT. Quantity vs PO cap: WITHIN. /no_think"}],\n    max_completion_tokens=128,\n    temperature=0,\n)\nprint(response.choices[0].message.content)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/own-your-intelligence-cortex-training/call-the-model.gif?v=5155e91e\" alt=\"Running the call cell in a Snowflake notebook: the cell runs for about a second and prints DECISION: BACKUP SUPPLIER, QTY: 360, POLICY: NW-4, with a planner note\"\u003E\u003C/p\u003E\n","\u003Cp\u003EIn one test run, the endpoint answered \u003Ccode\u003EDECISION: BACKUP SUPPLIER | QTY: 360 | POLICY: NW-4 | NOTE: Primary supplier is on watch. Place with the backup supplier and copy Seattle procurement.\u003C/code\u003E, which matches the policy.\u003C/p\u003E\n","\u003Ch3\u003EDrop the Deployment\u003C/h3\u003E\n","\u003Cp\u003EEvaluation deployments hold their GPUs until they are dropped. Run this cell when you finish calling the model, even if the call failed.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003Eclient.drop_model_deployment(deployment_id)\nprint(f"Deployment {deployment_id} dropped; serving GPUs released.")\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch2\u003EClean Up\u003C/h2\u003E\n","\u003Cp\u003EThe training job and the deployment are released by the cells above; make sure you ran the drop cell. To remove the tutorial setup, set \u003Ccode\u003ERUN_TEARDOWN = True\u003C/code\u003E in this cell and run it. It stops if a training job or deployment is still active. Otherwise it revokes the PAT and drops both integrations, the database (which removes the secret and experiments), the warehouse, and the role. A saved checkpoint is not recoverable after teardown. Suspend or delete the notebook service when you are done.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-python\"\u003ERUN_TEARDOWN = False  # Set True only when finished. This permanently deletes the setup.\n\nif not RUN_TEARDOWN:\n    print("Teardown skipped. Set RUN_TEARDOWN = True to remove the tutorial setup.")\nelse:\n    active_jobs = [j for j in client.list_jobs()\n                   if str(j.get("status", "")).lower().removeprefix("job_state_") not in TERMINAL_JOB]\n    active_deployments = [d for d in client.list_model_deployments()\n                          if d.get("status") not in {"DROPPED", "FAILED", "TERMINATED"}]\n    if active_jobs or active_deployments:\n        raise RuntimeError(f"{len(active_jobs)} job(s) and {len(active_deployments)} deployment(s) still active; nothing was dropped.")\n    session.use_role(ADMIN_ROLE)\n    remaining = []\n    for statement in [\n        f'ALTER USER "{USER}" REMOVE PROGRAMMATIC ACCESS TOKEN IF EXISTS {PAT_NAME}',\n        "DROP EXTERNAL ACCESS INTEGRATION IF EXISTS CT_QUICKSTART_API_EAI",\n        "DROP EXTERNAL ACCESS INTEGRATION IF EXISTS CT_QUICKSTART_DOWNLOAD_EAI",\n        f"DROP DATABASE IF EXISTS {DATABASE}",\n        f"DROP WAREHOUSE IF EXISTS {WAREHOUSE}",\n        f"DROP ROLE IF EXISTS {ROLE}",\n    ]:\n        try:\n            session.sql(statement).collect()\n            print("Done:", statement)\n        except Exception as exc:\n            remaining.append(statement)\n            print(f"Failed ({type(exc).__name__}): {statement}")\n    if remaining:\n        print("Run these in a worksheet as", ADMIN_ROLE + ":")\n        print(";\\n".join(remaining) + ";")\n    else:\n        print("Tutorial setup removed. Delete the notebook service when you are done.")\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch2\u003EConclusion And Resources\u003C/h2\u003E\n","\u003Cp\u003ECongratulations! You've successfully post-trained an open-weight model on a company decision policy with Cortex Training, measured it against the base model, and deployed it as an OpenAI-compatible endpoint.\u003C/p\u003E\n","\u003Ch3\u003EWhat You Learned\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003ECortex Training runs the GPU work while your notebook controls the training loop\u003C/li\u003E\u003Cli\u003EA small set of policy examples can teach an open model a company's decisions and house style without the policy in the prompt\u003C/li\u003E\u003Cli\u003EScoring the base and tuned models on the same held-out examples shows what post-training changed\u003C/li\u003E\u003Cli\u003EA weights-only checkpoint can be deployed as an OpenAI-compatible endpoint and called with the OpenAI SDK\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ERelated Resources\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/snowflakedb/cortex-training\"\u003ECortex Training SDK source\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E"],"description":"","title":"Own Your Intelligence: Post-Train and Deploy Open Models with Cortex Training","elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],":items":{},":itemsOrder":[],":type":"snowflake-site/components/contentfragment","elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"## Overview\n\nYour applications already know how your business makes decisions. This guide shows how to put that knowledge into the weights of an open model with Cortex Training (public preview), so the model follows your policy without the policy in the prompt.\n\nThe example is Northwind Supply Co., a fictional distributor. Its planning system computes the numbers for every SKU: days of cover, supplier lead time, suggested quantity, PO cap, supplier status, and flags for the two comparisons. What it needs from an agent is the decision and a planner note in Northwind's house style. You will post-train Qwen3-8B with LoRA on 200 of Northwind's decisions, score the base and tuned models side by side on 20 held-out SKUs, save a checkpoint, and deploy it as an OpenAI-compatible endpoint.\n\nEverything runs in one Snowflake notebook. Your training loop runs on a CPU notebook, and Cortex Training runs the GPU work: forward and backward passes, optimizer steps, weight sync, and generation.\n\n### What You'll Learn\n- How to set up a role, secret, PAT, and external access integrations for Cortex Training from a notebook\n- How to write a training loop with the Cortex Training primitives `forward_backward`, `step`, `weight_sync`, and `generate`\n- How to measure a base model and a tuned model on the same held-out examples\n- How to save a weights-only checkpoint and deploy it as an OpenAI-compatible endpoint\n- How to call the endpoint with the OpenAI Python SDK, including streaming\n\n### What You'll Build\nA Snowflake notebook that teaches Qwen3-8B Northwind's reorder decision policy and house style. It shows the base and tuned scores, a per-SKU results table, and a chart, then calls the deployed model with only a SKU's inputs.\n\n### Prerequisites\n- Access to a [Snowflake account](https://signup.snowflake.com/?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_cta=developer-guides)\n- Cortex Training enabled for the account and the tutorial role\n- Model deployments (the OpenAI-compatible endpoint) enabled. This is a separate enablement. Without it, deployment fails with `HTTP 403 (517600): ... model deployments are not enabled for this account`.\n- Eight H200 GPUs for training and sampling, then four for the endpoint, used one stage at a time. Expect about 20 to 30 minutes of GPU time.\n- A role that can create the tutorial objects (the notebook uses `ACCOUNTADMIN` for setup and teardown only), and an organization policy that allows a role-restricted programmatic access token (PAT)\n- A Snowflake Notebook on Container Runtime with Python 3.10 or later and a CPU compute pool such as `SYSTEM_COMPUTE_POOL_CPU`\n\nNo OpenAI account, local Python, Docker, or repository checkout is needed.\n\n## Setup\n\nDownload the [notebook](https://github.com/Snowflake-Labs/sfquickstarts/blob/master/site/sfguides/src/own-your-intelligence-cortex-training/assets/own-your-intelligence-cortex-training.ipynb). In **Projects \u003E Workspaces**, select **+ Add new \u003E Upload files** and upload it, then open it on a CPU Container Runtime with Python 3.10 or later. Give its service a 15-minute idle timeout. The notebook contains every code block below, in order, each with its explanation.\n\nOne restart is required. Run the configuration, object, and PAT cells first. Then open **Connected \u003E Manage service**, select the secret `CT_QUICKSTART_DB.LAB.CT_PAT`, and enable both `CT_QUICKSTART_API_EAI` and `CT_QUICKSTART_DOWNLOAD_EAI`. Save so the service restarts, rerun the configuration cell, and continue with the install cell.\n\n### Configure the Notebook\n\nSet `ACCOUNT_HOST` to this account's hostname, without `https://`. You can find it under your user menu \u003E Account \u003E View account details. Rerun this cell after every service restart.\n\n```python\n# Edit these values. Rerun this cell after every service restart.\nACCOUNT_HOST = \"YOUR_ORG-YOUR_ACCOUNT.snowflakecomputing.com\"  # exact hostname, no https://\nADMIN_ROLE = \"ACCOUNTADMIN\"\nNOTEBOOK_COMPUTE_POOL = \"SYSTEM_COMPUTE_POOL_CPU\"\nDATABASE = \"CT_QUICKSTART_DB\"\nSCHEMA = \"LAB\"\nROLE = \"CT_QUICKSTART_USER\"\nWAREHOUSE = \"CT_QUICKSTART_WH\"\nSECRET_NAME = \"CT_PAT\"\nPAT_NAME = \"CT_QUICKSTART_NOTEBOOK\"\nPAT_DAYS = 1\n\nMODEL_NAME = \"Qwen/Qwen3-8B\"\nTOKENIZER_REVISION = \"b968826d9c46dd6066d109eabc6255188de91218\"\nHARDWARE = \"H200\"\nTRAINING_GPUS = 4\nSAMPLING_GPUS = 4\nSERVING_GPUS = 4\nMAX_SEQ_LEN = 1024\nSTEPS = 60\nBATCH_SIZE = 4\nLEARNING_RATE = 1e-4\n\nif \"YOUR_\" in ACCOUNT_HOST:\n    raise ValueError(\"Set ACCOUNT_HOST to your account hostname, for example myorg-myaccount.snowflakecomputing.com\")\n\nimport os\n\nos.environ.setdefault(\"HF_HUB_DISABLE_XET\", \"1\")\nos.environ.setdefault(\"HF_HUB_ETAG_TIMEOUT\", \"30\")\nos.environ.setdefault(\"HF_HUB_DOWNLOAD_TIMEOUT\", \"60\")\n```\n\n### Create the Objects\n\nThis cell creates the role, database, warehouse, grants, and network rules. Every statement uses `IF NOT EXISTS` or is a grant, so rerunning it is safe.\n\n```python\nfrom snowflake.snowpark.context import get_active_session\n\nsession = get_active_session()\nUSER = session.sql(\"SELECT CURRENT_USER()\").collect()[0][0]\nsession.use_role(ADMIN_ROLE)\n\nfor statement in [\n    f\"CREATE ROLE IF NOT EXISTS {ROLE}\",\n    f\"GRANT ROLE {ROLE} TO ROLE SYSADMIN\",\n    f'GRANT ROLE {ROLE} TO USER \"{USER}\"',\n    f\"CREATE DATABASE IF NOT EXISTS {DATABASE}\",\n    f\"CREATE SCHEMA IF NOT EXISTS {DATABASE}.{SCHEMA}\",\n    (f\"CREATE WAREHOUSE IF NOT EXISTS {WAREHOUSE} WAREHOUSE_SIZE = 'XSMALL' \"\n     \"AUTO_SUSPEND = 60 AUTO_RESUME = TRUE INITIALLY_SUSPENDED = TRUE\"),\n    f\"GRANT USAGE ON DATABASE {DATABASE} TO ROLE {ROLE}\",\n    f\"GRANT USAGE ON SCHEMA {DATABASE}.{SCHEMA} TO ROLE {ROLE}\",\n    f\"GRANT CREATE EXPERIMENT ON SCHEMA {DATABASE}.{SCHEMA} TO ROLE {ROLE}\",\n    f\"GRANT CREATE SECRET ON SCHEMA {DATABASE}.{SCHEMA} TO ROLE {ROLE}\",\n    f\"GRANT USAGE ON WAREHOUSE {WAREHOUSE} TO ROLE {ROLE}\",\n    f\"GRANT USAGE ON COMPUTE POOL {NOTEBOOK_COMPUTE_POOL} TO ROLE {ROLE}\",\n    (f\"CREATE NETWORK RULE IF NOT EXISTS {DATABASE}.{SCHEMA}.CT_ACCOUNT_RULE MODE = EGRESS \"\n     f\"TYPE = HOST_PORT VALUE_LIST = ('{ACCOUNT_HOST.lower()}:443')\"),\n    (f\"CREATE NETWORK RULE IF NOT EXISTS {DATABASE}.{SCHEMA}.CT_DOWNLOAD_RULE MODE = EGRESS \"\n     \"TYPE = HOST_PORT VALUE_LIST = ('pypi.org:443', 'files.pythonhosted.org:443', \"\n     \"'github.com:443', 'codeload.github.com:443', 'huggingface.co:443', \"\n     \"'us.aws.cdn.hf.co:443', 'cdn-lfs.hf.co:443', 'cdn-lfs-us-1.hf.co:443', \"\n     \"'cas-bridge.xethub.hf.co:443', 'cas-server.xethub.hf.co:443', 'transfer.xethub.hf.co:443')\"),\n]:\n    session.sql(statement).collect()\nprint(f\"Objects ready for {USER}: {DATABASE}.{SCHEMA}, {WAREHOUSE}, role {ROLE}.\")\n```\n\n### Create the PAT and Integrations\n\nThis cell issues a one-day PAT restricted to the tutorial role and writes it straight into the secret. The token is never printed. An active PAT is reused. It then creates the two external access integrations: one for the account API and one for package and tokenizer downloads.\n\nIf your organization blocks PAT creation from a notebook session, create one in Snowsight under **Settings \u003E Authentication \u003E Programmatic access tokens**, restrict it to `CT_QUICKSTART_USER`, and store it with `CREATE SECRET`.\n\n```python\nSECRET_FQN = f\"{DATABASE}.{SCHEMA}.{SECRET_NAME}\"\nsession.use_role(ADMIN_ROLE)\ntokens = {row.as_dict()[\"name\"]: row.as_dict()\n          for row in session.sql(f'SHOW USER PROGRAMMATIC ACCESS TOKENS FOR USER \"{USER}\"').collect()}\nsecret_exists = bool(session.sql(f\"SHOW SECRETS LIKE '{SECRET_NAME}' IN SCHEMA {DATABASE}.{SCHEMA}\").collect())\nactive = str(tokens.get(PAT_NAME, {}).get(\"status\", \"\")).upper() == \"ACTIVE\"\n\nif active and secret_exists:\n    print(f\"Reusing active PAT {PAT_NAME}.\")\nelse:\n    if PAT_NAME in tokens:\n        session.sql(f'ALTER USER \"{USER}\" REMOVE PROGRAMMATIC ACCESS TOKEN {PAT_NAME}').collect()\n    created = session.sql(\n        f'ALTER USER \"{USER}\" ADD PROGRAMMATIC ACCESS TOKEN {PAT_NAME} '\n        f\"ROLE_RESTRICTION = '{ROLE}' DAYS_TO_EXPIRY = {int(PAT_DAYS)} \"\n        \"COMMENT = 'Cortex Training quickstart notebook'\"\n    ).collect()[0].as_dict()\n    token = {key.lower(): value for key, value in created.items()}[\"token_secret\"]\n    session.use_role(ROLE)\n    verb = \"ALTER SECRET\" if secret_exists else \"CREATE SECRET\"\n    clause = \"SET SECRET_STRING\" if secret_exists else \"TYPE = GENERIC_STRING SECRET_STRING\"\n    session.sql(f\"{verb} {SECRET_FQN} {clause} = '{token}'\").collect()\n    del token, created\n    print(f\"Stored a new {PAT_DAYS}-day PAT in {SECRET_FQN}.\")\n\nsession.use_role(ADMIN_ROLE)\nfor statement in [\n    (f\"CREATE EXTERNAL ACCESS INTEGRATION IF NOT EXISTS CT_QUICKSTART_API_EAI \"\n     f\"ALLOWED_NETWORK_RULES = ({DATABASE}.{SCHEMA}.CT_ACCOUNT_RULE) \"\n     f\"ALLOWED_AUTHENTICATION_SECRETS = ({SECRET_FQN}) ENABLED = TRUE\"),\n    (f\"CREATE EXTERNAL ACCESS INTEGRATION IF NOT EXISTS CT_QUICKSTART_DOWNLOAD_EAI \"\n     f\"ALLOWED_NETWORK_RULES = ({DATABASE}.{SCHEMA}.CT_DOWNLOAD_RULE) \"\n     \"ALLOWED_AUTHENTICATION_SECRETS = NONE ENABLED = TRUE\"),\n    f\"GRANT USAGE ON INTEGRATION CT_QUICKSTART_API_EAI TO ROLE {ROLE}\",\n    f\"GRANT USAGE ON INTEGRATION CT_QUICKSTART_DOWNLOAD_EAI TO ROLE {ROLE}\",\n]:\n    session.sql(statement).collect()\nprint(\"Integrations ready. Attach CT_QUICKSTART_DB.LAB.CT_PAT and both CT_QUICKSTART integrations, save, restart, then rerun configuration.\")\n```\n\n## Install and Connect\n\n### Install Packages\n\nAfter the restart, rerun the configuration cell, then run this cell. It installs the pinned Cortex Training SDK from a checksum-verified public archive and keeps the runtime's PyTorch, Snowpark, and connector versions in place.\n\n```python\nimport subprocess\nimport sys\nimport tempfile\nfrom importlib import metadata\nfrom pathlib import Path\n\nif sys.version_info \u003C (3, 10):\n    raise RuntimeError(\"Use a Container Runtime with Python 3.10 or later (the SDK requires \u003E= 3.10).\")\n\nSDK_REVISION = \"b4c26d57bcd741dce4cd63a6ed8f51cff6ba8829\"\nSDK_SHA256 = \"a7860f487ad542204cc0f2c39137ac1be0ef97d9845942d11021e991d839b4df\"\nSDK_URL = f\"https://github.com/snowflakedb/cortex-training/archive/{SDK_REVISION}.zip\"\n\nconstraints = [f\"{package}=={metadata.version(package)}\" for package in\n               (\"torch\", \"snowflake-snowpark-python\", \"snowflake-connector-python\", \"numpy\")]\nrequirements = [\n    f\"cortex-training @ {SDK_URL}#sha256={SDK_SHA256}\",\n    \"transformers==4.57.6\",\n    \"openai==2.6.1\",\n    f\"snowflake-ml-python=={metadata.version('snowflake-ml-python')}\",\n    f\"matplotlib=={metadata.version('matplotlib')}\",\n]\n\nwith tempfile.TemporaryDirectory(prefix=\"ct-install-\") as directory:\n    constraint_path = Path(directory) / \"constraints.txt\"\n    constraint_path.write_text(\"\\n\".join(constraints) + \"\\n\", encoding=\"utf-8\")\n    subprocess.run(\n        [sys.executable, \"-m\", \"pip\", \"install\", \"--disable-pip-version-check\",\n         \"--constraint\", str(constraint_path), *requirements],\n        check=True,\n    )\nprint(\"Packages installed.\")\n```\n\n### Connect to Cortex Training\n\nThis cell reads the PAT from the attached secret and opens the Cortex Training client. The token is kept in memory only.\n\n```python\nimport math\nimport random\nimport time\nfrom copy import deepcopy\nfrom pathlib import Path\n\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport torch\nfrom cortex_training import CortexTrainingClient, wire\nfrom IPython.display import Markdown, display\nfrom openai import OpenAI\nfrom snowflake.ml.experiment import ExperimentTracking\nfrom snowflake.snowpark.context import get_active_session\nfrom snowflake.snowpark.secrets import get_generic_secret_string\nfrom transformers import AutoTokenizer\n\nsession = get_active_session()\nsession.use_role(ROLE)\nsession.use_warehouse(WAREHOUSE)\nsession.use_database(DATABASE)\nsession.use_schema(SCHEMA)\n\npat = None\nfor reference in (f\"{DATABASE}/{SCHEMA}/{SECRET_NAME}\".lower(), f\"{DATABASE}.{SCHEMA}.{SECRET_NAME}\"):\n    try:\n        pat = get_generic_secret_string(reference)\n        break\n    except Exception:\n        pass\nif pat is None:\n    # Not attached: the secret is mounted only after it is selected in the service settings.\n    pat = (Path(\"/secrets\") / f\"{DATABASE}/{SCHEMA}/{SECRET_NAME}\".lower() / \"secret_string\").read_text().strip()\n\nclient = CortexTrainingClient.from_pat(\n    host=ACCOUNT_HOST, pat=pat, database=DATABASE, schema=SCHEMA,\n    poll_timeout=1800,\n    poll_interval=1.0, request_timeout=(10.0, 120.0), max_retries=0,\n)\nprint(f\"Connected as {session.get_current_role()} in {DATABASE}.{SCHEMA}.\")\n```\n\n## Define the Policy\n\nThe planning system supplies every number and flags both comparisons, so the model never calculates. It only decides and writes. Northwind's policy is checked in this order:\n\n| Condition | Decision | Policy |\n|---|---|---|\n| Suggested quantity is 0 | `HOLD` | NW-1 |\n| Supplier status is WATCH | `BACKUP SUPPLIER` | NW-4 |\n| Cover vs lead time is SHORT | `EXPEDITE` | NW-2 |\n| Quantity vs PO cap is OVER | `SPLIT PO` (first PO at the cap) | NW-3 |\n| Otherwise | `ORDER` | NW-5 |\n\nEvery answer uses the house format `DECISION: ... | QTY: ... | POLICY: NW-n | NOTE: ...`, with a one-line note for the site planner. The quantity is copied from the input or the cap.\n\nThe cell below generates 200 training decisions, 40 per decision, with half of them right at a threshold. It also creates 20 held-out SKUs, including five showcase SKUs with one per decision. NW-1005 has two triggers, a supplier on watch and short cover, so it tests the priority order. The model never sees the policy in a prompt.\n\n```python\nimport random\nimport re\n\nWAREHOUSES = [\"Dallas\", \"Chicago\", \"Atlanta\", \"Denver\", \"Seattle\", \"Newark\"]\nHOUSE_STYLE = re.compile(\n    r\"DECISION: (HOLD|BACKUP SUPPLIER|EXPEDITE|SPLIT PO|ORDER) \\| QTY: \\d+ \\| POLICY: NW-[1-5] \\| NOTE: .+\"\n)\n\n\ndef sku_prompt(sku, s):\n    return (\n        f\"Reorder check for {sku} at {s['warehouse']}. Days of cover: {s['cover_days']}. \"\n        f\"Supplier lead time: {s['lead_days']} days. Suggested quantity: {s['suggested_qty']}. \"\n        f\"PO cap: {s['po_cap']}. Supplier status: {s['supplier']}. \"\n        # The planning system flags both comparisons, so the model never compares numbers.\n        f\"Cover vs lead time: {'SHORT' if s['cover_days'] \u003C s['lead_days'] else 'OK'}. \"\n        f\"Quantity vs PO cap: {'OVER' if s['suggested_qty'] \u003E s['po_cap'] else 'WITHIN'}.\"\n    )\n\n\ndef expected_decision(s):\n    site = s[\"warehouse\"]\n    if s[\"suggested_qty\"] == 0:\n        return (\"HOLD\", 0, \"NW-1\", f\"Stock covers the plan. No PO; recheck {site} next cycle.\")\n    if s[\"supplier\"] == \"WATCH\":\n        return (\"BACKUP SUPPLIER\", s[\"suggested_qty\"], \"NW-4\",\n                f\"Primary supplier is on watch. Place with the backup supplier and copy {site} procurement.\")\n    if s[\"cover_days\"] \u003C s[\"lead_days\"]:\n        return (\"EXPEDITE\", s[\"suggested_qty\"], \"NW-2\",\n                f\"Cover runs out before the next delivery. Request air freight and alert the {site} planner.\")\n    if s[\"suggested_qty\"] \u003E s[\"po_cap\"]:\n        return (\"SPLIT PO\", s[\"po_cap\"], \"NW-3\",\n                f\"Above the PO cap. Release the first PO at the cap and queue the remainder for {site}.\")\n    return (\"ORDER\", s[\"suggested_qty\"], \"NW-5\", f\"Standard replenishment for {site}. Release on the normal cycle.\")\n\n\ndef expected_order(s):\n    decision_name, qty, policy, note = expected_decision(s)\n    return f\"DECISION: {decision_name} | QTY: {qty} | POLICY: {policy} | NOTE: {note}\"\n\n\ndef decision(answer):\n    found = re.search(r\"DECISION:\\s*([A-Z ]+?)\\s*\\|.*?POLICY:\\s*(NW-\\d)\", answer)\n    return (found.group(1), found.group(2)) if found else None\n\n\ndef is_correct(answer, s):\n    return decision(answer) == decision(expected_order(s))\n\n\ndef in_house_style(answer):\n    return bool(HOUSE_STYLE.fullmatch(answer.strip()))\n\n\nSHOWCASE = [\n    (\"NW-1001\", {\"warehouse\": \"Dallas\", \"cover_days\": 30, \"lead_days\": 14, \"suggested_qty\": 144, \"po_cap\": 500, \"supplier\": \"OK\"}),\n    (\"NW-1002\", {\"warehouse\": \"Chicago\", \"cover_days\": 5, \"lead_days\": 14, \"suggested_qty\": 240, \"po_cap\": 500, \"supplier\": \"OK\"}),\n    (\"NW-1003\", {\"warehouse\": \"Atlanta\", \"cover_days\": 21, \"lead_days\": 7, \"suggested_qty\": 768, \"po_cap\": 500, \"supplier\": \"OK\"}),\n    (\"NW-1004\", {\"warehouse\": \"Denver\", \"cover_days\": 60, \"lead_days\": 14, \"suggested_qty\": 0, \"po_cap\": 500, \"supplier\": \"OK\"}),\n    (\"NW-1005\", {\"warehouse\": \"Seattle\", \"cover_days\": 9, \"lead_days\": 21, \"suggested_qty\": 360, \"po_cap\": 500, \"supplier\": \"WATCH\"}),\n]\nassert [expected_decision(s)[0] for _, s in SHOWCASE] == [\"ORDER\", \"EXPEDITE\", \"SPLIT PO\", \"HOLD\", \"BACKUP SUPPLIER\"]\n\nrng = random.Random(7)\nDECISIONS = [\"HOLD\", \"BACKUP SUPPLIER\", \"EXPEDITE\", \"SPLIT PO\", \"ORDER\"]\n\n\ndef random_sku(target):\n    # Half of the examples sit right at a threshold, so the model learns where each rule flips.\n    lead_days = rng.choice([7, 14, 21])\n    edge = rng.random() \u003C 0.5\n    sku = {\"warehouse\": rng.choice(WAREHOUSES), \"lead_days\": lead_days, \"po_cap\": 500, \"supplier\": \"OK\",\n           \"cover_days\": lead_days if edge else rng.randint(lead_days, 60),\n           \"suggested_qty\": 492 if edge else 12 * rng.randint(1, 41)}\n    if target == \"HOLD\":\n        sku.update(suggested_qty=0, supplier=rng.choice([\"OK\", \"WATCH\"]), cover_days=rng.randint(0, 60))\n    elif target == \"BACKUP SUPPLIER\":\n        sku.update(supplier=\"WATCH\", suggested_qty=12 * rng.randint(1, 75),\n                   cover_days=rng.randint(0, lead_days - 1) if edge else rng.randint(0, 60))\n    elif target == \"EXPEDITE\":\n        sku.update(cover_days=lead_days - 1 if edge else rng.randint(0, lead_days - 1),\n                   suggested_qty=12 * rng.randint(1, 75))\n    elif target == \"SPLIT PO\":\n        sku.update(suggested_qty=504 if edge else 12 * rng.randint(42, 80))\n    assert expected_decision(sku)[0] == target\n    return sku\n\n\nTRAIN = [(f\"NW-{2000 + i}\", random_sku(DECISIONS[i % 5])) for i in range(200)]\nrng.shuffle(TRAIN)\nHELD_OUT = SHOWCASE + [(f\"NW-{3000 + i}\", random_sku(DECISIONS[i % 5])) for i in range(15)]\nTRAIN_CHATS = [\n    [{\"role\": \"user\", \"content\": sku_prompt(sku, s)}, {\"role\": \"assistant\", \"content\": expected_order(s)}]\n    for sku, s in TRAIN\n]\nHELD_PROMPTS = [sku_prompt(sku, s) for sku, s in HELD_OUT]\nassert len(TRAIN_CHATS) == 200 and len(HELD_OUT) == 20\nprint(f\"{len(TRAIN_CHATS)} training decisions and {len(HELD_OUT)} held-out SKUs (5 showcase, 15 random).\")\n```\n\n## Prepare Training\n\n### Tokenize the Examples\n\nOnly the assistant's decision is trained; prompt tokens are masked from the loss. The check at the end stops the run before any GPU work if the tokenizer's chat template differs from this formatter.\n\n```python\ntokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, revision=TOKENIZER_REVISION, trust_remote_code=False)\n\n\ndef prompt_token_ids(prompt):\n    return tokenizer.apply_chat_template(\n        [{\"role\": \"user\", \"content\": prompt}], tokenize=True,\n        add_generation_prompt=True, enable_thinking=False,\n    )\n\n\ndef encode_example(messages):\n    prompt, answer = messages[0][\"content\"], messages[1][\"content\"]\n    prefix = prompt_token_ids(prompt)\n    answer_ids = tokenizer.encode(answer, add_special_tokens=False)\n    end_id = tokenizer.convert_tokens_to_ids(\"\u003C|im_end|\u003E\")\n    ids = prefix + answer_ids + [end_id]\n    if not answer_ids or len(ids) \u003E MAX_SEQ_LEN:\n        raise ValueError(\"Empty answer or example exceeds MAX_SEQ_LEN.\")\n    weights = [0.0] * len(prefix) + [1.0] * (len(answer_ids) + 1)\n    return ids, weights[1:] + [0.0]\n\n\ndef to_batch(conversations):\n    sequences = [encode_example(messages) for messages in conversations]\n    width = max(len(ids) for ids, _ in sequences)\n    pad_id = tokenizer.pad_token_id if tokenizer.pad_token_id is not None else tokenizer.eos_token_id\n    ids_batch = [ids + [pad_id] * (width - len(ids)) for ids, _ in sequences]\n    attention = [[1] * len(ids) + [0] * (width - len(ids)) for ids, _ in sequences]\n    masks = [mask + [0.0] * (width - len(mask)) for _, mask in sequences]\n    input_ids = torch.tensor(ids_batch, dtype=torch.long)\n    kwargs = {\n        \"input_ids\": input_ids,\n        \"attention_mask\": torch.tensor(attention, dtype=torch.long),\n        \"position_ids\": torch.arange(width).expand(len(sequences), -1).contiguous(),\n        \"use_cache\": False,\n    }\n    context = {\"input_ids\": input_ids, \"loss_mask\": torch.tensor(masks, dtype=torch.float32)}\n    return kwargs, context\n\n\nfor messages in TRAIN_CHATS:\n    ids, mask = encode_example(messages)\n    expected = tokenizer.apply_chat_template(messages, tokenize=False, enable_thinking=False)\n    # The HF template adds a trailing newline after end-of-turn; it is not part of the target.\n    if tokenizer.decode(ids, skip_special_tokens=False) + \"\\n\" != expected:\n        raise RuntimeError(\"Pinned tokenizer template differs from the formatter; stop before allocating GPUs.\")\nprint(f\"Tokenizer template matches for all {len(TRAIN_CHATS)} examples.\")\n```\n\n### Configure the Job\n\nThe job uses LoRA on the attention and MLP projections, with four GPUs for sampling and four for training.\n\n```python\nLORA_CONFIG = {\n    \"peft_type\": \"Lora\", \"r\": 32, \"lora_alpha\": 32, \"lora_dropout\": 0.0, \"bias\": \"none\",\n    \"target_modules\": [\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\", \"gate_proj\", \"up_proj\", \"down_proj\"],\n}\nJOB_BODY = {\n    \"hardware\": HARDWARE,\n    \"idle_timeout_seconds\": 1800,\n    \"sub_job_configs\": [\n        {\n            \"job_type\": \"sampling\", \"model_name\": MODEL_NAME, \"dtype\": \"bfloat16\", \"seed\": 42,\n            \"inference_config\": {\n                \"max_seq_len\": MAX_SEQ_LEN, \"n_gpus\": SAMPLING_GPUS,\n                \"vllm_config\": {\"tensor_parallel_size\": SAMPLING_GPUS, \"gpu_memory_utilization\": 0.8},\n                \"peft_config\": deepcopy(LORA_CONFIG),\n            },\n        },\n        {\n            \"job_type\": \"training\", \"model_name\": MODEL_NAME, \"dtype\": \"bfloat16\", \"seed\": 42,\n            \"training_config\": {\n                \"n_gpus\": TRAINING_GPUS, \"max_seq_len\": MAX_SEQ_LEN,\n                \"train_batch_size\": BATCH_SIZE, \"gradient_clipping\": 1.0,\n                \"model_provider\": \"huggingface\", \"attn_implementation\": \"flash_attention_3\",\n                \"optimizer\": {\"name\": \"AdamW\", \"lr\": LEARNING_RATE, \"weight_decay\": 0.0,\n                              \"betas\": [0.9, 0.95], \"eps\": 1e-8},\n                \"ds_config\": {\"zero_optimization\": {\"stage\": 2, \"reduce_scatter\": True},\n                              \"bf16\": {\"enabled\": True}},\n                \"peft_config\": deepcopy(LORA_CONFIG),\n            },\n        },\n    ],\n}\nprint(f\"Training job: {TRAINING_GPUS + SAMPLING_GPUS} {HARDWARE} GPUs, {STEPS} steps.\")\n```\n\n### Define the Primitives\n\nCortex Training runs the GPU work. Your loop sends each batch to `forward_backward`, applies an optimizer step, and syncs the LoRA weights to the sampler before generating answers. `release_job` cancels the training job and waits until its GPUs are free.\n\n```python\nimport math\n\nCAUSAL_CROSS_ENTROPY = {\"loss_fn\": \"causal_cross_entropy\", \"post\": [\"compute_logprobs\"], \"config\": {}}\nTERMINAL_JOB = {\"cancelled\", \"failed\", \"done\", \"terminated\"}\n\n\ndef job_status(job_id):\n    return str(client.get_job(job_id).get(\"status\", \"\")).lower().removeprefix(\"job_state_\")\n\n\ndef forward_backward(job_id, kwargs, context):\n    payload = wire.dumps(\n        {\"args\": (), \"kwargs\": kwargs, \"context\": context, \"processing\": CAUSAL_CROSS_ENTROPY},\n        metadata={\"response_options\": {\"format\": \"dssst1\", \"delivery\": \"chunked\"}},\n    )\n    request_id = client.forward_backward(job_id, payload)\n    loss = float(client.poll_request(job_id, request_id)[\"avg_loss\"])\n    if not math.isfinite(loss):\n        raise RuntimeError(\"Non-finite loss; stop training and inspect the job.\")\n    return loss\n\n\ndef optim_step(job_id):\n    client.poll_request(job_id, client.step(job_id, learning_rate=LEARNING_RATE))\n\n\ndef sync_weights(job_id):\n    request_id = client.weight_sync(\n        job_id, source_sub_job_id=f\"{job_id}:training:0\",\n        target_sub_job_ids=[f\"{job_id}:sampling:0\"], weight_format=\"lora\",\n    )\n    client.poll_request(job_id, request_id)\n\n\ndef ask(job_id, prompts):\n    request_id = client.generate(\n        job_id, prompts=[prompt_token_ids(prompt) for prompt in prompts],\n        # max_tokens is the Cortex Training generate parameter, not the OpenAI endpoint's.\n        sampling_params={\"max_tokens\": 96, \"temperature\": 0.0,\n                         \"stop_token_ids\": [tokenizer.convert_tokens_to_ids(\"\u003C|im_end|\u003E\")]},\n    )\n    results = client.poll_request(job_id, request_id).get(\"results\") or []\n    if len(results) != len(prompts):\n        raise RuntimeError(\"Sampling returned an unexpected number of results.\")\n    return [str(result.get(\"text\", \"\")).strip() if isinstance(result, dict) else str(result)\n            for result in results]\n\n\ndef release_job(job_id, experiment_status):\n    global run_open\n    # Free the GPUs first, so an experiment-tracking error cannot leave the job running.\n    if job_status(job_id) not in TERMINAL_JOB:\n        client.cancel_job(job_id)\n    deadline = time.monotonic() + 300\n    while job_status(job_id) not in TERMINAL_JOB:\n        if time.monotonic() \u003E deadline:\n            raise TimeoutError(f\"Training job {job_id} has not stopped.\")\n        time.sleep(5)\n    print(f\"Training job {job_id} released.\")\n    if run_open:\n        try:\n            tracker.end_run(run_name, status=experiment_status)\n        except TypeError:  # snowflake-ml-python before 1.54 has no status argument\n            tracker.end_run(run_name)\n        run_open = False\n```\n\n## Train and Compare\n\n### Run the Training Loop\n\nThis cell creates the job, scores the untouched base model on the 20 held-out SKUs, runs 60 optimizer steps on batches of the 200 training decisions, and scores the tuned model on the same SKUs. If anything fails, it releases the training job before raising the error.\n\n```python\nrun_open, run_name, tracker = False, None, None\njob_id = None\ntry:\n    job_id = client.create_job_from_body(deepcopy(JOB_BODY))[\"job_id\"]\n    client.wait_for_job(job_id)\n    info = client.get_experiment_run(job_id)\n    run_name = info[\"experiment_run_name\"]\n    tracker = ExperimentTracking(session=session)\n    tracker.set_experiment(info[\"experiment_name\"].split(\".\")[-1].strip('\"'))\n    tracker.start_run(run_name)\n    run_open = True\n    tracker.log_params({\"model\": MODEL_NAME, \"steps\": STEPS, \"batch_size\": BATCH_SIZE,\n                        \"learning_rate\": LEARNING_RATE, \"tokenizer_revision\": TOKENIZER_REVISION})\n\n    base_answers = ask(job_id, HELD_PROMPTS)\n\n    losses = []\n    rng = random.Random(0)\n    for step in range(1, STEPS + 1):\n        kwargs, context = to_batch(rng.sample(TRAIN_CHATS, BATCH_SIZE))\n        loss = forward_backward(job_id, kwargs, context)\n        optim_step(job_id)\n        losses.append(loss)\n        tracker.log_metrics({\"train_loss\": loss}, step=step)\n        print(f\"Step {step:02d}/{STEPS}: loss {loss:.3f}\")\n    sync_weights(job_id)\n    tuned_answers = ask(job_id, HELD_PROMPTS)\nexcept BaseException:\n    if job_id:\n        release_job(job_id, \"FAILED\")\n    raise\n```\n\n### See the Effect\n\nEach row is a held-out SKU the model never trained on. The cell prints two scores for each model: whether the decision and policy code match Northwind's policy, and whether the answer follows the house format. It then shows a table with green and red results, and a chart of the training loss and both scores.\n\n```python\nbase_ok = [is_correct(answer, s) for answer, (_, s) in zip(base_answers, HELD_OUT, strict=True)]\ntuned_ok = [is_correct(answer, s) for answer, (_, s) in zip(tuned_answers, HELD_OUT, strict=True)]\nbase_style = [in_house_style(answer) for answer in base_answers]\ntuned_style = [in_house_style(answer) for answer in tuned_answers]\n\nheadline = (f\"Correct decisions: base {sum(base_ok)}/20, tuned {sum(tuned_ok)}/20. \"\n            f\"House style: base {sum(base_style)}/20, tuned {sum(tuned_style)}/20.\")\nprint(headline)\nfor (sku, s), tuned in zip(HELD_OUT, tuned_answers, strict=True):\n    print(f\"{sku}: expected {' '.join(decision(expected_order(s)))}; tuned {decision(tuned)}\")\ndisplay(Markdown(f\"## {headline}\"))\n\nrows = []\nfor (sku, s), base, tuned, b_ok, t_ok in zip(HELD_OUT, base_answers, tuned_answers, base_ok, tuned_ok, strict=True):\n    expected_name, _, expected_policy, _ = expected_decision(s)\n    rows.append({\n        \"SKU\": sku,\n        \"Expected\": f\"{expected_name} ({expected_policy})\",\n        \"Base model\": base[:70],\n        \"Base\": \"correct\" if b_ok else \"wrong\",\n        \"Tuned model\": tuned[:70],\n        \"Tuned\": \"correct\" if t_ok else \"wrong\",\n    })\nframe = pd.DataFrame(rows)\n\n\ndef colour(value):\n    return {\"correct\": \"background-color: #d4edda\", \"wrong\": \"background-color: #f8d7da\"}.get(value, \"\")\n\n\ndisplay(frame.style.map(colour, subset=[\"Base\", \"Tuned\"]))\n\nfig, (loss_ax, score_ax) = plt.subplots(1, 2, figsize=(12, 3.6))\nloss_ax.plot(range(1, STEPS + 1), losses, marker=\"o\", color=\"#29B5E8\")\nloss_ax.set(title=\"Training loss\", xlabel=\"Optimizer step\", ylabel=\"Loss\")\nlabels = [\"Correct decision\", \"House style\"]\npositions = range(len(labels))\nbase_scores = [sum(base_ok), sum(base_style)]\ntuned_scores = [sum(tuned_ok), sum(tuned_style)]\nbase_bars = score_ax.bar([p - 0.2 for p in positions], base_scores, width=0.4, color=\"#9AA0A6\", label=\"Base Qwen3-8B\")\ntuned_bars = score_ax.bar([p + 0.2 for p in positions], tuned_scores, width=0.4, color=\"#1F77B4\", label=\"Northwind-tuned\")\nscore_ax.set(ylim=(0, 22), ylabel=\"Held-out SKUs (of 20)\", title=\"Base vs tuned on 20 held-out SKUs\")\nscore_ax.set_xticks(list(positions), labels)\nfor bar in [*base_bars, *tuned_bars]:\n    score_ax.text(bar.get_x() + bar.get_width() / 2, bar.get_height() + 0.4,\n                  f\"{int(bar.get_height())}\", ha=\"center\", fontsize=11, weight=\"bold\")\nscore_ax.legend(loc=\"upper left\")\nplt.tight_layout()\nplt.show()\nplt.close(fig)\n```\n\nHere is one held-out SKU, NW-1005, sent with the same prompt to both models. The base model restates the inputs without making a decision. The tuned model applies policy NW-4, because a supplier on watch outranks short cover.\n\n![Before and after for NW-1005. Base Qwen3-8B restates the inputs and is cut off at the sampling limit without a decision. The Northwind-tuned model answers DECISION: BACKUP SUPPLIER, QTY: 360, POLICY: NW-4, with a planner note; its single-line answer is shown one field per line](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/own-your-intelligence-cortex-training/before-after-nw-1005.png?v=5155e91e)\n\n![Training loss falling over 60 steps, and base vs tuned scores on 20 held-out SKUs: base 0 of 20, tuned 20 of 20 for both correct decisions and house style](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/own-your-intelligence-cortex-training/training-loss-and-scores.png?v=5155e91e)\n\nIn the run shown above, the base model scored 0 of 20 on both measures, and the tuned model scored 20 of 20 on both. Results can vary between runs; another run scored 19 of 20 on decisions, with one wrong policy code.\n\n## Save a Checkpoint\n\nThis cell saves a weights-only checkpoint, then releases the training GPUs. The checkpoint stays in Cortex Training after the job ends, and the deployment uses it.\n\n```python\ncheckpoint_id = None\nif job_status(job_id) in TERMINAL_JOB:\n    # Rerun after the job was released: a released job cannot save, so reuse its checkpoint.\n    saved = [item[\"checkpoint_id\"] for item in client.list_checkpoints(job_id)\n             if item.get(\"checkpoint_type\") == \"weights-only\"]\n    if len(saved) != 1:\n        raise RuntimeError(f\"Expected one saved checkpoint for {job_id}, found {len(saved)}. Retrain to save one.\")\n    checkpoint_id = saved[0]\n    print(\"Training job already released; reusing checkpoint\", checkpoint_id)\ntry:\n    if checkpoint_id is None:\n        previous = {item[\"checkpoint_id\"] for item in client.list_checkpoints(job_id)}\n        save_result = client.poll_request(job_id, client.save(job_id, checkpoint_type=\"weights-only\"))\n        deadline = time.monotonic() + 300\n        while checkpoint_id is None:\n            checkpoint_id = save_result.get(\"checkpoint_id\")\n            if checkpoint_id is None:\n                new = [item[\"checkpoint_id\"] for item in client.list_checkpoints(job_id)\n                       if item.get(\"checkpoint_type\") == \"weights-only\" and item[\"checkpoint_id\"] not in previous]\n                checkpoint_id = new[0] if len(new) == 1 else None\n            if checkpoint_id is None:\n                if time.monotonic() \u003E deadline:\n                    raise TimeoutError(\"Save finished, but its checkpoint could not be identified.\")\n                time.sleep(5)\nexcept BaseException:\n    release_job(job_id, \"FAILED\")\n    raise\nrelease_job(job_id, \"FINISHED\")\nprint(\"Checkpoint retained:\", checkpoint_id)\n```\n\n## Deploy the Model\n\n### Create the Deployment\n\nThis cell creates a four-GPU evaluation deployment from the checkpoint and exposes an OpenAI-compatible endpoint. Training and serving share the account's GPU cap, so it first makes sure the training job is released. If the deployment fails to start, it is dropped.\n\n```python\nfrom urllib.parse import urlparse\n\nrelease_job(job_id, \"FINISHED\")  # Training and serving share the GPU cap; free training first. No-op if released.\ndeployment_id = client.create_model_deployment({\n    \"deployment\": {\"class\": \"EVALUATION\", \"min_replicas\": 1,\n                   \"gpus_per_replica\": SERVING_GPUS, \"gpu_type\": HARDWARE},\n    \"source\": {\"type\": \"checkpoint\", \"job_id\": job_id, \"checkpoint_id\": checkpoint_id},\n    \"inference\": {\"max_seq_len\": MAX_SEQ_LEN},\n})[\"deployment\"]\ntry:\n    base_url = client.wait_for_model_deployment(deployment_id)[\"base_url\"].rstrip(\"/\")\n    # Accounts have more than one valid hostname, so accept any HTTPS Snowflake host.\n    endpoint = urlparse(base_url)\n    if endpoint.scheme != \"https\" or not (endpoint.hostname or \"\").endswith(\".snowflakecomputing.com\"):\n        raise RuntimeError(\"Unexpected inference URL; refusing to send the PAT there.\")\nexcept BaseException:\n    client.drop_model_deployment(deployment_id)\n    raise\nprint(\"base_url:\", base_url)\nprint(\"model:\", deployment_id)\n```\n\n### Call the Model\n\nThat's the whole client. Any OpenAI-compatible SDK works: point it at your `base_url`, use the deployment ID as the model, and send your PAT with the `X-Snowflake-Authorization-Token-Type` header. The prompt carries only the SKU's facts, no policy, so the decision comes from the tuned weights. For token-by-token output, add `stream=True`.\n\n```python\nfrom openai import OpenAI\n\nnorthwind = OpenAI(\n    base_url=base_url,\n    api_key=pat,\n    default_headers={\"X-Snowflake-Authorization-Token-Type\": \"PROGRAMMATIC_ACCESS_TOKEN\"},\n)\nresponse = northwind.chat.completions.create(\n    model=deployment_id,\n    messages=[{\"role\": \"user\", \"content\": \"Reorder check for NW-1005 at Seattle. Days of cover: 9. Supplier lead time: 21 days. Suggested quantity: 360. PO cap: 500. Supplier status: WATCH. Cover vs lead time: SHORT. Quantity vs PO cap: WITHIN. /no_think\"}],\n    max_completion_tokens=128,\n    temperature=0,\n)\nprint(response.choices[0].message.content)\n```\n\n![Running the call cell in a Snowflake notebook: the cell runs for about a second and prints DECISION: BACKUP SUPPLIER, QTY: 360, POLICY: NW-4, with a planner note](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/own-your-intelligence-cortex-training/call-the-model.gif?v=5155e91e)\n\nIn one test run, the endpoint answered `DECISION: BACKUP SUPPLIER | QTY: 360 | POLICY: NW-4 | NOTE: Primary supplier is on watch. Place with the backup supplier and copy Seattle procurement.`, which matches the policy.\n\n### Drop the Deployment\n\nEvaluation deployments hold their GPUs until they are dropped. Run this cell when you finish calling the model, even if the call failed.\n\n```python\nclient.drop_model_deployment(deployment_id)\nprint(f\"Deployment {deployment_id} dropped; serving GPUs released.\")\n```\n\n## Clean Up\n\nThe training job and the deployment are released by the cells above; make sure you ran the drop cell. To remove the tutorial setup, set `RUN_TEARDOWN = True` in this cell and run it. It stops if a training job or deployment is still active. Otherwise it revokes the PAT and drops both integrations, the database (which removes the secret and experiments), the warehouse, and the role. A saved checkpoint is not recoverable after teardown. Suspend or delete the notebook service when you are done.\n\n```python\nRUN_TEARDOWN = False  # Set True only when finished. This permanently deletes the setup.\n\nif not RUN_TEARDOWN:\n    print(\"Teardown skipped. Set RUN_TEARDOWN = True to remove the tutorial setup.\")\nelse:\n    active_jobs = [j for j in client.list_jobs()\n                   if str(j.get(\"status\", \"\")).lower().removeprefix(\"job_state_\") not in TERMINAL_JOB]\n    active_deployments = [d for d in client.list_model_deployments()\n                          if d.get(\"status\") not in {\"DROPPED\", \"FAILED\", \"TERMINATED\"}]\n    if active_jobs or active_deployments:\n        raise RuntimeError(f\"{len(active_jobs)} job(s) and {len(active_deployments)} deployment(s) still active; nothing was dropped.\")\n    session.use_role(ADMIN_ROLE)\n    remaining = []\n    for statement in [\n        f'ALTER USER \"{USER}\" REMOVE PROGRAMMATIC ACCESS TOKEN IF EXISTS {PAT_NAME}',\n        \"DROP EXTERNAL ACCESS INTEGRATION IF EXISTS CT_QUICKSTART_API_EAI\",\n        \"DROP EXTERNAL ACCESS INTEGRATION IF EXISTS CT_QUICKSTART_DOWNLOAD_EAI\",\n        f\"DROP DATABASE IF EXISTS {DATABASE}\",\n        f\"DROP WAREHOUSE IF EXISTS {WAREHOUSE}\",\n        f\"DROP ROLE IF EXISTS {ROLE}\",\n    ]:\n        try:\n            session.sql(statement).collect()\n            print(\"Done:\", statement)\n        except Exception as exc:\n            remaining.append(statement)\n            print(f\"Failed ({type(exc).__name__}): {statement}\")\n    if remaining:\n        print(\"Run these in a worksheet as\", ADMIN_ROLE + \":\")\n        print(\";\\n\".join(remaining) + \";\")\n    else:\n        print(\"Tutorial setup removed. Delete the notebook service when you are done.\")\n```\n\n## Conclusion And Resources\n\nCongratulations! You've successfully post-trained an open-weight model on a company decision policy with Cortex Training, measured it against the base model, and deployed it as an OpenAI-compatible endpoint.\n\n### What You Learned\n- Cortex Training runs the GPU work while your notebook controls the training loop\n- A small set of policy examples can teach an open model a company's decisions and house style without the policy in the prompt\n- Scoring the base and tuned models on the same held-out examples shows what post-training changed\n- A weights-only checkpoint can be deployed as an OpenAI-compatible endpoint and called with the OpenAI SDK\n\n### Related Resources\n- [Cortex Training SDK source](https://github.com/snowflakedb/cortex-training)\n","multiValue":false,":type":"text/x-markdown"},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image","multiValue":false,":type":"text/plain"}},"isDeveloperGuidesPage":false,"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-cdc33de2b7","type":"2-column-75-25","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"none","bottomPadding":"none","spaceBetween":"none","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-96ba988698",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-1ba4504c32","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2026-10-30",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-68cd0d9270","additionalClasses":"qs-disclaimer-text","text":"\u003Cp\u003E\u003Cspan style=\"color: #666;\"\u003EThis content is provided as is, and is not maintained on an ongoing basis. It may be out of date with current Snowflake instances\u003C/span\u003E\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"}},":itemsOrder":["quickstart_last_modi","text"],":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"container-968ac1c5e1",":items":{},":itemsOrder":[],":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},":type":"snowflake-site/components/flexible-column-container","isBlogPage":false,"isActiveTOC":false}},":itemsOrder":["contentfragment","flexible_column_cont"],":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"container-b2df06b9b7",":items":{"quickstart_table_of_":{"layout":"SIMPLE","id":"container-8bfcbb98b9","isDeveloperGuidesPage":false,":items":{"quickstart_table_of_":{"id":"quickstart-table-of-content-047f2dd00b","headings":["\u003Ch2\u003EOverview\u003C/h2\u003E","\u003Ch2\u003ESetup\u003C/h2\u003E","\u003Ch2\u003EInstall and Connect\u003C/h2\u003E","\u003Ch2\u003EDefine the Policy\u003C/h2\u003E","\u003Ch2\u003EPrepare Training\u003C/h2\u003E","\u003Ch2\u003ETrain and Compare\u003C/h2\u003E","\u003Ch2\u003ESave a Checkpoint\u003C/h2\u003E","\u003Ch2\u003EDeploy the Model\u003C/h2\u003E","\u003Ch2\u003EClean Up\u003C/h2\u003E","\u003Ch2\u003EConclusion And 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href=\"https://www.snowflake.com/en/product/applications-and-collaboration/\"\u003EApplications & Collaboration\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/pricing-options/\"\u003EPricing\u003C/a\u003E\u003C/li\u003E\r\n\u003C/ul\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-size-small text-color-text-04"},"text_copy":{"id":"text-767ef85185","additionalClasses":"sf-footer__link-group","text":"\u003Cp class=\"sf-footer__column-title\"\u003ESupport\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/support/\"\u003ESupport\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://www.snowflake.com/en/legal/addenda/priority-support-services-description/\"\u003EPriority Support\u003C/a\u003E\u003C/li\u003E\r\n\u003Cli\u003E\u003Ca href=\"https://status.snowflake.com/\" target=\"_blank\" rel=\"noopener 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