Synthefy Python Client

synthefy is the lightweight SDK for Synthefy Nori. It owns the SynthefyNoriClient, shared feature preparation, and backend-neutral forecasting workflows. Hosted users do not install Torch or model weights; local execution is supplied by the separate synthefy-nori distribution.

The retired ForecastV2 API and /v2/forecast endpoint are not part of synthefy 7. Use SynthefyNoriClient directly for regression or synthefy.nori_ts.NoriTSForecaster for Nori-backed forecasting.

Features

• One regression client: the same SynthefyNoriClient contract runs through hosted Baseten, a named SageMaker endpoint, or the local runtime.
• Nori forecasting: optional feature preparation and result reconstruction use that same regression gateway.
• Prediction intervals: quantiles and full predictive distributions come from the same forward pass.
• DataFrame, categorical, and text preparation: shared preprocessing keeps local and hosted numeric requests aligned.
• Typed errors and requests: Pydantic request models and one HTTP error hierarchy across remote transports.

Installation

Hosted regression:

pip install synthefy

Local regression:

pip install synthefy-nori

Forecasting:

pip install "synthefy[forecasting]"       # hosted or SageMaker
pip install "synthefy-nori[forecasting]"  # local runtime

Optional text or SageMaker support:

pip install "synthefy[text]"
pip install "synthefy[aws]"

Nori — DataFrame Forecasting

NoriTSForecaster turns time-series DataFrames into regression requests and runs every request through a configured SynthefyNoriClient. That keeps the forecasting workflow identical across hosted Baseten, SageMaker, and local execution.

Use future_df= when the forecast horizon includes values known in advance. The target may use a domain-specific name such as sales:

import os

import pandas as pd

from synthefy import SynthefyNoriClient
from synthefy.nori_ts import NoriTSForecaster

history = pd.DataFrame({
    "timestamp": pd.date_range("2026-01-01", periods=48, freq="h"),
    "sales": [100.0 + hour for hour in range(48)],
    "promotion": [0.0] * 48,
})
future = pd.DataFrame({
    "timestamp": pd.date_range("2026-01-03", periods=12, freq="h"),
    "promotion": [0.0] * 6 + [1.0] * 6,
})

client = SynthefyNoriClient(
    mode="remote",
    model="nori-6m",
    api_key=os.environ["SYNTHEFY_NORI_API_KEY"],
)
forecaster = NoriTSForecaster(
    client=client,
    quantiles=[0.1, 0.5, 0.9],
)
forecast = forecaster.predict_df(
    history,
    future_df=future,
    target_column="sales",
)

future_df must contain future timestamps and every numeric covariate used in history. Its target must be absent or entirely missing; observed future targets would leak the answer. When there are no future covariates, pass prediction_length= instead and the forecaster generates the horizon. target_column accepts one column name per call; multiple target columns are not supported yet.

Nori — Tabular In-Context Regression

SynthefyNoriClient is the lightweight client for Synthefy Nori, an in-context learning regressor. Each call supplies labeled context rows (X_train, y_train) and query rows (X_test); the model returns one predicted value per query row in a single forward pass — there is no training step.

The same client runs predictions against the hosted endpoint, a named AWS SageMaker endpoint, or locally. Select mode="remote", mode="sagemaker", or mode="local"; there is no automatic backend selection and every constructor requires an explicit model=.

Use it from your AI coding assistant

Paste this into Claude Code, Cursor, or any AI coding assistant and it will wire Nori into your own project:

Look at my code/task/report here and figure out where Nori would best fit — it's
Synthefy's tabular foundation model for regression, used through the `synthefy`
client with no training loop and no hyperparameters. It runs fully on your own
machine (local mode, uses your GPU when one's available), or against the hosted
Synthefy API if you'd rather not run it locally.

1. Install it with this project's package manager, with the local runtime
   (e.g. `uv add "synthefy-nori"`, or `pip install -U "synthefy-nori"`).

2. Use it wherever a tabular regression / prediction step fits:

   ```python
   from synthefy import SynthefyNoriClient

   # model is required -- name a size: "nori-100m" (~98.3M), "nori-30m" (~29.2M),
   # or "nori-6m" (~6M base).
   client = SynthefyNoriClient(mode="local", model="nori-30m")   # runs on this machine, no API key

   y_pred = client.predict(
       X_train=X_train,   # lists, numpy arrays, or pandas — NaNs OK, imputed for you
       y_train=y_train,   # continuous target
       X_test=X_test,     # rows to score
   )                      # -> list of floats, one per X_test row (as_pandas=True for a Series)

   # Prediction intervals come free — no conformal/quantile add-ons:
   lo, mid, hi = client.predict(X_train, y_train, X_test,
                                output_type="quantiles", quantiles=[0.1, 0.5, 0.9])
   ```

X is a numeric feature matrix (or a pandas DataFrame — non-numeric columns are
encoded for you); y is a continuous target. If I already have a model, wire Nori
up alongside it on the same train/test split and metric so I can compare them. If
the best place to plug Nori in isn't obvious, show me where you'd put it and
confirm with me before making changes.

Prefer not to run it locally? Use the hosted API instead — create a key at
https://docs.synthefy.com/setup/api_key, then:
`client = SynthefyNoriClient(api_key="YOUR_API_KEY", model="nori-30m")` (or set
SYNTHEFY_NORI_API_KEY).

Hosted Usage (mode="remote")

from synthefy import SynthefyNoriClient

# The key is sent as `Authorization: Bearer <key>` (gateway default).
# Pass it explicitly or set the SYNTHEFY_NORI_API_KEY environment variable.
client = SynthefyNoriClient(api_key="your_api_key", model="nori-30m")

predictions = client.predict(
    X_train=[[0.0, 1.0], [1.0, 0.0], [1.0, 1.0]],  # context features
    y_train=[1.0, 1.0, 2.0],                        # context targets
    X_test=[[2.0, 2.0], [0.5, 0.5]],                # query features
)
print(predictions)  # -> [<float>, <float>]  (one per X_test row)

X_train, y_train, and X_test accept Python lists, numpy arrays, or pandas objects (a DataFrame for the feature matrices; a Series or single-column DataFrame for y_train). When both X_train and X_test are DataFrames, non-numeric columns are encoded for you — fit on X_train and applied to X_test — so you can pass raw categorical columns directly:

import pandas as pd

X_train = pd.DataFrame({"price": [9.99, 4.50, 7.25], "region": ["NW", "SE", "NW"]})
y_train = pd.Series([120.0, 305.0, 180.0])
X_test  = pd.DataFrame({"region": ["SE"], "price": [5.00]})  # order need not match

predictions = client.predict(X_train, y_train, X_test)  # 'region' is encoded

By default (categorical_columns="auto") each remaining non-numeric column becomes a single column of ordinal codes learned from X_train: retained categories receive deterministic 0..K-1 codes, a rare or unseen value maps to the bounded K other code, and a missing value stays NaN for server-side imputation. Pass categorical_columns=["region"] to encode exactly named columns and reject other strings, or categorical_columns=None to disable categorical inference. Text and categorical declarations may not overlap. Pass categorical_encoding="onehot" for the previous one-hot behavior (indicator columns per category; missing values get their own indicator; unseen values map to an all-zeros group). An automatically inferred column above max_categorical_cardinality (default 100) raises an ambiguity error instead of being silently dropped or embedded. Explicit categoricals use top-K plus other. Temporal columns require explicit conversion. Numeric columns (including bool) pass through unchanged, with NaN imputed server-side. Any object-dtype column is treated as categorical (including numeric-looking strings such as IDs or zip codes, and object date values) — cast genuine numeric columns to a numeric dtype if you want them kept as magnitudes. (Plain lists/numpy arrays must already be numeric — encoding needs column names.)

For raw text columns, install pip install "synthefy[text]" and name them with text_columns=. The client embeds those columns and optionally reduces them with SVD before sending the resulting numeric matrix, so this works in both local and remote modes:

predictions = client.predict(
    X_train,
    y_train,
    X_test,
    text_columns=["review"],
    svd_dim=128,
)

Text embedding always happens on the client machine. By default, text_device="auto" uses CUDA/ROCm when available, then Apple MPS, and falls back to CPU. Pass text_device="cpu" (or another PyTorch device such as "cuda:1") to override automatic selection. The remote service receives only the widened numeric features; remote mode does not move the sentence encoder to the server.

Shapes are validated client-side: X_train and y_train must have the same number of rows, and X_test must have the same number of features as X_train. When both X_train and X_test are DataFrames, X_test is aligned to X_train's columns by name (so column order is irrelevant), and a mismatch in the column sets raises. Missing values (NaN) are allowed — you don't need to fill them in beforehand; the model imputes them server-side.

predict returns a plain list[float] by default. Pass as_pandas=True to get a pandas Series instead — one value per X_test row, named after y_train and indexed by X_test's index (when X_test is a DataFrame), so predictions join straight back:

preds = client.predict(X_train, y_train, X_test, as_pandas=True)
# preds is a pd.Series named after y_train, sharing X_test's index

The client targets the Baseten inference gateway (https://inference.baseten.co/predict); model= is required and names a size — "nori-100m" (→ synthefy/nori-100m), "nori-30m" (→ synthefy/nori-30m), or "nori-6m" (→ synthefy/nori-6m). The gateway resolves that slug to a deployment, so you never name a deployment yourself.

timeout and max_retries are also configurable on the constructor.

Authentication

• The only credential is your Synthefy Nori API key, created in the Synthefy Console. It authenticates against the Baseten-hosted gateway, but you do not need a Baseten account.
• Provide it via the api_key argument or the SYNTHEFY_NORI_API_KEY environment variable. It is sent as the header Authorization: Bearer <key>, which is what the gateway requires.

Errors

The Nori client reuses the package's exception hierarchy:

• HTTP 400 → BadRequestError, carrying the server's error string as the message (e.g. a missing field or unsupported task).
• HTTP 401 → AuthenticationError (bad or missing key).
• Transient errors (timeouts, connection errors, 429, 5xx) are retried with exponential backoff, then surface as RateLimitError / InternalServerError / APITimeoutError / APIConnectionError.

Amazon SageMaker Usage (mode="sagemaker")

Install the optional AWS transport and invoke a named real-time endpoint:

pip install "synthefy[aws]"

from synthefy import SynthefyNoriClient

client = SynthefyNoriClient(
    mode="sagemaker",
    model="nori-30m",
    endpoint_name="nori-30m-prod",
    region_name="us-east-1",
)
predictions = client.predict(
    X_train=[[0.0], [1.0]],
    y_train=[0.0, 1.0],
    X_test=[[2.0]],
)

The client creates an argument-free boto3.Session() and therefore uses boto3's standard credential chain: environment/shared config, web identity (including GitHub OIDC), container or instance roles, and SSO profiles. It does not accept AWS access keys. model= and endpoint_name= are required: the endpoint selects the deployed model specification, while the request model is checked against it so a routing mistake fails closed. Backend selection is always explicit; installing another package never changes where a request runs.

SageMaker's request is the same Nori JSON contract used by the hosted transport, sent through InvokeEndpointWithResponseStream with application/json for all three models. The server emits 15-second heartbeat chunks and one final JSON result, which the client buffers into the normal predict() return value. This lets large 30M requests use SageMaker's streaming processing window (up to eight minutes) instead of the regular invocation's 60-second limit. Container errors retain their original status/message through the normal Synthefy exception hierarchy; AWS credential, signing, region, quota, and throttling errors remain native AWS SDK exceptions. The constructor timeout is SageMaker's per-read inactivity timeout, not a total stream deadline. Set timeout/retries on the constructor. HTTP-only extra_headers= are rejected for SageMaker. Per-call timeout= is ignored with a warning.

Streaming does not increase AWS Marketplace's 25,000,000-byte SageMaker endpoint request-body limit. The client checks the final encoded JSON before invoking the endpoint. It does not split oversized tables because every query must use the same complete in-context training set, so splitting can change the prediction. The planned large-input path is an explicit S3-backed SageMaker Asynchronous Inference API rather than a silent fallback; AWS documents payloads up to 1 GB and processing up to one hour for that service.

Local Usage (mode="local", Optional, No Network)

The same prediction can run locally — no network call and no API key — via the optional synthefy-nori package. Install the local runtime:

pip install "synthefy-nori"

Keep the installed synthefy-nori runtime current so it supports the client options you use and reports recoverable degradation explicitly.

from synthefy import SynthefyNoriClient

client = SynthefyNoriClient(mode="local", model="nori-30m")  # no API key needed
predictions = client.predict(
    X_train=[[0.0, 1.0], [1.0, 0.0], [1.0, 1.0]],
    y_train=[1.0, 1.0, 2.0],
    X_test=[[2.0, 2.0]],
)

predict has the same signature in every mode. The synthefy-nori dependency is imported lazily on first use; if it is not installed, a clear ImportError is raised telling you to pip install "synthefy-nori".

Local mode also preserves synthefy-nori's degradation warnings and their messages. With synthefy-nori>=0.13.1, an SVD failure warns under SvdFallbackWarning while still returning a prediction. Scored or audited runs can turn that warning into an exception around the client call; the client does not catch, wrap, or rewrite it:

from synthefy_nori import SvdFallbackWarning, strict_pipeline

with strict_pipeline(SvdFallbackWarning):
    predictions = client.predict(X_train, y_train, X_test)

Backend selection is explicit. Use mode="local" for in-process execution or mode="remote" for the hosted endpoint; installing synthefy-nori never changes an existing client's routing.

Large Tables and Memory (memory_policy=)

Nori does in-context regression, so your table is input: one prediction keeps a per-layer key/value cache over every context row, and that cache — not the ~6M-parameter model — is what exhausts GPU memory on a big table. memory_policy= decides what to do about it. Omit it and the defaults handle almost every request.

# A preset...
preds = client.predict(X_train, y_train, X_test, memory_policy="exact")        # never quantize
preds = client.predict(X_train, y_train, X_test, memory_policy="max_context")  # fit the largest table

# ...individual fields...
preds = client.predict(X_train, y_train, X_test, memory_policy={"cache_dtype": "int8"})
preds = client.predict(X_train, y_train, X_test,
                       memory_policy={"stream_context": True})  # bounded GPU staging

# ...or the typed model, which ships with the client — no synthefy-nori needed. Validated
# before the request goes out, so a typo or an out-of-range value costs no round trip.
from synthefy import MemoryPolicy
preds = client.predict(X_train, y_train, X_test,
                       memory_policy=MemoryPolicy(cache_dtype="int8", gpu_budget_frac=0.5))

print(client.last_memory_report["rung"])  # e.g. "resident_bf16"

last_memory_report is how you learn what actually happened, and it is worth reading: the fallback chosen depends on the replica's free VRAM at that moment, not on your request, so it is not knowable from your side.

Only the int8 rungs quantize the cache. Ordinary offload_* moves bytes to host RAM rather than approximating, so BF16 offload is bit-identical to staying resident. Explicit streaming is numerically close but not bit-exact even at BF16 because bounded online attention changes floating-point reduction order.

Use memory_policy={"stream_context": True} when context-attention GPU memory should stop scaling with context length. It keeps the full context/KV state on the host, reports stream_bf16 or stream_int8, disables cross-call context reuse, and defaults to a maximum staged-row cap of 2048. Runtime may use smaller K/V blocks to honor its fixed FP32 workspace cap; fit-time OOMs retry a bounded 2048 → 1024 → 512 → 256 row ladder. If the full cache cannot fit the allowed host budget, the request fails clearly instead of silently switching to plain_loop. Set memory_policy={"allow_subsample": False} to turn ordinary element-budget context shortening into an error as well.

One field behaves differently over the network: elements_budget. The cache is only built when the query set spans more than one chunk, and at default settings that needs far more query rows than the hosted request-body limit (~64 MiB) allows — so lowering elements_budget is what lets a hosted request reach the cached path at all.

In mode="local" the same argument works when the installed synthefy-nori exposes the field. An older runtime raises ImportError with an upgrade hint before inference. last_memory_report exposes the resolved local report just as it does for hosted calls.

Choosing Context on Large Tables (large_context_policy=)

When a context table is larger than one Nori call can use effectively, large_context_policy= makes row selection explicit instead of leaving it to a memory-pressure subsample. It works in local, remote, and sagemaker modes:

preds = client.predict(
    X_train,
    y_train,
    X_test,
    large_context_policy="cluster_route",
    large_context_threshold=50_000,
    large_context_seed=0,
)
print(client.last_large_context_report)

Here are some commonly used built-in policies:

Hosted and SageMaker support built-ins from the installed Nori version. See policies.py for the complete current list and configuration options. Hosted modes forward one policy-name string or a list of up to eight names unchanged, including parameter strings such as "safeboost[nu=0.25]". Custom callables and module/file policies remain local-only. large_context_cache_entries is also intentionally absent from the client: each client call is one-shot, fits the supplied X_train again, and hosted serving retains no customer context across requests.

For the 32k/64k target-rank comparison, send a list and choose the split that matches row semantics:

preds = client.predict(
    X_train,
    y_train,
    X_test,
    large_context_policy=[
        "target_rank[cap=32768]",
        "target_rank[cap=65536]",
    ],
    large_context_holdout="tail",  # chronological; use "random" for IID
)

The response echoes holdout_strategy; the client rejects a response that did not honor it. The global 64k screen was 3.02x slower than 32k and lost 0.0129 mean R² on four LaDe temporal tables, so 64k is not a default. The direct arms are measured; selecting between them with a tail gate still needs a frozen temporal replay before that gate should become a default.

last_large_context_report is cleared before every call and works in all three modes. It records whether the policy engaged, the honored policy, threshold and seed, the context window, internal nori_calls, and whether train-derived state was reused. A hosted client treats a missing or mismatched report as an unsupported deployment and raises instead of returning a valid-looking ordinary prediction.

Large-context policies currently return point predictions only (output_type="mean" or "median"). Quantile/full distributions and Nori Thinking variants reject the option before inference. Baseten and SageMaker share this contract; Snowflake SPCS's positional four-value envelope cannot carry it and rejects an appended options value.

Hosted use is still a full one-shot upload: X_train is sent and policy state is recomputed on every call. Upload-once/query-many needs a separate, tenant-isolated session API with authentication, routing, TTL cleanup, storage, and billing. Also note that cluster_route can make up to eight internal model calls while the existing gateway usage block still meters public request rows and columns; production enablement therefore requires an explicit pricing/cost decision after dev latency measurements.

Prediction Intervals (output_type= / quantiles=)

Nori's forward pass produces a whole predictive distribution, not just a point estimate, so prediction intervals cost nothing extra — no conformal wrapper, no separate quantile models:

lo, mid, hi = client.predict(
    X_train, y_train, X_test,
    output_type="quantiles", quantiles=[0.1, 0.5, 0.9],   # an 80% interval
)

output_type selects what comes back. Shared selectors use the same meanings as synthefy-nori's NoriRegressor.predict:

quantiles= takes tau levels strictly inside (0, 1); it is required by — and valid only with — output_type="quantiles". The returned rows follow your order, so quantiles=[0.9, 0.1] gives you high-then-low. Values come back in original-y units, sorted to a valid (monotone) quantile function per row.

as_pandas=True returns a DataFrame instead: one row per X_test row (indexed by X_test, so the bands join straight back) and one column per level, named "<target>[<level>]" — the same convention the forecasting client uses:

bands = client.predict(X_train, y_train, X_test, output_type="quantiles",
                       quantiles=[0.1, 0.9], as_pandas=True)
bands.columns   # ['price[0.1]', 'price[0.9]']  (named after y_train)

Use "full" for CRPS / interval scoring and calibration work; the bank is the checkpoint's full quantile head (K = 999 on the default checkpoint), so prefer "quantiles" when you only need a few levels — it keeps the response small.

Capability differs by mode:

• Local (pip install synthefy-nori): every output_type works. The installed runtime must support the requested distribution output; an older build raises ImportError with an upgrade hint. Quantile and full output require a compatible pinball checkpoint.

• Remote: needs a hosted deployment that serves distribution output. The server echoes back the output_type it honored, and the client raises rather than accept a mismatch:

  ValueError: The hosted deployment did not serve output_type='median': it omitted
  the output_type field entirely, so it predates distribution output. Such a
  deployment answers with the distribution mean, which is indistinguishable from a
  real 'median' result, so this is raised rather than returning means as if they
  were what you asked for. Use local mode (pip install "synthefy-nori", then
  mode="local"), or point base_url/endpoint at a deployment that serves
  distribution output.
  

  That handshake is the point: a deployment that ignores output_type answers with means, which look exactly like a valid "median" result — so silence here would be a confidently wrong answer, not a missing feature.

output_type/quantiles= cannot be combined with discretize= / categorical_levels= (below): discrete labels and a distribution summary are different answers, so asking for both raises ValueError. An ordinary predict(...) call is unaffected by any of this — the request body it sends is byte-for-byte what it always was.

Categorical / Ordinal Targets (discretize= / categorical_levels=)

When the target only takes a small set of discrete values (a 1–5 rating, a count, a quality score), pass discretize= and every returned prediction is one of the target's own levels instead of a continuous estimate:

labels = client.predict(X_train, y_train, X_test, discretize="snap-mean")
labels = client.predict(
    X_train, y_train, X_test,
    discretize="snap-mean",
    categorical_levels=[1, 2, 3, 4, 5],   # the full scale, if the context may under-cover it
)

Discretization is strictly opt-in — nothing is snapped unless you ask. categorical_levels is the set of values the target can take (numeric; order and duplicates don't matter); it defaults to the distinct values of y_train, which is leak-safe. A NaN prediction stays NaN rather than becoming a confident label.

Capability differs by mode:

• Remote: the hosted endpoint returns point predictions (the distribution mean), so the supported strategy is discretize="snap-mean" — the nearest level to the point prediction, computed client-side and identical to local "snap-mean". Other strategies raise a ValueError pointing here.
• Local (pip install "synthefy-nori", with a synthefy-nori recent enough to ship synthefy_nori.discretize): the full strategy set is forwarded — "map-cell" (accuracy-optimal), "median-cell" (MAE-optimal), "snap-mean" (QWK), "snap-median", "expected-level", "prior-match". Choose by the metric you are scored on; see the synthefy-nori docs. An older synthefy-nori raises an ImportError with an upgrade hint.

If your task is scored by squared error / R², don't discretize — the continuous mean is already optimal for those metrics.

API Reference

SynthefyNoriClient (Tabular Regression)

• SynthefyNoriClient(api_key=None, *, mode="remote", timeout=300.0, max_retries=2, base_url=..., endpoint=..., model, user_agent=None, endpoint_name=None, region_name=None) — model is required everywhere and accepts the released Nori variants (nori-6m, nori-30m, nori-100m, and nori-30m-thinking-medium) or an explicit custom HTTP slug; there is no None/default model path. SageMaker uses response streaming for every variant so large 30M/100M requests can run beyond the regular-response limit while predict() still returns one normal result.
  • mode: "remote" (hosted, default), "local" (in-process via synthefy-nori), or "sagemaker" (a named SageMaker endpoint using the AWS credential chain).
  • api_key (remote mode) falls back to the SYNTHEFY_NORI_API_KEY environment variable. Not required in local mode.
  • Hosted Nori is reached by gateway slug — that is the path Synthefy meters, rate-limits and grants per key. To target a single-model endpoint you host yourself, pass your own base_url/endpoint and an explicit custom model slug.
• predict(X_train, y_train, X_test, task="regression", *, output_type="mean", quantiles=None, categorical_columns="auto", max_categorical_cardinality=100, categorical_encoding="ordinal", text_columns=None, svd_dim=128, embedder="minilm", text_device="auto", timeout=None, extra_headers=None) -> List[float]
  • Returns one predicted value per row of X_test. timeout/extra_headers apply to remote mode only.
  • output_type= picks what comes back from the predictive distribution: "mean" (default), "median", "quantiles" (with quantiles=[...], returns (n_levels, n_query)), or "full" (the whole quantile bank as a dict). See Prediction Intervals. Everything other than "mean" needs local mode or a hosted deployment that serves distribution output.
  • Inputs accept Python lists, numpy arrays, or pandas DataFrames/Series. Lists/arrays must be numeric. DataFrame X_test is aligned to X_train by column name and named categorical/text roles are replayed from training; categorical_encoding="ordinal" is the default and "onehot" is available; missing values (NaN) are imputed server-side.
  • categorical_columns is "auto", an exact sequence of names, or None to disable inference. max_categorical_cardinality (default 100) bounds retained levels; ambiguous auto columns above it raise, while explicitly named categoricals use top-K plus other.
  • text_columns embeds named raw-text DataFrame columns client-side. The default text_device="auto" prefers CUDA/ROCm, then Apple MPS, then CPU; install the text extra and pass text_device="cpu" or another PyTorch device string to override it.
  • as_pandas=True returns a pandas Series (named after y_train, indexed by X_test) instead of the default list[float] — or a DataFrame with one column per level ("<target>[<level>]") for output_type="quantiles"/"full".
  • discretize= / categorical_levels= map predictions onto a discrete target's levels (see Categorical / Ordinal Targets); remote mode supports discretize="snap-mean", local mode the full strategy set of the installed synthefy-nori.
• mode: the explicitly selected execution mode.
• close() / context manager support (with SynthefyNoriClient(...) as client:).

Exception Hierarchy

Import these exceptions from synthefy.errors; all inherit from SynthefyError:

• APITimeoutError: Request timed out
• APIConnectionError: Network/connection issues
• APIStatusError: Base class for HTTP status errors
  • BadRequestError (400, 422): Invalid request data
  • AuthenticationError (401): Invalid API key
  • PermissionDeniedError (403): Access denied
  • NotFoundError (404): Resource not found
  • RateLimitError (429): Rate limit exceeded
  • InternalServerError (5xx): Server errors

Each status error includes:

• status_code: HTTP status code
• request_id: Request ID for debugging (if available)
• error_code: API-specific error code (if available)
• response_body: Raw response body

Configuration

Environment Variables

• SYNTHEFY_NORI_API_KEY: Your hosted-Nori API key (SynthefyNoriClient)

Support

For support and questions:

• Email: contact@synthefy.com

License

Apache License 2.0 - see LICENSE file for details.