Registry / http-networking / baseten-performance-client

baseten-performance-client

library0.1.9pypypi✓ verified 85d ago

The `baseten-performance-client` is a Python library designed for ultra-high performance interactions with Baseten's inference endpoints, particularly for embedding models. It provides a simple client interface for sending prediction requests. As of the current version `0.1.5`, it primarily focuses on optimizing HTTP requests to Baseten services. Its release cadence is tied to Baseten's internal development cycles, with updates typically driven by specific performance or feature needs.

http-networking ai-ml llm-agents

pip install baseten-performance-client

github ↗package ↗homepage ↗

INSTALL

IMPORT

SIG · BASETEN-PERFORMANC

baseten-performance-client

http-networkingpythonv0.1.9

Install

2.4s avg

Import

—

Disk

34MB

Pass rate

10/ 10

Env Coverage10 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v0.1.9 · pip install

no network on importno background threads

Install × environment matrix

Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux

musl

py 3.10–3.910 runs

installs and imports cleanly · install 0.0s · import 0.000s · 35.7MB

glibc

py 3.10–3.910 runs

installs and imports cleanly · install 2.4s · import 0.000s · 36MB

34MB installed

● package 34MB

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

PerformanceClient

✓ from baseten_performance_client import PerformanceClient

Initialize the `PerformanceClient` with your Baseten API key and make a prediction request to a specified model ID. The API key can be passed directly or set as the `BASETEN_API_KEY` environment variable.

import os from baseten_performance_client import PerformanceClient # Ensure your Baseten API key is set as an environment variable # os.environ['BASETEN_API_KEY'] = 'YOUR_BASETEN_API_KEY' api_key = os.environ.get('BASETEN_API_KEY', 'YOUR_BASETEN_API_KEY_HERE') model_id = 'YOUR_MODEL_ID' if api_key == 'YOUR_BASETEN_API_KEY_HERE' or not api_key: print("Warning: Please set the BASETEN_API_KEY environment variable or replace 'YOUR_BASETEN_API_KEY_HERE'.") print("Skipping prediction due to missing API key.") else: try: client = PerformanceClient(api_key=api_key) response = client.predict( model_id=model_id, input={'text': 'The quick brown fox jumps over the lazy dog.'} ) print("Prediction successful:") print(response) except Exception as e: print(f"An error occurred during prediction: {e}")

Debug

Known issues

gotchaThe client requires a valid Baseten API key for most models. Without it, requests will fail with authentication errors.

fix

Ensure the `BASETEN_API_KEY` environment variable is set, or pass `api_key` directly to the `PerformanceClient` constructor or `predict` method.

affects: All versions

gotchaThe `input` dictionary passed to the `predict` method must strictly adhere to the target Baseten model's expected input schema. Mismatched or malformed inputs will result in HTTP 400 Bad Request errors.

fix

Consult your Baseten model's API documentation or the model's 'deploy' tab on the Baseten platform for the exact expected input format (e.g., keys, types, array structures).

affects: All versions

gotchaWhile named 'performance client', the current implementation primarily uses standard HTTP/1.1 and `asyncio.run()` internally for synchronous calls. Users expecting ultra-low latency typical of gRPC or raw socket connections for very small requests might encounter higher overheads due to HTTP and Python's async-to-sync bridging.

fix

Understand that 'performance' here refers to optimized HTTP usage. For truly extreme low-latency scenarios, consider alternatives or ensure your Baseten model is optimized for such throughput. Be mindful of its blocking nature when integrating into existing asynchronous applications.

affects: All versions (0.1.x)

Errors

Common errors & fixes

baseten_performance_client.errors.BasetenAuthenticationError: Invalid API key

The Baseten API key provided is either missing, incorrect, or expired.

fix

Verify your `BASETEN_API_KEY` environment variable or the `api_key` argument. Generate a new API key from your Baseten account settings if needed.

baseten_performance_client.errors.BasetenModelNotFoundError: Model with ID '...' not found

The `model_id` specified in the `predict` call does not correspond to an existing, deployed, and accessible model on your Baseten account.

fix

Double-check the `model_id` for typos. Ensure the model is deployed and you have the correct permissions to access it on the Baseten platform.

baseten_performance_client.errors.BasetenAPIError: 400 Bad Request: Input validation failed

The `input` dictionary provided to the `predict` method does not match the schema expected by the Baseten model.

fix

Review the specific error message for details on the validation failure. Compare your `input` structure (keys, data types, nested objects/arrays) against the model's documented API schema.

Upgrade

Version history

0.1.9latest on PyPI · released Jun 4, 2026

Audit

Dependencies

pythonrequiredRequires Python 3.8 or newer

httpxrequiredUsed internally for making asynchronous HTTP requests

Agent activity

18 hits · last 30 days

node

OpenAI (training)

Resources

githubhttps://github.com/basetenlabs/truss ↗

packagehttps://pypi.org/project/baseten-performance-client/ ↗

homepagehttps://www.baseten.co ↗