chalkpy

library2.157.6pypypi✓ verified 26d ago

Chalkpy is the Python SDK for Chalk, a feature store designed to simplify feature engineering and deployment for machine learning teams. It allows users to define feature pipelines using familiar Python functions and data structures, orchestrating them on a Rust-based engine for parallel execution. The library facilitates defining features with Pydantic-inspired classes and creating resolvers to compute them for both online inference and offline training. The current version is 2.115.4, with frequent updates indicated by its changelog.

ai-ml data workflow

pip install chalkpy

package ↗homepage ↗docs ↗changelog ↗

INSTALL

IMPORT

SIG · CHALKPY

chalkpy

ai-mlpythonv2.157.6

Install

22.6s avg

Import

2806ms

Disk

666MB

Pass rate

5/ 10

Env Coverage5 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v2.157.6 · 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

glibc

py 3.10

✕ build_error

✓ 24s

py 3.11

✕ build_error

✓ 21.57s

py 3.12

✕ build_error

✓ 18.23s

py 3.13

✕ build_error

✓ 18.97s

py 3.9

2/3 runs

✓ 30.1s

666MB installed

● package 666MB

Code

Verified usage

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

ChalkClient

✓ from chalk.client import ChalkClient

features

✓ from chalk import features

Used as a decorator to define feature sets.

online

✓ from chalk import online

Used as a decorator to define online feature resolvers.

offline

✓ from chalk import offline

Used as a decorator to define offline feature resolvers.

✓ from chalk import _

Used to build features on feature classes, commonly imported as underscore.

This quickstart demonstrates how to initialize the ChalkClient and verify authentication. It requires `CHALK_CLIENT_ID` and `CHALK_CLIENT_SECRET` to be set as environment variables or for the user to have authenticated via the `chalk login` CLI command.

import os from chalk.client import ChalkClient # Ensure these environment variables are set or authenticate via `chalk login` CLI command CHALK_CLIENT_ID = os.environ.get("CHALK_CLIENT_ID", "") CHALK_CLIENT_SECRET = os.environ.get("CHALK_CLIENT_SECRET", "") if not CHALK_CLIENT_ID or not CHALK_CLIENT_SECRET: print("Warning: CHALK_CLIENT_ID and CHALK_CLIENT_SECRET environment variables are not set. Authentication will likely fail. Please refer to Chalk documentation for authentication.") try: client = ChalkClient( client_id=CHALK_CLIENT_ID, client_secret=CHALK_CLIENT_SECRET, branch='notebook' # or 'production', or a custom branch name ) # Verify the setup whoami_response = client.whoami() print(f"Successfully authenticated as user ID: {whoami_response.user_id}") except Exception as e: print(f"Authentication failed or API call error: {e}")

chalk --version

Debug

Known issues

gotchaAuthentication requires `CHALK_CLIENT_ID` and `CHALK_CLIENT_SECRET` environment variables to be set, or prior authentication through the `chalk cli login` command. Failing to do so will result in connection errors.

fix

Set `CHALK_CLIENT_ID` and `CHALK_CLIENT_SECRET` in your environment, or run `chalk login` from the command line and follow the prompts to authenticate.

affects: All versions

gotchaSpecific functionalities require installation with 'extras'. For example, `chalkpy[runtime]` is recommended for notebook environments, and `chalkpy[chalkdf]` is needed for features utilizing Chalk DataFrames. A bare `pip install chalkpy` may not include all necessary components for certain use cases.

fix

Install with the appropriate extras, e.g., `pip install "chalkpy[runtime]"` or `pip install "chalkpy[chalkdf]"`.

affects: All versions

gotchaThe `cache_nulls` parameter for features defaults to `True`, meaning Chalk will cache all values, including nulls. This can lead to unexpected caching behavior if not explicitly handled, as a null value will replace an existing cached value.

fix

Be aware of the `cache_nulls` default behavior. Set `cache_nulls=False` to prevent updating null entries in the cache, or `cache_nulls="evict_nulls"` to evict entries that would have been null.

affects: All versions

Errors

Common errors & fixes

Unexpected Chalk server error with status code 401

This error typically indicates that your Chalk client is unauthenticated, meaning the `CHALK_CLIENT_ID` and `CHALK_CLIENT_SECRET` environment variables are not set correctly, or you haven't authenticated via the `chalk login` CLI command.

fix

Ensure `CHALK_CLIENT_ID` and `CHALK_CLIENT_SECRET` are set in your environment variables, or run `chalk login` in your terminal and follow the prompts to authenticate.

```bash
export CHALK_CLIENT_ID="your_client_id"
export CHALK_CLIENT_SECRET="your_client_secret"
# Or, to log in via CLI
chalk login
```

ModuleNotFoundError: No module named 'chalk'

This error occurs when the `chalkpy` library, or specific modules within it, cannot be found by your Python environment, often due to an incomplete installation or incorrect import paths.

fix

Ensure `chalkpy` is installed in your active Python environment. If you need specific functionalities, install with the appropriate 'extras'.

```bash
pip install chalkpy
# Or for specific functionalities, e.g., for notebooks or Chalk DataFrames:
pip install "chalkpy[runtime]"
pip install "chalkpy[chalkdf]"
```

Please double check that all features have resolvers and resolvers do not have circular dependencies.

This error indicates that Chalk cannot construct a valid dependency graph for resolving features, either because a feature lacks a defined resolver or there's an unbreakable circular dependency in your feature computations.

fix

Review your feature definitions and resolvers to ensure every feature has a corresponding resolver and that no resolver directly or indirectly calls itself in a loop. Reduce the number of output features in your query to isolate the problematic set and debug circular dependencies.

AttributeError: module 'chalk' has no attribute 'ChalkClient' (or similar for other core components)

This usually happens when you try to access a component (like `ChalkClient`, `features`, `online`, `offline`) that is not directly available under the top-level `chalk` module, or there's a version mismatch/corrupted installation.

fix

Ensure you are importing core components from their correct submodules, as specified in the Chalkpy documentation. For `ChalkClient`, it's typically `from chalk.client import ChalkClient`. Other decorators like `features`, `online`, `offline` are often imported directly from `chalk`.

```python
from chalk.client import ChalkClient
from chalk import features, online, offline
```

Upgrade

Version history

2.157.6latest on PyPI · released Aug 28, 2026

Audit

Dependencies

pydanticrequiredUsed for defining features with Pydantic-inspired classes.

requestsrequiredCommon dependency for API interactions, mentioned in quickstart requirements.

chalkdfoptionalRequired for specific features related to Chalk DataFrames.

Agent activity

20 hits · last 30 days

node

OpenAI (training)

Resources

packagehttps://pypi.org/project/chalkpy/ ↗

homepagehttps://chalk.ai ↗

docshttps://docs.chalk.ai ↗

changeloghttps://docs.chalk.ai/docs/changelog ↗