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توضیحات

Python SDK for Chalk

ویژگی	مقدار
سیستم عامل	-
نام فایل	chalkpy-2.1.5
نام	chalkpy
نسخه کتابخانه	2.1.5
نگهدارنده	[]
ایمیل نگهدارنده	[]
نویسنده	Chalk AI, Inc.
ایمیل نویسنده	-
آدرس صفحه اصلی	-
آدرس اینترنتی	https://pypi.org/project/chalkpy/
مجوز	-

# Chalk Chalk enables innovative machine learning teams to focus on building the unique products and models which make their business stand out. Behind the scenes Chalk seamlessly handles data infrastructure with a best-in-class developer experience. Here’s how it works – --- ## Develop Chalk makes it simple to develop feature pipelines for machine learning. Define Python functions using the libraries and tools you're familiar with instead of specialized DSLs. Chalk then orchestrates your functions into pipelines that execute in parallel on a Rust-based engine and coordinates the infrastructure required to compute features. ### Features To get started, [define your features](/docs/features) with [Pydantic](https://pydantic-docs.helpmanual.io/)-inspired Python classes. You can define schemas, specify relationships, and add metadata to help your team share and re-use work. ```py @features class User: id: int full_name: str nickname: Optional[str] email: Optional[str] birthday: date credit_score: float datawarehouse_feature: float transactions = has_many(lambda: Transaction.user_id == User.id) ``` ### Resolvers Next, tell Chalk how to compute your features. Chalk ingests data from your existing data stores, and lets you use Python to compute features with [feature resolvers](/docs/resolver-overview). Feature resolvers are declared with the decorators `@online` and `@offline`, and can depend on the outputs of other feature resolvers. Resolvers make it easy to rapidly integrate a wide variety of data sources, join them together, and use them in your model. #### SQL ```python pg = PostgreSQLSource() @online def get_user(uid: User.id) -> Features[User.full_name, User.email]: return pg.query_string( "select email, full_name from users where id=:id", args=dict(id=uid) ).one() ``` #### REST ```python import requests @online def get_socure_score(uid: User.id) -> Features[User.socure_score]: return ( requests.get("https://api.socure.com", json={ id: uid }).json()['socure_score'] ) ``` --- ## Execute Once you've defined your features and resolvers, Chalk orchestrates them into flexible pipelines that make training and executing models easy. Chalk has built-in support for feature engineering workflows -- no need to manage Airflow or orchestrate complicated streaming flows. You can execute resolver pipelines with declarative caching, ingest batch data on a schedule, and easily make slow sources available online for low-latency serving. ### Caching Many data sources (like vendor APIs) are too slow for online use cases and/or charge a high dollar cost-per-call. Chalk lets you optimize latency and cost by defining declarative caching policies which are well-integrated throughout the system. You no longer have to manage Redis, Memcached, DynamodDB, or spend time tuning cache-warming pipelines. Add a caching policy with one line of code in your feature definition: ```python @features class ExternalBankAccount: - balance: int + balance: int = feature(max_staleness="**1d**") ``` Optionally warm feature caches by executing resolvers on a schedule: ```py @online(cron="**1d**") def fn(id: User.id) -> User.credit_score: return redshift.query(...).all() ``` Or override staleness tolerances at query time when you need fresher data for your models: ```py chalk.query( ..., outputs: [User.fraud_score], max_staleness: { User.fraud_score: "1m" } ) ``` ### Batch ETL ingestion Chalk also makes it simple to generate training sets from data warehouse sources -- join data from services like S3, Redshift, BQ, Snowflake (or other custom sources) with historical features computed online. Specify a cron schedule on an `@offline` resolver and Chalk automatically ingests data with support for incremental reads: ```py @offline(cron="**1h**") def fn() -> Feature[User.id, User.datawarehouse_feature]: return redshift.query(...).incremental() ``` Chalk makes this data available for point-in-time-correct dataset generation for data science use-cases. Every pipeline has built-in monitoring and alerting to ensure data quality and timeliness. ### Reverse ETL When your model needs to use features that are canonically stored in a high-latency data source (like a data warehouse), Chalk's Reverse ETL support makes it simple to bring those features online and serve them quickly. Add a single line of code to an `offline` resolver, and Chalk constructs a managed reverse ETL pipeline for that data source: ```py @offline(offline_to_online_etl="5m") ``` Now data from slow offline data sources is automatically available for low-latency online serving. --- ## Deploy + query Once you've defined your pipelines, you can rapidly deploy them to production with Chalk's CLI: ```bash chalk apply ``` This creates a deployment of your project, which is served at a unique preview URL. You can promote this deployment to production, or perform QA workflows on your preview environment to make sure that your Chalk deployment performs as expected. Once you promote your deployment to production, Chalk makes features available for low-latency [online inference](/docs/query-basics) and [offline training](/docs/training-client). Significantly, Chalk uses the exact same source code to serve temporally-consistent training sets to data scientists and live feature values to models. This re-use ensures that feature values from online and offline contexts match and dramatically cuts development time. ### Online inference Chalk's online store & feature computation engine make it easy to query features with ultra low-latency, so you can use your feature pipelines to serve online inference use-cases. Integrating Chalk with your production application takes minutes via Chalk's simple REST API: <RequestingFeaturesOnline style={{ height: 328 }} /> Features computed to serve online requests are also replicated to Chalk's offline store for historical performance tracking and training set generation. ### Offline training Data scientists can use Chalk's Jupyter integration to create datasets and train models. Datasets are stored and tracked so that they can be re-used by other modelers, and so that model provenance is tracked for audit and reproducibility. ```python X = ChalkClient.offline_query( input=labels[[User.uid, timestamp]], output=[ User.returned_transactions_last_60, User.user_account_name_match_score, User.socure_score, User.identity.has_verified_phone, User.identity.is_voip_phone, User.identity.account_age_days, User.identity.email_age, ], ) ``` Chalk datasets are always "temporally consistent." This means that you can provide labels with different past timestamps and get historical features that represent what your application would have retrieved online at those past times. Temporal consistency ensures that your model training doesn't mix "future" and "past" data.

نیازمندی

مقدار	نام
<23,>=22.1.0	cattrs
<2,>=1.2.0	executing
<2,>=1.0.0	pydantic
<7,>=6.0	pyyaml
<0.7,>=0.6.1	isodate
<3,>=2.8.0	python-dateutil
<2.30.0,>=2.28.0	requests
<1.6,>=1.5.1	pandas
<10.1,>=10.0.1	pyarrow
<4.5.0,>=4.4.0	typing-extensions
>=2.2.1	cloudpickle
>=13.3.5	rich
>=8.0.6	ipywidgets
<3,>=0.2.1	backports.zoneinfo[tzdata]
<0.2,>=0.1.1	aiomysql
<0.20,>=0.19.0	aiosqlite
<0.28,>=0.27.0	asyncpg
==0.6.0	duckdb
==0.17.2	polars[timezone]
<3,>=2.9.4	psycopg2
<2,>=1.0.2	pymysql
<2.1,>=2.0.909	redshift-connector
<3.1,>=3	snowflake-connector-python
<1.5,>=1.4.2	snowflake-sqlalchemy
<1.7,>=1.5.0	sqlalchemy-bigquery
<0.9,>=0.8.11	sqlalchemy-redshift
<2,>=1.4.26	sqlalchemy[asyncio]
>=11.3.2	sqlglot
<1.7,>=1.5.0	sqlalchemy-bigquery
==0.6.0	duckdb
==0.17.2	polars[timezone]
<2,>=1.4.26	sqlalchemy[asyncio]
>=11.3.2	sqlglot
-	mypy
-	pre-commit
-	pytest
-	pytest-env
-	pytest-timeout
-	pytest-xdist
-	python-dotenv
-	pytest-asyncio
-	sqlalchemy2-stubs
-	types-psycopg2
-	types-pymysql
-	types-pyyaml
-	types-requests
<2,>=1.0.2	pymysql
<0.2,>=0.1.1	aiomysql
==0.6.0	duckdb
==0.17.2	polars[timezone]
<2,>=1.4.26	sqlalchemy[asyncio]
>=11.3.2	sqlglot
==0.6.0	duckdb
==0.17.2	polars[timezone]
>=11.3.2	sqlglot
<3,>=2.9.4	psycopg2
<0.28,>=0.27.0	asyncpg
==0.6.0	duckdb
==0.17.2	polars[timezone]
<2,>=1.4.26	sqlalchemy[asyncio]
>=11.3.2	sqlglot
<0.9,>=0.8.11	sqlalchemy-redshift
<2.1,>=2.0.909	redshift-connector
<0.28,>=0.27.0	asyncpg
==0.6.0	duckdb
==0.17.2	polars[timezone]
<3,>=2.9.4	psycopg2
<2,>=1.4.26	sqlalchemy[asyncio]
>=11.3.2	sqlglot
==0.6.0	duckdb
>=11.3.2	sqlglot
==0.17.2	polars[timezone]
<3.1,>=3	snowflake-connector-python
<1.5,>=1.4.2	snowflake-sqlalchemy
==0.6.0	duckdb
==0.17.2	polars[timezone]
<2,>=1.4.26	sqlalchemy[asyncio]
>=11.3.2	sqlglot
<2,>=1.4.26	sqlalchemy[asyncio]
==0.6.0	duckdb
==0.17.2	polars[timezone]
>=11.3.2	sqlglot
<0.20,>=0.19.0	aiosqlite
==0.6.0	duckdb
==0.17.2	polars[timezone]
<2,>=1.4.26	sqlalchemy[asyncio]
>=11.3.2	sqlglot

زبان مورد نیاز

مقدار	نام
<3.12,>3.8	Python

نحوه نصب

نصب پکیج whl chalkpy-2.1.5:

pip install chalkpy-2.1.5.whl

نصب پکیج tar.gz chalkpy-2.1.5:

pip install chalkpy-2.1.5.tar.gz