PyPI 官网下载 | flytekit-0.14.4.tar.gz

# Flytekit [](https://pypi.python.org/pypi/flytekit/) [](https://pypi.python.org/pypi/flytekit/) [](https://pypi.python.org/pypi/flytekit/) [](https://pypi.python.org/pypi/flytekit/) [](https://pypi.python.org/pypi/flytekit/)  Python Library for easily authoring, testing, deploying, and interacting with Flyte tasks, workflows, and launch plans. To understand more about flyte refer to, - [Flyte homepage](https://flyte.org) - [Flyte master repository](https://github.com/lyft/flyte) ## Installation Flytekit is designed for minimal footprint, and thus some features must be installed as extras. ### Base Installation This is the lightest-weight SDK install. This installation includes everything you need to interact with Flyte. Modules include: 1. The full Flyte IDL and an additional model layer for easier extension of the data model. 2. gRPC client for communicating with the platform. 3. Implementations for authoring and extending all Flyte entities (including tasks, workflows, and launch plans). Tools include: 1. flyte-cli (Command-Line Interface for Interacting with the Flyte Platform) 2. pyflyte (Command-Line tool for easing the registration of Flyte entities) ```bash pip install flytekit ``` ### Plugin Installation #### Spark If `@spark_task` is to be used, one should install the `spark` plugin. ```bash pip install "flytekit[spark]" for Spark 2.4.x pip install "flytekit[spark3]" for Spark 3.x ``` #### Schema If `Types.Schema()` is to be used for computations involving large dataframes, one should install the `schema` extension. ```bash pip install "flytekit[schema]" ``` #### Sidecar If `@sidecar_task` is to be used, one should install the `sidecar` plugin. ```bash pip install "flytekit[sidecar]" ``` ### Pytorch If `@pytorch_task` is to be used, one should install the `pytorch` plugin. ```bash pip install "flytekit[pytorch]" ``` ### TensorFlow If `@tensorflow_task` is to be used, one should install the `tensorflow` plugin. ```bash pip install flytekit[tensorflow] ``` ### Full Installation To install all or multiple available plugins, one can specify them individually: ```bash pip install "flytekit[sidecar,spark,schema]" ``` Or install them with the `all` or `all-spark2.4` or `all-spark3` directives which will install all the plugins and a specific Spark version. Please note that `all` currently defaults to Spark 2.4.x. In a future release (starting 0.15.x), `all` will be switched to use Spark 3.x. ```bash pip install "flytekit[all]" ``` ## Development ### Recipes ``` $ make Available recipes: setup Install requirements fmt Format code with black and isort lint Run linters test Run tests requirements Compile requirements ``` ### Setup (Do Once) ```bash virtualenv ~/.virtualenvs/flytekit source ~/.virtualenvs/flytekit/bin/activate make setup ``` ### Formatting We use [black](https://github.com/psf/black) and [isort](https://github.com/timothycrosley/isort) to autoformat code. Run the following command to execute the formatters: ```bash source ~/.virtualenvs/flytekit/bin/activate make fmt ``` ### Testing #### Unit Testing ```bash source ~/.virtualenvs/flytekit/bin/activate make test ``` ### Updating requirements Update requirements in [`setup.py`](setup.py), or update requirements for development in [`dev-requirements.in`](dev-requirements.in). Then, validate, pin and freeze all requirements by running: ```bash source ~/.virtualenvs/flytekit/bin/activate make requirements ``` This will re-create the [`requirements.txt`](requirements.txt) and [`dev-requirements.txt`](dev-requirements.txt) files which will be used for testing. You will have also have to re-run `make setup` to update your local environment with the updated requirements.