fromconfig-yarn 0.1.3

# FromConfig Yarn

FromConfig Yarn

A fromconfig Launcher for yarn execution.

• Install
• Quickstart
• Usage Reference
  • Options

Install

pip install fromconfig_yarn

Quickstart

Once installed, the launcher is available with the name yarn.

Given the following module

class Model:
    def __init__(self, learning_rate: float):
        self.learning_rate = learning_rate

    def train(self):
        print(f"Training model with learning_rate {self.learning_rate}")

and config files

# config.yaml
model:
  _attr_: foo.Model
  learning_rate: "${params.learning_rate}"

# params.yaml
params:
  learning_rate: 0.001

# launcher.yaml
yarn:
  name: test-fromconfig

logging:
  level: 20

launcher:
  run: yarn

Run (assuming you are in a Hadoop environment)

fromconfig config.yaml params.yaml launcher.yaml - model - train

Which prints

INFO:fromconfig.launcher.logger:- yarn.name: test-fromconfig
INFO:fromconfig.launcher.logger:- logging.level: 20
INFO:fromconfig.launcher.logger:- params.learning_rate: 0.001
INFO:fromconfig.launcher.logger:- model._attr_: foo.Model
INFO:fromconfig.launcher.logger:- model.learning_rate: 0.001
INFO skein.Driver: Driver started, listening on 12345
INFO:fromconfig_yarn.launcher:Uploading pex to viewfs://root/user/path/to/pex
INFO:cluster_pack.filesystem:Resolved base filesystem: <class 'pyarrow.hdfs.HadoopFileSystem'>
INFO:cluster_pack.uploader:Zipping and uploading your env to viewfs://root/user/path/to/pex
INFO skein.Driver: Uploading application resources to viewfs://root/user/...
INFO skein.Driver: Submitting application...
INFO impl.YarnClientImpl: Submitted application application_12345
INFO:fromconfig_yarn.launcher:TRACKING_URL: http://12.34.56/application_12345

You can also monkeypatch the relevant functions to "fake" the Hadoop environment with

python monkeypatch_fromconfig.py config.yaml params.yaml launcher.yaml - model - train

This example can be found in docs/examples/quickstart.

Usage Reference

Options

To configure Yarn, add a yarn entry to your config.

You can set the following parameters.

• env_vars: A list of environment variables to forward to the container(s)
• hadoop_file_systems: The list of available filesystems
• ignored_packages: The list of packages not to include in the environment
• jvm_memory_in_gb: The JVM memory (default, 8)
• memory: The executor's memory (default, 32 GiB)
• num_cores: The executor's number of cores (default, 8)
• package_path: The HDFS location where to save the environment
• zip_file: The path to an existing pex file, either local or on HDFS
• name: The application name
• queue: The yarn queue to submit the application to
• node_label: The label of the hadoop node to be scheduled
• pre_script_hook: A script to be executed before python is invoked
• extra_env_vars: A mapping of extra environment variables to forward to the container(s)