<commit_before># An unique identifier for the head node and workers of this cluster.
cluster_name: default

## NOTE: Typically for local clusters, min_workers == initial_workers == max_workers.

# The minimum number of workers nodes to launch in addition to the head
# node. This number should be >= 0.
# Typically, min_workers == initial_workers == max_workers.
min_workers: 0
# The initial number of worker nodes to launch in addition to the head node.
# Typically, min_workers == initial_workers == max_workers.
initial_workers: 0

# The maximum number of workers nodes to launch in addition to the head node.
# This takes precedence over min_workers.
# Typically, min_workers == initial_workers == max_workers.
max_workers: 0

# Autoscaling parameters.
# Ignore this if min_workers == initial_workers == max_workers.
autoscaling_mode: default
target_utilization_fraction: 0.8
idle_timeout_minutes: 5

# This executes all commands on all nodes in the docker container,
# and opens all the necessary ports to support the Ray cluster.
# Empty string means disabled. Assumes Docker is installed.
docker:
    image: "" # e.g., tensorflow/tensorflow:1.5.0-py3
    container_name: "" # e.g. ray_docker
    # If true, pulls latest version of image. Otherwise, `docker run` will only pull the image
    # if no cached version is present.<commit_msg>[autoscaler] Better explain min/max/initial worker settings (#9132)<commit_after># An unique identifier for the head node and workers of this cluster.
cluster_name: default

## NOTE: Typically for local clusters, min_workers == initial_workers == max_workers == len(worker_ips).

# The minimum number of workers nodes to launch in addition to the head
# node. This number should be >= 0.
# Typically, min_workers == initial_workers == max_workers == len(worker_ips).
min_workers: 0
# The initial number of worker nodes to launch in addition to the head node.
# Typically, min_workers == initial_workers == max_workers == len(worker_ips).
initial_workers: 0

# The maximum number of workers nodes to launch in addition to the head node.
# This takes precedence over min_workers.
# Typically, min_workers == initial_workers == max_workers == len(worker_ips).
max_workers: 0

# Autoscaling parameters.
# Ignore this if min_workers == initial_workers == max_workers.
autoscaling_mode: default
target_utilization_fraction: 0.8
idle_timeout_minutes: 5

# This executes all commands on all nodes in the docker container,
# and opens all the necessary ports to support the Ray cluster.
# Empty string means disabled. Assumes Docker is installed.
docker:
    image: "" # e.g., tensorflow/tensorflow:1.5.0-py3
    container_name: "" # e.g. ray_docker
    # If true, pulls latest version of image. Otherwise, `docker run` will only pull the image
    # if no cached version is present.
{'fraction_non_alphanumeric': 0.07700496806245564, 'fraction_numerical': 0.009226401703335699, 'mean_word_length': 4.626746506986028, 'pattern_counts': {'":': 0, '<': 3, '<?xml version=': 0, '>': 5, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'dir': 'git-commits-cleaned', 'id': '2003174', 'n_tokens_mistral': 805, 'n_tokens_neox': 756, 'n_words': 339}