Task YAML#
SkyPilot provides an intuitive YAML interface to specify a task (resource requirements, setup commands, run commands, file mounts, storage mounts, and so on).
Task YAMLs can be used with the CLI, or the programmatic API (sky.Task.from_yaml()).
Available fields:
# Task name (optional), used for display purposes.
name: my-task
# Working directory (optional), synced to ~/sky_workdir on the remote cluster
# each time launch or exec is run with the yaml file.
#
# Commands in "setup" and "run" will be executed under it.
#
# If a .gitignore file (or a .git/info/exclude file) exists in the working
# directory, files and directories listed in it will be excluded from syncing.
workdir: ~/my-task-code
# Number of nodes (optional; defaults to 1) to launch including the head node.
#
# A task can set this to a smaller value than the size of a cluster.
num_nodes: 4
# Per-node resource requirements (optional).
resources:
cloud: aws # The cloud to use (optional).
# The region to use (optional). Auto-failover will be disabled
# if this is specified.
region: us-east-1
# The zone to use (optional). Auto-failover will be disabled
# if this is specified.
zone: us-east-1a
# Accelerator name and count per node (optional).
#
# Use `sky show-gpus` to view available accelerator configurations.
#
# The following three ways are valid for specifying accelerators for a cluster:
#
# To specify a single type of accelerator:
# Format: <name>:<count> (or simply <name>, short for a count of 1).
# accelerators: V100:4
#
# To specify an ordered list of accelerators (try the accelerators in
# the specified order):
# Format: [<name>:<count>, ...]
# accelerators: ['K80:1', 'V100:1', 'T4:1']
#
# To specify an unordered set of accelerators (optimize all specified
# accelerators together, and try accelerator with lowest cost first):
# Format: {<name>:<count>, ...}
# accelerators: {'K80:1', 'V100:1', 'T4:1'}
accelerators: V100:4
# Number of vCPUs per node (optional).
#
# Format:
# <count>: exactly <count> vCPUs
# <count>+: at least <count> vCPUs
#
# E.g., 4+ means first try to find an instance type with >= 4 vCPUs. If
# not found, use the next cheapest instance with more than 4 vCPUs.
cpus: 4+
# Memory in GiB per node (optional).
#
# Format:
# <num>: exactly <num> GiB
# <num>+: at least <num> GiB
#
# E.g., 32+ means first try to find an instance type with >= 32 GiB. If
# not found, use the next cheapest instance with more than 32 GiB.
memory: 32+
# Instance type to use (optional). If 'accelerators' is specified,
# the corresponding instance type is automatically inferred.
instance_type: p3.8xlarge
# Whether the cluster should use spot instances (optional).
# If unspecified, defaults to False (on-demand instances).
use_spot: False
# The recovery strategy for spot jobs (optional).
# `use_spot` must be True for this to have any effect. For now, only
# `FAILOVER` strategy is supported.
spot_recovery: none
# Disk size in GB to allocate for OS (mounted at /). Increase this if you
# have a large working directory or tasks that write out large outputs.
disk_size: 256
# Disk tier to use for OS (optional).
# Could be one of 'low', 'medium', 'high' or 'best' (default: 'medium').
# if 'best' is specified, use the best disk tier enabled.
# Rough performance estimate:
# low: 500 IOPS; read 20MB/s; write 40 MB/s
# medium: 3000 IOPS; read 220 MB/s; write 200 MB/s
# high: 6000 IOPS; 340 MB/s; write 250 MB/s
disk_tier: medium
# Ports to expose (optional).
#
# All ports specified here will be exposed to the public Internet. Under
# the hood, a firewall rule / inbound rule is automatically added to allow
# inbound traffic to these ports. Applies to all VMs of a cluster created
# with this field set.
#
# Currently only TCP protocol is supported.
#
# Ports Lifecycle:
# A cluster's ports will be updated whenever `sky launch` is executed.
# When launching an existing cluster, any new ports specified will be
# opened for the cluster, and the firewall rules for old ports will never
# be removed until the cluster is terminated.
#
# Could be an integer, a range, or a list of integers and ranges:
# To specify a single port:
# ports: 8081
# To specify a port range:
# ports: 10052-10100
# To specify multiple ports / port ranges:
# ports:
# - 8080
# - 10022-10040
ports: 8081
# Additional accelerator metadata (optional); only used for TPU node
# and TPU VM.
# Example usage:
#
# To request a TPU VM:
# accelerator_args:
# tpu_vm: True (optional, default: True)
#
# To request a TPU node:
# accelerator_args:
# tpu_name: ...
# tpu_vm: False
#
# By default, the value for "runtime_version" is decided based on which is
# requested and should work for either case. If passing in an incompatible
# version, GCP will throw an error during provisioning.
accelerator_args:
# Default is "tpu-vm-base" for TPU VM and "2.12.0" for TPU node.
runtime_version: tpu-vm-base
# tpu_name: mytpu
# tpu_vm: True # True to use TPU VM (the default); False to use TPU node.
# Custom image id (optional, advanced). The image id used to boot the
# instances. Only supported for AWS and GCP (for non-docker image). If not
# specified, SkyPilot will use the default debian-based image suitable for
# machine learning tasks.
#
# Docker support
# You can specify docker image to use by setting the image_id to
# `docker:<image name>` for Azure, AWS and GCP. For example,
# image_id: docker:ubuntu:latest
# Currently, only debian and ubuntu images are supported.
# If you want to use a docker image in a private registry, you can specify your
# username, password, and registry server as task environment variable. For
# details, please refer to the `envs` section below.
#
# AWS
# To find AWS AMI ids: https://leaherb.com/how-to-find-an-aws-marketplace-ami-image-id
# You can also change the default OS version by choosing from the
# following image tags provided by SkyPilot:
# image_id: skypilot:gpu-ubuntu-2004
# image_id: skypilot:k80-ubuntu-2004
# image_id: skypilot:gpu-ubuntu-1804
# image_id: skypilot:k80-ubuntu-1804
#
# It is also possible to specify a per-region image id (failover will only
# go through the regions specified as keys; useful when you have the
# custom images in multiple regions):
# image_id:
# us-east-1: ami-0729d913a335efca7
# us-west-2: ami-050814f384259894c
image_id: ami-0868a20f5a3bf9702
# GCP
# To find GCP images: https://cloud.google.com/compute/docs/images
# image_id: projects/deeplearning-platform-release/global/images/common-cpu-v20230615-debian-11-py310
# Or machine image: https://cloud.google.com/compute/docs/machine-images
# image_id: projects/my-project/global/machineImages/my-machine-image
#
# IBM
# Create a private VPC image and paste its ID in the following format:
# image_id: <unique_image_id>
# To create an image manually:
# https://cloud.ibm.com/docs/vpc?topic=vpc-creating-and-using-an-image-from-volume.
# To use an official VPC image creation tool:
# https://www.ibm.com/cloud/blog/use-ibm-packer-plugin-to-create-custom-images-on-ibm-cloud-vpc-infrastructure
# To use a more limited but easier to manage tool:
# https://github.com/IBM/vpc-img-inst
# Candidate resources (optional). If specified, SkyPilot will only use
# these candidate resources to launch the cluster. The fields specified
# outside of `any_of`, `ordered` will be used as the default values for
# all candidate resources, and any duplicate fields specified inside
# `any_of`, `ordered` will override the default values.
# `any_of:` means that SkyPilot will try to find a resource that matches
# any of the candidate resources, i.e. the failover order will be decided
# by the optimizer.
# `ordered:` means that SkyPilot will failover through the candidate
# resources with the specified order.
# Note: accelerators under `any_of` and `ordered` cannot be a list or set.
any_of:
- cloud: aws
region: us-west-2
acceraltors: V100
- cloud: gcp
acceraltors: A100
# Environment variables (optional). These values can be accessed in the
# `file_mounts`, `setup`, and `run` sections below.
#
# Values set here can be overridden by a CLI flag:
# `sky launch/exec --env ENV=val` (if ENV is present).
#
# If you want to use a docker image as runtime environment in a private
# registry, you can specify your username, password, and registry server as
# task environment variable. For example:
# envs:
# SKYPILOT_DOCKER_USERNAME: <username>
# SKYPILOT_DOCKER_PASSWORD: <password>
# SKYPILOT_DOCKER_SERVER: <registry server>
#
# SkyPilot will execute `docker login --username <username> --password
# <password> <registry server>` before pulling the docker image. For `docker
# login`, see https://docs.docker.com/engine/reference/commandline/login/
#
# You could also specify any of them through the CLI flag if you don't want
# to store them in your yaml file or if you want to generate them for
# constantly changing password. For example:
# sky launch --env SKYPILOT_DOCKER_PASSWORD=$(aws ecr get-login-password --region us-east-1).
#
# For more information about docker support in SkyPilot, please refer to the `image_id` section above.
envs:
MY_BUCKET: skypilot-temp-gcs-test
MY_LOCAL_PATH: tmp-workdir
MODEL_SIZE: 13b
file_mounts:
# Uses rsync to sync local files/directories to all nodes of the cluster.
#
# If symlinks are present, they are copied as symlinks, and their targets
# must also be synced using file_mounts to ensure correctness.
/remote/dir1/file: /local/dir1/file
/remote/dir2: /local/dir2
# Create a S3 bucket named sky-dataset, uploads the contents of
# /local/path/datasets to the bucket, and marks the bucket as persistent
# (it will not be deleted after the completion of this task).
# Symlinks and their contents are NOT copied.
#
# Mounts the bucket at /datasets-storage on every node of the cluster.
/datasets-storage:
name: sky-dataset # Name of storage, optional when source is bucket URI
source: /local/path/datasets # Source path, can be local or s3/gcs URL. Optional, do not specify to create an empty bucket.
store: s3 # Could be either 's3', 'gcs' or 'r2'; default: None. Optional.
persistent: True # Defaults to True; can be set to false to delete bucket after cluster is downed. Optional.
mode: MOUNT # Either MOUNT or COPY. Defaults to MOUNT. Optional.
# Copies a cloud object store URI to the cluster. Can be private buckets.
/datasets-s3: s3://my-awesome-dataset
# Demoing env var usage.
/checkpoint/${MODEL_SIZE}: ~/${MY_LOCAL_PATH}
/mydir:
name: ${MY_BUCKET} # Name of the bucket.
mode: MOUNT
# Setup script (optional) to execute on every `sky launch`.
# This is executed before the 'run' commands.
#
# The '|' separator indicates a multiline string. To specify a single command:
# setup: pip install -r requirements.txt
setup: |
echo "Begin setup."
pip install -r requirements.txt
echo "Setup complete."
# Main program (optional, but recommended) to run on every node of the cluster.
run: |
echo "Beginning task."
python train.py
# Demoing env var usage.
echo Env var MODEL_SIZE has value: ${MODEL_SIZE}