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 relative path is used, it's evaluated relative to the location from
# which `sky` is called.
#
# 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
  #
  # Azure
  # To find Azure images: https://docs.microsoft.com/en-us/azure/virtual-machines/linux/cli-ps-findimage
  # image_id: microsoft-dsvm:ubuntu-2004:2004:21.11.04
  #
  # 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 a relative path is used, it's evaluated relative to the location from
  # which `sky` is called.
  #
  # 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}