import numpy as np
import ray
ray.init(redis_address="172.31.15.101:6379")

@ray.remote
def f(_):
    return my_square(np.random.randint(0, 10))

def my_square(x):
    return x * x

ray.get([f.remote(x) for x in range(1)])

Remote function __main__.f failed with:

Traceback (most recent call last):
  File "<ipython-input-3-31e8a6f51ae7>", line 3, in f
NameError: name 'my_square' is not defined


  You can inspect errors by running

      ray.error_info()

  If this driver is hanging, start a new one with

      ray.init(redis_address="172.31.15.101:6379")
  
---------------------------------------------------------------------------
RayGetError                               Traceback (most recent call last)
<ipython-input-4-693bc96baeb6> in <module>()
----> 1 ray.get([f.remote(x) for x in range(1)])

~/anaconda3/lib/python3.6/site-packages/ray/worker.py in get(object_ids, worker)
   2243             for i, value in enumerate(values):
   2244                 if isinstance(value, RayTaskError):
-> 2245                     raise RayGetError(object_ids[i], value)
   2246             return values
   2247         else:

RayGetError: Could not get objectid ObjectID(2c2e76ba4dd326dd7b73540ab7933c73420b6bbf). It was created by remote function __main__.f which failed with:

Remote function __main__.f failed with:

Traceback (most recent call last):
  File "<ipython-input-3-31e8a6f51ae7>", line 3, in f
NameError: name 'my_square' is not defined

# An unique identifier for the head node and workers of this cluster.
cluster_name: ludwig_test_1

# The minimum number of workers nodes to launch in addition to the head
# node. This number should be >= 0.
min_workers: 4

# The maximum number of workers nodes to launch in addition to the head
# node. This takes precedence over min_workers.
max_workers: 4

# The autoscaler will scale up the cluster to this target fraction of resource
# usage. For example, if a cluster of 10 nodes is 100% busy and
# target_utilization is 0.8, it would resize the cluster to 13. This fraction
# can be decreased to increase the aggressiveness of upscaling.
target_utilization_fraction: 0.8

# If a node is idle for this many minutes, it will be removed.
idle_timeout_minutes: 5

# Cloud-provider specific configuration.
provider:
    type: aws
    region: us-west-2
    availability_zone: us-west-2c

# How Ray will authenticate with newly launched nodes.
auth:
    ssh_user: ubuntu
# By default Ray creates a new private keypair, but you can also use your own.
# If you do so, make sure to also set "KeyName" in the head and worker node
# configurations below.
#    ssh_private_key: /path/to/your/key.pem

# Provider-specific config for the head node, e.g. instance type. By default
# Ray will auto-configure unspecified fields such as SubnetId and KeyName.
# For more documentation on available fields, see:
# http://boto3.readthedocs.io/en/latest/reference/services/ec2.html#EC2.ServiceResource.create_instances
head_node:
    InstanceType: m5.large
    ImageId: ami-3b6bce43  # Amazon Deep Learning AMI (Ubuntu)

    # You can provision additional disk space with a conf as follows
    # BlockDeviceMappings:
    #     - DeviceName: /dev/sda1
    #       Ebs:
    #           VolumeSize: 100

    # Additional options in the boto docs.

# Provider-specific config for worker nodes, e.g. instance type. By default
# Ray will auto-configure unspecified fields such as SubnetId and KeyName.
# For more documentation on available fields, see:
# http://boto3.readthedocs.io/en/latest/reference/services/ec2.html#EC2.ServiceResource.create_instances
worker_nodes:
    InstanceType: m5.large
    ImageId: ami-3b6bce43  # Amazon Deep Learning AMI (Ubuntu)

    # Run workers on spot by default. Comment this out to use on-demand.
    InstanceMarketOptions:
        MarketType: spot
        # Additional options can be found in the boto docs, e.g.
        #   SpotOptions:
        #       MaxPrice: MAX_HOURLY_PRICE

    # Additional options in the boto docs.

# Files or directories to copy to the head and worker nodes. The format is a
# dictionary from REMOTE_PATH: LOCAL_PATH, e.g.
file_mounts: {
#    "/path1/on/remote/machine": "/path1/on/local/machine",
#    "/path2/on/remote/machine": "/path2/on/local/machine",
}

# List of shell commands to run to set up nodes.
setup_commands:
    # Note: if you're developing Ray, you probably want to create an AMI that
    # has your Ray repo pre-cloned. Then, you can replace the pip installs
    # below with a git checkout <your_sha> (and possibly a recompile).
    - pip install -U ray==0.3.1

# Custom commands that will be run on the head node after common setup.
head_setup_commands:
    - pip install boto3==1.4.8  # 1.4.8 adds InstanceMarketOptions

# Custom commands that will be run on worker nodes after common setup.
worker_setup_commands: []

# Command to start ray on the head node. You don't need to change this.
head_start_ray_commands:
    - ray stop
    - ray start --head --redis-port=6379 --autoscaling-config=~/ray_bootstrap_config.yaml

# Command to start ray on worker nodes. You don't need to change this.
worker_start_ray_commands:
    - ray stop
    - ray start --redis-address=$RAY_HEAD_IP:6379