text: """
  Each item in this list specifies a file_mount.
  
  local_dir: directory/file on the local machine.
  remote_dir: directory/file name to copy to on the remote instance.
  mount_point: where the directory/file will be mounted in the docker instance.
  """
  DIR_AND_MOUNT_POINT_MAPPINGS = [
      dict(
          local_dir='/home/jcoreyes/.mujoco',
          remote_dir='/home/ubuntu/.mujoco',
          mount_point='/root/.mujoco',
      ),
      dict(
          local_dir='/home/jcoreyes/objects/rlkit',
          remote_dir='/home/ubuntu/objects/rlkit',
          mount_point='/home/jcoreyes/objects/rlkit',
      ),
  
      dict(
          local_dir='/home/jcoreyes/.aws',
          remote_dir='/home/ubuntu/.aws',
          mount_point='/home/ubuntu/.aws',
      ),
      dict(
          local_dir='/tmp/local_exp.pkl',
          remote_dir='/home/ubuntu/local_exp.pkl',
          mount_point='/tmp/local_exp.pkl',
      ),
  ]
  # This can basically be anything. Used for launching on instances. The
  # local launch parameters (exo_func, exp_variant, etc) are saved at this location
  # on the local machine and then transfered to the remote machine.
  EXPERIMENT_INFO_PKL_FILEPATH = '/tmp/local_exp.pkl'
  # Again, can be anything. The Ray autoscaler yaml file is saved to this location
  # before launching.
  LAUNCH_FILEPATH = '/tmp/autoscaler_launch.yaml'
  
  # TODO:Steven remove this. File syncing from s3 still uses this.
  LOCAL_LOG_DIR = '/home/steven/logs'
  
  AWS_CONFIG_NO_GPU=dict(
      REGION='us-west-2',
      INSTANCE_TYPE = 'c5.large',
      SPOT_PRICE = 0.07,
      REGION_TO_AWS_IMAGE_ID = {
          'us-west-2': 'ami-0b294f219d14e6a82'
      },
      REGION_TO_AWS_AVAIL_ZONE = {
          'us-west-2': 'us-west-2a,us-west-2b'
      },
  
  )
  
  AWS_CONFIG_GPU = dict(
      REGION='us-west-2',
      INSTANCE_TYPE = 'g3.4xlarge',
      SPOT_PRICE = 0.6,
      REGION_TO_AWS_IMAGE_ID = {
          'us-west-2': 'ami-0b294f219d14e6a82'
      },
      REGION_TO_AWS_AVAIL_ZONE = {
          'us-west-2': 'us-west-2a,us-west-2b'
      },
  )
  
  GCP_CONFIG_GPU = dict(
      REGION='us-west1',
      INSTANCE_TYPE='n1-highmem-8',
      SOURCE_IMAGE='https://www.googleapis.com/compute/v1/projects/railrl-private-gcp/global/images/railrl-private-ray',
      PROJECT_ID='railrl-private-gcp',
      gpu_kwargs=dict(
          num_gpu=1,
      ),
      REGION_TO_GCP_AVAIL_ZONE = {
          'us-west1': "us-west1-b",
      },
  
  )
  
  
  AWS_CONFIG = {
      True: AWS_CONFIG_GPU,
      False: AWS_CONFIG_NO_GPU,
  }
  GCP_CONFIG = {
      True: GCP_CONFIG_GPU,
      False: GCP_CONFIG_GPU,
  }
  
  GPU_DOCKER_IMAGE = 'jcoreyes/op3-ray'
  """
  DOCKER_IMAGE['use_gpu']
  I think for our case, the GPU docker image can be used as the non-gpu image as well.
  """
  DOCKER_IMAGE = {
      True: GPU_DOCKER_IMAGE,
      False: GPU_DOCKER_IMAGE
  }
  LOG_BUCKET = 's3://op3-rlkit-ray'
  
  try:
      from rlkit.launchers.ray_config_personal import *
  except ImportError:
      print("No personal ray_config.py found")
  