def run(): launcher = doodad.AzureMode(azure_subscription_id=AZ_SUB_ID, azure_storage_connection_str=AZ_CONN_STR, azure_client_id=AZ_CLIENT_ID, azure_authentication_key=AZ_SECRET, azure_tenant_id=AZ_TENANT_ID, azure_storage_container=AZ_CONTAINER, log_path='doodad_test_experiment', region='eastus', instance_type='Standard_DS1_v2') az_mount = doodad.MountAzure( 'azure_script_output', mount_point='/output', ) local_mount = doodad.MountLocal(local_dir=TESTING_DIR, mount_point='/data', output=False) mounts = [local_mount, az_mount] doodad.run_command(command='cat /data/secret.txt > /output/secret.txt', mode=launcher, mounts=mounts, verbose=True)
def create_mounts( code_dirs_to_mount=config.CODE_DIRS_TO_MOUNT, non_code_dirs_to_mount=config.NON_CODE_DIRS_TO_MOUNT, remote_mount_configs=config.REMOTE_DIRS_TO_MOUNT, ): non_code_mounts = [ doodad.MountLocal(**non_code_mapping) for non_code_mapping in non_code_dirs_to_mount ] + [ doodad.MountRemote(**non_code_mapping) for non_code_mapping in remote_mount_configs ] if REPO_DIR not in config.CODE_DIRS_TO_MOUNT: config.CODE_DIRS_TO_MOUNT.append(REPO_DIR) code_mounts = [ doodad.MountLocal(local_dir=code_dir, pythonpath=True) for code_dir in code_dirs_to_mount ] mounts = code_mounts + non_code_mounts return mounts
def run(): gcp_mount = doodad.MountGCP(gcp_path='secret_output', mount_point='/output') local_mount = doodad.MountLocal(local_dir=TESTING_DIR, mount_point='/data', output=False) mounts = [local_mount, gcp_mount] launcher = doodad.GCPMode(gcp_bucket=GCP_BUCKET, gcp_log_path='test_doodad/gcp_test', gcp_project=GCP_PROJECT, instance_type='f1-micro', zone='us-west1-a', gcp_image=GCP_IMAGE, gcp_image_project=GCP_PROJECT) doodad.run_command(command='cat /data/secret.txt > /output/secret.txt', mode=launcher, mounts=mounts, verbose=True)
from doodad.wrappers.sweeper import launcher import d4rl import d4rl.flow import gym ENVS = [ #'flow-ring-random-v0', #'flow-ring-controller-v0', 'flow-merge-random-v0', 'flow-merge-controller-v0', ] mounts = [] mounts.append( doodad.MountLocal(local_dir='~/code/d4rl', mount_point='/code/d4rl', pythonpath=True)) sweeper = launcher.DoodadSweeper(mounts=mounts, docker_img='justinfu/brac_flow:0.3', docker_output_dir='/root/tmp/offlinerl', gcp_bucket_name='justin-doodad', gcp_image='ubuntu-1804-docker-gpu', gcp_project='qlearning000') for env_name in ENVS: env = gym.make(env_name) _, dataset = os.path.split(env.dataset_filepath) dirname, _ = os.path.splitext(dataset) params = {
import d4rl import d4rl.flow import gym # env_name = 'maze2d-eval-umaze-v1' env_name = "flow-merge-random-v0" env = gym.make(env_name) _, dataset = os.path.split(env.dataset_filepath) dirname, _ = os.path.splitext(dataset) mounts = [] # mounts.append(doodad.MountLocal(local_dir='~/code/batch_rl_aviral', # mount_point='/code/batch_rl_private', pythonpath=True)) mounts.append( doodad.MountLocal(local_dir="~/code/d4rl", mount_point="/code/d4rl", pythonpath=True)) # mounts.append(doodad.MountLocal(local_dir='~/.d4rl/rlkit/%s' % dirname, # mount_point='/datasets')) mounts.append( doodad.MountLocal(local_dir="/data/doodad_results", mount_point="/root/tmp/offlinerl", output=True)) gcp_launcher = doodad.GCPMode( gcp_bucket="justin-doodad", gcp_log_path="doodad/logs/bear", gcp_project="qlearning000", instance_type="n1-standard-1", zone="us-west1-a", gcp_image="ubuntu-1804-docker-gpu",
'kitchen-complete-v0', 'kitchen-partial-v0', 'kitchen-mixed-v0', ] FLOW_ENVS = [ 'flow-ring-random-v0', 'flow-ring-controller-v0', 'flow-merge-random-v0', 'flow-merge-controller-v0', ] ENVS.extend(FLOW_ENVS) mounts = [] mounts.append(doodad.MountLocal(local_dir='~/code/awr', mount_point='/code/awr', pythonpath=True)) mounts.append(doodad.MountLocal(local_dir='~/code/d4rl', mount_point='/code/d4rl', pythonpath=True)) sweeper = launcher.DoodadSweeper( mounts=mounts, docker_img='justinfu/awr:0.1', gcp_bucket_name='justin-doodad', gcp_image='ubuntu-1804-docker-gpu', gcp_project='qlearning000' ) flow_sweeper = launcher.DoodadSweeper( mounts=mounts, docker_img='justinfu/awr_flow:0.1',
import doodad from doodad.wrappers.sweeper import launcher import d4rl import d4rl.flow import gym #env_name = 'maze2d-eval-umaze-v1' env_name = 'flow-merge-random-v0' env = gym.make(env_name) _, dataset = os.path.split(env.dataset_filepath) dirname, _ = os.path.splitext(dataset) mounts = [] #mounts.append(doodad.MountLocal(local_dir='~/code/batch_rl_aviral', # mount_point='/code/batch_rl_private', pythonpath=True)) mounts.append(doodad.MountLocal(local_dir='~/code/d4rl', mount_point='/code/d4rl', pythonpath=True)) #mounts.append(doodad.MountLocal(local_dir='~/.d4rl/rlkit/%s' % dirname, # mount_point='/datasets')) mounts.append(doodad.MountLocal(local_dir='/data/doodad_results/merge-random', mount_point='/root/tmp/offlinerl', output=True)) gcp_launcher = doodad.GCPMode( gcp_bucket='justin-doodad', gcp_log_path='doodad/logs/bear', gcp_project='qlearning000', instance_type='n1-standard-1', zone='us-west1-a', gcp_image='ubuntu-1804-docker-gpu', gcp_image_project='qlearning000' ) local_launcher = doodad.LocalMode()
from doodad.wrappers.sweeper import launcher import d4rl import d4rl.flow import gym ENVS = [ 'flow-ring-random-v0', 'flow-ring-controller-v0', 'flow-merge-random-v0', 'flow-merge-controller-v0', ] mounts = [] mounts.append( doodad.MountLocal(local_dir='~/code/d4rl', mount_point='/code/d4rl', pythonpath=True)) sweeper = launcher.DoodadSweeper(mounts=mounts, docker_img='justinfu/brac_flow:0.3', docker_output_dir='/root/tmp/offlinerl', gcp_bucket_name='justin-doodad', gcp_image='ubuntu-1804-docker-gpu', gcp_project='qlearning000') for env_name in ENVS: env = gym.make(env_name) _, dataset = os.path.split(env.dataset_filepath) dirname, _ = os.path.splitext(dataset) params = {
import d4rl import d4rl.flow import gym #env_name = 'maze2d-eval-umaze-v1' env_name = 'flow-merge-random-v0' env = gym.make(env_name) _, dataset = os.path.split(env.dataset_filepath) dirname, _ = os.path.splitext(dataset) mounts = [] #mounts.append(doodad.MountLocal(local_dir='~/code/batch_rl_aviral', # mount_point='/code/batch_rl_private', pythonpath=True)) mounts.append( doodad.MountLocal(local_dir='~/code/d4rl', mount_point='/code/d4rl', pythonpath=True)) #mounts.append(doodad.MountLocal(local_dir='~/.d4rl/rlkit/%s' % dirname, # mount_point='/datasets')) mounts.append( doodad.MountLocal(local_dir='/data/doodad_results', mount_point='/root/tmp/offlinerl', output=True)) gcp_launcher = doodad.GCPMode(gcp_bucket='justin-doodad', gcp_log_path='doodad/logs/bear', gcp_project='qlearning000', instance_type='n1-standard-1', zone='us-west1-a', gcp_image='ubuntu-1804-docker-gpu', gcp_image_project='qlearning000')
from doodad.wrappers.sweeper import launcher import d4rl import d4rl.flow import gym ENVS = [ "flow-ring-random-v0", "flow-ring-controller-v0", "flow-merge-random-v0", "flow-merge-controller-v0", ] mounts = [] mounts.append( doodad.MountLocal(local_dir="~/code/d4rl", mount_point="/code/d4rl", pythonpath=True)) sweeper = launcher.DoodadSweeper( mounts=mounts, docker_img="justinfu/brac_flow:0.3", docker_output_dir="/root/tmp/offlinerl", gcp_bucket_name="justin-doodad", gcp_image="ubuntu-1804-docker-gpu", gcp_project="qlearning000", ) for env_name in ENVS: env = gym.make(env_name) _, dataset = os.path.split(env.dataset_filepath) dirname, _ = os.path.splitext(dataset)
import os import doodad from doodad.wrappers.sweeper import launcher from d4rl import infos import gym #env_name = 'maze2d-eval-umaze-v1' env_name = 'flow-ring-random-v0' _, dataset = os.path.split(infos.DATASET_URLS[env_name]) dirname, _ = os.path.splitext(dataset) mounts = [] mounts.append( doodad.MountLocal(local_dir='~/code/awr', mount_point='/code/awr', pythonpath=True, filter_dir=('data', '.git', 'awr_env'))) mounts.append( doodad.MountLocal(local_dir='~/code/d4rl', mount_point='/code/d4rl', pythonpath=True, filter_dir=('data', '.git', 'scripts'))) mounts.append( doodad.MountLocal(local_dir='~/.d4rl/rlkit/%s' % dirname, mount_point='/datasets')) mounts.append( doodad.MountLocal(local_dir='/data/doodad/awr', mount_point='/data', output=True)) gcp_launcher = doodad.GCPMode(gcp_bucket='justin-doodad',
import d4rl import d4rl.flow import gym # env_name = 'maze2d-eval-umaze-v1' env_name = "flow-merge-random-v0" env = gym.make(env_name) _, dataset = os.path.split(env.dataset_filepath) dirname, _ = os.path.splitext(dataset) mounts = [] # mounts.append(doodad.MountLocal(local_dir='~/code/batch_rl_aviral', # mount_point='/code/batch_rl_private', pythonpath=True)) mounts.append( doodad.MountLocal(local_dir="~/code/d4rl", mount_point="/code/d4rl", pythonpath=True)) # mounts.append(doodad.MountLocal(local_dir='~/.d4rl/rlkit/%s' % dirname, # mount_point='/datasets')) mounts.append( doodad.MountLocal( local_dir="/data/doodad_results/merge-random", mount_point="/root/tmp/offlinerl", output=True, )) gcp_launcher = doodad.GCPMode( gcp_bucket="justin-doodad", gcp_log_path="doodad/logs/bear", gcp_project="qlearning000", instance_type="n1-standard-1",