def run():
gcp_mount = doodad.MountGCP(gcp_path='secret_output',
mount_point='/output')
mounts = [gcp_mount]
launcher = doodad.GCPMode(gcp_bucket=GCP_BUCKET,
gcp_log_path='test_doodad/gcp_gpu_test',
gcp_project=GCP_PROJECT,
instance_type='n1-standard-1',
zone='us-west1-a',
gcp_image=GCP_IMAGE,
gcp_image_project=GCP_PROJECT,
use_gpu=True,
gpu_model='nvidia-tesla-t4')
doodad.run_command(command='nvidia-smi > /output/secret.txt',
mode=launcher,
mounts=mounts,
verbose=True)
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)
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",
)
local_launcher = doodad.LocalMode()
#!/bin/bash
# ALPHA=1.0
# PLR=3e-05
# VALUE_PENALTY=False
# DIVERGENCE=kl
# ENV=walker2d-medium-v0
# DATA=example
# B_CKPT=$HOME/tmp/offlinerl/learn/$ENV/train_bc/bc/0/0/agent_behavior
# python train_offline.py \
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()
"""
ENV=walker2d-medium-v0
python scripts/brac/train_offline.py \
--alsologtostderr --sub_dir=0 \
--env_name=$ENV --identifier="train_bc" \
--agent_name=bc \
--total_train_steps=300000 \