def upload(training_dir, algorithm_id=None, writeup=None, api_key=None, ignore_open_monitors=False):
"""Upload the results of training (as automatically recorded by your
env's monitor) to OpenAI Gym.
Args:
training_dir (Optional[str]): A directory containing the results of a training run.
algorithm_id (Optional[str]): An arbitrary string indicating the paricular version of the algorithm (including choices of parameters) you are running.
writeup (Optional[str]): A Gist URL (of the form https://gist.github.com/<user>/<id>) containing your writeup for this evaluation.
api_key (Optional[str]): Your OpenAI API key. Can also be provided as an environment variable (OPENAI_GYM_API_KEY).
"""
if not ignore_open_monitors:
open_monitors = monitoring._open_monitors()
if len(open_monitors) > 0:
envs = [m.env.spec.id if m.env.spec else '(unknown)' for m in open_monitors]
raise error.Error("Still have an open monitor on {}. You must run 'env.monitor.close()' before uploading.".format(', '.join(envs)))
env_info, training_episode_batch, training_video = upload_training_data(training_dir, api_key=api_key)
env_id = env_info['env_id']
training_episode_batch_id = training_video_id = None
if training_episode_batch:
training_episode_batch_id = training_episode_batch.id
if training_video:
training_video_id = training_video.id
if logger.level <= logging.INFO:
if training_episode_batch_id is not None and training_video_id is not None:
logger.info('[%s] Creating evaluation object from %s with learning curve and training video', env_id, training_dir)
elif training_episode_batch_id is not None:
logger.info('[%s] Creating evaluation object from %s with learning curve', env_id, training_dir)
elif training_video_id is not None:
logger.info('[%s] Creating evaluation object from %s with training video', env_id, training_dir)
else:
raise error.Error("[%s] You didn't have any recorded training data in {}. Once you've used 'env.monitor.start(training_dir)' to start recording, you need to actually run some rollouts. Please join the community chat on https://gym.openai.com if you have any issues.".format(env_id, training_dir))
evaluation = resource.Evaluation.create(
training_episode_batch=training_episode_batch_id,
training_video=training_video_id,
env=env_info['env_id'],
algorithm={
'id': algorithm_id,
},
writeup=writeup,
gym_version=env_info['gym_version'],
api_key=api_key,
)
logger.info(
"""
****************************************************
You successfully uploaded your evaluation on %s to
OpenAI Gym! You can find it at:
%s
****************************************************
""".rstrip(), env_id, evaluation.web_url())
return evaluation
def upload(training_dir, algorithm_id=None, writeup=None, api_key=None, ignore_open_monitors=False):
"""Upload the results of training (as automatically recorded by your
env's monitor) to OpenAI Gym.
Args:
training_dir (Optional[str]): A directory containing the results of a training run.
algorithm_id (Optional[str]): An arbitrary string indicating the paricular version of the algorithm (including choices of parameters) you are running.
writeup (Optional[str]): A Gist URL (of the form https://gist.github.com/<user>/<id>) containing your writeup for this evaluation.
api_key (Optional[str]): Your OpenAI API key. Can also be provided as an environment variable (OPENAI_GYM_API_KEY).
"""
if not ignore_open_monitors:
open_monitors = monitoring._open_monitors()
if len(open_monitors) > 0:
envs = [m.env.spec.id if m.env.spec else '(unknown)' for m in open_monitors]
raise error.Error("Still have an open monitor on {}. You must run 'env.monitor.close()' before uploading.".format(', '.join(envs)))
env_info, training_episode_batch, training_video = upload_training_data(training_dir, api_key=api_key)
env_id = env_info['env_id']
training_episode_batch_id = training_video_id = None
if training_episode_batch:
training_episode_batch_id = training_episode_batch.id
if training_video:
training_video_id = training_video.id
if logger.level <= logging.INFO:
if training_episode_batch_id is not None and training_video_id is not None:
logger.info('[%s] Creating evaluation object from %s with learning curve and training video', env_id, training_dir)
elif training_episode_batch_id is not None:
logger.info('[%s] Creating evaluation object from %s with learning curve', env_id, training_dir)
elif training_video_id is not None:
logger.info('[%s] Creating evaluation object from %s with training video', env_id, training_dir)
else:
raise error.Error("[%s] You didn't have any recorded training data in {}. Once you've used 'env.monitor.start(training_dir)' to start recording, you need to actually run some rollouts. Please join the community chat on https://gym.openai.com if you have any issues.".format(env_id, training_dir))
evaluation = resource.Evaluation.create(
training_episode_batch=training_episode_batch_id,
training_video=training_video_id,
env=env_info['env_id'],
algorithm={
'id': algorithm_id,
},
writeup=writeup,
gym_version=env_info['gym_version'],
api_key=api_key,
)
logger.info(
"""
****************************************************
You successfully uploaded your evaluation on %s to
OpenAI Gym! You can find it at:
%s
****************************************************
""".rstrip(), env_id, evaluation.web_url())
return evaluation
def _upload(training_dir,
algorithm_id=None,
writeup=None,
benchmark_run_id=None,
api_key=None,
ignore_open_monitors=False,
skip_videos=False):
if not ignore_open_monitors:
open_monitors = monitoring._open_monitors()
if len(open_monitors) > 0:
envs = [
m.env.spec.id if m.env.spec else '(unknown)'
for m in open_monitors
]
raise error.Error(
"Still have an open monitor on {}. You must run 'env.close()' before uploading."
.format(', '.join(envs)))
env_info, training_episode_batch, training_video = upload_training_data(
training_dir, api_key=api_key, skip_videos=skip_videos)
env_id = env_info['env_id']
training_episode_batch_id = training_video_id = None
if training_episode_batch:
training_episode_batch_id = training_episode_batch.id
if training_video:
training_video_id = training_video.id
if logger.level <= logging.INFO:
if training_episode_batch_id is not None and training_video_id is not None:
logger.info(
'[%s] Creating evaluation object from %s with learning curve and training video',
env_id, training_dir)
elif training_episode_batch_id is not None:
logger.info(
'[%s] Creating evaluation object from %s with learning curve',
env_id, training_dir)
elif training_video_id is not None:
logger.info(
'[%s] Creating evaluation object from %s with training video',
env_id, training_dir)
else:
raise error.Error(
"[%s] You didn't have any recorded training data in %s. Once you've used 'env = gym.wrappers.Monitor(env, directory)' to start recording, you need to actually run some rollouts. Please join the community chat on https://gym.openai.com if you have any issues."
% (env_id, training_dir))
evaluation = resource.Evaluation.create(
training_episode_batch=training_episode_batch_id,
training_video=training_video_id,
env=env_info['env_id'],
algorithm={
'id': algorithm_id,
},
benchmark_run_id=benchmark_run_id,
writeup=writeup,
gym_version=env_info['gym_version'],
api_key=api_key,
)
return evaluation
def _upload(training_dir, algorithm_id=None, writeup=None, benchmark_run_id=None, api_key=None, ignore_open_monitors=False):
if not ignore_open_monitors:
open_monitors = monitoring._open_monitors()
if len(open_monitors) > 0:
envs = [m.env.spec.id if m.env.spec else '(unknown)' for m in open_monitors]
raise error.Error("Still have an open monitor on {}. You must run 'env.monitor.close()' before uploading.".format(', '.join(envs)))
env_info, training_episode_batch, training_video = upload_training_data(training_dir, api_key=api_key)
env_id = env_info['env_id']
training_episode_batch_id = training_video_id = None
if training_episode_batch:
training_episode_batch_id = training_episode_batch.id
if training_video:
training_video_id = training_video.id
if logger.level <= logging.INFO:
if training_episode_batch_id is not None and training_video_id is not None:
logger.info('[%s] Creating evaluation object from %s with learning curve and training video', env_id, training_dir)
elif training_episode_batch_id is not None:
logger.info('[%s] Creating evaluation object from %s with learning curve', env_id, training_dir)
elif training_video_id is not None:
logger.info('[%s] Creating evaluation object from %s with training video', env_id, training_dir)
else:
raise error.Error("[%s] You didn't have any recorded training data in {}. Once you've used 'env.monitor.start(training_dir)' to start recording, you need to actually run some rollouts. Please join the community chat on https://gym.openai.com if you have any issues.".format(env_id, training_dir))
evaluation = resource.Evaluation.create(
training_episode_batch=training_episode_batch_id,
training_video=training_video_id,
env=env_info['env_id'],
algorithm={
'id': algorithm_id,
},
benchmark_run_id=benchmark_run_id,
writeup=writeup,
gym_version=env_info['gym_version'],
api_key=api_key,
)
logger.info(
"""
****************************************************
You successfully uploaded your evaluation on %s to
OpenAI Gym! You can find it at:
%s
****************************************************
""".rstrip(), env_id, evaluation.web_url())
return evaluation
