Commit a9c2418d authored by nilabha's avatar nilabha
Browse files

added merge changes and documentation

parent 6e97f18d
# Saving Recorded Video
We use `gym monitor` for video recording
To use human rendering with recording and uploading videos, update the env_cnfig as follows
render: human
This saves videos to the folder `flatland` in the same location as the running script.
To add a custom location, set the config `video_dir` under env_config
render: human
# For saving videos in custom folder and to wandb.
# By default if not specified folder is flatland
video_dir: small_tree_video
By default, the folder is cleared before saving the new videos. This is done as all videos with the pattern `*0.mp4` are used for uploading to wandb and we do not want to upload videos from older runs
This behavior can be changed using the config parameter `resume: True`
Also, note that
When we process videos, we sort videos which are more recent (based on the latest modified time). Hence videos would appear in descending order in wandb under the section media
## Sample video uploaded to wandb
## Video Folder Names
Currently video naming convention is
The first part comprising of `iteration,steps,percentage_completition_mean` are the env stats from when the first result occurs just immediately after the video was first created. Look at the section [TODO](#TODO) for more details regarding this choice.
The second part `{episode_id}.{pid}.video000000` is basically as per the convention used for video names in the openai.gym monitor. episode_id is the current worker's episode number, pid is the process ID for the process which saved the video recording. Often, the last frames are recorded as `*video000001.mp4` which we ignore and don't upload to wandb to reduce the number of videos uploaded to wandb.
We also finally save the last modified time as often there can be multiple versions of same video in different stages of recording.
# Evaluation
We can enable rendering only during evalaution and not in the normal training process. This will take the new config settings and videos will be saved. All videos will be saved in the `video_dir` or the default `flatland` folder. In the same wandb process we only resume videos and we don't overwrite any content in the same folder. Caution should be taken to manually delete the existing videos in the folder or specify a new `video_dir` folder in such cases.
If we are running multiple wandb process, caution should be taken to avoid writing to the same folder. The recommended practice is to create a different `video_dir` for each run to avoid any conflicts and accidently deleting other run files.
- Cannot match exact env details on which video was created and hence defaulting to the env details from when the video was first created. To help identify we must record the video file with the env iteration or/and steps etc.
- Using the env details when the video was created may be useful when recording video during evaluation
where we are more interested in the current training state
import multiprocessing
import numbers
from pprint import pprint
import wandb
from ray import tune
from datetime import datetime
import os, fnmatch
import shutil
......@@ -32,7 +33,7 @@ class WandbLogger(tune.logger.Logger):
def _init(self):
self._config = None
wandb.init(**self.config.get("env_config", {}).get("wandb", {}))
self.metrics_queue_dict = {}
if self.config.get("env_config", {}).get("render", None):
......@@ -55,30 +56,39 @@ class WandbLogger(tune.logger.Logger):
def reset_state(self):
# Holds list of uploaded/moved files so that we dont upload them again
self._upload_files = []
self._upload_files = {}
# Holds information of env state and put them in an unique file name
# and maps it to the original video file
self._file_map = {}
self._save_folder = None
def on_result(self, result):
experiment_tag = result.get('experiment_tag', 'no_experiment_tag')
experiment_id = result.get('experiment_id', 'no_experiment_id')
if experiment_tag not in self.metrics_queue_dict:
print("=" * 50)
print("Setting up new w&b logger")
print("Experiment tag:", experiment_tag)
print("Experiment id:", experiment_id)
config = result.get("config")
if config and self._config is None:
for k in config.keys():
if k != "callbacks":
if wandb.config.get(k) is None:
wandb.config[k] = config[k]
self._config = config
queue = multiprocessing.Queue()
p = multiprocessing.Process(target=wandb_process, args=(queue, config,))
self.metrics_queue_dict[experiment_tag] = queue
print("=" * 50)
queue = self.metrics_queue_dict[experiment_tag]
tmp = result.copy()
for k in ["done", "config", "pid", "timestamp"]:
if k in tmp:
del tmp[k]
metrics = {}
for key, value in flatten_dict(tmp, delimiter="/").items():
if not isinstance(value, numbers.Number):
metrics[key] = value
if self.config.get("env_config", {}).get("render", None):
# uploading relevant videos to wandb
......@@ -87,13 +97,18 @@ class WandbLogger(tune.logger.Logger):
self.clear_folders(resume= True)
if self._save_folder:
metrics = self.update_video_metrics(result,metrics)
def update_video_metrics(self, result, metrics):
iterations = result['training_iteration']
steps = result['timesteps_total']
perc_comp_mean = result['custom_metrics'].get('percentage_complete_mean',0)*100
# We ignore *1.mp4 videos which just has the last frame
_video_file = f'*0.mp4'
_found_videos = find(_video_file,self._save_folder)
_found_videos = list(set(_found_videos) - set(self._upload_files))
#_found_videos = list(set(_found_videos) - set(self._upload_files))
# Sort by create time for uploading to wandb
for _found_video in _found_videos:
......@@ -117,19 +132,48 @@ class WandbLogger(tune.logger.Logger):
# We only move videos that have been flushed out.
# This is done by checking against a threshold size of 1000 bytes
if _check_file.st_size > 1000:
# Also check if file has changed from last time
if _check_file.st_size > 1000 and _check_file.st_ctime > self._upload_files.get(_found_video, True):
_video_file_name = self._file_map.get(_key,"Unknown")
wandb.log({_video_file_name: wandb.Video(_found_video, format="mp4")})
# wandb.log({_video_file_name: wandb.Video(_found_video, format="mp4")})
metrics[_video_file_name] = wandb.Video(_found_video, format="mp4")
self._upload_files[_found_video] = _check_file.st_size
return metrics
def close(self):
all_uploaded_videos = self._upload_files.keys()
for _found_video in all_uploaded_videos:
# Move upload videos and their meta data once done to the logdir
# Copy upload videos and their meta data once done to the logdir
src = _found_video
_video_file = _found_video.split(os.sep)[-1]
dst = os.path.join(self.logdir,_video_file)
shutil.move(src, dst)
shutil.move(src.replace("mp4","meta.json"), dst.replace("mp4","meta.json"))
shutil.copy2(src, dst)
shutil.copy2(src.replace("mp4","meta.json"), dst.replace("mp4","meta.json"))
except OSError as e:
print ("Error: %s - %s." % (e.filename, e.strerror))
def close(self):
# each logger has to run in a separate process
def wandb_process(queue, config):
run = wandb.init(reinit=True, **config.get("env_config", {}).get("wandb", {}))
if config:
for k in config.keys():
if k != "callbacks":
if wandb.config.get(k) is None:
wandb.config[k] = config[k]
if 'yaml_config' in config['env_config']:
yaml_config = config['env_config']['yaml_config']
print("Saving full experiment config:", yaml_config)
while True:
metrics = queue.get()
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