Add TensorBoard Support

Adds TensorBoard support for basic key-value pairs. Anything logged via
`logger.record_tabular()` is also available via TensorBoard.

environment.yml

@@ -62,3 +62,4 @@ dependencies:
         - pylru==1.0.9
         - hyperopt
         - polling
+        - tensorboard

rllab/misc/logger.py

@@ -15,6 +15,7 @@
 import json
 import pickle
 import base64
+import tensorflow as tf
 
 _prefixes = []
 _prefix_str = ''
@@ -31,13 +32,17 @@
 _tabular_fds = {}
 _tabular_header_written = set()
 
+_tensorboard_writer = None
 _snapshot_dir = None
 _snapshot_mode = 'all'
 _snapshot_gap = 1
 
 _log_tabular_only = False
 _header_printed = False
 
+_tensorboard_default_step = 0
+_tensorboard_step_key = None
+
 
 def _add_output(file_name, arr, fds, mode='a'):
     if file_name not in arr:
@@ -77,6 +82,20 @@ def remove_tabular_output(file_name):
     _remove_output(file_name, _tabular_outputs, _tabular_fds)
 
 
+def set_tensorboard_dir(dir_name):
+    global _tensorboard_writer
+    if dir_name:
+        if _tensorboard_writer:
+            _tensorboard_writer.close()
+            _tensorboard_writer = None
+    else:
+        mkdir_p(os.path.dirname(dir_name))
+        _tensorboard_writer = tf.summary.FileWriter(dir_name)
+        _tensorboard_default_step = 0
+        assert _tensorboard_writer is not None
+        print("tensorboard data will be logged into:", dir_name)
+
+
 def set_snapshot_dir(dir_name):
     global _snapshot_dir
     _snapshot_dir = dir_name
@@ -94,18 +113,26 @@ def set_snapshot_mode(mode):
     global _snapshot_mode
     _snapshot_mode = mode
 
+
 def get_snapshot_gap():
     return _snapshot_gap
 
+
 def set_snapshot_gap(gap):
     global _snapshot_gap
     _snapshot_gap = gap
 
+
 def set_log_tabular_only(log_tabular_only):
     global _log_tabular_only
     _log_tabular_only = log_tabular_only
 
 
+def set_tensorboard_step_key(key):
+    global _tensorboard_step_key
+    _tensorboard_step_key = key
+
+
 def get_log_tabular_only():
     return _log_tabular_only
 
@@ -186,6 +213,23 @@ def refresh(self):
 table_printer = TerminalTablePrinter()
 
 
+def dump_tensorboard(*args, **kwargs):
+    if len(_tabular) > 0 and _tensorboard_writer:
+        tabular_dict = dict(_tabular)
+        if _tensorboard_step_key and _tensorboard_step_key in tabular_dict:
+            step = tabular_dict[_tensorboard_step_key]
+        else:
+            global _tensorboard_default_step
+            step = _tensorboard_default_step
+            _tensorboard_default_step += 1
+
+        summary = tf.Summary()
+        for k, v in tabular_dict.items():
+            summary.value.add(tag=k, simple_value=float(v))
+        _tensorboard_writer.add_summary(summary, int(step))
+        _tensorboard_writer.flush()
+
+
 def dump_tabular(*args, **kwargs):
     wh = kwargs.pop("write_header", None)
     if len(_tabular) > 0:
@@ -195,11 +239,18 @@ def dump_tabular(*args, **kwargs):
             for line in tabulate(_tabular).split('\n'):
                 log(line, *args, **kwargs)
         tabular_dict = dict(_tabular)
+
+        # write to the tensorboard folder
+        # This assumes that the keys in each iteration won't change!
+        dump_tensorboard(args, kwargs)
+
         # Also write to the csv files
         # This assumes that the keys in each iteration won't change!
         for tabular_fd in list(_tabular_fds.values()):
-            writer = csv.DictWriter(tabular_fd, fieldnames=list(tabular_dict.keys()))
-            if wh or (wh is None and tabular_fd not in _tabular_header_written):
+            writer = csv.DictWriter(
+                tabular_fd, fieldnames=list(tabular_dict.keys()))
+            if wh or (wh is None
+                      and tabular_fd not in _tabular_header_written):
                 writer.writeheader()
                 _tabular_header_written.add(tabular_fd)
             writer.writerow(tabular_dict)
@@ -245,7 +296,8 @@ def log_parameters(log_file, args, classes):
             log_params[name] = params
         else:
             log_params[name] = getattr(cls, "__kwargs", dict())
-            log_params[name]["_name"] = cls.__module__ + "." + cls.__class__.__name__
+            log_params[name][
+                "_name"] = cls.__module__ + "." + cls.__class__.__name__
     mkdir_p(os.path.dirname(log_file))
     with open(log_file, "w") as f:
         json.dump(log_params, f, indent=2, sort_keys=True)
@@ -258,13 +310,13 @@ def stub_to_json(stub_sth):
         data = dict()
         for k, v in stub_sth.kwargs.items():
             data[k] = stub_to_json(v)
-        data["_name"] = stub_sth.proxy_class.__module__ + "." + stub_sth.proxy_class.__name__
+        data[
+            "_name"] = stub_sth.proxy_class.__module__ + "." + stub_sth.proxy_class.__name__
         return data
     elif isinstance(stub_sth, instrument.StubAttr):
         return dict(
             obj=stub_to_json(stub_sth.obj),
-            attr=stub_to_json(stub_sth.attr_name)
-        )
+            attr=stub_to_json(stub_sth.attr_name))
     elif isinstance(stub_sth, instrument.StubMethodCall):
         return dict(
             obj=stub_to_json(stub_sth.obj),
@@ -294,7 +346,10 @@ def default(self, o):
         if isinstance(o, type):
             return {'$class': o.__module__ + "." + o.__name__}
         elif isinstance(o, Enum):
-            return {'$enum': o.__module__ + "." + o.__class__.__name__ + '.' + o.name}
+            return {
+                '$enum':
+                o.__module__ + "." + o.__class__.__name__ + '.' + o.name
+            }
         return json.JSONEncoder.default(self, o)
 
 

scripts/run_experiment_lite.py

@@ -29,41 +29,95 @@ def run_experiment(argv):
 
     default_exp_name = 'experiment_%s_%s' % (timestamp, rand_id)
     parser = argparse.ArgumentParser()
-    parser.add_argument('--n_parallel', type=int, default=1,
-                        help='Number of parallel workers to perform rollouts. 0 => don\'t start any workers')
-    parser.add_argument(
-        '--exp_name', type=str, default=default_exp_name, help='Name of the experiment.')
-    parser.add_argument('--log_dir', type=str, default=None,
-                        help='Path to save the log and iteration snapshot.')
-    parser.add_argument('--snapshot_mode', type=str, default='all',
-                        help='Mode to save the snapshot. Can be either "all" '
-                             '(all iterations will be saved), "last" (only '
-                             'the last iteration will be saved), "gap" (every'
-                             '`snapshot_gap` iterations are saved), or "none" '
-                             '(do not save snapshots)')
-    parser.add_argument('--snapshot_gap', type=int, default=1,
-                        help='Gap between snapshot iterations.')
-    parser.add_argument('--tabular_log_file', type=str, default='progress.csv',
-                        help='Name of the tabular log file (in csv).')
-    parser.add_argument('--text_log_file', type=str, default='debug.log',
-                        help='Name of the text log file (in pure text).')
-    parser.add_argument('--params_log_file', type=str, default='params.json',
-                        help='Name of the parameter log file (in json).')
-    parser.add_argument('--variant_log_file', type=str, default='variant.json',
-                        help='Name of the variant log file (in json).')
-    parser.add_argument('--resume_from', type=str, default=None,
-                        help='Name of the pickle file to resume experiment from.')
-    parser.add_argument('--plot', type=ast.literal_eval, default=False,
-                        help='Whether to plot the iteration results')
-    parser.add_argument('--log_tabular_only', type=ast.literal_eval, default=False,
-                        help='Whether to only print the tabular log information (in a horizontal format)')
-    parser.add_argument('--seed', type=int,
-                        help='Random seed for numpy')
-    parser.add_argument('--args_data', type=str,
-                        help='Pickled data for stub objects')
-    parser.add_argument('--variant_data', type=str,
-                        help='Pickled data for variant configuration')
-    parser.add_argument('--use_cloudpickle', type=ast.literal_eval, default=False)
+    parser.add_argument(
+        '--n_parallel',
+        type=int,
+        default=1,
+        help=
+        'Number of parallel workers to perform rollouts. 0 => don\'t start any workers'
+    )
+    parser.add_argument(
+        '--exp_name',
+        type=str,
+        default=default_exp_name,
+        help='Name of the experiment.')
+    parser.add_argument(
+        '--log_dir',
+        type=str,
+        default=None,
+        help='Path to save the log and iteration snapshot.')
+    parser.add_argument(
+        '--snapshot_mode',
+        type=str,
+        default='all',
+        help='Mode to save the snapshot. Can be either "all" '
+        '(all iterations will be saved), "last" (only '
+        'the last iteration will be saved), "gap" (every'
+        '`snapshot_gap` iterations are saved), or "none" '
+        '(do not save snapshots)')
+    parser.add_argument(
+        '--snapshot_gap',
+        type=int,
+        default=1,
+        help='Gap between snapshot iterations.')
+    parser.add_argument(
+        '--tabular_log_file',
+        type=str,
+        default='progress.csv',
+        help='Name of the tabular log file (in csv).')
+    parser.add_argument(
+        '--text_log_file',
+        type=str,
+        default='debug.log',
+        help='Name of the text log file (in pure text).')
+    parser.add_argument(
+        '--tensorboard_log_dir',
+        type=str,
+        default='progress',
+        help='Name of the folder for tensorboard_summary.')
+    parser.add_argument(
+        '--tensorboard_step_key',
+        type=str,
+        default=None,
+        help=
+        'Name of the step key in log data which shows the step in tensorboard_summary.'
+    )
+    parser.add_argument(
+        '--params_log_file',
+        type=str,
+        default='params.json',
+        help='Name of the parameter log file (in json).')
+    parser.add_argument(
+        '--variant_log_file',
+        type=str,
+        default='variant.json',
+        help='Name of the variant log file (in json).')
+    parser.add_argument(
+        '--resume_from',
+        type=str,
+        default=None,
+        help='Name of the pickle file to resume experiment from.')
+    parser.add_argument(
+        '--plot',
+        type=ast.literal_eval,
+        default=False,
+        help='Whether to plot the iteration results')
+    parser.add_argument(
+        '--log_tabular_only',
+        type=ast.literal_eval,
+        default=False,
+        help=
+        'Whether to only print the tabular log information (in a horizontal format)'
+    )
+    parser.add_argument('--seed', type=int, help='Random seed for numpy')
+    parser.add_argument(
+        '--args_data', type=str, help='Pickled data for stub objects')
+    parser.add_argument(
+        '--variant_data',
+        type=str,
+        help='Pickled data for variant configuration')
+    parser.add_argument(
+        '--use_cloudpickle', type=ast.literal_eval, default=False)
 
     args = parser.parse_args(argv[1:])
 
@@ -87,6 +141,7 @@ def run_experiment(argv):
     tabular_log_file = osp.join(log_dir, args.tabular_log_file)
     text_log_file = osp.join(log_dir, args.text_log_file)
     params_log_file = osp.join(log_dir, args.params_log_file)
+    tensorboard_log_dir = osp.join(log_dir, args.tensorboard_log_dir)
 
     if args.variant_data is not None:
         variant_data = pickle.loads(base64.b64decode(args.variant_data))
@@ -100,12 +155,14 @@ def run_experiment(argv):
 
     logger.add_text_output(text_log_file)
     logger.add_tabular_output(tabular_log_file)
+    logger.set_tensorboard_dir(tensorboard_log_dir)
     prev_snapshot_dir = logger.get_snapshot_dir()
     prev_mode = logger.get_snapshot_mode()
     logger.set_snapshot_dir(log_dir)
     logger.set_snapshot_mode(args.snapshot_mode)
     logger.set_snapshot_gap(args.snapshot_gap)
     logger.set_log_tabular_only(args.log_tabular_only)
+    logger.set_tensorboard_step_key(args.tensorboard_step_key)
     logger.push_prefix("[%s] " % args.exp_name)
 
     if args.resume_from is not None: