remote logging docs

docs/tutorial/summary.md

@@ -69,6 +69,24 @@ As a result, tensorboard will show not only summaries in the graph, but also you
 For example, a precise validation error often needs to be computed manually, outside the TensorFlow graph.
 With a uniform monitor backend, this number will show up in tensorboard as well.
 
+### Remote Logging
+
+It is also easy to send data to online logging services
+for experiment management and reproducibility.
+
+For example, to send logging data to [comet.ml](https://www.comet.ml/), you can use
+[CometMLMonitor](../modules/callbacks.html#tensorpack.callbacks.CometMLMonitor).
+
+To send logging data to [WandB](https://www.wandb.com/),
+it's even simpler -- you only need to do:
+```python
+import wandb; wandb.init(..., sync_tensorboard=True)
+```
+
+Refer to their documentation for more types of logging you can do by using
+their APIs directly: [comet.ml](https://www.comet.ml/docs/python-sdk/Experiment/),
+[WandB](https://docs.wandb.com/docs/init.html).
+
 ### Textual Logs
 
 ```python

tensorpack/callbacks/monitor.py

@@ -550,33 +550,34 @@ class SendMonitorData(MonitorBase):
 
 class CometMLMonitor(MonitorBase):
     """
-    Send data to https://www.comet.ml.
+    Send scalar data and the graph to https://www.comet.ml.
 
     Note:
         1. comet_ml requires you to `import comet_ml` before importing tensorflow or tensorpack.
         2. The "automatic output logging" feature of comet_ml will make the training progress bar appear to freeze.
            Therefore the feature is disabled by default.
     """
-    def __init__(self, experiment=None, api_key=None, tags=None, **kwargs):
+    def __init__(self, experiment=None, tags=None, **kwargs):
         """
         Args:
             experiment (comet_ml.Experiment): if provided, invalidate all other arguments
-            api_key (str): your comet.ml API key
             tags (list[str]): experiment tags
-            kwargs: other arguments passed to :class:`comet_ml.Experiment`.
+            kwargs: arguments used to initialize :class:`comet_ml.Experiment`,
+                such as project name, API key, etc.
+                Refer to its documentation for details.
         """
         if experiment is not None:
             self._exp = experiment
-            assert api_key is None and tags is None and len(kwargs) == 0
+            assert tags is None and len(kwargs) == 0
         else:
             from comet_ml import Experiment
             kwargs.setdefault('log_code', True)  # though it's not functioning, git patch logging requires it
             kwargs.setdefault('auto_output_logging', None)
-            self._exp = Experiment(api_key=api_key, **kwargs)
+            self._exp = Experiment(**kwargs)
             if tags is not None:
                 self._exp.add_tags(tags)
 
-        self._exp.set_code("Code logging is impossible because there are too many files ...")
+        self._exp.set_code("Code logging is impossible ...")
         self._exp.log_dependency('tensorpack', __git_version__)
 
     @property
@@ -593,6 +594,17 @@ class CometMLMonitor(MonitorBase):
     def process_scalar(self, name, val):
         self._exp.log_metric(name, val, step=self.global_step)
 
+    @HIDE_DOC
+    def process_image(self, name, val):
+        self._exp.set_step(self.global_step)
+        for idx, v in enumerate(val):
+            log_name = "{}_step{}{}".format(
+                name,
+                self.global_step,
+                "_" + str(idx) if len(val) > 1 else "")
+
+            self._exp.log_image(v, image_format="jpeg", name=log_name, image_minmax=(0, 255))
+
     def _after_train(self):
         self._exp.end()