[tune] Clean up result logging: move out of /tmp, add timestamp (#1297)

doc/source/example-a3c.rst

@@ -153,6 +153,6 @@ workers, we can train the agent in around 25 minutes.
 
 You can visualize performance by running
 :code:`tensorboard --logdir [directory]` in a separate screen, where
-:code:`[directory]` is defaulted to :code:`/tmp/ray/`. If you are running
+:code:`[directory]` is defaulted to :code:`~/ray_results/`. If you are running
 multiple experiments, be sure to vary the directory to which Tensorflow saves
 its progress (found in :code:`a3c.py`).

doc/source/example-policy-gradient.rst

@@ -28,7 +28,7 @@ TensorBoard to the log output directory as follows.
 
 .. code-block:: bash
 
-  tensorboard --logdir=/tmp/ray
+  tensorboard --logdir=~/ray_results
 
 Many of the TensorBoard metrics are also printed to the console, but you might
 find it easier to visualize and compare between runs using the TensorBoard UI.

doc/source/rllib.rst

@@ -59,7 +59,7 @@ You can train a simple DQN agent with the following command
 
     python ray/python/ray/rllib/train.py --run DQN --env CartPole-v0
 
-By default, the results will be logged to a subdirectory of ``/tmp/ray``.
+By default, the results will be logged to a subdirectory of ``~/ray_results``.
 This subdirectory will contain a file ``params.json`` which contains the
 hyperparameters, a file ``result.json`` which contains a training summary
 for each episode and a TensorBoard file that can be used to visualize
@@ -67,7 +67,7 @@ training process with TensorBoard by running
 
 ::
 
-     tensorboard --logdir=/tmp/ray
+     tensorboard --logdir=~/ray_results
 
 
 The ``train.py`` script has a number of options you can show by running

doc/source/tune.rst

@@ -50,7 +50,7 @@ This script runs a small grid search over the ``my_func`` function using ray.tun
     == Status ==
     Using FIFO scheduling algorithm.
     Resources used: 4/8 CPUs, 0/0 GPUs
-    Result logdir: /tmp/ray/my_experiment
+    Result logdir: ~/ray_results/my_experiment
      - my_func_0_alpha=0.2,beta=1:	RUNNING [pid=6778], 209 s, 20604 ts, 7.29 acc
      - my_func_1_alpha=0.4,beta=1:	RUNNING [pid=6780], 208 s, 20522 ts, 53.1 acc
      - my_func_2_alpha=0.6,beta=1:	TERMINATED [pid=6789], 21 s, 2190 ts, 101 acc
@@ -63,14 +63,14 @@ In order to report incremental progress, ``my_func`` periodically calls the ``re
 Visualizing Results
 -------------------
 
-Ray.tune logs trial results to a unique directory per experiment, e.g. ``/tmp/ray/my_experiment`` in the above example. The log records are compatible with a number of visualization tools:
+Ray.tune logs trial results to a unique directory per experiment, e.g. ``~/ray_results/my_experiment`` in the above example. The log records are compatible with a number of visualization tools:
 
 To visualize learning in tensorboard, run:
 
 ::
 
     $ pip install tensorboard
-    $ tensorboard --logdir=/tmp/ray/my_experiment
+    $ tensorboard --logdir=~/ray_results/my_experiment
 
 .. image:: ray-tune-tensorboard.png
 
@@ -79,7 +79,7 @@ To use rllab's VisKit (you may have to install some dependencies), run:
 ::
 
     $ git clone https://github.com/rll/rllab.git
-    $ python rllab/rllab/viskit/frontend.py /tmp/ray/my_experiment
+    $ python rllab/rllab/viskit/frontend.py ~/ray_results/my_experiment
 
 .. image:: ray-tune-viskit.png