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[autoscaler] Improve argument handling for submit (#7986)

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Co-Authored-By: Kristian Hartikainen <kristian.hartikainen@gmail.com>

* ok

Co-authored-by: Kristian Hartikainen <kristian.hartikainen@gmail.com>
This commit is contained in:
Richard Liaw
2020-04-13 15:53:42 -07:00
committed by GitHub
co-authored by Kristian Hartikainen
parent e68d601ec7
commit e97adba6ac
2 changed files with 26 additions and 13 deletions
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doc/source/tune/guides/plot_tune-distributed.rst
+8 -9
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@@ -50,7 +50,7 @@ If you used a cluster configuration (starting a cluster with ``ray up`` or ``ray
.. code-block:: bash
ray submit tune-default.yaml tune_script.py --args="--ray-address=localhost:6379"
ray submit tune-default.yaml tune_script.py -- --ray-address=localhost:6379
.. tip::
@@ -77,7 +77,7 @@ If you already have a list of nodes, you can follow the local private cluster se
.. code-block:: bash
ray submit tune-default.yaml tune_script.py --args="--ray-address=localhost:6379"
ray submit tune-default.yaml tune_script.py -- --ray-address=localhost:6379
Manual Local Cluster Setup
~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -133,7 +133,7 @@ Ray currently supports AWS and GCP. Follow the instructions below to launch node
.. code-block:: bash
ray submit tune-default.yaml tune_script.py --start --args="--ray-address=localhost:6379"
ray submit tune-default.yaml tune_script.py --start -- --ray-address=localhost:6379
.. image:: /images/tune-upload.png
:scale: 50%
@@ -221,9 +221,8 @@ Here is an example for running Tune on spot instances. This assumes your AWS cre
.. code-block:: bash
ray submit tune-default.yaml mnist_pytorch_trainable.py \
--args="--ray-address=localhost:6379" \
--start
ray submit tune-default.yaml mnist_pytorch_trainable.py --start -- --ray-address=localhost:6379
4. Optionally for testing on AWS or GCP, you can use the following to kill a random worker node after all the worker nodes are up
@@ -237,7 +236,7 @@ To summarize, here are the commands to run:
wget https://raw.githubusercontent.com/ray-project/ray/master/python/ray/tune/examples/mnist_pytorch_trainable.py
wget https://raw.githubusercontent.com/ray-project/ray/master/python/ray/tune/tune-default.yaml
ray submit tune-default.yaml mnist_pytorch_trainable.py --args="--ray-address=localhost:6379" --start
ray submit tune-default.yaml mnist_pytorch_trainable.py --start -- --ray-address=localhost:6379
# wait a while until after all nodes have started
ray kill-random-node tune-default.yaml --hard
@@ -257,12 +256,12 @@ Below are some commonly used commands for submitting experiments. Please see the
# Upload `tune_experiment.py` from your local machine onto the cluster. Then,
# run `python tune_experiment.py --address=localhost:6379` on the remote machine.
$ ray submit CLUSTER.YAML tune_experiment.py --args="--address=localhost:6379"
$ ray submit CLUSTER.YAML tune_experiment.py -- --address=localhost:6379
# Start a cluster and run an experiment in a detached tmux session,
# and shut down the cluster as soon as the experiment completes.
# In `tune_experiment.py`, set `tune.run(upload_dir="s3://...")` to persist results
$ ray submit CLUSTER.YAML --tmux --start --stop tune_experiment.py --args="--address=localhost:6379"
$ ray submit CLUSTER.YAML --tmux --start --stop tune_experiment.py -- --address=localhost:6379
# To start or update your cluster:
$ ray up CLUSTER.YAML [-y]