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Replace --redis-address with --address in test, docs, tune, rllib (#5602)

Browse Source 
* wip

* add tests and tune

* add ci

* test fix

* lint

* fix tests

* wip

* sugar dep
This commit is contained in:
Eric Liang
2019-09-01 16:53:02 -07:00
committed by GitHub
parent c49b98cfc4
commit a101812b9f
49 changed files with 183 additions and 177 deletions
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ci/long_running_tests/workloads/actor_deaths.py
+1 -1
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@@ -33,7 +33,7 @@ for i in range(num_nodes):
resources={str(i): 2},
object_store_memory=object_store_memory,
redis_max_memory=redis_max_memory)
ray.init(redis_address=cluster.redis_address)
ray.init(address=cluster.address)
# Run the workload.
ci/long_running_tests/workloads/apex.py
+1 -1
View File
@@ -30,7 +30,7 @@ for i in range(num_nodes):
resources={str(i): 2},
object_store_memory=object_store_memory,
redis_max_memory=redis_max_memory)
ray.init(redis_address=cluster.redis_address)
ray.init(address=cluster.address)
# Run the workload.
ci/long_running_tests/workloads/impala.py
+1 -1
View File
@@ -30,7 +30,7 @@ for i in range(num_nodes):
resources={str(i): 2},
object_store_memory=object_store_memory,
redis_max_memory=redis_max_memory)
ray.init(redis_address=cluster.redis_address)
ray.init(address=cluster.address)
# Run the workload.
ci/long_running_tests/workloads/many_actor_tasks.py
+1 -1
View File
@@ -31,7 +31,7 @@ for i in range(num_nodes):
resources={str(i): 2},
object_store_memory=object_store_memory,
redis_max_memory=redis_max_memory)
ray.init(redis_address=cluster.redis_address)
ray.init(address=cluster.address)
# Run the workload.
ci/long_running_tests/workloads/many_drivers.py
+3 -3
View File
@@ -32,7 +32,7 @@ for i in range(num_nodes):
resources={str(i): 5},
object_store_memory=object_store_memory,
redis_max_memory=redis_max_memory)
ray.init(redis_address=cluster.redis_address)
ray.init(address=cluster.address)
# Run the workload.
@@ -41,7 +41,7 @@ ray.init(redis_address=cluster.redis_address)
driver_script = """
import ray
ray.init(redis_address="{}")
ray.init(address="{}")
num_nodes = {}
@@ -65,7 +65,7 @@ for _ in range(5):
assert ray.get(actor.method.remote()) == 1
print("success")
""".format(cluster.redis_address, num_nodes)
""".format(cluster.address, num_nodes)
@ray.remote
ci/long_running_tests/workloads/many_tasks.py
+1 -1
View File
@@ -31,7 +31,7 @@ for i in range(num_nodes):
resources={str(i): 2},
object_store_memory=object_store_memory,
redis_max_memory=redis_max_memory)
ray.init(redis_address=cluster.redis_address)
ray.init(address=cluster.address)
# Run the workload.
ci/long_running_tests/workloads/node_failures.py
+1 -1
View File
@@ -31,7 +31,7 @@ for i in range(num_nodes):
resources={str(i): 2},
object_store_memory=object_store_memory,
redis_max_memory=redis_max_memory)
ray.init(redis_address=cluster.redis_address)
ray.init(address=cluster.address)
# Run the workload.
ci/long_running_tests/workloads/pbt.py
+1 -1
View File
@@ -31,7 +31,7 @@ for i in range(num_nodes):
resources={str(i): 2},
object_store_memory=object_store_memory,
redis_max_memory=redis_max_memory)
ray.init(redis_address=cluster.redis_address)
ray.init(address=cluster.address)
# Run the workload.
ci/performance_tests/test_performance.py
+5 -5
View File
@@ -26,7 +26,7 @@ parser.add_argument(
help="True if the object store should not be warmed up. This could cause "
"the benchmarks to appear slower than usual.")
parser.add_argument(
"--redis-address",
"--address",
required=False,
type=str,
help="The address of the cluster to connect to. If this is ommitted, then "
@@ -57,7 +57,7 @@ def start_local_cluster(num_nodes, object_store_memory):
resources={str(i): 500},
object_store_memory=object_store_memory,
redis_max_memory=redis_max_memory)
ray.init(redis_address=cluster.redis_address)
ray.init(address=cluster.address)
return cluster
@@ -228,12 +228,12 @@ if __name__ == "__main__":
if num_nodes < 2:
raise ValueError("The --num-nodes argument must be at least 2.")
if args.redis_address:
ray.init(redis_address=args.redis_address)
if args.address:
ray.init(address=args.address)
wait_for_and_check_cluster_configuration(num_nodes)
logger.warning(
"Running performance benchmarks on the cluster with "
"address %s.", args.redis_address)
"address %s.", args.address)
else:
logger.warning(
"Running performance benchmarks on a simulated cluster "
ci/stress_tests/test_dead_actors.py
+1 -1
View File
@@ -12,7 +12,7 @@ import ray
logger = logging.getLogger(__name__)
ray.init(redis_address="localhost:6379")
ray.init(address="localhost:6379")
@ray.remote
ci/stress_tests/test_many_tasks.py
+1 -1
View File
@@ -13,7 +13,7 @@ import ray
logging.basicConfig(level=logging.DEBUG)
logger = logging.getLogger(__name__)
ray.init(redis_address="localhost:6379")
ray.init(address="localhost:6379")
# These numbers need to correspond with the autoscaler config file.
# The number of remote nodes in the autoscaler should upper bound
ci/travis/install-dependencies.sh
+4 -4
View File
@@ -25,7 +25,7 @@ if [[ "$PYTHON" == "2.7" ]] && [[ "$platform" == "linux" ]]; then
bash miniconda.sh -b -p $HOME/miniconda
export PATH="$HOME/miniconda/bin:$PATH"
pip install -q scipy tensorflow cython==0.29.0 gym opencv-python-headless pyyaml pandas==0.24.2 requests \
feather-format lxml openpyxl xlrd py-spy setproctitle faulthandler pytest-timeout mock flaky networkx tabulate psutil
feather-format lxml openpyxl xlrd py-spy setproctitle faulthandler pytest-timeout pytest-sugar mock flaky networkx tabulate psutil
elif [[ "$PYTHON" == "3.5" ]] && [[ "$platform" == "linux" ]]; then
sudo apt-get update
sudo apt-get install -y python-dev python-numpy build-essential curl unzip tmux gdb
@@ -34,21 +34,21 @@ elif [[ "$PYTHON" == "3.5" ]] && [[ "$platform" == "linux" ]]; then
bash miniconda.sh -b -p $HOME/miniconda
export PATH="$HOME/miniconda/bin:$PATH"
pip install -q scipy tensorflow cython==0.29.0 gym opencv-python-headless pyyaml pandas==0.24.2 requests \
feather-format lxml openpyxl xlrd py-spy setproctitle pytest-timeout flaky networkx tabulate psutil aiohttp
feather-format lxml openpyxl xlrd py-spy setproctitle pytest-timeout pytest-sugar flaky networkx tabulate psutil aiohttp
elif [[ "$PYTHON" == "2.7" ]] && [[ "$platform" == "macosx" ]]; then
# Install miniconda.
wget https://repo.continuum.io/miniconda/Miniconda2-4.5.4-MacOSX-x86_64.sh -O miniconda.sh -nv
bash miniconda.sh -b -p $HOME/miniconda
export PATH="$HOME/miniconda/bin:$PATH"
pip install -q cython==0.29.0 tensorflow gym opencv-python-headless pyyaml pandas==0.24.2 requests \
feather-format lxml openpyxl xlrd py-spy setproctitle faulthandler pytest-timeout mock flaky networkx tabulate psutil
feather-format lxml openpyxl xlrd py-spy setproctitle faulthandler pytest-timeout pytest-sugar mock flaky networkx tabulate psutil
elif [[ "$PYTHON" == "3.5" ]] && [[ "$platform" == "macosx" ]]; then
# Install miniconda.
wget https://repo.continuum.io/miniconda/Miniconda3-4.5.4-MacOSX-x86_64.sh -O miniconda.sh -nv
bash miniconda.sh -b -p $HOME/miniconda
export PATH="$HOME/miniconda/bin:$PATH"
pip install -q cython==0.29.0 tensorflow gym opencv-python-headless pyyaml pandas==0.24.2 requests \
feather-format lxml openpyxl xlrd py-spy setproctitle pytest-timeout flaky networkx tabulate psutil aiohttp
feather-format lxml openpyxl xlrd py-spy setproctitle pytest-timeout pytest-sugar flaky networkx tabulate psutil aiohttp
elif [[ "$LINT" == "1" ]]; then
sudo apt-get update
sudo apt-get install -y build-essential curl unzip
doc/source/autoscaling.rst
+4 -4
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@@ -14,7 +14,7 @@ as described in `the boto docs <http://boto3.readthedocs.io/en/latest/guide/conf
Then you're ready to go. The provided `ray/python/ray/autoscaler/aws/example-full.yaml <https://github.com/ray-project/ray/tree/master/python/ray/autoscaler/aws/example-full.yaml>`__ cluster config file will create a small cluster with a m5.large head node (on-demand) configured to autoscale up to two m5.large `spot workers <https://aws.amazon.com/ec2/spot/>`__.
Try it out by running these commands from your personal computer. Once the cluster is started, you can then
SSH into the head node, ``source activate tensorflow_p36``, and then run Ray programs with ``ray.init(redis_address="localhost:6379")``.
SSH into the head node, ``source activate tensorflow_p36``, and then run Ray programs with ``ray.init(address="localhost:6379")``.
.. code-block:: bash
@@ -37,7 +37,7 @@ First, install the Google API client (``pip install google-api-python-client``),
Then you're ready to go. The provided `ray/python/ray/autoscaler/gcp/example-full.yaml <https://github.com/ray-project/ray/tree/master/python/ray/autoscaler/gcp/example-full.yaml>`__ cluster config file will create a small cluster with a n1-standard-2 head node (on-demand) configured to autoscale up to two n1-standard-2 `preemptible workers <https://cloud.google.com/preemptible-vms/>`__. Note that you'll need to fill in your project id in those templates.
Try it out by running these commands from your personal computer. Once the cluster is started, you can then
SSH into the head node and then run Ray programs with ``ray.init(redis_address="localhost:6379")``.
SSH into the head node and then run Ray programs with ``ray.init(address="localhost:6379")``.
.. code-block:: bash
@@ -59,7 +59,7 @@ This is used when you have a list of machine IP addresses to connect in a Ray cl
Be sure to specify the proper ``head_ip``, list of ``worker_ips``, and the ``ssh_user`` field.
Try it out by running these commands from your personal computer. Once the cluster is started, you can then
SSH into the head node and then run Ray programs with ``ray.init(redis_address="localhost:6379")``.
SSH into the head node and then run Ray programs with ``ray.init(address="localhost:6379")``.
.. code-block:: bash
@@ -77,7 +77,7 @@ SSH into the head node and then run Ray programs with ``ray.init(redis_address="
Running commands on new and existing clusters
---------------------------------------------
You can use ``ray exec`` to conveniently run commands on clusters. Note that scripts you run should connect to Ray via ``ray.init(redis_address="localhost:6379")``.
You can use ``ray exec`` to conveniently run commands on clusters. Note that scripts you run should connect to Ray via ``ray.init(address="localhost:6379")``.
.. code-block:: bash
doc/source/configure.rst
+2 -2
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@@ -36,7 +36,7 @@ When starting Ray from the command line, pass the ``--num-cpus`` and ``--num-cpu
$ ray start --head --num-cpus=<NUM_CPUS> --num-gpus=<NUM_GPUS>
# To start a non-head node.
$ ray start --redis-address=<redis-address> --num-cpus=<NUM_CPUS> --num-gpus=<NUM_GPUS>
$ ray start --address=<address> --num-cpus=<NUM_CPUS> --num-gpus=<NUM_GPUS>
# Specifying custom resources
ray start [--head] --num-cpus=<NUM_CPUS> --resources='{"Resource1": 4, "Resource2": 16}'
@@ -46,7 +46,7 @@ If using the command line, connect to the Ray cluster as follow:
.. code-block:: python
# Connect to ray. Notice if connected to existing cluster, you don't specify resources.
ray.init(redis_address=<redis-address>)
ray.init(address=<address>)
Logging and Debugging
doc/source/deploy-on-kubernetes.rst
+1 -1
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@@ -57,7 +57,7 @@ Start an IPython interpreter, e.g., ``ipython``
# Note that if you run this script on a non-head node, then you must replace
# "localhost" with socket.gethostbyname("ray-head").
ray.init(redis_address="localhost:6379")
ray.init(address="localhost:6379")
@ray.remote
def f(x):
doc/source/deploying-on-slurm.rst
+3 -3
View File
@@ -23,7 +23,7 @@ Clusters managed by Slurm may require that Ray is initialized as a part of the s
node1=${nodes_array[0]}
ip_prefix=$(srun --nodes=1 --ntasks=1 -w $node1 hostname --ip-address) # Making redis-address
ip_prefix=$(srun --nodes=1 --ntasks=1 -w $node1 hostname --ip-address) # Making address
suffix=':6379'
ip_head=$ip_prefix$suffix
@@ -35,7 +35,7 @@ Clusters managed by Slurm may require that Ray is initialized as a part of the s
for (( i=1; i<=$worker_num; i++ ))
do
node2=${nodes_array[$i]}
srun --nodes=1 --ntasks=1 -w $node2 ray start --block --redis-address=$ip_head & # Starting the workers
srun --nodes=1 --ntasks=1 -w $node2 ray start --block --address=$ip_head & # Starting the workers
sleep 5
done
@@ -49,7 +49,7 @@ Clusters managed by Slurm may require that Ray is initialized as a part of the s
import time
import ray
ray.init(redis_address=os.environ["ip_head"])
ray.init(address=os.environ["ip_head"])
@ray.remote
def f():
doc/source/example-resnet.rst
+2 -2
View File
@@ -40,8 +40,8 @@ Then run the training script that matches the dataset you downloaded.
--num_gpus=1
To run the training script on a cluster with multiple machines, you will need
to also pass in the flag ``--redis-address=<redis_address>``, where
``<redis-address>`` is the address of the Redis server on the head node.
to also pass in the flag ``--address=<address>``, where
``<address>`` is the address of the Redis server on the head node.
The script will print out the IP address that the log files are stored on. In
the single-node case, you can ignore this and run tensorboard on the current
doc/source/example-rl-pong.rst
+1 -1
View File
@@ -20,7 +20,7 @@ Then you can run the example as follows.
python ray/doc/examples/rl_pong/driver.py --batch-size=10
To run the example on a cluster, simply pass in the flag
``--redis-address=<redis-address>``.
``--address=<address>``.
At the moment, on a large machine with 64 physical cores, computing an update
with a batch of size 1 takes about 1 second, a batch of size 10 takes about 2.5
doc/source/internals-overview.rst
+1 -1
View File
@@ -25,7 +25,7 @@ Connecting to an existing Ray cluster
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
To connect to an existing Ray cluster, simply pass the argument address of the
Redis server as the ``redis_address=`` keyword argument into ``ray.init``. In
Redis server as the ``address=`` keyword argument into ``ray.init``. In
this case, no new processes will be started when ``ray.init`` is called, and
similarly the processes will continue running when the script exits. In this
case, all processes except workers that correspond to actors are shared between
doc/source/tune-distributed.rst
+16 -16
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@@ -8,7 +8,7 @@ Tune is commonly used for large-scale distributed hyperparameter optimization. T
**Quick Summary**: To run a distributed experiment with Tune, you need to:
1. Make sure your script has ``ray.init(redis_address=...)`` to connect to the existing Ray cluster.
1. Make sure your script has ``ray.init(address=...)`` to connect to the existing Ray cluster.
2. If a ray cluster does not exist, start a Ray cluster (instructions for `local machines <tune-distributed.html#local-cluster-setup>`_, `cloud <tune-distributed.html#launching-a-cloud-cluster>`_).
3. Run the script on the head node (or use ``ray submit``).
@@ -19,7 +19,7 @@ Running a distributed (multi-node) experiment requires Ray to be started already
Across your machines, Tune will automatically detect the number of GPUs and CPUs without you needing to manage ``CUDA_VISIBLE_DEVICES``.
To execute a distributed experiment, call ``ray.init(redis_address=XXX)`` before ``tune.run``, where ``XXX`` is the Ray redis address, which defaults to ``localhost:6379``. The Tune python script should be executed only on the head node of the Ray cluster.
To execute a distributed experiment, call ``ray.init(address=XXX)`` before ``tune.run``, where ``XXX`` is the Ray redis address, which defaults to ``localhost:6379``. The Tune python script should be executed only on the head node of the Ray cluster.
One common approach to modifying an existing Tune experiment to go distributed is to set an ``argparse`` variable so that toggling between distributed and single-node is seamless.
@@ -29,22 +29,22 @@ One common approach to modifying an existing Tune experiment to go distributed i
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--ray-redis-address")
parser.add_argument("--ray-address")
args = parser.parse_args()
ray.init(redis_address=args.ray_redis_address)
ray.init(address=args.ray_address)
tune.run(...)
.. code-block:: bash
# On the head node, connect to an existing ray cluster
$ python tune_script.py --ray-redis-address=localhost:XXXX
$ python tune_script.py --ray-address=localhost:XXXX
If you used a cluster configuration (starting a cluster with ``ray up`` or ``ray submit --start``), use:
.. code-block:: bash
ray submit tune-default.yaml tune_script.py --args="--ray-redis-address=localhost:6379"
ray submit tune-default.yaml tune_script.py --args="--ray-address=localhost:6379"
.. tip::
@@ -69,7 +69,7 @@ If you have already have a list of nodes, you can follow the local private clust
.. code-block:: bash
ray submit tune-default.yaml tune_script.py --args="--ray-redis-address=localhost:6379"
ray submit tune-default.yaml tune_script.py --args="--ray-address=localhost:6379"
Manual Local Cluster Setup
~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -85,18 +85,18 @@ If you run into issues using the local cluster setup (or want to add nodes manua
The command will print out the address of the Redis server that was started (and some other address information).
**Then on all of the other nodes**, run the following. Make sure to replace ``<redis-address>`` with the value printed by the command on the head node (it should look something like ``123.45.67.89:6379``).
**Then on all of the other nodes**, run the following. Make sure to replace ``<address>`` with the value printed by the command on the head node (it should look something like ``123.45.67.89:6379``).
.. code-block:: bash
$ ray start --redis-address=<redis-address>
$ ray start --address=<address>
Then, you can run your Tune Python script on the head node like:
.. code-block:: bash
# On the head node, execute using existing ray cluster
$ python tune_script.py --ray-redis-address=<redis-address>
$ python tune_script.py --ray-address=<address>
Launching a cloud cluster
-------------------------
@@ -121,7 +121,7 @@ Ray currently supports AWS and GCP. Below, we will launch nodes on AWS that will
.. code-block:: bash
ray submit tune-default.yaml tune_script.py --start --args="--ray-redis-address=localhost:6379"
ray submit tune-default.yaml tune_script.py --start --args="--ray-address=localhost:6379"
.. image:: images/tune-upload.png
:scale: 50%
@@ -208,7 +208,7 @@ 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-redis-address=localhost:6379" \
--args="--ray-address=localhost:6379" \
--start
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
@@ -223,7 +223,7 @@ 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-redis-address=localhost:6379" --start
ray submit tune-default.yaml mnist_pytorch_trainable.py --args="--ray-address=localhost:6379" --start
# wait a while until after all nodes have started
ray kill-random-node tune-default.yaml --hard
@@ -240,13 +240,13 @@ Below are some commonly used commands for submitting experiments. Please see the
.. code-block:: bash
# Upload `tune_experiment.py` from your local machine onto the cluster. Then,
# run `python tune_experiment.py --redis-address=localhost:6379` on the remote machine.
$ ray submit CLUSTER.YAML tune_experiment.py --args="--redis-address=localhost:6379"
# run `python tune_experiment.py --address=localhost:6379` on the remote machine.
$ ray submit CLUSTER.YAML tune_experiment.py --args="--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="--redis-address=localhost:6379"
$ ray submit CLUSTER.YAML --tmux --start --stop tune_experiment.py --args="--address=localhost:6379"
# To start or update your cluster:
$ ray up CLUSTER.YAML [-y]
doc/source/tune.rst
+4 -4
View File
@@ -57,9 +57,9 @@ Distributed Quick Start
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--ray-redis-address")
parser.add_argument("--ray-address")
args = parser.parse_args()
ray.init(redis_address=args.ray_redis_address)
ray.init(address=args.ray_address)
Alternatively, download a full example script here: :download:`mnist_pytorch.py <../../python/ray/tune/examples/mnist_pytorch.py>`
@@ -74,7 +74,7 @@ Alternatively, download it here: :download:`tune-local-default.yaml <../../pytho
.. code-block:: bash
ray submit tune-local-default.yaml mnist_pytorch.py --args="--ray-redis-address=localhost:6379" --start
ray submit tune-local-default.yaml mnist_pytorch.py --args="--ray-address=localhost:6379" --start
This will start Ray on all of your machines and run a distributed hyperparameter search across them.
@@ -84,7 +84,7 @@ To summarize, here are the full set of commands:
wget https://raw.githubusercontent.com/ray-project/ray/master/python/ray/tune/examples/mnist_pytorch.py
wget https://raw.githubusercontent.com/ray-project/ray/master/python/ray/tune/tune-local-default.yaml
ray submit tune-local-default.yaml mnist_pytorch.py --args="--ray-redis-address=localhost:6379" --start
ray submit tune-local-default.yaml mnist_pytorch.py --args="--ray-address=localhost:6379" --start
Take a look at the `Distributed Experiments <tune-distributed.html>`_ documentation for more details, including:
doc/source/using-ray-on-a-cluster.rst
+3 -3
View File
@@ -33,12 +33,12 @@ The command will print out the address of the Redis server that was started
(and some other address information).
**Then on all of the other nodes**, run the following. Make sure to replace
``<redis-address>`` with the value printed by the command on the head node (it
``<address>`` with the value printed by the command on the head node (it
should look something like ``123.45.67.89:6379``).
.. code-block:: bash
ray start --redis-address=<redis-address>
ray start --address=<address>
If you wish to specify that a machine has 10 CPUs and 1 GPU, you can do this
with the flags ``--num-cpus=10`` and ``--num-gpus=1``. See the `Configuration <configure.html>`__ page for more information.
@@ -56,7 +56,7 @@ the following.
.. code-block:: python
import ray
ray.init(redis_address="<redis-address>")
ray.init(address="<address>")
Now you can define remote functions and execute tasks. For example, to verify
that the correct number of nodes have joined the cluster, you can run the
doc/source/walkthrough.rst
+1 -1
View File
@@ -15,7 +15,7 @@ large cluster. To run this walkthrough, install Ray with ``pip install -U ray``.
import ray
# Start Ray. If you're connecting to an existing cluster, you would use
# ray.init(redis_address=<cluster-redis-address>) instead.
# ray.init(address=<cluster-address>) instead.
ray.init()
See the `Configuration <configure.html>`__ documentation for the various ways to
python/ray/node.py
+5
View File
@@ -201,6 +201,11 @@ class Node(object):
"""Get the cluster Redis address."""
return self._node_ip_address
@property
def address(self):
"""Get the cluster address."""
return self._redis_address
@property
def redis_address(self):
"""Get the cluster Redis address."""
python/ray/tests/cluster_utils.py
+6 -2
View File
@@ -45,13 +45,17 @@ class Cluster(object):
if connect:
self.connect()
@property
def address(self):
return self.redis_address
def connect(self):
"""Connect the driver to the cluster."""
assert self.redis_address is not None
assert not self.connected
output_info = ray.init(
ignore_reinit_error=True,
redis_address=self.redis_address,
address=self.redis_address,
redis_password=self.redis_password)
logger.info(output_info)
self.connected = True
@@ -167,7 +171,7 @@ class Cluster(object):
Exception: An exception is raised if we time out while waiting for
nodes to join.
"""
ip_address, port = self.redis_address.split(":")
ip_address, port = self.address.split(":")
redis_client = redis.StrictRedis(
host=ip_address, port=int(port), password=self.redis_password)
python/ray/tests/conftest.py
+6 -6
View File
@@ -105,7 +105,7 @@ def _ray_start_cluster(**kwargs):
for _ in range(num_nodes):
remote_nodes.append(cluster.add_node(**init_kwargs))
if do_init:
ray.init(redis_address=cluster.redis_address)
ray.init(address=cluster.address)
yield cluster
# The code after the yield will run as teardown code.
ray.shutdown()
@@ -158,12 +158,12 @@ def call_ray_start(request):
subprocess.check_output(command_args, stderr=subprocess.STDOUT))
# Get the redis address from the output.
redis_substring_prefix = "redis_address=\""
redis_address_location = (
address_location = (
out.find(redis_substring_prefix) + len(redis_substring_prefix))
redis_address = out[redis_address_location:]
redis_address = redis_address.split("\"")[0]
address = out[address_location:]
address = address.split("\"")[0]
yield redis_address
yield address
# Disconnect from the Ray cluster.
ray.shutdown()
@@ -182,7 +182,7 @@ def two_node_cluster():
for _ in range(2):
remote_node = cluster.add_node(
num_cpus=1, _internal_config=internal_config)
ray.init(redis_address=cluster.redis_address)
ray.init(address=cluster.address)
yield cluster, remote_node
# The code after the yield will run as teardown code.
python/ray/tests/test_actor.py
+8 -8
View File
@@ -869,7 +869,7 @@ def test_actor_load_balancing(ray_start_cluster):
num_nodes = 3
for i in range(num_nodes):
cluster.add_node(num_cpus=1)
ray.init(redis_address=cluster.redis_address)
ray.init(address=cluster.address)
@ray.remote
class Actor1(object):
@@ -916,7 +916,7 @@ def test_actor_lifetime_load_balancing(ray_start_cluster):
num_nodes = 3
for i in range(num_nodes):
cluster.add_node(num_cpus=1)
ray.init(redis_address=cluster.redis_address)
ray.init(address=cluster.address)
@ray.remote(num_cpus=1)
class Actor(object):
@@ -940,7 +940,7 @@ def test_actor_gpus(ray_start_cluster):
for i in range(num_nodes):
cluster.add_node(
num_cpus=10 * num_gpus_per_raylet, num_gpus=num_gpus_per_raylet)
ray.init(redis_address=cluster.redis_address)
ray.init(address=cluster.address)
@ray.remote(num_gpus=1)
class Actor1(object):
@@ -979,7 +979,7 @@ def test_actor_multiple_gpus(ray_start_cluster):
for i in range(num_nodes):
cluster.add_node(
num_cpus=10 * num_gpus_per_raylet, num_gpus=num_gpus_per_raylet)
ray.init(redis_address=cluster.redis_address)
ray.init(address=cluster.address)
@ray.remote(num_gpus=2)
class Actor1(object):
@@ -1049,7 +1049,7 @@ def test_actor_different_numbers_of_gpus(ray_start_cluster):
cluster.add_node(num_cpus=10, num_gpus=0)
cluster.add_node(num_cpus=10, num_gpus=5)
cluster.add_node(num_cpus=10, num_gpus=10)
ray.init(redis_address=cluster.redis_address)
ray.init(address=cluster.address)
@ray.remote(num_gpus=1)
class Actor1(object):
@@ -1093,7 +1093,7 @@ def test_actor_multiple_gpus_from_multiple_tasks(ray_start_cluster):
_internal_config=json.dumps({
"num_heartbeats_timeout": 1000
}))
ray.init(redis_address=cluster.redis_address)
ray.init(address=cluster.address)
@ray.remote
def create_actors(i, n):
@@ -1168,7 +1168,7 @@ def test_actors_and_tasks_with_gpus(ray_start_cluster):
for i in range(num_nodes):
cluster.add_node(
num_cpus=num_gpus_per_raylet, num_gpus=num_gpus_per_raylet)
ray.init(redis_address=cluster.redis_address)
ray.init(address=cluster.address)
def check_intervals_non_overlapping(list_of_intervals):
for i in range(len(list_of_intervals)):
@@ -2034,7 +2034,7 @@ def test_custom_label_placement(ray_start_cluster):
cluster = ray_start_cluster
cluster.add_node(num_cpus=2, resources={"CustomResource1": 2})
cluster.add_node(num_cpus=2, resources={"CustomResource2": 2})
ray.init(redis_address=cluster.redis_address)
ray.init(address=cluster.address)
@ray.remote(resources={"CustomResource1": 1})
class ResourceActor1(object):
python/ray/tests/test_basic.py
+9 -9
View File
@@ -1235,7 +1235,7 @@ def test_wait_cluster(ray_start_cluster):
cluster = ray_start_cluster
cluster.add_node(num_cpus=1, resources={"RemoteResource": 1})
cluster.add_node(num_cpus=1, resources={"RemoteResource": 1})
ray.init(redis_address=cluster.redis_address)
ray.init(address=cluster.address)
@ray.remote(resources={"RemoteResource": 1})
def f():
@@ -1263,7 +1263,7 @@ def test_object_transfer_dump(ray_start_cluster):
num_nodes = 3
for i in range(num_nodes):
cluster.add_node(resources={str(i): 1}, object_store_memory=10**9)
ray.init(redis_address=cluster.redis_address)
ray.init(address=cluster.address)
@ray.remote
def f(x):
@@ -1534,7 +1534,7 @@ def test_free_objects_multi_node(ray_start_cluster):
num_cpus=1,
resources={"Custom{}".format(i): 1},
_internal_config=config)
ray.init(redis_address=cluster.redis_address)
ray.init(address=cluster.address)
class RawActor(object):
def get(self):
@@ -1996,7 +1996,7 @@ def test_zero_cpus_actor(ray_start_cluster):
cluster = ray_start_cluster
cluster.add_node(num_cpus=0)
cluster.add_node(num_cpus=2)
ray.init(redis_address=cluster.redis_address)
ray.init(address=cluster.address)
local_plasma = ray.worker.global_worker.plasma_client.store_socket_name
@@ -2069,7 +2069,7 @@ def test_multiple_raylets(ray_start_cluster):
cluster.add_node(num_cpus=11, num_gpus=0)
cluster.add_node(num_cpus=5, num_gpus=5)
cluster.add_node(num_cpus=10, num_gpus=1)
ray.init(redis_address=cluster.redis_address)
ray.init(address=cluster.address)
cluster.wait_for_nodes()
# Define a bunch of remote functions that all return the socket name of
@@ -2191,7 +2191,7 @@ def test_custom_resources(ray_start_cluster):
cluster = ray_start_cluster
cluster.add_node(num_cpus=3, resources={"CustomResource": 0})
cluster.add_node(num_cpus=3, resources={"CustomResource": 1})
ray.init(redis_address=cluster.redis_address)
ray.init(address=cluster.address)
@ray.remote
def f():
@@ -2235,7 +2235,7 @@ def test_two_custom_resources(ray_start_cluster):
"CustomResource1": 3,
"CustomResource2": 4
})
ray.init(redis_address=cluster.redis_address)
ray.init(address=cluster.address)
@ray.remote(resources={"CustomResource1": 1})
def f():
@@ -2468,7 +2468,7 @@ def test_load_balancing(ray_start_cluster):
num_cpus = 7
for _ in range(num_nodes):
cluster.add_node(num_cpus=num_cpus)
ray.init(redis_address=cluster.redis_address)
ray.init(address=cluster.address)
@ray.remote
def f():
@@ -2486,7 +2486,7 @@ def test_load_balancing_with_dependencies(ray_start_cluster):
num_nodes = 3
for _ in range(num_nodes):
cluster.add_node(num_cpus=1)
ray.init(redis_address=cluster.redis_address)
ray.init(address=cluster.address)
@ray.remote
def f(x):
python/ray/tests/test_component_failures.py
+1 -1
View File
@@ -198,7 +198,7 @@ def ray_start_workers_separate_multinode(request):
cluster = Cluster()
for _ in range(num_nodes):
cluster.add_node(num_cpus=num_initial_workers)
ray.init(redis_address=cluster.redis_address)
ray.init(address=cluster.address)
yield num_nodes, num_initial_workers
# The code after the yield will run as teardown code.
python/ray/tests/test_credis.py
+2 -2
View File
@@ -9,8 +9,8 @@ import ray
def parse_client(addr_port_str):
redis_address, redis_port = addr_port_str.split(":")
return redis.StrictRedis(host=redis_address, port=redis_port)
address, redis_port = addr_port_str.split(":")
return redis.StrictRedis(host=address, port=redis_port)
@unittest.skipIf(not os.environ.get("RAY_USE_NEW_GCS", False),
python/ray/tests/test_dynres.py
+10 -10
View File
@@ -86,7 +86,7 @@ def test_dynamic_res_updation_clientid(ray_start_cluster):
for i in range(num_nodes):
cluster.add_node()
ray.init(redis_address=cluster.redis_address)
ray.init(address=cluster.address)
target_node_id = ray.nodes()[1]["NodeID"]
@@ -120,7 +120,7 @@ def test_dynamic_res_creation_clientid(ray_start_cluster):
for i in range(num_nodes):
cluster.add_node()
ray.init(redis_address=cluster.redis_address)
ray.init(address=cluster.address)
target_node_id = ray.nodes()[1]["NodeID"]
@@ -150,7 +150,7 @@ def test_dynamic_res_creation_clientid_multiple(ray_start_cluster):
for i in range(num_nodes):
cluster.add_node()
ray.init(redis_address=cluster.redis_address)
ray.init(address=cluster.address)
target_node_ids = [node["NodeID"] for node in ray.nodes()]
@@ -191,7 +191,7 @@ def test_dynamic_res_deletion_clientid(ray_start_cluster):
# target node
cluster.add_node(resources={res_name: res_capacity})
ray.init(redis_address=cluster.redis_address)
ray.init(address=cluster.address)
target_node_id = ray.nodes()[1]["NodeID"]
@@ -223,7 +223,7 @@ def test_dynamic_res_creation_scheduler_consistency(ray_start_cluster):
for i in range(num_nodes):
cluster.add_node()
ray.init(redis_address=cluster.redis_address)
ray.init(address=cluster.address)
node_ids = [node["NodeID"] for node in ray.nodes()]
@@ -260,7 +260,7 @@ def test_dynamic_res_deletion_scheduler_consistency(ray_start_cluster):
for i in range(num_nodes):
cluster.add_node()
ray.init(redis_address=cluster.redis_address)
ray.init(address=cluster.address)
node_ids = [node["NodeID"] for node in ray.nodes()]
@@ -313,7 +313,7 @@ def test_dynamic_res_concurrent_res_increment(ray_start_cluster):
for i in range(num_nodes):
cluster.add_node()
ray.init(redis_address=cluster.redis_address)
ray.init(address=cluster.address)
node_ids = [node["NodeID"] for node in ray.nodes()]
target_node_id = node_ids[1]
@@ -392,7 +392,7 @@ def test_dynamic_res_concurrent_res_decrement(ray_start_cluster):
for i in range(num_nodes):
cluster.add_node()
ray.init(redis_address=cluster.redis_address)
ray.init(address=cluster.address)
node_ids = [node["NodeID"] for node in ray.nodes()]
target_node_id = node_ids[1]
@@ -469,7 +469,7 @@ def test_dynamic_res_concurrent_res_delete(ray_start_cluster):
for i in range(num_nodes):
cluster.add_node()
ray.init(redis_address=cluster.redis_address)
ray.init(address=cluster.address)
node_ids = [node["NodeID"] for node in ray.nodes()]
target_node_id = node_ids[1]
@@ -538,7 +538,7 @@ def test_dynamic_res_creation_stress(ray_start_cluster):
for i in range(num_nodes):
cluster.add_node()
ray.init(redis_address=cluster.redis_address)
ray.init(address=cluster.address)
node_ids = [node["NodeID"] for node in ray.nodes()]
target_node_id = node_ids[1]
python/ray/tests/test_failure.py
+6 -8
View File
@@ -625,20 +625,18 @@ def test_warning_for_too_many_nested_tasks(shutdown_only):
def test_redis_module_failure(ray_start_regular):
address_info = ray_start_regular
redis_address = address_info["redis_address"]
redis_address = redis_address.split(":")
assert len(redis_address) == 2
address = address_info["redis_address"]
address = address.split(":")
assert len(address) == 2
def run_failure_test(expecting_message, *command):
with pytest.raises(
Exception, match=".*{}.*".format(expecting_message)):
client = redis.StrictRedis(
host=redis_address[0], port=int(redis_address[1]))
client = redis.StrictRedis(host=address[0], port=int(address[1]))
client.execute_command(*command)
def run_one_command(*command):
client = redis.StrictRedis(
host=redis_address[0], port=int(redis_address[1]))
client = redis.StrictRedis(host=address[0], port=int(address[1]))
client.execute_command(*command)
run_failure_test("wrong number of arguments", "RAY.TABLE_ADD", 13)
@@ -722,7 +720,7 @@ def test_connect_with_disconnected_node(shutdown_only):
})
cluster = Cluster()
cluster.add_node(num_cpus=0, _internal_config=config)
ray.init(redis_address=cluster.redis_address)
ray.init(address=cluster.address)
info = relevant_errors(ray_constants.REMOVED_NODE_ERROR)
assert len(info) == 0
# This node is killed by SIGKILL, ray_monitor will mark it to dead.
python/ray/tests/test_monitors.py
+3 -3
View File
@@ -25,7 +25,7 @@ def _test_cleanup_on_driver_exit(num_redis_shards):
lines = [m.strip() for m in output.split("\n")]
init_cmd = [m for m in lines if m.startswith("ray.init")]
assert 1 == len(init_cmd)
redis_address = init_cmd[0].split("redis_address=\"")[-1][:-2]
address = init_cmd[0].split("address=\"")[-1][:-2]
max_attempts_before_failing = 100
# Wait for monitor.py to start working.
time.sleep(2)
@@ -38,7 +38,7 @@ def _test_cleanup_on_driver_exit(num_redis_shards):
def Driver(success):
success.value = True
# Start driver.
ray.init(redis_address=redis_address)
ray.init(address=address)
summary_start = StateSummary()
if (0, 1) != summary_start:
success.value = False
@@ -81,7 +81,7 @@ def _test_cleanup_on_driver_exit(num_redis_shards):
# Just make sure Driver() is run and succeeded.
assert success.value
# Check that objects, tasks, and functions are cleaned up.
ray.init(redis_address=redis_address)
ray.init(address=address)
attempts = 0
while (0, 1) != StateSummary():
time.sleep(0.1)
python/ray/tests/test_multi_node.py
+30 -30
View File
@@ -14,9 +14,9 @@ from ray.tests.utils import (run_string_as_driver,
def test_error_isolation(call_ray_start):
redis_address = call_ray_start
address = call_ray_start
# Connect a driver to the Ray cluster.
ray.init(redis_address=redis_address)
ray.init(address=address)
# There shouldn't be any errors yet.
assert len(ray.errors()) == 0
@@ -48,7 +48,7 @@ def test_error_isolation(call_ray_start):
import ray
import time
ray.init(redis_address="{}")
ray.init(address="{}")
time.sleep(1)
assert len(ray.errors()) == 0
@@ -70,7 +70,7 @@ assert len(ray.errors()) == 1
assert "{}" in ray.errors()[0]["message"]
print("success")
""".format(redis_address, error_string2, error_string2)
""".format(address, error_string2, error_string2)
out = run_string_as_driver(driver_script)
# Make sure the other driver succeeded.
@@ -85,16 +85,16 @@ print("success")
def test_remote_function_isolation(call_ray_start):
# This test will run multiple remote functions with the same names in
# two different drivers. Connect a driver to the Ray cluster.
redis_address = call_ray_start
address = call_ray_start
ray.init(redis_address=redis_address)
ray.init(address=address)
# Start another driver and make sure that it can define and call its
# own commands with the same names.
driver_script = """
import ray
import time
ray.init(redis_address="{}")
ray.init(address="{}")
@ray.remote
def f():
return 3
@@ -105,7 +105,7 @@ for _ in range(10000):
result = ray.get([f.remote(), g.remote(0, 0)])
assert result == [3, 4]
print("success")
""".format(redis_address)
""".format(address)
out = run_string_as_driver(driver_script)
@@ -128,32 +128,32 @@ print("success")
def test_driver_exiting_quickly(call_ray_start):
# This test will create some drivers that submit some tasks and then
# exit without waiting for the tasks to complete.
redis_address = call_ray_start
address = call_ray_start
ray.init(redis_address=redis_address)
ray.init(address=address)
# Define a driver that creates an actor and exits.
driver_script1 = """
import ray
ray.init(redis_address="{}")
ray.init(address="{}")
@ray.remote
class Foo(object):
def __init__(self):
pass
Foo.remote()
print("success")
""".format(redis_address)
""".format(address)
# Define a driver that creates some tasks and exits.
driver_script2 = """
import ray
ray.init(redis_address="{}")
ray.init(address="{}")
@ray.remote
def f():
return 1
f.remote()
print("success")
""".format(redis_address)
""".format(address)
# Create some drivers and let them exit and make sure everything is
# still alive.
@@ -205,14 +205,14 @@ ray.get([a.log.remote(), f.remote()])
"call_ray_start", ["ray start --head --num-cpus=1 --num-gpus=1"],
indirect=True)
def test_drivers_release_resources(call_ray_start):
redis_address = call_ray_start
address = call_ray_start
# Define a driver that creates an actor and exits.
driver_script1 = """
import time
import ray
ray.init(redis_address="{}")
ray.init(address="{}")
@ray.remote
def f(duration):
@@ -237,7 +237,7 @@ foos = [Foo.remote() for _ in range(100)]
[f.remote(10 ** 6) for _ in range(100)]
print("success")
""".format(redis_address)
""".format(address)
driver_script2 = (driver_script1 +
"import sys\nsys.stdout.flush()\ntime.sleep(10 ** 6)\n")
@@ -392,7 +392,7 @@ def test_calling_start_ray_head():
],
indirect=True)
def test_using_hostnames(call_ray_start):
ray.init(node_ip_address="localhost", redis_address="localhost:6379")
ray.init(node_ip_address="localhost", address="localhost:6379")
@ray.remote
def f():
@@ -407,7 +407,7 @@ def test_connecting_in_local_case(ray_start_regular):
# Define a driver that just connects to Redis.
driver_script = """
import ray
ray.init(redis_address="{}")
ray.init(address="{}")
print("success")
""".format(address_info["redis_address"])
@@ -436,7 +436,7 @@ def train_func(config, reporter): # add a reporter arg
reporter(timesteps_total=i, mean_accuracy=i+97) # report metrics
os.environ["TUNE_RESUME_PROMPT_OFF"] = "True"
ray.init(redis_address="{}")
ray.init(address="{}")
ray.tune.register_trainable("train_func", train_func)
tune.run_experiments({{
@@ -463,16 +463,16 @@ print("success")
def test_driver_exiting_when_worker_blocked(call_ray_start):
# This test will create some drivers that submit some tasks and then
# exit without waiting for the tasks to complete.
redis_address = call_ray_start
address = call_ray_start
ray.init(redis_address=redis_address)
ray.init(address=address)
# Define a driver that creates two tasks, one that runs forever and the
# other blocked on the first in a `ray.get`.
driver_script = """
import time
import ray
ray.init(redis_address="{}")
ray.init(address="{}")
@ray.remote
def f():
time.sleep(10**6)
@@ -482,7 +482,7 @@ def g():
g.remote()
time.sleep(1)
print("success")
""".format(redis_address)
""".format(address)
# Create some drivers and let them exit and make sure everything is
# still alive.
@@ -496,7 +496,7 @@ print("success")
driver_script = """
import time
import ray
ray.init(redis_address="{}")
ray.init(address="{}")
@ray.remote
def f():
time.sleep(10**6)
@@ -506,7 +506,7 @@ def g():
g.remote()
time.sleep(1)
print("success")
""".format(redis_address)
""".format(address)
# Create some drivers and let them exit and make sure everything is
# still alive.
@@ -520,7 +520,7 @@ print("success")
driver_script_template = """
import time
import ray
ray.init(redis_address="{}")
ray.init(address="{}")
@ray.remote
def g(x):
return
@@ -534,7 +534,7 @@ print("success")
for _ in range(3):
nonexistent_id_bytes = _random_string()
nonexistent_id_hex = ray.utils.binary_to_hex(nonexistent_id_bytes)
driver_script = driver_script_template.format(redis_address,
driver_script = driver_script_template.format(address,
nonexistent_id_hex)
out = run_string_as_driver(driver_script)
# Simulate the nonexistent dependency becoming available.
@@ -547,7 +547,7 @@ print("success")
driver_script_template = """
import time
import ray
ray.init(redis_address="{}")
ray.init(address="{}")
@ray.remote
def g():
ray.wait(ray.ObjectID(ray.utils.hex_to_binary("{}")))
@@ -561,7 +561,7 @@ print("success")
for _ in range(3):
nonexistent_id_bytes = _random_string()
nonexistent_id_hex = ray.utils.binary_to_hex(nonexistent_id_bytes)
driver_script = driver_script_template.format(redis_address,
driver_script = driver_script_template.format(address,
nonexistent_id_hex)
out = run_string_as_driver(driver_script)
# Simulate the nonexistent dependency becoming available.
python/ray/tests/test_multi_node_2.py
+4 -4
View File
@@ -59,8 +59,8 @@ def test_internal_config(ray_start_cluster_head):
assert ray.cluster_resources()["CPU"] == 1
def setup_monitor(redis_address):
monitor = Monitor(redis_address, None)
def setup_monitor(address):
monitor = Monitor(address, None)
monitor.subscribe(ray.gcs_utils.XRAY_HEARTBEAT_BATCH_CHANNEL)
monitor.subscribe(ray.gcs_utils.XRAY_JOB_CHANNEL) # TODO: Remove?
monitor.update_raylet_map(_append_port=True)
@@ -113,7 +113,7 @@ def test_heartbeats_single(ray_start_cluster_head):
"""
cluster = ray_start_cluster_head
timeout = 5
monitor = setup_monitor(cluster.redis_address)
monitor = setup_monitor(cluster.address)
total_cpus = ray.state.cluster_resources()["CPU"]
verify_load_metrics(monitor, (0.0, {"CPU": 0.0}, {"CPU": total_cpus}))
@@ -159,7 +159,7 @@ def test_heartbeats_cluster(ray_start_cluster_head):
num_nodes_total = int(num_workers_nodes + 1)
[cluster.add_node() for i in range(num_workers_nodes)]
cluster.wait_for_nodes()
monitor = setup_monitor(cluster.redis_address)
monitor = setup_monitor(cluster.address)
verify_load_metrics(monitor, (0.0, {"CPU": 0.0}, {"CPU": num_nodes_total}))
python/ray/tests/test_node_manager.py
+3 -3
View File
@@ -16,7 +16,7 @@ def test_infeasible_tasks(ray_start_cluster):
return
cluster.add_node(resources={str(0): 100})
ray.init(redis_address=cluster.redis_address)
ray.init(address=cluster.address)
# Submit an infeasible task.
x_id = f._submit(args=[], kwargs={}, resources={str(1): 1})
@@ -30,14 +30,14 @@ def test_infeasible_tasks(ray_start_cluster):
driver_script = """
import ray
ray.init(redis_address="{}")
ray.init(address="{}")
@ray.remote(resources={})
def f():
{}pass # This is a weird hack to insert some blank space.
f.remote()
""".format(cluster.redis_address, "{str(2): 1}", " ")
""".format(cluster.address, "{str(2): 1}", " ")
run_string_as_driver(driver_script)
python/ray/tests/test_object_manager.py
+2 -2
View File
@@ -28,7 +28,7 @@ def create_cluster(num_nodes):
for i in range(num_nodes):
cluster.add_node(resources={str(i): 100}, object_store_memory=10**9)
ray.init(redis_address=cluster.redis_address)
ray.init(address=cluster.address)
return cluster
@@ -226,7 +226,7 @@ def test_object_transfer_retry(ray_start_cluster):
num_gpus=1,
object_store_memory=object_store_memory,
_internal_config=config)
ray.init(redis_address=cluster.redis_address)
ray.init(address=cluster.address)
@ray.remote(num_gpus=1)
def f(size):
python/ray/tests/test_ray_init.py
+2 -2
View File
@@ -28,8 +28,8 @@ class TestRedisPassword(object):
return 1
info = ray.init(redis_password=password)
redis_address = info["redis_address"]
redis_ip, redis_port = redis_address.split(":")
address = info["redis_address"]
redis_ip, redis_port = address.split(":")
# Check that we can run a task
object_id = f.remote()
python/ray/tests/test_stress.py
+2 -2
View File
@@ -49,7 +49,7 @@ def ray_start_combination(request):
})
for i in range(num_nodes - 1):
cluster.add_node(num_cpus=10)
ray.init(redis_address=cluster.redis_address)
ray.init(address=cluster.address)
yield num_nodes, num_workers_per_scheduler, cluster
# The code after the yield will run as teardown code.
@@ -219,7 +219,7 @@ def ray_start_reconstruction(request):
_internal_config=json.dumps({
"initial_reconstruction_timeout_milliseconds": 200
}))
ray.init(redis_address=cluster.redis_address)
ray.init(address=cluster.address)
yield plasma_store_memory, num_nodes, cluster
python/ray/tests/test_tempfile.py
+3 -4
View File
@@ -14,7 +14,7 @@ def test_conn_cluster():
# plasma_store_socket_name
with pytest.raises(Exception) as exc_info:
ray.init(
redis_address="127.0.0.1:6379",
address="127.0.0.1:6379",
plasma_store_socket_name="/tmp/this_should_fail")
assert exc_info.value.args[0] == (
"When connecting to an existing cluster, "
@@ -23,7 +23,7 @@ def test_conn_cluster():
# raylet_socket_name
with pytest.raises(Exception) as exc_info:
ray.init(
redis_address="127.0.0.1:6379",
address="127.0.0.1:6379",
raylet_socket_name="/tmp/this_should_fail")
assert exc_info.value.args[0] == (
"When connecting to an existing cluster, "
@@ -31,8 +31,7 @@ def test_conn_cluster():
# temp_dir
with pytest.raises(Exception) as exc_info:
ray.init(
redis_address="127.0.0.1:6379", temp_dir="/tmp/this_should_fail")
ray.init(address="127.0.0.1:6379", temp_dir="/tmp/this_should_fail")
assert exc_info.value.args[0] == (
"When connecting to an existing cluster, "
"temp_dir must not be provided.")
python/ray/tune/examples/async_hyperband_example.py
+2 -2
View File
@@ -51,10 +51,10 @@ if __name__ == "__main__":
parser.add_argument(
"--smoke-test", action="store_true", help="Finish quickly for testing")
parser.add_argument(
"--ray-redis-address",
"--ray-address",
help="Address of Ray cluster for seamless distributed execution.")
args, _ = parser.parse_known_args()
ray.init(redis_address=args.ray_redis_address)
ray.init(address=args.ray_address)
# asynchronous hyperband early stopping, configured with
# `episode_reward_mean` as the
python/ray/tune/examples/bohb_example.py
+2 -2
View File
@@ -19,7 +19,7 @@ parser = argparse.ArgumentParser()
parser.add_argument(
"--smoke-test", action="store_true", help="Finish quickly for testing")
parser.add_argument(
"--ray-redis-address",
"--ray-address",
help="Address of Ray cluster for seamless distributed execution.")
args, _ = parser.parse_known_args()
@@ -56,7 +56,7 @@ class MyTrainableClass(Trainable):
if __name__ == "__main__":
import ConfigSpace as CS
ray.init(redis_address=args.ray_redis_address)
ray.init(address=args.ray_address)
# BOHB uses ConfigSpace for their hyperparameter search space
config_space = CS.ConfigurationSpace()
python/ray/tune/examples/mnist_pytorch.py
+3 -3
View File
@@ -116,11 +116,11 @@ if __name__ == "__main__":
parser.add_argument(
"--smoke-test", action="store_true", help="Finish quickly for testing")
parser.add_argument(
"--ray-redis-address",
"--ray-address",
help="Address of Ray cluster for seamless distributed execution.")
args = parser.parse_args()
if args.ray_redis_address:
ray.init(redis_address=args.ray_redis_address)
if args.ray_address:
ray.init(address=args.ray_address)
sched = AsyncHyperBandScheduler(
time_attr="training_iteration", metric="mean_accuracy")
analysis = tune.run(
python/ray/tune/examples/mnist_pytorch_trainable.py
+2 -2
View File
@@ -25,7 +25,7 @@ parser.add_argument(
default=False,
help="enables CUDA training")
parser.add_argument(
"--ray-redis-address", type=str, help="The Redis address of the cluster.")
"--ray-address", type=str, help="The Redis address of the cluster.")
parser.add_argument(
"--smoke-test", action="store_true", help="Finish quickly for testing")
@@ -64,7 +64,7 @@ class TrainMNIST(tune.Trainable):
if __name__ == "__main__":
args = parser.parse_args()
ray.init(redis_address=args.ray_redis_address)
ray.init(address=args.ray_address)
sched = ASHAScheduler(metric="mean_accuracy")
analysis = tune.run(
TrainMNIST,
python/ray/tune/tests/example.py
+2 -2
View File
@@ -6,9 +6,9 @@
# import ray
# import argparse
# parser = argparse.ArgumentParser()
# parser.add_argument("--redis-address")
# parser.add_argument("--address")
# args = parser.parse_args()
# ray.init(redis_address=args.redis_address)
# ray.init(address=args.address)
# __quick_start_begin__
import torch.optim as optim
python/ray/tune/tests/test_cluster.py
+4 -4
View File
@@ -357,7 +357,7 @@ import time
import ray
from ray import tune
ray.init(redis_address="{redis_address}")
ray.init(address="{address}")
tune.run(
@@ -372,7 +372,7 @@ tune.run(
dict(experiment=kwargs),
raise_on_failed_trial=False)
""".format(
redis_address=cluster.redis_address, checkpoint_dir=dirpath)
address=cluster.address, checkpoint_dir=dirpath)
run_string_as_driver_nonblocking(script)
# Wait until the right checkpoint is saved.
# The trainable returns every 0.5 seconds, so this should not miss
@@ -446,7 +446,7 @@ import time
import ray
from ray import tune
ray.init(redis_address="{redis_address}")
ray.init(address="{address}")
{fail_class_code}
@@ -460,7 +460,7 @@ tune.run(
max_failures=1,
raise_on_failed_trial=False)
""".format(
redis_address=cluster.redis_address,
address=cluster.address,
checkpoint_dir=dirpath,
fail_class_code=reformatted,
fail_class=_Mock.__name__)
python/ray/tune/tests/tutorial.py
+1 -1
View File
@@ -40,7 +40,7 @@ search_space = {
}
# Uncomment this to enable distributed execution
# `ray.init(redis_address=...)`
# `ray.init(address=...)`
analysis = tune.run(train_mnist, config=search_space)
# __eval_func_end__
python/ray/worker.py
+5 -5
View File
@@ -1298,8 +1298,8 @@ def _initialize_serialization(job_id, worker=global_worker):
class_id="ray.signature.FunctionSignature")
def init(redis_address=None,
address=None,
def init(address=None,
redis_address=None,
num_cpus=None,
num_gpus=None,
memory=None,
@@ -1346,14 +1346,14 @@ def init(redis_address=None,
.. code-block:: python
ray.init(redis_address="123.45.67.89:6379")
ray.init(address="123.45.67.89:6379")
Args:
redis_address (str): The address of the Redis server to connect to. If
address (str): The address of the Ray cluster to connect to. If
this address is not provided, then this command will start Redis, a
raylet, a plasma store, a plasma manager, and some workers.
It will also kill these processes when Python exits.
address (str): Same as redis_address.
redis_address (str): Deprecated; same as address.
num_cpus (int): Number of cpus the user wishes all raylets to
be configured with.
num_gpus (int): Number of gpus the user wishes all raylets to
rllib/train.py
+1 -1
View File
@@ -156,7 +156,7 @@ def run(args, parser):
object_store_memory=args.ray_object_store_memory,
memory=args.ray_memory,
redis_max_memory=args.ray_redis_max_memory)
ray.init(address=cluster.redis_address)
ray.init(address=cluster.address)
else:
ray.init(
address=args.ray_address,