example

Converting a real-life use case to Ray on Golem

This tutorial explains step-by-step how to parallelize an example app, set Ray on Golem cluster up, run the script on the cluster, and finally stop the cluster.

The parallelization part is also explained in our PoC demo video

The example Double Dummy Solver app

info

We recommend creating a new directory and a clean Python virtual environment before you proceed. This avoids cluttering your system installation with unnecessary packages.

In our case, the application is a very simple script running a simple bridge (the card game) simulation.

Download our example bridge script

# Download the example bridge app
wget https://github.com/golemfactory/ray-on-golem/raw/main/examples/dds.py

The script uses the endplay library

# Install endplay lib (recommended within a clean virtual environment)
pip3 install endplay==0.4.11b0

When you run the code it randomly generates 50 bridge deals and for each deal, it calculates potential winnings for each player and each trump suit - get_deal() and get_lots_of_deals() functions. It prints the results in a rather cryptic bridge language. You can learn more on endplay docs site.

Finally, the code outputs the time it took to calculate all the deals.

In this form, it works in a very single-threaded way - it waits for each deal to be calculated before starting with the next one.

# Run the example bridge app
python3 dds.py
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deal count: 50 time: 0:00:07.907285

Parallelize with Ray

Let's now proceed to parallelization.

We need to install ray-on-golem. It will install ray as a dependency.

# Install ray-on-golem and ray (recommended in a clean virtual environment)
pip3 install -U ray-on-golem
info

Note: As an added convenience, the installation of ray-on-golem ensures that both ray and yagna are set up for you. With these components in place, you're well-prepared to harness the full potential of Ray on the Golem Network.

Ray initialization

We will now modify the original ddy.py to make it use Ray.

Ray needs to be imported and initialized. Add the following before get_deal() definition:

import ray

# Use the default ray cluster or start a local one
ray.init()

Empty ray.init() arguments tell ray to look for an existing cluster (we will use this path later) or start a local instance for the time of app execution.

Run the code, and notice information about a local Ray instance starting.

python3 dds.py
2023-08-29 14:45:30,359	INFO worker.py:1612 -- Started a local Ray instance. View the dashboard at 127.0.0.1:8265
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deal count: 50 time: 0:00:08.060884

@ray.remote decorator

We call the get_deal() method a lot, it uses the CPU intensively and doesn't need any external communication - it is a great candidate for parallelization.

Let's decorate it with @ray.remote:

@ray.remote
def get_deal():

The decorator tells Ray that this piece of code can be scheduled to run on a remote node.

Please note, that if the remote task is too small, the scheduling overhead might be too big for the parallelization to be beneficial. Consider grouping smaller tasks into chunks that need more CPU time.

Remote call and waiting for results with ray.get()

We need to explicitly acknowledge the remoteness of the call by adding .remote() inside the get_lots_of_deals() function. Additionally, now we aren't getting the results right away. get_deal.remote() merely schedules the execution and returns a future - an id - needed to get the results later with ray.get():

def get_lots_of_deals():

    result_ids = [get_deal.remote() for i in range(DEAL_COUNT)]

    results = ray.get(result_ids)

    return results

And that's it! Run the code now - the output should look the same, but the whole thing should take a little less time.

The time difference is the result of Ray using more cores of your local CPU (vs. using only one core by the original app).

python3 dds.py
2023-08-29 15:00:40,130	INFO worker.py:1612 -- Started a local Ray instance. View the dashboard at 127.0.0.1:8265
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AJ.J752.42.KQ843 KQ9.T943.J6.AJ96 82.A8.T9875.T752 ♣,♦,♥,♠,NT;N:7,8,10,11,10;E:6,4,3,2,3;S:7,8,9,11,10;W:6,4,3,2,3', 'N:T9.865.T92.JT743 J874.Q97.Q84.A96 A65.AJ2.765.K852 KQ32.KT43.AKJ3.Q ♣,♦,♥,♠,NT;N:6,2,2,2,5;E:7,10,10,11,7;S:6,2,2,2,5;W:7,10,10,11,7', 'N:K9654.K9642..KQ9 87.AQJ3.J76.T873 AJ32.T.AQ943.J52 QT.875.KT852.A64 ♣,♦,♥,♠,NT;N:6,7,7,11,6;E:5,5,5,2,5;S:6,7,8,11,6;W:5,5,5,2,5', 'N:K4.K98532.QT842. 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T62.AT853.A5.J74 J.J.K832.AKQT986 AQ954.64.T64.532 ♣,♦,♥,♠,NT;N:10,10,6,6,8;E:3,3,5,6,3;S:10,10,6,6,8;W:3,3,5,7,3', 'N:532.A.J9875.7542 KJT4.J9743.3.Q98 A987.Q52.AQT2.A6 Q6.KT86.K64.KJT3 ♣,♦,♥,♠,NT;N:6,8,4,7,7;E:7,4,8,6,6;S:6,9,4,7,7;W:7,4,8,6,6', 'N:KT973.QT5.732.T6 J42.K84.KJ.KQ542 8.963.98654.AJ87 AQ65.AJ72.AQT.93 ♣,♦,♥,♠,NT;N:4,5,3,3,3;E:9,7,9,10,10;S:4,5,3,3,3;W:9,7,9,10,10', 'N:AK983.A6.AQJ.QT5 JT.KQ74.K52.AJ72 Q65.JT9852.93.93 742.3.T8764.K864 ♣,♦,♥,♠,NT;N:5,5,8,9,7;E:8,7,5,4,5;S:5,5,8,9,7;W:8,7,5,4,5']
deal count: 50 time: 0:00:04.643217

Output cluster information

As the one last touch before proceeding to run our script on a Ray on Golem cluster, we'll add some information about the cluster to the output. Add this after ray.init() and at the end of the code:

print('''This cluster consists of
          {} nodes in total
          {} CPU resources in total
      '''.format(len(ray.nodes()), ray.cluster_resources()['CPU']))

Run it and see how many cores Ray finds on your machine:

python3 dds.py
2023-08-29 15:08:48,590	INFO worker.py:1612 -- Started a local Ray instance. View the dashboard at 127.0.0.1:8265
This cluster consists of
          1 nodes in total
          8.0 CPU resources in total

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This cluster consists of
          1 nodes in total
          8.0 CPU resources in total

deal count: 50 time: 0:00:04.550863

Set up your Ray on Golem cluster

We will now scale the execution even further - we will use a Ray on Golem cluster.

Run the following to make sure the needed software is installed:

# install ray-on-golem (just make sure you have it after the previous steps)
pip3 install -U ray-on-golem

Now, run the following to start a basic cluster, based on our example configuration. Your cluster will run on our testnet - it is free, but not very powerful.

# Download the golem-cluster.yaml
wget https://github.com/golemfactory/ray-on-golem/raw/main/golem-cluster.yaml

# In this command:
# * yagna starts in the background (if not running)
# * ray-on-golem cluster manager starts in the background
# * ray head node is started on a golem provider
ray up golem-cluster.yaml --yes

# Check if Ray on Golem cluster is running
ray exec golem-cluster.yaml 'ray status'

The example cluster config file contains payment information. As a default it runs for free on the Golem testnet - it should be enough to try it out (which is all we are supporting for now).

The provided example Golem cluster config file defines a small cluster with three nodes that is configured to autoscale up to 10 worker nodes. The availability of the testnet machines might cap the size of your cluster.

If you want more details about the config file, check out the cluster yaml reference.

info

Consult the troubleshooting guide if anything goes wrong.

Run the app

Pass endplay library requirement to Ray

Now that the app will be running on a cluster, we need to inform it that the endplay library is required on individual worker nodes.

Replacing ray.init() with the following will do the trick:

# endplay library dependency
runtime_env = {
    "pip": [
        "Pillow",  # explicit uppercased "Pillow", as pip complains about mismatched metadata in endplay dependency tree
        "endplay==0.4.11b0",
    ]
}


# Use the default ray cluster or start a local one
# Make sure endplay lib is installed
ray.init(runtime_env=runtime_env)

When you run the code, it takes a bit longer than previously, as Ray needs to create a virtual environment before the execution.

Submit the code to cluster and observe the autoscaling

With the cluster up and running, with Ray notified about endplay lib dependency, executing our app is as simple as:

ray submit golem-cluster.yaml dds.py
2023-09-18 09:16:38,986	WARNING util.py:249 -- Dropping the empty legacy field head_node. head_nodeis not supported for ray>=2.0.0. It is recommended to removehead_node from the cluster config.
2023-09-18 09:16:38,986	WARNING util.py:249 -- Dropping the empty legacy field worker_nodes. worker_nodesis not supported for ray>=2.0.0. It is recommended to removeworker_nodes from the cluster config.
Loaded cached provider configuration
If you experience issues with the cloud provider, try re-running the command with --no-config-cache.
Fetched IP: 192.168.0.3
2023-09-18 09:16:40,697	WARNING util.py:249 -- Dropping the empty legacy field head_node. head_nodeis not supported for ray>=2.0.0. It is recommended to removehead_node from the cluster config.
2023-09-18 09:16:40,697	WARNING util.py:249 -- Dropping the empty legacy field worker_nodes. worker_nodesis not supported for ray>=2.0.0. It is recommended to removeworker_nodes from the cluster config.
Fetched IP: 192.168.0.3
2023-09-18 07:16:40,451	INFO worker.py:1364 -- Connecting to existing Ray cluster at address: 192.168.0.3:6379...
[2023-09-18 07:16:40,458 I 892 892] global_state_accessor.cc:356: This node has an IP address of 10.42.42.2, while we can not find the matched Raylet address. This maybe come from when you connect the Ray cluster with a different IP address or connect a container.
2023-09-18 07:16:40,458	INFO worker.py:1544 -- Connected to Ray cluster. View the dashboard at 10.42.42.2:8265
This cluster consists of
          1 nodes in total
          1.0 CPU resources in total

(autoscaler +2s) Tip: use `ray status` to view detailed cluster status. To disable these messages, set RAY_SCHEDULER_EVENTS=0.
(autoscaler +2s) Adding 1 node(s) of type ray.worker.default.
(autoscaler +16s) Resized to 2 CPUs.
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This cluster consists of
          2 nodes in total
          2.0 CPU resources in total

deal count: 50 time: 0:00:18.404510
shared connection to 192.168.0.3 closed.

Notice how at first, there is only one node, and after the computation, there is more. This is Ray autoscaler at work. The time needed to add new nodes might be too long for you to notice when you run the dds.py code as it is. If you increase DEAL_CNT and/or rerun the app, the difference will be more visible

ray submit golem-cluster.yaml dds.py
2023-09-18 09:18:23,831	WARNING util.py:249 -- Dropping the empty legacy field head_node. head_nodeis not supported for ray>=2.0.0. It is recommended to removehead_node from the cluster config.
2023-09-18 09:18:23,831	WARNING util.py:249 -- Dropping the empty legacy field worker_nodes. worker_nodesis not supported for ray>=2.0.0. It is recommended to removeworker_nodes from the cluster config.
Loaded cached provider configuration
If you experience issues with the cloud provider, try re-running the command with --no-config-cache.
Fetched IP: 192.168.0.3
2023-09-18 09:18:25,526	WARNING util.py:249 -- Dropping the empty legacy field head_node. head_nodeis not supported for ray>=2.0.0. It is recommended to removehead_node from the cluster config.
2023-09-18 09:18:25,527	WARNING util.py:249 -- Dropping the empty legacy field worker_nodes. worker_nodesis not supported for ray>=2.0.0. It is recommended to removeworker_nodes from the cluster config.
Fetched IP: 192.168.0.3
2023-09-18 07:18:25,278	INFO worker.py:1364 -- Connecting to existing Ray cluster at address: 192.168.0.3:6379...
[2023-09-18 07:18:25,285 I 1351 1351] global_state_accessor.cc:356: This node has an IP address of 10.42.42.2, while we can not find the matched Raylet address. This maybe come from when you connect the Ray cluster with a different IP address or connect a container.
2023-09-18 07:18:25,285	INFO worker.py:1544 -- Connected to Ray cluster. View the dashboard at 10.42.42.2:8265
This cluster consists of
          4 nodes in total
          4.0 CPU resources in total

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This cluster consists of
          7 nodes in total
          7.0 CPU resources in total

deal count: 100 time: 0:00:14.398623
shared connection to 192.168.0.3 closed.

It is worth noting that if you want to pass arguments to the submitted script you need to use the double-dash symbol (--). It allows Ray to distinguish submit arguments from the script ones (more details).

Scale up

Our DDS script computes 50 bridge deals by default - a rather small number - so that the execution time does not get in the way of your following this tutorial. However, the small size of the sample probably won't benefit from the distribution to the Golem Network.

To see actual profits from such parallelization, increase the DEAL_COUNT 10 or 50 times, however, you want.

Then you can run it locally (you can comment out the runtime_env argument for ray.init() if you want the fastest local execution):

python3 dds.py

And on Golem:

ray submit golem-cluster.yaml dds.py

At some point (typically 100-200 deals tend to be enough), the execution on your Ray on Golem cluster will be visibly faster.

You can find the resulting code in our repo.

Play around

Everything is set up at this point and you can play around.

You can play with the script and with the cluster config yaml - change the maximum number of nodes, autoscaling speed, etc.

Stop the cluster

When you are done, it is a good practice to stop the cluster. In the default configuration, it runs on the free testnet, but keeping it running impairs the provider availability for others. On the other hand, when you run on the mainnet, stopping the cluster saves you money.

ray down golem-cluster.yaml --yes

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