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Merge pull request #74 from golemfactory/mateusz/ray-on-golem-pre-alpha
Ray on Golem docs - don't merge yet :)
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import LiteYouTubeEmbed from 'react-lite-youtube-embed' | ||
import 'react-lite-youtube-embed/dist/LiteYouTubeEmbed.css' | ||
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function extract_id_from_link(url) { | ||
var regExp = | ||
/^.*((youtu.be\/)|(v\/)|(\/u\/\w\/)|(embed\/)|(watch\?))\??v?=?([^#&?]*).*/ | ||
var match = url.match(regExp) | ||
return match && match[7].length == 11 ? match[7] : false | ||
} | ||
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export function YoutubeEmbed({ link }) { | ||
return <LiteYouTubeEmbed id={extract_id_from_link(link)} /> | ||
} |
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import * as React from 'react' | ||
export const RayIcon = (props) => ( | ||
<svg | ||
width="683px" | ||
height="512px" | ||
viewBox="0 0 682 512" | ||
version="1.1" | ||
{...props} | ||
> | ||
<g id="surface1"> | ||
<path d="M 287.386719 242.429688 C 289.878906 232.722656 294.90625 223.851562 301.949219 216.734375 C 312.320312 206.328125 326.410156 200.492188 341.09375 200.515625 C 356.386719 200.515625 370.21875 206.710938 380.238281 216.734375 C 387.285156 223.851562 392.308594 232.722656 394.800781 242.429688 L 435.574219 242.429688 C 436.878906 237.160156 438.964844 232.121094 441.769531 227.476562 L 369.496094 155.132812 C 360.898438 160.242188 351.089844 162.953125 341.09375 162.976562 C 326.410156 163 312.320312 157.160156 301.949219 146.757812 C 291.554688 136.375 285.722656 122.273438 285.746094 107.574219 C 285.746094 92.265625 291.9375 78.414062 301.949219 68.390625 C 312.320312 57.984375 326.410156 52.148438 341.09375 52.171875 C 356.386719 52.171875 370.21875 58.371094 380.238281 68.390625 C 390.632812 78.773438 396.464844 92.875 396.441406 107.574219 C 396.441406 117.585938 393.734375 127.410156 388.605469 136.003906 L 460.878906 208.347656 C 469.480469 203.238281 479.292969 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</g> | ||
</svg> | ||
) |
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src/pages/docs/creators/ray/basic-ray-tasks-usage-tutorial.md
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--- | ||
description: anatomy of Ray application based on a simple code showcasing Ray tasks | ||
title: Basic Ray tasks usage tutorial | ||
type: example | ||
--- | ||
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# Basic Ray tasks usage examples | ||
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The purpose of this article is to show you an example of a Ray app. It shows how Ray tasks are executed on different machines. | ||
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We have also prepared a couple of other example Ray apps to make it easier for you to play with Ray on Golem and to help you see the power of Ray. | ||
You can find the examples in [`ray-on-golem` repository](https://github.com/golemfactory/golem-ray/tree/main/examples). | ||
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## The app | ||
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Have a look at the code (but you can find it also in [the repository](https://github.com/golemfactory/golem-ray/blob/main/examples/simple-task.py): | ||
```python | ||
from collections import Counter | ||
import socket | ||
import time | ||
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import ray | ||
ray.init() | ||
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print('''This cluster consists of | ||
{} nodes in total | ||
{} CPU resources in total | ||
'''.format(len(ray.nodes()), ray.cluster_resources()['CPU'])) | ||
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@ray.remote | ||
def f(): | ||
time.sleep(0.5) | ||
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# Return IP address. | ||
return socket.gethostbyname(socket.gethostname()) | ||
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object_ids = [f.remote() for _ in range(1000)] | ||
ip_addresses = ray.get(object_ids) | ||
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print('Tasks executed') | ||
for ip_address, num_tasks in Counter(ip_addresses).items(): | ||
print(' {} tasks on {}'.format(num_tasks, ip_address)) | ||
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print('''This cluster consists of | ||
{} nodes in total | ||
{} CPU resources in total | ||
'''.format(len(ray.nodes()), ray.cluster_resources()['CPU'])) | ||
``` | ||
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You can run the app on your local machine with | ||
```bash | ||
python3 simple-task.py | ||
``` | ||
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Or, you can execute it on Ray on the Golem cluster with `ray submit`. Let's have a look at ray usage in the app. | ||
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You need to run certain steps: | ||
- initialize ray, so your code would be able to use ray infrastructure | ||
- declare the methods that can be run remotely by ray | ||
- apply ray pattern to execute tasks remote with `remote()` and await the results with `ray.get()` | ||
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## Ray initialization | ||
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```python | ||
import ray | ||
ray.init() | ||
``` | ||
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This is what Ray initialization looks like. `ray.init()` without parameters tells Ray to look for an existing Ray cluster or (if not found) to start up a local Ray instance. | ||
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## Cluster information | ||
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The `ray` module exposes methods we use to print the information about the cluster (before and after ray computation). | ||
When you run the script on a fresh Ray on the Golem cluster, the number of nodes will increase as a result of the computation. | ||
This happens because Ray autoscaler monitors the amount of work pending and requests additional nodes as the queues get longer. | ||
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It also decommissions the nodes when they start to idle (5 mins in the example config we are providing). | ||
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## Remote tasks declaration and execution | ||
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```python | ||
@ray.remote | ||
def f: | ||
``` | ||
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Ray's `remote` decorator turns a regular local function into Ray's object, which enables the function to be executed remotely (on a different node). | ||
When you subsequently call its `.remote` method, the Ray scheduler will queue its execution. | ||
In our case, the function just sleeps for a moment and then returns the IP address of the node it was executed on. | ||
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```python | ||
object_ids = [f.remote() for _ in range(1000)] | ||
``` | ||
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What happens when you call the `remote` method added by the `@ray.remote` decorator, is that it immediately returns an id - a kind of future promise - that can be used to get the results later. | ||
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## Waiting for the results of remote calls | ||
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```python | ||
ip_addresses = ray.get(object_ids) | ||
``` | ||
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The future(s) can be awaited with `ray.get`. It returns only when all the remote tasks are executed. | ||
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## Conclussion | ||
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And that's it - the code parallelization with Ray is done by choosing which parts of code can be executed remotely, decorating them with `@ray.remote`, then changing their execution to `.remote()`, and finally waiting for the results with `ray.get()`. Of course, remote Ray tasks can call other Ray remote tasks. | ||
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You can now proceed to run your app on a [Ray on Golem cluster](/docs/creators/ray/setup-tutorial) | ||
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Read more information about Ray tasks on [Ray Core docs website](https://docs.ray.io/en/latest/ray-core/walkthrough.html) | ||
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