The gpu watcher will help to monitor gpu devices (memory, power, versions, limits).This watcher must obviously be run on a machine with one or more GPU devices.
Let’s first create a watcher for your tasks to live under:
Now let’s walk through the
gpu_task
, which is currentlythe only one available for the gpu watcher. If you aren’t familiar with howto add a task and otherwise manage them, see the getting started.Add a Task
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- $ watchme create gpu Now let’s walk through the gputask, which is currently the only one available for the gpu watcher. If you aren’t familiar with how to add a task and otherwise manage them, see the getting started.
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Remember that we are going to be added tasks to our watcher, “gpu” above.The general format to add a task looks like this:
The key and value pairs are going to vary based on the watcher task.
Task Parameters
The gpu task doesn’t have any required arguments, but you can definea comma separated list of entries to skip.
Name | Required | Default | Example | Notes |
---|---|---|---|---|
skip | No | undefined | skip@ | varies by task (see below) |
Also note that for all of the tasks below, you need to select the type of task with
--type gpu
.Task Environment
For any task, if you have a variable exporter to the environment that startswith
WATCHMEENV_
, it will be found and added to the result. For example,WATCHMEENV_result_id=123
will be saved to the results dictionary with key“result_id” set to “123.”Return Values
All of the return values here will be dictionaries, meaning appropriate to export to json.You shouldn’t need to define a
file_name
parameter, as it will be set automatically tobe your host plus username. However, you are free to set this parameter to change this value.1. The GPU Task
This task is useful for monitoring a specific process that uses a GPU. You can run it as atask (discussed first) or a decorator to a function to enable continuous monitoring.Either way, you will want to create your watcher first:
Run on the Fly, Python
It’s most likely you want to run and monitor a command on the fly, either fromwithin Python or the command line. First, let’s take a look at running fromwithin Python. We are going to use a
TerminalRunner
to run the command:You’ll get a list of timepoints, collected at intervals of 3 seconds! Hereis a look at the first timepoint:
Do you need to add data to the structure? Just export it with prefix
WATCHMEENV_*
and it will appear in the result.
Run on the Fly, Command Line
If you choose, the same function can be run via the watchme command line client.If you provide no additional arguments, it will print the data structure tothe screen:
Want to add an environment variable? The same applies - you can export
WATCHMEENV_*
and they will be added to results.If you want to save to a watcher, then provide the watcher name as the first argument.For example, here we run a task on the fly, and save the result to the watcher “decorator.”Since we don’t provide a
--name
argument, the name defaults to a derivation of the command run.List the folders in the watcher named “decorators” to see the newly added result:
And then use export to export the data!
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For both of the command line above, you can define
--name
to givea custom name, or --seconds
to set the interval at which to collect metrics(default is 3).And along with the interactive Python version, you can optionally specifya comma separated value string of keys to include, skip, or only use.Here we skip two fields:
And don’t forget you can use the default “func” argument (monitor_pid_task)to look at cpu metrics:
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And there you have it! With these methods to monitor any process on the fly ata particular interval, you are good to go!
Run as a Task
To run as a task, you will want to provide
func@gpu_task
when you create the task. You are also required to add a pid, and this can be a number, or the name of the process.Either way, likely you would be running this for a long runningprocess, and in the case of providing an integer, you would need to updatethe pid of the process when you restart your computer. Here is an exampleadding a process name I know will be running when my computer starts:If you choose a process name, remember that different processes can have the samename (for example, think about the case of having multiple Python interpreters open!)This means that watchme will select the first in the list that it finds. Vellum 2 0 4. If youhave preference for a specific one, then it’s recommended that you provide theprocess id directly.
Customize
You can also add a parameter called “skip”, including one or more (comma separate)indices in the results to skip.
To test out the task, you can do the following:
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You’ll see the results.json print to the screen! When it’s time to use thewatcher, you can active and schedule it.
Use as a Decorator
Although the parameters are not stored in the
watchme.cfg
, if you usethe decorator to run the same task, the .git repository is still usedas a database, and you can collect data to monitor your different Pythonfunctions on the fly. Specifically, the decorator is going to usemultiprocessing to run your function, and then watch it via the process id (pid).You get to choose how often (in seconds) you want to record metrics likememory, io counters, and cpu, and threads. See here for an example of default output for one timepoint.This decorator function uses the same task function, that we discussed first,but with a different invocation.The first argument is the name of the watcher (e.g., gpu) and you are also allowedto specify the following arguments (not shown):
- include: a list of keys to add back in (e.g., “environ”)
- skip: a list of keys to skip (see section above for default)
- create: create the watcher if it doesn’t exist (default is False, must exist)
- only: forget about include/skip, only include these fields
- name: if you want to call the result folder something other than the function name
Why don’t you need specify a pid? Your running function will produce the process id, so you don’t need to provide it. Let’s run this script. You can get the full thing from the gist here.You’ll notice in the gist example we demonstrate “myfunc” taking arguments too.
Great! So it ran for the watcher we created called
gpu
, but whereare the results? Let’s take a look in our watcher folder:In addition to the task that we ran, “task-monitor-python,” we also haveresults in a new “decorator-gpu-myfunc” folder. You’ve guessed it right -the decorator namespace creates folders of the format
decorator-gpu-<name>
,where name is the name of the function, or a “name” parameter you provide to thedecorator.Watchme 2 0 5 Sezonas
What is a result?
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Remember that we are monitoring our function every 3 seconds, so for a functionthat lasts about 10, we will record process metrics three times. How would we export that data? Like this:
We ask for
--json
because we know the result is provided in json.For the above export, we will find three commits, each with a commit id,timestamp, and full result.Verify the Addition
Once you add one or more tasks, you can inspect your watcher configurationfile at $HOME/.watchme/gpu/watchme.cfg:
You can also use inspect:
At this point, you can test running the watcher with the
--test
flag forrun:and when you are ready, activate the entire watcher to run:
And don’t forget to set a schedule to automated it (if appropriate)
Every time your task runs, new files (or old files will be updated) andyou can choose to push the entire watcher folder to GitHub to havereproducible monitoring! Since the parameter result files are namedbased on your host and username, others could fork the repo, runon their system, and pull request to combine data.
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When you are ready, read more about exporting data. Endurance 2 0 – helps your mac battery by 20.
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