# ensemble
An HPC ensemble is an orchestration of jobs that can ideally be controlled by an algorithm. Ensemble (in python) is a project to do exactly that. As a user, you specify the parameters for your job, and then an algorithm and options for it. But when you think about it, an algorithm *could* have a known name or label, but in its simplest form it is a set of rules (triggers and actions) that make up a state machine. In that light, ensemble python is a simple tool to create state machines to orchestrate units of work with workload managers. Specific sets of rules could be packaged up to be called an algorithm, and in fact that might be what we eventually call the yaml file that defines them. For now, we call them ensembles.
The library here listens for the heartbeat of your ensemble -- events that come directly from the queue or entity that is controlling the jobs.
This means that we define:
- A number of executors (typically queues) that can deliver events (job completion events to start)
- Rules for when to submit jobs (at onset, at periods during running)
- Rules for when to stop, cancel, or terminate
- Parameters for the jobs (what will be converted into a job specification for the queue to consume)
- Rules for when to change the environment (cluster) like growing or shrinking, if supported
- A set of online (streaming) ML metrics (e.g., mean, median or MAD, min/max, etc) that are recorded for each job group and state (finished, failed succeeded)
At a high level, we need to be able to define events, and rules for transitioning to new states. This even means we could make infinite loops (I accidentally already did). There should be an executor or queue interface that can support any kind of workload manager queue that can return the expected types.
🚧 Under Construction! 🚧
## Design
This design will be translated into more consolidated design documentation. For now I'm putting it here.
### Concepts
- **Executor** provides a queue backend that should handle sending events. While a poll oriented design could work, it's not ideal. In the context of the ensemble, the queue executor is referred to as a **member**.
- **Ensemble Service**: provides grpc endpoints for one or more ensemble members to communicate with. This is an explicit design decision that, for example, would allow deploying one service that is orchestrating multiple things at once.
- **Rules**: A rule is composed of a trigger and action to take, and this is what drives the ensemble, more akin to a state machine than a traditional workflow DAG because the structure can be unknown at the start. For example, you might say "on the start of the ensemble, submit these jobs with label X."
- **Triggers**: A trigger is part of a rule (described above) and in the configuration file, and can be read as "when this trigger happens, do this action."
- **Action**: An actual is an operation that is the result of hitting a trigger condition. One type of action is performed by the queue, and are known to it. For example, queue actions include submit, scale-up, scale-down, or terminate. Actions are typically called by triggers under rules. Actions can be customized, and these are called custom actions.
- **Plugins**: A plugin is a collection of custom actions that are typically associated with a particular application. For example, a plugin for LAMMPS might know how to check LAMMPS output and act on a specific parsing of a result. Plugins are used equivalently to custom functions, and can accept arguments.
- **Metrics** are summary metrics collected for groups of jobs, for customized algorithms. To support this, we use online (or streaming) ML algorithms for things like mean, IQR, etc. While there could be a named entity called an algorithm, since it's just a set of rules (triggers and actions) that means we don't need to explicitly define them (yet). I can see at some point creating "packaged" rule sets that are called that.
#### Logging
By default, we have a pretty printed set of the "main" events (triggers and submissions). However, to see ALL events from Flux (good for debugging) you can turn them on:
```yaml
logging:
debug: true
```
To set the event heartbeat to fire at some increment, set it:
```yaml
logging:
debug: true
heartbeat: 60
```
Note that by default it is turned off (set to 0 seconds) unless you include a grow or shrink action. In that case, it turns on and defaults to 60, unless you've specified another interval.
If you have grow/shrink and explicitly turn it off, it will still default to 60 seconds, because grow/shrink won't work as expected without the heartbeat.
#### Rules
A rule defines a trigger and action to take. The library is event driven, meaning that the queue is expected to send events, and we don't do any polling.
##### Triggers
The current triggers supported are [events for flux](https://flux-framework.readthedocs.io/projects/flux-rfc/en/latest/spec_21.html). In addition, we have added:
- metric: Triggered when a queue metric is updated
For example, the following snippet defines two rules. The first says "on the start of the ensemble, submit the job group labeled as lammps." And then when lammps has 3 successful runs, submit the job group amg (groups are not shown).
```yaml
rules:
- trigger: start
action:
name: submit
label: lammps
- trigger: metric
name: count.lammps.success
when: 3
action:
name: submit
label: amg
```
Note that yes, this means "submit" is both an action and an event.
##### Actions
The design of a rule is to have an action, and the action is something your ensemble can be tasked to do when an event is triggered. Right now we support the following:
**Flux and MiniCluster**
- *submit*: submit a job
- *terminate*: terminate the member. This is usually what you want to call to have the entire thing exit on 0
- *custom*: run a custom function that will receive known kwargs, and then should return an action (read more below)
**MiniCluster Only**
- *grow*: grow (or request) the cluster to scale up
- *shrink*: shrink (or request) the cluster to scale down
For the scale operations, since this brings in the issue of resource contention between different ensembles, we have to assume to start that a request to scale:
1. Should only happen once. If it's granted, great, if not, we aren't going to ask again.
2. Does not need to consider another level of scheduler (e.g., fair share)
I started thinking about putting a second scheduler (or fair share algorithm) in the grpc service, but realized this was another level of complexity that although we might work on it later, is not warranted yet.
Also note that since scale operation triggers might not be linked to job events (e.g., if we want to trigger when a job group has been in the queue for too long) we added support for a heartbeat. The heartbeat
isn't a trigger in and of itself, but when it runs, it will run through rules that are relevant to queue metrics.
We see "submit" as two examples in the above, which is a common thing you'd want to do! For each action, you should minimally define the "name" and a "label" that typically corresponds to a job group.
You can also optionally define "repetitions," which are the number of times the action should be run before expiring. If you want a backoff period between repetitions, set "backoff" to a non zero value.
By default, when no repetitions or backoff are set, the action is assumed to have a repetition of 1. It will be run once! Let's now look at a custom action. Here is what your function should look like in your `ensemble.yaml`
##### Actions
Custom actions are also supported, where you define a custom function that in and of itself returns an action! Here is an example to extend the above. Let's say we want to run the group "echo" again, we might do the following:
```python
def echo_again(**kwargs):
"""
For a custom function, you can assume the following in kwargs,
and note that this set of metadata can vary based on the ensemble type.
metrics: the full QueueMetrics object to use as needed
event: the full event that triggered the custom action
rule: the original rule that triggered the action
action: your custom action
handle: the active flux handle (Flux ensemble specific!)
You should (must) return an action in your response, or None to take
no action. E.g.,: return Action({"name": "submit", "label": "echo"})
"""
return Action({"name": "submit", "label": "echo"})
```
Note that in the above, we get access to the following via kwargs:
- metrics: the full QueueMetrics object to use as needed
- event: the full event that triggered the custom action
- rule: the original rule that triggered the action
- action: your custom action
- handle: the active flux handle (Flux ensemble specific!)
- A custom rule can have a trigger, and then return another rule. This means that:
- return "None" to do nothing
- return an action to follow up your custom function
#### Metrics
We use streaming ML "stats" for each job group, and then a subset of variables. Right now we support, for each job group:
- **duration** Each of variance, mean, iqr, max, min, and mad (mean absolute deviation) for the duration of the job
- **pending-time** Each of variance, mean, iqr, max, min, and mad (mean absolute deviation) for the time the job spent in the queue (pending state)
Here is an example that shows duration for a job group called "echo."
```console
🌊 Streaming ML Model Summary:
name : echo-duration
variance : 3.561605860855515e-07
mean : 0.013609373569488525
iqr : 0.00041562974899014317
max : 0.014329195022583008
min : 0.011783838272094727
mad : 0.00024357753771322735
```
Along with that, we take counts of everything! Here is after running two groups of jobs, where one job group was triggered to run after a count of the first was recorded.
```console
{'variance': {'sleep-duration': Var: 0.000006, 'echo-duration': Var: 0.}, 'mean': {'sleep-duration': Mean: 10.015157, 'echo-duration': Mean: 0.013609}, 'iqr': {'sleep-duration': IQR: 0.003083, 'echo-duration': IQR: 0.000416}, 'max': {'sleep-duration': Max: 10.020326, 'echo-duration': Max: 0.014329}, 'min': {'sleep-duration': Min: 10.012975, 'echo-duration': Min: 0.011784}, 'mad': {'sleep-duration': MAD: 0.001754, 'echo-duration': MAD: 0.000244}, 'count': {'sleep': {'finished': Count: 10., 'success': Count: 10.}, 'echo': {'finished': Count: 20., 'success': Count: 20.}}}
```
### Overview
Since this will need to handle many ensembles, I'm going to try designing it as a service. But note that it doesn't _need_ to be run as one (I'm developing and just running flux directly with the command line client, which works too). There will be grpc endpoints that can receive messages. Each queue or executor will have its own separate running process. E.g., for Flux we will have a script running alongside a broker with a flux handle. This should ideally use events (but is not required to if the queue does not support that).
## Example
Let's do an example running in the Development container, where we have flux. You can do the following:
```bash
# Start a flux instance
flux start --test-size=4
# Install in development mode, and run "make" to rebuild proto if necessary
sudo pip install -e .
# Start the server (actually you don't need to do this, I'm not using it yet)
ensemble-server start
# Run the hello-world example ensemble! it will submit and monitor job events, etc
ensemble run examples/hello-world.yaml
# Here is how to add on the fly debug (logging->debug true)
ensemble run --debug examples/hello-world.yaml
# This example shows using repetitions and backoff
ensemble run examples/backoff-example.yaml
# This shows a custom action
ensemble run examples/custom-action-example.yaml
# This shows termination, which is necessary for when you want an exit
ensemble run examples/terminate-example.yaml
# Run a heartbeat every 3 seconds.
# This will trigger a check to see if actions need to be performed
ensemble run examples/heartbeat-example.yaml
# Grow/shrink requires a minicluster (flux doesn't support it) but we can mock it here
# Note that a --name with <namespace>/<name> is required
ensemble run --name default/ensemble --executor minicluster examples/grow-shrink-example.yaml
```
Right now, this will run any rules with "start" triggers, which for this hello world example includes a few hello world jobs! You'll then be able to watch and see flux events coming in!
Here is the full run - we run a bunch of sleep jobs (10) and when we hit a count of 5, we launch a bunch of echo jobs.
```console
$ ensemble run examples/hello-world.yaml
=> trigger start
submit sleep (name:sleep),(command:sleep 10),(count:5),(nodes:1)
=> trigger count.sleep.success
submit echo (name:echo),(command:echo hello world),(count:5),(nodes:1)
```
The above output is colored! Note that in any ensemble file, you can turn on debug mode to see more verbose output (events, etc.)
<details>
<summary>Example Ensemble Run</summary>
This run shows having verbose enabled.
```console
$ ensemble run examples/hello-world.yaml
=> trigger start
submit sleep (name:sleep),(command:sleep 10),(count:5),(nodes:1)
{'id': 513684799488, 'events': [{'timestamp': 1729181750.7627614, 'name': 'submit', 'context': {'userid': 1000, 'urgency': 16, 'flags': 0, 'version': 1}}, {'timestamp': 1729181750.7731683, 'name': 'validate'}, {'timestamp': 1729181750.7839293, 'name': 'depend'}, {'timestamp': 1729181750.7839663, 'name': 'priority', 'context': {'priority': 16}}, {'timestamp': 1729181750.785146, 'name': 'alloc'}, {'timestamp': 1729181750.7863054, 'name': 'start'}], 'jobspec': {'resources': [{'type': 'node', 'count': 1, 'with': [{'type': 'slot', 'count': 1, 'with': [{'type': 'core', 'count': 1}], 'label': 'task'}]}], 'tasks': [{'command': ['sleep', '10'], 'slot': 'task', 'count': {'per_slot': 1}}], 'attributes': {'system': {'duration': 0.0}, 'user': {'group': 'sleep'}}, 'version': 1}, 'R': {'version': 1, 'execution': {'R_lite': [{'rank': '1', 'children': {'core': '6'}}], 'nodelist': ['08c63b4a360d'], 'starttime': 1729181750, 'expiration': 4882781720}}}
{'id': 512543948800, 'events': [{'timestamp': 1729181750.6949837, 'name': 'submit', 'context': {'userid': 1000, 'urgency': 16, 'flags': 0, 'version': 1}}, {'timestamp': 1729181750.7062836, 'name': 'validate'}, {'timestamp': 1729181750.7169266, 'name': 'depend'}, {'timestamp': 1729181750.7169478, 'name': 'priority', 'context': {'priority': 16}}, {'timestamp': 1729181750.717982, 'name': 'alloc'}, {'timestamp': 1729181750.7212884, 'name': 'start'}], 'jobspec': {'resources': [{'type': 'node', 'count': 1, 'with': [{'type': 'slot', 'count': 1, 'with': [{'type': 'core', 'count': 1}], 'label': 'task'}]}], 'tasks': [{'command': ['sleep', '10'], 'slot': 'task', 'count': {'per_slot': 1}}], 'attributes': {'system': {'duration': 0.0}, 'user': {'group': 'sleep'}}, 'version': 1}, 'R': {'version': 1, 'execution': {'R_lite': [{'rank': '2', 'children': {'core': '7'}}], 'nodelist': ['08c63b4a360d'], 'starttime': 1729181750, 'expiration': 4882781720}}}
{'id': 514053898240, 'events': [{'timestamp': 1729181750.7850096, 'name': 'submit', 'context': {'userid': 1000, 'urgency': 16, 'flags': 0, 'version': 1}}, {'timestamp': 1729181750.7954807, 'name': 'validate'}, {'timestamp': 1729181750.8061984, 'name': 'depend'}, {'timestamp': 1729181750.806234, 'name': 'priority', 'context': {'priority': 16}}], 'jobspec': {'resources': [{'type': 'node', 'count': 1, 'with': [{'type': 'slot', 'count': 1, 'with': [{'type': 'core', 'count': 1}], 'label': 'task'}]}], 'tasks': [{'command': ['sleep', '10'], 'slot': 'task', 'count': {'per_slot': 1}}], 'attributes': {'system': {'duration': 0.0}, 'user': {'group': 'sleep'}}, 'version': 1}}
{'id': 512929824768, 'events': [{'timestamp': 1729181750.7181482, 'name': 'submit', 'context': {'userid': 1000, 'urgency': 16, 'flags': 0, 'version': 1}}, {'timestamp': 1729181750.7286258, 'name': 'validate'}, {'timestamp': 1729181750.7391565, 'name': 'depend'}, {'timestamp': 1729181750.7391846, 'name': 'priority', 'context': {'priority': 16}}, {'timestamp': 1729181750.7405195, 'name': 'alloc'}, {'timestamp': 1729181750.7415988, 'name': 'start'}], 'jobspec': {'resources': [{'type': 'node', 'count': 1, 'with': [{'type': 'slot', 'count': 1, 'with': [{'type': 'core', 'count': 1}], 'label': 'task'}]}], 'tasks': [{'command': ['sleep', '10'], 'slot': 'task', 'count': {'per_slot': 1}}], 'attributes': {'system': {'duration': 0.0}, 'user': {'group': 'sleep'}}, 'version': 1}, 'R': {'version': 1, 'execution': {'R_lite': [{'rank': '2', 'children': {'core': '6'}}], 'nodelist': ['08c63b4a360d'], 'starttime': 1729181750, 'expiration': 4882781720}}}
{'id': 513315700736, 'events': [{'timestamp': 1729181750.7404878, 'name': 'submit', 'context': {'userid': 1000, 'urgency': 16, 'flags': 0, 'version': 1}}, {'timestamp': 1729181750.7509809, 'name': 'validate'}, {'timestamp': 1729181750.7615387, 'name': 'depend'}, {'timestamp': 1729181750.761586, 'name': 'priority', 'context': {'priority': 16}}, {'timestamp': 1729181750.762535, 'name': 'alloc'}, {'timestamp': 1729181750.7638192, 'name': 'start'}], 'jobspec': {'resources': [{'type': 'node', 'count': 1, 'with': [{'type': 'slot', 'count': 1, 'with': [{'type': 'core', 'count': 1}], 'label': 'task'}]}], 'tasks': [{'command': ['sleep', '10'], 'slot': 'task', 'count': {'per_slot': 1}}], 'attributes': {'system': {'duration': 0.0}, 'user': {'group': 'sleep'}}, 'version': 1}, 'R': {'version': 1, 'execution': {'R_lite': [{'rank': '1', 'children': {'core': '7'}}], 'nodelist': ['08c63b4a360d'], 'starttime': 1729181750, 'expiration': 4882781720}}}
{'id': -1, 'events': []}
Sentinel is seen, starting event monitoring.
{'id': 514053898240, 'events': [{'timestamp': 1729181750.8073554, 'name': 'annotations', 'context': {'annotations': None}}]}
{'id': 514053898240, 'events': [{'timestamp': 1729181750.8073688, 'name': 'alloc'}], 'R': {'version': 1, 'execution': {'R_lite': [{'rank': '0', 'children': {'core': '7'}}], 'nodelist': ['08c63b4a360d'], 'starttime': 1729181750, 'expiration': 4882781720}}}
{'id': 514053898240, 'events': [{'timestamp': 1729181750.8085647, 'name': 'start'}]}
{'id': 512543948800, 'events': [{'timestamp': 1729181760.7343254, 'name': 'finish', 'context': {'status': 0}}]}
{'id': 512543948800, 'events': [{'timestamp': 1729181760.735187, 'name': 'release', 'context': {'ranks': 'all', 'final': True}}]}
{'id': 512543948800, 'events': [{'timestamp': 1729181760.7352166, 'name': 'free'}]}
{'id': 512543948800, 'events': [{'timestamp': 1729181760.735229, 'name': 'clean'}]}
{'id': 512929824768, 'events': [{'timestamp': 1729181760.7541575, 'name': 'finish', 'context': {'status': 0}}]}
{'id': 512929824768, 'events': [{'timestamp': 1729181760.755071, 'name': 'release', 'context': {'ranks': 'all', 'final': True}}]}
{'id': 512929824768, 'events': [{'timestamp': 1729181760.7551124, 'name': 'free'}]}
{'id': 512929824768, 'events': [{'timestamp': 1729181760.755127, 'name': 'clean'}]}
{'id': 513315700736, 'events': [{'timestamp': 1729181760.7783182, 'name': 'finish', 'context': {'status': 0}}]}
{'variance': {'sleep-pending': Var: 0.000002, 'sleep-duration': Var: 0.000001}, 'mean': {'sleep-pending': Mean: 0.024037, 'sleep-duration': Mean: 10.013365}, 'iqr': {'sleep-pending': IQR: 0.000104, 'sleep-duration': IQR: 0.00194}, 'max': {'sleep-pending': Max: 0.026305, 'sleep-duration': Max: 10.014499}, 'min': {'sleep-pending': Min: 0.023331, 'sleep-duration': Min: 10.012559}, 'mad': {'sleep-pending': MAD: 0.000011, 'sleep-duration': MAD: 0.000478}, 'count': {'sleep': {'finished': Count: 3., 'success': Count: 3.}}}
=> trigger count.sleep.success
submit echo (name:echo),(command:echo hello world),(count:5),(nodes:1)
{'id': 513315700736, 'events': [{'timestamp': 1729181760.7791471, 'name': 'release', 'context': {'ranks': 'all', 'final': True}}]}
{'variance': {'sleep-pending': Var: 0.000002, 'sleep-duration': Var: 0.000001}, 'mean': {'sleep-pending': Mean: 0.024037, 'sleep-duration': Mean: 10.013365}, 'iqr': {'sleep-pending': IQR: 0.000104, 'sleep-duration': IQR: 0.00194}, 'max': {'sleep-pending': Max: 0.026305, 'sleep-duration': Max: 10.014499}, 'min': {'sleep-pending': Min: 0.023331, 'sleep-duration': Min: 10.012559}, 'mad': {'sleep-pending': MAD: 0.000011, 'sleep-duration': MAD: 0.000478}, 'count': {'sleep': {'finished': Count: 3., 'success': Count: 3.}}}
{'id': 513315700736, 'events': [{'timestamp': 1729181760.7791893, 'name': 'free'}]}
{'variance': {'sleep-pending': Var: 0.000002, 'sleep-duration': Var: 0.000001}, 'mean': {'sleep-pending': Mean: 0.024037, 'sleep-duration': Mean: 10.013365}, 'iqr': {'sleep-pending': IQR: 0.000104, 'sleep-duration': IQR: 0.00194}, 'max': {'sleep-pending': Max: 0.026305, 'sleep-duration': Max: 10.014499}, 'min': {'sleep-pending': Min: 0.023331, 'sleep-duration': Min: 10.012559}, 'mad': {'sleep-pending': MAD: 0.000011, 'sleep-duration': MAD: 0.000478}, 'count': {'sleep': {'finished': Count: 3., 'success': Count: 3.}}}
{'id': 513315700736, 'events': [{'timestamp': 1729181760.7792032, 'name': 'clean'}]}
{'variance': {'sleep-pending': Var: 0.000002, 'sleep-duration': Var: 0.000001}, 'mean': {'sleep-pending': Mean: 0.024037, 'sleep-duration': Mean: 10.013365}, 'iqr': {'sleep-pending': IQR: 0.000104, 'sleep-duration': IQR: 0.00194}, 'max': {'sleep-pending': Max: 0.026305, 'sleep-duration': Max: 10.014499}, 'min': {'sleep-pending': Min: 0.023331, 'sleep-duration': Min: 10.012559}, 'mad': {'sleep-pending': MAD: 0.000011, 'sleep-duration': MAD: 0.000478}, 'count': {'sleep': {'finished': Count: 3., 'success': Count: 3.}}}
{'id': 513684799488, 'events': [{'timestamp': 1729181760.799435, 'name': 'finish', 'context': {'status': 0}}]}
{'id': 513684799488, 'events': [{'timestamp': 1729181760.8005102, 'name': 'release', 'context': {'ranks': 'all', 'final': True}}]}
{'id': 513684799488, 'events': [{'timestamp': 1729181760.800557, 'name': 'free'}]}
{'id': 513684799488, 'events': [{'timestamp': 1729181760.8005707, 'name': 'clean'}]}
{'id': 514053898240, 'events': [{'timestamp': 1729181760.8197052, 'name': 'finish', 'context': {'status': 0}}]}
{'id': 514053898240, 'events': [{'timestamp': 1729181760.8207376, 'name': 'release', 'context': {'ranks': 'all', 'final': True}}]}
{'id': 514053898240, 'events': [{'timestamp': 1729181760.8207989, 'name': 'free'}]}
{'id': 514053898240, 'events': [{'timestamp': 1729181760.8208258, 'name': 'clean'}]}
{'id': 683252121600, 'events': [{'timestamp': 1729181760.8696713, 'name': 'submit', 'context': {'userid': 1000, 'urgency': 16, 'flags': 0, 'version': 1}}], 'jobspec': {'resources': [{'type': 'node', 'count': 1, 'with': [{'type': 'slot', 'count': 1, 'with': [{'type': 'core', 'count': 1}], 'label': 'task'}]}], 'tasks': [{'command': ['echo', 'hello', 'world'], 'slot': 'task', 'count': {'per_slot': 1}}], 'attributes': {'system': {'duration': 0.0}, 'user': {'group': 'echo'}}, 'version': 1}}
{'id': 683252121600, 'events': [{'timestamp': 1729181760.8811576, 'name': 'validate'}]}
{'id': 683252121600, 'events': [{'timestamp': 1729181760.8918009, 'name': 'depend'}]}
{'id': 683252121600, 'events': [{'timestamp': 1729181760.8918545, 'name': 'priority', 'context': {'priority': 16}}]}
{'id': 683252121600, 'events': [{'timestamp': 1729181760.8930376, 'name': 'annotations', 'context': {'annotations': None}}]}
{'id': 683252121600, 'events': [{'timestamp': 1729181760.8930573, 'name': 'alloc'}], 'R': {'version': 1, 'execution': {'R_lite': [{'rank': '0', 'children': {'core': '7'}}], 'nodelist': ['08c63b4a360d'], 'starttime': 1729181760, 'expiration': 4882781720}}}
{'id': 683252121600, 'events': [{'timestamp': 1729181760.8941934, 'name': 'start'}]}
{'id': 683637997568, 'events': [{'timestamp': 1729181760.8929186, 'name': 'submit', 'context': {'userid': 1000, 'urgency': 16, 'flags': 0, 'version': 1}}], 'jobspec': {'resources': [{'type': 'node', 'count': 1, 'with': [{'type': 'slot', 'count': 1, 'with': [{'type': 'core', 'count': 1}], 'label': 'task'}]}], 'tasks': [{'command': ['echo', 'hello', 'world'], 'slot': 'task', 'count': {'per_slot': 1}}], 'attributes': {'system': {'duration': 0.0}, 'user': {'group': 'echo'}}, 'version': 1}}
{'id': 683252121600, 'events': [{'timestamp': 1729181760.9039655, 'name': 'finish', 'context': {'status': 0}}]}
{'id': 683252121600, 'events': [{'timestamp': 1729181760.9044635, 'name': 'release', 'context': {'ranks': 'all', 'final': True}}]}
{'id': 683252121600, 'events': [{'timestamp': 1729181760.9044845, 'name': 'free'}]}
{'id': 683252121600, 'events': [{'timestamp': 1729181760.9044933, 'name': 'clean'}]}
{'id': 683637997568, 'events': [{'timestamp': 1729181760.9033427, 'name': 'validate'}]}
{'id': 683637997568, 'events': [{'timestamp': 1729181760.9143605, 'name': 'depend'}]}
{'id': 683637997568, 'events': [{'timestamp': 1729181760.9143932, 'name': 'priority', 'context': {'priority': 16}}]}
{'id': 683637997568, 'events': [{'timestamp': 1729181760.9153101, 'name': 'annotations', 'context': {'annotations': None}}]}
{'id': 683637997568, 'events': [{'timestamp': 1729181760.9153209, 'name': 'alloc'}], 'R': {'version': 1, 'execution': {'R_lite': [{'rank': '0', 'children': {'core': '7'}}], 'nodelist': ['08c63b4a360d'], 'starttime': 1729181760, 'expiration': 4882781720}}}
{'id': 683637997568, 'events': [{'timestamp': 1729181760.916482, 'name': 'start'}]}
{'id': 684023873536, 'events': [{'timestamp': 1729181760.9156003, 'name': 'submit', 'context': {'userid': 1000, 'urgency': 16, 'flags': 0, 'version': 1}}], 'jobspec': {'resources': [{'type': 'node', 'count': 1, 'with': [{'type': 'slot', 'count': 1, 'with': [{'type': 'core', 'count': 1}], 'label': 'task'}]}], 'tasks': [{'command': ['echo', 'hello', 'world'], 'slot': 'task', 'count': {'per_slot': 1}}], 'attributes': {'system': {'duration': 0.0}, 'user': {'group': 'echo'}}, 'version': 1}}
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{'id': 683637997568, 'events': [{'timestamp': 1729181760.9271212, 'name': 'release', 'context': {'ranks': 'all', 'final': True}}]}
{'id': 683637997568, 'events': [{'timestamp': 1729181760.927144, 'name': 'free'}]}
{'id': 683637997568, 'events': [{'timestamp': 1729181760.9271522, 'name': 'clean'}]}
{'id': 684023873536, 'events': [{'timestamp': 1729181760.92615, 'name': 'validate'}]}
{'id': 684023873536, 'events': [{'timestamp': 1729181760.9372776, 'name': 'depend'}]}
{'id': 684023873536, 'events': [{'timestamp': 1729181760.9373105, 'name': 'priority', 'context': {'priority': 16}}]}
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{'id': 684023873536, 'events': [{'timestamp': 1729181760.939761, 'name': 'start'}]}
{'id': 684409749504, 'events': [{'timestamp': 1729181760.9384136, 'name': 'submit', 'context': {'userid': 1000, 'urgency': 16, 'flags': 0, 'version': 1}}], 'jobspec': {'resources': [{'type': 'node', 'count': 1, 'with': [{'type': 'slot', 'count': 1, 'with': [{'type': 'core', 'count': 1}], 'label': 'task'}]}], 'tasks': [{'command': ['echo', 'hello', 'world'], 'slot': 'task', 'count': {'per_slot': 1}}], 'attributes': {'system': {'duration': 0.0}, 'user': {'group': 'echo'}}, 'version': 1}}
{'id': 684023873536, 'events': [{'timestamp': 1729181760.9494925, 'name': 'finish', 'context': {'status': 0}}]}
{'id': 684023873536, 'events': [{'timestamp': 1729181760.9499779, 'name': 'release', 'context': {'ranks': 'all', 'final': True}}]}
{'id': 684023873536, 'events': [{'timestamp': 1729181760.9500043, 'name': 'free'}]}
{'id': 684023873536, 'events': [{'timestamp': 1729181760.9500136, 'name': 'clean'}]}
{'id': 684409749504, 'events': [{'timestamp': 1729181760.9491577, 'name': 'validate'}]}
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{'id': 684409749504, 'events': [{'timestamp': 1729181760.962481, 'name': 'start'}]}
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{'id': 684409749504, 'events': [{'timestamp': 1729181760.9745595, 'name': 'free'}]}
{'id': 684409749504, 'events': [{'timestamp': 1729181760.9745681, 'name': 'clean'}]}
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{'id': 684795625472, 'events': [{'timestamp': 1729181760.986103, 'name': 'start'}]}
{'id': 684795625472, 'events': [{'timestamp': 1729181760.9960618, 'name': 'finish', 'context': {'status': 0}}]}
🌊 Streaming ML Model Summary sleep-duration: (variance:1.459105192225026e-06),(mean:10.01287293434143),(iqr:0.0005707740783691406),(max:10.014498949050903),(min:10.011140584945679),(mad:0.0004782676696777344)
🌊 Streaming ML Model Summary echo-duration: (variance:3.7348486898736154e-07),(mean:0.01016392707824707),(iqr:0.0003674030303955078),(max:0.011217832565307617),(min:0.009731531143188477),(mad:0.0)
🌊 Streaming ML Model Summary echo-pending: (variance:2.0732844063786618e-07),(mean:0.02421259880065918),(iqr:0.0004546642303466797),(max:0.0247495174407959),(min:0.023563385009765625),(mad:9.322166442871094e-05)
🌊 Streaming ML Model Summary sleep-pending: (variance:1.614884990885912e-06),(mean:0.02403717041015625),(iqr:0.00010442733764648438),(max:0.02630472183227539),(min:0.023331403732299805),(mad:1.0967254638671875e-05)
{'id': 684795625472, 'events': [{'timestamp': 1729181760.9969702, 'name': 'release', 'context': {'ranks': 'all', 'final': True}}]}
{'id': 684795625472, 'events': [{'timestamp': 1729181760.9970036, 'name': 'free'}]}
{'id': 684795625472, 'events': [{'timestamp': 1729181760.997014, 'name': 'clean'}]}
```
</details>
It's a little verbose because I'm still debugging a lot. We can likely stream this down a bit!
## Development
We are going to use flux, so the provided [Development Container](.devcontainer) supports that.
Once you are in the container:
```bash
flux start --test-size=4
```
You can then test the MiniCluster monitoring tool (separately):
```bash
python3 -m ensemble.members.flux
```
### Questions or Items to DO
- We probably want some kind of cron or heartbeat functionality (does flux have a job like this?)
- We will want parameters, etc. to vary based on custom functions.
- Likely a custom function should be able to return None and then actions or other rules.
- Move (this) verbose readme into proper docs
- Likely bug - when we have a huge backlog to parse and can't get all previous ids.
#### Next step applications
- If the output data of lammps (or some app) looks one one, do action X. Otherwise Y. We could use "plugins" that provide custom actions here that are app specific.
- Real world use case is AMS - running either surrogate or multi-physics model. whenever runs the physics model, saves the input/output pairs and uses them to retrain. "Under what conditions would AMS need to start training." We would also want to look at cases with conditional logic.
- Flux operator - implement with grpc, and set duration criteria to grow until we reach. As example, could look at queue wait times - "This job is running too long, run lammps with 8 instead of 16." In simpler terms, if my rate of job completions is too low then I want more resources. High level, we want to change the per job shape based on amount of ime to run the job
#### Thinking through service design
1. The ensemble (python) would be started as a MiniCluster in a Kubernetes Cluster.
2. The ensemble operator would be in charge of creating the MiniCluster, along with a pod that orchestrates the service (the ensemble-service grpc)
3. To start there would be one ensemble running per Ensemble Operator CRD (but could be multiple)
4. We would only want the service to ping the operator when the MiniCluster needs to change (scale, etc).
5. This means that when we create the service and MiniCluster, each needs to know about one another (the address)
## License
HPCIC DevTools is distributed under the terms of the MIT license.
All new contributions must be made under this license.
See [LICENSE](https://github.com/converged-computing/cloud-select/blob/main/LICENSE),
[COPYRIGHT](https://github.com/converged-computing/cloud-select/blob/main/COPYRIGHT), and
[NOTICE](https://github.com/converged-computing/cloud-select/blob/main/NOTICE) for details.
SPDX-License-Identifier: (MIT)
LLNL-CODE- 842614
Raw data
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"name": "ensemble-python",
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"keywords": "multi-cluster, scheduler",
"author": "Vanessasaurus",
"author_email": "vsoch@users.noreply.github.com",
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"platform": null,
"description": "# ensemble\n\n\n\nAn HPC ensemble is an orchestration of jobs that can ideally be controlled by an algorithm. Ensemble (in python) is a project to do exactly that. As a user, you specify the parameters for your job, and then an algorithm and options for it. But when you think about it, an algorithm *could* have a known name or label, but in its simplest form it is a set of rules (triggers and actions) that make up a state machine. In that light, ensemble python is a simple tool to create state machines to orchestrate units of work with workload managers. Specific sets of rules could be packaged up to be called an algorithm, and in fact that might be what we eventually call the yaml file that defines them. For now, we call them ensembles.\n\nThe library here listens for the heartbeat of your ensemble -- events that come directly from the queue or entity that is controlling the jobs.\nThis means that we define:\n\n- A number of executors (typically queues) that can deliver events (job completion events to start)\n- Rules for when to submit jobs (at onset, at periods during running)\n- Rules for when to stop, cancel, or terminate\n- Parameters for the jobs (what will be converted into a job specification for the queue to consume)\n- Rules for when to change the environment (cluster) like growing or shrinking, if supported\n- A set of online (streaming) ML metrics (e.g., mean, median or MAD, min/max, etc) that are recorded for each job group and state (finished, failed succeeded)\n\nAt a high level, we need to be able to define events, and rules for transitioning to new states. This even means we could make infinite loops (I accidentally already did). There should be an executor or queue interface that can support any kind of workload manager queue that can return the expected types.\n\n\ud83d\udea7 Under Construction! \ud83d\udea7\n\n## Design\n\nThis design will be translated into more consolidated design documentation. For now I'm putting it here.\n\n### Concepts\n\n- **Executor** provides a queue backend that should handle sending events. While a poll oriented design could work, it's not ideal. In the context of the ensemble, the queue executor is referred to as a **member**.\n- **Ensemble Service**: provides grpc endpoints for one or more ensemble members to communicate with. This is an explicit design decision that, for example, would allow deploying one service that is orchestrating multiple things at once.\n- **Rules**: A rule is composed of a trigger and action to take, and this is what drives the ensemble, more akin to a state machine than a traditional workflow DAG because the structure can be unknown at the start. For example, you might say \"on the start of the ensemble, submit these jobs with label X.\"\n- **Triggers**: A trigger is part of a rule (described above) and in the configuration file, and can be read as \"when this trigger happens, do this action.\"\n- **Action**: An actual is an operation that is the result of hitting a trigger condition. One type of action is performed by the queue, and are known to it. For example, queue actions include submit, scale-up, scale-down, or terminate. Actions are typically called by triggers under rules. Actions can be customized, and these are called custom actions.\n- **Plugins**: A plugin is a collection of custom actions that are typically associated with a particular application. For example, a plugin for LAMMPS might know how to check LAMMPS output and act on a specific parsing of a result. Plugins are used equivalently to custom functions, and can accept arguments.\n- **Metrics** are summary metrics collected for groups of jobs, for customized algorithms. To support this, we use online (or streaming) ML algorithms for things like mean, IQR, etc. While there could be a named entity called an algorithm, since it's just a set of rules (triggers and actions) that means we don't need to explicitly define them (yet). I can see at some point creating \"packaged\" rule sets that are called that.\n\n\n#### Logging\n\nBy default, we have a pretty printed set of the \"main\" events (triggers and submissions). However, to see ALL events from Flux (good for debugging) you can turn them on:\n\n```yaml\nlogging:\n debug: true\n```\n\nTo set the event heartbeat to fire at some increment, set it:\n\n```yaml\nlogging:\n debug: true\n heartbeat: 60\n```\n\nNote that by default it is turned off (set to 0 seconds) unless you include a grow or shrink action. In that case, it turns on and defaults to 60, unless you've specified another interval.\nIf you have grow/shrink and explicitly turn it off, it will still default to 60 seconds, because grow/shrink won't work as expected without the heartbeat.\n\n#### Rules\n\nA rule defines a trigger and action to take. The library is event driven, meaning that the queue is expected to send events, and we don't do any polling.\n\n##### Triggers\n\nThe current triggers supported are [events for flux](https://flux-framework.readthedocs.io/projects/flux-rfc/en/latest/spec_21.html). In addition, we have added:\n\n- metric: Triggered when a queue metric is updated\n\nFor example, the following snippet defines two rules. The first says \"on the start of the ensemble, submit the job group labeled as lammps.\" And then when lammps has 3 successful runs, submit the job group amg (groups are not shown).\n\n```yaml\nrules:\n - trigger: start\n action:\n name: submit\n label: lammps\n\n - trigger: metric\n name: count.lammps.success\n when: 3\n action:\n name: submit\n label: amg\n```\n\nNote that yes, this means \"submit\" is both an action and an event.\n\n##### Actions\n\nThe design of a rule is to have an action, and the action is something your ensemble can be tasked to do when an event is triggered. Right now we support the following:\n\n**Flux and MiniCluster**\n\n- *submit*: submit a job\n- *terminate*: terminate the member. This is usually what you want to call to have the entire thing exit on 0\n- *custom*: run a custom function that will receive known kwargs, and then should return an action (read more below)\n\n**MiniCluster Only**\n\n- *grow*: grow (or request) the cluster to scale up\n- *shrink*: shrink (or request) the cluster to scale down\n\nFor the scale operations, since this brings in the issue of resource contention between different ensembles, we have to assume to start that a request to scale:\n\n1. Should only happen once. If it's granted, great, if not, we aren't going to ask again.\n2. Does not need to consider another level of scheduler (e.g., fair share)\n\nI started thinking about putting a second scheduler (or fair share algorithm) in the grpc service, but realized this was another level of complexity that although we might work on it later, is not warranted yet.\nAlso note that since scale operation triggers might not be linked to job events (e.g., if we want to trigger when a job group has been in the queue for too long) we added support for a heartbeat. The heartbeat\nisn't a trigger in and of itself, but when it runs, it will run through rules that are relevant to queue metrics.\nWe see \"submit\" as two examples in the above, which is a common thing you'd want to do! For each action, you should minimally define the \"name\" and a \"label\" that typically corresponds to a job group.\nYou can also optionally define \"repetitions,\" which are the number of times the action should be run before expiring. If you want a backoff period between repetitions, set \"backoff\" to a non zero value.\nBy default, when no repetitions or backoff are set, the action is assumed to have a repetition of 1. It will be run once! Let's now look at a custom action. Here is what your function should look like in your `ensemble.yaml`\n\n##### Actions\n\n\nCustom actions are also supported, where you define a custom function that in and of itself returns an action! Here is an example to extend the above. Let's say we want to run the group \"echo\" again, we might do the following:\n\n```python\n def echo_again(**kwargs):\n \"\"\"\n For a custom function, you can assume the following in kwargs,\n and note that this set of metadata can vary based on the ensemble type.\n metrics: the full QueueMetrics object to use as needed\n event: the full event that triggered the custom action\n rule: the original rule that triggered the action\n action: your custom action\n handle: the active flux handle (Flux ensemble specific!)\n\n You should (must) return an action in your response, or None to take\n no action. E.g.,: return Action({\"name\": \"submit\", \"label\": \"echo\"})\n \"\"\"\n return Action({\"name\": \"submit\", \"label\": \"echo\"})\n```\n\nNote that in the above, we get access to the following via kwargs:\n\n- metrics: the full QueueMetrics object to use as needed\n- event: the full event that triggered the custom action\n- rule: the original rule that triggered the action\n- action: your custom action\n- handle: the active flux handle (Flux ensemble specific!)\n\n- A custom rule can have a trigger, and then return another rule. This means that:\n - return \"None\" to do nothing\n - return an action to follow up your custom function\n\n\n#### Metrics\n\nWe use streaming ML \"stats\" for each job group, and then a subset of variables. Right now we support, for each job group:\n\n- **duration** Each of variance, mean, iqr, max, min, and mad (mean absolute deviation) for the duration of the job\n- **pending-time** Each of variance, mean, iqr, max, min, and mad (mean absolute deviation) for the time the job spent in the queue (pending state)\n\nHere is an example that shows duration for a job group called \"echo.\"\n\n```console\n\ud83c\udf0a Streaming ML Model Summary:\n name : echo-duration\n variance : 3.561605860855515e-07\n mean : 0.013609373569488525\n iqr : 0.00041562974899014317\n max : 0.014329195022583008\n min : 0.011783838272094727\n mad : 0.00024357753771322735\n```\n\nAlong with that, we take counts of everything! Here is after running two groups of jobs, where one job group was triggered to run after a count of the first was recorded.\n\n```console\n{'variance': {'sleep-duration': Var: 0.000006, 'echo-duration': Var: 0.}, 'mean': {'sleep-duration': Mean: 10.015157, 'echo-duration': Mean: 0.013609}, 'iqr': {'sleep-duration': IQR: 0.003083, 'echo-duration': IQR: 0.000416}, 'max': {'sleep-duration': Max: 10.020326, 'echo-duration': Max: 0.014329}, 'min': {'sleep-duration': Min: 10.012975, 'echo-duration': Min: 0.011784}, 'mad': {'sleep-duration': MAD: 0.001754, 'echo-duration': MAD: 0.000244}, 'count': {'sleep': {'finished': Count: 10., 'success': Count: 10.}, 'echo': {'finished': Count: 20., 'success': Count: 20.}}}\n```\n\n### Overview\n\nSince this will need to handle many ensembles, I'm going to try designing it as a service. But note that it doesn't _need_ to be run as one (I'm developing and just running flux directly with the command line client, which works too). There will be grpc endpoints that can receive messages. Each queue or executor will have its own separate running process. E.g., for Flux we will have a script running alongside a broker with a flux handle. This should ideally use events (but is not required to if the queue does not support that).\n\n\n## Example\n\nLet's do an example running in the Development container, where we have flux. You can do the following:\n\n```bash\n# Start a flux instance\nflux start --test-size=4\n\n# Install in development mode, and run \"make\" to rebuild proto if necessary\nsudo pip install -e .\n\n# Start the server (actually you don't need to do this, I'm not using it yet)\nensemble-server start\n\n# Run the hello-world example ensemble! it will submit and monitor job events, etc\nensemble run examples/hello-world.yaml\n\n# Here is how to add on the fly debug (logging->debug true)\nensemble run --debug examples/hello-world.yaml\n\n# This example shows using repetitions and backoff\nensemble run examples/backoff-example.yaml\n\n# This shows a custom action\nensemble run examples/custom-action-example.yaml\n\n# This shows termination, which is necessary for when you want an exit\nensemble run examples/terminate-example.yaml\n\n# Run a heartbeat every 3 seconds.\n# This will trigger a check to see if actions need to be performed\nensemble run examples/heartbeat-example.yaml\n\n# Grow/shrink requires a minicluster (flux doesn't support it) but we can mock it here\n# Note that a --name with <namespace>/<name> is required\nensemble run --name default/ensemble --executor minicluster examples/grow-shrink-example.yaml\n```\n\nRight now, this will run any rules with \"start\" triggers, which for this hello world example includes a few hello world jobs! You'll then be able to watch and see flux events coming in!\nHere is the full run - we run a bunch of sleep jobs (10) and when we hit a count of 5, we launch a bunch of echo jobs.\n\n```console\n$ ensemble run examples/hello-world.yaml\n => trigger start\n submit sleep (name:sleep),(command:sleep 10),(count:5),(nodes:1)\n => trigger count.sleep.success\n submit echo (name:echo),(command:echo hello world),(count:5),(nodes:1)\n```\n\nThe above output is colored! Note that in any ensemble file, you can turn on debug mode to see more verbose output (events, etc.)\n\n<details>\n\n<summary>Example Ensemble Run</summary>\n\nThis run shows having verbose enabled.\n\n```console\n$ ensemble run examples/hello-world.yaml\n => trigger start\n submit sleep (name:sleep),(command:sleep 10),(count:5),(nodes:1)\n{'id': 513684799488, 'events': [{'timestamp': 1729181750.7627614, 'name': 'submit', 'context': {'userid': 1000, 'urgency': 16, 'flags': 0, 'version': 1}}, {'timestamp': 1729181750.7731683, 'name': 'validate'}, {'timestamp': 1729181750.7839293, 'name': 'depend'}, {'timestamp': 1729181750.7839663, 'name': 'priority', 'context': {'priority': 16}}, {'timestamp': 1729181750.785146, 'name': 'alloc'}, {'timestamp': 1729181750.7863054, 'name': 'start'}], 'jobspec': {'resources': [{'type': 'node', 'count': 1, 'with': [{'type': 'slot', 'count': 1, 'with': [{'type': 'core', 'count': 1}], 'label': 'task'}]}], 'tasks': [{'command': ['sleep', '10'], 'slot': 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{'system': {'duration': 0.0}, 'user': {'group': 'sleep'}}, 'version': 1}, 'R': {'version': 1, 'execution': {'R_lite': [{'rank': '2', 'children': {'core': '7'}}], 'nodelist': ['08c63b4a360d'], 'starttime': 1729181750, 'expiration': 4882781720}}}\n{'id': 514053898240, 'events': [{'timestamp': 1729181750.7850096, 'name': 'submit', 'context': {'userid': 1000, 'urgency': 16, 'flags': 0, 'version': 1}}, {'timestamp': 1729181750.7954807, 'name': 'validate'}, {'timestamp': 1729181750.8061984, 'name': 'depend'}, {'timestamp': 1729181750.806234, 'name': 'priority', 'context': {'priority': 16}}], 'jobspec': {'resources': [{'type': 'node', 'count': 1, 'with': [{'type': 'slot', 'count': 1, 'with': [{'type': 'core', 'count': 1}], 'label': 'task'}]}], 'tasks': [{'command': ['sleep', '10'], 'slot': 'task', 'count': {'per_slot': 1}}], 'attributes': {'system': {'duration': 0.0}, 'user': {'group': 'sleep'}}, 'version': 1}}\n{'id': 512929824768, 'events': [{'timestamp': 1729181750.7181482, 'name': 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'events': [{'timestamp': 1729181760.9837554, 'name': 'depend'}]}\n{'id': 684795625472, 'events': [{'timestamp': 1729181760.983788, 'name': 'priority', 'context': {'priority': 16}}]}\n{'id': 684795625472, 'events': [{'timestamp': 1729181760.9848664, 'name': 'annotations', 'context': {'annotations': None}}]}\n{'id': 684795625472, 'events': [{'timestamp': 1729181760.984877, 'name': 'alloc'}], 'R': {'version': 1, 'execution': {'R_lite': [{'rank': '0', 'children': {'core': '7'}}], 'nodelist': ['08c63b4a360d'], 'starttime': 1729181760, 'expiration': 4882781720}}}\n{'id': 684795625472, 'events': [{'timestamp': 1729181760.986103, 'name': 'start'}]}\n{'id': 684795625472, 'events': [{'timestamp': 1729181760.9960618, 'name': 'finish', 'context': {'status': 0}}]}\n\ud83c\udf0a Streaming ML Model Summary sleep-duration: (variance:1.459105192225026e-06),(mean:10.01287293434143),(iqr:0.0005707740783691406),(max:10.014498949050903),(min:10.011140584945679),(mad:0.0004782676696777344)\n\ud83c\udf0a Streaming ML Model Summary echo-duration: (variance:3.7348486898736154e-07),(mean:0.01016392707824707),(iqr:0.0003674030303955078),(max:0.011217832565307617),(min:0.009731531143188477),(mad:0.0)\n\ud83c\udf0a Streaming ML Model Summary echo-pending: (variance:2.0732844063786618e-07),(mean:0.02421259880065918),(iqr:0.0004546642303466797),(max:0.0247495174407959),(min:0.023563385009765625),(mad:9.322166442871094e-05)\n\ud83c\udf0a Streaming ML Model Summary sleep-pending: (variance:1.614884990885912e-06),(mean:0.02403717041015625),(iqr:0.00010442733764648438),(max:0.02630472183227539),(min:0.023331403732299805),(mad:1.0967254638671875e-05)\n{'id': 684795625472, 'events': [{'timestamp': 1729181760.9969702, 'name': 'release', 'context': {'ranks': 'all', 'final': True}}]}\n{'id': 684795625472, 'events': [{'timestamp': 1729181760.9970036, 'name': 'free'}]}\n{'id': 684795625472, 'events': [{'timestamp': 1729181760.997014, 'name': 'clean'}]}\n```\n\n</details>\n\nIt's a little verbose because I'm still debugging a lot. We can likely stream this down a bit!\n\n## Development\n\nWe are going to use flux, so the provided [Development Container](.devcontainer) supports that.\nOnce you are in the container:\n\n```bash\nflux start --test-size=4\n```\n\nYou can then test the MiniCluster monitoring tool (separately):\n\n```bash\npython3 -m ensemble.members.flux\n```\n\n### Questions or Items to DO\n\n- We probably want some kind of cron or heartbeat functionality (does flux have a job like this?)\n- We will want parameters, etc. to vary based on custom functions.\n- Likely a custom function should be able to return None and then actions or other rules.\n- Move (this) verbose readme into proper docs\n- Likely bug - when we have a huge backlog to parse and can't get all previous ids.\n\n#### Next step applications\n\n- If the output data of lammps (or some app) looks one one, do action X. Otherwise Y. We could use \"plugins\" that provide custom actions here that are app specific.\n- Real world use case is AMS - running either surrogate or multi-physics model. whenever runs the physics model, saves the input/output pairs and uses them to retrain. \"Under what conditions would AMS need to start training.\" We would also want to look at cases with conditional logic.\n- Flux operator - implement with grpc, and set duration criteria to grow until we reach. As example, could look at queue wait times - \"This job is running too long, run lammps with 8 instead of 16.\" In simpler terms, if my rate of job completions is too low then I want more resources. High level, we want to change the per job shape based on amount of ime to run the job\n\n#### Thinking through service design\n\n1. The ensemble (python) would be started as a MiniCluster in a Kubernetes Cluster.\n2. The ensemble operator would be in charge of creating the MiniCluster, along with a pod that orchestrates the service (the ensemble-service grpc)\n3. To start there would be one ensemble running per Ensemble Operator CRD (but could be multiple)\n4. We would only want the service to ping the operator when the MiniCluster needs to change (scale, etc).\n5. This means that when we create the service and MiniCluster, each needs to know about one another (the address)\n\n## License\n\nHPCIC DevTools is distributed under the terms of the MIT license.\nAll new contributions must be made under this license.\n\nSee [LICENSE](https://github.com/converged-computing/cloud-select/blob/main/LICENSE),\n[COPYRIGHT](https://github.com/converged-computing/cloud-select/blob/main/COPYRIGHT), and\n[NOTICE](https://github.com/converged-computing/cloud-select/blob/main/NOTICE) for details.\n\nSPDX-License-Identifier: (MIT)\n\nLLNL-CODE- 842614\n",
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