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Arcee
The OptScale ML profiling tool by Hystax
Arcee is a tool that hepls you to integrate ML tasks with OptScale. This tool can automatically collect executor metadata from cloud and process stats.
Installation
Arcee requires python 3.7+ to run.
pip install optscale-arcee
Usage
First of all you need to import and init arcee in your code:
import optscale_arcee as arcee
# init arcee using context manager syntax
with arcee.init('token', 'model_key'):
# some code
To use custom endpoint and enable\disable ssl checks (supports using self-signed ssl certificates):
with arcee.init('token', 'model_key', endpoint_url='https://my.custom.endpoint:443/arcee/v2', ssl=False):
# some code
Alternatively arcee can be initialized via function call. However manual finish is required:
arcee.init('token', 'model_key')
# some code
arcee.finish()
Or in error case:
arcee.init('token', 'model_key')
# some code
arcee.error()
To send stats:
arcee.send({"loss": 2.0012, "iter": 2, "epoch": 1})
(key should be string, value - int or float, multiple values can be sent)
To add tags to model run (key, value):
arcee.tag("project", "torchvision demo")
To add milestones:
arcee.milestone("Download test data")
To add stages:
arcee.stage("calculation")
To add hyperparameters:
arcee.hyperparam("epochs", 5)
Logging datasets
To log a dataset, use the dataset method with the following parameter:
- path (str): the path of the dataset.
arcee.dataset("dataset_path")
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