mle-monitor 0.0.2

Machine Learning Experiment Resource Monitoring

Lightweight Experiment & Resource Monitoring 📺

"Did I already run this experiment before? How many resources are currently available on my cluster?" If these are common questions you encounter during your daily life as a researcher, then mle-monitor is made for you. It provides a lightweight API for tracking your experiments using a pickle protocol database (e.g. for hyperparameter searches and/or multi-configuration/multi-seed runs). Furthermore, it comes with built-in resource monitoring on Slurm/Grid Engine clusters and local machines/servers.

mle-monitor provides three core functionalities:

• MLEProtocol: A composable protocol database API for ML experiments.
• MLEResource: A tool for obtaining server/cluster usage statistics.
• MLEDashboard: A dashboard visualizing resource usage & experiment protocol.

To get started I recommend checking out the colab notebook and an example workflow.

MLEProtocol: Keeping Track of Your Experiments 📝

from mle_monitor import MLEProtocol

# Load protocol database or create new one -> print summary
protocol_db = MLEProtocol("mle_protocol.db", verbose=False)
protocol_db.summary(tail=10, verbose=True)

# Draft data to store in protocol & add it to the protocol
meta_data = {
    "purpose": "Grid search",  # Purpose of experiment
    "project_name": "MNIST",  # Project name of experiment
    "experiment_type": "hyperparameter-search",  # Type of experiment
    "experiment_dir": "experiments/logs",  # Experiment directory
    "num_total_jobs": 10,  # Number of total jobs to run
    ...
}
new_experiment_id = protocol_db.add(meta_data)

# ... train your 10 (pseudo) networks/complete respective jobs
for i in range(10):
    protocol_db.update_progress_bar(new_experiment_id)

# Wrap up an experiment (store completion time, etc.)
protocol_db.complete(new_experiment_id)

The meta data can contain the following keys:

Search Type	Description	Default
purpose	Purpose of experiment	'None provided'
project_name	Project name of experiment	'default'
exec_resource	Resource jobs are run on	'local'
experiment_dir	Experiment log storage directory	'experiments'
experiment_type	Type of experiment to run	'single'
base_fname	Main code script to execute	'main.py'
config_fname	Config file path of experiment	'base_config.yaml'
num_seeds	Number of evaluations seeds	1
num_total_jobs	Number of total jobs to run	1
num_job_batches	Number of jobs in single batch	1
num_jobs_per_batch	Number of sequential job batches	1
time_per_job	Expected duration: days-hours-minutes	'00:01:00'
num_cpus	Number of CPUs used in job	1
num_gpus	Number of GPUs used in job	0

Additionally you can synchronize the protocol with a Google Cloud Storage (GCS) bucket by providing cloud_settings. In this case also the results stored in experiment_dir will be uploaded to the GCS bucket, when you call protocol.complete().

# Define GCS settings - requires 'GOOGLE_APPLICATION_CREDENTIALS' env var.
cloud_settings = {
    "project_name": "mle-toolbox",  # GCP project name
    "bucket_name": "mle-protocol",  # GCS bucket name
    "use_protocol_sync": True,  # Whether to sync the protocol to GCS
    "use_results_storage": True,  # Whether to sync experiment_dir to GCS
}
protocol_db = MLEProtocol("mle_protocol.db", cloud_settings, verbose=True)

The MLEResource: Keeping Track of Your Resources 📉

On Your Local Machine

from mle_monitor import MLEResource

# Instantiate local resource and get usage data
resource = MLEResource(resource_name="local")
resource_data = resource.monitor()

On a Slurm Cluster

resource = MLEResource(
    resource_name="slurm-cluster",
    monitor_config={"partitions": ["<partition-1>", "<partition-2>"]},
)

On a Grid Engine Cluster

resource = MLEResource(
    resource_name="sge-cluster",
    monitor_config={"queues": ["<queue-1>", "<queue-2>"]}
)

The MLEDashboard: Dashboard Visualization 🎞️

from mle_monitor import MLEDashboard

# Instantiate dashboard with protocol and resource
dashboard = MLEDashboard(protocol, resource)

# Get a static snapshot of the protocol & resource utilisation printed in console
dashboard.snapshot()

# Run monitoring in while loop - dashboard
dashboard.live()

Installation ⏳

A PyPI installation is available via:

pip install mle-monitor

If you want to get the most recent commit, please install directly from the repository:

pip install git+https://github.com/mle-infrastructure/mle-monitor.git@main

Citing the MLE-Infrastructure ✏️

If you use mle-monitor in your research, please cite it as follows:

@software{mle_infrastructure2021github,
  author = {Robert Tjarko Lange},
  title = {{MLE-Infrastructure}: A Set of Lightweight Tools for Distributed Machine Learning Experimentation},
  url = {http://github.com/mle-infrastructure},
  year = {2021},
}

Development 👷

You can run the test suite via python -m pytest -vv tests/. If you find a bug or are missing your favourite feature, feel free to create an issue and/or start contributing 🤗.