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
Alternatively, you can clone this repository and afterwards 'manually' install it:
git clone https://github.com/mle-infrastructure/mle-monitor.git
cd mle-monitor
pip install -e .
Development & Milestones for Next Release
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 contact me @RobertTLange or create an issue
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