A Lightweight Experiment & Resource Monitoring Tool 📺

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Deep Learningmle-monitor
Overview

Lightweight Experiment & Resource Monitoring 📺

Pyversions PyPI version Code style: black Colab codecov

"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.

drawing

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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Comments
  • Is the dashboard pooling squeue?

    Is the dashboard pooling squeue?

    Hey, Thanks for publishing the library, the dashboard looks great!

    However, I was a bit concerned to see you are using squeue since the official documentation says

    "Executing squeue sends a remote procedure call to slurmctld. If enough calls from squeue or other Slurm client commands that send remote procedure calls to the slurmctld daemon come in at once, it can result in a degradation of performance of the slurmctld daemon, possibly resulting in a denial of service.

    Do not run squeue or other Slurm client commands that send remote procedure calls to slurmctld from loops in shell scripts or other programs. Ensure that programs limit calls to squeue to the minimum necessary for the information you are trying to gather."

    Do you poll squeue or is there some other, smarter management of it that I missed?

    Thanks, Eliahu

    opened by eliahuhorwitz 0
Releases(v0.0.1)

  • v0.0.1(Dec 9, 2021)

    Basic API for MLEProtocol, MLEResource & MLEDashboard:

    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)
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    Source code(zip)
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