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# EasySweeps - A CLI tool for automating Weights & Biases sweeps across multiple GPUs.

Watch EasySweeps quick usage video

Why EasySweeps?

W&B is great for experiment tracking, but managing sweeps at scale has pain points:

• Repetitive setup – Launching sweeps across datasets requires manual duplication
• Limited agent control – No built-in way to stop agents by GPU or sweep

• Manual management – Multi-GPU sweep orchestration requires custom scripts

EasySweeps solves these by providing simple commands to create, launch, monitor, and manage sweep agents.

Installation

pip install easysweeps

Requirements: Python 3.7+, W&B account, CUDA GPUs (optional)

Quick Start

# Initialize project structure
ez init

# Create sweeps from the template and variant in the "sweeps" directory
ez sweep

# Launch agents
ez agent <SWEEP_ID> --gpu-list 0 1 2

# Monitor status of sweeps and agents
ez status

# Kill agents on specific GPU
ez kill --sweep <SWEEP_ID> --gpu <gpu-number>

Configuration

After running ez init a file named ez_config.yaml is built. This file defines the structure of your project:

sweep_dir: "sweeps"          # Directory for sweep template and variants files
agent_log_dir: "agent_logs"  # Directory for agent logs
entity: "your_entity"        # W&B entity (null = current user logged in to wandb)
project: "your_project"      # W&B project name

Commands

ez init

Scaffolds project structure with config file and example templates.

ez sweep

Creates W&B sweeps from a template and variants file.

The folder with the template and variants files is configured in the ez_config.yaml file.

Why Templates & Variants?

When running hyperparameter sweeps across multiple datasets or environments, you often want: - Separate sweeps per dataset – Avoid mixing data from different distributions, which can skew optimization - Shared hyperparameter configurations – Reuse the same search space and method across datasets - DRY principle – Maintain one template instead of duplicating configs for each dataset

For example, if optimizing learning rate and batch size for both MNIST and CIFAR-10, you don't want the optimizer to see loss data from both datasets together. Instead, EasySweeps creates isolated sweeps for each dataset using the same hyperparameter template.

Template (sweeps/sweep_template.yaml):

name: "example_{dataset}" # Will create one sweep per dataset
method: "grid"
metric:
  name: "loss"
  goal: "minimize"
parameters:
  learning_rate:
    values: [0.001, 0.01]
  dataset:
    value: None  # Replaced by variants
program: "train.py"

Variants (sweeps/sweep_variants.yaml):

dataset: ['mnist', 'cifar10']  # Creates one sweep configuration per dataset

ez agent

Launches sweep agents on specified GPUs as systemd scope units.

ez agent                                # List available sweeps
ez agent abc123 --gpu-list 0 1 2        # Launch on GPUs 0,1,2
ez agent abc123 --gpu-list 0 --agents-per-sweep 3  # 3 agents on GPU 0

ez status

Shows all sweeps and running agents with GPU assignments and runtime.

ez kill

Stops running agents with flexible targeting.

ez kill                         # List active sweeps
ez kill --force                 # Kill all agents
ez kill --gpu 0                 # Kill agents on GPU 0
ez kill --sweep abc123          # Kill agents for specific sweep
ez kill --sweep abc123 --gpu 0  # Target specific sweep + GPU

Examples

How to use examples:

cd example
ez init
ez sweep
ez agent <sweep_id> --gpu-list 0 1 2

Contributors ✨

EasySweeps is built and maintained by:

Yaniv Galron 💻 🔌	Ron Tohar 💻 🔌

License

MIT License - see LICENSE for details.

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