The `mle-scheduler` Package

Resource allocation made easy 🚂

mle-scheduler provides a lightweight API to launch and monitor job queues on Slurm/Open Grid Engine clusters, SSH servers or Google Cloud Platform VMs. It smoothly orchestrates simultaneous runs for different configurations and/or random seeds. It is meant to reduce boilerplate and to make job resource specification intuitive. It comes with two core pillars:

MLEJob: Launches and monitors a single job on a resource (Slurm, Open Grid Engine, GCP, SSH, etc.).
MLEQueue: Launches and monitors a queue of jobs with different training configurations and/or seeds.

For a quickstart check out the notebook blog or the example scripts 📖

	Local	Slurm	Grid Engine	SSH	GCP

Installation ⏳

pip install mle-scheduler

Managing a Single Job with `MLEJob` Locally 🚀

from mle_scheduler import MLEJob

# python train.py -config base_config_1.yaml -exp_dir logs_single -seed_id 1
job = MLEJob(
    resource_to_run="local",
    job_filename="train.py",
    config_filename="base_config_1.yaml",
    experiment_dir="logs_single",
    seed_id=1
)

_ = job.run()

Managing a Queue of Jobs with `MLEQueue` Locally 🚀...🚀

from mle_scheduler import MLEQueue

# python train.py -config base_config_1.yaml -seed 0 -exp_dir logs_queue/<date>_base_config_1
# python train.py -config base_config_1.yaml -seed 1 -exp_dir logs_queue/<date>_base_config_1
# python train.py -config base_config_2.yaml -seed 0 -exp_dir logs_queue/<date>_base_config_2
# python train.py -config base_config_2.yaml -seed 1 -exp_dir logs_queue/<date>_base_config_2
queue = MLEQueue(
    resource_to_run="local",
    job_filename="train.py",
    config_filenames=["base_config_1.yaml",
                      "base_config_2.yaml"],
    random_seeds=[0, 1],
    experiment_dir="logs_queue"
)

queue.run()

Launching Slurm Cluster-Based Jobs 🐒

# Each job requests 5 CPU cores & 1 V100S GPU & loads CUDA 10.0
job_args = {
    "partition": "<SLURM_PARTITION>",  # Partition to schedule jobs on
    "env_name": "mle-toolbox",  # Env to activate at job start-up
    "use_conda_venv": True,  # Whether to use anaconda venv
    "num_logical_cores": 5,  # Number of requested CPU cores per job
    "num_gpus": 1,  # Number of requested GPUs per job
    "gpu_type": "V100S",  # GPU model requested for each job
    "modules_to_load": "nvidia/cuda/10.0"  # Modules to load at start-up
}

queue = MLEQueue(
    resource_to_run="slurm-cluster",
    job_filename="train.py",
    job_arguments=job_args,
    config_filenames=["base_config_1.yaml",
                      "base_config_2.yaml"],
    experiment_dir="logs_slurm",
    random_seeds=[0, 1]
)
queue.run()

Launching GridEngine Cluster-Based Jobs 🐘

# Each job requests 5 CPU cores & 1 V100S GPU w. CUDA 10.0 loaded
job_args = {
    "queue": "<GRID_ENGINE_QUEUE>",  # Queue to schedule jobs on
    "env_name": "mle-toolbox",  # Env to activate at job start-up
    "use_conda_venv": True,  # Whether to use anaconda venv
    "num_logical_cores": 5,  # Number of requested CPU cores per job
    "num_gpus": 1,  # Number of requested GPUs per job
    "gpu_type": "V100S",  # GPU model requested for each job
    "gpu_prefix": "cuda"  #$ -l {gpu_prefix}="{num_gpus}"
}

queue = MLEQueue(
    resource_to_run="slurm-cluster",
    job_filename="train.py",
    job_arguments=job_args,
    config_filenames=["base_config_1.yaml",
                      "base_config_2.yaml"],
    experiment_dir="logs_grid_engine",
    random_seeds=[0, 1]
)
queue.run()

Launching SSH Server-Based Jobs 🦊

ssh_settings = {
    "user_name": "<SSH_USER_NAME>",  # SSH server user name
    "pkey_path": "<PKEY_PATH>",  # Private key path (e.g. ~/.ssh/id_rsa)
    "main_server": "<SSH_SERVER>",  # SSH Server address
    "jump_server": '',  # Jump host address
    "ssh_port": 22,  # SSH port
    "remote_dir": "mle-code-dir",  # Dir to sync code to on server
    "start_up_copy_dir": True,  # Whether to copy code to server
    "clean_up_remote_dir": True  # Whether to delete remote_dir on exit
}

job_args = {
    "env_name": "mle-toolbox",  # Env to activate at job start-up
    "use_conda_venv": True  # Whether to use anaconda venv
}

queue = MLEQueue(
    resource_to_run="ssh-node",
    job_filename="train.py",
    config_filenames=["base_config_1.yaml",
                      "base_config_2.yaml"],
    random_seeds=[0, 1],
    experiment_dir="logs_ssh_queue",
    job_arguments=job_args,
    ssh_settings=ssh_settings)

queue.run()

Launching GCP VM-Based Jobs 🦄

cloud_settings = {
    "project_name": "<GCP_PROJECT_NAME>",  # Name of your GCP project
    "bucket_name": "<GCS_BUCKET_NAME>", # Name of your GCS bucket
    "remote_dir": "<GCS_CODE_DIR_NAME>",  # Name of code dir in bucket
    "start_up_copy_dir": True,  # Whether to copy code to bucket
    "clean_up_remote_dir": True  # Whether to delete remote_dir on exit
}

job_args = {
    "num_gpus": 0,  # Number of requested GPUs per job
    "gpu_type": None,  # GPU requested e.g. "nvidia-tesla-v100"
    "num_logical_cores": 1,  # Number of requested CPU cores per job
}

queue = MLEQueue(
    resource_to_run="gcp-cloud",
    job_filename="train.py",
    config_filenames=["base_config_1.yaml",
                      "base_config_2.yaml"],
    random_seeds=[0, 1],
    experiment_dir="logs_gcp_queue",
    job_arguments=job_args,
    cloud_settings=cloud_settings,
)
queue.run()

The mle-scheduler Package