Batch System Integration

from toil.batchSystems.abstractBatchSystem import (
    AbstractBatchSystem, 
    AbstractScalableBatchSystem,
    UpdatedBatchJobInfo,
    BatchJobExitReason
)
from toil.job import JobDescription
from typing import Optional, Dict, List

class CustomBatchSystem(AbstractBatchSystem):
    """Custom batch system implementation."""
    
    def issueBatchJob(self, jobNode: JobDescription) -> int:
        """Submit job to batch system and return job ID."""
        # Extract resource requirements
        memory_mb = jobNode.memory // (1024 * 1024)
        cores = jobNode.cores  
        disk_mb = jobNode.disk // (1024 * 1024)
        
        # Submit to underlying scheduler
        batch_job_id = self.submit_to_scheduler(
            command=jobNode.command,
            memory=memory_mb,
            cores=cores,
            disk=disk_mb
        )
        
        return batch_job_id
    
    def killBatchJobs(self, jobIDs: List[int]) -> None:
        """Terminate specified jobs."""
        for job_id in jobIDs:
            self.cancel_job(job_id)
    
    def getIssuedBatchJobIDs(self) -> List[int]:
        """Get list of all submitted job IDs."""
        return list(self.issued_jobs.keys())
    
    def getRunningBatchJobIDs(self) -> Dict[int, float]:
        """Get running jobs with their runtime in seconds."""
        running_jobs = {}
        for job_id in self.issued_jobs:
            if self.is_job_running(job_id):
                runtime = self.get_job_runtime(job_id)
                running_jobs[job_id] = runtime
        return running_jobs
    
    def getUpdatedBatchJob(self, maxWait: int) -> Optional[UpdatedBatchJobInfo]:
        """Poll for completed job, waiting up to maxWait seconds."""
        completed_job = self.poll_for_completion(maxWait)
        
        if completed_job:
            return UpdatedBatchJobInfo(
                jobID=completed_job.id,
                exitReason=BatchJobExitReason.FINISHED,
                wallTime=completed_job.wall_time,
                exitCode=completed_job.exit_code
            )
        return None
    
    def getSchedulingStatusMessage(self) -> Optional[str]:
        """Get current scheduling status for monitoring."""
        return f"Jobs queued: {self.get_queued_count()}, Running: {self.get_running_count()}"

# Scalable batch system for cloud environments
class CloudBatchSystem(AbstractScalableBatchSystem):
    """Scalable batch system with auto-provisioning."""
    
    def nodeTypes(self) -> List['NodeInfo']:
        """Get available node types for scaling."""
        from toil.batchSystems import NodeInfo
        
        return [
            NodeInfo(
                cores=4,
                memory=8 * 1024 * 1024 * 1024,  # 8GB
                disk=100 * 1024 * 1024 * 1024,  # 100GB
                preemptible=True,
                nodeType="m5.large"
            ),
            NodeInfo(
                cores=16, 
                memory=32 * 1024 * 1024 * 1024,  # 32GB
                disk=500 * 1024 * 1024 * 1024,   # 500GB
                preemptible=False,
                nodeType="m5.4xlarge"
            )
        ]
    
    def provisioner(self) -> Optional['AbstractProvisioner']:
        """Get associated provisioner for auto-scaling."""
        return self.cloud_provisioner

from toil.batchSystems.singleMachine import SingleMachineBatchSystem
from toil.common import Config

# Configuration for local execution
config = Config()
config.batchSystem = "local"
config.maxCores = 8        # Maximum cores to use locally
config.maxMemory = "16G"   # Maximum memory to use
config.maxDisk = "100G"    # Maximum disk space

# Local batch system handles resource contention automatically
local_batch = SingleMachineBatchSystem(config)

# Jobs run as separate processes on local machine
# Resource limits enforced through process monitoring
# Automatic cleanup of failed processes

from toil.batchSystems.slurm import SlurmBatchSystem

# Slurm-specific configuration
config = Config()
config.batchSystem = "slurm"

# Slurm partition and account settings
config.slurmPartition = "compute"      # Slurm partition to use
config.slurmAccount = "my_account"     # Account for billing
config.slurmQoS = "normal"             # Quality of service

# Advanced Slurm options
config.slurmArgs = [
    "--constraint=cpu",                 # Node constraints
    "--exclusive",                      # Exclusive node access
    "--mail-type=FAIL",                # Email on failure
    "--mail-user=user@example.com"     # Email address
]

# GPU allocation in Slurm
config.slurmGres = "gpu:2"             # Request 2 GPUs per job

slurm_batch = SlurmBatchSystem(config)

# Job submission generates Slurm sbatch scripts
# Automatic SLURM_JOB_ID tracking
# Integration with Slurm accounting and limits

from toil.batchSystems.kubernetes import KubernetesBatchSystem

# Kubernetes configuration
config = Config()
config.batchSystem = "kubernetes"

# Kubernetes namespace and service account
config.kubernetesNamespace = "toil-workflows"
config.kubernetesServiceAccount = "toil-service"

# Container configuration
config.kubernetesDefaultImage = "ubuntu:20.04"
config.kubernetesDockerImage = "my-org/toil-worker:latest"

# Resource limits and requests
config.kubernetesNodeSelector = {"nodeType": "compute"}
config.kubernetesTolerationsJson = '[{"key": "dedicated", "operator": "Equal", "value": "toil"}]'

# Persistent volume configuration
config.kubernetesPersistentVolumeSize = "10G"
config.kubernetesStorageClass = "fast-ssd"

k8s_batch = KubernetesBatchSystem(config)

# Jobs run as Kubernetes Jobs/Pods
# Automatic volume mounting for job store access
# Integration with Kubernetes RBAC and networking

from toil.batchSystems.lsf import LSFBatchSystem

# LSF configuration
config = Config()
config.batchSystem = "lsf"

# LSF queue and project settings
config.lsfQueue = "normal"            # LSF queue name
config.lsfProject = "research_proj"   # Project for accounting

# Resource specification
config.lsfArgs = [
    "-R", "select[mem>8000]",         # Memory requirements
    "-R", "span[hosts=1]",            # Single host allocation  
    "-W", "4:00"                      # Wall time limit
]

lsf_batch = LSFBatchSystem(config)

# Job submission using bsub command
# LSF job array support for parallel jobs  
# Integration with LSF resource reservation

from toil.batchSystems.awsBatch import AWSBatchSystem

# AWS Batch configuration
config = Config()
config.batchSystem = "aws_batch"

# AWS Batch job queue and definition
config.awsBatchJobQueue = "toil-job-queue"
config.awsBatchJobDefinition = "toil-worker"

# AWS region and credentials
config.awsRegion = "us-west-2" 
config.awsCredentials = "~/.aws/credentials"

# Container and compute environment settings
config.awsBatchComputeEnvironment = "toil-compute-env"
config.awsBatchDockerImage = "amazonlinux:2"

aws_batch = AWSBatchSystem(config)

# Jobs submitted to AWS Batch queues
# Automatic EC2 instance provisioning
# Integration with AWS IAM and VPC
# Support for Spot instances for cost optimization

from toil.batchSystems.mesos.batchSystem import MesosBatchSystem

# Mesos configuration  
config = Config()
config.batchSystem = "mesos"

# Mesos master and framework settings
config.mesosMaster = "zk://localhost:2181/mesos"  # Zookeeper URL
config.mesosFrameworkId = "toil-framework"        # Framework identifier

# Resource allocation
config.mesosRole = "production"                   # Mesos role
config.mesosCheckpoint = True                     # Enable checkpointing

mesos_batch = MesosBatchSystem(config)

# Framework registration with Mesos master
# Dynamic resource allocation and deallocation  
# Fault tolerance through framework checkpointing

from toil.batchSystems.abstractBatchSystem import BatchJobExitReason
import logging

def monitor_batch_system(batch_system: AbstractBatchSystem):
    """Monitor batch system status and job progress."""
    
    # Get current job status
    issued_jobs = batch_system.getIssuedBatchJobIDs()
    running_jobs = batch_system.getRunningBatchJobIDs() 
    
    print(f"Issued jobs: {len(issued_jobs)}")
    print(f"Running jobs: {len(running_jobs)}")
    
    # Check for completed jobs
    while True:
        updated_job = batch_system.getUpdatedBatchJob(maxWait=10)
        
        if updated_job is None:
            continue
            
        job_id = updated_job.jobID
        exit_reason = updated_job.exitReason
        wall_time = updated_job.wallTime
        exit_code = updated_job.exitCode
        
        if exit_reason == BatchJobExitReason.FINISHED:
            if exit_code == 0:
                print(f"Job {job_id} completed successfully in {wall_time}s")
            else:
                print(f"Job {job_id} failed with exit code {exit_code}")
                
        elif exit_reason == BatchJobExitReason.FAILED:
            print(f"Job {job_id} failed due to batch system error")
            
        elif exit_reason == BatchJobExitReason.KILLED:
            print(f"Job {job_id} was killed")
            
        elif exit_reason == BatchJobExitReason.ERROR:
            print(f"Job {job_id} encountered an error")

    # Get scheduling status message
    status_msg = batch_system.getSchedulingStatusMessage()
    if status_msg:
        print(f"Scheduler status: {status_msg}")

from toil.batchSystems import NodeInfo
from toil.provisioners.abstractProvisioner import AbstractProvisioner

class NodeInfo:
    """Information about available compute nodes."""
    
    def __init__(self, cores: int, memory: int, disk: int, 
                 preemptible: bool, nodeType: str):
        self.cores = cores           # CPU cores available
        self.memory = memory         # Memory in bytes
        self.disk = disk            # Disk space in bytes  
        self.preemptible = preemptible  # Whether node can be preempted
        self.nodeType = nodeType    # Cloud provider node type

# Define available node types
def get_cloud_node_types():
    """Define node types for cloud auto-scaling."""
    return [
        NodeInfo(
            cores=2,
            memory=4 * 1024**3,      # 4GB
            disk=50 * 1024**3,       # 50GB
            preemptible=True,
            nodeType="t3.small"
        ),
        NodeInfo(
            cores=8, 
            memory=32 * 1024**3,     # 32GB
            disk=200 * 1024**3,      # 200GB
            preemptible=False,
            nodeType="m5.2xlarge"
        ),
        NodeInfo(
            cores=32,
            memory=128 * 1024**3,    # 128GB 
            disk=1000 * 1024**3,     # 1TB
            preemptible=False,
            nodeType="m5.8xlarge"
        )
    ]

# Node filtering for job placement
def custom_node_filter(batch_system: AbstractScalableBatchSystem):
    """Filter nodes based on custom criteria."""
    
    def node_filtering_func(node_info: dict) -> bool:
        # Only use nodes with sufficient resources
        min_memory = 8 * 1024**3  # 8GB minimum
        min_cores = 4             # 4 cores minimum
        
        return (node_info.get('memory', 0) >= min_memory and 
                node_info.get('cores', 0) >= min_cores)
    
    # Apply custom filtering
    batch_system.nodeFiltering = node_filtering_func

from toil.batchSystems.abstractBatchSystem import (
    InsufficientSystemResources,
    AcquisitionTimeoutException, 
    DeadlockException
)

def handle_batch_system_errors():
    """Handle common batch system errors."""
    
    try:
        # Submit job to batch system
        job_id = batch_system.issueBatchJob(job_description)
        
    except InsufficientSystemResources as e:
        # Handle resource shortage
        logging.warning(f"Insufficient resources: {e}")
        # Reduce resource requirements or wait
        time.sleep(60)
        
    except AcquisitionTimeoutException as e:
        # Handle timeout acquiring resources
        logging.error(f"Resource acquisition timeout: {e}")
        # Retry with different parameters
        
    except DeadlockException as e:
        # Handle batch system deadlock
        logging.critical(f"Batch system deadlock: {e}")
        # May require manual intervention
        
def robust_job_submission(batch_system, job_description, max_retries=3):
    """Submit job with automatic retry on failure."""
    
    for attempt in range(max_retries):
        try:
            return batch_system.issueBatchJob(job_description)
            
        except Exception as e:
            logging.warning(f"Job submission attempt {attempt + 1} failed: {e}")
            if attempt == max_retries - 1:
                raise
            time.sleep(2 ** attempt)  # Exponential backoff