from magpie import Magpie

client = Magpie(api_key="your_api_key_here")

# Create an ephemeral job
job = client.jobs.create(
    name="data-processor",
    script="python process_data.py",
    vcpus=4,
    memory_mb=2048
)

print(f"Job ID: {job.id}")
print(f"Status: {job.status}")

# Create a stateful job with environment variables
stateful_job = client.jobs.create(
    name="ml-training",
    script="""
        pip install torch
        python train.py --epochs 100
    """,
    vcpus=8,
    memory_mb=16384,
    stateful=True,
    env={
        "MODEL_PATH": "/workspace/models",
        "DATASET": "imagenet"
    },
    timeout=3600
)

# Create a persistent VM
persistent_vm = client.jobs.create(
    name="dev-environment",
    script="echo 'VM ready for development'",
    vcpus=2,
    memory_mb=4096,
    persistent=True
)

from magpie import Magpie

client = Magpie(api_key="your_api_key_here")

# Run a job and wait for completion
job = client.jobs.run_and_wait(
    name="batch-report",
    script="""
        echo 'Generating report...'
        python generate_report.py
        echo 'Report complete!'
    """,
    vcpus=2,
    memory_mb=1024,
    poll_interval=10
)

print(f"Job finished with status: {job.status}")

# Get the logs after completion
logs = client.jobs.get_logs(job.id)
print(logs)

from magpie import Magpie

client = Magpie(api_key="your_api_key_here")

# Get job details
job = client.jobs.get("job_abc123xyz")

print(f"Job Name: {job.name}")
print(f"Status: {job.status}")
print(f"vCPUs: {job.vcpus}")
print(f"Memory: {job.memory_mb} MB")
print(f"Created: {job.created_at}")
print(f"Stateful: {job.stateful}")
print(f"Persistent: {job.persistent}")

from magpie import Magpie
import time

client = Magpie(api_key="your_api_key_here")

# Create a job
job = client.jobs.create(
    name="long-task",
    script="sleep 60 && echo 'Done!'",
    vcpus=1,
    memory_mb=512
)

# Poll for status
while True:
    status = client.jobs.get_status(job.id)
    print(f"Current status: {status}")

    if status in ["completed", "failed", "stopped"]:
        break

    time.sleep(5)

from magpie import Magpie

client = Magpie(api_key="your_api_key_here")

# Get all logs
logs = client.jobs.get_logs("job_abc123xyz")
print("Complete logs:")
print(logs)

# Get only the last 50 lines
recent_logs = client.jobs.get_logs("job_abc123xyz", tail=50)
print("\nLast 50 lines:")
print(recent_logs)

# Monitor logs for a running job
job = client.jobs.create(
    name="log-generator",
    script="""
        for i in {1..10}; do
            echo "Processing item $i"
            sleep 2
        done
    """,
    vcpus=1,
    memory_mb=512
)

import time
time.sleep(5)

logs = client.jobs.get_logs(job.id)
print(f"Current output:\n{logs}")

from magpie import Magpie

client = Magpie(api_key="your_api_key_here")

# Create a persistent VM
vm = client.jobs.create(
    name="interactive-dev",
    script="echo 'VM ready'",
    vcpus=4,
    memory_mb=8192,
    persistent=True
)

# Wait for VM to be ready
import time
while client.jobs.get_status(vm.id) != "running":
    time.sleep(2)

# Get SSH connection details
ssh_info = client.jobs.ssh(vm.id)

print(f"Host: {ssh_info.host}")
print(f"Port: {ssh_info.port}")
print(f"Username: {ssh_info.username}")
print(f"\nConnect with:\n{ssh_info.command}")

# Example output:
# Connect with:
# ssh -p 2222 magpie@vm-abc123.magpie.cloud

from magpie import Magpie

client = Magpie(api_key="your_api_key_here")

# Create a long-running job
job = client.jobs.create(
    name="background-task",
    script="while true; do echo 'Working...'; sleep 10; done",
    vcpus=1,
    memory_mb=512,
    persistent=True
)

# Work with the job...
import time
time.sleep(30)

# Stop the job when done
result = client.jobs.stop(job.id)
print(f"Job stopped: {result}")

# Verify status
status = client.jobs.get_status(job.id)
print(f"Current status: {status}")  # Output: stopped

from magpie import Magpie

client = Magpie(api_key="your_api_key_here")

# Create a temporary job
job = client.jobs.create(
    name="temp-calculation",
    script="echo 'Quick calculation'",
    vcpus=1,
    memory_mb=512
)

# Wait for completion
job = client.jobs.run_and_wait(
    name="temp-calculation",
    script="echo 'Quick calculation'",
    vcpus=1,
    memory_mb=512
)

# Get logs before deletion
logs = client.jobs.get_logs(job.id)
print(f"Final output: {logs}")

# Delete the job
result = client.jobs.delete(job.id)
print(f"Job deleted: {result}")

# Attempting to get the job now will raise an error
try:
    client.jobs.get(job.id)
except Exception as e:
    print(f"Error: {e}")  # Job not found