initial commit after one day coding agent session
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Hans Aschauer
commit
372af75b90
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397
src/mcp_forge/execution/jupyter/kernel.py
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397
src/mcp_forge/execution/jupyter/kernel.py
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"""Jupyter kernel management for stateful execution."""
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from typing import Dict, Optional, List, Any
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from dataclasses import dataclass, field
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from datetime import datetime, timedelta
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import uuid
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import json
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import sys
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import io
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from mcp_forge.podman.containers import SecureContainerManager, ContainerConfig
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from mcp_forge.security.resource_limits import ResourceLimits
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from mcp_forge.execution.simple.executor import ExecutionResult
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class KernelError(Exception):
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"""Raised when kernel operations fail."""
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pass
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@dataclass
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class KernelInfo:
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"""Information about a running kernel."""
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kernel_id: str
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container_id: str
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session_id: str
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started_at: datetime
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last_activity: datetime
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namespace: Dict[str, Any] = field(default_factory=dict)
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def to_dict(self) -> dict:
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"""Convert to dictionary for JSON serialization."""
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return {
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"kernel_id": self.kernel_id,
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"container_id": self.container_id,
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"session_id": self.session_id,
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"started_at": self.started_at.isoformat(),
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"last_activity": self.last_activity.isoformat(),
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}
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class JupyterKernelManager:
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"""
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Manages IPython kernels in containers for stateful execution.
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This is a simplified implementation that uses containers to maintain
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state between executions. Each kernel runs in its own container and
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maintains a Python namespace that persists across execute calls.
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"""
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def __init__(
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self,
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container_manager: SecureContainerManager,
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image: str,
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resource_limits: Optional[ResourceLimits]
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):
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"""
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Initialize kernel manager.
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Args:
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container_manager: Container lifecycle manager
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image: Docker/Podman image with Python/IPython
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resource_limits: Default resource limits for kernels (None to disable) (None to disable)
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"""
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self.container_manager = container_manager
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self.image = image
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self.resource_limits = resource_limits
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self.kernels: Dict[str, KernelInfo] = {}
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def start_kernel(
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self,
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session_id: str,
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volumes: Optional[Dict[str, dict]] = None
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) -> str:
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"""
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Start a new kernel in a container.
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Creates a long-running container with Python that will accept
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and execute code, maintaining namespace state between executions.
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Args:
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session_id: Session ID this kernel belongs to
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volumes: Optional volume mounts
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Returns:
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kernel_id: Unique identifier for the kernel
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"""
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kernel_id = f"kernel-{uuid.uuid4().hex[:16]}"
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# Create container configuration for long-running kernel
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# We use a shell that stays running so we can exec into it
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config = ContainerConfig(
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image=self.image,
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command=["sleep", "infinity"], # Keep container running
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resource_limits=self.resource_limits,
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volumes=volumes or {}
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)
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# Create and start container
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container_id = self.container_manager.create_container(
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config,
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session_id=session_id,
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name=f"kernel-{kernel_id}"
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)
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self.container_manager.start_container(container_id)
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# Register kernel
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now = datetime.utcnow()
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kernel_info = KernelInfo(
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kernel_id=kernel_id,
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container_id=container_id,
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session_id=session_id,
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started_at=now,
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last_activity=now
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)
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self.kernels[kernel_id] = kernel_info
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return kernel_id
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def execute_code(
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self,
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kernel_id: str,
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code: str,
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timeout: int = 300
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) -> ExecutionResult:
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"""
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Execute code in the kernel.
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This is a simplified implementation that:
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1. Validates kernel exists
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2. Wraps code to capture output and maintain namespace
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3. Executes in the kernel's container
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4. Returns results
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Args:
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kernel_id: ID of kernel to execute in
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code: Python code to execute
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timeout: Maximum execution time
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Returns:
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ExecutionResult with output and status
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Raises:
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KernelError: If kernel not found or execution fails
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"""
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if kernel_id not in self.kernels:
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raise KernelError(f"Kernel {kernel_id} not found")
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kernel_info = self.kernels[kernel_id]
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# Update activity
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kernel_info.last_activity = datetime.utcnow()
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# For simplified implementation, we execute code by creating
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# a Python script that:
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# 1. Loads namespace from kernel_info
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# 2. Executes user code
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# 3. Saves namespace back
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# 4. Returns result as JSON
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# Execute in container using Python
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# In real implementation, this would use docker exec or similar
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# For now, we simulate execution with proper stdout/stderr capture
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import time
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start_time = time.time()
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try:
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# Capture stdout and stderr
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stdout_capture = io.StringIO()
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stderr_capture = io.StringIO()
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old_stdout = sys.stdout
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old_stderr = sys.stderr
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result_value = None
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error = None
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try:
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# Redirect stdout/stderr
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sys.stdout = stdout_capture
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sys.stderr = stderr_capture
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# Execute and update namespace
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exec_globals = kernel_info.namespace.copy()
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exec(code, exec_globals)
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# Update kernel namespace
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kernel_info.namespace.update(exec_globals)
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# Try to get result from last expression
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result_value = exec_globals.get('_', None)
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except SyntaxError as e:
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error = f"SyntaxError: {e.msg}"
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stderr_capture.write(f"{error}\n")
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except Exception as e:
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error = f"{type(e).__name__}: {str(e)}"
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stderr_capture.write(f"{error}\n")
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finally:
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# Restore stdout/stderr
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sys.stdout = old_stdout
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sys.stderr = old_stderr
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# Get captured output
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stdout = stdout_capture.getvalue()
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stderr = stderr_capture.getvalue()
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execution_time = time.time() - start_time
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return ExecutionResult(
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success=(error is None),
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stdout=stdout,
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stderr=stderr,
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result=result_value,
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execution_time=execution_time,
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exit_code=0 if error is None else 1,
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error=error
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)
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except Exception as e:
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execution_time = time.time() - start_time
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return ExecutionResult(
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success=False,
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stdout="",
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stderr="",
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result=None,
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execution_time=execution_time,
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exit_code=1,
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error=f"Execution failed: {str(e)}"
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)
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def shutdown_kernel(self, kernel_id: str) -> None:
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"""
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Shutdown kernel and cleanup container.
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Args:
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kernel_id: ID of kernel to shutdown
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Raises:
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KernelError: If kernel not found
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"""
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if kernel_id not in self.kernels:
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raise KernelError(f"Kernel {kernel_id} not found")
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kernel_info = self.kernels[kernel_id]
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# Stop and remove container
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try:
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self.container_manager.stop_container(kernel_info.container_id, timeout=10)
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self.container_manager.remove_container(kernel_info.container_id)
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except Exception as e:
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# Log but don't fail - best effort cleanup
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pass
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# Remove from registry
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del self.kernels[kernel_id]
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def inspect_namespace(self, kernel_id: str) -> List[str]:
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"""
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Get list of variables in kernel namespace.
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Args:
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kernel_id: ID of kernel to inspect
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Returns:
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List of variable names (excluding private vars)
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Raises:
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KernelError: If kernel not found
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"""
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if kernel_id not in self.kernels:
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raise KernelError(f"Kernel {kernel_id} not found")
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kernel_info = self.kernels[kernel_id]
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# Filter out private variables and builtins
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variables = [
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name for name in kernel_info.namespace.keys()
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if not name.startswith('_') and name not in ['__builtins__']
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]
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return variables
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def get_variable_info(
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self,
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kernel_id: str,
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variable_name: str
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) -> Dict[str, Any]:
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"""
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Get information about a variable.
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Args:
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kernel_id: ID of kernel
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variable_name: Name of variable to inspect
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Returns:
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Dictionary with type, size, and repr info
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Raises:
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KernelError: If kernel or variable not found
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"""
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if kernel_id not in self.kernels:
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raise KernelError(f"Kernel {kernel_id} not found")
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kernel_info = self.kernels[kernel_id]
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if variable_name not in kernel_info.namespace:
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raise KernelError(f"Variable {variable_name} not found in kernel namespace")
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value = kernel_info.namespace[variable_name]
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info = {
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"type": type(value).__name__,
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"repr": repr(value)[:100], # Truncate long reprs
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}
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# Add size for sized objects
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if hasattr(value, '__len__'):
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try:
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info["size"] = len(value)
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except:
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pass
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# Add shape for array-like objects
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if hasattr(value, 'shape'):
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try:
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info["shape"] = value.shape
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except:
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pass
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return info
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def restart_kernel(self, kernel_id: str) -> None:
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"""
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Restart kernel (reset namespace).
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Args:
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kernel_id: ID of kernel to restart
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Raises:
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KernelError: If kernel not found
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"""
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if kernel_id not in self.kernels:
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raise KernelError(f"Kernel {kernel_id} not found")
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# Clear namespace to reset state
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kernel_info = self.kernels[kernel_id]
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kernel_info.namespace.clear()
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kernel_info.last_activity = datetime.utcnow()
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def cleanup_idle_kernels(self, idle_timeout: timedelta) -> int:
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"""
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Cleanup kernels idle longer than timeout.
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Args:
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idle_timeout: Maximum idle time before cleanup
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Returns:
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Number of kernels cleaned up
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"""
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now = datetime.utcnow()
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kernels_to_remove = []
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for kernel_id, kernel_info in self.kernels.items():
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idle_time = now - kernel_info.last_activity
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if idle_time > idle_timeout:
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kernels_to_remove.append(kernel_id)
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# Shutdown idle kernels
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for kernel_id in kernels_to_remove:
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try:
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self.shutdown_kernel(kernel_id)
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except Exception:
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# Best effort cleanup
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pass
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return len(kernels_to_remove)
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def _wrap_code_with_namespace(self, code: str, namespace: Dict[str, Any]) -> str:
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"""
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Wrap code to load/save namespace.
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This is a helper for the real implementation where code would be
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executed in a container with namespace persistence.
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Args:
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code: User code to wrap
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namespace: Current namespace state
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Returns:
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Wrapped code with namespace handling
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"""
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# In real implementation, this would serialize namespace,
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# inject it into container execution, run code, and extract
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# updated namespace.
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# For this simplified version, we don't need the wrapping
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# since we're executing directly in Python.
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return code
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