AKernel Python SDK

akernel-sdk is the Python interface for creating and managing remote AKernel sandboxes. Applications use one stable API for commands, files, interactive PTYs, port forwarding, and reverse tunnels.

It supports two backends:

• openyuanrong-sandbox (default), using a RESTful API and Rust runtime.
• openyuanrong-sdk (legacy), using YuanRong actors and a Python runtime.

Navigation

• AKernel Python SDK
  • Navigation
  • Install and configure
  • Create a sandbox
    • Experimental GPU and writable storage
    • Network ACLs
  • Sandbox runtimes
  • Commands
  • Filesystem
  • Interactive PTYs
  • Port forwarding
  • Reverse tunnels
  • Rootfs and mounts
  • Launch from a Dockerfile
  • Resources and lifecycle
  • CLI
  • Examples and tests
  • Public value types

Install and configure

AKernel SDK requires Python 3.10 or newer.

pip install akernel-sdk

To install from source:

python -m pip install ./sdk/python

Configure the public AKernel entrypoint and a signed JWT token:

export AKERNEL_SERVER_ADDRESS="akernel.example.com"
export AKERNEL_TOKEN="<token>"

Address behavior is deterministic:

• A host or IP without a port uses HTTPS/WSS on 443 for the frontend and HTTP on 80 for public sandbox port URLs.
• host:port uses that port as a shared HTTPS/WSS endpoint.
• AKERNEL_GATEWAY_ADDRESS overrides the port-forwarding and exec gateway for standalone or custom topologies. An override without a scheme uses HTTP/WS.

The legacy actor backend is optional. Install and select it before importing akernel_sdk:

pip install "akernel-sdk[openyuanrong-sdk]"
export AKERNEL_BACKEND=openyuanrong-sdk

Create a sandbox

from akernel_sdk import Sandbox

with Sandbox(cpu=1000, memory=2048) as sandbox:
    result = sandbox.commands.run("printf hello")
    print(result.stdout)

The constructor accepts:

Sandbox(
    image: str | None = None,
    rootfs: S3Config | None = None,
    runtime: str = "runsc",
    cpu: int = 1000,
    memory: int = 4096,
    cpu_limit: int = 0,
    mem_limit: int = 0,
    idle_timeout: int = 300,
    schedule_timeout: int = 30,
    env: dict[str, str] | None = None,
    name: str | None = None,
    cwd: str | None = None,
    port_forwardings: list[int] | None = None,
    mounts: list[Mount] | None = None,
    reverse_tunnel: HttpReverseTunnel | None = None,
    detached: bool = False,
    node_id: str | None = None,
    *,
    xpu: str | None = None,
    storage_mb: int | None = None,
    network_policy: NetworkPolicy | None = None,
    dockerfile: DockerfileLaunch | None = None,
    extra_config: Mapping[str, object] | None = None,
)

Experimental GPU and writable storage

Request a whole NVIDIA GPU by type, exact product model, and count:

with Sandbox(xpu="gpu:l20:1") as sandbox:
    print(sandbox.commands.run("nvidia-smi -L").stdout)

The type:model:count value is case-insensitive and canonicalized to lower case. The model is required and matched exactly; wildcard models are not supported. The bundled backend currently requires the gVisor runsc runtime and a node configured for gVisor nvproxy. Runtime compatibility is validated by the backend rather than the SDK.

Set the writable root filesystem quota in MiB:

with Sandbox(storage_mb=20 * 1024) as sandbox:
    print(sandbox.commands.run("df -h /").stdout)

The bundled backend currently requires runsc for an explicit storage_mb quota and uses sandboxd's disk-backed XFS filestore. Runtime compatibility is validated by the backend. When storage_mb is omitted, sandboxd retains its configured default 10 GiB memory-backed writable overlay. See examples/gpu_sandbox.py and examples/storage_sandbox.py.

Network ACLs

Omit network_policy to leave all sandbox networking unrestricted. An empty NetworkPolicy() is equivalent and is omitted from the creation request:

from akernel_sdk import NetworkPolicy, Sandbox

with Sandbox() as unrestricted:
    print(unrestricted.commands.run("python3 -c 'import socket; "
                                    "socket.getaddrinfo(\"github.com\", 443)'"))

Block new sandbox flows except the YuanRong control proxy and published sandbox-port routes:

with Sandbox(network_policy=NetworkPolicy.block()) as sandbox:
    result = sandbox.commands.run("printf 'control plane still works'")
    assert result.exit_code == 0

Commands and lifecycle operations continue to work in block mode. The policy also publishes the sandbox targets required by direct filesystem I/O, reverse tunnels, and explicit port_forwardings. Replies on those allowed paths are stateful. Other new network flows remain denied.

Deny conventional DNS lookups for exact names or leading *. suffix patterns:

policy = NetworkPolicy.deny_dns("github.com", "*.github.com")
with Sandbox(network_policy=policy) as sandbox:
    blocked = sandbox.commands.run(
        "python3 -c 'import socket; socket.getaddrinfo(\"github.com\", 443)'"
    )
    assert blocked.exit_code != 0

An exact pattern matches only that name. For example, github.com does not match api.github.com, while *.github.com matches descendants but not the apex. Supply both when both should be denied. Patterns are normalized to lower case without a trailing dot; international names must use ASCII punycode. Each pattern may be an exact ASCII name or begin with one leading *.. The remaining name is at most 253 characters; each dot-separated label is 1-63 letters, digits, underscores, or hyphens, and a hyphen cannot start or end a label. Other wildcard placements and ? are rejected.

Network policies are fixed when a sandbox is created. block_network and dns_blacklist cannot be combined in the current SDK. DNS blacklists cover ordinary UDP and TCP DNS and return a refused response for blocked queries; DNS-over-HTTPS and connections to a known IP are outside their scope. The block-network packet ACL is currently stateful IPv4. DNS-only policies do not install a general packet allowlist.

See examples/network_policy.py for all three modes. Deployment nodes must have network ACL support enabled; the bundled standalone, Helm, and Terraform configurations enable it. Drain existing sandboxes before upgrading a node to an ACL-enabled sandboxd configuration, as described in the deployment guide.

Sandbox runtimes

AKernel uses the gVisor runsc runtime when runtime is omitted. Runtime identifiers and optional extra_config are forwarded to the selected backend instead of being restricted or interpreted by an SDK-owned registry. Values in extra_config must be JSON-compatible. The bundled deployment advertises runsc and Kata Containers by default:

default_sandbox = Sandbox()
runsc_sandbox = Sandbox(runtime="runsc")
kata_sandbox = Sandbox(runtime="kata")

Kata requires at least one cluster node whose sandboxd instance successfully initialized the Kata runtime with a usable /dev/kvm device. Nodes without KVM remain available for runsc workloads and do not advertise Kata. A runtime that is unavailable in the cluster fails scheduling or backend validation.

The all-in-one image can optionally package a native Linux runc backend. Operators build it with AKERNEL_ENABLE_RUNC=true and enable it explicitly because it provides host-kernel container isolation, not the user-space kernel boundary of runsc. Guided cloud profiles use make config ENABLE_RUNC=true; standalone deployments use AKERNEL_ENABLE_RUNC=true, and direct Helm deployments use node.config.sandboxd.enableRunc=true. After it is advertised:

with Sandbox(runtime="runc") as sandbox:
    print(sandbox.commands.run("uname -s").stdout)

with Sandbox(
    runtime="runc",
    extra_config={"enableKVM": True},
) as sandbox_with_kvm:
    print(sandbox_with_kvm.commands.run("test -c /dev/kvm").exit_code)

enableKVM is owned by the runc backend and requires a usable /dev/kvm on the selected node. Runc supports OCI/EROFS root filesystems, read-only mounts, networking, command execution, and the default writable overlay. Experimental GPU requests and explicit storage_mb quotas remain runsc-only. See the sandbox runtime comparison for the runtime capability boundaries.

See examples/sandbox_runtime.py for a runnable example.

Commands

Run a foreground command:

result = sandbox.commands.run(
    "printf $GREETING",
    envs={"GREETING": "hello"},
    cwd="/tmp",
    timeout=60,
)
print(result.stdout, result.stderr, result.exit_code)

Run and control a background command:

handle = sandbox.commands.run("sleep 30", background=True)
print(handle.pid)

for process in sandbox.commands.list():
    print(process.pid, process.command, process.running)

handle.kill()

Enable stdin only when it is needed:

handle = sandbox.commands.run("wc -l", background=True, stdin=True)
handle.send_stdin("one\ntwo\n")
handle.close_stdin()
result = handle.wait(timeout=15)

Foreground commands return a backend-neutral CommandResult. Background commands return an AKernel CommandHandle; its lifecycle operations are delegated to the selected backend.

Filesystem

sandbox.files.write("/tmp/message.txt", "hello")
print(sandbox.files.read("/tmp/message.txt"))

sandbox.files.write("/tmp/data.bin", b"\x00\x01")
print(sandbox.files.read("/tmp/data.bin", format="bytes"))

for entry in sandbox.files.list("/tmp"):
    print(entry.path, entry.type, entry.size)

sandbox.files.make_dir("/workspace")
sandbox.files.rename("/tmp/message.txt", "/workspace/message.txt")
sandbox.files.remove("/workspace/message.txt")

Copy local files or directories through the frontend exec WebSocket:

sandbox.files.copy_from_local("./project", "/workspace/project")
sandbox.files.copy_to_local("/workspace/result.json", "./result.json")

Interactive PTYs

Use sandbox.pty for an interactive byte stream with stdin, streaming output, terminal resizing, and an exit status:

import sys

from akernel_sdk import Sandbox


def write_output(data: bytes) -> None:
    sys.stdout.buffer.write(data)
    sys.stdout.buffer.flush()


with Sandbox() as sandbox:
    with sandbox.pty.create(on_data=write_output) as session:
        session.send_stdin(b"echo hello from PTY\n")
        session.resize(rows=40, cols=120)
        session.send_stdin(b"exit 7\n")
        print(session.wait())

PTY output remains bytes so the SDK does not guess the terminal encoding. Use session.close_stdin() to signal end-of-input while continuing to receive output. A session belongs to its WebSocket connection: closing it terminates the remote interactive process, and reconnecting to an existing session is not supported.

Use sandbox.commands instead when the caller needs separate stdout and stderr, a complete CommandResult, or a controllable background process. The former Shell API and its actor bash_* methods were removed before the v0.1.0 public API was released.

Port forwarding

Declare each sandbox port at creation time:

from akernel_sdk import Sandbox

with Sandbox(port_forwardings=[8080]) as sandbox:
    server = sandbox.commands.run(
        "python3 -m http.server 8080 --bind 0.0.0.0",
        background=True,
    )
    print(sandbox.get_port_url(8080))
    server.kill()

get_port_url() rejects undeclared ports. Pass internal=True only when a deployment operator explicitly wants the direct Traefik address instead of the public gateway.

Reverse tunnels

A reverse tunnel lets sandbox code call an HTTP or HTTPS service reachable from the machine running the SDK:

from akernel_sdk import HttpReverseTunnel, Sandbox

tunnel = HttpReverseTunnel(
    target="https://service.example.com",
    reverse_port=8765,
    listen_port=8766,
    connect_timeout=60,
)

with Sandbox(reverse_tunnel=tunnel) as sandbox:
    result = sandbox.commands.run(
        f"curl {sandbox.reverse_tunnel.url}/health"
    )

reverse_port carries the WebSocket tunnel through Traefik. listen_port is the loopback HTTP listener used inside the sandbox. Consequently, sandbox.reverse_tunnel.url is always http://127.0.0.1:<listen_port>, even when target uses HTTPS.

For an HTTPS target, the SDK-side tunnel client performs the TLS handshake and certificate verification. The sandbox application talks only to its loopback HTTP listener. AKernel supports one HTTP/HTTPS reverse tunnel per sandbox and does not expose a general TCP tunnel.

The default openyuanrong-sandbox backend supports custom internal tunnel ports. Its frontend derives the WebSocket port from the HTTP listener, so reverse_port must equal listen_port - 1. Both ports are reserved inside that sandbox while the tunnel is active and must not also appear in port_forwardings; they do not occupy ports on the SDK host.

Rootfs and mounts

Use a public OCI image:

with Sandbox(image="ubuntu:24.04") as sandbox:
    print(sandbox.commands.run("cat /etc/os-release").stdout)

Or use an object in S3-compatible storage as the rootfs:

from akernel_sdk import S3Config, Sandbox

rootfs = S3Config(
    endpoint="https://s3.example.com",
    bucket="akernel-rootfs",
    object="ubuntu-24.04/rootfs.img",
    access_key="<optional>",
    secret_key="<optional>",
)

with Sandbox(rootfs=rootfs) as sandbox:
    print(sandbox.commands.run("cat /etc/os-release").stdout)

image and rootfs are mutually exclusive. The SDK generates the backend wire representation; callers do not pass raw rootfs JSON or override the runtime inside an S3 object. When neither source is supplied, AKernel sends only the selected isolation runtime and openYuanRong overlays it onto the rootfs configured by the deployed service.

The same S3Config type can be used as a read-only mount source:

from akernel_sdk import Mount

mount = Mount(target="/models", type="erofs", s3_config=rootfs)
with Sandbox(mounts=[mount]) as sandbox:
    print(sandbox.commands.run("ls /models").stdout)

OCI images can also be mounted read-only:

mount = Mount(target="/opt/tools", image_url="ubuntu:24.04")

Launch from a Dockerfile

Dockerfile direct launch is a supported AKernel SDK capability and will remain available. Its documented strict subset evolves incrementally with production experience; unsupported inputs continue to fail closed. The specific API surface may evolve, with documentation and migration guidance for material changes. It is not a general-purpose Docker build.

FROM supplies only the root filesystem; inherited OCI configuration is not applied. Precheck the context, then pass its launch configuration to Sandbox:

from akernel_sdk import DockerfileLaunch, LocalDockerContext, Sandbox, check_direct_launch
context = LocalDockerContext("Dockerfile", context_dir=".")
if check_direct_launch(context).direct_launchable:
    with Sandbox(dockerfile=DockerfileLaunch(context, run_timeout=300)) as sandbox:
        pass

RUN, COPY, and ADD run on every launch without a snapshot or cache; unsupported Dockerfiles must be built externally. Read the complete contract, security boundaries, and supported syntax in the Dockerfile launch guide. See the runnable example.

Resources and lifecycle

resources() returns stable NodeInfo values rather than backend objects:

from akernel_sdk import resources

for node in resources():
    print(node.id, node.status, node.capacity, node.allocatable, node.labels)

Accelerators appear under keys such as GPU/l20. Capacity is the total card count and allocatable is the currently free count. ak resources renders the same information as, for example, gpu/l20 1/4.

Use the context manager for ordinary sandboxes. For a named detached sandbox, explicitly delete it when it is no longer needed:

sandbox = Sandbox(name="worker", detached=True)
sandbox.kill()             # closes local clients; remote sandbox remains
Sandbox.delete("worker")   # terminates the named remote sandbox

sandbox.id is the physical ID shown by ak list. get_info() returns a SandboxInfo containing id, state, requested CPU, memory, XPU and storage, and the OCI image when one was configured.

CLI

The ak CLI is installed with the SDK package:

ak resources
ak list
ak list --quiet
ak exec <sandbox-id>
ak exec <sandbox-id> -- /bin/sh
ak delete <sandbox-id> [<sandbox-id> ...]

It uses the same AKERNEL_SERVER_ADDRESS and AKERNEL_TOKEN environment as the Python API.

Examples and tests

Maintained examples are under examples/:

• basic_usage.py
• command_stdin.py
• custom_image.py
• dockerfile_launch.py
• gpu_sandbox.py
• named_sandbox.py
• network_policy.py
• pty.py
• port_forwarding.py
• reverse_tunnel.py
• s3_rootfs_and_mounts.py
• storage_sandbox.py

Run unit tests without a deployment:

PYTHONPATH=sdk/python \
  python -m unittest discover -s sdk/python/tests/unit -t sdk/python -v

Run the integration suite against a configured deployment:

export AKERNEL_RUN_INTEGRATION=1
PYTHONPATH=sdk/python \
  python -m unittest discover -s sdk/python/tests/integration -t sdk/python -v

Load and transfer benchmarks live under benchmarks/ and are not part of the default test suite.

Public value types

Type	Fields
CommandResult	stdout, stderr, exit_code
CommandInfo	pid, command, running
EntryInfo	name, path, type, size, permissions, modified_time
SandboxInfo	id, state, cpu, memory, image, xpu, storage_mb
NodeInfo	id, status, capacity, allocatable, labels
S3Config	endpoint, bucket, object, optional credentials
Mount	target, one source, and type
HttpReverseTunnel	target, reverse_port, listen_port, connect_timeout
NetworkPolicy	block_network, dns_blacklist
DockerfileLaunch	context, auto_start_cmd, run_timeout
DockerContext	Abstract Dockerfile and build-context source
DockerContextEntry	path, kind, mode
LocalDockerContext	Local Dockerfile and context implementation