Chalk Sandbox SDK

Python SDK for the Chalk Sandbox gRPC service. Create sandboxes, execute commands, and stream output over bidirectional gRPC streams.

Contributor note: for testing deployed functions against local chalkcompute or local chalk-remote-call-python changes, see local-sdk-remote-call-testing.md.

Install

pip install grpcio protobuf

Quick start

from chalkcompute import SandboxClient

with SandboxClient.from_env() as client:
    # Create a sandbox from a pre-built image
    sandbox = client.create(image="ubuntu:latest")

    # Run a command
    result = sandbox.exec("echo", "hello world")
    print(result.stdout_text)  # "hello world"
    print(result.exit_code)    # 0

    # Clean up
    sandbox.terminate()

Declarative images

Build custom container images with a fluent API instead of writing Dockerfiles. The image spec is serialized as protobuf and transmitted to the sandbox service, which builds and caches the image before starting the container.

from chalkcompute import Image, SandboxClient

# Build a data-science image declaratively
img = (
    Image.debian_slim()
    .pip_install(["pandas", "numpy", "scikit-learn"])
    .run_commands(
        "apt-get update && apt-get install -y git curl",
    )
    .workdir("/home/user/app")
    .env({"PYTHONDONTWRITEBYTECODE": "1"})
)

with SandboxClient.from_env() as client:
    sandbox = client.create(image=img)
    result = sandbox.exec("python", "-c", "import pandas; print(pandas.__version__)")
    print(result.stdout_text)
    sandbox.terminate()

Base images

# Arbitrary base image
img = Image.base("node:25-trixie-slim")

# Convenience: python + debian slim
img = Image.debian_slim()  # python:3.14-slim-trixie

# From an existing Dockerfile (contents are inlined, so you can chain more steps)
img = Image.from_dockerfile("Dockerfile").pip_install(["extra-dep"])

Build steps

img = (
    Image.debian_slim()
    # Install Python packages
    .pip_install(["requests", "flask"])

    # Install from a requirements.txt (read locally, inlined into the spec)
    .pip_install_from_requirements("requirements.txt")

    # Run shell commands (each becomes a Docker RUN layer)
    .run_commands(
        "apt-get update && apt-get install -y git",
        "mkdir -p /app/data",
    )

    # Add local files into the image
    .add_local_file("config.yaml", "/app/config.yaml")
    .add_local_file("entrypoint.sh", "/app/entrypoint.sh", mode=0o755)
    .add_local_dir("src", "/app/src")

    # Raw Dockerfile instructions
    .dockerfile_commands(["EXPOSE 8080", "HEALTHCHECK CMD curl -f http://localhost:8080/"])

    # Image-level configuration
    .workdir("/app")
    .env({"FLASK_APP": "app:create_app"})
    .entrypoint(["/app/entrypoint.sh"])
    .cmd(["serve"])
)

Immutable composition

Each builder method returns a new Image, so intermediate images can be shared:

base = Image.debian_slim().pip_install(["requests"])

# Two different images that share the same base
api_image = base.pip_install(["flask"]).workdir("/api")
worker_image = base.pip_install(["celery"]).workdir("/worker")

api_sandbox = client.create(image=api_image)
worker_sandbox = client.create(image=worker_image)

api_sandbox.terminate()
worker_sandbox.terminate()

Connecting

from chalkcompute import SandboxClient

# Insecure (local dev)
client = SandboxClient("localhost:50051")

# With TLS
client = SandboxClient("sandbox.example.com:443", use_tls=True)

# As a context manager
with SandboxClient("localhost:50051") as client:
    ...

Rotating workload identity

The SDK can use a directly usable Chalk JWT from a rotating token file instead of a client ID and secret:

export CHALK_WEB_IDENTITY_TOKEN_FILE=/var/run/secrets/chalk/identity-token
export CHALK_API_SERVER=https://api.chalk.ai

Each SDK client caches the token for the shorter of one hour or half of the token's remaining lifetime from its exp claim, then re-reads the file on its next authenticated operation. Tokens without exp use the one-hour limit. Changing the configured file path bypasses the cache. The JWT's environment_id claim selects the environment unless CHALK_ENVIRONMENT or CHALK_ENVIRONMENT_ID is set explicitly. Queued function calls additionally require CHALK_GRPC_ENGINE, because identity JWTs do not contain engine-routing data.

Workload identity federation

Use the authenticated Connect client to mint a short-lived OIDC token for a third-party workload identity provider. For example, with Snowflake configured to trust Chalk's issuer and JWKS:

from chalkcompute import ConnectClient

token = ConnectClient().get_workload_identity_token("snowflakecomputing.com")

The token is scoped to the active Chalk environment. Its audience is the value passed to get_workload_identity_token, and its signing key is published by the Chalk API server at /.well-known/jwks.json.

Evaluations

Create a reusable evaluation by pinning a completed dataset revision to a deployed task function and one or more deployed scorer functions. A dataset name resolves to its latest revision when the evaluation is created, and the resolved revision is then pinned. Function parameters bind to dataset columns by name; scorers may additionally declare output and trace parameters.

import chalkcompute as cc

dataset = cc.DatasetClient().upload(
    "support_goldens",
    "support_goldens.csv",
)

suite = cc.EvaluationSuite.create("Release")

@cc.function(name="support-answer")
def answer(input: str) -> str:
    return call_support_model(input)

@cc.function(name="response-quality")
def response_quality(input: str, output: str) -> cc.EvaluationScorerResult:
    score, details = score_response(input=input, output=output)
    return cc.EvaluationScorerResult(
        score=score,
        metadata={"details": details},
    )

evaluation = cc.Evaluation.create(
    "Customer Support Chatbot",
    dataset=dataset,
    task=answer,
    scorers=[response_quality],
    suite_id=suite.id,
)

run = evaluation.run(metadata={"git_sha": "abc123"}).wait()
print(run.status, run.result_dataset)

DatasetClient.upload accepts CSV or Parquet paths, multiple same-schema files, PyArrow tables and record batches, column/row mappings, and dataframes convertible to Arrow. It uploads ordinary tabular data and does not require ChalkPy feature definitions. Uploading to an existing name creates a new dataset revision.

@cc.function starts deployment in the background, allowing consecutive definitions to build concurrently. Evaluation.create waits on those handles before reading their immutable function version IDs. Existing functions can instead be attached by reference:

evaluation = cc.Evaluation.create(
    "Customer Support Chatbot",
    dataset=dataset,
    task=cc.RemoteFunction.from_name("support-answer"),
    scorers=[cc.RemoteFunction.from_version_id("fn_brand_alignment_v2")],
)

RemoteFunction.from_name resolves the currently selected version at lookup time; evaluation creation then pins that version. from_id remains a compatibility alias for from_version_id. An imperative RemoteFunction must be explicitly deployed before it can be used in an evaluation.

Built-in scorers

cc.scorers deploys common scorers without a function body. Each one takes an inputs pair naming the candidate column and the column it is compared against, scores between 0 and 1, and records the raw quantity as row metadata.

match = cc.scorers.exact_match(case_insensitive=True)

evaluation = cc.Evaluation.create(
    "Capitals",
    dataset=dataset,
    task=answer,
    scorers=[match],
)
  • exact_match — the two columns hold the same text, after optional trimming, whitespace collapsing, and case folding.

LLM judges

cc.scorers.llm_judge deploys a scorer from a pydantic model (v1 or v2) instead of a function body. The model's score field becomes the score; every other field is recorded as row metadata. The reply is requested through the OpenAI client with structured outputs and validated against the model, so a malformed grade fails the row.

from pydantic import BaseModel, Field

class TraceQuality(BaseModel):
    score: float = Field(ge=0, le=1, description="Overall quality.")
    directness: int = Field(ge=0, le=2, description="Shortest reasonable path to the goal.")
    task_correctness: int = Field(ge=0, le=2, description="Was the task actually completed?")
    reason: str

trace_quality = cc.scorers.llm_judge(
    TraceQuality,
    model="gpt-5",
    instructions="You are grading a browser agent's login attempt.",
    api_key=cc.Secret.from_chalk_env("OPENAI_API_KEY"),
)

The scorer deploys as trace-quality, the model's name in kebab case; pass name= to choose another. inputs names the dataset columns the judge reads (default ("output",)); prompt_fn replaces the default prompt; parse_fn replaces structured outputs for an endpoint without them. completion_kwargs (for example {"temperature": 0, "max_tokens": 400}) go on every request as given. Any OpenAI-compatible endpoint works via base_url, including Chalk's AI router. Every function deployed from the same module imports that module, so give sibling functions an image with pydantic as well. A judge is generated at import time, so it cannot be deployed in strip mode.

Deployment revisions and rollback

Scaling groups and functions have stable parent IDs with immutable deployment revisions beneath them. Scaling groups append a revision on each deploy(). Functions and class methods reuse an existing version when the source, image recipe, configuration, and managed secret revisions are unchanged—even across separate runs of your script. The unchanged path makes one ensure request without building an image or uploading source files. Only missing source content is uploaded on a change; unchanged images and source snapshots are shared within the environment.

Use deploy(force_new_version=True) to create a fresh function version while still reusing image/source preparation. External secret providers and integration secrets conservatively disable function-version reuse because their values can rotate outside Chalk. Referenced data volumes retain their existing semantics. This requires a server with EnsureExternalFunction support.

group = cc.ScalingGroup(name="api", image="registry.example/api:v1").deploy()
group.deploy()  # updates the same group and creates another revision
for revision in group.revisions():
    print(revision.id, revision.status, revision.is_current)
group.rollback("sgr_previous")

@cc.function(name="rank")
def rank(query: str) -> str:
    return query

rank.deploy()
rank.deploy()  # reuses the unchanged version
rank.deploy(force_new_version=True)  # explicitly creates a new version
for version in rank.versions():
    print(version.id, version.created_at, version.is_current)
rank.rollback("efv_previous")

Use ScalingGroup.from_id(...) or RemoteFunction.from_function_id(...) to attach to a stable parent. RemoteFunction.from_version_id(...) attaches through an immutable version and still exposes its parent lifecycle. refresh() follows the parent's currently selected revision, and delete() deletes the stable parent and all of its revisions.

Scorers may return a numeric scalar, one EvaluationScorerResult, or a list[EvaluationScorerResult]. Returning a list lets one scorer emit multiple scores from shared computation; an empty list emits no scores for that row. Each result carries a normalized score and optional row-level JSON-serializable metadata. The return annotation declares the Arrow schema, and the class-level Arrow hooks handle nested serialization, so the generic function runtime does not need scorer-specific behavior.

Sandbox lifecycle

# Create with resource limits
sandbox = client.create(
    image="ubuntu:latest",
    cpu="2",
    memory="4Gi",
    env={"DEBIAN_FRONTEND": "noninteractive"},
    chalk_identity=True,
)

# List all sandboxes
for info in client.list():
    print(f"{info.id} {info.status} {info.name}")

# Get a handle to an existing sandbox by ID
existing_sandbox = client.get(id="550e8400-e29b-41d4-a716-446655440000")

# Fetch info from server
info = existing_sandbox.refresh()  # force re-fetch
print(info.status)

# Terminate, optionally with a grace period
sandbox.terminate()
existing_sandbox.terminate(grace_period_seconds=30)

Set chalk_identity=True to give the sandbox a platform-managed Chalk identity. The sandbox receives CHALK_WEB_IDENTITY_TOKEN_FILE and the Chalk API/environment settings it needs to authenticate without caller credentials being copied into the workload.

Executing commands

Run and wait

result = sandbox.exec("ls", "-la", "/tmp")
for line in result.stdout:
    print(line)
for line in result.stderr:
    print(f"ERR: {line}")
print(f"exit code: {result.exit_code}")

# Or get the full text at once
print(result.stdout_text)
print(result.stderr_text)

Stream output in real time

for event in sandbox.exec_stream("make", "build", workdir="/app"):
    if event.stdout:
        print(event.stdout, end="")
    if event.stderr:
        print(event.stderr, end="", file=sys.stderr)
    if event.is_exited:
        print(f"\nDone: exit code {event.exit_code}")

Interactive processes (stdin + signals)

process = sandbox.exec_start("bash")

process.write_stdin("echo hello\n")
process.write_stdin("exit\n")
process.close_stdin()

for event in process.output():
    if event.stdout:
        print(event.stdout, end="")

Send signals to running processes:

import signal

process = sandbox.exec_start("sleep", "300")
process.send_signal(signal.SIGTERM)
result = process.wait()

Options

All exec methods accept the same keyword arguments:

result = sandbox.exec(
    "python", "train.py",
    workdir="/app",                     # working directory
    timeout_secs=3600,                  # kill after 1 hour
    env={"CUDA_VISIBLE_DEVICES": "0"},  # environment variables
)

Examples

Clone a GitHub repo into a sandbox

from chalkcompute import SandboxClient

client = SandboxClient.from_env()
sandbox = client.create(image="ubuntu:latest")

# Install git
sandbox.exec("apt-get", "update")
sandbox.exec("apt-get", "install", "-y", "git")

# Clone
result = sandbox.exec(
    "git", "clone", "https://github.com/chalk-ai/chalk.git", "/workspace/chalk"
)
if result.exit_code != 0:
    print(f"Clone failed: {result.stderr_text}")
else:
    # List what we got
    result = sandbox.exec("ls", "-la", "/workspace/chalk")
    for line in result.stdout:
        print(line)

sandbox.terminate()
client.close()

Spawn an OpenCode agent in a sandbox

OpenCode is a terminal-based AI coding agent. You can run it inside a sandbox to give it an isolated environment to work in.

from chalkcompute import SandboxClient

client = SandboxClient.from_env()
sandbox = client.create(
    image="ubuntu:latest",
    cpu="2",
    memory="4Gi",
    env={
        "ANTHROPIC_API_KEY": "sk-ant-...",
    },
)

# Install dependencies
sandbox.exec("apt-get", "update")
sandbox.exec("apt-get", "install", "-y", "git", "curl", "build-essential")

# Install Go (opencode is a Go binary)
sandbox.exec("bash", "-c", "curl -fsSL https://go.dev/dl/go1.26.3.linux-amd64.tar.gz | tar -C /usr/local -xz")
sandbox.exec("bash", "-c", "echo 'export PATH=$PATH:/usr/local/go/bin:/root/go/bin' >> /root/.bashrc")

# Install opencode
sandbox.exec("bash", "-c", "export PATH=$PATH:/usr/local/go/bin:/root/go/bin && go install github.com/opencode-ai/opencode@latest")

# Clone a repo to work on
sandbox.exec("git", "clone", "https://github.com/your-org/your-repo.git", "/workspace/repo")

# Run opencode non-interactively with a prompt
result = sandbox.exec(
    "bash", "-c",
    "export PATH=$PATH:/usr/local/go/bin:/root/go/bin && cd /workspace/repo && opencode -p 'fix the failing tests in pkg/auth'",
    timeout_secs=600,
)
print(result.stdout_text)

# Or run it interactively and feed it commands
process = sandbox.exec_start(
    "bash", "-c",
    "export PATH=$PATH:/usr/local/go/bin:/root/go/bin && cd /workspace/repo && opencode",
)

# Stream its output
for event in process.output():
    if event.stdout:
        print(event.stdout, end="")
    if event.stderr:
        print(event.stderr, end="", file=sys.stderr)
    if event.is_exited:
        break

sandbox.terminate()
client.close()

Long-running build with real-time output

from chalkcompute import SandboxClient

client = SandboxClient.from_env()
sandbox = client.create(image="node:25-trixie-slim")

sandbox.exec("git", "clone", "https://github.com/your-org/frontend.git", "/app")
sandbox.exec("npm", "install", workdir="/app")

# Stream the build output as it happens
for event in sandbox.exec_stream("npm", "run", "build", workdir="/app"):
    if event.stdout:
        print(event.stdout, end="")
    if event.stderr:
        print(event.stderr, end="", file=sys.stderr)
    if event.is_exited and event.exit_code != 0:
        print(f"Build failed with exit code {event.exit_code}")

sandbox.terminate()
client.close()

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Newer release available (2.11.8)

Owner

PyPI data
Data sourced directly from PyPI's database.

Requires

Python <3.15, >=3.11

Provides Extra

dev

Report project as malware

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

chalkcompute-2.11.5.tar.gz (457.9 kB view details)

Uploaded Source

Built Distributions

If you're not sure about the file name format, learn more about wheel file names.

chalkcompute-2.11.5-cp314-cp314-musllinux_1_2_x86_64.whl (5.6 MB view details)

Uploaded CPython 3.14musllinux: musl 1.2+ x86-64

chalkcompute-2.11.5-cp314-cp314-manylinux_2_28_x86_64.whl (5.4 MB view details)

Uploaded CPython 3.14manylinux: glibc 2.28+ x86-64

chalkcompute-2.11.5-cp314-cp314-macosx_11_0_arm64.whl (4.9 MB view details)

Uploaded CPython 3.14macOS 11.0+ ARM64

chalkcompute-2.11.5-cp313-cp313-musllinux_1_2_x86_64.whl (5.6 MB view details)

Uploaded CPython 3.13musllinux: musl 1.2+ x86-64

chalkcompute-2.11.5-cp313-cp313-manylinux_2_28_x86_64.whl (5.4 MB view details)

Uploaded CPython 3.13manylinux: glibc 2.28+ x86-64

chalkcompute-2.11.5-cp313-cp313-macosx_11_0_arm64.whl (4.9 MB view details)

Uploaded CPython 3.13macOS 11.0+ ARM64

chalkcompute-2.11.5-cp312-cp312-musllinux_1_2_x86_64.whl (5.6 MB view details)

Uploaded CPython 3.12musllinux: musl 1.2+ x86-64

chalkcompute-2.11.5-cp312-cp312-manylinux_2_28_x86_64.whl (5.4 MB view details)

Uploaded CPython 3.12manylinux: glibc 2.28+ x86-64

chalkcompute-2.11.5-cp312-cp312-macosx_11_0_arm64.whl (4.9 MB view details)

Uploaded CPython 3.12macOS 11.0+ ARM64

chalkcompute-2.11.5-cp311-cp311-musllinux_1_2_x86_64.whl (5.6 MB view details)

Uploaded CPython 3.11musllinux: musl 1.2+ x86-64

chalkcompute-2.11.5-cp311-cp311-manylinux_2_28_x86_64.whl (5.4 MB view details)

Uploaded CPython 3.11manylinux: glibc 2.28+ x86-64

chalkcompute-2.11.5-cp311-cp311-macosx_11_0_arm64.whl (4.9 MB view details)

Uploaded CPython 3.11macOS 11.0+ ARM64

File details

Details for the file chalkcompute-2.11.5.tar.gz.

File metadata

  • Download URL: chalkcompute-2.11.5.tar.gz
  • Upload date:
  • Size: 457.9 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/7.0.0 CPython/3.13.14

File hashes

Hashes for chalkcompute-2.11.5.tar.gz
Algorithm Hash digest
SHA256 1b66be249668da873d9759c30486b425c7c8bca50fce2f3c318450bbe690a668
MD5 9095c7b23087a94d46e4ee29fe3fbd4b
BLAKE2b-256 a7510ba8e18c902999d1434bef6fc426a9fe19e0d820989d1a27e80a23f6ffc6

See more details on using hashes here.

Provenance

The following attestation bundles were made for chalkcompute-2.11.5.tar.gz:

Publisher: release.yml on chalk-ai/chalk-sandbox-sdk

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file chalkcompute-2.11.5-cp314-cp314-musllinux_1_2_x86_64.whl.

File metadata

File hashes

Hashes for chalkcompute-2.11.5-cp314-cp314-musllinux_1_2_x86_64.whl
Algorithm Hash digest
SHA256 5107cc32e2188a7a963028df280c99fa8b00b28502efeee9b4f9f23236492580
MD5 a1fe0bf92ebb05d59556194115853bb3
BLAKE2b-256 d4c0735d0887e368ba3bc07f20d4c347d53d067d7cbad425a612c6fc2e1fd8a7

See more details on using hashes here.

Provenance

The following attestation bundles were made for chalkcompute-2.11.5-cp314-cp314-musllinux_1_2_x86_64.whl:

Publisher: release.yml on chalk-ai/chalk-sandbox-sdk

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file chalkcompute-2.11.5-cp314-cp314-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for chalkcompute-2.11.5-cp314-cp314-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 fadbd2f7108308da57eca20bc9eb6ebeec31f5ce9982f618013a2dd033b3be5b
MD5 f847b5347339a530e2d299f26e8fbc51
BLAKE2b-256 d2b8e7b33f0bc9cd6de0a9638370452992aff15b1239b8cd6b61d6b4108c10c5

See more details on using hashes here.

Provenance

The following attestation bundles were made for chalkcompute-2.11.5-cp314-cp314-manylinux_2_28_x86_64.whl:

Publisher: release.yml on chalk-ai/chalk-sandbox-sdk

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file chalkcompute-2.11.5-cp314-cp314-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for chalkcompute-2.11.5-cp314-cp314-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 363f6894263c4252e859cdd238cb6ce31c3ef28386a998e3fd4b8e352716f4c9
MD5 f2c35fbf81bb80de25088913dd989cc7
BLAKE2b-256 2d0130a07bbcbcdc3dd41ec9f6334e12791104973624ee092ed11cbe36b8549c

See more details on using hashes here.

Provenance

The following attestation bundles were made for chalkcompute-2.11.5-cp314-cp314-macosx_11_0_arm64.whl:

Publisher: release.yml on chalk-ai/chalk-sandbox-sdk

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file chalkcompute-2.11.5-cp313-cp313-musllinux_1_2_x86_64.whl.

File metadata

File hashes

Hashes for chalkcompute-2.11.5-cp313-cp313-musllinux_1_2_x86_64.whl
Algorithm Hash digest
SHA256 12c02471e4ed3e7436294aaefbf59f7205fd5fbe6ebae2ab4e1203816a053905
MD5 107e86198695583d8bb613bed9aead48
BLAKE2b-256 da251df03cea0cffd1ffce43c72365a55db39632183453ab2df9a2ad9053c2c2

See more details on using hashes here.

Provenance

The following attestation bundles were made for chalkcompute-2.11.5-cp313-cp313-musllinux_1_2_x86_64.whl:

Publisher: release.yml on chalk-ai/chalk-sandbox-sdk

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file chalkcompute-2.11.5-cp313-cp313-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for chalkcompute-2.11.5-cp313-cp313-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 6649ad2e850d4e9c7cd9eeb35194df4c0b68adb9899f8ec05f3ab13b816f25f1
MD5 24c45e3abbe62d0fc6ea8f75e0c2ea5c
BLAKE2b-256 04c2c9ac5fcb9c46aad01724cb471cceb4cca8afa29a6f3f2c6a8aedef398afb

See more details on using hashes here.

Provenance

The following attestation bundles were made for chalkcompute-2.11.5-cp313-cp313-manylinux_2_28_x86_64.whl:

Publisher: release.yml on chalk-ai/chalk-sandbox-sdk

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file chalkcompute-2.11.5-cp313-cp313-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for chalkcompute-2.11.5-cp313-cp313-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 0b0326551d79e55da76b6a8306ef278c2a0eccf1f2617db5144e4650a7101c9c
MD5 3269595bc2a71fc6dc3cf8c3e5439634
BLAKE2b-256 7a420b78d1aa312623c9efc8a8d943335dab2db360127b32e7026f2b0899f0b7

See more details on using hashes here.

Provenance

The following attestation bundles were made for chalkcompute-2.11.5-cp313-cp313-macosx_11_0_arm64.whl:

Publisher: release.yml on chalk-ai/chalk-sandbox-sdk

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file chalkcompute-2.11.5-cp312-cp312-musllinux_1_2_x86_64.whl.

File metadata

File hashes

Hashes for chalkcompute-2.11.5-cp312-cp312-musllinux_1_2_x86_64.whl
Algorithm Hash digest
SHA256 083a9494b6cc2d3469f65933b480d2ac9413951502baaf15d2f12adf0c87fe08
MD5 693e841e0d88330c3e4d79cc7540ff40
BLAKE2b-256 db99bee84be31c5bbc8e5d111753ddc7be6b57f34e488e6c977f1efc460c519c

See more details on using hashes here.

Provenance

The following attestation bundles were made for chalkcompute-2.11.5-cp312-cp312-musllinux_1_2_x86_64.whl:

Publisher: release.yml on chalk-ai/chalk-sandbox-sdk

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file chalkcompute-2.11.5-cp312-cp312-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for chalkcompute-2.11.5-cp312-cp312-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 8a43b7d0bc478f32791a1e50479838afbe70f7255823f10bda4772af38a92cfd
MD5 b16c77a4f6ae604d6909a9c49ee042bf
BLAKE2b-256 96cf593f9be37c9f97d3cc7fcc92c3891bb9899ae42118319a9a075955770403

See more details on using hashes here.

Provenance

The following attestation bundles were made for chalkcompute-2.11.5-cp312-cp312-manylinux_2_28_x86_64.whl:

Publisher: release.yml on chalk-ai/chalk-sandbox-sdk

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file chalkcompute-2.11.5-cp312-cp312-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for chalkcompute-2.11.5-cp312-cp312-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 6ecee02ec24fc68df5e6cc907a5902bb8c73d624edcd9496e4082773abcf7f37
MD5 25fea65cc2c45ff476f417404a65c87b
BLAKE2b-256 271f59d6c89ded8ab7ce09733261e02caadcd009fa4aec5c8352bbaefdcda069

See more details on using hashes here.

Provenance

The following attestation bundles were made for chalkcompute-2.11.5-cp312-cp312-macosx_11_0_arm64.whl:

Publisher: release.yml on chalk-ai/chalk-sandbox-sdk

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file chalkcompute-2.11.5-cp311-cp311-musllinux_1_2_x86_64.whl.

File metadata

File hashes

Hashes for chalkcompute-2.11.5-cp311-cp311-musllinux_1_2_x86_64.whl
Algorithm Hash digest
SHA256 f1118b5a40d92bf7429e8f80a7429c2c32c6b543720b5dbe1a85cb4da3b9b644
MD5 c844834e2238afb9fa12b60305a114de
BLAKE2b-256 b930dd206631daaa9670d1682ace9ad7851ce32c27095726292a7f9a2cd93bca

See more details on using hashes here.

Provenance

The following attestation bundles were made for chalkcompute-2.11.5-cp311-cp311-musllinux_1_2_x86_64.whl:

Publisher: release.yml on chalk-ai/chalk-sandbox-sdk

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file chalkcompute-2.11.5-cp311-cp311-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for chalkcompute-2.11.5-cp311-cp311-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 6c41fee84188a19751d132ebcfc234b850d0440ff3ce7c8fe673a7a6870024c4
MD5 85b2acd79acd290da40f4581fd7e3032
BLAKE2b-256 eb6a86c90fe264db6800fc394595e737c0a4dba2c9283140ef97630c1a1ade6c

See more details on using hashes here.

Provenance

The following attestation bundles were made for chalkcompute-2.11.5-cp311-cp311-manylinux_2_28_x86_64.whl:

Publisher: release.yml on chalk-ai/chalk-sandbox-sdk

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file chalkcompute-2.11.5-cp311-cp311-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for chalkcompute-2.11.5-cp311-cp311-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 ca755c657d4a2746aa13d35b5096d6604d3e3510642ab79adc13526cdda42ee3
MD5 5df9ee53e6b0ef3e297c8bbd8c0b375e
BLAKE2b-256 6e377585d64c023d2e109b49d8440e87afae8c370fe809cf5d33d64b5c930e20

See more details on using hashes here.

Provenance

The following attestation bundles were made for chalkcompute-2.11.5-cp311-cp311-macosx_11_0_arm64.whl:

Publisher: release.yml on chalk-ai/chalk-sandbox-sdk

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

Release history Release notifications | RSS feed

2.11.8

13 files

2.11.7

13 files

2.11.6

13 files

This release

2.11.5 This release

13 files

2.11.4

13 files

2.11.3

13 files

2.11.2

13 files

2.11.1

13 files

2.9.8

13 files

2.9.7

13 files

2.9.6

13 files

2.9.5

13 files

2.9.4

13 files

2.9.3

13 files

2.9.2

13 files

2.9.1

13 files

2.9.0

13 files

2.8.1

13 files

2.8.0

13 files

2.7.0

13 files

2.6.2

13 files

2.6.1

9 files

2.5.3

9 files

2.5.2

9 files

2.5.1

9 files

2.5.0

9 files

2.4.1

9 files

2.3.9

9 files

2.3.8

9 files

2.3.7

9 files

2.3.6

9 files

2.3.5

9 files

2.3.4

9 files

2.3.3

9 files

2.3.2

9 files

2.3.1

9 files

2.3.0

9 files

2.2.0

9 files

2.1.8

9 files

2.1.3

9 files

2.1.2

9 files

2.1.1

9 files

2.1.0

9 files

2.0.1

9 files

2.0.0

9 files

1.5.17

9 files

1.5.16

9 files

1.5.15

9 files

1.5.14

9 files

1.5.13

9 files

1.5.12

9 files

1.5.11

9 files

1.5.10

9 files

1.5.9

5 files

1.5.6

5 files

1.5.5

2 files

1.5.3

2 files

1.5.2

2 files

1.5.1

2 files

1.5.0

2 files

1.4.2

2 files

1.4.1

2 files

1.4.0

2 files

1.3.0

2 files

1.2.0

2 files

1.1.1

2 files

1.1.0

2 files

1.0.0

2 files

0.1.1

2 files

0.1.0

2 files

0.0.0

9 files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page