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.

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-2.11.4.tar.gz (456.0 kB view details)

Uploaded Source

Built Distributions

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

chalkcompute-2.11.4-cp314-cp314-musllinux_1_2_x86_64.whl (5.6 MB view details)

Uploaded CPython 3.14musllinux: musl 1.2+ x86-64

chalkcompute-2.11.4-cp314-cp314-manylinux_2_28_x86_64.whl (5.4 MB view details)

Uploaded CPython 3.14manylinux: glibc 2.28+ x86-64

chalkcompute-2.11.4-cp314-cp314-macosx_11_0_arm64.whl (4.9 MB view details)

Uploaded CPython 3.14macOS 11.0+ ARM64

chalkcompute-2.11.4-cp313-cp313-musllinux_1_2_x86_64.whl (5.6 MB view details)

Uploaded CPython 3.13musllinux: musl 1.2+ x86-64

chalkcompute-2.11.4-cp313-cp313-manylinux_2_28_x86_64.whl (5.4 MB view details)

Uploaded CPython 3.13manylinux: glibc 2.28+ x86-64

chalkcompute-2.11.4-cp313-cp313-macosx_11_0_arm64.whl (4.9 MB view details)

Uploaded CPython 3.13macOS 11.0+ ARM64

chalkcompute-2.11.4-cp312-cp312-musllinux_1_2_x86_64.whl (5.6 MB view details)

Uploaded CPython 3.12musllinux: musl 1.2+ x86-64

chalkcompute-2.11.4-cp312-cp312-manylinux_2_28_x86_64.whl (5.4 MB view details)

Uploaded CPython 3.12manylinux: glibc 2.28+ x86-64

chalkcompute-2.11.4-cp312-cp312-macosx_11_0_arm64.whl (4.9 MB view details)

Uploaded CPython 3.12macOS 11.0+ ARM64

chalkcompute-2.11.4-cp311-cp311-musllinux_1_2_x86_64.whl (5.6 MB view details)

Uploaded CPython 3.11musllinux: musl 1.2+ x86-64

chalkcompute-2.11.4-cp311-cp311-manylinux_2_28_x86_64.whl (5.4 MB view details)

Uploaded CPython 3.11manylinux: glibc 2.28+ x86-64

chalkcompute-2.11.4-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-2.11.4.tar.gz.

File metadata

  • Download URL: chalkcompute-2.11.4.tar.gz
  • Upload date:
  • Size: 456.0 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/7.0.0 CPython/3.13.14

File hashes

Hashes for chalkcompute-2.11.4.tar.gz
Algorithm Hash digest
SHA256 d978df6573c25d33c70495472090bebe719ca7d92080df7fa6ba4ea971415694
MD5 9d7143962ed86f8c7b617027190ebad6
BLAKE2b-256 d1e438560d34e4917b0952e3545562bfac507d22859b66ab86c78a8f565bb1d0

See more details on using hashes here.

Provenance

The following attestation bundles were made for chalkcompute-2.11.4.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-2.11.4-cp314-cp314-musllinux_1_2_x86_64.whl.

File metadata

File hashes

Hashes for chalkcompute-2.11.4-cp314-cp314-musllinux_1_2_x86_64.whl
Algorithm Hash digest
SHA256 000ed4cc32ae024951a233a54a53a1d675c635702c6ed6b730f51c0d981d4de7
MD5 b2810be31c852e8ee540340d43305308
BLAKE2b-256 bb819eddb1d6e39dd8fe1b647d12cfb49f3933091fb4c9fc6d25ceb8072d3d4b

See more details on using hashes here.

Provenance

The following attestation bundles were made for chalkcompute-2.11.4-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-2.11.4-cp314-cp314-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for chalkcompute-2.11.4-cp314-cp314-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 2a00739c46af3c0697b8cdb0ef89569b696b8ad7ce85a0833b9a77c9f567fdb5
MD5 02dae5a32e158368ee124c4b29f1f563
BLAKE2b-256 2fe9992a2fc350d93e0cab5f2598e4e77a7141bf463c059837893cee7df4020f

See more details on using hashes here.

Provenance

The following attestation bundles were made for chalkcompute-2.11.4-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-2.11.4-cp314-cp314-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for chalkcompute-2.11.4-cp314-cp314-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 c82a03c639c612dcbe8c671404f1c80e706f2a8f5c85bc3734fbf383136bc3e4
MD5 f9fcc4ddc139e70cc6382b260e83b48f
BLAKE2b-256 5d20c340c7a2bbaf1b583c1f5680c237fc7c0381ddb9f607edcee07ac364c8d9

See more details on using hashes here.

Provenance

The following attestation bundles were made for chalkcompute-2.11.4-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-2.11.4-cp313-cp313-musllinux_1_2_x86_64.whl.

File metadata

File hashes

Hashes for chalkcompute-2.11.4-cp313-cp313-musllinux_1_2_x86_64.whl
Algorithm Hash digest
SHA256 bf9387e9574cbe86e0669cebab821b9fbf9b852ffc58482595ca974767763715
MD5 53ec5a5c98b2811f6ff9f89f7aea7ec9
BLAKE2b-256 b312944270037a9ba7dda76d5799aa8b36dc5a5b4e4968a60889c206463d3d96

See more details on using hashes here.

Provenance

The following attestation bundles were made for chalkcompute-2.11.4-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-2.11.4-cp313-cp313-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for chalkcompute-2.11.4-cp313-cp313-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 8e0c57bd302da624956e286eccc72bf3bd6dffbf989f73c464ebd343a9f61ace
MD5 ebbe75b8a4e94a3c0ff9e3dbe37e1d0b
BLAKE2b-256 0d7b282fc6dda3a8cb205493bf46d7366a05d351c35e30ed3d34b8a1a80549da

See more details on using hashes here.

Provenance

The following attestation bundles were made for chalkcompute-2.11.4-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-2.11.4-cp313-cp313-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for chalkcompute-2.11.4-cp313-cp313-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 1633aadc6fe518b6a932018ab5d3cd70d06c5f3ecb1efa931e625a9b172ed227
MD5 96f5ffa35a64ed868c6f7452a3d51060
BLAKE2b-256 632d6ea586b838c03951fc4c4800090323842741716bf5ffe2cd6e5e701ac768

See more details on using hashes here.

Provenance

The following attestation bundles were made for chalkcompute-2.11.4-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-2.11.4-cp312-cp312-musllinux_1_2_x86_64.whl.

File metadata

File hashes

Hashes for chalkcompute-2.11.4-cp312-cp312-musllinux_1_2_x86_64.whl
Algorithm Hash digest
SHA256 ae6cc76115a5617308d4186f00f1326ab647a35e2edf9440e7f2dbe7d89f53b7
MD5 494a1f5eb326d1f0e3260860000a7940
BLAKE2b-256 61c7444bc9fcb73a7869f9019aa8e3d34cdaaed269db50e2d99ea747a7f49464

See more details on using hashes here.

Provenance

The following attestation bundles were made for chalkcompute-2.11.4-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-2.11.4-cp312-cp312-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for chalkcompute-2.11.4-cp312-cp312-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 5f91760711bc27e1192ee2bfc5a0dda738dad8cd6822680272c40547447d0ea6
MD5 6ec7d908623ca455253dfea77f1dae8e
BLAKE2b-256 668a2aec9a3ccec9001a2cedfb54b015e4b1ed915107884da1dbcf01388f99e2

See more details on using hashes here.

Provenance

The following attestation bundles were made for chalkcompute-2.11.4-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-2.11.4-cp312-cp312-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for chalkcompute-2.11.4-cp312-cp312-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 7b05b43ffee5a327c7b9f95341b0a708ac3691d83f07157c5a9ef718f23e79c8
MD5 3d98b1959e4c21080734d3d61cfde5e1
BLAKE2b-256 f3426f131e6c45b97bdf982d2c89ac659df1307e390dd99205fa8e5019292fc6

See more details on using hashes here.

Provenance

The following attestation bundles were made for chalkcompute-2.11.4-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-2.11.4-cp311-cp311-musllinux_1_2_x86_64.whl.

File metadata

File hashes

Hashes for chalkcompute-2.11.4-cp311-cp311-musllinux_1_2_x86_64.whl
Algorithm Hash digest
SHA256 f8cd16e66bd541b17067bb03d7b437b3b4ffe15ccadb86681dfeddf1a790fae8
MD5 781f614420fed83e47eda8f87c787499
BLAKE2b-256 35b397318fbfc256452297dc315da08c68263888f3ad9da2965123031dc9f94f

See more details on using hashes here.

Provenance

The following attestation bundles were made for chalkcompute-2.11.4-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-2.11.4-cp311-cp311-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for chalkcompute-2.11.4-cp311-cp311-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 0650e9a1bba8053f5fdf3fed0ec553552e7a32e411e464dfc9a204eab7d2e348
MD5 87cb3fe2fd78f0d8f2b1accfb2ffadb1
BLAKE2b-256 02599df1233dca5a41c355e071a823b40606ceeb46c586ed64f9efcef1e9e590

See more details on using hashes here.

Provenance

The following attestation bundles were made for chalkcompute-2.11.4-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-2.11.4-cp311-cp311-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for chalkcompute-2.11.4-cp311-cp311-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 88c29d55086f97d3f9a2b75b02ddbed3539ae03cce85c2b4034095e144511928
MD5 c4b0e97fea54f203947e44e869747b63
BLAKE2b-256 bcc6680b67b591fd2fe5298f8b141cecd3363f2fe8b7d8f2936399e599d9183d

See more details on using hashes here.

Provenance

The following attestation bundles were made for chalkcompute-2.11.4-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

2.11.5

13 files

This release

2.11.4 This release

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