amds 0.5.5

American Data Science Python SDK and CLI

amds

Developer-friendly & type-safe Python SDK specifically catered to leverage amds API.

[!IMPORTANT] This SDK is not yet ready for production use. To complete setup please follow the steps outlined in your workspace. Delete this section before > publishing to a package manager.

Summary

Table of Contents

• amds
  • SDK Installation
  • IDE Support
  • SDK Example Usage
  • Authentication
  • Available Resources and Operations
  • Retries
  • Error Handling
  • Server Selection
  • Custom HTTP Client
  • Resource Management
  • Debugging
• Development
  • Maturity
  • Contributions
  • Command Line Interface (CLI)

SDK Installation

[!NOTE] Python version upgrade policy

Once a Python version reaches its official end of life date, a 3-month grace period is provided for users to upgrade. Following this grace period, the minimum python version supported in the SDK will be updated.

The SDK can be installed with either pip or poetry package managers.

PIP

PIP is the default package installer for Python, enabling easy installation and management of packages from PyPI via the command line.

pip install amds

Poetry

Poetry is a modern tool that simplifies dependency management and package publishing by using a single pyproject.toml file to handle project metadata and dependencies.

poetry add amds

Shell and script usage with uv

You can use this SDK in a Python shell with uv and the uvx command that comes with it like so:

uvx --from amds python

It's also possible to write a standalone Python script without needing to set up a whole project like so:

#!/usr/bin/env -S uv run --script
# /// script
# requires-python = ">=3.9"
# dependencies = [
#     "amds",
# ]
# ///

from amds import Amds

sdk = Amds(
  # SDK arguments
)

# Rest of script here...

Once that is saved to a file, you can run it with uv run script.py where script.py can be replaced with the actual file name.

IDE Support

PyCharm

Generally, the SDK will work well with most IDEs out of the box. However, when using PyCharm, you can enjoy much better integration with Pydantic by installing an additional plugin.

• PyCharm Pydantic Plugin

SDK Example Usage

Example

# Synchronous Example
from amds import Amds
import os


with Amds(
    api_key=os.getenv("AMDS_API_KEY", ""),
) as a_client:

    res = a_client.environments.get()

    # Handle response
    print(res)

The same SDK client can also be used to make asychronous requests by importing asyncio.

# Asynchronous Example
from amds import Amds
import asyncio
import os

async def main():

    async with Amds(
        api_key=os.getenv("AMDS_API_KEY", ""),
    ) as a_client:

        res = await a_client.environments.get_async()

        # Handle response
        print(res)

asyncio.run(main())

Authentication

Per-Client Security Schemes

This SDK supports the following security scheme globally:

Name	Type	Scheme	Environment Variable
api_key	apiKey	API key	AMDS_API_KEY

To authenticate with the API the api_key parameter must be set when initializing the SDK client instance. For example:

from amds import Amds
import os


with Amds(
    api_key=os.getenv("AMDS_API_KEY", ""),
) as a_client:

    res = a_client.environments.get()

    # Handle response
    print(res)

Available Resources and Operations

Available methods

alph

• gpt4o_mini - gpt-4o-mini
• gpt4o - gpt-4o
• gpt4 - gpt-4
• claude35_haiku - claude-3-5-haiku
• claude35_sonnet - claude-3-5-sonnet

compute

• get - Get Compute

environments

• get - Get Environments

integrated_servers

• get - Get Integrated Servers
• add - Add Integrated Server
• delete - Delete Integrated Server

servers

• get - Get Servers
• create - Create Server
• stop - Stop Server
• start - Start Server
• delete - Delete Server
• run_code - Run Code

servers.files

• get - Get File
• upload - Upload File

Retries

Some of the endpoints in this SDK support retries. If you use the SDK without any configuration, it will fall back to the default retry strategy provided by the API. However, the default retry strategy can be overridden on a per-operation basis, or across the entire SDK.

To change the default retry strategy for a single API call, simply provide a RetryConfig object to the call:

from amds import Amds
from amds.utils import BackoffStrategy, RetryConfig
import os


with Amds(
    api_key=os.getenv("AMDS_API_KEY", ""),
) as a_client:

    res = a_client.environments.get(,
        RetryConfig("backoff", BackoffStrategy(1, 50, 1.1, 100), False))

    # Handle response
    print(res)

If you'd like to override the default retry strategy for all operations that support retries, you can use the retry_config optional parameter when initializing the SDK:

from amds import Amds
from amds.utils import BackoffStrategy, RetryConfig
import os


with Amds(
    retry_config=RetryConfig("backoff", BackoffStrategy(1, 50, 1.1, 100), False),
    api_key=os.getenv("AMDS_API_KEY", ""),
) as a_client:

    res = a_client.environments.get()

    # Handle response
    print(res)

Error Handling

Handling errors in this SDK should largely match your expectations. All operations return a response object or raise an exception.

By default, an API error will raise a models.APIError exception, which has the following properties:

Property	Type	Description
.status_code	int	The HTTP status code
.message	str	The error message
.raw_response	httpx.Response	The raw HTTP response
.body	str	The response content

When custom error responses are specified for an operation, the SDK may also raise their associated exceptions. You can refer to respective Errors tables in SDK docs for more details on possible exception types for each operation. For example, the get_async method may raise the following exceptions:

Error Type	Status Code	Content Type
models.APIError	4XX, 5XX	*/*

Example

from amds import Amds, models
import os


with Amds(
    api_key=os.getenv("AMDS_API_KEY", ""),
) as a_client:
    res = None
    try:

        res = a_client.environments.get()

        # Handle response
        print(res)

    except models.APIError as e:
        # handle exception
        raise(e)

Server Selection

Override Server URL Per-Client

The default server can be overridden globally by passing a URL to the server_url: str optional parameter when initializing the SDK client instance. For example:

from amds import Amds
import os


with Amds(
    server_url="https://dashboard.amdatascience.com",
    api_key=os.getenv("AMDS_API_KEY", ""),
) as a_client:

    res = a_client.environments.get()

    # Handle response
    print(res)

Custom HTTP Client

The Python SDK makes API calls using the httpx HTTP library. In order to provide a convenient way to configure timeouts, cookies, proxies, custom headers, and other low-level configuration, you can initialize the SDK client with your own HTTP client instance. Depending on whether you are using the sync or async version of the SDK, you can pass an instance of HttpClient or AsyncHttpClient respectively, which are Protocol's ensuring that the client has the necessary methods to make API calls. This allows you to wrap the client with your own custom logic, such as adding custom headers, logging, or error handling, or you can just pass an instance of httpx.Client or httpx.AsyncClient directly.

For example, you could specify a header for every request that this sdk makes as follows:

from amds import Amds
import httpx

http_client = httpx.Client(headers={"x-custom-header": "someValue"})
s = Amds(client=http_client)

or you could wrap the client with your own custom logic:

from amds import Amds
from amds.httpclient import AsyncHttpClient
import httpx

class CustomClient(AsyncHttpClient):
    client: AsyncHttpClient

    def __init__(self, client: AsyncHttpClient):
        self.client = client

    async def send(
        self,
        request: httpx.Request,
        *,
        stream: bool = False,
        auth: Union[
            httpx._types.AuthTypes, httpx._client.UseClientDefault, None
        ] = httpx.USE_CLIENT_DEFAULT,
        follow_redirects: Union[
            bool, httpx._client.UseClientDefault
        ] = httpx.USE_CLIENT_DEFAULT,
    ) -> httpx.Response:
        request.headers["Client-Level-Header"] = "added by client"

        return await self.client.send(
            request, stream=stream, auth=auth, follow_redirects=follow_redirects
        )

    def build_request(
        self,
        method: str,
        url: httpx._types.URLTypes,
        *,
        content: Optional[httpx._types.RequestContent] = None,
        data: Optional[httpx._types.RequestData] = None,
        files: Optional[httpx._types.RequestFiles] = None,
        json: Optional[Any] = None,
        params: Optional[httpx._types.QueryParamTypes] = None,
        headers: Optional[httpx._types.HeaderTypes] = None,
        cookies: Optional[httpx._types.CookieTypes] = None,
        timeout: Union[
            httpx._types.TimeoutTypes, httpx._client.UseClientDefault
        ] = httpx.USE_CLIENT_DEFAULT,
        extensions: Optional[httpx._types.RequestExtensions] = None,
    ) -> httpx.Request:
        return self.client.build_request(
            method,
            url,
            content=content,
            data=data,
            files=files,
            json=json,
            params=params,
            headers=headers,
            cookies=cookies,
            timeout=timeout,
            extensions=extensions,
        )

s = Amds(async_client=CustomClient(httpx.AsyncClient()))

Resource Management

The Amds class implements the context manager protocol and registers a finalizer function to close the underlying sync and async HTTPX clients it uses under the hood. This will close HTTP connections, release memory and free up other resources held by the SDK. In short-lived Python programs and notebooks that make a few SDK method calls, resource management may not be a concern. However, in longer-lived programs, it is beneficial to create a single SDK instance via a context manager and reuse it across the application.

from amds import Amds
import os
def main():

    with Amds(
        api_key=os.getenv("AMDS_API_KEY", ""),
    ) as a_client:
        # Rest of application here...


# Or when using async:
async def amain():

    async with Amds(
        api_key=os.getenv("AMDS_API_KEY", ""),
    ) as a_client:
        # Rest of application here...

Debugging

You can setup your SDK to emit debug logs for SDK requests and responses.

You can pass your own logger class directly into your SDK.

from amds import Amds
import logging

logging.basicConfig(level=logging.DEBUG)
s = Amds(debug_logger=logging.getLogger("amds"))

You can also enable a default debug logger by setting an environment variable AMDS_DEBUG to true.

Development

Maturity

This SDK is in beta, and there may be breaking changes between versions without a major version update. Therefore, we recommend pinning usage to a specific package version. This way, you can install the same version each time without breaking changes unless you are intentionally looking for the latest version.

Contributions

While we value open-source contributions to this SDK, this library is generated programmatically. Any manual changes added to internal files will be overwritten on the next generation. We look forward to hearing your feedback. Feel free to open a PR or an issue with a proof of concept and we'll do our best to include it in a future release.

SDK Created by Speakeasy

Command Line Interface (CLI)

In addition to the SDK, this project includes a command-line interface (CLI) for interacting with the American Data Science API.

Installation

The CLI is automatically installed when you install the Python package.

pip install amds

Commands

• amds environments - Manage environments
• amds servers - Manage servers
• amds compute - Manage compute resources
• amds login - Authenticate with American Data Science API
• amds jupyter - Launch and manage local Jupyter Lab instances

Jupyter Command

The jupyter command allows you to launch a local Jupyter Lab instance with advanced features:

# Launch a local Jupyter Lab instance with ngrok proxy
amds jupyter launch

# Launch with alph-editor
amds jupyter launch --with-alph

# Specify custom port and directory
amds jupyter launch --port 9999 --directory /path/to/notebooks

This command:

• Launches a local Jupyter Lab instance
• Creates a secure public URL using ngrok
• Uploads instance information to dashboard.amdatascience.com
• Can launch with the alph-editor extension

See amds jupyter launch --help for all options.