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Client Initialization (Start Here)

Learn how to install the Pachyderm SDK, import a client, and initialize it with your configuration settings.

February 22, 2024

The Pachyderm SDK enables you to interact with HPE ML Data Management’s API, client, and configuration directly in a powerful way.

1. Installation #

Before using the Pachyderm SDK, make sure you have it installed. You can install the SDK using pip:

pip install pachyderm_sdk

2. Import the Client #

To use the Client class, you need to import it from the Pachyderm SDK:

from pachyderm_sdk import Client

3. Creating a Client Instance #

To interact with a Pachyderm cluster, you need to create an instance of the Client class. The Client class provides multiple ways to create a client instance based on your requirements.

Default Settings #

client = Client()

This creates a client that connects to the local Pachyderm cluster running on localhost:30650 with default authentication settings.

Custom Settings #

You can customize the client settings by providing the relevant parameters to the Client constructor. Here’s an example:

client = Client(
    host='localhost',
    port=8080,
    auth_token='your-auth-token',
    root_certs=None,
    transaction_id=None,
    tls=False
)

In the above example, the client is configured to connect to the local Pachyderm cluster running on localhost:8080 without TLS encryption.

  • The auth_token parameter allows you to specify an authentication token for accessing the cluster.
  • The root_certs parameter can be used to provide custom root certificates for secure connections.
  • The transaction_id parameter allows you to specify a transaction ID to run operations on.
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By default, the client will attempt to read the authentication token from the AUTH_TOKEN_ENV environment variable. You can also set the authentication token after creating the client using the auth_token property:

4. Connect #

The Client class provides different methods to connect to a Pachyderm cluster based on your deployment configuration.

From Within a Cluster #

If you’re running the code within a Pachyderm cluster, you can use the new_in_cluster method to create a client instance that operates within the cluster. This method reads the cluster configuration from the environment and creates a client based on the available configuration.

client = Client.new_in_cluster(auth_token='your-auth-token', transaction_id='your-transaction-id')

Via PachD Address #

If you have the Pachd address (host:port) of the HPE ML Data Management cluster, you can create a client instance using the from_pachd_address method:

client = Client.from_pachd_address('pachd-address', auth_token='your-auth-token', root_certs='your-root-certs', transaction_id='your-transaction-id')

Referencing a Config File #

If you have a HPE ML Data Management configuration file, you can create a client instance using the from_config method:

client = Client.from_config('path-to-config-file')

Test Connection #

If you’d like to quickly test out working with the Pachyderm SDK on your local machine (e.g., using a locally deployed Docker Desktop instance), try out the following:

from pachyderm_sdk import Client 

client = Client(host="localhost", port="80")
version = client.get_version()
print(version)

Example Output

Version(major=2, minor=6, micro=4, git_commit='358bd1229130eb262c22caf82ed87b3cc91ec81c', git_tree_modified='false', build_date='2023-06-22T14:49:32Z', go_version='go1.20.5', platform='arm64')

If you see this, you are ready to start working with the SDK.