Platform For AI:Installation and configuration

Prerequisites

• You have obtained an AccessKey pair.

• You have created a workspace.

• You have created an OSS bucket.

• Python 3.8 or later.

• RAM users require permissions on workspace and Object Storage Service (OSS) buckets:

  • Assign developer or administrator role to submit training jobs. For more information, see Manage members of the workspace.

  • Grant read and write permissions to store code and models in OSS buckets. For more information, see "Grant your RAM user or RAM role the permissions to access OSS" in Grant the permissions that are required to use Machine Learning Designer.

Installation

Install PAI SDK for Python:

pip install "alipai>=0.4.0"

Configuration

Configure workspace credentials and region settings:

# Run after SDK installation.
python -m pai.toolkit.config

Configuration example:

Upon successful configuration, the system displays: Configuration saved successfully: <Path to which the configurations are saved>.

Configuration parameters

The configuration wizard prompts for the following parameters:

Parameter Description Example workspace_id PAI workspace ID. Find it in PAI console. 12345 region_id Alibaba Cloud region where workspace or EAS service is deployed. Required for cross-region access. us-southeast-1 oss_bucket_name OSS bucket name for storing code and models. my-pai-bucket

Available regions

Supported region IDs for region_id parameter:

Region Region ID China (Hangzhou) cn-hangzhou China (Shanghai) cn-shanghai China (Beijing) cn-beijing China (Shenzhen) cn-shenzhen China (Zhangjiakou) cn-zhangjiakou China (Hong Kong) cn-hongkong Singapore ap-southeast-1 Indonesia (Jakarta) ap-southeast-5 US (Silicon Valley) us-west-1 US (Virginia) us-east-1 US (Atlanta) us-southeast-1 Germany (Frankfurt) eu-central-1 UK (London) eu-west-1 Japan (Tokyo) ap-northeast-1

Cross-region configuration

Programmatic configuration for cross-region scenarios, such as deploying EAS services in a different region from workspace:

from pai.session import Session

# Initialize session with US region
session = Session(
    workspace_id="12345",
    region_id="us-southeast-1",  # EAS service deployed in US region
    oss_bucket_name="my-pai-bucket"
)

# Deploy model to EAS service in us-southeast-1
from pai.model import Model

model = Model(
    model_data="oss://my-pai-bucket/models/my_model/",
    session=session
)

# Deploy with region-specific configuration
predictor = model.deploy(
    service_name="my-service",
    instance_type="ecs.g6.xlarge",
    options={
        "metadata.region": "us-southeast-1"  # Explicit region specification
    }
)

Note: Ensure OSS bucket and EAS service are in the same region for optimal performance. Cross-region data transfer may incur additional latency and costs.

Next steps

After configuration, submit training jobs. For more information, see Submit a training job.