Compute
Elastic Cloud Server
Huawei Cloud Flexus
Bare Metal Server
Auto Scaling
Image Management Service
Dedicated Host
FunctionGraph
Cloud Phone Host
Huawei Cloud EulerOS
Networking
Virtual Private Cloud
Elastic IP
Elastic Load Balance
NAT Gateway
Direct Connect
Virtual Private Network
VPC Endpoint
Cloud Connect
Enterprise Router
Enterprise Switch
Global Accelerator
Management & Governance
Cloud Eye
Identity and Access Management
Cloud Trace Service
Resource Formation Service
Tag Management Service
Log Tank Service
Config
OneAccess
Resource Access Manager
Simple Message Notification
Application Performance Management
Application Operations Management
Organizations
Optimization Advisor
IAM Identity Center
Cloud Operations Center
Resource Governance Center
Migration
Server Migration Service
Object Storage Migration Service
Cloud Data Migration
Migration Center
Cloud Ecosystem
KooGallery
Partner Center
User Support
My Account
Billing Center
Cost Center
Resource Center
Enterprise Management
Service Tickets
HUAWEI CLOUD (International) FAQs
ICP Filing
Support Plans
My Credentials
Customer Operation Capabilities
Partner Support Plans
Professional Services
Analytics
MapReduce Service
Data Lake Insight
CloudTable Service
Cloud Search Service
Data Lake Visualization
Data Ingestion Service
GaussDB(DWS)
DataArts Studio
Data Lake Factory
DataArts Lake Formation
IoT
IoT Device Access
Others
Product Pricing Details
System Permissions
Console Quick Start
Common FAQs
Instructions for Associating with a HUAWEI CLOUD Partner
Message Center
Security & Compliance
Security Technologies and Applications
Web Application Firewall
Host Security Service
Cloud Firewall
SecMaster
Anti-DDoS Service
Data Encryption Workshop
Database Security Service
Cloud Bastion Host
Data Security Center
Cloud Certificate Manager
Edge Security
Situation Awareness
Managed Threat Detection
Blockchain
Blockchain Service
Web3 Node Engine Service
Media Services
Media Processing Center
Video On Demand
Live
SparkRTC
MetaStudio
Storage
Object Storage Service
Elastic Volume Service
Cloud Backup and Recovery
Storage Disaster Recovery Service
Scalable File Service Turbo
Scalable File Service
Volume Backup Service
Cloud Server Backup Service
Data Express Service
Dedicated Distributed Storage Service
Containers
Cloud Container Engine
SoftWare Repository for Container
Application Service Mesh
Ubiquitous Cloud Native Service
Cloud Container Instance
Databases
Relational Database Service
Document Database Service
Data Admin Service
Data Replication Service
GeminiDB
GaussDB
Distributed Database Middleware
Database and Application Migration UGO
TaurusDB
Middleware
Distributed Cache Service
API Gateway
Distributed Message Service for Kafka
Distributed Message Service for RabbitMQ
Distributed Message Service for RocketMQ
Cloud Service Engine
Multi-Site High Availability Service
EventGrid
Dedicated Cloud
Dedicated Computing Cluster
Business Applications
Workspace
ROMA Connect
Message & SMS
Domain Name Service
Edge Data Center Management
Meeting
AI
Face Recognition Service
Graph Engine Service
Content Moderation
Image Recognition
Optical Character Recognition
ModelArts
ImageSearch
Conversational Bot Service
Speech Interaction Service
Huawei HiLens
Video Intelligent Analysis Service
Developer Tools
SDK Developer Guide
API Request Signing Guide
Terraform
Koo Command Line Interface
Content Delivery & Edge Computing
Content Delivery Network
Intelligent EdgeFabric
CloudPond
Intelligent EdgeCloud
Solutions
SAP Cloud
High Performance Computing
Developer Services
ServiceStage
CodeArts
CodeArts PerfTest
CodeArts Req
CodeArts Pipeline
CodeArts Build
CodeArts Deploy
CodeArts Artifact
CodeArts TestPlan
CodeArts Check
CodeArts Repo
Cloud Application Engine
MacroVerse aPaaS
KooMessage
KooPhone
KooDrive
On this page
Help Center/ ModelArts/ Image Management/ Using a Custom Image to Train Models (Model Training)/ Using a Custom Image to Create a CPU- or GPU-based Training Job

Using a Custom Image to Create a CPU- or GPU-based Training Job

Updated on 2024-08-14 GMT+08:00

Model training is an iterative optimization process. Through unified training management, you can flexibly select algorithms, data, and hyperparameters to obtain the optimal input configuration and model. After comparing metrics between job versions, you can determine the most satisfactory training job.

Prerequisites

Creating a Training Job

  1. Log in to the ModelArts management console. In the left navigation pane, choose Model Training > Training Jobs. The training job list is displayed by default.
  2. Click Create Training Job and set parameters.
    Table 1 Creating a training job using a custom image

    Parameter

    Description

    Algorithm Type

    Select Custom algorithm. This parameter is mandatory.

    Boot Mode

    Select Custom image. This parameter is mandatory.

    Image

    Container image path. This parameter is mandatory.

    You can set the container image path in either of the following ways:
    • To select your image or an image shared by others, click Select on the right and select a container image for training. The required image must be uploaded to SWR beforehand.
    • To select a public image, enter the address of the public image in SWR. Enter the image path in the format of "Organization name/Image name:Version name". Do not contain the domain name in the path because the system will automatically add the domain name to the path.

    Code Directory

    Select the OBS directory where the training code file is stored. If the custom image does not contain training code, you need to set this parameter. If the custom image contains training code, you do not need to set this parameter.

    • Upload code to the OBS bucket beforehand. The total size of files in the directory cannot exceed 5 GB, the number of files cannot exceed 1000, and the folder depth cannot exceed 32.
    • The training code file is automatically downloaded to the ${MA_JOB_DIR}/demo-code directory of the training container when the training job is started. demo-code is the last-level OBS directory for storing the code. For example, if Code Directory is set to /test/code, the training code file is downloaded to the ${MA_JOB_DIR}/code directory of the training container.

    User ID

    User ID for running the container. The default value 1000 is recommended.

    If the UID needs to be specified, its value must be within the specified range. The UID ranges of different resource pools are as follows:

    • Public resource pool: 1000 to 65535
    • Dedicated resource pool: 0 to 65535

    Boot Command

    Command for booting an image. This parameter is mandatory.

    When a training job is running, the boot command is automatically executed after the code directory is downloaded.
    • If the training boot script is a .py file, train.py for example, the boot command is as follows:
      python ${MA_JOB_DIR}/demo-code/train.py
    • If the training boot script is a .sh file, main.sh for example, the boot command is as follows:
      bash ${MA_JOB_DIR}/demo-code/main.sh

    You can use semicolons (;) and ampersands (&&) to combine multiple commands. demo-code in the command is the last-level OBS directory where the code is stored. Replace it with the actual one.

    NOTE:

    To ensure data security, do not enter sensitive information, such as plaintext passwords.

    Local Code Directory

    Specify the local directory of a training container. When a training starts, the system automatically downloads the code directory to this directory.

    The default local code directory is /home/ma-user/modelarts/user-job-dir. This parameter is optional.

    Work Directory

    During training, the system automatically runs the cd command to execute the boot file in this directory.

    Table 2 Parameters for creating a training job

    Parameter

    Sub-Parameter

    Description

    Input

    Parameter

    The algorithm code reads the training input data based on the input parameter name.

    Set this parameter to data_url, which is the same as the parameter for parsing the input data in the training code. You can set multiple training input parameters. The name of each training input parameter must be unique.

    For example, if you use argparse in the training code to parse data_url into the data input, set the parameter name of the training input to data_url.

    import argparse
    # Create a parsing task.
    parser = argparse.ArgumentParser(description="train mnist", formatter_class=argparse.ArgumentDefaultsHelpFormatter)
    # Add parameters.
    parser.add_argument('--train_url', type=str, help='the path model saved')
    parser.add_argument('--data_url', type=str, help='the training data')
    # Parse the parameters.
    args, unknown = parser.parse_known_args()

    Dataset

    Click Dataset and select the target dataset and its version in the ModelArts dataset list.

    When the training job is started, ModelArts automatically downloads the data in the input path to the training container.

    NOTE:

    ModelArts data management is being upgraded and is invisible to users who have not used data management. It is recommended that new users store their training data in OBS buckets.

    Data path

    Click Data path and select the storage path to the training input data from an OBS bucket.

    When the training job is started, ModelArts automatically downloads the data in the input path to the training container.

    How to Obtain

    The following uses training input data_path as an example.

    • If you select Hyperparameters, use this code to obtain the data:
      import argparse
      parser = argparse.ArgumentParser()
      parser.add_argument('--data_path')
      args, unknown = parser.parse_known_args()
      data_path = args.data_path 
    • If you select Environment variables, use this code to obtain the data:
      import os
      data_path = os.getenv("data_path", "")

    Output

    Parameter

    The algorithm code reads the training output data based on the output parameter name.

    Set this parameter to train_url, which is the same as the parameter for parsing the output data in the training code. You can set multiple training output parameters. The name of each training output parameter must be unique.

    Data path

    Click Data path and select the storage path to the training output data from an OBS bucket. During training, the system automatically synchronizes files from the local code directory of the training container to the data path.

    NOTE:

    The data path can only be an OBS path. To prevent any issues with data storage, choose an empty directory as the data path.

    How to Obtain

    The following uses the training output train_url as an example.

    • If you select Hyperparameters, use this code to obtain the data:
      import argparse
      parser = argparse.ArgumentParser()
      parser.add_argument('--train_url')
      args, unknown = parser.parse_known_args()
      train_url = args.train_url 
    • If you select Environment variables, use this code to obtain the data:
      import os
      train_url = os.getenv("train_url", "")

    Predownload

    Indicates whether to pre-download the files in the output directory to a local directory.

    • If you set Predownload to No, the system does not download the files in the training output data path to a local directory of the training container when the training job is started.
    • If you set Predownload to Yes, the system automatically downloads the files in the training output data path to a local directory of the training container when the training job is started. The larger the file size, the longer the download time. To avoid excessive training time, remove any unneeded files from the local code directory of the training container as soon as possible. To use resumable training and incremental training, Download must be selected.

    Hyperparameters

    -

    Used for training tuning. This parameter is optional.

    Environment Variable

    -

    Add environment variables based on service requirements. For details about preset environment variables in the training container, see Viewing Environment Variables of a Training Container.

    Auto Restart

    -

    Once this function is enabled, you can set the number of restarts and whether to enable Unconditional auto restart.

    If a training job encounters an exception due to environment issues, ModelArts automatically handles the exception or isolates the abnormal node and then restarts the training job, improving the training success rate. To avoid training progress loss and computing power waste, ensure that the code has adapted to resumable training before enabling this function.

    The value ranges from 1 to 128. The default value is 3. The value cannot be changed once the training job is created. Set this parameter based on your needs.

    If Unconditional auto restart is selected, the training job will be restarted unconditionally once the system detects a training exception. To prevent invalid restarts, the training job can be unconditionally restarted three times consecutively.

    If auto restart is triggered during training, the system records the restart information. You can check the fault recovery details on the training job details page.

  3. Select an instance flavor. The value range of the training parameters is consistent with the constraints of existing custom images. Select a public resource pool or dedicated resource pool as required. For details about the parameters, see Creating a Training Job.
  1. Click Submit to create the training job.

    It takes a period of time to create a training job.

    To view the real-time status of a training job, go to the training job list and click the name of the training job. On the training job details page that is displayed, view the basic information of the training job. For details, see Viewing Training Job Details.

We use cookies to improve our site and your experience. By continuing to browse our site you accept our cookie policy. Find out more

Feedback

Feedback

Feedback

0/500

Selected Content

Submit selected content with the feedback