What's New

1

Password-free SSH mutual trust between nodes for a training job created using a custom image

If you use a custom image based on the MPI or Horovod framework for distributed training, you can configure password-free SSH mutual trust between training job nodes.

Commercial use

Configuring Password-free SSH Mutual Trust Between Nodes for a Training Job Created Using a Custom Image

2

Batch input of hyperparameters and environment variables for training jobs

When creating or modifying a training job, you can input hyperparameters and environment variables in batches.

Commercial use

Creating a Production Training Job

3

Training job suspension detection

The environment variable MA_HANG_DETECT_TIME is set to 30 by default, which means a job is considered suspended if its process I/O does not change for 30 minutes. This parameter can be modified.

Commercial use

Detecting Training Job Suspension

4

Starting MindSpore using msrun or torchrun

To run the preset Ascend-Powered-Engine training framework, you can configure the environment variable MA_RUN_METHOD to set the boot mode as either msrun or torchrun.

Commercial use

Starting a Preset Image's Boot File

5

Accessing a real-time service through a VPC channel

To connect to a ModelArts VPC endpoint service, you can create a VPC endpoint in a specific VPC within your account.

Commercial use

Accessing a Real-Time Service Through a VPC Channel

6

ModelArts Notebook scheduled task

You can create scheduled tasks, run notebook files with just one click, and customize the time and frequency for executing code blocks.

Commercial use

Creating a Scheduled Job in JupyterLab

1

ModelArts documentation upgrade

The user guide is now organized by product, with separate sections for each. The ModelArts console operation guide is now included in the ModelArts Standard User Guide.

Added user guides for ModelArts Lite Server, ModelArts Lite Cluster.

Commercial use

ModelArts Standard Usage

Lite Server Usage

1

Training fault recovery details

If a training job experiences a fault, such as process-level recovery, POD-level rescheduling, or job-level rescheduling, you can access the job details page to view the fault recovery details. This page displays the start and stop details of the training job.

Commercial use

Training Job Rescheduling

2

Customizable training specifications

When creating a training job, you can customize dedicated resource pool specifications to improve resource utilization.

Commercial use

Creating a Training Job

1

Billing details

The new topic introduces ModelArts billing modes and items. You can learn how to change the billing mode, renew a subscription, view bills, and stop being charged.

Commercial use

Billing

1

Storage volume mounting for real-time services

You can enable this function when you create a real-time service. A storage volume will be mounted to a compute node (compute instance) as a local directory.

Commercial use

Deploying as a Real-Time Service

2

Master Distribution parameter for creating dedicated resource pools

You can set Master Distribution to either Random or Custom when you create a dedicated resource pool.

Commercial use

Creating a Resource Pool

3

Node replacing

You can replace a single node in dedicated resource pools.

Commercial use

Viewing Details About a Resource Pool

4

IPv6 for creating dedicated resource pools

You can enable IPv6 when you create a network and a dedicated resource pool.

Commercial use

Creating a Resource Pool

5

IPv6 for real-time services

You can enable IPv6 for a real-time service during creation.

Commercial use

Deploying as a Real-Time Service

6

List of faulty node isolation codes

Isolation codes for faulty nodes and fault locating methods are provided.

Commercial use

Abnormal Status of a Dedicated Resource Pool

7

Tips for locating faults for Cloud Shell

Cloud Shell cannot be used if a training job is not in running state or the permission is insufficient. You can locate the fault as prompted.

Commercial use

Using Cloud Shell to Debug a Production Training Job

8

Runtime User ID configuration

If you set Boot Mode to Preset Image and the framework version to Customize during training job creation, you can customize the user ID used during container runtime.

Commercial use

Creating a Training Job

9

New topic for Starting a Preset Framework

Startup principles of preset frameworks such as TensorFlow and PyTorch are added.

Commercial use

Starting a Preset Framework

10

Notebook cache directory capacity alarms

There were no capacity alarms for cache directories in the development environment. Once the capacity usage exceeds the limit, notebook instances will be restarted, and multiple configurations will be reset. As a result, your data and the environment are discarded. Monitoring and alarms of cache directories are now available so the data can be reported to AOM.

Commercial use

Reporting Notebook Cache Directory Capacity

11

CIDR Block parameter for creating dedicated resource pools

You can use the default CIDR block or set a custom CIDR block when you create a dedicated resource pool.

Commercial use

Creating a Resource Pool

1

Tool Guide deleted

The original content in the Tool Guide are now provided in "DevEnviron" > "Local IDE" > "Local IDE (PyCharm)" > "Connecting to a Notebook Instance Through PyCharm Toolkit".

Discontinued

PyCharm Toolkit

1

Filtering training jobs by resource pool

You can filter training jobs by resource pool in the training job list.

Commercial use

Rebuilding

Stopping

or Deleting a Training Job

2

Importing large models for ModelArts inference

You can import large models to create AI applications and deploy services in ModelArts.

Commercial use

Using a Large Model to Create an AI Application and Deploy a Real-Time Service

1

Optimized sub-section titles of "Creating an AI Application" under "Managing AI Applications

The sub-section titles of "Creating an AI Application" show the meta model sources on the page for creating an AI application on the management console.

Commercial use

Creating an AI Application

2

Added information about using Grafana to view AOM monitoring metrics

Grafana is a tool for data visualization that offers various monitoring views and templates. You can import some common monitoring view templates from ModelArts to Grafana. Then, you can use Grafana to see the ModelArts monitoring metrics that are sent to AOM.

Commercial use

Using Grafana to View AOM Monitoring Metrics

3

Accessing a real-time service through WebSocket supported

For a real-time service that uses WebSocket, the client and the server can exchange data in both directions through a persistent connection if they complete the initial handshake successfully.

Commercial use

Accessing a Real-Time Service Through WebSocket

4

Assigning fine-grained permissions for setting training job priorities released

You can assign a priority level to a training job that uses a new-version dedicated resource pool. The default priority levels are 1 or 2. If you have the permission to use the highest priority level, you can assign 1, 2, or 3 to your job.

Commercial use

Priority of a Training Job

5

Submitting jobs, creating images, and image switching using ModelArts CLI for local development supported

ModelArts CLI, also called ma-cli, is a cross-platform command line tool used to connect to ModelArts and run management commands on ModelArts resources. ma-cli allows you to interact with cloud services through ModelArts notebook and on-premises VMs. You can run ma-cli commands for command autocomplete and authentication, as well as creating images, submitting ModelArts training jobs and DLI Spark jobs, and copying OBS data.

Commercial use

ModelArts CLI Command Reference

6

Notebook instance events added

Added events of a notebook instance's lifecycle.

Commercial use

Viewing Notebook Instance Events

1

New-version dedicated resource pools released

Compared with old-version dedicated resource pools, new-version dedicated resource pools have made improvements in both product and functions. For example, the original dedicated resource pools for development/training and service deployment have been unified, the network of a dedicated resource pool can be customized to access, the cluster information is more complete, the accelerator card driver of a cluster can be customized to manage, and resource allocation is fine-grained.

Commercial use

Resource Pool Management

1

TMS

ModelArts can work with Tag Management Service (TMS). When creating resource-consuming tasks in ModelArts, for example, training jobs, configure tags for these tasks so that ModelArts can use tags to manage resources by group.

Commercial use

How Does ModelArts Use Tags to Manage Resources by Group?

Creating a Notebook Instance

Deploying as a Real-Time Service

2

Event viewing optimized in new-version job training

The event display has been optimized so that you can clearly obtain the current training phase and the duration.

Commercial use

Viewing Training Job Events

3

Data labeling platform discontinued

The data labeling platform has been discontinued. The labeling console of the original team labeling has been integrated into the ModelArts console.

Discontinued

Logging In to the Data Labeling Platform

4

Viewing notebook instance events released

When a notebook instance starts or is running, you can view its events on the notebook instance details page. Through events, you can obtain details about the running or abnormal status of an instance.

Commercial use

Viewing Notebook Instance Events

1

Fine-grained permissions for new-version DevEnviron and training jobs

Permission policies and supported actions are added for fine-grained permissions.

Open beta testing

DevEnviron Permissions (Recommended)

Training Job Permissions (Recommended)

2

File transferring supported

Through file transferring, local files and folders can be uploaded to OBS, and the files and folders in OBS can be downloaded to a local path.

Open beta testing

Transferring Files (Recommended)

1

Examples for calling real-time service APIs in multiple development languages

ModelArts supports the calling of a real-time service for prediction through APIs. To provide better guidance for you, Python and Java request examples in multiple authentication modes have been provided.

Open beta testing

Accessing Real-Time Services

2

Detecting training job suspension

A training job may be suspended due to unknown reasons. If the suspension cannot be detected promptly, resources cannot be released, leading to a waste. To minimize resource cost and improve user experience, ModelArts provides suspension detection for training jobs. With this function, suspension can be automatically detected and displayed on the log details page. You can also enable notification so that you can be promptly notified of job suspension.

Open beta testing

Detecting Training Job Suspension

3

Modifying the SSH configuration in a notebook instance

To change the key pair or modify the whitelist of a notebook instance with remote SSH enabled, perform the modification on the notebook details page.

Open beta testing

Modifying the SSH Configuration for Notebook

4

Tool guide for PyCharm

AI developers use PyCharm to develop algorithms or models. ModelArts provides the PyCharm Toolkit plug-in to help AI developers quickly submit locally developed code to the ModelArts training environment. With PyCharm Toolkit, developers can quickly upload code, submit training jobs, and obtain training logs for local display so that they can better focus on local code development.

Open beta testing

Local IDE (PyCharm)

5

Viewing the notebook instances of all IAM users under one tenant account

Any IAM user granted with the listAllNotebooks and listUsers permissions can click View all on the notebook page to view the instances of all IAM users in the current IAM project.

Open beta testing

Viewing All Notebook Instances of an IAM Project

1

Viewing events of training jobs

During the running of a new-version training job, you can view its events for accurate fault locating.

Open beta testing

Viewing Training Job Events

2

Automatic recovery for training jobs

If a new-version training job failed due to a hardware fault, fault tolerance check and automatic recovery are now available.

Open beta testing

Training Job Fault Tolerance Check

3

Custom images for creating algorithms and training jobs

In new-version training jobs, custom images can be used to create and run training jobs.

Open beta testing

Using a Custom Image to Train Models

1

Notebook of the new version released

Notebook of the new version is now available. Compared with Notebook 1.0, Notebook 2.0 provides more convenient functions such as remote access to notebook through local IDEs.

Open beta testing

Introduction to Development Tools

1

Optimized agency authorization

Agency authorization has been optimized for refined permissions management. This allows you to custom authorization for exchanging data between ModelArts and other services.

Open beta testing

Configuring Access Authorization

1

Model Management renamed AI Application Management

To better distinguish between models and AI applications, Model Management has been renamed AI Application Management. A model refers to a model file. An AI application refers to an image that can be deployed, a packed model, or an application obtained through ExeML.

Open beta testing

Introduction to AI Application Management

1

Access key authorization discontinued

To better distinguish between models and AI applications, Model Management has been renamed AI Application Management. A model refers to a model file. An AI application refers to an image that can be deployed, a packed model, or an application obtained through ExeML.

Open beta testing

Configuring Access Authorization

1

Training logs of the new version released

On the page for viewing training logs of the new version, common errors leading to a training job failure can be automatically identified, and solutions are provided accordingly.

Open beta testing

Training Log Failure Analysis

2

Datasets of the new version released

In the new version, creating a dataset is decoupled from creating a labeling job, which is more user-friendly.

Open beta testing

Creating a Dataset

Creating a Labeling Job

1

Training management of the new version released

Both training jobs and algorithm management of the new version are coupled for better training experience. Training management of the old version is retained.

Open beta testing

Model Training

2

Hyperparameter search available in training management of the new version

ModelArts hyperparameter search automatically tunes hyperparameters, which surpasses manual tuning in both speed and precision.

Commercial use

Automatic Model Tuning (AutoSearch)

1

Basic tutorials for assigning permissions for using ModelArts

If you already have a Huawei Cloud account and need to assign the account permissions to multiple users so that they can access ModelArts, you can assign permissions by referring to this case.

Commercial use

Assigning Basic Permissions for Using ModelArts

2

Authorization management APIs

You can call APIs for authorization management.

Commercial use

Authorization Management

1

Development environment update for notebook instances (The old-version notebook has been discontinued. Use the new-version notebook.)

Each development environment supports multiple AI engines. You can use all supported AI engines in the same notebook instance. This version updates the display of the development environment when a notebook instance is created.

Commercial use

Creating and Opening a Notebook Instance

1

Development environment: reconstruction of the notebook user guide for easier access

The ModelArts development environment integrates open source Jupyter Notebook and JupyterLab. You can choose one of them to develop models based on your habits. This version separates Jupyter Notebook from JupyterLab for easy search. You can use either of them to complete tasks.

Commercial use

Notebook User Guide

2

Data management: table datasets

Newly created table datasets support a wide range of data sources, such as Object Storage Service (OBS), Data Warehouse Service (DWS), Data Lake Insight (DLI), and MapReduce Service (MRS). In this version, DWS table data can be used as the data source to meet the requirements of different application scenarios.

Open beta testing

Table Datasets

1

GitHub code library for notebook (This function of the old-version notebook has been discontinued. For details about how to download the Git code library for the new version, see Using the Git Plug-in.)

When creating notebook instances, you can download the public and private repositories of GitHub and perform operations on the graphical user interface (GUI) using the Git plug-in of JupyterLab.

Commercial use

GitHub Code Library

2

Data management: video dataset management

Video datasets are supported. You can label a video in ModelArts to identify the location and class of each object in the video.

Open beta testing

Video Datasets