Getting Started with Training: Training a Model with One Click

Scenario

This case is only available in the new console.

To lower the usage threshold and enhance user experience, we provide one-click training cases on the ModelArts console. Currently, ModelArts supports SFT and LoRA fine-tuning for the Qwen3-8B, Qwen3-32B, and Qwen3-30B-A3B models, helping you quickly master the model training functions of ModelArts.

After the model training is successful, you can directly use the model for subsequent operations such as model deployment, or create a model again on the training job creation page by referring to this training process as required.

Prerequisites

1. You have registered a Huawei Cloud account, completed real-name authentication, and been granted related permissions. For details, see 1. Prerequisites: Accounts and Permissions.
2. To use one-click model training, you need to configure the ModelArts FullAccess permission. Otherwise, you cannot access OBS. For details, see Configuring Common Operations Permissions for ModelArts.

Constraints

One-click training is supported only on the new console.

Billing

Model training in ModelArts uses compute and storage resources, which are billed. Compute resources are billed for running training jobs. Storage resources are billed for storing data in OBS or SFS. For details, see Model Training Billing Items.

Procedure

1. Log in to the ModelArts console.
2. In the navigation pane, choose Getting Started. In the Popular Models area, select the model you want to train. Click Train on the card. The page for creating a training job is displayed.

   Currently, the following models support one-click training.

   Table 1 Models that support one-click training

   Model Name	Model Type	Description
   Qwen3-8B	Dense model	Additionally, this model supports seamless switching between thinking and non-thinking modes. You can switch between the two as needed during a dialog. The model's inference capability reaches the SOTA level among models of the same scale in the industry, and its general capability significantly surpasses that of Qwen2.5-7B.

3. Quickly configure basic information.

   Table 2 Configuring parameters

   Parameter	Description	Default Value
   Model	Create a model on which the training job depends.	Selected model
   Name	Job name, which is mandatory. The system automatically generates a name. You can rename it based on the following naming rules: The name contains 1 to 64 characters. Only letters, digits, hyphens (-), and underscores (_) are allowed.	Generated randomly
   Training Method	Training method used to create a training job.	LoRA fine-tuning
   Training Framework	Training framework used to create a training job.	MindSpeed
   Model Output Path	Checkpoints and model assets generated by this training task will be saved to this path. The storage address must start with obs:// or / and end with a slash (/). It cannot contain double slashes (//) except in the prefix. For example, obs://bucketname/path/ or /bucketname/path/.	obs://bucketname/path/
   Resource Pool Type	Public resource pool: The public resource pool is available for all tenants and does not require user creation. Dedicated resource pool: Dedicated resource pools are created separately and used exclusively. For details, see Creating a Dedicated Resource Pool.	Public resource pool
   Resource Pool	This parameter appears only for dedicated resource pools. In the Resource Pool section, click Select Resource Pool and choose your desired dedicated resource pool or logical subpool from the menu on the right. Click OK. You can view the dedicated resource pool name, node pool specifications, number of available nodes/maximum number of nodes, number of available NPU/GPUs, available CPUs (vCPUs), available memory (GiB), and resource fragments. Hover over View in the Resource Fragment column to check fragment details and check whether the resource pool meets the training requirements. Once you choose a resource pool, its details appear. To choose a different one, click Reselect.	-
   Instance Specifications	Resource instance specifications used to create a training job. Determines the hardware specifications for the training instances. For Dedicated resource pool, you must select a pool first. For Public resource pool, select a specification directly from the list. 8 x Ascend-snt9b2 | 192 vCPUs | 1536 GiB (modelarts.bm.npu.arm.8snt9b2) or higher is recommended.	8 x Ascend-snt9b2 | 192 vCPUs | 1536 GiB (modelarts.bm.npu.arm.8snt9b2)
   Instances	Number of resource instances used to create a training job. The value range displayed on the GUI is related to the instance specifications.	1
   Select Dataset	Dataset used for creating a training job. You can choose Preset Data or My Data. In this example, choose a ModelArts preset dataset.	First dataset option
   More Configurations	Environment variables of the training job. For details, see Table 4.	The parameters are set by default.

4. Confirm the configuration and price, and click OK. The model training job list page is displayed.

   A training job runs for a period of time. On the training job list page, view the basic information about the training job.

   • In the training job list, Status of a newly created training job is Pending.
   • When the status of a training job changes to Completed, the training job is finished, and the generated model is stored in the corresponding output path.
   • If the status is Failed or Abnormal, click the training job name to go to the job details page, and view information such as the model output, task details, events, logs, and resource usage.

After the model training is successful, you can directly use the model for subsequent operations such as model deployment, or create a model again on the training job creation page by referring to this training process as required.

FAQ

For details about common errors and solutions, see Table 3.

INFO: root: Train valid number is 0.

ValueError: label_map not match. {1:'apple', 2:'orange', 3:'banana', 4:'pear'} & {1:'apple', 2:'orange', 3:'banana'}

Default Environment Variables