Preparing a Model Training Image

ModelArts provides deep learning-powered base images such as TensorFlow, PyTorch, and MindSpore images. In these images, the software mandatory for running training jobs has been installed. If the software in the base images cannot meet your service requirements, create new images based on the base images and use the new images to create training jobs.

Preset Training Images

The following table lists the preset training base images of ModelArts.

**Table 1** ModelArts training base images
Engine	Version
PyTorch	pytorch_1.8.0-cuda_10.2-py_3.7-ubuntu_18.04-x86_64
TensorFlow	tensorflow_2.1.0-cuda_10.1-py_3.7-ubuntu_18.04-x86_64
Horovod	horovod_0.20.0-tensorflow_2.1.0-cuda_10.1-py_3.7-ubuntu_18.04-x86_64
Horovod	horovod_0.22.1-pytorch_1.8.0-cuda_10.2-py_3.7-ubuntu_18.04-x86_64
MPI	mindspore_1.3.0-cuda_10.1-py_3.7-ubuntu_1804-x86_64

Creating a Custom Training Image

If the software in the base images cannot meet your service requirements, create new images based on the base images and use the new images to create training jobs. Figure 1 shows the process of creating an image.

Figure 1 Process of creating a custom training image

Scenario 1: If the preset images meet ModelArts training constraints but lack necessary code dependencies, install additional software packages.

For details, see Creating a Custom Image Using a Preset Image for Model Training.

Scenario 2: If the local images meet code dependency requirements but not ModelArts training constraints, adapt them to ModelArts.

For details, see Migrating Existing Images to ModelArts for Model Training.

Scenario 3: If neither the preset nor local images meet your needs, create an image that meets both code dependency and ModelArts training constraints. See the following examples:

Parent topic: Model Training

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