What Are the Precautions for Switching Training Jobs from the Old Version to the New Version?

Differences in Training Job Creation

• In earlier versions, you can create a training job using Algorithm Management, Frequently-used, and Custom.
• In the new version, you can create a training job using Custom algorithmor My algorithm.

This allows you to select algorithms by category.

• The saved algorithms in Algorithm Management in the old version are in My algorithm in the new version.
• The Frequently-used in the old version is the Custom algorithm in the new version. Select Preset image for Boot Mode when you create jobs using the new version.
• The Custom in the old version is the Custom algorithm in the new version. Select Custom image for Boot Mode when you create jobs using the new version.

Differences in Training Code Adaptation

In the old version, you are required to configure data input and output as follows:

# Parse CLI parameters.
import argparse
parser = argparse.ArgumentParser(description='MindSpore Lenet Example')
parser.add_argument('--data_url', type=str, default="./Data",
                    help='path where the dataset is saved')
parser.add_argument('--train_url', type=str, default="./Model", help='if is test, must provide\
                    path where the trained ckpt file')
args = parser.parse_args()
...
# Download data to your local container. In the code, local_data_path specifies the training input path.
mox.file.copy_parallel(args.data_url, local_data_path)
...
# Upload the local container data to the OBS path.
mox.file.copy_parallel(local_output_path, args.train_url)

Differences in Built-in Training Engines

• In the new version, MoXing 2.0.0 or later is installed by default for built-in training engines.
• In the new version, Python 3.7 or later is used for built-in training engines.
• In the new image, the default home directory has been changed from /home/work to /home/ma-user. Check whether the training code contains hard coding of /home/work.
• Built-in training engines are different between the old and new versions. Commonly used built-in training engines have been upgraded in the new version.
  To use a training engine in the old version, switch to the old version. Table 1 lists the differences between the built-in training engines in the old and new versions.

  Table 1 Differences between the built-in training engines in the old and new versions

  Runtime Environment	Built-in Training Engine and Version	Old Version	New Version
  TensorFlow	TensorFlow-1.8.0	√	x
  	TensorFlow-1.13.1	√	Coming soon
  	TensorFlow-2.1.0	√	√
  MXNet	MXNet-1.2.1	√	x
  Caffe	Caffe-1.0.0	√	x
  Spark MLlib	Spark-2.3.2	√	x
  Ray	Ray-0.7.4	√	x
  XGBoost with scikit-learn	XGBoost-0.80-Sklearn-0.18.1	√	x
  PyTorch	PyTorch-1.0.0	√	x
  	PyTorch-1.3.0	√	x
  	PyTorch-1.4.0	√	x
  	PyTorch-1.8.0	x	√
  MPI	MindSpore-1.3.0	x	√
  Horovod	Horovod_0.20.0-TensorFlow_2.1.0	x	√
  	horovod_0.22.1-pytorch_1.8.0	x	√
  MindSpore-GPU	MindSpore-1.1.0	√	x
  	MindSpore-1.2.0	√	x