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

Frequently-used Frameworks

Updated on 2023-09-06 GMT+08:00

This section describes frequently-used AI frameworks supported by ModelArts and how to use these frameworks to compile training code for creating training jobs.

Frequently-used AI Frameworks for Training Management

ModelArts supports the following AI engines and versions.

NOTE:
  • MoXing is a distributed training acceleration framework developed by the ModelArts team. It is built on the open-source deep learning engines TensorFlow, MXNet, PyTorch, and Keras. If you use MoXing to compile a training script, select the corresponding AI engine and version based on your selected API when you create a training job.

Developing Training Code Using Frequently-used Frameworks

When creating a training job using a common framework, set the code directory, boot file, input path, and output path. These settings enable the interaction between you and ModelArts.

  • Code directory

    Specify the code directory in the OBS bucket and upload training data such as training code, dependency installation packages, or pre-generated model to the directory. After you create the training job, ModelArts downloads the code directory and its subdirectories to the container.

  • Boot file

    The boot file in the code directory is used to start the training. Only Python boot files are supported.

  • OBS path for storing training data

    Do not store training data in the code directory. When the training job starts, the data stored in the code directory will be downloaded to the backend. A large amount of training data may lead to a download failure.

    After the training job is started, ModelArts mounts a disk to the /cache directory. You can use this directory to store temporary files. For details about the size of the /cache directory, see What Are Sizes of the /cache Directories for Different Resource Specifications in the Training Environment?

    You must upload the training data to another OBS path rather than the code directory and parse data_url to download the training data to the /cache directory. Ensure that you have the read permission to the OBS bucket.

  • Output path

    You are advised to set an empty directory as the training output path. In the training code, you must parse train_url to upload the training output to the output path. Ensure that you have the write and read permissions to the OBS bucket.

When you use a frequently-used framework to create a training job, develop training code. The following figure shows the process of developing training code.

  1. (Optional) Introduce dependencies.

    If your model references other dependencies, place the corresponding file or installation package in Code Directory you set during training job creation.

    Figure 1 Selecting a frequently-used framework and specifying the model boot file
  2. Parse mandatory parameters data_url and train_url.

    When using a frequently-used framework to create a training job, set job parameters on the page for creating a training job.

    data_url: Training data is mandatory for developing training code. When creating a training job, set Data Source. In the training code, data_url indicates the path of the data source.

    train_url: After model training is complete, the trained model and the output information must be stored in an OBS path. When creating a training job, set Training Output Path. In the training code, train_url indicates the OBS path specified for Training Output Path.

    Figure 2 Job parameters

    In the training code, data_url and train_url must be parsed. Use the following parameter parsing methods for ModelArts:

     1
     2
     3
     4
     5
     6
     7
     8
     9
    10
    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, default='obs://obs-test/ckpt/mnist',
                        help='the path model saved')
    parser.add_argument('--data_url', type=str, default='obs://obs-test/data/', help='the training data')
    # Parse the parameters.
    args, unkown = parser.parse_known_args()
    
  3. Import training data from data_url.

    Training data is stored in data_url. Use the MoXing API to download the data to the cache directory.

    1
    2
    import moxing as mox 
    mox.file.copy_parallel(args.data_url, "/cache")
    
  4. Compile training code and save the model.

    Training code and the code for saving the model are closely related to the AI engine you use. The following uses the TensorFlow framework as an example. In the training code, the TensorFlow API tf.flags.FLAGS is used to receive CLI parameters.

    from __future__ import absolute_import
    from __future__ import division
    from __future__ import print_function
    
    import os
    
    import tensorflow as tf
    from tensorflow.examples.tutorials.mnist import input_data
    
    import moxing as mox
    
    tf.flags.DEFINE_integer('max_steps', 1000, 'number of training iterations.')
    tf.flags.DEFINE_string('data_url', '/home/jnn/nfs/mnist', 'dataset directory.')
    tf.flags.DEFINE_string('train_url', '/home/jnn/temp/delete', 'saved model directory.')
    
    FLAGS = tf.flags.FLAGS
    
    
    def main(*args):
        mox.file.copy_parallel(FLAGS.data_url, '/cache/data_url')
    
        # Train model
        print('Training model...')
        mnist = input_data.read_data_sets('/cache/data_url', one_hot=True)
        sess = tf.InteractiveSession()
        serialized_tf_example = tf.placeholder(tf.string, name='tf_example')
        feature_configs = {'x': tf.FixedLenFeature(shape=[784], dtype=tf.float32),}
        tf_example = tf.parse_example(serialized_tf_example, feature_configs)
        x = tf.identity(tf_example['x'], name='x')
        y_ = tf.placeholder('float', shape=[None, 10])
        w = tf.Variable(tf.zeros([784, 10]))
        b = tf.Variable(tf.zeros([10]))
        sess.run(tf.global_variables_initializer())
        y = tf.nn.softmax(tf.matmul(x, w) + b, name='y')
        cross_entropy = -tf.reduce_sum(y_ * tf.log(y))
    
        tf.summary.scalar('cross_entropy', cross_entropy)
    
        train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)
    
        correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
        accuracy = tf.reduce_mean(tf.cast(correct_prediction, 'float'))
        tf.summary.scalar('accuracy', accuracy)
        merged = tf.summary.merge_all()
        test_writer = tf.summary.FileWriter('/cache/train_url', flush_secs=1)
    
        for step in range(FLAGS.max_steps):
            batch = mnist.train.next_batch(50)
            train_step.run(feed_dict={x: batch[0], y_: batch[1]})
            if step % 10 == 0:
                summary, acc = sess.run([merged, accuracy], feed_dict={x: mnist.test.images, y_: mnist.test.labels})
                test_writer.add_summary(summary, step)
                print('training accuracy is:', acc)
        print('Done training!')
    
        builder = tf.saved_model.builder.SavedModelBuilder(os.path.join('/cache/train_url', 'model'))
    
        tensor_info_x = tf.saved_model.utils.build_tensor_info(x)
        tensor_info_y = tf.saved_model.utils.build_tensor_info(y)
    
        prediction_signature = (
            tf.saved_model.signature_def_utils.build_signature_def(
                inputs={'images': tensor_info_x},
                outputs={'scores': tensor_info_y},
                method_name=tf.saved_model.signature_constants.PREDICT_METHOD_NAME))
    
        builder.add_meta_graph_and_variables(
            sess, [tf.saved_model.tag_constants.SERVING],
            signature_def_map={
                'predict_images':
                    prediction_signature,
            },
            main_op=tf.tables_initializer(),
            strip_default_attrs=True)
    
        builder.save()
    
        print('Done exporting!')
    
        mox.file.copy_parallel('/cache/train_url', FLAGS.train_url)
    
    
    if __name__ == '__main__':
        tf.app.run(main=main)

  5. Export the trained model to train_url.

    The training output path is train_url. You are advised to use the MoXing API to export the output from /cache/train_url to train_url.

    mox.file.copy_parallel("/cache/train_url", args.train_url)

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