Quick Start

This section uses a simple example to help you quickly understand how to port and execute TensorFlow scripts to the Ascend platform.

Script Porting

Script before porting:

import tensorflow as tf
a = tf.random_normal([2, 3], dtype=tf.float32)
b = tf.random_uniform([2, 3], dtype=tf.float32)
c = tf.add(a, b)
with tf.Session() as sess:
    result = sess.run(c)
    print("result: ", result)

Script after porting:

import tensorflow as tf
# Import NPU-related libraries.
from npu_bridge.npu_init import *
a = tf.random_normal([2, 3], dtype=tf.float32)
b = tf.random_uniform([2, 3], dtype=tf.float32)
c = tf.add(a, b)
# Add allow_soft_placement=True for the session configurations to allow TensorFlow to automatically allocate devices.
config = tf.ConfigProto(allow_soft_placement=True)
# Add an NPU optimizer named NpuOptimizer. During network compilation, the NPU traverses only the session configurations under NpuOptimizer.
custom_op = config.graph_options.rewrite_options.custom_optimizers.add()
custom_op.name = "NpuOptimizer"
# Explicitly disable the remapping and memory_optimization functions of TensorFlow to avoid conflicts with the functions of the NPU.
config.graph_options.rewrite_options.remapping = RewriterConfig.OFF  # Explicitly disable the function.
config.graph_options.rewrite_options.memory_optimization = RewriterConfig.OFF  # Explicitly disable the function.
with tf.Session(config=config) as sess:
    result = sess.run(c)
    print("result: ", result)

Execute the script.

Set environment variables.

# Set one of the following environment variables for the installation path of the infrastructure software on which training depends. The following assumes that the installation user is HwHiAiUser:
# Method 1: Install Ascend-CANN-Toolkit for training on an Ascend AI device, which serves as the development environment.
. /home/HwHiAiUser/Ascend/ascend-toolkit/set_env.sh 
# Method 2: Install Ascend-CANN-NNAE on an Ascend AI device.
# . /home/HwHiAiUser/Ascend/nnae/set_env.sh 
# TF Adapter Python library. ${TFPLUGIN_INSTALL_PATH} indicates the installation path of the TF Adapter package.
export PYTHONPATH=${TFPLUGIN_INSTALL_PATH}:$PYTHONPATH

export JOB_ID=10087        # User-defined training job ID. Only letters, digits, hyphens (-), and underscores (_) are supported. You are advised not to use a number starting with 0.
export ASCEND_DEVICE_ID=0  # Logical ID of the Ascend AI Processor, optional in single-device training and defaulted to 0, indicating that training is performed on device 0.

Execute the script.
Assume that the ported script is named tf_quickstart.py. The command example is as follows:
```
python3 tf_quickstart.py
```