![]() The script also reports a list of detailed changes: for example, argument renames:Īll of this information is included in the report.txt file that will be exported to your current directory. Tf_upgrade_v2 - intree foo/ - outtree foo-upgraded/ - copyotherfiles Tf_upgrade_v2 - intree foo/ - outtree foo-upgraded/ py files and copy all the other files to the outtree Tf_upgrade_v2 - infile foo.py - outfile foo-upgraded.py The upgrade script can be run on a single Python file: Note: tf_upgrade_v2 is installed automatically by pip install for TensorFlow 1.13 and later (incl. If you want to try upgrading your models from TensorFlow 1.12 to TensorFlow 2.0, follow the instructions below:įirst, install tf-nightly-2.0-preview / tf-nightly-gpu-2.0-preview. absl.flags) or switching to a package in tensorflow/addons. Upgrading code that uses these modules might require using an additional library (for e.g. It is recommended, however, to manually proofread such replacements and migrate them to new APIs in tf.* namespace instead of tf.compat.v1.* namespace as quickly as feasible.įurthermore, due to module deprecations (for example, tf.flags and tf.contrib), TensorFlow 2.0 will include changes that cannot be worked around by switching to compat.v1. This module will replace calls of the form tf.foo with equivalent tf.compat.v1.foo references. To ensure your code is still supported in TensorFlow 2.0, the upgrade script includes a compat.v1 module. ![]() We have attempted to automate as many of the upgrade tasks as possible: however, there are still syntactical and stylistic changes that cannot be performed by the script.Ĭertain API symbols cannot be upgraded simply by using a string replacement. The tf_upgrade_v2 utility is included automatically with a pip install of TF 2.0, and will help accelerate your upgrade processes by converting existing TensorFlow 1.13 Python scripts to TensorFlow 2.0. To streamline the changes, and to make your transition to TF 2.0 as seamless as possible, the TensorFlow engineering team has created a tf_upgrade_v2 utility that will help transition legacy code to the new API. ![]() Manually performing all of these modifications would be tedious, and prone to error. TensorFlow 2.0 will include many API changes, such as reordering arguments, renaming symbols, and changing default values for parameters. Posted by Paige Bailey and Anna Revinskaya
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