This document is for users who need backwards compatibility across different versions of TensorFlow (either for code or data), and for developers who want to modify TensorFlow while preserving compatibility.
Semantic Versioning 2.0
TensorFlow follows Semantic Versioning 2.0 (semver) for its
public API. Each release version of TensorFlow has the form
For example, TensorFlow version 1.2.3 has
MAJOR version 1,
MINOR version 2,
PATCH version 3. Changes to each number have the following meaning:
MAJOR: Potentially backwards incompatible changes. Code and data that worked with a previous major release will not necessarily work with the new release. However, in some cases existing TensorFlow graphs and checkpoints may be migratable to the newer release; see Compatibility of graphs and checkpoints for details on data compatibility.
MINOR: Backwards compatible features, speed improvements, etc. Code and data that worked with a previous minor release and which depends only on the public API will continue to work unchanged. For details on what is and is not the public API, see What is covered.
PATCH: Backwards compatible bug fixes.
For example, release 1.0.0 introduced backwards incompatible changes from release 0.12.1. However, release 1.1.1 was backwards compatible with release 1.0.0.
What is covered
Only the public APIs of TensorFlow are backwards compatible across minor and patch versions. The public APIs consist of
- All the documented Python functions and classes in the
tensorflowmodule and its submodules, except for
- functions and classes in
- functions and classes whose names start with
_(as these are private) Note that the code in the
tools/directories is not reachable through the
tensorflowPython module and is thus not covered by the compatibility guarantee.
- functions and classes in
If a symbol is available through the
tensorflow Python module or its
submodules, but is not documented, then it is not considered part of the
The C API.
The following protocol buffer files:
What is not covered
Some API functions are explicitly marked as "experimental" and can change in backward incompatible ways between minor releases. These include:
Experimental APIs: The
tf.contribmodule and its submodules in Python and any functions in the C API or fields in protocol buffers that are explicitly commented as being experimental.
Other languages: TensorFlow APIs in languages other than Python and C, such as:
Details of composite ops: Many public functions in Python expand to several primitive ops in the graph, and these details will be part of any graphs saved to disk as
GraphDefs. These details may change for minor releases. In particular, regressions tests that check for exact matching between graphs are likely to break across minor releases, even though the behavior of the graph should be unchanged and existing checkpoints will still work.
Floating point numerical details: The specific floating point values computed by ops may change at any time. Users should rely only on approximate accuracy and numerical stability, not on the specific bits computed. Changes to numerical formulas in minor and patch releases should result in comparable or improved accuracy, with the caveat that in machine learning improved accuracy of specific formulas may result in decreased accuracy for the overall system.
Random numbers: The specific random numbers computed by the random ops may change at any time. Users should rely only on approximately correct distributions and statistical strength, not the specific bits computed. However, we will make changes to random bits rarely (or perhaps never) for patch releases. We will, of course, document all such changes.
Version skew in distributed Tensorflow: Running two different versions of TensorFlow in a single cluster is unsupported. There are no guarantees about backwards compatibility of the wire protocol.
Bugs: We reserve the right to make backwards incompatible behavior (though not API) changes if the current implementation is clearly broken, that is, if it contradicts the documentation or if a well-known and well-defined intended behavior is not properly implemented due to a bug. For example, if an optimizer claims to implement a well-known optimization algorithm but does not match that algorithm due to a bug, then we will fix the optimizer. Our fix may break code relying on the wrong behavior for convergence. We will note such changes in the release notes.
Error messages: We reserve the right to change the text of error messages. In addition, the type of an error may change unless the type is specified in the documentation. For example, a function documented to raise an
InvalidArgumentexception will continue to raise
InvalidArgument, but the human-readable message contents can change.
Compatibility of graphs and checkpoints
You'll sometimes need to preserve graphs and checkpoints. Graphs describe the data flow of ops to be run during training and inference, and checkpoints contain the saved tensor values of variables in a graph.
Many TensorFlow users save graphs and trained models to disk for later evaluation or additional training, but end up running their saved graphs or models on a later release. In compliance with semver, any graph or checkpoint written out with one version of TensorFlow can be loaded and evaluated with a later version of TensorFlow with the same major release. However, we will endeavor to preserve backwards compatibility even across major releases when possible, so that the serialized files are usable over long periods of time.
Graphs are serialized via the
GraphDef protocol buffer. To facilitate (rare)
backwards incompatible changes to graphs, each
GraphDef has a version number
separate from the TensorFlow version. For example,
GraphDef version 17
inv op in favor of
reciprocal. The semantics are:
Each version of TensorFlow supports an interval of
GraphDefversions. This interval will be constant across patch releases, and will only grow across minor releases. Dropping support for a
GraphDefversion will only occur for a major release of TensorFlow.
Newly created graphs are assigned the latest
If a given version of TensorFlow supports the
GraphDefversion of a graph, it will load and evaluate with the same behavior as the TensorFlow version used to generate it (except for floating point numerical details and random numbers), regardless of the major version of TensorFlow. In particular, all checkpoint files will be compatible.
GraphDefupper bound is increased to X in a (minor) release, there will be at least six months before the lower bound is increased to X. For example (we're using hypothetical version numbers here):
- TensorFlow 1.2 might support
GraphDefversions 4 to 7.
- TensorFlow 1.3 could add
GraphDefversion 8 and support versions 4 to 8.
- At least six months later, TensorFlow 2.0.0 could drop support for versions 4 to 7, leaving version 8 only.
- TensorFlow 1.2 might support
Finally, when support for a
GraphDef version is dropped, we will attempt to
provide tools for automatically converting graphs to a newer supported
Graph and checkpoint compatibility when extending TensorFlow
This section is relevant only when making incompatible changes to the
format, such as when adding ops, removing ops, or changing the functionality
of existing ops. The previous section should suffice for most users.
Backward and partial forward compatibility
Our versioning scheme has three requirements:
- Backward compatibility to support loading graphs and checkpoints created with older versions of TensorFlow.
- Forward compatibility to support scenarios where the producer of a graph or checkpoint is upgraded to a newer version of TensorFlow before the consumer.
- Enable evolving TensorFlow in incompatible ways. For example, removing Ops, adding attributes, and removing attributes.
Note that while the
GraphDef version mechanism is separate from the TensorFlow
version, backwards incompatible changes to the
GraphDef format are still
restricted by Semantic Versioning. This means functionality can only be removed
or changed between
MAJOR versions of TensorFlow (such as
Additionally, forward compatibility is enforced within Patch releases (
1.x.2 for example).
To achieve backward and forward compatibility and to know when to enforce changes
in formats, graphs and checkpoints have metadata that describes when they
were produced. The sections below detail the TensorFlow implementation and
guidelines for evolving
Independent data version schemes
There are different data versions for graphs and checkpoints. The two data
formats evolve at different rates from each other and also at different rates
from TensorFlow. Both versioning systems are defined in
Whenever a new version is added, a note is added to the header detailing what
changed and the date.
Data, producers, and consumers
We distinguish between the following kinds of data version information:
producers: binaries that produce data. Producers have a version
producer) and a minimum consumer version that they are compatible with
consumers: binaries that consume data. Consumers have a version
consumer) and a minimum producer version that they are compatible with
Each piece of versioned data has a
field which records the
producer that made the data, the
that it is compatible with, and a list of
bad_consumers versions that are
By default, when a producer makes some data, the data inherits the producer's
bad_consumers can be set if specific
consumer versions are known to contain bugs and must be avoided. A consumer can
accept a piece of data if the following are all true:
consumernot in data's
Since both producers and consumers come from the same TensorFlow code base,
contains a main data version which is treated as either
consumer depending on context and both
(needed by producers and consumers, respectively). Specifically,
GraphDefversions, we have
- For checkpoint versions, we have
Evolving GraphDef versions
This section explains how to use this versioning mechanism to make different
types of changes to the
Add an Op
Add the new Op to both consumers and producers at the same time, and do not
GraphDef versions. This type of change is automatically
backward compatible, and does not impact forward compatibility plan since
existing producer scripts will not suddenly use the new functionality.
Add an Op and switch existing Python wrappers to use it
- Implement new consumer functionality and increment the
- If it is possible to make the wrappers use the new functionality only in cases that did not work before, the wrappers can be updated now.
- Change Python wrappers to use the new functionality. Do not increment
min_consumer, since models that do not use this Op should not break.
Remove or restrict an Op's functionality
- Fix all producer scripts (not TensorFlow itself) to not use the banned Op or functionality.
- Increment the
GraphDefversion and implement new consumer functionality that bans the removed Op or functionality for GraphDefs at the new version and above. If possible, make TensorFlow stop producing
GraphDefswith the banned functionality. To do so, add the
- Wait for a major release for backward compatibility purposes.
min_producerto the GraphDef version from (2) and remove the functionality entirely.
Change an Op's functionality
- Add a new similar Op named
SomethingV2or similar and go through the process of adding it and switching existing Python wrappers to use it, which may take three weeks if forward compatibility is desired.
- Remove the old Op (Can only take place with a major version change due to backward compatibility).
min_consumerto rule out consumers with the old Op, add back the old Op as an alias for
SomethingV2, and go through the process to switch existing Python wrappers to use it.
- Go through the process to remove
Ban a single unsafe consumer version
- Bump the
GraphDefversion and add the bad version to
bad_consumersfor all new GraphDefs. If possible, add to
bad_consumersonly for GraphDefs which contain a certain Op or similar.
- If existing consumers have the bad version, push them out as soon as possible.