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Back-ticks are now converted to links in the api_docs generator. With the new docs repo we're moving to simplify the docs pipeline, and make everything more readable.
By doing this we no longer get test failures for symbols that don't exist (`tf.does_not_exist` will not get a link).
There is also no way, not to set custom link text. That's okay.
This is the result of the following regex replacement (+ a couple of manual edits.):
re: @\{([^$].*?)(\$.+?)?}
sub: `\1`
Which does the following replacements:
"@{tf.symbol}" --> "`tf.symbol`"
"@{tf.symbol$link_text}" --> "`tf.symbol`"
PiperOrigin-RevId: 208042358
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Example code as follow:
config = tf.estimator.RunConfig(protocol='grpc+verbs')
nn = tf.estimator.Estimator(model_fn=model_fn,
model_dir=model_dir,
params=params,
config=config)
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PiperOrigin-RevId: 183936100
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to github issues.
For example : #14942
PiperOrigin-RevId: 178296636
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ClusterSpec propagation is a capability upgrade for TensorFlow that should make
it much easier to (1) build distributed TensorFlow clusters, and (2) handle
node failures. The ClusterSpec propagation capability allows TensorFlow workers
to be booted independently of each other, and with no knowledge about others.
The client can then construct a ClusterDef (ClusterSpec), and then send it
to the TF master at session creation. The master in turn then propagates the
ClusterDef along to all of the workers.
Change: 155159972
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Change: 147499520
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TASK 2. //training/... Python class doc strings
Change: 147434408
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Change: 134626795
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Change: 133602571
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A TensorFlow server (tf.train.Server) is configured with a list of
jobs, where each job includes the addresses of the tasks in that
job. At present, the tasks are provided as a dense list, and a server
must be configured with the addresses of all tasks in every job, even
when that server might never contact a particular task.
This CL adds support for configuring individual jobs with a sparse
mapping from task index to network address. The net effect is that a
server in (e.g.) a worker job need not know the addresses of the other
worker tasks. This reduces the amount of configuration needed in two
ways: (i) the cluster specification for an individual server contains
only the server with which it makes contact, and (ii) there is no need
to specify a device filter to prevent the server pinging all known
tasks on session creation (which can lead to unavailability when
unrelated tasks fail).
This CL also cleans up the code in grpc_channel.{cc,h} in three ways:
1. Move unnecessarily public methods into an anonymous namespace.
2. Shorten some of the unwieldy function and class names.
3. Use std::move() where appropriate to avoid copying vectors and maps
of strings.
Change: 131490850
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facilitate comparisons.
Change: 131096794
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Change: 130695673
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This makes it easier to set properties such as the
`gpu_options.per_process_gpu_memory_fraction`, which have to be set on
the server, rather than individual serssions.
Fixes #3057.
Change: 126009942
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Change: 123900456
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in Python.
- Add a tf_status util file to convert between Status and TF_Status.
- Use TF_Status for the swigged APIs in session, checkpoint_reader, and server_lib.
- Converts all these tf_status to exceptions in Python with the new context handler.
- Remove the old StatusNotOK exception.
Change: 121644982
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Creating a `tf.train.ClusterSpec` from another ClusterSpec was broken,
which in turn broke creating a `tf.train.Server` from a ClusterSpec.
Fixes #1961.
Change: 119954117
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Change: 119533248
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Previously, if the port was undefined, an out-of-bounds access would
be made. This change adds the appropriate checks.
Change: 119424297
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This is a breaking change! The following classes have been renamed:
tf.GrpcServer -> tf.train.Server
tf.ClusterSpec -> tf.train.ClusterSpec
tf.ServerDef -> tf.train.ServerDef
tf.JobDef -> tf.train.JobDef
tf.ClusterDef -> tf.train.ClusterDef
The constructor for tf.train.Server is more permissive and now accepts
tf.train.ClusterSpec, tf.train.ClusterDef, and dictionary inputs for
specifying the cluster part of the server.
For consistency, the server library moves from python/client to python/training.
Change: 119335624
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Mark Daoust
Li Liangbin
Anna R
Mustafa Ispir
Brennan Saeta
Patrick Nguyen
A. Unique TensorFlower
Derek Murray
Benoit Steiner
A. Unique TensorFlower