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Diffstat (limited to 'tensorflow/docs_src/guide/using_tpu.md')
| -rw-r--r-- | tensorflow/docs_src/guide/using_tpu.md | 16 |
1 files changed, 8 insertions, 8 deletions
diff --git a/tensorflow/docs_src/guide/using_tpu.md b/tensorflow/docs_src/guide/using_tpu.md index 90a663b75e..59b34e19e0 100644 --- a/tensorflow/docs_src/guide/using_tpu.md +++ b/tensorflow/docs_src/guide/using_tpu.md @@ -22,8 +22,8 @@ Standard `Estimators` can drive models on CPU and GPUs. You must use `tf.contrib.tpu.TPUEstimator` to drive a model on TPUs. Refer to TensorFlow's Getting Started section for an introduction to the basics -of using a @{$premade_estimators$pre-made `Estimator`}, and -@{$custom_estimators$custom `Estimator`s}. +of using a [pre-made `Estimator`](../guide/premade_estimators.md), and +[custom `Estimator`s](../guide/custom_estimators.md). The `TPUEstimator` class differs somewhat from the `Estimator` class. @@ -171,9 +171,9 @@ This section details the changes you must make to the model function During regular usage TensorFlow attempts to determine the shapes of each `tf.Tensor` during graph construction. During execution any unknown shape dimensions are determined dynamically, -see @{$guide/tensors#shape$Tensor Shapes} for more details. +see [Tensor Shapes](../guide/tensors.md#shape) for more details. -To run on Cloud TPUs TensorFlow models are compiled using @{$xla$XLA}. +To run on Cloud TPUs TensorFlow models are compiled using [XLA](../performance/xla/index.md). XLA uses a similar system for determining shapes at compile time. XLA requires that all tensor dimensions be statically defined at compile time. All shapes must evaluate to a constant, and not depend on external data, or stateful @@ -184,7 +184,7 @@ operations like variables or a random number generator. Remove any use of `tf.summary` from your model. -@{$summaries_and_tensorboard$TensorBoard summaries} are a great way see inside +[TensorBoard summaries](../guide/summaries_and_tensorboard.md) are a great way see inside your model. A minimal set of basic summaries are automatically recorded by the `TPUEstimator`, to `event` files in the `model_dir`. Custom summaries, however, are currently unsupported when training on a Cloud TPU. So while the @@ -343,7 +343,7 @@ weight when creating your `tf.metrics`. Efficient use of the `tf.data.Dataset` API is critical when using a Cloud TPU, as it is impossible to use the Cloud TPU's unless you can feed it data -quickly enough. See @{$datasets_performance} for details on dataset performance. +quickly enough. See [Input Pipeline Performance Guide](../performance/datasets_performance.md) for details on dataset performance. For all but the simplest experimentation (using `tf.data.Dataset.from_tensor_slices` or other in-graph data) you will need to @@ -361,7 +361,7 @@ Small datasets can be loaded entirely into memory using `tf.data.Dataset.cache`. Regardless of the data format used, it is strongly recommended that you -@{$performance_guide#use_large_files$use large files}, on the order of +[use large files](../performance/performance_guide.md#use_large_files), on the order of 100MB. This is especially important in this networked setting as the overhead of opening a file is significantly higher. @@ -391,5 +391,5 @@ to make a Cloud TPU compatible model are the example models published in: For more information about tuning TensorFlow code for performance see: - * The @{$performance$Performance Section.} + * The [Performance Section.](../performance/index.md) |