diff options
| author |  Neal Wu <wun@google.com> | 2017-11-10 19:16:53 -0800 |
|---|---|---|
| committer |  TensorFlower Gardener <gardener@tensorflow.org> | 2017-11-10 19:23:53 -0800 |
| commit | 03c150d3d3cd00c8fbcb7e84b5fa1db08256ed3c (patch) | |
| tree | 82f1059ab3c5f31408969018499943df0151fd81 | |
| parent | 973987fbe2d9448e15f6efa613aae090703457e0 (diff) | |
Minor cleanup of links in the performance guides
PiperOrigin-RevId: 175368372
| -rw-r--r-- | tensorflow/docs_src/performance/performance_guide.md | 2 | ||||
| -rw-r--r-- | tensorflow/docs_src/performance/performance_models.md | 6 |
2 files changed, 4 insertions, 4 deletions
diff --git a/tensorflow/docs_src/performance/performance_guide.md b/tensorflow/docs_src/performance/performance_guide.md index da556bd848..17f71a6d77 100644 --- a/tensorflow/docs_src/performance/performance_guide.md +++ b/tensorflow/docs_src/performance/performance_guide.md @@ -127,7 +127,7 @@ Reading large numbers of small files significantly impacts I/O performance. One approach to get maximum I/O throughput is to preprocess input data into larger (~100MB) `TFRecord` files. For smaller data sets (200MB-1GB), the best approach is often to load the entire data set into memory. The document -[Downloading and converting to TFRecord format](https://github.com/tensorflow/models/tree/master/research/slim#Data) +[Downloading and converting to TFRecord format](https://github.com/tensorflow/models/tree/master/research/slim#downloading-and-converting-to-tfrecord-format) includes information and scripts for creating `TFRecords` and this [script](https://github.com/tensorflow/models/tree/master/tutorials/image/cifar10_estimator/generate_cifar10_tfrecords.py) converts the CIFAR-10 data set into `TFRecords`. diff --git a/tensorflow/docs_src/performance/performance_models.md b/tensorflow/docs_src/performance/performance_models.md index fcda19e74c..359b0e904d 100644 --- a/tensorflow/docs_src/performance/performance_models.md +++ b/tensorflow/docs_src/performance/performance_models.md @@ -29,8 +29,8 @@ implementation is made up of 3 stages: The dominant part of each stage is executed in parallel with the other stages using `data_flow_ops.StagingArea`. `StagingArea` is a queue-like operator -similar to @{tf.FIFOQueue}. The difference is that `StagingArea` does not -guarantee FIFO ordering, but offers simpler functionality and can be executed +similar to @{tf.FIFOQueue}. The difference is that `StagingArea` does not +guarantee FIFO ordering, but offers simpler functionality and can be executed on both CPU and GPU in parallel with other stages. Breaking the input pipeline into 3 stages that operate independently in parallel is scalable and takes full advantage of large multi-core environments. The rest of this section details @@ -344,7 +344,7 @@ executing the main script `alexnet`. * **`num_gpus`**: Number of GPUs to use. * **`data_dir`**: Path to data to process. If not set, synthetic data is used. - To use Imagenet data use these + To use ImageNet data use these [instructions](https://github.com/tensorflow/models/tree/master/research/inception#getting-started) as a starting point. * **`batch_size`**: Batch size for each GPU. |