R1.3 #13830
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R1.3 #13830
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Context: After r1.2, TensorBoard moved out of the TensorFlow repository, into its own repository and its own pip package (presently tensorflow-tensorboard, will later switch to just tensorboard). The new pip package specifies the `tensorboard` command, so I removed it from the list of console scripts forTensorFlow. I also added tensorflow-tensorboard as a pip dependency. However, it turns out that the pip order of operations is: - install pip dependencies (thus getting tensorflow-tensorboard and the new tensorboard command) - remove deprecated console scripts (thus erroneously removing the new pointer to tensorboard) To fix this, I returned the `tensorboard` console script to tensorflow's setup.py, except it now references the tensorboard package rather than the tensorflow package. Thus, the console script declaration in tensorflow and tensorboard are identical. We can be confident that the tensorboard package is available, because it is specified by the pip dependency. Test Plan: - Create a clean virtualenv. - pip install tensorflow < 1.3. - verify that the tensorboard command works properly - pip install tensorflow 1.3 using a pip package generated with this change - verify that the tensorboard command still works
It now takes about 400ms rather than 800ms, if the file system cache is warm. Most of the latency was due to parsing text_format OpList protocol buffers in our generated sources. We now use a binary representation, while preserving the text proto as a comment for readability. Note: This change does not improve the latency of dereferencing tf.contrib, which takes about 340ms.
Every summary op writes data for a single plugin to process. Hence, each SummaryMetadata proto should have a single PluginData optional field (instead of a repeated one). This removes much complexity from TensorBoard logic that loops over the plugin data. It also simplifies the SQL schema - it can now enforce a one-to-one relationship between summary op and plugin.
Make 'import tensorflow' go faster
Turn off grappler for 1.3
Make plugin_data optional instead of repeated
Updating version to rc2.
Pin 1.3.x package to tensorflow-tensorboard 0.1.x
…rd-install Ensure that TensorBoard is still available when pip installed. (r1.3)
Make layout optimizer_test manual due to Grappler being off in the r1.3 branch.
In #11952, I had set made some logic within debug_grpc_testlib return too early, breaking some debugger-related behavior. This PR fixes that.
Fix debugger logic in r1.3
Update docs symposium
* fix broken links, add links check to sanity * fix broken link in export.md
Updating protobuf and llvm hashes.
TF Docs fix for 1.3
GitHub only 1.3.1 release.
Rename set to depset (#13443)
This makes the definition consistent with TensorBoard and TensorFlow Serving. It's better to track HEAD than the release versions. PiperOrigin-RevId: 170788851
I find the comment say that embedding shape is [num_encoder_symbols x input_size], I was so confused and checked the source code, I believe it will be better to change the input_size to embedding_size
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…ction more clearly Imported from GitHub PR openxla/xla#13830 I find the previous comments about swapping {k, n_div_k} which I write before in tree reduction rewriter may make others feel confuse. I rewrite them for easier understanding. Copybara import of the project: -- ac30db74938721956a7cf86f45b8b4a46d9b5b58 by Zhou, Lingzhi <lingzhi.zhou@intel.com>: make the comments about swapping {k, n_div_k} in tree reduction more clearly -- 62df053ba87cb906bf988033dc63c50ced004691 by Zhou, Lingzhi <lingzhi.zhou@intel.com>: modify the comments about where should swap or not Merging this change closes #13830 FUTURE_COPYBARA_INTEGRATE_REVIEW=openxla/xla#13830 from lingzhi98:lingzhi/fix_tree_reduction_comments 753b4a9f75221901d230a188cf84a6d6cf760a2e PiperOrigin-RevId: 644264354
copybara-service bot
pushed a commit
that referenced
this pull request
Jun 18, 2024
…ction more clearly Imported from GitHub PR openxla/xla#13830 I find the previous comments about swapping {k, n_div_k} which I write before in tree reduction rewriter may make others feel confuse. I rewrite them for easier understanding. Copybara import of the project: -- ac30db74938721956a7cf86f45b8b4a46d9b5b58 by Zhou, Lingzhi <lingzhi.zhou@intel.com>: make the comments about swapping {k, n_div_k} in tree reduction more clearly -- 62df053ba87cb906bf988033dc63c50ced004691 by Zhou, Lingzhi <lingzhi.zhou@intel.com>: modify the comments about where should swap or not Merging this change closes #13830 PiperOrigin-RevId: 644275543
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On Windows, after reinstalling Anaconda3 I run the command
conda create -n tensorflow python=3.5to create a tensorflow environment within Anaconda installation in the hidden user appdata folder.
Then I opened the new and updated navigator of anaconda. Changed the environment from root to tensorflow. I was given the option to install Spyder. That I did. Within minutes it installed Spyder within the tensorflow environment.
The first two Tensorflow programs in the guide ran perfectly and smoothly.