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Summary: Testing on fresh Trusty VMs on GCP suggests that this may suffice to stop the strange errors wherein Bazel invokes GCC with invalid flags, though it should be noted that we do not actually understand the root cause or the mechanism of failure. Test Plan: That this build should pass on Travis does not actually indicate that this commit fixes the underlying problem, because the build will use a new Bazel cache due to the version change. So: merge now, and hope that this fixes the problem. wchargin-branch: travis-bazel-0.21.0
Summary: This reverts part of d285c7d because the upstream package has been fixed. Test Plan: Running `from tensorflow.compat import v1` fails in the 20190114 virtualenv but works in the 20190115 virtualenv. Building the Pip package with `bazel run //tensorboard/pip_package:build_pip_package` also works. wchargin-branch: unpin-tf-nightly
* Add flags to TBContext * Add flags to TBContext in application * Add flags to tbcontext * gitignore changes * fix link * spacing
d3.formatter now supports trim trailing zero with "~". For instance, number 0.1 used to result in "0.100" but now is "0.1". This does not affect numbers like "0.1234" and both formatter result in "0.123".
Summary: We’ve migrated all users inside Google to use the explicitly versioned endpoints (either `summary:summary_v1` or `summary:summary_v2`), so we can now enforce that going forward. Any existing users should migrate to `//tensorboard/summary:summary_v1`, which is API-compatible and stable. Test Plan: That `bazel build //tensorboard` and `bazel query 'deps(//...)'` still work suffices. wchargin-branch: restrict-summary-visibility
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Feb 26, 2019
Summary: This commit adds a module `tensorboard.notebook` that can be loaded as a Jupyter extension, providing the `%tensorboard` cell magic to launch and display TensorBoard instances within a notebook. This code doesn’t have to be perfect right now; one main goal for this PR is to sync the structure of this code into google3, where it’s easier to work on and test Colab integration. The UI looks like this: ![Screenshot of a `%tensorboard` command in the Jupyter UI.][1] [1]: https://user-images.githubusercontent.com/4317806/52386325-7ae7eb80-2a3a-11e9-93ab-fc9a689de51c.png Test Plan: Currently, this requires a bit of path hackery to get working: 1. Run `bazel build //tensorboard` to build the main binary. 2. Run `which tensorboard` to find the `tensorboard` binary provided by your virtualenv, and hit it with `chmod -x` to make it not executable. 3. Run `export PATH="$(readlink -e ./bazel-bin/tensorboard):$PATH"` to add it to your path, and confirm that `which tensorboard` points to the built version rather than the version in your virtualenv. 4. Run `jupyter notebook` to start the Jupyter server. 5. Create a notebook and execute `%load_ext tensorboard.notebook` to load the extension; henceforth, `%tensorboard` should work until you restart the Jupyter kernel. (Step (2) is necessary because the `jupyter notebook` runtime adds the virtualenv _back_ to the front of your `PATH`. An alternative is to patch `os.environ["PATH"]` from within the Jupyter notebook.) After setting it up as above, the following makes a good test plan (assuming that you have no other TensorBoard instances running): - `%tensorboard --logdir ~/tb/mnist --port 6006` (should launch) - `%tensorboard --logdir ~/tb/mnist --port 6006` (should reuse) - `%tensorboard --logdir ~/tb/images_demo --port 6006` (should fail) - `%tensorboard --logdir ~/tb/images_demo --port 6007` (should launch) - `%tensorboard --logdir ~/tb/mnist --port 6006` (should reuse #1) - multiple `%tensorboard`s in a single cell: ```py for i in ("images_demo", "audio_demo"): %tensorboard --logdir ~/tb/$i --port 0 ``` - `from tensorboard import notebook` - `notebook.list()` (should list four instances) - `notebook.display(port=6006)` - `notebook.display(height=800)` Finally, if you skip (or revert) step (2) from the setup instructions, you can see the timeout behavior, because we’ll invoke the `tensorboard` provided by PyPI, which does not yet know how to write TensorboardInfo. wchargin-branch: notebook-jupyter
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