* An Argo workflow to use as the E2E test for code_search example.
* The workflow builds the Docker images and then runs the python test
to train and export a model
* Move common utilities into util.libsonnet.
* Add the workflow to the set of triggered workflows.
* Update the test environment used by the test ksonnet app; we've since
changed the location of the app.
Related to #295
* Refactor the jsonnet file defining the GCB build workflow
* Use an external variable to conditionally pull and use a previous
Docker image as a cache
* Reduce code duplication by building a shared template for all the different
workflows.
* BUILD_ID needs to be defined in the default parameters otherwise we get an error when adding a new environment.
* Define suitable defaults.
* Fix model export, loss function, and add some manual tests.
Fix Model export to support computing code embeddings: Fix#260
* The previous exported model was always using the embeddings trained for
the search query.
* But we need to be able to compute embedding vectors for both the query
and code.
* To support this we add a new input feature "embed_code" and conditional
ops. The exported model uses the value of the embed_code feature to determine
whether to treat the inputs as a query string or code and computes
the embeddings appropriately.
* Originally based on #233 by @activatedgeek
Loss function improvements
* See #259 for a long discussion about different loss functions.
* @activatedgeek was experimenting with different loss functions in #233
and this pulls in some of those changes.
Add manual tests
* Related to #258
* We add a smoke test for T2T steps so we can catch bugs in the code.
* We also add a smoke test for serving the model with TFServing.
* We add a sanity check to ensure we get different values for the same
input based on which embeddings we are computing.
Change Problem/Model name
* Register the problem github_function_docstring with a different name
to distinguish it from the version inside the Tensor2Tensor library.
* * Skip the test when running under prow because its a manual test.
* Fix some lint errors.
* * Fix lint and skip tests.
* Fix lint.
* * Fix lint
* Revert loss function changes; we can do that in a follow on PR.
* * Run generate_data as part of the test rather than reusing a cached
vocab and processed input file.
* Modify SimilarityTransformer so we can overwrite the number of shards
used easily to facilitate testing.
* Comment out py-test for now.
Jeremy Lewi