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This repository was archived by the owner on Sep 3, 2022. It is now read-only.
* Add gcs_copy_file() that is missing but is referenced in a couple of places. (#110)
* Add gcs_copy_file() that is missing but is referenced in a couple of places.
* Add DataFlow to pydatalab dependency list.
* Fix travis test errors by reimplementing gcs copy.
* Remove unnecessary shutil import.
* Flake8 configuration. Set max line length to 100. Ignore E111, E114 (#102)
* Add datalab user agent to CloudML trainer and predictor requests. (#112)
* Update oauth2client to 2.2.0 to satisfy cloudml in Cloud Datalab (#111)
* Update README.md (#114)
Added docs link.
* Generate reST documentation for magic commands (#113)
Auto generate docs for any added magics by searching through the source files for lines with register_line_cell_magic, capturing the names for those magics, and calling them inside an ipython kernel with the -h argument, then storing that output into a generated datalab.magics.rst file.
* Fix an issue that %%chart failed with UDF query. (#116)
* Fix an issue that %%chart failed with UDF query.
The problem is that the query is submitted to BQ without replacing variable values from user namespace.
* Fix chart tests by adding ip.user_ns mock.
* Fix charting test.
* Add missing import "mock".
* Fix chart tests.
* Fix "%%bigquery schema" issue -- the command generates nothing in output. (#119)
* Add some missing dependencies, remove some unused ones (#122)
* Remove scikit-learn and scipy as dependencies
* add more required packages
* Add psutil as dependency
* Update packages versions
* Cleanup (#123)
* Remove unnecessary semicolons
* remove unused imports
* remove unncessary defined variable
* Fix query_metadata tests (#128)
Fix query_metadata tests
* Make the library pip-installable (#125)
This PR adds tensorflow and cloudml in setup.py to make the lib pip-installable. I had to install them explicitly using pip from inside the setup.py script, even though it's not a clean way to do it, it gets around the two issues we have at the moment with these two packags:
- Pypi has Tensorflow version 0.12, while we need 0.11 for the current version of pydatalab. According to the Cloud ML docs, that version exists as a pip package for three supported platforms.
- Cloud ML SDK exists as a pip package, but also not on Pypi, and while we could add it as a dependency link, there exists another package on Pypi called cloudml, and pip ends up installing that instead (see #124). I cannot find a way to force pip to install the package from the link I included.
* Set command description so it is displayed in --help. argparser's format_help() prints description but not help. (#131)
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