Four fixes for RASP Tutorial (#391)

[MisterTea]

Summary:
When trying to follow the tutorial there are a few things that need fixing:

1. When running the script serving/scripts/rasp_to_model.py I came across this error

python serving/scripts/rasp_to_model.py /tmp/rasp_logging/log.txt /tmp/input_df.pkl

Traceback (most recent call last):
  File "serving/scripts/rasp_to_model.py", line 13, in <module>
    logger.setLevel(logging.info)
  File "/usr/local/anaconda3/envs/reagent/lib/python3.7/logging/__init__.py", line 1353, in setLevel
    self.level = _checkLevel(level)
  File "/usr/local/anaconda3/envs/reagent/lib/python3.7/logging/__init__.py", line 195, in _checkLevel
    raise TypeError("Level not an integer or a valid string: %r" % level)
TypeError: Level not an integer or a valid string: <function info at 0x7fb8000d73b0>

Luckily it is an easy fix to pass an actual loglevel.

2. This config file probably is outdated: serving/examples/ecommerce/training/contextual_bandit.yaml

• changed indentation level
• changed key name

3. There is an init.py file missing in the gym tests therefore leading to an error

4. The path to the SPARK_JAR was not resolving correctly.

Pull Request resolved: #391

Test Plan:
Imported from GitHub, without a Test Plan: line.
...but without running open source tests.

Reviewed By: czxttkl

Differential Revision: D27842451

Pulled By: MisterTea

[facebook-github-bot]

This pull request was exported from Phabricator. Differential Revision: D27842451

[facebook-github-bot]

@MisterTea merged this pull request in 322a542.

[ebraheemf]

Hello @MisterTea,

When I try to run the contextual bandit section of the demo:
python serving/examples/ecommerce/customer_simulator.py contextual_bandit.json

The RASP server is throwing the following error:
[F PytorchActionValueScorer.cpp:74] TORCH ERROR: forward() Expected a value of type '__torch__.reagent.core.types.ServingFeatureData' for argument 'state' but instead found type 'Tuple[Tensor, Tensor]'.

so I believe there are more changes needed to the format the data from the /api/request endpoint into the model?