Please see the set of transform project conventions for details on general project conventions, transform configuration, testing and IDE set up.
This project wraps the profiler transform with a Ray runtime.
Transform runtime is responsible for creation cache actors and sending their handles to the transforms themselves Additionally it writes created word counts to the data storage (as .csv files) and enhances statistics information with the information about cache size and utilization
In addition to the configuration parameters, defined here Ray version adds the following parameters:
- aggregator_cpu - specifies an amount of CPUs per aggregator actor
- num_aggregators - specifies number of aggregator actors
When running the transform with the Ray launcher (i.e. TransformLauncher), the following command line arguments are available in addition to the options provided by the launcher.
--profiler_aggregator_cpu PROFILER_AGGREGATOR_CPU
number of CPUs per aggrigator
--profiler_num_aggregators PROFILER_NUM_AGGREGATORS
number of agregator actors to use
--profiler_doc_column PROFILER_DOC_COLUMN
key for accessing data
These correspond to the configuration keys described above.
To run the samples, use the following make
targets
run-cli-sample
- runs src/ededup_transform_ray.py using command line argsrun-local-sample
- runs src/ededup_local_ray.pyrun-s3-sample
- runs src/ededup_s3_ray.py
These targets will activate the virtual environment and set up any configuration needed.
Use the -n
option of make
to see the detail of what is done to run the sample.
For example,
make run-cli-sample
...
Then
ls output
To see results of the transform.
To use the transform image to transform your data, please refer to the running images quickstart, substituting the name of this transform image and runtime as appropriate.