This adds some more definite types for our NLP tasks and tokenization configurations.
This is the first step in allowing users to more easily import their own transformer models via something other than hugging face.
The Cloud ID simplifies sending data to a cluster on Elastic Cloud.
With this change, the user will have the option specify a Cloud ID using the `--cloud-id` argument as an alternative to an Elasticsearch URL (`--url` argument).
`--cloud-id` and `--url` are mutually exclusive arguments.
## Changes
### Better logging
Switched from `print` statements to `logging` for a cleaner and more informative output - timestamps and log level are shown. The logging is now a bit more verbose, but it will help users to better understand what the script is doing.
### Add support for ES authentication using username/password or api key
Instead of being limited to passing credentials in the URL, there are now 2 additional methods:
- username/password using `--es-username` and `--es-password`
- API key using `--es-api-key`
Credentials can also be specified as environment variables with `ES_USERNAME`/`ES_PASSWORD` or `ES_API_KEY`
### Graceful handling of missing PyTorch requirements
In order to use the `eland_import_hub_model` script, PyTorch extras are required to be installed. If the user does not have the required packages installed, a helpful message is logged with a hint to install `eland[pytorch]` with `pip`.
### Graceful handling of already existing trained model
If a trained model with the same ID as the one we're trying to import already exists, and `--clear-previous` was not specified, we now log a clearer message about why the script can't proceed along with a hint to use the `--clear-previous` flag.
Prior to this change, we were letting the API exception seep through and the user was faced with a stack trace.
### `tqdm` added to main dependencies
If the user doesn't have `eland[pytorch]` extras installed, the first module to be reported as missing is `tqdm`. Since this module is [used in eland codebase](8294224e34/eland/ml/pytorch/_pytorch_model.py (L24)) directly, it makes sense to me to have it as part of the main set of requirements.
### Nit: Set tqdm unit to `parts` in `_pytorch_model.put_model`
The default unit is `it`, but `parts` better describes what the progress bar is tracking - uploading trained model definition parts.
