Skip to main content

ExplainaBoard Client

Project description

ExplainaBoard Client

This is a command line and API client that makes it easy for you to upload systems to ExplainaBoard.

Preparation

Install

  • For CLI/api users
    • pip install explainaboard_client
  • For explainaboard client developers
    • pip install .

Acquiring a Login and API Key

First, create an account at the ExplainaBoard site and remember the email address you used. Once you are logged in, you can click on the upper-right corner of the screen, and it will display your API key, which you can copy-paste.

You can save these into environmental variables for convenient use in the commands below:

export EB_EMAIL="[your email]"
export EB_API_KEY="[your API key]"

Usage

Uploading/Browsing/Deleting Systems from the Command Line

Uploading Systems: The most common usage of this client will probably be to upload systems. You can do that from the command line. If you are using a pre-existing dataset viewable from the ExplainaBoard datasets page then you can use something like the following command:

python -m explainaboard_client.cli.upload_system \
  --email $EB_EMAIL --api_key $EB_API_KEY \
  --task [TASK_ID] \
  --system_name [MODEL_NAME] \
  --system_output [SYSTEM_OUTPUT] --output_file_type [FILE_TYPE] \
  --dataset [DATASET] --sub_dataset [SUB_DATASET] --split [SPLIT] \
  --source_language [SOURCE] --target_language [TARGET] \
  [--public]

You will need to fill in all the settings appropriately, for example:

  • [TASK_ID] is the ID of the task you want to perform. A full list is here.
  • [MODEL_NAME] is whatever name you want to give to your model.
  • [SYSTEM_OUTPUT] is the file that you want to upload.
  • [FILE_TYPE] is the type of the file, "text", "tsv", "csv", "conll", or "json".
  • [DATASET], [SUB_DATASET] and [SPLIT] indicate which dataset you're uploading a system output for.
  • [SOURCE] and [TARGET] language indicate the language of the input and output of the system. If the inputs and outputs are the in the same language you only need to specify one or the other.
  • By default your systems will be private, but if you add the --public flag they will be made public on the public leaderboards and system listing.

Uploading w/ Custom Datasets: You can also upload results for custom datasets that are not supported by DataLab yet:

python -m explainaboard_client.cli.upload_system \
  --email $EB_EMAIL --api_key $EB_API_KEY \
  --task [TASK_ID] \
  --system_name [MODEL_NAME] \
  --system_output [SYSTEM_OUTPUT] --output_file_type [FILE_TYPE] \
  --custom_dataset [CUSTOM_DATASET] --custom_dataset_file_type [FILE_TYPE] \
  --source_language [SOURCE] --target_language [TARGET]

with similar file and file-type arguments to the system output above. If you're interested in getting your datasets directly supported within ExplainaBoard, please open an issue or send a PR to DataLab, and we'll be happy to help out!

Finding Uploaded Systems: You can also find systems that have already been uploaded using the following syntax

python -m explainaboard_client.cli.find_systems \
  --email $EB_EMAIL --api_key $EB_API_KEY --output_format tsv

By default this outputs in a summarized TSV format (similar to the online system browser), but you can set --output_format json to get more extensive information. There are many options for how you can specify which systems you want to find, which you can take a look at by running python -m explainaboard_client.cli.find_systems without any arguments.

Deleting System Outputs: You can delet existing system outputs using the following command:

python -m explainaboard_client.cli.delete_systems \
  --email $EB_EMAIL --api_key $EB_API_KEY --system_ids XXX YYY

Here the system_ids are the unique identifier of each system returned in the system_id field of the JSON returned by the find_systems command above. The system IDs are not the system name as displayed in the interface.

Programmatic Usage

Please see examples in ./tests. We will be working on more examples and documentation shortly.

Update

There are two packages associated with this CLI: explainaboard_api_client and explainaboard_client

  • explainaboard_api_client: auto generated according to OpenAPI definition specified in openapi.yaml. Version of this client is specified in the same yaml file (info.version).
    • To update: pip install -U explainaboard_api_client or specify a specific version
    • To check the API version used in the live environment: curl https://explainaboard.inspiredco.ai/api/info (this information will be added to the UI in the future)
  • explainaboard_client: a thin wrapper for the API client to make it easy to use. It helps users configure API keys, choose host names, load files from local FS, etc. Usually, this package is relatively stable so you don't need to update unless a new feature of the CLI is released.
    • To update: pip install -U explainaboard_client

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

explainaboard_client-0.0.5.tar.gz (12.3 kB view details)

Uploaded Source

Built Distribution

explainaboard_client-0.0.5-py2.py3-none-any.whl (14.6 kB view details)

Uploaded Python 2 Python 3

File details

Details for the file explainaboard_client-0.0.5.tar.gz.

File metadata

  • Download URL: explainaboard_client-0.0.5.tar.gz
  • Upload date:
  • Size: 12.3 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.9.13

File hashes

Hashes for explainaboard_client-0.0.5.tar.gz
Algorithm Hash digest
SHA256 c3c756f86f35a83bd78c597659c99ace0360f6b954e1010f5b3f4ce5256fd2d6
MD5 cce6b30e90a2facf551d9e945b4f6ffc
BLAKE2b-256 af3c6d62a32933f322dcc76ac36bc43a7e34a50144598c6b0318f292bd265da7

See more details on using hashes here.

File details

Details for the file explainaboard_client-0.0.5-py2.py3-none-any.whl.

File metadata

File hashes

Hashes for explainaboard_client-0.0.5-py2.py3-none-any.whl
Algorithm Hash digest
SHA256 ea03069bfd09baac88c1aa234e4580c231262424c132efa82e6266c1b18454d3
MD5 3dcb792a518ba313afcff88c31077492
BLAKE2b-256 f3b64e9abff08ab1242aeb6a27cdc6adb36a5ab5315e741e1a58eadc000851ee

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page