lewtun
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README.md CHANGED
@@ -44,15 +44,44 @@ Next, copy the example file of environment variables:
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  cp .env.template .env
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  ```
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- and set the `HF_TOKEN` variable with a valid API token from the `autoevaluator` user. Finally, spin up the application by running:
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  ```
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  streamlit run app.py
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  ```
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  ## AutoTrain configuration details
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- Models are evaluated by AutoTrain, with the payload sent to the `AUTOTRAIN_BACKEND_API` environment variable. The current configuration for evaluation jobs running on Spaces is:
 
 
 
 
 
 
 
 
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  ```
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  AUTOTRAIN_BACKEND_API=https://api.autotrain.huggingface.co
@@ -63,3 +92,22 @@ To evaluate models with a _local_ instance of AutoTrain, change the environment
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  ```
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  AUTOTRAIN_BACKEND_API=http://localhost:8000
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  ```
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  cp .env.template .env
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  ```
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+ and set the `HF_TOKEN` variable with a valid API token from the [`autoevaluator`](https://huggingface.co/autoevaluator) bot user. Finally, spin up the application by running:
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  ```
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  streamlit run app.py
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  ```
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+ ## Usage
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+ Evaluation on the Hub involves two main steps:
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+ 1. Submitting an evaluation job via the UI. This creates an AutoTrain project with `N` models for evaluation. At this stage, the dataset is also processed and prepared for evaluation.
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+ 2. Triggering the evaluation itself once the dataset is processed.
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+ From the user perspective, only step (1) is needed since step (2) is handled by a cron job on GitHub Actions that executes the `run_evaluation_jobs.py` script every 15 minutes.
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+ See below for details on manually triggering evaluation jobs.
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+ ### Triggering an evaluation
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+ To evaluate the models in an AutoTrain project, run:
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+ ```
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+ python run_evaluation_jobs.py
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+ ```
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+ This will download the [`autoevaluate/evaluation-job-logs`](https://huggingface.co/datasets/autoevaluate/evaluation-job-logs) dataset from the Hub and check which evaluation projects are ready for evaluation (i.e. those whose dataset has been processed).
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+
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  ## AutoTrain configuration details
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+ Models are evaluated by the [`autoevaluator`](https://huggingface.co/autoevaluator) bot user in AutoTrain, with the payload sent to the `AUTOTRAIN_BACKEND_API` environment variable. Evaluation projects are created and run on either the `prod` or `staging` environments. You can view the status of projects in the AutoTrain UI by navigating to one of the links below (ask internally for access to the staging UI):
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+ | AutoTrain environment | AutoTrain UI URL | `AUTOTRAIN_BACKEND_API` |
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+ |:---------------------:|:--------------------------------------------------------------------------------------------------------------:|:--------------------------------------------:|
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+ | `prod` | [`https://ui.autotrain.huggingface.co/projects`](https://ui.autotrain.huggingface.co/projects) | https://api.autotrain.huggingface.co |
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+ | `staging` | [`https://ui-staging.autotrain.huggingface.co/projects`](https://ui-staging.autotrain.huggingface.co/projects) | https://api-staging.autotrain.huggingface.co |
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+ The current configuration for evaluation jobs running on [Spaces](https://huggingface.co/spaces/autoevaluate/model-evaluator) is:
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  ```
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  AUTOTRAIN_BACKEND_API=https://api.autotrain.huggingface.co
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  ```
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  AUTOTRAIN_BACKEND_API=http://localhost:8000
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  ```
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+ ### Migrating from staging to production (and vice versa)
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+ In general, evaluation jobs should run in AutoTrain's `prod` environment, which is defined by the following environment variable:
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+ ```
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+ AUTOTRAIN_BACKEND_API=https://api.autotrain.huggingface.co
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+ ```
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+ However, there are times when it is necessary to run evaluation jobs in AutoTrain's `staging` environment (e.g. because a new evaluation pipeline is being deployed). In these cases the corresponding environement variable is:
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+ ```
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+ AUTOTRAIN_BACKEND_API=https://api-staging.autotrain.huggingface.co
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+ ```
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+ To migrate between these two environments, update the `AUTOTRAIN_BACKEND_API` in two places:
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+ * In the [repo secrets](https://huggingface.co/spaces/autoevaluate/model-evaluator/settings) associated with the `model-evaluator` Space. This will ensure evaluation projects are created in the desired environment.
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+ * In the [GitHub Actions secrets](https://github.com/huggingface/model-evaluator/settings/secrets/actions) associated with this repo. This will ensure that the correct evaluation jobs are approved and launched via the `run_evaluation_jobs.py` script.
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