| `minio_cluster_replication_current_active_workers`         | Total number of active replication workers                                                               |
| `minio_cluster_replication_average_active_workers`         | Average number of active replication workers                                                             |

```console
$ pip install "hypercorn[trio]"
---> 100%
```

</div>

### Run with Trio

Then you can pass the command line option `--worker-class` with the value `trio`:

<div class="termy">

```console
$ hypercorn main:app --worker-class trio
```

</div>

And that will start Hypercorn with your app using Trio as the backend.

It is made up of 3 architecture modules:

- **Runtime**: Provides the runtimes or "containers" in which code runs. These runtimes include the Gradle client, the daemon and the worker processes. This is the base module on which all other architecture modules depend.
- **Configuration**: Allows the build structure and work, such as tasks, to be specified. This includes the project model, the DSL and so on.

        val expectedInstallationPaths = if (os == Os.WINDOWS) windowsPaths else linuxPaths

          "expr": "sum by (server) (minio_cluster_replication_average_active_workers{job=\"$scrape_jobs\"})",
          "interval": "1m",
          "intervalFactor": 2,
          "legendFormat": "{{server}}",
          "refId": "A"
        }
      ],
      "thresholds": [],
      "timeRegions": [],
      "title": "Avg. Active Workers",
      "tooltip": {
        "shared": true,
        "sort": 0,

        "//tensorflow/core/distributed_runtime:remote_device",
        "//tensorflow/core/distributed_runtime:server_lib",
        "//tensorflow/core/distributed_runtime:worker_env",
        "//tensorflow/core/distributed_runtime:worker_interface",
        "//tensorflow/core/distributed_runtime/eager:cluster_function_library_runtime",
        "//tensorflow/core/distributed_runtime/eager:eager_client",

It is made up of 3 architecture modules:

- **core-runtime**: Provides the runtimes or "containers" in which code runs. These runtimes include the Gradle client, the daemon and the worker processes. This is the base module on which all other architecture modules depend.
- **core-configuration**: Allows the build structure and work, such as tasks, to be specified. This includes the project model, the DSL and so on.

	totalFailedCount    MetricName = "total_failed_count"
	totalFailedBytes    MetricName = "total_failed_bytes"

	currActiveWorkers  MetricName = "current_active_workers"
	avgActiveWorkers   MetricName = "average_active_workers"
	maxActiveWorkers   MetricName = "max_active_workers"
	recentBacklogCount MetricName = "recent_backlog_count"
	currInQueueCount   MetricName = "last_minute_queued_count"

## Imagem Oficial do Docker com Gunicorn - Uvicorn

Há uma imagem oficial do Docker que inclui o Gunicorn executando com trabalhadores Uvicorn, conforme detalhado em um capítulo anterior: [Server Workers - Gunicorn com Uvicorn](server-workers.md){.internal-link target=_blank}.

          "expr": "sum by (server) (minio_cluster_replication_average_active_workers{job=\"$scrape_jobs\"})",
          "interval": "1m",
          "intervalFactor": 2,
          "legendFormat": "{{server}}",
          "refId": "A"
        }
      ],
      "thresholds": [],
      "timeRegions": [],
      "title": "Avg. Active Workers",
      "tooltip": {
        "shared": true,
        "sort": 0,