44 lines
1.7 KiB
Markdown
44 lines
1.7 KiB
Markdown
# Fly.io deployment example
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This project modifies the `bot_runner.py` server to launch a new machine for each user session. This is a recommended approach for production vs. running shell processess as your deployment will quickly run out of system resources under load.
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To speed up machine boot times, we also download and cache Silero VAD as part of the Dockerfile (`install_deps.py`). If you are using other custom models, you can add them here too.
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For this example, we are using Daily as a WebRTC transport and provisioning a new room and token for each session. You can use another transport, such as WebSockets, by modifying the `bot.py` and `bot_runner.py` files accordingly.
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## Setting up your fly.io deployment
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### Create your fly.toml file
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You can copy the `example-fly.toml` as a reference. Be sure to change the app name to something unique.
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### Create your .env file
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Copy the base `env.example` to `.env` and enter the necessary API keys.
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`FLY_APP_NAME` should match that in the `fly.toml` file.
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### Launch a new fly.io project
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`fly launch` or `fly launch --org your-org-name`
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### Set the necessary app secrets from your .env
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Note: you can do this manually via the fly.io dashboard under the "secrets" sub-section of your deployment (e.g. "https://fly.io/apps/fly-app-name/secrets") or run the following terminal command:
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`cat .env | tr '\n' ' ' | xargs flyctl secrets set`
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### Deploy your machine
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`fly deploy`
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## Connecting to your bot
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Send a post request to your running fly.io instance:
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`curl --location --request POST 'https://YOUR_FLY_APP_NAME/start_bot'`
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This request will wait until the machine enters into a `starting` state, before returning the a room URL and token to join.
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