docs: improve
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# Docker
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There is a supplied docker image to make deploying a server as a container easier.
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## CPU
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**Command line**
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```bash
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docker run \
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-p 8080:8080 -v $HOME/.tabby:/data \
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tabbyml/tabby serve --model TabbyML/SantaCoder-1B
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```
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**Docker Compose**
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```yaml
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version: '3.5'
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services:
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tabby:
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restart: always
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image: tabbyml/tabby
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command: serve --model TabbyML/SantaCoder-1B
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volumes:
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- "$HOME/.tabby:/data"
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ports:
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- 8080:8080
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```
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## CUDA (requires NVIDIA Container Toolkit)
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**Command line**
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```bash
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docker run \
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--gpus all -p 8080:8080 -v $HOME/.tabby:/data \
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tabbyml/tabby \
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serve --model TabbyML/SantaCoder-1B --device cuda
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```
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**Docker Compose**
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```yaml
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version: '3.5'
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services:
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tabby:
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restart: always
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image: tabbyml/tabby
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command: serve --model TabbyML/SantaCoder-1B --device cuda
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volumes:
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- "$HOME/.tabby:/data"
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ports:
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- 8080:8080
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resources:
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reservations:
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devices:
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- driver: nvidia
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count: 1
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capabilities: [gpu]
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```
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@ -0,0 +1,76 @@
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# Docker
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There is a supplied docker image to make deploying a server as a container easier.
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import Tabs from '@theme/Tabs';
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import TabItem from '@theme/TabItem';
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## CPU
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<Tabs>
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<TabItem value="shell" label="Shell" default>
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```bash title="run.sh"
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docker run \
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-p 8080:8080 -v $HOME/.tabby:/data \
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tabbyml/tabby serve --model TabbyML/SantaCoder-1B
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```
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</TabItem>
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<TabItem value="compose" label="Docker Compose">
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```yaml title="docker-compose.yml"
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version: '3.5'
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services:
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tabby:
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restart: always
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image: tabbyml/tabby
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command: serve --model TabbyML/SantaCoder-1B
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volumes:
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- "$HOME/.tabby:/data"
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ports:
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- 8080:8080
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```
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</TabItem>
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</Tabs>
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## CUDA (requires NVIDIA Container Toolkit)
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<Tabs>
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<TabItem value="shell" label="Shell" default>
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```bash title="run.sh"
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docker run \
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--gpus all -p 8080:8080 -v $HOME/.tabby:/data \
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tabbyml/tabby \
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serve --model TabbyML/SantaCoder-1B --device cuda
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```
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</TabItem>
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<TabItem value="compose" label="Docker Compose">
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```yaml title="docker-compose.yml"
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version: '3.5'
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services:
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tabby:
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restart: always
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image: tabbyml/tabby
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command: serve --model TabbyML/SantaCoder-1B --device cuda
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volumes:
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- "$HOME/.tabby:/data"
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ports:
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- 8080:8080
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resources:
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reservations:
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devices:
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- driver: nvidia
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count: 1
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capabilities: [gpu]
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```
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</TabItem>
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</Tabs>
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@ -7,3 +7,7 @@ Thanks to Apple's Accelerate and CoreML frameworks, we can now run Tabby on edge
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2. Run `tabby --help` to verify successful installation.
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3. Start the server with `tabby serve model --models TabbyML/T5P-220M`.
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:::tip
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The compute power of M1/M2 is limited and is likely to be sufficient only for individual usage. If you require a shared instance for a team, we recommend considering Docker hosting with CUDA. You can find more information about Docker [here](./docker).
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