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OlivierDehaene 2023-11-30 15:18:15 +01:00
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@ -8,7 +8,7 @@ members = [
]
[workspace.package]
version = "1.1.1"
version = "1.2.0"
edition = "2021"
authors = ["Olivier Dehaene"]
homepage = "https://github.com/huggingface/text-generation-inference"

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@ -62,7 +62,7 @@ For a detailed starting guide, please see the [Quick Tour](https://huggingface.c
model=HuggingFaceH4/zephyr-7b-beta
volume=$PWD/data # share a volume with the Docker container to avoid downloading weights every run
docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:1.1.1 --model-id $model
docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:1.2 --model-id $model
```
And then you can make requests like
@ -76,7 +76,7 @@ curl 127.0.0.1:8080/generate \
**Note:** To use NVIDIA GPUs, you need to install the [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html). We also recommend using NVIDIA drivers with CUDA version 11.8 or higher. For running the Docker container on a machine with no GPUs or CUDA support, it is enough to remove the `--gpus all` flag and add `--disable-custom-kernels`, please note CPU is not the intended platform for this project, so performance might be subpar.
**Note:** TGI supports AMD Instinct MI210 and MI250 [to some extent](https://huggingface.co/docs/text-generation-inference/supported_models#supported-hardware). To use AMD GPUs, please use `docker run --device /dev/kfd --device /dev/dri --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:1.1.0+rocm --model-id $model` instead of the command above.
**Note:** TGI supports AMD Instinct MI210 and MI250 [to some extent](https://huggingface.co/docs/text-generation-inference/supported_models#supported-hardware). To use AMD GPUs, please use `docker run --device /dev/kfd --device /dev/dri --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:1.2+rocm --model-id $model` instead of the command above.
To see all options to serve your models (in the [code](https://github.com/huggingface/text-generation-inference/blob/main/launcher/src/main.rs) or in the cli):
```
@ -106,7 +106,7 @@ model=meta-llama/Llama-2-7b-chat-hf
volume=$PWD/data # share a volume with the Docker container to avoid downloading weights every run
token=<your cli READ token>
docker run --gpus all --shm-size 1g -e HUGGING_FACE_HUB_TOKEN=$token -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:1.1.1 --model-id $model
docker run --gpus all --shm-size 1g -e HUGGING_FACE_HUB_TOKEN=$token -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:1.2 --model-id $model
```
### A note on Shared Memory (shm)

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@ -10,7 +10,7 @@
"name": "Apache 2.0",
"url": "https://www.apache.org/licenses/LICENSE-2.0"
},
"version": "1.1.1"
"version": "1.2.0"
},
"paths": {
"/": {

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@ -19,6 +19,6 @@ docker run --gpus all \
--shm-size 1g \
-e HUGGING_FACE_HUB_TOKEN=$token \
-p 8080:80 \
-v $volume:/data ghcr.io/huggingface/text-generation-inference:1.1.1 \
-v $volume:/data ghcr.io/huggingface/text-generation-inference:1.2 \
--model-id $model
```

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@ -8,14 +8,14 @@ Let's say you want to deploy [Falcon-7B Instruct](https://huggingface.co/tiiuae/
model=tiiuae/falcon-7b-instruct
volume=$PWD/data # share a volume with the Docker container to avoid downloading weights every run
docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:1.1.1 --model-id $model
docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:1.2 --model-id $model
```
<Tip warning={true}>
To use GPUs, you need to install the [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html) . We also recommend using NVIDIA drivers with CUDA version 11.8 or higher.
To use TGI on RoCm-enabled AMD GPUs (only MI210 and MI250 are tested), please use the image `ghcr.io/huggingface/text-generation-inference:1.1.1+rocm` instead. For details about the usage on RoCm, please refer to the [Supported Hardware section](./supported_models#supported-hardware) and [AMD documentation](https://rocm.docs.amd.com/en/latest/deploy/docker.html).
To use TGI on RoCm-enabled AMD GPUs (only MI210 and MI250 are tested), please use the image `ghcr.io/huggingface/text-generation-inference:1.2+rocm` instead. For details about the usage on RoCm, please refer to the [Supported Hardware section](./supported_models#supported-hardware) and [AMD documentation](https://rocm.docs.amd.com/en/latest/deploy/docker.html).
</Tip>
@ -87,7 +87,7 @@ curl 127.0.0.1:8080/generate \
To see all possible deploy flags and options, you can use the `--help` flag. It's possible to configure the number of shards, quantization, generation parameters, and more.
```bash
docker run ghcr.io/huggingface/text-generation-inference:1.1.1 --help
docker run ghcr.io/huggingface/text-generation-inference:1.2 --help
```
</Tip>

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@ -1,6 +1,6 @@
[tool.poetry]
name = "text-generation-integration-tests"
version = "1.1.1"
version = "1.2.0"
description = "Text Generation Inference integration tests"
authors = ["Nicolas Patry <nicolas@huggingface.co>"]

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@ -1,6 +1,6 @@
[tool.poetry]
name = "text-generation-server"
version = "1.1.1"
version = "1.2.0"
description = "Text Generation Inference Python gRPC Server"
authors = ["Olivier Dehaene <olivier@huggingface.co>"]