# OpenGPT
`OpenGPT` is an open-source _cloud-native_ large **multi-modal models** (LMMs) serving solution.
It is designed to simplify the deployment and management of large language models, on a distributed cluster of GPUs.
> The content of README.md is just a placeholder to remind me of what I want to do.
## Features
OpenGPT provides the following features to make it easy to deploy and serve large multi-modal models (LMMs) in production:
- Support for multi-modal models
- Scalable architecture for handling high traffic loads
- Optimized for low-latency inference
- Automatic model partitioning and distribution across multiple GPUs
- Centralized model management and monitoring
- REST API for easy integration with existing applications
You can learn more about OpenGPT’s [architecture in our documentation](https://opengpt.readthedocs.io/en/latest/).
## Roadmap
You can view our roadmap with features that are planned, started, and completed on the [Roadmap discussion](discussions/categories/roadmap) category.
## Installation
Install the package with pip:
```bash
pip install open_gpt
```
## Quickstart
```python
import open_gpts
model = open_gpts.create_model('facebook/llama-7b', device='cuda', precision='fp16')
prompt = "The quick brown fox jumps over the lazy dog."
output = model.generate(
prompt,
max_length=100,
temperature=0.9,
top_k=50,
top_p=0.95,
repetition_penalty=1.2,
do_sample=True,
num_return_sequences=1,
)
```
We also provide some advanced features to allow you to host your models cost-effectively:
- **Offloading**: you can offload parts of the model to CPU to reduce the cost of inference.
- **Quantization**: you can quantize the model to reduce the cost of inference.
For more details, please see the [documentation](https://opengpt.readthedocs.io/en/latest/).
## Serving Models
You can serve your models with OpenGPT. To do so, you can use the `serve` command:
```bash
opengpt serve facebook/llama-9b --device cuda --precision fp16 --port 5000
```
This will start a server on port 5000. You can then send requests to the server:
```python
import requests
prompt = "The quick brown fox jumps over the lazy dog."
response = requests.post(
"http://localhost:5000/generate",
json={
"prompt": prompt,
"max_length": 100,
"temperature": 0.9,
"top_k": 50,
"top_p": 0.95,
"repetition_penalty": 1.2,
"do_sample": True,
"num_return_sequences": 1,
},
)
# SSE support
from aiohttp_sse_client import client as sse_client
async with sse_client.EventSource(
'http://localhost:5000/stream/generate?prompt=The+quick+brown+fox+jumps+over+the+lazy+dog.&max_length=100&temperature=0.9&top_k=50&top_p=0.95&repetition_penalty=1.2&do_sample=True&num_return_sequences=1'
) as event_source:
try:
async for event in event_source:
print(event)
except ConnectionError:
pass
```
Note that the server will only accept requests from the same machine. If you want to accept requests from other machines, you can use the `--host` flag to specify the host to bind to.
## Deploying Models
You can also deploy the server to a cloud provider like Jina Cloud or AWS.
To do so, you can use `deploy` command:
- Jina Cloud
```bash
opengpt deploy facebook/llama-9b --device cuda --precision fp16 --provider jina --name opengpt --replicas 2
```
- AWS
To deploy to AWS, you need to install extra dependencies:
```bash
pip install opegpt[aws]
```
And you need to specify the region:
```bash
opengpt deploy facebook/llama-9b --device cuda --precision fp16 --provider aws --region us-east-1 --name opengpt --replicas 2
```
This will deploy the model to the cloud provider. You can then send requests to the server:
```python
import requests
prompt = "The quick brown fox jumps over the lazy dog."
response = requests.post(
"https://opengpt.jina.ai/generate",
json={
"prompt": prompt,
"max_length": 100,
"temperature": 0.9,
"top_k": 50,
"top_p": 0.95,
"repetition_penalty": 1.2,
"do_sample": True,
"num_return_sequences": 1,
},
)
```
## Kubernetes
To deploy OpenGPT on your Kubernetes cluster, follow these steps:
1. Install the OpenGPT operator on your Kubernetes cluster using Helm:
```bash
helm install opengpt ./helm/opengpt --namespace opengpt
```
2. Create a custom resource for your GPT model:
```YAML
apiVersion: opengpt.io/v1alpha1
kind: GptModel
metadata:
name: my-gpt-model
namespace: opengpt
spec:
modelPath: s3://my-bucket/my-model
modelName: my-model
maxBatchSize: 16
inputShape:
- 1024
- 1024
- 3
outputShape:
- 1024
- 1024
- 3
```
3. Apply the custom resource to your cluster:
```bash
kubectl apply -f my-gpt-model.yaml
```
4. Monitor the status of your GPT model using the OpenGPT dashboard:
```bash
kubectl port-forward -n opengpt svc/opengpt-dashboard 8080:80
```
## Accessing models via API
You can also access the online models via API. To do so, you can use the `inference_client` package:
```python
from inference_client import Client
client = Client(token='<your access token>')
model = client.get_model('facebook/llama-9b')
prompt = "The quick brown fox jumps over the lazy dog."
output = model.generate(
prompt,
max_length=100,
temperature=0.9,
top_k=50,
top_p=0.95,
repetition_penalty=1.2,
do_sample=True,
num_return_sequences=1,
)
```
By this way, you can access the models without deploying them to your own machine.
## Advanced Usage
### Model Offloading
You can also apply the model offloading techniques (based on [FlexTensor](https://github.com/numb3r3/flex-tensor)) to OpenGPT. To do so, you can use the `--offload-percents` flag:
```bash
opengpt serve facebook/llama-9b --device cuda --precision fp16 --port 5000 --offload-percents 10,90,50,50,0,100
```
This will offload parts of the model to the CPU. You can also use the `--offload-strategy` flag to specify the offloading strategy:
```bash
opengpt serve facebook/llama-9b --device cuda --precision fp16 --port 5000 --offload-strategy "cpu,cpu,cpu,cpu,cpu,cpu"
```
### Model Quantization
You can also apply the model quantization techniques.
- 8-bit quantization
```bash
opengpt serve facebook/llama-9b --device cuda --precision fp16 --port 5000 --quantize 8bit
```
## Fine-tuning Models
We currently support fine-tuning models by using the `finetune` command:
```bash
opengpt finetune facebook/llama-9b --dataset wikitext-2 --device cuda --precision fp16 --batch-size 32 --learning-rate 1e-4 --epochs 10
```
Specifically, we implement the following fine-tuning methods:
- [LLaMA-Adapter: Efficient Fine-tuning of LLaMA](https://github.com/ZrrSkywalker/LLaMA-Adapter): Fine-tuning model to follow instructions within 1 Hour and 1.2M Parameters
- [low-rank adaptation (LoRA)](https://arxiv.org/pdf/2106.09685.pdf): Low-Rank Adaptation for Efficient Language Model Fine-Tuning
## Documentation
For more information, check out the [documentation](https://opengpt.readthedocs.io/en/latest/).
## Contributing
We welcome contributions from the community! To contribute, please submit a pull request following our contributing guidelines.
## License
OpenGPT is licensed under the Apache License, Version 2.0. See LICENSE for the full license text.
Copyright 2020-2022 Jina AI Limited. All rights reserved.
Apache License
Version 2.0, January 2004
http://www.apache.org/licenses/
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Raw data
{
"_id": null,
"home_page": "https://open-gpt.jina.ai",
"name": "open-gpts",
"maintainer": "",
"docs_url": null,
"requires_python": ">=3.8",
"maintainer_email": "",
"keywords": "Pytorch,LLM,GPT",
"author": "Felix Wang",
"author_email": "felix.wang@jina.ai",
"download_url": "https://files.pythonhosted.org/packages/55/0b/95c964839739de929a81a598d89b67316eee343b4e39ca4bb64d1c6a6738/open_gpts-0.0.1rc1.tar.gz",
"platform": null,
"description": "# OpenGPT\n\n`OpenGPT` is an open-source _cloud-native_ large **multi-modal models** (LMMs) serving solution. \nIt is designed to simplify the deployment and management of large language models, on a distributed cluster of GPUs.\n\n> The content of README.md is just a placeholder to remind me of what I want to do.\n\n## Features\n\nOpenGPT provides the following features to make it easy to deploy and serve large multi-modal models (LMMs) in production:\n\n- Support for multi-modal models\n- Scalable architecture for handling high traffic loads\n- Optimized for low-latency inference\n- Automatic model partitioning and distribution across multiple GPUs\n- Centralized model management and monitoring\n- REST API for easy integration with existing applications\n\nYou can learn more about OpenGPT\u2019s [architecture in our documentation](https://opengpt.readthedocs.io/en/latest/).\n\n\n## Roadmap\n\nYou can view our roadmap with features that are planned, started, and completed on the [Roadmap discussion](discussions/categories/roadmap) category.\n\n## Installation\n\nInstall the package with pip:\n\n```bash\npip install open_gpt\n```\n\n## Quickstart\n\n```python\nimport open_gpts\n\nmodel = open_gpts.create_model('facebook/llama-7b', device='cuda', precision='fp16')\n\nprompt = \"The quick brown fox jumps over the lazy dog.\"\n\noutput = model.generate(\n prompt,\n max_length=100,\n temperature=0.9,\n top_k=50,\n top_p=0.95,\n repetition_penalty=1.2,\n do_sample=True,\n num_return_sequences=1,\n)\n```\n\nWe also provide some advanced features to allow you to host your models cost-effectively:\n\n- **Offloading**: you can offload parts of the model to CPU to reduce the cost of inference.\n\n- **Quantization**: you can quantize the model to reduce the cost of inference.\n\nFor more details, please see the [documentation](https://opengpt.readthedocs.io/en/latest/).\n\n## Serving Models\n\nYou can serve your models with OpenGPT. To do so, you can use the `serve` command:\n\n```bash\nopengpt serve facebook/llama-9b --device cuda --precision fp16 --port 5000\n```\n\nThis will start a server on port 5000. You can then send requests to the server:\n\n```python\nimport requests\n\nprompt = \"The quick brown fox jumps over the lazy dog.\"\n\nresponse = requests.post(\n \"http://localhost:5000/generate\",\n json={\n \"prompt\": prompt,\n \"max_length\": 100,\n \"temperature\": 0.9,\n \"top_k\": 50,\n \"top_p\": 0.95,\n \"repetition_penalty\": 1.2,\n \"do_sample\": True,\n \"num_return_sequences\": 1,\n },\n)\n\n\n# SSE support\nfrom aiohttp_sse_client import client as sse_client\n\nasync with sse_client.EventSource(\n 'http://localhost:5000/stream/generate?prompt=The+quick+brown+fox+jumps+over+the+lazy+dog.&max_length=100&temperature=0.9&top_k=50&top_p=0.95&repetition_penalty=1.2&do_sample=True&num_return_sequences=1'\n) as event_source:\n try:\n async for event in event_source:\n print(event)\n except ConnectionError:\n pass\n```\n\nNote that the server will only accept requests from the same machine. If you want to accept requests from other machines, you can use the `--host` flag to specify the host to bind to.\n\n## Deploying Models\n\nYou can also deploy the server to a cloud provider like Jina Cloud or AWS.\nTo do so, you can use `deploy` command:\n\n- Jina Cloud\n\n```bash\nopengpt deploy facebook/llama-9b --device cuda --precision fp16 --provider jina --name opengpt --replicas 2\n```\n\n- AWS\n\nTo deploy to AWS, you need to install extra dependencies: \n\n```bash\npip install opegpt[aws]\n```\n\nAnd you need to specify the region:\n\n```bash\nopengpt deploy facebook/llama-9b --device cuda --precision fp16 --provider aws --region us-east-1 --name opengpt --replicas 2\n```\n\nThis will deploy the model to the cloud provider. You can then send requests to the server:\n\n```python\nimport requests\n\nprompt = \"The quick brown fox jumps over the lazy dog.\"\n\nresponse = requests.post(\n \"https://opengpt.jina.ai/generate\",\n json={\n \"prompt\": prompt,\n \"max_length\": 100,\n \"temperature\": 0.9,\n \"top_k\": 50,\n \"top_p\": 0.95,\n \"repetition_penalty\": 1.2,\n \"do_sample\": True,\n \"num_return_sequences\": 1,\n },\n)\n```\n\n## Kubernetes\n\nTo deploy OpenGPT on your Kubernetes cluster, follow these steps:\n\n1. Install the OpenGPT operator on your Kubernetes cluster using Helm:\n\n ```bash\n helm install opengpt ./helm/opengpt --namespace opengpt\n ```\n\n2. Create a custom resource for your GPT model:\n \n ```YAML\n apiVersion: opengpt.io/v1alpha1\n kind: GptModel\n metadata:\n name: my-gpt-model\n namespace: opengpt\n spec:\n modelPath: s3://my-bucket/my-model\n modelName: my-model\n maxBatchSize: 16\n inputShape:\n - 1024\n - 1024\n - 3\n outputShape:\n - 1024\n - 1024\n - 3\n\n ```\n \n3. Apply the custom resource to your cluster:\n\n ```bash\n kubectl apply -f my-gpt-model.yaml\n ```\n\n4. Monitor the status of your GPT model using the OpenGPT dashboard:\n\n ```bash\n kubectl port-forward -n opengpt svc/opengpt-dashboard 8080:80\n ```\n\n## Accessing models via API\n\nYou can also access the online models via API. To do so, you can use the `inference_client` package:\n\n```python\nfrom inference_client import Client\n\nclient = Client(token='<your access token>')\n\nmodel = client.get_model('facebook/llama-9b')\n\nprompt = \"The quick brown fox jumps over the lazy dog.\"\n\noutput = model.generate(\n prompt,\n max_length=100,\n temperature=0.9,\n top_k=50,\n top_p=0.95,\n repetition_penalty=1.2,\n do_sample=True,\n num_return_sequences=1,\n)\n```\n\nBy this way, you can access the models without deploying them to your own machine.\n\n## Advanced Usage\n\n### Model Offloading\n\nYou can also apply the model offloading techniques (based on [FlexTensor](https://github.com/numb3r3/flex-tensor)) to OpenGPT. To do so, you can use the `--offload-percents` flag:\n\n```bash\nopengpt serve facebook/llama-9b --device cuda --precision fp16 --port 5000 --offload-percents 10,90,50,50,0,100\n```\n\nThis will offload parts of the model to the CPU. You can also use the `--offload-strategy` flag to specify the offloading strategy:\n\n```bash\nopengpt serve facebook/llama-9b --device cuda --precision fp16 --port 5000 --offload-strategy \"cpu,cpu,cpu,cpu,cpu,cpu\"\n```\n\n### Model Quantization\n\nYou can also apply the model quantization techniques.\n\n- 8-bit quantization\n\n```bash\nopengpt serve facebook/llama-9b --device cuda --precision fp16 --port 5000 --quantize 8bit\n```\n\n## Fine-tuning Models\n\nWe currently support fine-tuning models by using the `finetune` command:\n\n```bash\nopengpt finetune facebook/llama-9b --dataset wikitext-2 --device cuda --precision fp16 --batch-size 32 --learning-rate 1e-4 --epochs 10\n```\n\nSpecifically, we implement the following fine-tuning methods:\n- [LLaMA-Adapter: Efficient Fine-tuning of LLaMA](https://github.com/ZrrSkywalker/LLaMA-Adapter): Fine-tuning model to follow instructions within 1 Hour and 1.2M Parameters\n- [low-rank adaptation (LoRA)](https://arxiv.org/pdf/2106.09685.pdf): Low-Rank Adaptation for Efficient Language Model Fine-Tuning\n\n## Documentation\n\nFor more information, check out the [documentation](https://opengpt.readthedocs.io/en/latest/).\n\n\n## Contributing\n\nWe welcome contributions from the community! To contribute, please submit a pull request following our contributing guidelines.\n\n## License\n\nOpenGPT is licensed under the Apache License, Version 2.0. See LICENSE for the full license text.\nCopyright 2020-2022 Jina AI Limited. All rights reserved.\n\n Apache License\n Version 2.0, January 2004\n http://www.apache.org/licenses/\n\n TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION\n\n 1. Definitions.\n\n \"License\" shall mean the terms and conditions for use, reproduction,\n and distribution as defined by Sections 1 through 9 of this document.\n\n \"Licensor\" shall mean the copyright owner or entity authorized by\n the copyright owner that is granting the License.\n\n \"Legal Entity\" shall mean the union of the acting entity and all\n other entities that control, are controlled by, or are under common\n control with that entity. For the purposes of this definition,\n \"control\" means (i) the power, direct or indirect, to cause the\n direction or management of such entity, whether by contract or\n otherwise, or (ii) ownership of fifty percent (50%) or more of the\n outstanding shares, or (iii) beneficial ownership of such entity.\n\n \"You\" (or \"Your\") shall mean an individual or Legal Entity\n exercising permissions granted by this License.\n\n \"Source\" form shall mean the preferred form for making modifications,\n including but not limited to software source code, documentation\n source, and configuration files.\n\n \"Object\" form shall mean any form resulting from mechanical\n transformation or translation of a Source form, including but\n not limited to compiled object code, generated documentation,\n and conversions to other media types.\n\n \"Work\" shall mean the work of authorship, whether in Source or\n Object form, made available under the License, as indicated by a\n copyright notice that is included in or attached to the work\n (an example is provided in the Appendix below).\n\n \"Derivative Works\" shall mean any work, whether in Source or Object\n form, that is based on (or derived from) the Work and for which the\n editorial revisions, annotations, elaborations, or other modifications\n represent, as a whole, an original work of authorship. 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