Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Update README.md #1304

Merged
merged 1 commit into from
May 10, 2023
Merged

Update README.md #1304

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
30 changes: 19 additions & 11 deletions README.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -5,7 +5,7 @@
<img src="https://aqueduct-public-assets-bucket.s3.us-east-2.amazonaws.com/webapp/logos/aqueduct-logo-two-tone/1x/aqueduct-logo-two-tone-1x.png" width="40%" />
</a>

<h2 style="border: 0px white;">The Python API to run machine learning in your cloud</h2>
<h2 style="border: 0px white;">Run LLMs and ML on any cloud infrastructure</h2>

### 📢 [Slack](https://slack.aqueducthq.com)&nbsp;&nbsp;|&nbsp;&nbsp;🗺️ [Roadmap](https://roadmap.aqueducthq.com)&nbsp;&nbsp;|&nbsp;&nbsp;🐞 [Report a bug](https://github.com/aqueducthq/aqueduct/issues/new?assignees=&labels=bug&template=bug_report.md&title=%5BBUG%5D)&nbsp;&nbsp;|&nbsp;&nbsp;✍️ [Blog](https://blog.aqueducthq.com)

Expand All @@ -18,7 +18,7 @@
[![Tests](https://github.com/aqueducthq/aqueduct/actions/workflows/integration-tests.yml/badge.svg)](https://github.com/aqueducthq/aqueduct/actions/workflows/integration-tests.yml)
</div>

**Aqueduct enables you to easily define, run, and manage AI & ML tasks on any cloud infrastructure. [Check out our quickstart guide! →](https://docs.aqueducthq.com/quickstart-guide)**
**Aqueduct is an MLOps framework that allows you to define and deploy machine learning and LLM workloads on any cloud infrastructure. [Check out our quickstart guide! →](https://docs.aqueducthq.com/quickstart-guide)**

<p align="center">
<img src="https://user-images.githubusercontent.com/867892/230214641-b0aec53b-4988-4581-84ed-134f97ed9276.png" width="80%" />
Expand All @@ -41,23 +41,31 @@ Aqueduct's Python native API allows you to define ML tasks in regular Python cod
For example, we can define a pipeline that trains a model on Kubernetes using a GPU and validates that model in AWS Lambda in a few lines of Python:

```python
# Use an existing LLM.
vicuna = aq.llm_op('vicuna_7b', engine='eks-us-east-2')
features = vicuna(
raw_logs,
{
prompt:
"Turn this log entry into a CSV: {text}"
}
)

# Or write a custom op on your favorite infrastructure!
@op(
engine='eks-us-east-2',
engine='kubernetes',
# Get a GPU.
resources={'gpu_resource_name': 'nvidia.com/gpu'}
)
def train(features):
return model.train(features)

@metric(engine='lambda-us-east-2')
def validate(model):
return validation_test(model)
def train(featurized_logs):
return model.train(features) # Train your model.

validate(train(features))
train(features)
```

Once you publish this workflow to Aqueduct, you can see it on the UI:

![image](https://user-images.githubusercontent.com/867892/228295996-4ba3de23-3106-431d-93a9-afd8d77a707b.png)
![image](https://github.com/aqueducthq/aqueduct/assets/867892/d0561772-8799-4046-92ae-3c975d70e47d)

To see how to build your first workflow, check out our **[quickstart guide! →](https://docs.aqueducthq.com/quickstart-guide)**

Expand Down