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update awesome ai infrastructures
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1duo authored Nov 30, 2018
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Expand Up @@ -30,7 +30,7 @@ in **no specific order**. This list cares more about overall architectures of AI

#### Architecture:

![fig-tfx](images/google-tfx-arch.png)
<p align="center"><img src="images/google-tfx-arch.png" width="80%"/></p>

#### Components:

Expand All @@ -42,7 +42,7 @@ in **no specific order**. This list cares more about overall architectures of AI

- **TensorFlow Serving**: a flexible, high-performance serving system for machine learning models, designed for production environments

### [KubeFlow](https://www.kubeflow.org/) - The Machine Learning Toolkit for [Kubernetes](https://kubernetes.io/) ([Google](https://www.google.com/about/))
### [Kubeflow](https://www.kubeflow.org/) - The Machine Learning Toolkit for [Kubernetes](https://kubernetes.io/) ([Google](https://www.google.com/about/))

> The Kubeflow project is dedicated to making deployments of machine learning (ML) workflows on [Kubernetes](https://kubernetes.io/) simple, portable and scalable. Our goal is not to recreate other services, but to provide a straightforward way to deploy best-of-breed open-source systems for ML to diverse infrastructures. Anywhere you are running [Kubernetes](https://kubernetes.io/), you should be able to run Kubeflow.
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#### Architecture:

![fig-michelangelo](images/uber-michelangelo-arch.png)
<p align="center"><img src="images/uber-michelangelo-arch.png" width="80%"/></p>

#### Components:

Expand All @@ -93,7 +93,7 @@ in **no specific order**. This list cares more about overall architectures of AI

#### Architecture:

![fig-rapids](images/nvidia-rapids-arch.png)
<p align="center"><img src="images/nvidia-rapids-arch.png" width="80%"/></p>

#### Components:

Expand All @@ -117,7 +117,7 @@ in **no specific order**. This list cares more about overall architectures of AI

#### Architecture:

![fig-fblearner](images/facebook-fblearnerflow-arch.png)
<p align="center"><img src="images/facebook-fblearnerflow-arch.png" width="80%"/></p>

#### Components:

Expand All @@ -136,7 +136,7 @@ up for easy, fast, and scalable distributed training.

#### Architecture:

![fig-alchemist](images/apple-alchemist-arch.png)
<p align="center"><img src="images/apple-alchemist-arch.png" width="80%"/></p>

#### Components:

Expand All @@ -157,7 +157,7 @@ allows them to upload and browse the code assets, submit distributed jobs, and q

#### Architecture:

![fig-ffdl](images/ibm-ffdl-arch-2.png)
<p align="center"><img src="images/ibm-ffdl-arch-2.png" width="80%"/></p>

#### Components:

Expand All @@ -177,7 +177,7 @@ allows them to upload and browse the code assets, submit distributed jobs, and q

#### Architecture:

![fig-bigdl](images/intel-bigdl-arch.png)
<p align="center"><img src="images/intel-bigdl-arch.png" width="80%"/></p>

#### Components:

Expand All @@ -195,7 +195,7 @@ allows them to upload and browse the code assets, submit distributed jobs, and q

#### Architecture:

![fig-sagemaker](images/amazon-sagemaker-arch.png)
<p align="center"><img src="images/amazon-sagemaker-arch.png" width="80%"/></p>

### TransmogrifAI ([Salesforce](https://www.salesforce.com/))

Expand All @@ -205,7 +205,7 @@ allows them to upload and browse the code assets, submit distributed jobs, and q

#### Architecture:

![fig-transmogrifai](images/salesforce-transmogrifai-arch.png)
<p align="center"><img src="images/salesforce-transmogrifai-arch.png" width="80%"/></p>

#### Components:

Expand All @@ -223,7 +223,7 @@ allows them to upload and browse the code assets, submit distributed jobs, and q

#### Architecture:

![fig-mlflow](images/databricks-mlflow-arch.png)
<p align="center"><img src="images/databricks-mlflow-arch.png" width="80%"/></p>

#### Components:

Expand All @@ -241,7 +241,7 @@ allows them to upload and browse the code assets, submit distributed jobs, and q
| [__h2o__](https://www.h2o.ai/products/h2o/) | [__h2o4gpu__](https://www.h2o.ai/products/h2o4gpu/) |

![fig-h2o](images/h2o-arch.png)
<p align="center"><img src="images/h2o-arch.png" width="80%"/></p>

# Machine Learning Model Deployment

Expand All @@ -253,31 +253,31 @@ allows them to upload and browse the code assets, submit distributed jobs, and q
| [__documentation__](https://developer.apple.com/documentation/coreml) |

![fig-coreml](images/apple-coreml-arch.png)
<p align="center"><img src="images/apple-coreml-arch.png" width="80%"/></p>

### Greengrass ([Amazon Web Service](https://aws.amazon.com/?nc2=h_lg))

> AWS Greengrass is software that lets you run local compute, messaging, data caching, sync, and ML inference capabilities for connected devices in a secure way. With AWS Greengrass, connected devices can run AWS Lambda functions, keep device data in sync, and communicate with other devices securely – even when not connected to the Internet. Using AWS Lambda, Greengrass ensures your IoT devices can respond quickly to local events, use Lambda functions running on Greengrass Core to interact with local resources, operate with intermittent connections, stay updated with over the air updates, and minimize the cost of transmitting IoT data to the cloud.
| [__blog__](https://aws.amazon.com/greengrass/) |

![fig-greengrass](images/amazon-greengrass-arch.png)
<p align="center"><img src="images/amazon-greengrass-arch.png" width="80%"/></p>

### GraphPipe ([Oracle](https://www.oracle.com/index.html))

> GraphPipe is a protocol and collection of software designed to simplify machine learning model deployment and decouple it from framework-specific model implementations.
| [__homepage__](https://oracle.github.io/graphpipe/#/) | [__github__](https://github.com/oracle/graphpipe) | [__documentation__](https://oracle.github.io/graphpipe/#/guide/user-guide/overview) |

![fig-graphpipe](images/oracle-graphpipe-arch.jpg)
<p align="center"><img src="images/oracle-graphpipe-arch.jpg" width="80%"/></p>

### PocketFlow ([Tencent](https://www.tencent.com/en-us/))

> PocketFlow is an open-source framework for compressing and accelerating deep learning models with minimal human effort. Deep learning is widely used in various areas, such as computer vision, speech recognition, and natural language translation. However, deep learning models are often computational expensive, which limits further applications on **mobile devices** with limited computational resources.
| [__homepage__](https://pocketflow.github.io/) | [__github__](https://github.com/Tencent/PocketFlow) |

![fig-pocketflow](images/tencent-pocketflow-arch.png)
<p align="center"><img src="images/tencent-pocketflow-arch.png" width="80%"/></p>

# Large-Scale Distributed AI Training Efforts

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