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Fix(docs): fix broken link in doc and format doc (#165)
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@FogDong
FogDong committed Dec 29, 2020
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99 changes: 49 additions & 50 deletions docs/docs_zh/introduction.md
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Original file line number Diff line number Diff line change
Expand Up @@ -65,23 +65,23 @@ Docker 很好地解决了传统应用分发的问题，那么在机器学习场
```python
# 建立模型，设定 Optimizer，进行训练
model = keras.Sequential([
keras.layers.Conv2D(input_shape=(28,28,1),
filters=8,
kernel_size=3,
strides=2,
activation='relu',
keras.layers.Conv2D(input_shape=(28,28,1),
filters=8,
kernel_size=3,
strides=2,
activation='relu',
name='Conv1'),
keras.layers.Flatten(),
keras.layers.Dense(10,
activation=tf.nn.softmax,
keras.layers.Dense(10,
activation=tf.nn.softmax,
name='Softmax')
])
model.compile(optimizer='adam',
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
model.fit(train_images, train_labels, epochs=epochs)

test_loss, test_acc = model.evaluate(test_images,
test_loss, test_acc = model.evaluate(test_images,
test_labels)
import tempfile

Expand Down Expand Up @@ -123,7 +123,7 @@ v1: pushed to remote (1 layer, 162.1 KiB total)
以 Harbor 为例，在 Harbor 镜像仓库中，我们可以看到这一模型的元数据等。

<p align="center">
<img src="./images/intro/harbor.png" height="350">
<img src="../images/intro/harbor.png" height="350">
</p>

随后，我们可以在服务器上将模型下载下来。下载的过程也与推送到镜像仓库的方法类似。
Expand Down Expand Up @@ -176,21 +176,21 @@ spec:
name: mnist
protocol: tensorflow
predictors:
- graph:
children: []
implementation: TENSORFLOW_SERVER
modelUri: demo.goharbor.io/tensorflow/fashion_model:v1
serviceAccountName: ormb
name: mnist-model
parameters:
- name: signature_name
type: STRING
value: predict_images
- name: model_name
type: STRING
value: mnist-model
name: default
replicas: 1
- graph:
children: []
implementation: TENSORFLOW_SERVER
modelUri: demo.goharbor.io/tensorflow/fashion_model:v1
serviceAccountName: ormb
name: mnist-model
parameters:
- name: signature_name
type: STRING
value: predict_images
- name: model_name
type: STRING
value: mnist-model
name: default
replicas: 1
```
当算法工程师迭代了新版本的模型时，可以打包新的版本，利用 [ormb][] 拉取新的镜像后重新部署。[ormb][] 可以配合任何符合 [OCI Distribution Specification](https://github.com/opencontainers/distribution-spec) 的镜像仓库使用，这意味着 [ormb][] 支持公有云上的镜像仓库，和 Harbor 等开源镜像仓库项目。
Expand Down Expand Up @@ -219,31 +219,30 @@ spec:

```json
{
"created": "2015-10-31T22:22:56.015925234Z", "architecture": "amd64",
"os": "linux",
"config": {
"Entrypoint": [
"/bin/my-app-binary"
],
"created": "2015-10-31T22:22:56.015925234Z",
"architecture": "amd64",
"os": "linux",
"config": {
"Entrypoint": ["/bin/my-app-binary"]
},
"rootfs": {
"diff_ids": [
"sha256:c6f988f4874bb0add23a778f753c65efe992244e148a1d2ec2a8b664fb66bbd1",
"sha256:5f70bf18a086007016e948b04aed3b82103a36bea41755b6cddfaf10ace3c6ef"
],
"type": "layers"
},
"history": [
{
"created": "2015-10-31T22:22:54.690851953Z",
"created_by": "/bin/sh -c #(nop) ADD file:a3bc1e842b69636f9df5256c49c5374fb4eef1e281fe3f282c65fb853ee171c5 in /"
},
"rootfs": {
"diff_ids": [
"sha256:c6f988f4874bb0add23a778f753c65efe992244e148a1d2ec2a8b664fb66bbd1",
"sha256:5f70bf18a086007016e948b04aed3b82103a36bea41755b6cddfaf10ace3c6ef"
],
"type": "layers"
},
"history": [
{
"created": "2015-10-31T22:22:54.690851953Z",
"created_by": "/bin/sh -c #(nop) ADD file:a3bc1e842b69636f9df5256c49c5374fb4eef1e281fe3f282c65fb853ee171c5 in /"
},
{
"created": "2015-10-31T22:22:55.613815829Z",
"created_by": "/bin/sh -c #(nop) CMD [\"sh\"]",
"empty_layer": true
}
]
{
"created": "2015-10-31T22:22:55.613815829Z",
"created_by": "/bin/sh -c #(nop) CMD [\"sh\"]",
"empty_layer": true
}
]
}
```

Expand Down Expand Up @@ -277,7 +276,7 @@ spec:
"framework": "TensorFlow",
"format": "SavedModel",
"size": 9223372036854775807,
"metrics": {
"metrics": {
"training": [
{
"name": "acc",
Expand Down
105 changes: 52 additions & 53 deletions docs/introduction.md
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Original file line number Diff line number Diff line change
@@ -1,6 +1,6 @@
# Introducing `ORMB`

The container virtualization technology like Docker has become the mainstay of cloud computing industry. Software engineers from around the world have flocked to support it. Based on the `Open Container Initiative` technology, the ecology of containers is evolving rapidly. There are also a lot of fast-growing projects such as `Docker Compose` and `Kata Containers`, in which `Kubernetes` has become the standard in the field of cluster scheduling.
The container virtualization technology like Docker has become the mainstay of cloud computing industry. Software engineers from around the world have flocked to support it. Based on the `Open Container Initiative` technology, the ecology of containers is evolving rapidly. There are also a lot of fast-growing projects such as `Docker Compose` and `Kata Containers`, in which `Kubernetes` has become the standard in the field of cluster scheduling.

When we look back, the reason why container virtualization technology represented by Docker can take a place in the world is because of its image distribution capability. The traditional application deployment is denounced by its complexity of deploying in different environments, which is solved by the image distribution. Docker brings the capability of `Build Once, Deploy Anywhere` to the traditional application scenario. Languages like Java, NodeJS, Rust, etc as well as various dependency libraries, can be built locally once into the OCI compliant container images, which can then be stored and distributed using image registry.

Expand Down Expand Up @@ -58,30 +58,30 @@ And so born the `ORMB`.`ORMB`(oh, RMB) is named from the OCI-based Registry for

In this section, we'll use image recognition as an example to show how `ORMB` can be used to distribute machine learning models.

We will train a simple CNN image recognition model locally using [Fashion MNIST](https://github.com/zalandoresearch/fashion-mnist) and push it to a remote image registry using `ORMB`. Later, we'll also use `ORMB` to pull the model from the registry in our remote server, and use the third-party model server to provide external serving. Then we can call the service with a RESTful interface and see the results.
We will train a simple CNN image recognition model locally using [Fashion MNIST](https://github.com/zalandoresearch/fashion-mnist) and push it to a remote image registry using `ORMB`. Later, we'll also use `ORMB` to pull the model from the registry in our remote server, and use the third-party model server to provide external serving. Then we can call the service with a RESTful interface and see the results.

The code of model training is shown below, but the specific training process will not be repeated in this article. Finally, the trained model is saved in SavedModel format. You can see the model in the [examples of ormb](https://github.com/caicloud/ormb/tree/master/examples/SavedModel-fashion).

```python
# Modeling, set optimizer, and train
model = keras.Sequential([
keras.layers.Conv2D(input_shape=(28,28,1),
filters=8,
kernel_size=3,
strides=2,
activation='relu',
keras.layers.Conv2D(input_shape=(28,28,1),
filters=8,
kernel_size=3,
strides=2,
activation='relu',
name='Conv1'),
keras.layers.Flatten(),
keras.layers.Dense(10,
activation=tf.nn.softmax,
keras.layers.Dense(10,
activation=tf.nn.softmax,
name='Softmax')
])
model.compile(optimizer='adam',
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
model.fit(train_images, train_labels, epochs=epochs)

test_loss, test_acc = model.evaluate(test_images,
test_loss, test_acc = model.evaluate(test_images,
test_labels)
import tempfile

Expand Down Expand Up @@ -182,21 +182,21 @@ spec:
name: mnist
protocol: tensorflow
predictors:
- graph:
children: []
implementation: TENSORFLOW_SERVER
modelUri: demo.goharbor.io/tensorflow/fashion_model:v1
serviceAccountName: ormb
name: mnist-model
parameters:
- name: signature_name
type: STRING
value: predict_images
- name: model_name
type: STRING
value: mnist-model
name: default
replicas: 1
- graph:
children: []
implementation: TENSORFLOW_SERVER
modelUri: demo.goharbor.io/tensorflow/fashion_model:v1
serviceAccountName: ormb
name: mnist-model
parameters:
- name: signature_name
type: STRING
value: predict_images
- name: model_name
type: STRING
value: mnist-model
name: default
replicas: 1
```
As the algorithm engineer iterates over the new version of the model and package it, the new image can be pulled with `ORMB` and redeployed. `ORMB` can be used with any image registry that meets the [OCI Distribution Specification](https://github.com/opencontainers/distribution-spec), which means that `ORMB` can both support image registry on the public cloud and other open source image registry projects like Harbor.
Expand All @@ -213,7 +213,7 @@ In a word, `ORMB` is designed to be `Docker` in machine learning scenarios, aimi

### What happened with `docker pull`

Before we get to the design, let's take a look at what happens when we download a container image. For image registries that conform to the OCI specification, they all follow the same rule, which is the [OCI Distribution Specification](https://github.com/opencontainers/distribution-spec/blob/master/spec.md#pulling-an-image).
Before we get to the design, let's take a look at what happens when we download a container image. For image registries that conform to the OCI specification, they all follow the same rule, which is the [OCI Distribution Specification](https://github.com/opencontainers/distribution-spec/blob/master/spec.md#pulling-an-image).

<p align="center">
<img src="./images/intro/docker-pull.png" height="550">
Expand All @@ -225,31 +225,30 @@ The config of container image has a fixed mediaType: `application/vnd.oci.image.

```json
{
"created": "2015-10-31T22:22:56.015925234Z", "architecture": "amd64",
"os": "linux",
"config": {
"Entrypoint": [
"/bin/my-app-binary"
],
"created": "2015-10-31T22:22:56.015925234Z",
"architecture": "amd64",
"os": "linux",
"config": {
"Entrypoint": ["/bin/my-app-binary"]
},
"rootfs": {
"diff_ids": [
"sha256:c6f988f4874bb0add23a778f753c65efe992244e148a1d2ec2a8b664fb66bbd1",
"sha256:5f70bf18a086007016e948b04aed3b82103a36bea41755b6cddfaf10ace3c6ef"
],
"type": "layers"
},
"history": [
{
"created": "2015-10-31T22:22:54.690851953Z",
"created_by": "/bin/sh -c #(nop) ADD file:a3bc1e842b69636f9df5256c49c5374fb4eef1e281fe3f282c65fb853ee171c5 in /"
},
"rootfs": {
"diff_ids": [
"sha256:c6f988f4874bb0add23a778f753c65efe992244e148a1d2ec2a8b664fb66bbd1",
"sha256:5f70bf18a086007016e948b04aed3b82103a36bea41755b6cddfaf10ace3c6ef"
],
"type": "layers"
},
"history": [
{
"created": "2015-10-31T22:22:54.690851953Z",
"created_by": "/bin/sh -c #(nop) ADD file:a3bc1e842b69636f9df5256c49c5374fb4eef1e281fe3f282c65fb853ee171c5 in /"
},
{
"created": "2015-10-31T22:22:55.613815829Z",
"created_by": "/bin/sh -c #(nop) CMD [\"sh\"]",
"empty_layer": true
}
]
{
"created": "2015-10-31T22:22:55.613815829Z",
"created_by": "/bin/sh -c #(nop) CMD [\"sh\"]",
"empty_layer": true
}
]
}
```

Expand Down Expand Up @@ -283,7 +282,7 @@ When it comes to the specific supporting for model, we define our own [config](h
"framework": "TensorFlow",
"format": "SavedModel",
"size": 9223372036854775807,
"metrics": {
"metrics": {
"training": [
{
"name": "acc",
Expand Down Expand Up @@ -350,4 +349,4 @@ We work closely with the Harbor community to use Harbor as the default registry

For example, `ORMB` takes advantage of Harbor's Replication feature to enable model synchronization between image registry in multiple environments, such as training environments, model production environments, and so on. We can also configure webhooks for the model to add custom processing logic to the model lifecycle.

[ormb]: https://github.com/kleveross/ormb
[ormb]: https://github.com/kleveross/ormb

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