@@ -9,15 +8,15 @@ A version control file system for data linage & data collaboration.
+
----
-JiaoZiFS is an industry-leading **Data-Centric Version Control** File System, helps ensure Responsible AI Engineering by improving **Data Versioning**, **Provenance**, and **Reproducibility**.
+JZFS is an industry-leading **Data-Centric Version Control** File System, helps ensure Responsible AI Engineering by improving **Data Versioning**, **Provenance**, and **Reproducibility**.
Note:
-* The name JiaoZi pays tribute to the world's earliest paper money: [Song Dynasty JiaoZi](https://en.wikipedia.org/wiki/Jiaozi_(currency)).
-* JiaoZiFS is yet another implementation of [IPFS (InterPlanetary File System)](https://ipfs.tech/) as JiaoZiFS will be compatible with the [implementation requirements](https://specs.ipfs.tech/architecture/principles/#ipfs-implementation-requirements) of IPFS.
-* As a filesystem of data versioning at scale, although JiaoZiFS is built for machine learning, It has a wide range of use scenarios (refer A Universe of Uses) and can be seamlessly integrated into all your data stack.
+* The name JZFS pays tribute to the world's earliest paper money: [Song Dynasty JiaoZi](https://en.wikipedia.org/wiki/Jiaozi_(currency)).
+* JZFS is yet another implementation of [IPFS (InterPlanetary File System)](https://ipfs.tech/) as JZFS will be compatible with the [implementation requirements](https://specs.ipfs.tech/architecture/principles/#ipfs-implementation-requirements) of IPFS.
+* As a filesystem of data versioning at scale, although JZFS is built for machine learning, It has a wide range of use scenarios (refer A Universe of Uses) and can be seamlessly integrated into all your data stack.
Data-centric AI is about the practice of iterating and collaborating on data, used to build AI systems, programmatically. Machine learning pioneer Andrew Ng [argues that focusing on the quality of data fueling AI systems will help unlock its full power](https://youtu.be/TU6u_T-s68Y).
@@ -35,15 +34,15 @@ In production systems with machine learning components, updates and experiments
#### Requirement
-1. To build JiaoZiFS, you need a working installation of [Go 1.22.0 or higher](https://golang.org/dl/)
-2. JiaoZiFS use postgres to store running data, you can install at [postgres install installation guide](https://www.postgresql.org/docs/current/installation.html)
+1. To build JZFS, you need a working installation of [Go 1.22.0 or higher](https://golang.org/dl/)
+2. JZFS use postgres to store running data, you can install at [postgres install installation guide](https://www.postgresql.org/docs/current/installation.html)
#### Build And Running
1. clone and build
```bash
-git clone https://github.com/GitDataAI/jiaozifs.git
-cd jiaozifs
+git clone https://github.com/GitDataAI/jzfs.git
+cd jzfs
make build
```
@@ -62,16 +61,16 @@ docker run -v :/app -p 34913:34913 gitdatateam/jzfs:latest --db "postgres
```
#### Cloud
-[Try without installing](https://cloud.jiaozifs.com)
+[Try without installing](https://console.gitdata.ai)
-Note: storage config for IPFS backend storage as you create a new repository in JiaoZiFS UI.
+Note: storage config for IPFS backend storage as you create a new repository in JZFS Console.
```
{"type":"ipfs","ipfs":{"url":"/dns/kubo-service.ipfs.svc.cluster.local/tcp/5001"}}
```
#### Examples
-Build AL/ML pipeline over JiaoZiFS
+Build AL/ML pipeline over JZFS
[Face detection and recognition inference pipeline](https://colab.research.google.com/drive/1wsv-KMxTdsCLZ64eLq4W1MTfspid-vv6?usp=sharing)
----
@@ -100,6 +99,6 @@ Build AL/ML pipeline over JiaoZiFS
----
### License
-Dual-licensed under [MIT](https://github.com/GitDataAI/jiaozifs/blob/main/LICENSE-MIT) + [Apache 2.0](https://github.com/GitDataAI/jiaozifs/blob/main/LICENSE-APACHE)
+Dual-licensed under [MIT](https://github.com/GitDataAI/jzfs/blob/main/LICENSE-MIT) + [Apache 2.0](https://github.com/GitDataAI/jzfs/blob/main/LICENSE-APACHE)
diff --git a/api/resp.gen.go b/api/resp.gen.go
index e66c7ab..0070f7e 100644
--- a/api/resp.gen.go
+++ b/api/resp.gen.go
@@ -20,6 +20,7 @@ import (
type MockResponseWriter struct {
ctrl *gomock.Controller
recorder *MockResponseWriterMockRecorder
+ isgomock struct{}
}
// MockResponseWriterMockRecorder is the mock recorder for MockResponseWriter.
@@ -69,13 +70,13 @@ func (mr *MockResponseWriterMockRecorder) Write(arg0 any) *gomock.Call {
}
// WriteHeader mocks base method.
-func (m *MockResponseWriter) WriteHeader(arg0 int) {
+func (m *MockResponseWriter) WriteHeader(statusCode int) {
m.ctrl.T.Helper()
- m.ctrl.Call(m, "WriteHeader", arg0)
+ m.ctrl.Call(m, "WriteHeader", statusCode)
}
// WriteHeader indicates an expected call of WriteHeader.
-func (mr *MockResponseWriterMockRecorder) WriteHeader(arg0 any) *gomock.Call {
+func (mr *MockResponseWriterMockRecorder) WriteHeader(statusCode any) *gomock.Call {
mr.mock.ctrl.T.Helper()
- return mr.mock.ctrl.RecordCallWithMethodType(mr.mock, "WriteHeader", reflect.TypeOf((*MockResponseWriter)(nil).WriteHeader), arg0)
+ return mr.mock.ctrl.RecordCallWithMethodType(mr.mock, "WriteHeader", reflect.TypeOf((*MockResponseWriter)(nil).WriteHeader), statusCode)
}