Fix yaml file format bug (#75)

* Add pages

* publish plugin

* link

* Update plugin

* update plugin

* Update plugin

* Update plugin

* Update plugin

* Update plugin

* Update summary

* Update plugin

* Update plugin

* Test submit job v2 plugin

* update link

* Fix url link

* Update plugin.js (#32)

* Add chunk.js (#33)

* Update plugin

* Update new deployment (#43)

* Protocol (#45)

* Rename couplet_dataset copy.yaml to couplet_dataset.yaml

* New yaml file

* Update Protocol (#46)

* Update protocol examples (#47)

* Fix protocol (#48)

* Update plugin (#52)

* Update examples and plugins (#55)

* Change title as link to enable open in new tab (#59)

* Covid examples (#62)

* Fix name for yaml

* Update grammar check examples

* Update plugin

* Fix plugin

* Update example

* Add covid-chestxray-dataset

* Fix covid-chestxray-dataset

* update plugin.js

* fix covid-chestxray-dataset protocal name

* fix covid-chestxray-dataset protocal name

* update X-ray chest check description

* fix link to covid-chestxray-dataset image & metedata

* Add gpu sharing example (#64)

* Add mock data file

* add covid_chestxray_training code

* Add covid_chestxray_training.yaml

* update training code path

* add covid chestxray model training job

* fix covid image data training job item name

* Update test data

* update data

* Update test data

* Add pytorch distributed job examples (#71)

* Patch

* Patch

* type=template

* Add new files

* Changed the folder

* Change the word old to template

* Update data

* Rename

* Update description of couplet examples

* deploy distributed job templates

* Change github pages publish branch to master

* delete

* fix lint

* package lock

* Fix yaml format bug

Co-authored-by: yiyione <yiyi@microsoft.com>
Co-authored-by: Zhiyuan He <362583303@qq.com>
Co-authored-by: vvfreesoul <285009003@qq.com>
Co-authored-by: TobeyQin <paula_qin@hotmail.com>

examples/item_protocols/cifar10-single-mul-DDP-nccl-Apex-mixed.yaml

@@ -2,15 +2,15 @@ protocolVersion: 2
 name: Apex-cifar-10-nccl-2node-4gpu_2_748a8371
 type: job
 jobRetryCount: 0
-  description: |
-    # Pytorch Cifar10 Example
+description: |
+  # Pytorch Cifar10 Example
 
-    This example shows how to train a custom neural network on cifar10 with Pytorch on OpenPAI.
+  This example shows how to train a custom neural network on cifar10 with Pytorch on OpenPAI.
 
-    We installed Apex before running `python <script.py>`,and the sample program will be trained on two machines, each with two gpus.
+  We installed Apex before running `python <script.py>`,and the sample program will be trained on two machines, each with two gpus.
 
-    This example can be run on single-node or on multi-node, using nccl backend for distributed GPU training,nccl currently provides the best distributed GPU training performance, especially for multiprocess single-node or multi-node distributed training.
-    And mixed precision training (training in a combination of float (FP32) and half (FP16) precision) allows us to use larger batch sizes and take advantage of NVIDIA Tensor Cores for faster computation.
+  This example can be run on single-node or on multi-node, using nccl backend for distributed GPU training,nccl currently provides the best distributed GPU training performance, especially for multiprocess single-node or multi-node distributed training.
+  And mixed precision training (training in a combination of float (FP32) and half (FP16) precision) allows us to use larger batch sizes and take advantage of NVIDIA Tensor Cores for faster computation.
 
 
 

examples/item_protocols/cifar10-single-mul-DDP-nccl.yaml

@@ -2,16 +2,14 @@ protocolVersion: 2
 name: cifar10-single-mul-DDP-nccl-1
 type: job
 jobRetryCount: 0
-  description: |
-    # Pytorch Cifar10 Example
+description: |
+  # Pytorch Cifar10 Example
 
-    This example shows how to train a custom neural network on cifar10 with Pytorch on OpenPAI.
-
-    We installed Apex before running `python <script.py>`,and the sample program will be trained on two machines, each with two gpus.
-
-    This example can be run on single-node or on multi-node, using nccl backend for distributed GPU training,nccl currently provides the best distributed GPU training performance, especially for multiprocess single-node or multi-node distributed training.
+  This example shows how to train a custom neural network on cifar10 with Pytorch on OpenPAI.
 
+  We installed Apex before running `python <script.py>`,and the sample program will be trained on two machines, each with two gpus.
 
+  This example can be run on single-node or on multi-node, using nccl backend for distributed GPU training,nccl currently provides the best distributed GPU training performance, especially for multiprocess single-node or multi-node distributed training.
 
 prerequisites:
   - type: dockerimage

examples/item_protocols/cifar10-single-node-gpus-cpu-DP.yaml

@@ -2,13 +2,12 @@ protocolVersion: 2
 name: vv-cifar10-single-node-gpus-cpu-DP-4
 type: job
 jobRetryCount: 0
-  description: |
-    # Pytorch Cifar10 Example
+description: |
+  # Pytorch Cifar10 Example
 
-    This example shows how to train a custom neural network on cifar10 with Pytorch on OpenPAI.
-
-    This example program can only run on single-node, but you can choose to use multiple gpus and cpus.
+  This example shows how to train a custom neural network on cifar10 with Pytorch on OpenPAI.
 
+  This example program can only run on single-node, but you can choose to use multiple gpus and cpus.
 
 prerequisites:
   - type: dockerimage

webportal/src/app/utils/marketplace_api.js

@@ -35,7 +35,13 @@ export async function getItem(itemId) {
     newItem.protocol = protocol;
     return newItem;
   } catch (error) {
-    alert(`could not get marketplace item from uri ${uri}`);
+    if (error.name === 'YAMLException') {
+      alert(`wrong yaml file format of ${uri}`);
+      window.location.href = `http://localhost:9286/plugin.html?index=0`;
+    } else {
+      alert(`could not get marketplace item from uri ${uri}`);
+      window.location.href = `http://localhost:9286/plugin.html?index=0`;
+    }
   }
 }