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Edited developer guide

Change-Id: Iba98d20a7d80fa35a93269d19b68e2f2eaaee712
changes/19/618619/1
Viswanath KSP 7 months ago
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commit
6a63677ec7
1 changed files with 26 additions and 12 deletions
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      doc/source/developer-guide.md

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doc/source/developer-guide.md View File

@@ -9,9 +9,12 @@ Openstack Gyan is the Machine Learning Infra as a service project. This document
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 # How to setup Gyan
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   - Clone the devstack master branch 
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-    > git clone https://github.com/openstack-dev/devstack
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+    ```
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+    git clone https://github.com/openstack-dev/devstack
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+    ```
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   - Copy the contents below to local.conf in devstack
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-    > [[local|localrc]]
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+    ```
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+    [[local|localrc]]
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     HOST_IP=192.168.1.188
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     SERVICE_HOST=192.168.1.188
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     DATABASE_PASSWORD=password
@@ -24,30 +27,41 @@ Openstack Gyan is the Machine Learning Infra as a service project. This document
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     enable_plugin heat https://git.openstack.org/openstack/heat
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     LIBS_FROM_GIT="python-gyanclient"
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     ENABLED_SERVICES+=gyan-api
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+    ```
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   - Run stack.sh
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-     > ./stack.sh
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+    ```
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+    ./stack.sh
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+    ```
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 # Workflow of Gyan:
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   - Create hints.yaml to specify the size of the compute node. For example
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-    > python_version: 2.7
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+    ```
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+      python_version: 2.7
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       cpu: 2
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       memory: 1024 Mb
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       disk: 20 Gb
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       driver: TensorflowDriver
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       additional_details: {}
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+    ```
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   - After that create the flavor using hints template
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-    > gyan flavor-create --hints-path flavor.yaml tensorflow
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+    ```
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+    gyan flavor-create --hints-path flavor.yaml tensorflow
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+    ```
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   - Compress your ML model that is already trained  and create a Gyan model
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-    > gyan create-model --trained-model model.zip --type Tensorflow MNIST
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+    ```
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+    gyan create-model --trained-model model.zip --type Tensorflow MNIST
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+    ```
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   - Deploy model that was created in the previous step.
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-    > gyan deploy-model <model-id>
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-  - The above command will launch the compute node based on the flavor details we      gave in the first step. Once the compute node is launched, the gyan-compute will   be installed and connected to gyan-server.
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+    ```
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+    gyan deploy-model <model-id>
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+    ```
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+  - The above command will launch the compute node based on the flavor details we gave in the first step. Once the compute node is launched, the gyan-compute will   be installed and connected to gyan-server.
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   - We should get new host in `gyan host-list`.
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-  - In the last step you should see the deployed url of the model. We can find out     using `gyan model-list` 
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+  - In the last step you should see the deployed url of the model. We can find out using `gyan model-list` 
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 # How to use run sample example KnowThyNumber provided with Gyan:
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   - Make sure you have installed golang in your system and set the GOROOT and GOPATH properly.
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-  -  Copy the KnowThyNumber example from gyan/examples to GOPATH/src/github.com/<user-id>/KnowThyNumber.
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-  -  Now run `go run server.go`. This will start local server on 9000 port.
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+  -  Copy the KnowThyNumber example from `gyan/examples` to `GOPATH/src/github.com/<user-id>/KnowThyNumber`
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+  -  Now run `go run server.go`. This will start local server on `9000` port.
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   -  We got deployed_url in the previous section. Get the openstack token using `openstack token issue`. 
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-  -  Open the browser and navigate to http://localhost:9000. Provide the `deployed_url` and `token` in the app. Now you can draw any number in the canvas and use your model to predict it.
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+  -  Open the browser and navigate to `http://localhost:9000`. Provide the `deployed_url` and `token` in the app. Now you can draw any number in the canvas and use your model to predict it.

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