From 6a63677ec7494524209a0fa9dc5d401ab56c0961 Mon Sep 17 00:00:00 2001 From: Viswanath KSP Date: Sat, 17 Nov 2018 19:19:41 +0530 Subject: [PATCH] Edited developer guide Change-Id: Iba98d20a7d80fa35a93269d19b68e2f2eaaee712 --- doc/source/developer-guide.md | 38 ++++++++++++++++++++++++----------- 1 file changed, 26 insertions(+), 12 deletions(-) diff --git a/doc/source/developer-guide.md b/doc/source/developer-guide.md index cff16bb..04e6a72 100644 --- a/doc/source/developer-guide.md +++ b/doc/source/developer-guide.md @@ -9,9 +9,12 @@ Openstack Gyan is the Machine Learning Infra as a service project. This document # How to setup Gyan - Clone the devstack master branch - > git clone https://github.com/openstack-dev/devstack + ``` + git clone https://github.com/openstack-dev/devstack + ``` - Copy the contents below to local.conf in devstack - > [[local|localrc]] + ``` + [[local|localrc]] HOST_IP=192.168.1.188 SERVICE_HOST=192.168.1.188 DATABASE_PASSWORD=password @@ -24,30 +27,41 @@ Openstack Gyan is the Machine Learning Infra as a service project. This document enable_plugin heat https://git.openstack.org/openstack/heat LIBS_FROM_GIT="python-gyanclient" ENABLED_SERVICES+=gyan-api + ``` - Run stack.sh - > ./stack.sh + ``` + ./stack.sh + ``` # Workflow of Gyan: - Create hints.yaml to specify the size of the compute node. For example - > python_version: 2.7 + ``` + python_version: 2.7 cpu: 2 memory: 1024 Mb disk: 20 Gb driver: TensorflowDriver additional_details: {} + ``` - After that create the flavor using hints template - > gyan flavor-create --hints-path flavor.yaml tensorflow + ``` + gyan flavor-create --hints-path flavor.yaml tensorflow + ``` - Compress your ML model that is already trained and create a Gyan model - > gyan create-model --trained-model model.zip --type Tensorflow MNIST + ``` + gyan create-model --trained-model model.zip --type Tensorflow MNIST + ``` - Deploy model that was created in the previous step. - > gyan deploy-model - - 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. + ``` + gyan deploy-model + ``` + - 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. - We should get new host in `gyan host-list`. - - In the last step you should see the deployed url of the model. We can find out using `gyan model-list` + - In the last step you should see the deployed url of the model. We can find out using `gyan model-list` # How to use run sample example KnowThyNumber provided with Gyan: - Make sure you have installed golang in your system and set the GOROOT and GOPATH properly. - - Copy the KnowThyNumber example from gyan/examples to GOPATH/src/github.com//KnowThyNumber. - - Now run `go run server.go`. This will start local server on 9000 port. + - Copy the KnowThyNumber example from `gyan/examples` to `GOPATH/src/github.com//KnowThyNumber` + - Now run `go run server.go`. This will start local server on `9000` port. - We got deployed_url in the previous section. Get the openstack token using `openstack token issue`. - - 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. + - 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.