For all of our quick installations options, please have your account key ready. If you don't have one yet, please obtain one by registering. You can continue with the installation on AWS, on GKE or on a bare Kubernetes cluster.
If you have chosen to try out RiseML on AWS, we provide a quick start installer. The installer will guide you through installing Kubernetes and RiseML on AWS. All you need is a workstation with Linux (x84_64) or MacOS (x84_64) and an access key for your AWS account.
To get started execute this command on your favourite terminal
bash -c "$(curl -fsSL https://get.riseml.com)"
This command will guide you through the installation process. Note: if you register for an account on the homepage you will get a personalized link for downloading the installer, and you don't need to provide the account key as shown below.
The following shows a sample output of the installer using 1 GPU worker and 3 CPU worker nodes with autoscaling enabled:
Choose a region or availability zone in which to install RiseML. If a region is chosen the cluster will be in the spread across all of the region's availability zones. * AWS region or availability zone [default: us-east-1]: Configure CPU as well as GPU worker nodes. Make sure that the instance type is available in your region and that instance limits suffice. Autoscaling is enabled by default. Set min/max to the same value to disable autoscaling. * CPU workers min count [default: 0]: max count [default: 3]: instance type [default: m4.2xlarge]: * GPU workers min count [default: 0]: max count [default: 3]: 1 instance type [default: p3.2xlarge]: Your cluster ID is 5f76fb19-cf34-481b-bb95-7b3185bcd498 RiseML account key: dc6s49mblq5ifxokdkorqtdx3h06nkwm --- output trimmed --- To install RiseML on AWS we need your credentails. AWS access key: AKI***************** AWS secret access key: 9azJha********************************** We are about to create these components on AWS: 1 (m4.2xlarge) nodes for the Kubernetes master 1 (m4.2xlarge) nodes for the RiseML system 0-3 (m4.2xlarge) nodes for the CPU workers 0-1 (p3.2xlarge) nodes for the GPU workers These will be created using AWS Access Key: AKI***************** Are you sure you wish to continue (y/n)[default: n]: y --- output trimmed --- ___ _ _ ___ ___ ___ ___ ___ / __| | | |/ __/ __/ _ \/ __/ __| \__ \ |_| | (_| (_| __/\__ \__ \ |___/\__,_|\___\___\___||___/___/ RiseML is successfully installed and registered! Your Account Key: dc6s49mblq5ifxokdkorqtdx3h06nkwm We have also created a user for you. This user has administrative access rights. User Name: admin API Key: RnrjsdzpdwGDu4bvi9B9bscgvJyGUe5d The RiseML client is installed in /home/satran/.riseml/bin directory. Add these to your profile environment. export PATH=/home/satran/.riseml/bin:$PATH To destroy this cluster: riseml-install -cleanup You can run these commands to get started with your cluster: riseml init riseml train -l For more information check out our docs https://docs.riseml.com
On a successful execution of the command, it will provide you with details about your cluster and how to run a simple experiment. You can store these in a document or run
riseml-install again. Ensure you add the riseml
bin directory to your path.
The installer requires you have an AWS IAM user with these permissions:
These requirements are mandated by the open source tool we use: kops. You will also need the access key and secret assess key for your account. You can find out more on how to get these keys here: http://docs.aws.amazon.com/general/latest/gr/managing-aws-access-keys.html
Currently, only the following OS(architecture) are supported:
Resources are expensive and it is necessary to delete the cluster after you have experimented with RiseML. The command below will delete all resources that was created by the installer.
bin directory is in your path by running the command below. The installer is copied to
$HOME/.riseml/bin directory when you downloaded the script.
In case you run into trouble when executing the installer you can look through the logs which is stored in
$HOME/.riseml/install/log. Sometimes the easiest step is to run the installer again (inside the
The installer keeps track of the installation progress and continues where it stopped.
If it doesn't seem to work you can run
riseml-install -cleanup riseml-install -reset
to delete the cluster and clean up old cached files.
riseml-install to try it out again.
Inspecting the Kubernetes cluster
To avoid overwriting existing Kubernetes configuration file the installer creates a separate configuration file:
~/.riseml/install/kubeconfig. You should export
KUBECONFIG variable using the following command:
With this set you can troubleshoot your K8s cluster with
This quick installation will show you how to setup a beta GKE cluster that is GPU-enabled.
You will need Google's
gcloud CLI and
kubectl for that.
Start by creating a GKE Kubernetes cluster with some GPUs:
$ gcloud beta container clusters create test-3x4-k80 --project test-clusters --num-nodes=3 --machine-type=n1-standard-8 --accelerator type=nvidia-tesla-k80,count=4 --zone us-central1-c --cluster-version 1.9.2-gke.1
Next, install the Nvidia drivers to your cluster:
$ kubectl create -f https://raw.githubusercontent.com/GoogleCloudPlatform/container-engine-accelerators/k8s-1.9/nvidia-driver-installer/cos/daemonset-preloaded.yaml
Wait a few minutes to let the driver installation finish. Continue with setting up a RiseML namespace and Helm:
$ kubectl create namespace riseml $ kubectl create serviceaccount tiller --namespace kube-system $ kubectl create clusterrolebinding tiller-cluster-admin-binding \ --clusterrole=cluster-admin --serviceaccount=kube-system:tiller $ helm init --service-account tiller --tiller-namespace kube-system $ helm repo add riseml-charts https://cdn.riseml.com/helm-charts
Create a configuration file
riseml-config.yml and adjust accountKey and
adminEmail to your values:
$ cat riseml-config.yml accountKey: <your_account_key> adminApiKey: mlriseapikey adminEmail: <your_email> git: persistence: enabled: true logs: persistence: enabled: true nfsProvisioner: enabled: true path: /tmp/riseml persistence: enabled: true nodePorts: false nvidiaDriverDir: /home/kubernetes/bin/nvidia postgresql: persistence: enabled: true subPath: data registry: persistence: enabled: true scheduleOnMaster: false
Next, install RiseML into your GKE cluster and wait for it to spin up completely:
$ helm install riseml-charts/riseml --name riseml --namespace riseml -f riseml-config.yml $ watch kubectl -n riseml get pods
Wait until all pods are running and execute the commands the
helm install gave you to get the connection info needed to login.
Next, download the RiseML CLI from http://docs.riseml.com/install/cli.html and login using the info from above:
$ riseml user login --api-key XYZ --api-host XYZ:80
Finally, check your installation:
$ riseml system info RiseML Client/Server Version: 1.0.3/1.1.0 RiseML Cluster ID: 575cbb40-1cae-11e8-ad4d-0a580a380032 Kubernetes Version 1.9+ (Build Date: 2018-01-31T22:30:55Z) NODE CPU MEM GPU GPU MEM gke-test-3x4-k80-default-pool-1e0c6fe8-dd26 7 26.0 4 44.7 gke-test-3x4-k80-default-pool-1e0c6fe8-qv7l 7 26.0 4 44.7 gke-test-3x4-k80-default-pool-1e0c6fe8-h2t3 7 26.0 4 44.7 -------------------------------------------------------------------- Total 21 77.9 12 134.1
For this installation, you will need a Kubernetes cluster with version at least 1.8 that is already installed and working. If you don't have a Kubernetes cluster, you can check the Kubernetes docs for the various installation options. An easy solution that works well in most cases is installing using kubeadm.
Your cluster needs one node that is big enough (at least 4 CPUs, check requirements) and not the Kubernetes master. If you want to use the master node for RiseML, you need to enable the master for regular workloads, see below.
The installation will be good for testing and evaluating RiseML. It uses temporary internal storage on your nodes. If you restart some nodes, it is possible that information on experiments or your experiment data is lost. If you want to avoid this, you should perform a custom installation.
Run the following, to install Helm on your Kubernetes cluster:
$ curl https://raw.githubusercontent.com/kubernetes/helm/master/scripts/get | bash $ kubectl create serviceaccount tiller --namespace kube-system $ kubectl create clusterrolebinding tiller-cluster-admin-binding \ --clusterrole=cluster-admin --serviceaccount=kube-system:tiller $ helm init --service-account tiller --tiller-namespace kube-system $ helm repo add riseml-charts https://cdn.riseml.com/helm-charts
Label RiseML Node
We will use one node, where RiseML runs all of its services.
You can get all node's names by running
kubectl get nodes.
Label the node of your choosing (as long as it has enough resources) using the following command:
kubectl label node <your_node> riseml.com/system-node=true
Create RiseML Configuration
Create the configuration file
$ cat riseml-config.yml accountKey: <your_account_key> adminApiKey: mlriseapikey adminEmail: <your_email> nvidiaDriverDir: "/var/lib/nvidia-driver" minio: enabled: true secretKey: mlriseapikey nfsProvisioner: enabled: true path: /tmp/risemlnfs nodeSelectors: system: riseml.com/system-node: "true" imageBuilder: riseml.com/system-node: "true"
Adjust the following:
- accountKey: enter your account key
- adminEmail: enter your email
- nvidiaDriverDir: if you want GPU support, provide the directory where the driver can be found on all nodes; see instructions
If you want, you can also change:
- adminApiKey: select an api key (use only alphanumeric characters and 0-9)
- minio.secretKey: select a secret key (use only alphanumeric characters and 0-9)
- nfsProvisioner.path: a path on the RiseML system node where data is placed
Use Helm to install RiseML with your configuration:
$ helm install riseml-charts/riseml --name riseml --namespace riseml -f riseml-config.yml NAME: riseml LAST DEPLOYED: Fri Dec 15 11:13:04 2017 NAMESPACE: riseml STATUS: DEPLOYED ... NOTES: RiseML was deployed. It may take a while for all services to be operational. You can watch the progress with this command (all Pods should be RUNNING): watch -n 1 kubectl get pods -n=riseml To set up your client, look up your RiseML master's hostname or ip address and run: export RISEML_HOSTNAME=<YOUR MASTER HOSTNAME/IP> ### RiseML Client You can get the RiseML client from here: http://docs.riseml.com/install/cli.html To configure the RiseML client, run: riseml user login --api-key mlriseapikey --host $RISEML_HOSTNAME ### Minio Client (for accessing data) You can get the Minio client from here: https://docs.minio.io/docs/minio-client-quickstart-guide To configure the Minio client, run: mc config host add data http://$RISEML_HOSTNAME:31874 minioaccess mlriseapikey mc config host add output http://$RISEML_HOSTNAME:31875 minioaccess mlriseapikey You can find some examples to run on https://github.com/riseml/examples More information is available in our documentation: https://docs.riseml.com
Thats it! You can now download and setup the RiseML command line client as output by the installation above. For accessing internal data (e.g., uploading training data, our downloading the results of experiments) you can setup Minio.
Optional: Run Regular Workloads on Master
By default, the Kubernetes master does not allow any regular workloads to run on it. If you want RiseML services to run on the master, you need to run:
kubectl taint nodes --all node-role.kubernetes.io/master-
Note that this will also allow other workloads besides RiseML to run on your master.