Onboard your team¶

Once your organization is set up in Valohai, you’ll be able to easily share projects within your teams.

☁️ Connect to a cloud storage¶

Configuring a data store will ensure that all your execution outputs will stay centralized inside your cloud subscription.

You can either create data stores per project, or create data stores that are shared across the whole organization.
- It’s recommended to create at least one organization data store and setting it as the default data store, to ensure all data that is outputted from execution will go to your own data store, instead of the Valohai managed data storage.
- Each project owner can configure their data store, if needed, and override the default upload location with their new data store.
Access existing data from your cloud storage (e.g. training data sets, labels, etc.)
Upload new data from executions (e.g. trained model, weights, metrics, graphics, etc.)
The project’s default upload store determines where to save output files

Configure a shared data store

Login at https://app.valohai.com
Navigate to Hi, <name> (the top-right menu) > Manage <organization>.
Go to Data Stores under the organization controls
Follow the tutorial below for adding your own data store.

Follow one of our tutorials below to setup your own data stores:

Projects without a data store

Valohai will upload execution outputs by default to the Valohai S3 Store if a data store hasn’t been configured for the project.
Only users with access to the project will be able to access data in the Valohai owned storage.

⚙️ Configure environment settings¶

Every time you run an execution or a task Valohai will either launch a new machine or use an already running one to run your execution.

Each machine type has its own configuration:

Minimum Scale: What’s the minimum number of machines (of a certain type) that Valohai should keep running? By default, this is set to 0.
- If you set the minimum number to 1 (or greater) we’ll make sure that there is constantly a machine available in your environment. This also means that your cloud provider will also charge you for having the machine running all the time.
Maximum Scale: What’s the maximum number of machines (of a certain type) that Valohai can run in parallel? By default, this is set to 5.
- After a maximum number of machines has been launched, new executions will get queued and executed once a machine frees up from a previous execution.
- The maximum limit is also determined by the quotas/limits for virtual machines you have with your cloud provider.
Scale-Down Grace Period: How many minutes should we wait after executions have completed to shut down the machine? By default, 15min.
Per-User Quota: You can limit how many machines (of a certain type) can a user run in parallel? By default, this is 0 (no limit)

Change your default environment

Login at https://app.valohai.com
Navigate to Hi, <name> (the top-right menu) > Manage <organization>.
Go to Environments under the organization controls
Choose the default environment for your users.

Available only for Enterprise subscriptions

The environment settings are available only to customers who have Enterprise subscription and are using their own workers (cloud or on-prem).

🐳 Access Private Docker Repositories¶

You can use custom Docker images from private repositories by providing the credentials in your organization’s Registries settings. Valohai executions support private repositories from Docker Hub and the container registries from AWS, Azure and GCP.

Login at https://app.valohai.com
Navigate to Hi, <name> (the top right menu) > Manage <organization>.
Go to Registries under the organization controls
Add a new entry
Insert the name in the format of docker.io/myusername/* or myregistry.azurecr.io/*.
Use the previously generated credentials as the username and password (Docker username and access token or Azure Service Principal credentials)

To use a private Docker image in your executions specify the image in the step of your valohai.yaml using the full name like docker.io/myusername/name:tag.

🔑 Access Private GitHub Repositories¶

You can easily configure Git repositories for each project.

For public repositories, you can just paste the HTTPS link to the repository to Project->Settings->Repository
You can easily authenticate to private GitHub repositories using the app at Project->Settings->Repository

If you’re connecting to a private repository:

Generate a private SSH key pair on your machine with ssh-keygen -t rsa -b 4096 -N '' -f my-project-deploy-key will generate two files
Add the generated public key (my-project-deploy-key.pub) to your source control (e.g. GitHub, GitLab, BitBucket)
Get the Clone with SSH link to your repository from your source control
Go to your project settings’ Repository tab.
- URL: Paste here the clone with SSH url
- SSH Private Key: Copy & paste the generated private key contents (my-project-deploy-key)

Follow our step-by-step guides to connect your private repository:

👩‍💻👨‍💻 Invite Team Members¶

Login at https://app.valohai.com
Navigate to Hi, <name> (the top-right menu) > Manage <organization>.
Go to Invitations under the organization controls
Add a new email and send them an invite

The recipient will get an email to join your organization on Valohai.

You can manage your organization’s members and promote members to an admin under the Members control.

Note: You can also enforce Two-Factor Authentication to all users, and manage Azure AD integration under the Settings section.

🗄 Reproducibility and lineage¶

Valohai automatically keeps track of key information about your executions, making it easier to reproduce your experiments in the future and understand how they work.

Details on all past executions¶

What code was ran to get the results of this execution?
- For all executions that are based on a Git commit, Valohai will provide details of the commit and link back to it for details.
- If the execution was ran as –adhoc or as a Notebook execution, you’ll see adhoc under commit. Clicking the link will take you to details of the valohai.yaml configuration file, and allow you to download the code.
Where the execution was ran (cloud or onprem) and what kind of hardware was used to run it?
Which Docker image was used to run the execution?
What was used as the input data for this execution? This could be for example training-data, labels, etc.
Commands that were executed (for example if you executed a pip install to install additional dependencies that are not part of the original Docker image.
Who, when and how much did it cost?

Trace models and data files¶

In addition to seeing the outputs of each execution, you can trace files that you’ve connected to Valohai (inputs/outputs). This allows you to easily see which executions and deployments are relying on certain models, datasets, or output files.

Tracing a file will create a graph for you, that’ll show:

How was this file generated? Which executions resulted in this file?
Which executions and deployments are relying on this file?

Go to your project’s data tab to see all your files and trace them.

Metadata¶

On top of all the data that Valohai is collecting about your executions, you can also easily create your metadata from your executions.

Metadata can be anything: performance metrics, details about the libraries you’re using, and anything else.
This data is then visible on the Metadata tab inside each execution.

Use tags to easily identify certain executions¶

Tags are useful when you for example want to highlight an execution that lead to an update in production. Or just want to make it easier for your team members to find certain executions, so they don’t want to scroll through hundreds of experiments you ran in the project.

Set tags at the bottom of each execution’s Details tab.

💡 Additional organization settings¶

There are a variety of other settings that you can manage for your organization:

Manage users and teams inside your organization
- You can set project access and visibility to only to members of a certain team, instead the whole organization.
Connect Valohai to Azure AD and keep access control on Azure, avoiding access control setting duplication.
Require two-factor authentication for all users in your organization
Prevent users from running personal projects under organization execution environments