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We have renamed Views to Projects in Iterative Studio.
Accordingly, Views dashboard is now called Projects dashboard; View settings are now called Project settings; and so on.
You can configure additional settings for your projects. Some of these settings, such as project name, are optional. Some other settings, such as data remotes, may be required depending on how your Git repository has been set up.
If you are connecting to a DVC repo which is at the root of the Git repository and does not reference remote/cloud storage, then you can successfully visualize it without configuring additional settings.
Alternatively, you could create projects from:
In each of these scenarios, you will need to configure additional settings for Iterative Studio to be able to access the data required for visualization.
Additionally, you can also configure project settings to change the name of your project and to select mandatory columns to import in your project.
In the section on preparing your repositories, you saw that you can use Iterative Studio with DVC as well as non-DVC repositories. If you are connecting to a non-DVC repository, then you will need to specify the custom files that contain the metrics and hyperparameters that you want to visualize.
Depending on how you have set up your Git repositories, your DVC repo (to which you are trying to connect from Iterative Studio) may not be in the root of your Git repo. Instead, it could be in a sub-directory of a monorepo. If this is the case, you will need to specify the full path to the sub-directory that contains the data you want to visualize in Iterative Studio.
The metrics and parameters that you want to include in the project may also be present in a data remote (cloud storage or another location outside the Git repo). If you want to include such data in your projects, then you will have to grant Iterative Studio access to the data remote.
You can configure a project's settings at any time after creating the project.
For this, click on the
icon in
the project. In the menu that opens up, click on
Settings
.
To change the project name, enter the new name for your project as shown below.
If you have connected to a monorepo, then specify the full path to the sub-directory that contains the DVC repo to which you are trying to connect.
If you need to provide credentials for
DVC data remotes, you will need to
do it after your project has been created. First, create your project without
specifying the data remotes. Once your project is created, open its settings.
Open the Data remotes / cloud storage credentials
section. The data remotes
that are used in your DVC repo will be listed.
Now, click on Add new credentials
. In the form that opens up, select the
provider (Amazon S3, GCP, etc.). For details on what types of remote storage
(protocols) are supported, refer to the DVC documentation on
supported storage types.
Depending on the provider, you will be asked for more details such as the credentials name, username, password etc. Note that for each supported storage type, the required details may be different.
You will also have to ensure that the credentials you enter have the required permissions on the cloud / remote storage. In the DVC documentation on supported storage types, expand the section for the storage type you want to add. There, you will find the details of the permissions that you need to grant to the account (credentials) that you are configuring on Iterative Studio.
Note that Iterative Studio uses the credentials only to read plots/metrics files if they are not saved into Git. It does not access any other data in your remote storage. And you do not need to provide the credentials if any DVC data remote in not used in your Git repository.
If your repository exceeds 200 columns, Iterative Studio will import a subset. The columns that are not imported will not be available to display in your project. In the settings for "Mandatory columns", You can select which columns are mandatory to import. Iterative Studio will also import unselected columns up to a maximum of 200.
Note that some non-mandatory columns will also be imported if there are less than 200 mandatory columns. If you would like to hide specific columns from your project, you can do so in the project's Display preferences.
If your project is missing some required columns or includes columns that you do not want, refer to the following troubleshooting sections to understand why this may have happened.
Note: The Mandatory columns section was earlier called Tracking scope.
If you want to connect custom files, you can add them by clicking the Add file
button. Enter the full file path within your Git repository, and specify whether
the file is for Metrics
or Parameters
.