cluster profiles dataiku

If the model predicts True but the value is False, the gain is -1. The administrator assigns global permissions for each group. You can verify group membership and permissions in one chart. A group of analysts and data scientists creates a Flow. From the Deployer, go to Projects or API Services. You can visit the other sections available in the course, Intro to Machine Learning and then move on to Machine Learning Basics. In the Spark configuration, enable the Managed K8S configuration checkbox, In Target namespace, enter something like dss-ns-${dssUserLogin}, Set Authentication mode to Create service accounts dynamically. Note that you can override this behavior in the scenario settings. A Kubernetes service to expose a publicly available URL which applications can use to query your API. . Else, returns None. Allows group members to have full administrative control over the code environment. An analyst in Ireland and I have been working together on the Dataiku Clustering Analysis and we are having a hard time understanding the results for the Agglomerative and Interactive models. This includes the ability to create new datasets, recipes, and run all jobs in this project. Infrastructure elements of the Deployer: How to grant group permissions with certain privileges. The data is of medium sensitivity so all dashboard users could use any of the Flow. Allows group member to click on the Download button to retrieve the content of a dataset. We can use this horizontal line to see how many clusters wed have at this threshold by counting how many vertical lines it bisects. The available sampling methods depend on the machine learning engine. In this example, we have selected multiple groups. In this method, instead of beginning with a randomly-selected value of K, hierarchical clustering starts by declaring each data point as a cluster of its own. Each API Service Deployment (see Concepts) is setup on Kubernetes as: A Kubernetes deployment made of several replicas of a single pod. If DSS unexpectedly stops while the scenario is running, the cluster resources will keep running on your cloud provider. The Settings tab allows you to fully customize all aspects of your clustering. You are viewing the documentation for version, # Start the cluster. However, once the users code has been started, a fundamental property of Kubernetes is that each container is independent and cannot access others. We notice that some variables seem to have a stronger impact than others. If selected this will automatically also select Create code envs.. You may have other profiles available, or only some of them. list clusters: list_clusters() obtain a handle on a cluster: get_cluster() create a cluster: create_cluster() DSSClusterSettings is an opaque type and its content is specific to each . The first steps in configuring Hadoop security support consist in setting up the Kerberos account which DSS will use for accessing cluster resources: Create a Kerberos principal (user or service account) for this DSS instance in your Kerberos account database. Only global administrators can create infrastructures such as nodes and deployments. If not there already, be sure the Result tab is selected near the top center of the page. This does not previously stop it. Recall from our business objectives that the business analyst wants to define built-in checks for ML assertions. Deletes the cluster. A group of analysts and data scientists creates a Flow. Setup with Kubernetes Dataiku DSS 12 documentation Check out the use cases! This applies both to static Kubernetes clusters and dynamic / managed by DSS Kubernetes clusters. A common use case for clusters is to run one or multiple scenarios. Examples include: Reading a Dataiku DSS dataset to create a Bokeh web app, A clustering task would have a heatmap and cluster profiles. In both cases, the steeper the curves are at the beginning of the graphs, the better the model. Figure 12. DSS automatically uses the requested cluster and the limits defined in the container runtime configuration. To build our elbow plot, we iteratively run the K-Means algorithm, first with K=1, then K=2, and so forth, and computing the total variation within clusters at each value of K. As we increase the value of K and the number of clusters increases, the total variation within clusters will always decrease, or at least remain constant. The definition should come from a call to the get_definition() In this section, well show you how to grant group access with view, deploy, and admin permissions for infrastructures on the Project Deployer or API Deployer. Azure through AKS Google Cloud Platform through GKE Creating a cluster To create managed clusters, you must first install the DSS plugin corresponding to your cloud provider ( EKS, AKS, or GKE ). This requires that the account running DSS has credentials on the Kubernetes cluster that allow it to create namespaces. An exception is thrown in case of error, Setting up Dashboards and Flow export to PDF or images, Projects, Folders, Dashboards, Wikis Views, Changing the Order of Sections on the Homepage, Fuzzy join with other dataset (memory-based), Fill empty cells with previous/next value, In-memory Python (Scikit-learn / XGBoost), How to Manage Large Flows with Flow Folding, Reference architecture: managed compute on EKS with Glue and Athena, Reference architecture: manage compute on AKS and storage on ADLS gen2, Reference architecture: managed compute on GKE and storage on GCS, Hadoop filesystems connections (HDFS, S3, EMRFS, WASB, ADLS, GS), Using Amazon Elastic Kubernetes Service (EKS), Using Microsoft Azure Kubernetes Service (AKS), Using code envs with containerized execution, Importing code from Git in project libraries, Automation scenarios, metrics, and checks, Components: Custom chart palettes and map backgrounds, Authentication information and impersonation, Hadoop Impersonation (HDFS, YARN, Hive, Impala), DSS crashes / The Disconnected overlay appears, Your user profile does not allow issues, ERR_BUNDLE_ACTIVATE_CONNECTION_NOT_WRITABLE: Connection is not writable, ERR_CODEENV_CONTAINER_IMAGE_FAILED: Could not build container image for this code environment, ERR_CODEENV_CONTAINER_IMAGE_TAG_NOT_FOUND: Container image tag not found for this Code environment, ERR_CODEENV_CREATION_FAILED: Could not create this code environment, ERR_CODEENV_DELETION_FAILED: Could not delete this code environment, ERR_CODEENV_EXISTING_ENV: Code environment already exists, ERR_CODEENV_INCORRECT_ENV_TYPE: Wrong type of Code environment, ERR_CODEENV_INVALID_CODE_ENV_ARCHIVE: Invalid code environment archive, ERR_CODEENV_JUPYTER_SUPPORT_INSTALL_FAILED: Could not install Jupyter support in this code environment, ERR_CODEENV_JUPYTER_SUPPORT_REMOVAL_FAILED: Could not remove Jupyter support from this code environment, ERR_CODEENV_MISSING_ENV: Code environment does not exists, ERR_CODEENV_MISSING_ENV_VERSION: Code environment version does not exists, ERR_CODEENV_NO_CREATION_PERMISSION: User not allowed to create Code environments, ERR_CODEENV_NO_USAGE_PERMISSION: User not allowed to use this Code environment, ERR_CODEENV_UNSUPPORTED_OPERATION_FOR_ENV_TYPE: Operation not supported for this type of Code environment, ERR_CODEENV_UPDATE_FAILED: Could not update this code environment, ERR_CONNECTION_ALATION_REGISTRATION_FAILED: Failed to register Alation integration, ERR_CONNECTION_API_BAD_CONFIG: Bad configuration for connection, ERR_CONNECTION_AZURE_INVALID_CONFIG: Invalid Azure connection configuration, ERR_CONNECTION_DUMP_FAILED: Failed to dump connection tables, ERR_CONNECTION_INVALID_CONFIG: Invalid connection configuration, ERR_CONNECTION_LIST_HIVE_FAILED: Failed to list indexable Hive connections, ERR_CONNECTION_S3_INVALID_CONFIG: Invalid S3 connection configuration, ERR_CONNECTION_SQL_INVALID_CONFIG: Invalid SQL connection configuration, ERR_CONNECTION_SSH_INVALID_CONFIG: Invalid SSH connection configuration, ERR_CONTAINER_CONF_NO_USAGE_PERMISSION: User not allowed to use this containerized execution configuration, ERR_CONTAINER_CONF_NOT_FOUND: The selected container configuration was not found, ERR_CONTAINER_IMAGE_PUSH_FAILED: Container image push failed, ERR_DATASET_ACTION_NOT_SUPPORTED: Action not supported for this kind of dataset, ERR_DATASET_CSV_UNTERMINATED_QUOTE: Error in CSV file: Unterminated quote, ERR_DATASET_HIVE_INCOMPATIBLE_SCHEMA: Dataset schema not compatible with Hive, ERR_DATASET_INVALID_CONFIG: Invalid dataset configuration, ERR_DATASET_INVALID_FORMAT_CONFIG: Invalid format configuration for this dataset, ERR_DATASET_INVALID_METRIC_IDENTIFIER: Invalid metric identifier, ERR_DATASET_INVALID_PARTITIONING_CONFIG: Invalid dataset partitioning configuration, ERR_DATASET_PARTITION_EMPTY: Input partition is empty, ERR_DATASET_TRUNCATED_COMPRESSED_DATA: Error in compressed file: Unexpected end of file, ERR_ENDPOINT_INVALID_CONFIG: Invalid configuration for API Endpoint, ERR_FOLDER_INVALID_PARTITIONING_CONFIG: Invalid folder partitioning configuration, ERR_FSPROVIDER_CANNOT_CREATE_FOLDER_ON_DIRECTORY_UNAWARE_FS: Cannot create a folder on this type of file system, ERR_FSPROVIDER_DEST_PATH_ALREADY_EXISTS: Destination path already exists, ERR_FSPROVIDER_FSLIKE_REACH_OUT_OF_ROOT: Illegal attempt to access data out of connection root path, ERR_FSPROVIDER_HTTP_CONNECTION_FAILED: HTTP connection failed, ERR_FSPROVIDER_HTTP_INVALID_URI: Invalid HTTP URI, ERR_FSPROVIDER_HTTP_REQUEST_FAILED: HTTP request failed, ERR_FSPROVIDER_ILLEGAL_PATH: Illegal path for that file system, ERR_FSPROVIDER_INVALID_CONFIG: Invalid configuration, ERR_FSPROVIDER_INVALID_FILE_NAME: Invalid file name, ERR_FSPROVIDER_LOCAL_LIST_FAILED: Could not list local directory, ERR_FSPROVIDER_PATH_DOES_NOT_EXIST: Path in dataset or folder does not exist, ERR_FSPROVIDER_ROOT_PATH_DOES_NOT_EXIST: Root path of the dataset or folder does not exist, ERR_FSPROVIDER_SSH_CONNECTION_FAILED: Failed to establish SSH connection, ERR_HIVE_HS2_CONNECTION_FAILED: Failed to establish HiveServer2 connection, ERR_HIVE_LEGACY_UNION_SUPPORT: Your current Hive version doesnt support UNION clause but only supports UNION ALL, which does not remove duplicates, ERR_METRIC_DATASET_COMPUTATION_FAILED: Metrics computation completely failed, ERR_METRIC_ENGINE_RUN_FAILED: One of the metrics engine failed to run, ERR_ML_MODEL_DETAILS_OVERFLOW: Model details exceed size limit, ERR_NOT_USABLE_FOR_USER: You may not use this connection, ERR_OBJECT_OPERATION_NOT_AVAILABLE_FOR_TYPE: Operation not supported for this kind of object, ERR_PLUGIN_CANNOT_LOAD: Plugin cannot be loaded, ERR_PLUGIN_COMPONENT_NOT_INSTALLED: Plugin component not installed or removed, ERR_PLUGIN_DEV_INVALID_COMPONENT_PARAMETER: Invalid parameter for plugin component creation, ERR_PLUGIN_DEV_INVALID_DEFINITION: The descriptor of the plugin is invalid, ERR_PLUGIN_INVALID_DEFINITION: The plugins definition is invalid, ERR_PLUGIN_NOT_INSTALLED: Plugin not installed or removed, ERR_PLUGIN_WITHOUT_CODEENV: The plugin has no code env specification, ERR_PLUGIN_WRONG_TYPE: Unexpected type of plugin, ERR_PROJECT_INVALID_ARCHIVE: Invalid project archive, ERR_PROJECT_INVALID_PROJECT_KEY: Invalid project key, ERR_PROJECT_UNKNOWN_PROJECT_KEY: Unknown project key, ERR_RECIPE_CANNOT_CHANGE_ENGINE: Cannot change engine, ERR_RECIPE_CANNOT_CHECK_SCHEMA_CONSISTENCY: Cannot check schema consistency, ERR_RECIPE_CANNOT_CHECK_SCHEMA_CONSISTENCY_EXPENSIVE: Cannot check schema consistency: expensive checks disabled. In the Flow, select the customers_labeled dataset, and then click Actions in the top right corner. You can visit the model evaluation concept to learn more about these and other model evaluation tools. From the top navigation bar, go to the Applications menu and choose Administration. For a detailed description, see Using Amazon EKS. The data is of medium sensitivity so all or some DSS users should be able to reuse it on other projects. and arent sure which types of Dataiku actions require membership in the allowed_user_groups local Unix group, below is a quick summary. Please read carefully the Prerequisites and limitations documentation and check that you have all required information. Clusters may be listed, created and obtained using methods of the DSSClient: obtain a handle on a cluster: get_cluster(). Lets look at each of these methods in more detail. You can change this and choose to configure which groups can view the code environment. Prepare your local aws, docker, and kubectl commands Follow the AWS documentation to ensure the following on your local machine (where Dataiku DSS is installed): If the model predicts True but the value is False, the gain is -1. Thus, the dashboard users (or a subgroup of them) has this permission to gain access to source datasets. The account running Dataiku only needs full access to these namespaces in order to create service accounts in them. This permission is generally not very useful without the Read project content permission. To do this: Based on the permissions model within your organization, select the permissions you want to allow for this group. Resources are elements where the administrator might want to manage security, including projects, code environments, managed clusters, containerized execution, and infrastructure elements of the Deployer. Gets the whole status as a raw dictionary. A User can have only one profile at a time. In your scenario, add an initial step Setup cluster: Select the cluster type that you want to create (depending on the plugin you are using), Fill in the configuration form (depending on the plugin you are using), Set clusterForScenario as the Target variable. If you are transferring ownership, select Confirm and then Save. Allows users to create and publish projects to a Dataiku Automation node through the Deployer. You can also share the charts to Dashboards (after a model has been deployed to the Flow), and the feature importance plot is included in the model documentation. A code environment is a standalone and self-contained environment to run Python or R code. You can access the sampling settings in Models > Settings > Sampling. All permissions are cumulative. Allows users to create and edit development plugins. Using spark-submit with scala into kubernetes cluster DrissiReda Level 4 03-15-2021 12:14 PM I'm using dataiku version 9.0 in my kubernetes cluster, I managed to do a spark-submit of a jar with a shell. Profiles can be different between licenses. Sampling Features Dimensionality reduction Outliers detection Algorithms Sampling Note You can access the sampling settings in Models > Settings > Sampling The available sampling methods depend on the machine learning engine. In your Dataiku instance, choose Administration from the Applications menu. Allows group members to modify cluster settings. Otherwise this permission is not needed. The fundamental local isolation code layer, Security for running regular workloads on Kubernetes (Python, R, Machine Learning), Security for running Spark workloads on Kubernetes, dssuser means the UNIX user which runs the DSS software, DATADIR means the directory in which DSS is running. In the chart for our model, we can see that the age_first_order values that had the most effect on the revenue are around 20 (negatively affecting the revenue) and around 60 to 70 (positively affecting the revenue). You can change this and choose to allow all groups on the instance to view the cluster. Recalculate the midpoint (centroid) of each cluster. You can select each group name to view its settings. Configure the Cost matrix weights as follows: If the model predicts True and the value is True, the gain is 5. Well do that in the section on tuning the model. You are viewing the Knowledge Base for version, Deploying Dataiku Instances to Cloud Stacks, Preferred Connections and Format for Dataset Storage, Compute and Resource Quotas on Dataiku Cloud, Tutorial | Evaluate the model (ML Practitioner part 2). This group may execute the corresponding application if the application is configured to be instantiated only by a user with this permission. Assertions can save time in the development phase of our model, speed up model evaluation, and help us improve the model when needed. This permission should be the default for a data team working within a project. The actual predicted value depends on which cut-off threshold we decide to use on this probability; i.e., at which probability do we decide to classify our customer as a high revenue one? Read and write settings of clusters. You have discovered ways to interpret your model and understand prediction quality and model results. Can be selected separately without giving access to manage all clusters. Not doing so could generate very skewed clusters, or many small clusters and one cluster containing almost the whole dataset. Completing these steps will help you understand the various permissions available and how to assign permissions to different groups. Users who run any kind of local code (Python or R - be it in recipes, notebooks, webapps, scenarios, reports, etc. Hierarchical clustering iteratively repeats this process until all data points are merged into one large cluster. Architecture with Dataiku | Dataiku In hierarchical clustering, we aim to first understand the relationship between the clusters visually, and then determine the number of clusters, or hierarchy level, that best portrays the different groupings. The exact definition of user profiles that are available depends on your DSS license. You are viewing the Knowledge Base for version, Deploying Dataiku Instances to Cloud Stacks, Preferred Connections and Format for Dataset Storage, Compute and Resource Quotas on Dataiku Cloud, FAQ | Which activities require that a user be added to the. Shapley values are a measure of each feature values relative importance to the model. For instance, you could group customers into clusters based on their payment history, which could be used to guide sales strategies. One popular use case for clustering is recommendation engines, which are systems built to predict what users might like, such as a movie or a book. Here we discuss options for our classification task, but a regression task would include a scatter plot and error distribution. See above for how this will work. Allows users to create projects using predefined templates from Dataiku samples and tutorials. To learn more about the User Isolation Framework, visit our reference documentation. Dataiku now shows the group you added, along with permission options. The Lift charts and ROC curve are visual aids, perhaps the most useful, to assess the performance of your model. It looks like, in our case, the train and test datasets are imbalanced. You are viewing the documentation for version, Running Unsupervised Machine Learning in DSS, Automation scenarios, metrics, and checks. In case of any doubt, please contact your Dataiku Customer Success Manager. FAQ | Which activities require that a user be added to the allowed_user_groups local Unix group? By default, only the owner can see the cluster and choose to use it. To visualize the hierarchical relationship between the clusters and determine the optimal number of clusters for our use case, we can use a dendrogram. DSS has scenario steps available for starting and stopping clusters. Code will be executed with the UNIX privileges of the user. Reference | User profiles Dataiku Knowledge Base In versions 12.0 and above, Dataiku automatically produces the three feature importance plots for all algorithms except K-nearest neighbors and support vector machine models, which both require long computation time. Well modify feature handling when we tune the model. Disabling this permission removes the most obvious way to download whole datasets, but users who have at least Read project content permission will still be able to download datasets. In our case, if we set the distance threshold so that each cluster is as dissimilar as possible, we would have six bisected lines, resulting in six clusters. This includes opaque data for the cluster if this is Dataiku has rejected customerID because this feature was detected as an unique identifier and was not helpful to predict high-value customers. The new Yuzu version of Dataiku integrates tools to help you with natural language processing (NLP). Topic Modeling and Image Classification with Dataiku and NVIDIA Data They can also create their own dashboards and insights, but only based on datasets/models/, Other profiles may be available, and not all of these may be available. Before trying to improve its performance, lets look at ways to interpret it, and understand its prediction quality and model results. The returned object can be used to save settings. Visit User profiles in the reference documentation .
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