openapi: 3.2.0 info: title: proto/clarifai/api/service.proto My Scopes API version: version not set tags: - name: My Scopes paths: /v2/my_scopes: get: summary: API Keys in the public API -- request is itself Key authorized, and will tell… operationId: V2_MyScopes3 responses: '200': description: A successful response. content: application/json: schema: $ref: '#/components/schemas/apiMultiScopeResponse' default: description: An unexpected error response. content: application/json: schema: $ref: '#/components/schemas/googlerpcStatus' parameters: - name: user_app_id.user_id description: Note user_id 'me' is reserved - it is the alias for the id of authorized user in: query required: false schema: type: string - name: user_app_id.app_id in: query required: false schema: type: string tags: - My Scopes components: schemas: apiTimeRange: type: object properties: start_time: type: string format: date-time description: Begin of the time range, optional, inclusive. end_time: type: string format: date-time description: End of the time range, optional, inclusive. title: TimeRange apiOutputConfig: type: object properties: concepts_mutually_exclusive: type: boolean description: 'For custom concept model training: whether the concept predictions must sum to 1.' existing_model_id: type: string description: 'DEPRECATED: For custom models, this is the base model to use for image embeddings. Default is general model.' language: type: string description: 'For concept model predictions: Overrides the default_language for the app in a predict call.' hyper_parameters: type: string description: 'DEPRECATED: Hyper-parameters for custom training. Use new hyper_params field instead.' max_concepts: type: integer format: int64 description: 'For concept model predictions: Maximum number of concepts in result. Defaults to 0 which under the hood will return default of 20. We do a server side default in order to control this feature in the future.' min_value: type: number format: float description: 'For concept model predictions: Minimum value of concept''s probability score in result. Defaults to 0.0 which means we won''t do any thresholding as all probabilities will likely be > 0.0.' select_concepts: type: array items: type: object $ref: '#/components/schemas/apiConcept' title: 'For concept model predictions: Select concepts in result by name or by id' training_timeout: type: integer format: int64 title: 'For custom concept model training: Training timeout of the model (in seconds)' sample_ms: type: integer format: int64 title: 'For model predictions on video: Sample delay for video predicting (1 frame per N milliseconds)' hyper_params: type: object title: 'For custom model training: Hyperparameters for custom training' embed_model_version_id: type: string description: 'For custom model training: this is the base model version to use for image embeddings. This has to be one of the embed models in the app workflow. Use input_info.base_embed_model instead.' fail_on_missing_positive_examples: type: boolean title: 'For custom model training: Use this flag to fail on missing positive examples By default we fill in the missing with random examples' model_metadata: type: object description: 'For custom model training: This is any additional metadata as a JSON object that we want want to persist in the model''s output config. This is a useful quick way to set fields for introducing fields for new model types so we don''t have to add a new proto field and DB field each time. Please refer to the documentation or model implementation internally for more details on what fields are supported for which models. TODO(zeiler): remove this field after Portal is updated.' description: 'OutputConfig is a collection of parameters controlling either inference or training settings for the given Model. This message will be deprecated over time in favor or output_info.params and train_params in OutputInfo which are cleaner and more extensible for many ModelTypes.' apiApp: type: object properties: id: type: string name: type: string default_language: type: string default_workflow_id: type: string description: 'Deprecated, use default_workflow instead. @exclude TODO (EAGLE-4506): Remove this field and associated code' default_workflow: $ref: '#/components/schemas/apiWorkflow' user_id: type: string title: 'why is user_id present here when this message type is used in PostApps but completely ignored there? PostApp already specifies the userid in path but doesn''t even actually use neither of userids, it instead used the id from auth context. This creates a lot of ambiguity, should always have different message types for Post/Get endpoints so that the minimum interface for each op can be described' created_at: type: string format: date-time title: 'When the app was created. We follow the XXXX timestamp format. We use https://www.ietf.org/rfc/rfc3339.txt format: "2006-01-02T15:04:05.999999Z" so you can expect results like the following from the API: "2017-04-11T21:50:50.223962Z"' modified_at: type: string format: date-time title: When the app was last modified legal_consent_status: type: integer format: int64 title: if user accept legal consent for face recognition metadata: type: object title: 'To handle arbitrary json metadata you can use a struct field: https://github.com/google/protobuf/blob/master/src/google/protobuf/struct.proto' description: type: string description: short description about the app. sample_ms: type: integer format: int64 title: 'Default value for model predictions on video: Sample delay for video predicting (1 frame per N milliseconds)' visibility: $ref: '#/components/schemas/clarifaiapiVisibility' description: 'The visibility field represents whether this message is privately/publicly visible. To be visible to the public the App that contains it AND the User that contains the App must also be publicly visible.' data_tier_id: type: string description: data tier id this app is using. is_starred: type: boolean title: 'Is starred by the requesting user (only showed on get/list requests) Please use PostAppStars/DeleteAppStars endpoints to star/unstar an app' star_count: type: integer format: int32 title: 'How many users have starred the app (only showed on get/list requests) Computed value, not editable' notes: type: string description: 'Notes for the application This field should be used for in-depth notes and supports up to 64Kbs.' image: $ref: '#/components/schemas/apiImage' title: Representative image for this app is_template: type: boolean description: 'An app marked as a template can be duplicated by any user that can see it, including all visible resources within it.' extra_info: $ref: '#/components/schemas/apiAppExtraInfo' embeddings_storage: $ref: '#/components/schemas/AppEmbeddingsStorage' title: 'Where app embeddings are stored postgres (default), qdrant' title: Application with tasks and datasets apiModelVersion: type: object properties: id: type: string created_at: type: string format: date-time description: When the version was created. status: $ref: '#/components/schemas/apistatusStatus' description: The status of the version (whether it's untrained, training, trained, etc.). active_concept_count: type: integer format: int64 metrics: $ref: '#/components/schemas/apiEvalMetrics' total_input_count: type: integer format: int64 title: number of inputs in the model version pretrained_model_config: $ref: '#/components/schemas/apiPretrainedModelConfig' title: 'When a model has already been trained externally, you can upload it directly to the platform as a model version directly by setting the PretrainedModelConfig' completed_at: type: string format: date-time description: When training of this version was completed. description: type: string title: Description about this version visibility: $ref: '#/components/schemas/clarifaiapiVisibility' description: 'The visibility field represents whether this message is privately/publicly visible. To be visible to the public the App that contains it AND the User that contains the App must also be publicly visible.' app_id: type: string description: The app the model version belongs to. user_id: type: string description: The user the model version belongs to. modified_at: type: string format: date-time title: When this model version was last modified metadata: type: object title: 'To handle arbitrary json metadata you can use a struct field: https://github.com/google/protobuf/blob/master/src/google/protobuf/struct.proto' license: type: string output_info: $ref: '#/components/schemas/apiOutputInfo' description: 'Info about the model''s output. Besides `output_info.data`, these fields should be reserved for parameters that affect the models outputs when inferencing. `output_info.data` is used to specify the training concepts for this model version.' input_info: $ref: '#/components/schemas/apiInputInfo' description: 'Info about preprocessing the models inputs, before they are sent to this model for training or inferencing. E.g.: `input_info.base_embed_model` lets us know inputs should be ran through a base model before being sent to an embedding-classifier.' train_info: $ref: '#/components/schemas/apiTrainInfo' description: Configuration for the training process of this model version. import_info: $ref: '#/components/schemas/apiImportInfo' title: 'Configuration used to import model from third-party toolkits DEPRECATED: no longer support importing models from third party toolkits' train_log: type: string title: Contains the training logs if available inference_compute_info: $ref: '#/components/schemas/apiComputeInfo' description: The minimum required compute resource for this model for inference. build_info: $ref: '#/components/schemas/apiBuildInfo' title: Build information for the model version method_signatures: type: array items: type: object $ref: '#/components/schemas/apiMethodSignature' title: Model signature information for the model version special_handling: type: array items: type: object $ref: '#/components/schemas/apiSpecialHandling' description: List of special handling instructions for this model version. num_threads: type: integer format: int32 description: The number of threads to use for this model version. title: ModelVersion apiCluster: type: object properties: id: type: string count: type: integer format: int64 title: Number of annotations tied to the cluster in the app score: type: number format: float description: 'The score assigned to this cluster. For List Clusters endpoint, this represents percentage of inputs in the app assigned to this cluster.' hits: type: array items: type: object $ref: '#/components/schemas/apiHit' title: Representative hits for cluster (for now we only return 1) projection: type: array items: type: number format: float title: Cluster data apiModelTypeEnumOption: type: object properties: id: type: string description: The unique value of the enum option. aliases: type: array items: type: object $ref: '#/components/schemas/apiModelTypeEnumOptionAlias' title: 'List of other ID values that are equivalent with this ID. This allows the user to choose this option by multiple IDs. Example: if enum is "Phone Number Prefix", you could add an option that is selectable by two values: 1. ID: "Estonia" 2. Alias: 37' description: type: string description: Optional description for this enum option. model_type_fields: type: array items: type: object $ref: '#/components/schemas/apiModelTypeField' description: 'These are additional fields that are specific to this enum choice. This allows us to use enums to control configuration settings as well.' internal_only: type: boolean description: If this enum option should be internal only. recommended: type: boolean description: 'Whether this is the recommended enum option. Set to `true` when there are multiple options, and one is shown to be better than the others.' title: ModelTypeEnumOption apiTrainInfo: type: object properties: params: type: object description: 'To control the training process when PostModelVersions is used we allow a list of parameters defined for each ModelType as a Struct (JSON object) here. During training, the settings contained within are sent to the training processor to alter the training process.' dataset: $ref: '#/components/schemas/apiDataset' title: The dataset and dataset version this model version was or will be trained on resume_from_model: $ref: '#/components/schemas/apiModel' description: The model to resume training from. apiDatasetVersionMetrics: type: object properties: inputs_count: type: string format: uint64 title: Number of inputs unlabeled_inputs_count: type: string format: uint64 description: 'Number of unlabeled inputs An input is considered unlabeled if it there are no annotations with positive labels for that input.' inputs_with_metadata_count: type: string format: uint64 title: Number of inputs that have metadata inputs_with_geo_count: type: string format: uint64 title: Number of inputs that have geo information regions_count: type: string format: uint64 title: Number of regions region_location_matrix: $ref: '#/components/schemas/MatrixUint64' title: 'The matrix shows where the regions are located. Example: If the matrix has 2x2 dimensions, then * region_location_matrix[0][0] = the number of regions that appear in the top left corner, i.e. [0,0]..(0.5,0.5) * region_location_matrix[0][1] = the number of regions that appear in the top right corner, i.e. [0,0.5]..[0.5,1] * region_location_matrix[1][0] = the number of regions that appear in the bottom left corner, i.e. [0.5,0]..[1,0.5) * region_location_matrix[1][1] = the number of regions that appear in the bottom right corner, i.e. [0.5,0.5]..[1,1]' bounding_boxes_count: type: string format: uint64 title: Number of bounding boxes polygons_count: type: string format: uint64 title: Number of polygons points_count: type: string format: uint64 title: Number of points masks_count: type: string format: uint64 title: Number of masks region_inputs_count: type: string format: uint64 description: 'Number of inputs that have regions attached Note that this is not a recursive count: if an input contains frames that contains regions, then the region_frames_count is increased, but region_inputs_count is not increased.' region_frames_count: type: string format: uint64 title: Number of frames that have regions attached frames_count: type: string format: uint64 title: Number of frames frame_inputs_count: type: string format: uint64 title: Number of inputs that have frames attached embeddings_count: type: string format: uint64 title: Number of embeddings positive_input_tags_count: type: string format: uint64 title: Number of positive tags added at input-level positive_region_tags_count: type: string format: uint64 title: Number of positive tags added at region-level positive_frame_tags_count: type: string format: uint64 title: Number of positive tags added at frame-level apiModelPredictConfig: type: object properties: model: $ref: '#/components/schemas/apiModel' description: Assumed to be owned by the calling users app unless user_id and app_id are filled out. apiGeoBoxedPoint: type: object properties: geo_point: $ref: '#/components/schemas/apiGeoPoint' title: GeoBoxedPoint apiSpecialHandling: type: object properties: id: type: string reason: $ref: '#/components/schemas/SpecialHandlingReason' done: type: boolean description: Whether special handling is done. description: Tracks special handling reason and whether it's been done. apiBookmarkOrigin: type: object properties: id: type: string title: original resource id app_id: type: string title: original resource app id user_id: type: string title: original resource user id resource_type: $ref: '#/components/schemas/BookmarkOriginBookmarkType' description: resource type. apiLabelCount: type: object properties: concept_name: type: string count: type: integer format: int64 concept: $ref: '#/components/schemas/apiConcept' title: LabelCount SearchMetric: type: string enum: - METRIC_NOT_SET - EUCLIDEAN_DISTANCE - COSINE_DISTANCE default: METRIC_NOT_SET description: 'Metric used for search. Can be EUCLIDEAN_DISTANCE (default) or COSINE_DISTANCE. Currently only brute force search supports non-eudlicean metrics.' apiAppExtraInfo: type: object properties: search_revision_marker: type: string title: 'Revision marker for this application. The value of the revision changes when * inputs are added, updated or deleted * annotations are added, updated or deleted * inputs are added to or removed from datasets For example, this value can be used to detect if client side caches related to searching should be invalidated. Field not filled in for list endpoints, use GetApp' counts: $ref: '#/components/schemas/apiAppResourceCounts' apiKeypointInfo: type: object properties: keypoint_names: type: array items: type: string title: Names of the keypoints skeleton: type: array items: type: object $ref: '#/components/schemas/apiKeypointEdge' description: Defines the connections between keypoint_names. Each value represents the index in keypoint_names. apiWorker: type: object properties: user: $ref: '#/components/schemas/apiUser' description: 'User is the human that created the annotation. By default no real names of users are returned in responses. These can be requested with the ''names'' additional field.' model: $ref: '#/components/schemas/apiModel' description: Model is the model that created the annotation. workflow: $ref: '#/components/schemas/apiWorkflow' description: Workflow is the workflow that created the annotation. description: Worker is the author of an annotation. apiFrameInfo: type: object properties: index: type: integer format: int64 title: 'Deprecated. Use Time instead. The index of the frame, informational and optional. Depends on the sampling rate used during processing May be 0 for interpolated frames that are generated for brief time (training) or if new frame is manually added' time: type: string format: uint64 description: 'time in the video in milliseconds. This is independent of the sampling rates used during processing. Changed from uint32 to uint64 to support video livestreams longer than 49.7 days.' number: type: integer format: int64 title: 'The absolute number of the frame in the (original) video Different from index. Index is just the order in which frames were processed for search (and can be 0 for manual annotations)' description: The information of the location of the Frame. apiModelTypeRangeInfo: type: object properties: min: type: number format: float description: The start of the range as a float. max: type: number format: float description: The end of the range as a float. step: type: number format: float description: 'An optional step size for the range. If provided then only values at that step size will be rounded to. For example if step is 0.02 then 0.0245 will round to 0.02.' title: ModelTypeRangeInfo apiAnd: type: object properties: input: $ref: '#/components/schemas/apiInput' title: "FILTER by input.data... information.\nThis can include human provided concepts, geo location info, metadata, etc.\nThis is effectively searching over only the trusted annotation attached to an input in your\napp. To search by more specific annotation fields use the Annotation object here.\n########## Supported fields ##########\n - data.concepts[].id\n - data.concepts[].name\n - data.concepts[].value\n - data.geo.geo_box[].geo_point.latitude\n - data.geo.geo_box[].geo_point.longitude\n - data.geo.geo_limit.type\n - data.geo.geo_limit.value\n - data.geo.geo_point.latitude\n - data.geo.geo_point.longitude\n - data.image.url\n - data.metadata.fields - filter by metadata. metadata key&value fields are OR-ed.\n - dataset_ids[] - filter by dataset IDs\n - id - filter by input ID\n - status.code - filter by input status" output: $ref: '#/components/schemas/apiOutput' description: "RANK based predicted outputs from models such as custom trained models, pre-trained models,\netc. This is also where you enter the image url for a visual search because what we're asking\nthe system to do is find output embedding most visually similar to the provided input (that\ninput being in And.output.input.data.image.url for example). This will return the Hits\nsorted by visual similarity (1.0 being very similar or exact match and 0.0 being very\ndissimlar). For a search by Output concept, this means we're asking the system to rank\nthe Hits by confidence of our model's predicted Outputs. So for example if the model\npredicts an image is 0.95 likely there is a \"dog\" present, that should related directly\nto the score returned if you search for Output concept \"dog\" in your query. This provides\na natural ranking to search results based on confidence of predictions from the models and\nis used when ANDing multiple of these types of RANK by Output queries together as well.\n\n########## Supported fields ##########\n - data.clusters[].id\n - data.concepts[].id\n - data.concepts[].name\n - data.concepts[].value\n - input.data.image.base64[]\n - input.data.image.url\n - input.id" negate: type: boolean title: 'If True then this will flip the meaning of this part of the query. This allow for queries such as dog AND ! metadata=={"blah":"value"}' annotation: $ref: '#/components/schemas/clarifaiapiAnnotation' description: "FILTER by annotation information. This is more flexible than just filtering by\nInput information because in the general case each input can have several annotations.\nSome example use cases for filtering by annotations:\n1) find all the inputs annotated \"dog\" by worker_id = \"XYZ\"\n2) find all the annotations associated with embed_model_version_id = \"123\"\n3) find all the annotations that are trusted, etc.\n\nSince all the annotations under the hood are joined to the embedding model's annotation\nusing worker_id's of other models like cluster models or concept models should be\ncombinable with queries like visual search (a query with Output filled in).\n\n########## Supported fields ##########\n - annotation_info.fields - filter by annotation info\n - data.concepts[].id\n - data.concepts[].name\n - data.concepts[].value\n - data.geo.geo_box[].geo_point.latitude\n - data.geo.geo_box[].geo_point.longitude\n - data.geo.geo_limit.type\n - data.geo.geo_limit.value\n - data.geo.geo_point.latitude\n - data.geo.geo_point.longitude\n - data.image.url\n - data.metadata.fields - filter by metadata. metadata key&value fields are OR-ed.\n - input_id\n - input_level\n - model_version_id\n - status.code\n - task_id\n - trusted\n - user_id" description: 'This is the common building block of a query which is a sequence of And messages ANDed together. Note that some fields are used too RANK results (affect the scores) and some are used to FILTER results (unordered subset of your app''s contents). In general, FILTER operations are more efficient queries at scale and when combined with RANK operations can speed up search performance as you effectively operate on a smaller sub-set of your entire app.' apiConcept: type: object properties: id: type: string description: The concept's unique id. name: type: string description: The name of the concept in the given language. value: type: number format: float description: 'Used to indicate presence (1.0) or not (0.0) of this concept when making a request. This is also the prediction probability when returning predictions from our API. For convenience we use the default of 1.0 when making requests so the concept you provide is is treated as a positive (1.0) and not a negative (which would be value == 0.0).' created_at: type: string format: date-time description: 'When the concept was created. The format is https://www.ietf.org/rfc/rfc3339.txt . Example: "2006-01-02T15:04:05.999999Z". This field is used only in a response.' language: type: string description: 'The language in which the concept name is in. This is *ONLY* used in the response and setting it in a request is ignored since the default language of your app is used when creating or patching a Concept. To set other languages for your concept use the ConceptLanguage object and its corresponding endpoints.' app_id: type: string description: The application id that this concept is within. This can be ignored by most users. definition: type: string description: The definition for the concept. Similar to name. This can be ignored by most users. vocab_id: type: string description: 'The vocabulary that this concept belongs to. This is useful if you have different unique sets of concepts that you can separate out based on this field. For example "age_appearance" vs "gender_appearance" in a list of concept returned from the demographics model.' visibility: $ref: '#/components/schemas/clarifaiapiVisibility' description: 'The visibility field represents whether this message is privately/publicly visible. To be visible to the public the App that contains it AND the User that contains the App must also be publicly visible.' user_id: type: string description: The user the concept belongs to. keypoint_info: $ref: '#/components/schemas/apiKeypointInfo' title: Information about keypoints for this concept extra_info: $ref: '#/components/schemas/apiConceptExtraInfo' description: Optional extra info. metadata: type: object title: 'To handle arbitrary json metadata: https://github.com/google/protobuf/blob/master/src/google/protobuf/struct.proto' image: $ref: '#/components/schemas/apiImage' title: Representative image for the concept title: Concept or tag apiData: type: object properties: image: $ref: '#/components/schemas/apiImage' description: Input and output images. video: $ref: '#/components/schemas/apiVideo' description: Input and output videos. concepts: type: array items: type: object $ref: '#/components/schemas/apiConcept' description: A list of concepts. metadata: type: object title: 'To handle arbitrary json metadata you can use a struct field: https://github.com/google/protobuf/blob/master/src/google/protobuf/struct.proto' geo: $ref: '#/components/schemas/apiGeo' description: Geography information. colors: type: array items: type: object $ref: '#/components/schemas/apiColor' description: The dominant colors within an image. clusters: type: array items: type: object $ref: '#/components/schemas/apiCluster' description: Clustering centroids for inputs. embeddings: type: array items: type: object $ref: '#/components/schemas/apiEmbedding' description: Embedding vectors representing each input. regions: type: array items: type: object $ref: '#/components/schemas/apiRegion' description: For recursing into localized regions of an input. frames: type: array items: type: object $ref: '#/components/schemas/apiFrame' description: For temporal content like video. text: $ref: '#/components/schemas/apiText' description: Input, output or annotation text. audio: $ref: '#/components/schemas/apiAudio' description: Input and output audio. tracks: type: array items: type: object $ref: '#/components/schemas/apiTrack' description: Track information. time_segments: type: array items: type: object $ref: '#/components/schemas/apiTimeSegment' description: Time segments information. hits: type: array items: type: object $ref: '#/components/schemas/apiHit' title: Holds score, rank, and user, app, input IDs and search hit data heatmaps: type: array items: type: object $ref: '#/components/schemas/apiImage' title: Heatmap as 2d image parts: type: array items: type: object $ref: '#/components/schemas/apiPart' description: 'For data messages that have multiple parts such as multi-modal requests, we allow you to specify those as a list of Data objects.' ndarray: $ref: '#/components/schemas/apiNDArray' description: 'A proto representation for numpy arrays, useful to pass information from python SDK to a python based model implementation.' int_value: type: string format: int64 title: Input and output integer number float_value: type: number format: double title: Input and output floating number bytes_value: type: string format: byte title: Input and output bytes data bool_value: type: boolean title: Input and output bool data string_value: type: string title: Input and output string data struct_value: type: object title: To handle Input and output json title: Data apiDatasetVersionRequestOrigin: type: string enum: - DATASET_VERSION_REQUEST_ORIGIN_NOT_SET - MANUAL - TRAINING - EVAL_GROUND_TRUTH - EVAL_PREDICTIONS default: DATASET_VERSION_REQUEST_ORIGIN_NOT_SET apiDatasetVersionProcessingInfo: type: object properties: frame_interpolation_info: $ref: '#/components/schemas/apiFrameInterpolationInfo' description: 'If frame_interpolation_info is set, then these settings are used to interpolate new frame annotation from other video annotations. If frame_interpolation_info is set in the dataset default_processing_info, then it can be disabled for a single dataset version by setting processing_info but not setting processing_info.frame_interpolation_info.' description: 'DatasetVersionProcessingInfo contains information about processing applied to a dataset version.' apiFrame: type: object properties: frame_info: $ref: '#/components/schemas/apiFrameInfo' description: Information aboue frame such as number and time. data: $ref: '#/components/schemas/apiData' description: 'A recursive definition of the data within the Frame. For example, this will contain data.concepts if the Frame also has annotations or predictions of concepts within it. This can also have data.regions for annotation or predictions of detection regions, which can then recursively have their data field filled in as well.' id: type: string description: An ID for the frame. description: A Frame of time-series Data such as a Video. AppEmbeddingsStorage: type: string enum: - EMBEDDING_STORAGE_NOT_SET - POSTGRES - QDRANT default: EMBEDDING_STORAGE_NOT_SET apiTimeInfo: type: object properties: num_frames: type: integer format: int64 title: Number of frames begin_time: type: number format: float description: Timestamp where track begins. end_time: type: number format: float description: Timestamp where track ends. title: TimeInfo apiPart: type: object properties: data: $ref: '#/components/schemas/apiData' description: The data for this part. id: type: string description: A unique id for the part. description: A part of data used for multi-modal processing. apiROC: type: object properties: fpr: type: array items: type: number format: float tpr: type: array items: type: number format: float thresholds: type: array items: type: number format: float fpr_per_image: type: array items: type: number format: float fpr_per_object: type: array items: type: number format: float title: ROC apiSpan: type: object properties: char_start: type: integer format: int64 char_end: type: integer format: int64 raw_text: type: string apiDatasetVersionExportFormat: type: string enum: - DATASET_VERSION_EXPORT_FORMAT_NOT_SET - CLARIFAI_DATA_PROTOBUF - CLARIFAI_DATA_JSON - COCO default: DATASET_VERSION_EXPORT_FORMAT_NOT_SET description: " - CLARIFAI_DATA_PROTOBUF: CLARIFAI_DATA_PROTOBUF is the proprietary Clarifai API Data format. It\nis a ZIP-archive containing batches of serialized InputBatch protobuf messages.\n\nNote that only the \"id\" and \"data\" fields of exported inputs are set.\n - CLARIFAI_DATA_JSON: CLARIFAI_DATA_JSON is the proprietary Clarifai API Data format in JSON. It\nis a ZIP-archive containing batches of serialized InputBatch JSON messages.\n\nNote that only the \"id\" and \"data\" fields of exported inputs are set.\n - COCO: COCO is the data format used by Common Objects in Context. It is a\nZIP-archive containing JSON files with the dataset version annotations.\nSee https://cocodataset.org/#format-data." apiRank: type: object properties: negate: type: boolean title: 'If True then this will flip the meaning of this part of the query. This allow for queries such as !dog' annotation: $ref: '#/components/schemas/clarifaiapiAnnotation' title: "RANK by annotation information.\n########## Supported fields ##########\n - data.concepts[].id\n - data.concepts[].name\n - data.concepts[].value\n - data.embeddings[].num_dimensions\n - data.embeddings[].vector[]\n - data.image.base64[]\n - data.image.url\n - data.text.raw\n - input_id\n - model_version_id" title: Rank apiInputSettings: type: object properties: worker: $ref: '#/components/schemas/apiWorker' title: 'Default model used for annotation generation (SAM2, etc.) Make sure to set correct model version id, app id and user id etc. Workflow is not supported here yet' sample_rate_ms: type: integer format: int64 title: Sampling settings used sample_rate_frame: type: integer format: int64 pinned_concepts: type: array items: type: object $ref: '#/components/schemas/apiConcept' title: Pinned concept ids apiImage: type: object properties: url: type: string description: 'This is a URL to a publicly accessible image file. The platform will download this file server side and then process.' base64: type: string format: byte description: 'The base64 field is using image file bytes directly in the request. NOTE: if you''re sending a json request, then this MUST be base64 encoded before sending (hence the name here). When using our grpc clients, you DO NOT need to base64 encode it yourself since the clients know how to do this for you automatically and will avoid the base64 encoding if they send a binary request.' allow_duplicate_url: type: boolean hosted: $ref: '#/components/schemas/apiHostedURL' description: The hosted field lists images in different sizes hosted in Clarifai storage. image_info: $ref: '#/components/schemas/apiImageInfo' title: image info for original size. for image info for other sizes, use hosted_image_info hosted_image_info: type: object additionalProperties: $ref: '#/components/schemas/apiImageInfo' title: 'The map of hosted image info of different sizes (see hosted.sizes), excluding the original image. Note: keys(hosted_image_info) = hosted.sizes - "orig"' decoded_bytes: type: string format: byte description: For internal processing of already decoded bytes. title: Image apiPrecisionRecallCurve: type: object properties: recall: type: array items: type: number format: float precision: type: array items: type: number format: float thresholds: type: array items: type: number format: float title: PrecisionRecallCurve apiFilter: type: object properties: negate: type: boolean title: 'If True then this will flip the meaning of this part of the query. This allow for queries such as dog AND ! metadata=={"blah":"value"}' annotation: $ref: '#/components/schemas/clarifaiapiAnnotation' description: "# Filter by worker fields such as model, workflow and user IDs\n - worker.model.model_version.id\n - worker.user.id\n - worker.workflow.version.id\n\n # Filter by other top-level fields\n - annotation_info.fields - filter by annotation info\n - input_level - filter only input-level annotations\n - status.code - filter by annotation status code\n - trusted - filter only trusted annotations\n\n # Filter by space-time info fields, i.e. region, frames and time-segments\n - data - filter only annotations without space-time info, e.g. classifications\n - in order to enable this, you need to set the field to an empty object, i.e. `{\"data\": {}}`\n - data.frames[].frame_info - filter only frame annotations\n - in order to enable this, you need to set the field to an empty object, i.e. `{\"data\": {\"frames\": [{\"frame_info\": {}}]}}`\n - data.regions[].region_info.bounding_box - filter only bounding box annotations\n - in order to enable this, you need to set the field to an empty object, i.e. `{\"data\": {\"regions\": [{\"region_info\": {\"bounding_box\":{}}}]}}`\n - data.regions[].region_info.mask - filter only mask annotations\n - in order to enable this, you need to set the field to an empty object, i.e. `{\"data\": {\"regions\": [{\"region_info\": {\"mask\":{}}}]}}`\n - data.regions[].region_info.point - filter only point annotations\n - in order to enable this, you need to set the field to an empty object, i.e. `{\"data\": {\"regions\": [{\"region_info\": {\"point\":{}}}]}}`\n - data.regions[].region_info.polygon - filter only polygon annotations\n - in order to enable this, you need to set the field to an empty object, i.e. `{\"data\": {\"regions\": [{\"region_info\": {\"polygon\":{}}}]}}`\n - data.regions[].region_info.span - filter only span annotations\n - in order to enable this, you need to set the field to an empty object, i.e. `{\"data\": {\"regions\": [{\"region_info\": {\"span\":{}}}]}}`\n - data.regions[].track_id - filter annotations by track_id\n - in order to enable this, you need to provide \"track_id_value\" i.e. `{\"data\": {\"regions\": [{\"track_id\" : \"track_id_value\"}]}}`\n - data.time_segments[].time_info - filter only time-segment annotations\n - in order to enable this, you need to set the field to an empty object, i.e. `{\"data\": {\"time_segments\": [{\"time_info\": {}}]}}`\n\n # Filter by other data fields\n - data.clusters[].id\n - data.concepts[].id\n - data.concepts[].name\n - data.concepts[].value\n - data.geo.geo_box[].geo_point.latitude\n - data.geo.geo_box[].geo_point.longitude\n - data.geo.geo_limit.type\n - data.geo.geo_limit.value\n - data.geo.geo_point.latitude\n - data.geo.geo_point.longitude\n - data.metadata.fields - filter by metadata\n - Important to note: metadata key&value fields are OR-ed.\n - example with 1 metadata key: searching by\n `{\n ` \"data\": {\n ` \"metadata\": {\n ` \"fields\": {\n ` \"foo\": {\n ` \"string_value\": \"bar\"\n ` },\n ` }\n ` }\n ` }\n `}\n will result in a search condition like `metadata includes {\"foo\": \"bar}`;\n - example with 2 metadata keys: searching by\n `{\n ` \"data\": {\n ` \"metadata\": {\n ` \"fields\": {\n ` \"foo1\": {\n ` \"string_value\": \"bar2\"\n ` },\n ` \"foo2\": {\n ` \"string_value\": \"bar2\"\n ` }\n ` }\n ` }\n ` }\n `}\n will result in a search condition like `(metadata includes {\"foo1\": \"bar1\"}) OR (metadata includes {\"foo2\": \"bar2\"})`.\n - data.text - filter only text annotations\n - in order to enable this, you need to set the field to an empty object, i.e. `{\"data\": {\"text\": {}}}`" title: "FILTER by annotation information.\n########## Supported fields ##########\n # Filter by ID fields\n - id - example: `{\"id\": \"xyz\"}`\n - input_id\n - model_version_id\n - task_id\n - user_id" input: $ref: '#/components/schemas/apiInput' title: "FILTER by input information.\n########## Supported fields ##########\n - data.audio - filter only audio inputs\n - in order to enable this, you need to set the field to an empty object, i.e. `{\"audio\": {}}`\n - data.image - filter only image inputs\n - enable using `{\"image\": {}}`\n - data.text - filter only text inputs\n - enable using `{\"text\": {}}`\n - data.video - filter only video inputs\n - enable using `{\"video\": {}}`\n - dataset_ids[] - filter by dataset IDs\n - example: `{\"dataset_ids\": [\"d1\", \"d2\"]}` will filter for inputs in d1 OR d2\n - status.code - filter by input status\n - example: `{\"status\": {\"code\": 30000}}` to filter only for SUCCESS inputs" last_updated_time_range: $ref: '#/components/schemas/apiTimeRange' description: Filter by annotation last updated time range. title: Filter apiImportInfo: type: object properties: params: type: object title: 'Used to configure model imports from third-party toolkits. DEPRECATED: no longer support importing models from third party toolkits' title: 'DEPRECATED: no longer support importing models from third-party toolkits' apiSearch: type: object properties: query: $ref: '#/components/schemas/apiQuery' description: Search query. id: type: string description: 'Customer facing, external ID for search to be saved. Provided by the user, e.g. "saved-search-1. It is unique per application.' application_id: type: string description: Application that owns this saved search. name: type: string description: Human readable display name of the saved search. as_of: type: string format: date-time description: '"As of" timestamp, indicating a time in the past as of which we want to retrieve the annotations satisfying the query.' git_hash: type: string description: Git hash of the code that ran the filter. created_at: type: string format: date-time description: When the saved search was created. modified_at: type: string format: date-time description: When the saved search was updated. algorithm: type: string title: 'The search algorithm to be used. Options are are ''nearest_neighbor'', ''brute_force'', and ''avg_concept_brute_force'' The last two perform a brute force search visual search instead of a more scalable distributed nearest neighbor search and should be used by advanced users only. If not specified we default to nearest neighbor' save: type: boolean title: 'If true, save this search, and exit without executing the search. If false execute the query' min_value: type: number format: float description: 'Minimum value of confidence threshold score in result. Defaults to 0.0 which means we won''t do any thresholding as all probabilities will likely be > 0.0.' visibility: $ref: '#/components/schemas/clarifaiapiVisibility' description: 'The visibility field represents whether this message is privately/publicly visible. To be visible to the public the App that contains it AND the User that contains the App must also be publicly visible.' metric: $ref: '#/components/schemas/SearchMetric' description: This is the new Search object used in saved searches. ModelTypeFieldModelTypeFieldType: type: string enum: - INVALID_MODEL_TYPE_FIELD_TYPE - BOOLEAN - STRING - NUMBER - ARRAY_OF_CONCEPTS - ARRAY_OF_CONCEPTS_WITH_THRESHOLD - RANGE - ENUM - COLLABORATORS - JSON - ARRAY_OF_NUMBERS - WORKFLOW_EMBED_MODELS - ARRAY_OF_STRINGS - RECURSIVE_ENUM - PYTHON_CODE - DATASET_ID - DATASET_VERSION_ID - ARRAY_OF_MODEL_CONCEPTS - DATASET - DATASET_VERSION - ENCRYPTED_STRING - CHECKPOINT_MODEL - ARRAY_OF_SECRETS default: INVALID_MODEL_TYPE_FIELD_TYPE description: "These are various types of fields that we have UIs for.\n\n - ARRAY_OF_CONCEPTS: For auto-completing to concepts in the app. This goes into an data.concepts field.\n - ARRAY_OF_CONCEPTS_WITH_THRESHOLD: For auto-completing to concepts in the app. This goes into an data.concepts field.\n - RANGE: A range for a float value.\n - ENUM: If ENUM is used then the \"enum_options\" field should also be filled in with the respective ID and description\nfor the different ENUM options.\n - COLLABORATORS: For listing collaborators of the app. The field is a string of the collaborator's user_id.\n - JSON: For arbitrary json object: \"{...}\"\n - ARRAY_OF_NUMBERS: Such as [1.0, 2.0, 3.5]\n - WORKFLOW_EMBED_MODELS: For selecting the embed_model_version_id for context based models.\n - ARRAY_OF_STRINGS: Such as ['a', 'b', 'cantaloupe']\n - RECURSIVE_ENUM: If RECURSIVE_ENUM is used then the \"enum_options\" field should also be filled in with the respective ID and\ndescription for the different RECURSIVE_ENUM options, as well as model_type_fields for each enum choice.\n - PYTHON_CODE: For blocks of code that need to be specified by the user for setup or execution during workflow runs.\n - DATASET_ID: For selecting a dataset id in model parameters. String in API request.\n - DATASET_VERSION_ID: For selecting a dataset version id. String.\n - ARRAY_OF_MODEL_CONCEPTS: For auto-completing to concepts in the model.\n - DATASET: For selecting a dataset\n - DATASET_VERSION: For selecting a dataset version\n - ENCRYPTED_STRING: To pass a string downstream, that is encrypted in the DB and API.\n - CHECKPOINT_MODEL: For selecting a model version of the same model type to resume training from.\n - ARRAY_OF_SECRETS: For model secrets" apiRegionInfo: type: object properties: bounding_box: $ref: '#/components/schemas/apiBoundingBox' description: Details of the region's rectangular bounding box. mask: $ref: '#/components/schemas/apiMask' description: Details of the region's segmentation mask. polygon: $ref: '#/components/schemas/apiPolygon' description: A polygon of points. point: $ref: '#/components/schemas/apiPoint' description: A landmark point location. span: $ref: '#/components/schemas/apiSpan' description: Span char sequence for NLP. token: $ref: '#/components/schemas/apiToken' description: Token char sequence for NLP. keypoint_locations: type: array items: type: object $ref: '#/components/schemas/apiPoint' description: 'The locations of detected keypoints, which are to be used in conjunction with the detected concept''s skeleton to connect the keypoint locations. These will be in the same order as the respective keypoint_names inside the concept.' description: The information of the location of the Region. apiText: type: object properties: raw: type: string description: This is a raw text string. url: type: string title: Url to a text file allow_duplicate_url: type: boolean hosted: $ref: '#/components/schemas/apiHostedURL' description: 'The hosted field lists original text hosted in Clarifai storage. This field is currently used only in response.' text_info: $ref: '#/components/schemas/apiTextInfo' title: text info title: Text apiComputeInfo: type: object properties: cpu_limit: type: string description: 'Amount of CPUs to use as a limit. This follows kubernetes notation like: "1", "100m", "4.5", etc. See https://kubernetes.io/docs/reference/kubernetes-api/common-definitions/quantity/ For instances, this is the instance''s CPU count. For runners, this is the maximum amount of CPU that the runner pod can use.' cpu_memory: type: string description: 'Amount of CPU memory to use as a limit. This follows kubernetes notation like: 1Ki, 1500Mi, 3Gi, 4Ti, etc. See https://kubernetes.io/docs/reference/kubernetes-api/common-definitions/quantity/ For instances, this is the instance''s CPU memory. For runners, this is the maximum amount of CPU memory that the runner pod can use.' cpu_requests: type: string description: 'Amount of CPUs to use as a minimum. This follows kubernetes notation like: "1", "100m", "4.5", etc. See https://kubernetes.io/docs/reference/kubernetes-api/common-definitions/quantity/ For runners, this is the minimum amount of CPU requested for the runner pod. Optional. If unspecified, a small default is used.' cpu_memory_requests: type: string description: 'Amount of CPU memory to use as a minimum. This follows kubernetes notation like: 1Ki, 1500Mi, 3Gi, 4Ti, etc. For runners, this is the minimum amount of CPU memory requested for the runner pod. Optional. If unspecified, a small default is used.' num_accelerators: type: integer format: int64 description: Amount of GPU/TPUs to use. accelerator_memory: type: string description: "The supported accelerators that the resource can run on. If the resource requires a specific\n accelerator type then it will only be scheduled on nodes that have that type of accelerator.\n If there is no hard requirements beyond the number of accelerators and their memory then this\n field can be left empty.\n This setting will cause an error if num_accelerators == 0" title: 'Amount of accelerator/GPU memory to use as a minimum. This is defined per accelerator. This follows the format used by kubernetes like 1Ki, 2Mi, 3Gi, 4Ti. See https://kubernetes.io/docs/reference/kubernetes-api/common-definitions/quantity/' accelerator_type: type: array items: type: string description: 'Or should it be removed completely and use the nodepool accelerator type itself. These are the supported accelerators that the model can run on.' accelerator_topology: type: array items: type: string description: 'For multi-host accelerators (i.e., TPU Slices), this defines the slice topology. Corresponds to the tpu.googleapis.com/topology annotation. Example: "2x2x1" for a 16-chip slice using v4 TPUs. Leave empty for single-host accelerators like GPUs or non-slice TPUs.' description: 'These are the resource needs of a given API object such as a model. This is what they require as a minimum to run and will be used upon scheduling as the request and limit for the k8s pod. If we want to separate limits and requests in the future we can allow setting a limits ComputeInfo and a requests ComputeInfo.' apiQuery: type: object properties: ands: type: array items: type: object $ref: '#/components/schemas/apiAnd' description: 'The query syntax is simply a list of And operatiosn that will be ANDed together to fetch results which are returned to the user as Hit messages. Deprecated: Only used by the deprecated PostSearches endpoint. Use filters and ranks instead with PostInputsSearches or PostAnnotationsSearches.' language: type: string description: 'This allows the query to override any default language the app was setup in when doing Concept based searches. This currently only affects public Models Output searches when those public Models have translations for their Concepts.' filters: type: array items: type: object $ref: '#/components/schemas/apiFilter' title: 'filters in this query e.q. only fetch annotations that have certain metadata' ranks: type: array items: type: object $ref: '#/components/schemas/apiRank' title: 'rankings in this query e.g. visual search by a url' description: This is the search query used in /searches, model training requests, bulk data exports, etc. apiEvalTestSetEntry: type: object properties: input: $ref: '#/components/schemas/apiInput' title: the input information predicted_concepts: type: array items: type: object $ref: '#/components/schemas/apiConcept' ground_truth_concepts: type: array items: type: object $ref: '#/components/schemas/apiConcept' title: All the ground truth concepts will be show on the top level annotation: $ref: '#/components/schemas/clarifaiapiAnnotation' title: 'Only region-based/frame-based app contains this annotation Each annotation only contains one region And the concepts is in ground_truth_concepts instead of this annotation' predicted_annotation: $ref: '#/components/schemas/clarifaiapiAnnotation' description: For region based models, region and associated concepts are stored together. ground_truth_annotation: $ref: '#/components/schemas/clarifaiapiAnnotation' title: EvalTestSetEntry apiModelTypeField: type: object properties: path: type: string description: 'The path where the value of the field will be stored in the model version object. Example: "output_info.data" would be the Data message in the OutputInfo message. "output_info.output_config.language" is in the OutputConfig message within OutputInfo "input_info.params" is in the params struct within InputInfo. "output_info.params" is in the params struct within OutputInfo. "train_info.params" is in the params struct within TrainInfo. and so on.' field_type: $ref: '#/components/schemas/ModelTypeFieldModelTypeFieldType' description: 'The field for this field. This is often used for displaying the field in the UI whereas the DataType enum below defines the specific type of datain the Python function.' default_value: description: 'A default value. We use the Value field because we want to have structured data (just like google.protobuf.Struct but this is just a single value).' description: type: string description: Description for this field. placeholder: type: string description: Placeholder text for the UI element. model_type_enum_options: type: array items: type: object $ref: '#/components/schemas/apiModelTypeEnumOption' description: List of options of the ENUM type and potentially additional fields they bring with them. internal_only: type: boolean description: If this field should appear for internal users only. required: type: boolean description: 'If this field is a required field. If True then during validation you won''t be able to create a model of this type with providing a value for this field. When False, the ModelType''s default_value will be used for this field.' model_type_range_info: $ref: '#/components/schemas/apiModelTypeRangeInfo' description: If the field_type is RANGE, this must be filled in. name: type: string title: name of method signature argument type: $ref: '#/components/schemas/apiModelTypeFieldDataType' description: The type of the argument. type_args: type: array items: type: object $ref: '#/components/schemas/apiModelTypeField' title: 'type enum, and recursively set type_args with the inner type argumets in complex objects (e.g. List[Tuple[int, str]])' iterator: type: boolean description: this will be use to define whether the method argument supports streaming as an iterator. default: type: string description: 'This specify the default value of the method argument If this argument is not passed, the input is required. If it is explicitly set to None, the input is optional We define this as a string because the default value can be a string, int, float, bool, or a complex object like a JSON The default_value field above should not also be used.' is_param: type: boolean title: wheather it's a inference param or a modeltype field description: ModelTypeField stores a field value of a configurable type. apiTrackerMetrics: type: object properties: mot_mota: type: number format: float title: Multiple object tracking accuracy mot_num_switches: type: integer format: int32 title: Number of switches between tracks morse_frag: type: number format: float title: MORSE fragmentation rate (a.k.a unique switch rate, only calculated in public sector) avg_precision: type: number format: float title: Average precision calculated from all processed frames aiid: type: string title: The concept that we are evaluating the tracker unique_switch_rate: type: number format: float title: Same as morse_frag but calculated using MOT mapping/metrics title: TrackerMetrics apiModel: type: object properties: id: type: string description: The model's ID. Must be unique within a particular app and URL-friendly. name: type: string description: 'DEPRECATED: Please use the model id to name the model.' created_at: type: string format: date-time title: "When the model was created. We follow the XXXX timestamp\nformat. We use https://www.ietf.org/rfc/rfc3339.txt format:\n\"2006-01-02T15:04:05.999999Z\" so you can expect results like\n the following from the API:\n \"2017-04-11T21:50:50.223962Z\"" modified_at: type: string format: date-time title: When was the most recent model version created at app_id: type: string description: The app the model belongs to. output_info: $ref: '#/components/schemas/apiOutputInfo' title: 'Info about the model''s output and configuration. DEPRECATED: Will be moved to model version' model_version: $ref: '#/components/schemas/apiModelVersion' description: 'A particular version of the model, e.g., to specify the version when creating a workflow or when listing Models to include the latest ModelVersion of the model in the response.' display_name: type: string description: 'DEPRECATED: Please use the model id to name the model.' user_id: type: string description: The user id that the model belongs to. default_eval_info: $ref: '#/components/schemas/apiEvalInfo' description: The default evaluation info. Can be overwritten by eval request. model_type_id: type: string description: 'The ModelType.Id that is used for this model. This is used for all versions and you cannot change model_type_id between versions of the same model.' task: type: string title: The task the model was trained to do visibility: $ref: '#/components/schemas/clarifaiapiVisibility' description: 'The visibility field represents whether this message is privately/publicly visible. To be visible to the public the App that contains it AND the User that contains the App must also be publicly visible.' description: type: string title: Short description about this model metadata: type: object title: 'To handle arbitrary json metadata you can use a struct field: https://github.com/google/protobuf/blob/master/src/google/protobuf/struct.proto' presets: type: object notes: type: string description: 'Notes for the model This field should be used for in-depth notes and supports up to 64Kbs.' toolkits: type: array items: type: string title: Tags from toolkits category use_cases: type: array items: type: string title: Tags from use_cases category languages: type: array items: type: string description: Tags from languages category. languages_full: type: array items: type: object $ref: '#/components/schemas/apiFullTag' description: Tags from languages category with names, only used in responses. check_consents: type: array items: type: string is_starred: type: boolean title: 'Is starred by the requesting user (only showed on get/list requests) Please use PostModelStars/DeleteModelStars endpoints to star/unstar a model' star_count: type: integer format: int32 title: 'How many users have starred the model (only showed on get/list requests) Computed value, not editable' workflow_recommended: type: boolean title: Whether it's recommended that this model is used within a workflow bookmark_origin: $ref: '#/components/schemas/apiBookmarkOrigin' description: 'bookmark info. When set, this model is a bookmarked model of this app. Info in this field will allow you to find/access original model.' image: $ref: '#/components/schemas/apiImage' title: Representative image for this model license_type: $ref: '#/components/schemas/apiLicenseType' title: License Type source: $ref: '#/components/schemas/ModelSource' creator: type: string title: Creator of Model version_count: type: integer format: int32 billing_type: $ref: '#/components/schemas/ModelBillingType' featured_order: type: integer format: int32 description: 'Whether the model should be featured, and if so, the order in which it should be featured. The order is relative to other models that are also featured. Models with a higher order will be featured first.' deploy_restriction: $ref: '#/components/schemas/apiDeployRestriction' description: Deploy restriction for the model. replica_count: type: integer format: int64 description: 'replica_count indicates the number of deployed model replicas. This field is populated when `show_replicas` is true in ListModelsRequest.' open_router_info: $ref: '#/components/schemas/apiOpenRouterInfo' title: OpenRouter Info for the model description: "This is the Model object which represents a created model in the platform.\nEach model has a particular type denoted by the model_type_id.\nWhen creating a Model with PostModels the following happens:\n - if the ModelType is trainable, then a new ModelVersion is created that is\n - UNTRAINED status by default\n - TRAINED status if a ModelVersion was included with PretrainedModelConfig in PostModels\n - if the ModelType is not trainable, then a new ModelVersion is created with TRAINED status.\nTo modify config settings like OutputInfo for the Model you an use PatchModels. This will\nalso create a new ModelVersion, potentially UNTRAINED following the same rules as above.\nThe fields that are patchable include Model.name, Model.display_name and Model.output_info\n(except the Model.output_info.type and Model.output_info.type_ext)." apiModelTypeEnumOptionAlias: type: object properties: id_int: type: string format: int64 description: Integer alias for id. wildcard_string: type: string description: String that can contain wild cards and the regex needs to match. apiGeoPoint: type: object properties: longitude: type: number format: float latitude: type: number format: float title: GeoPoint apiExtendedMetrics: type: object properties: user_metrics: type: object apiMask: type: object properties: image: $ref: '#/components/schemas/apiImage' description: The image of the mask in a non-raster format. description: Segmentation mask. apiLicenseType: type: string enum: - UNKNOWN_LICENSE_TYPE - FIRST_PARTY - OPEN_SOURCE - CLOSED_SOURCE default: UNKNOWN_LICENSE_TYPE apiAudio: type: object properties: url: type: string description: 'This is a URL to a publicly accessible image file. The platform will download this file server side and then process.' base64: type: string format: byte description: 'The base64 field is using image file bytes directly in the request. NOTE: if you''re sending a json request, then this MUST be base64 encoded before sending (hence the name here). When using our grpc clients, you DO NOT need to base64 encode it yourself since the clients know how to do this for you automatically and will avoid the base64 encoding if they send a binary request.' allow_duplicate_url: type: boolean description: If True then you will be allowed to have multiple urls. hosted: $ref: '#/components/schemas/apiHostedURL' description: 'The hosted field lists original audio hosted in Clarifai storage. This field is currently used only in response.' audio_info: $ref: '#/components/schemas/apiAudioInfo' title: audio info title: Audio asset struct apiAppResourceCounts: type: object properties: datasets: type: string format: int64 models: type: string format: int64 workflows: type: string format: int64 inputs: type: string format: int64 pipelines: type: string format: int64 apiToken: type: object properties: char_start: type: integer format: int64 char_end: type: integer format: int64 raw_text: type: string apiOutputInfo: type: object properties: data: $ref: '#/components/schemas/apiData' description: List of concepts or other output related data for the model. output_config: $ref: '#/components/schemas/apiOutputConfig' title: 'Model configuration...going away in favor of output_info.params and train_params over time. TO BE DEPRECATED' message: type: string description: For returning where to look for the Output info if not returning it. fields_map: type: object description: 'Map from the api.Data field names to the underlying model graph''s outputs. When using a PretrainedModelConfig the values in this map need to match the Triton config.pbtxt output names.' params: type: object description: 'For predicting with the various ModelType''s we accept a Struct (JSON object) worth of args that the ModelTypeField defines. During inference, the settings contained within are sent to the model predictor to alter predictions from this Model.' params_specs: type: array items: type: object $ref: '#/components/schemas/apiModelTypeField' description: 'These allow you to specifcy addition fields that a specific model supports beyond those defined in it''s ModelType. This field is to be deprecated and will be replaced by MethodSignature proto.' description: 'OutputInfo defines some of the settings for each model version that PatchModels can effect. These parameters control some of the training or inference operations that this model can do. As the number of parameters continued to grow when we launched more ModelTypes we decided to move to using the OutputInfo.params field which is a Struct (or JSON object if you''re using our JSON REST APIs). This allows each ModelType to define the set of fields, their default values and description of each field so that we can display those in Portal and make the creation of Model''s very extensible. The OutputConfig object will eventually go away in favor of output_info.params struct.' apiMetricsSummary: type: object properties: top1_accuracy: type: number format: float top5_accuracy: type: number format: float macro_avg_roc_auc: type: number format: float macro_std_roc_auc: type: number format: float macro_avg_f1_score: type: number format: float macro_std_f1_score: type: number format: float macro_avg_precision: type: number format: float macro_avg_recall: type: number format: float mean_avg_precision_iou_50: type: number format: float mean_avg_precision_iou_range: type: number format: float title: MetricsSummary apiPoint: type: object properties: row: type: number format: float description: 'The row location of the point. This has a [0.0-1.0] range with 0.0 being top row and 1.0 being the bottom row.' col: type: number format: float description: 'The column location of the point. This has a [0.0-1.0] range with 0.0 being left col and 1.0 being the right col.' z: type: number format: float description: Depth if applicable for the point. visibility: $ref: '#/components/schemas/apiPointVisibility' title: Point apiPolygon: type: object properties: points: type: array items: type: object $ref: '#/components/schemas/apiPoint' description: A list of points connected together to form the polygon. title: Polygon apiConceptExtraInfo: type: object properties: is_rankable: type: boolean description: Whether this concept is rankable based on ConceptExtraInfoRequest configuration. description: 'ConceptExtraInfo represents extra information related to a concept that is context-dependent. It is only set when requested in ConceptExtraInfoRequest.' apiInput: type: object properties: id: type: string title: The ID for the input data: $ref: '#/components/schemas/apiData' description: The data passed along in this input. created_at: type: string format: date-time title: 'When the input was created. We follow the XXXX timestamp format. We use https://www.ietf.org/rfc/rfc3339.txt format: "2006-01-02T15:04:05.999999Z" so you can expect results like the following from the API: "2017-04-11T21:50:50.223962Z"' modified_at: type: string format: date-time description: When the input was modified. status: $ref: '#/components/schemas/apistatusStatus' description: 'This is the status at a per Input level which allows for partial failures.' dataset_ids: type: array items: type: string description: 'List of dataset IDs that this input is part of Currently, this field is ONLY used to * search inputs part of dataset(s), e.g. in `PostSearches`, `PostInputsSearches` and `PostAnnotationsSearches` endpoints, and * to add inputs to dataset(s) in `PostInputs` endpoint. Note that this field is ignored for other endpoints, e.g. `GetInput`, `ListInputs` and `PatchInputs`.' settings: $ref: '#/components/schemas/apiInputSettings' description: Global settings for annotation tracks. title: Input apiEvalInfo: type: object properties: params: type: object description: 'To control the evaluation process. Allow a list of parameters.' apiEvalMetrics: type: object properties: status: $ref: '#/components/schemas/apistatusStatus' user_id: type: string title: user id that owns this evaluation app_id: type: string title: app id that owns this evaluation id: type: string title: Id of this evaluation model: $ref: '#/components/schemas/apiModel' title: Model to evaluate ground_truth_dataset: $ref: '#/components/schemas/apiDataset' title: The ground truth dataset predictions_dataset: $ref: '#/components/schemas/apiDataset' title: The dataset with predictions summary: $ref: '#/components/schemas/apiMetricsSummary' confusion_matrix: $ref: '#/components/schemas/apiConfusionMatrix' cooccurrence_matrix: $ref: '#/components/schemas/apiCooccurrenceMatrix' label_counts: $ref: '#/components/schemas/apiLabelDistribution' binary_metrics: type: array items: type: object $ref: '#/components/schemas/apiBinaryMetrics' test_set: type: array items: type: object $ref: '#/components/schemas/apiEvalTestSetEntry' metrics_by_area: type: array items: type: object $ref: '#/components/schemas/apiBinaryMetrics' metrics_by_class: type: array items: type: object $ref: '#/components/schemas/apiBinaryMetrics' tracker_metrics: type: array items: type: object $ref: '#/components/schemas/apiTrackerMetrics' eval_info: $ref: '#/components/schemas/apiEvalInfo' description: 'Evaluation parameters to pass. Expected to match what is defined in the model type for the respective model.' extended_metrics: $ref: '#/components/schemas/apiExtendedMetrics' title: EvalMetrics apiMultiScopeResponse: type: object properties: status: $ref: '#/components/schemas/apistatusStatus' description: The status of the request. scopes: type: array items: type: string description: This is a list of the scopes that your key has. app: $ref: '#/components/schemas/apiApp' description: The app that the key has access to. endpoints: type: array items: type: string description: This is a list of endpoint permissions that your key has. user_feature_flags: type: string title: MultiScopeResponse apiAudioInfo: type: object properties: audio_format: type: string title: audio format sample_rate: type: integer format: int32 title: sample rate duration_seconds: type: number format: float title: audio track duration in seconds bit_rate: type: integer format: int32 title: audio track bit rate apiTimeSegment: type: object properties: id: type: string description: A unique id for the time segment. data: $ref: '#/components/schemas/apiData' time_info: $ref: '#/components/schemas/apiTimeInfo' title: TimeSegment apiDatasetVersionExportInfo: type: object properties: clarifai_data_protobuf: $ref: '#/components/schemas/apiDatasetVersionExport' description: clarifai_data_protobuf is a CLARIFAI_DATA_PROTOBUF export of the dataset version. clarifai_data_json: $ref: '#/components/schemas/apiDatasetVersionExport' description: clarifai_data_json is a CLARIFAI_DATA_JSON export of the dataset version. coco: $ref: '#/components/schemas/apiDatasetVersionExport' description: coco is a COCO export of the dataset version. description: 'DatasetVersionExportInfo contains information about all exports of a dataset version. If the dataset version has not been exported in a format, then the DatasetVersionExport field for that format is empty instead of having a "not exported" status.' apiEmailAddress: type: object properties: email: type: string primary: type: boolean verified: type: boolean title: EmailAddress MatrixUint64: type: object properties: n_cols: type: string format: uint64 title: Number of columns data: type: array items: type: string format: uint64 description: 'Matrix data stored as an array. In order to access matrix element at row i & column j, use data[i*n_cols+j].' description: 'Store matrix of uint64s values. It is recommended to store the matrix as a 1D array because it produces less bytes during serialization than a 2D array. The matrix does store the number of columns, but it does not store the number of rows. The number of rows can automatically be calculated as length(data)/n_cols.' apiDataset: type: object properties: id: type: string title: The ID for the dataset created_at: type: string format: date-time description: 'When the dataset was created. The format is https://www.ietf.org/rfc/rfc3339.txt. Example: "2006-01-02T15:04:05.999999Z".' modified_at: type: string format: date-time description: 'When the dataset was modified. The format is https://www.ietf.org/rfc/rfc3339.txt. Example: "2006-01-02T15:04:05.999999Z".' app_id: type: string description: The app the dataset belongs to. user_id: type: string description: The user the dataset belongs to. description: type: string title: Description of the dataset metadata: type: object title: 'To handle arbitrary json metadata you can use a struct field: https://github.com/google/protobuf/blob/master/src/google/protobuf/struct.proto' visibility: $ref: '#/components/schemas/clarifaiapiVisibility' description: 'The visibility field represents whether this message is privately/publicly visible. To be visible to the public the App that contains it AND the User that contains the App must also be publicly visible.' default_annotation_filter: $ref: '#/components/schemas/apiAnnotationFilter' description: Default annotation filter used for this dataset. default_processing_info: $ref: '#/components/schemas/apiDatasetVersionProcessingInfo' description: Default processing info used for this dataset. notes: type: string description: 'Notes for the dataset This field should be used for in-depth notes and supports up to 64Kbs.' version: $ref: '#/components/schemas/apiDatasetVersion' description: 'Dataset version associated with this dataset. This is used in listing Datasets and including the latest version.' is_starred: type: boolean description: Whether the dataset is starred by the requesting user. star_count: type: integer format: int32 description: Number of users that starred this dataset. bookmark_origin: $ref: '#/components/schemas/apiBookmarkOrigin' description: 'bookmark info. When set, this dataset is a bookmarked dataset of this app. Info in this field will allow you to find/access original dataset.' image: $ref: '#/components/schemas/apiImage' title: Representative image for this dataset title: Dataset apiConfusionMatrix: type: object properties: matrix: type: array items: type: object $ref: '#/components/schemas/apiConfusionMatrixEntry' concept_ids: type: array items: type: string description: These concept_ids are ordered by the strength of the diagonal in the ConfusionMatrix. title: ConfusionMatrix apiWorkflowNode: type: object properties: id: type: string description: 'An identifier for this node in the graph. This is used when connecting NodeInputs together.' model: $ref: '#/components/schemas/apiModel' description: 'The model that will do the processing at this node. We only vlidate the model.id and model.model_version.id fields.' node_inputs: type: array items: type: object $ref: '#/components/schemas/apiNodeInput' description: 'Each WorkflowNode can connect to multiple input nodes so that we can handle multi-model data and more complex workflow operations.' suppress_output: type: boolean title: suppress the output for workflow prediction output_info_override: $ref: '#/components/schemas/apiOutputInfo' description: 'Used to override the output_info.data and output_info.params of the model specified by the node. Values for fields_map, message, and output_config are ignored.' title: WorkflowNode apiConfusionMatrixEntry: type: object properties: predicted: type: string actual: type: string value: type: number format: float predicted_concept: $ref: '#/components/schemas/apiConcept' actual_concept: $ref: '#/components/schemas/apiConcept' title: ConfusionMatrixEntry apiAnnotationFilterConfig: type: object properties: annotation_filter: $ref: '#/components/schemas/apiAnnotationFilter' description: The annotation filter that is used. ignore_empty_inputs: type: boolean description: 'If true, empty inputs are not included in the dataset version. If false, empty inputs are included in the dataset version. We define an empty input as an input without any annotations after annotation filter is applied.' apiRegion: type: object properties: id: type: string description: A unique id for the region. region_info: $ref: '#/components/schemas/apiRegionInfo' description: The details about the location of the region. data: $ref: '#/components/schemas/apiData' description: 'A recursive definition of the data within the Region. For example, this will contain data.concepts if the region also has annotations or predictions of concepts within it.' value: type: number format: float description: This is the confidence score of the overall Region. track_id: type: string description: For tracking algorithsm and annotations we tie regions together with this track id. description: A region within the data. apiGeo: type: object properties: geo_point: $ref: '#/components/schemas/apiGeoPoint' geo_limit: $ref: '#/components/schemas/apiGeoLimit' geo_box: type: array items: type: object $ref: '#/components/schemas/apiGeoBoxedPoint' title: 'NOTE: inconsistency: should have been geo_boxed_points' title: Geo apiPointVisibility: type: string enum: - NOT_SET - VISIBLE - NOT_VISIBLE - NOT_PRESENT default: NOT_SET description: "- NOT_SET: Visibility of the point is not set\n - VISIBLE: Point is visible\n - NOT_VISIBLE: Point is occluded\n - NOT_PRESENT: Point is not in the image" title: Whether this point is visible or occluded apiOpenRouterInfo: type: object properties: params: type: object apiImageInfo: type: object properties: width: type: integer format: int32 title: width height: type: integer format: int32 title: height format: type: string title: image format color_mode: type: string title: image color mode mode: type: string title: mode (when used for decoded_bytes) (RGB, RGBA, P, L, etc.) ModelBillingType: type: string enum: - Unknown - Tokens - Ops default: Unknown apiDeployRestriction: type: string enum: - USAGE_RESTRICTION_NOT_SET - NO_LIMITS - SHARED_COMPUTE_ONLY - DEDICATED_COMPUTE_ONLY - MODEL_OWNER_DEDICATED_COMPUTE_ONLY default: USAGE_RESTRICTION_NOT_SET description: " - USAGE_RESTRICTION_NOT_SET: Default uninitialized state.\n - NO_LIMITS: No restrictions on usage.\n - SHARED_COMPUTE_ONLY: Model can only be used on shared compute resources.\n - DEDICATED_COMPUTE_ONLY: Model can only be used on dedicated compute resources.\n - MODEL_OWNER_DEDICATED_COMPUTE_ONLY: Model can only be used on dedicated compute resources owned by the model owner." apiOutput: type: object properties: id: type: string title: One of these outputs per Input status: $ref: '#/components/schemas/apistatusStatus' created_at: type: string format: date-time title: 'When the object was created. We follow the XXXX timestamp format. We use https://www.ietf.org/rfc/rfc3339.txt format: "2006-01-02T15:04:05.999999Z" so you can expect results like the following from the API: "2017-04-11T21:50:50.223962Z"' model: $ref: '#/components/schemas/apiModel' description: The model that created this Output. input: $ref: '#/components/schemas/apiInput' description: 'The input that was passed to the model to create this Output. For example if we have an image model then it will take as input here an Input object with Image filled in.' data: $ref: '#/components/schemas/apiData' description: 'The output data for this Output. For example if we have a concept model then the predicted concepts will appear here.' prompt_tokens: type: integer format: int64 description: Number of prompt tokens as reported by the model or third-party API. completion_tokens: type: integer format: int64 description: Number of completion tokens as reported by the model or third-party API. cached_tokens: type: integer format: int64 description: 'Number of cached prompt tokens as reported by the model (subset of prompt_tokens). Cached tokens are prompt tokens served from the model''s KV cache rather than recomputed.' title: Output clarifaiapiAnnotation: type: object properties: id: type: string title: The ID for the annotation input_id: type: string title: ID of the input this annotation is tied to data: $ref: '#/components/schemas/apiData' description: The data passed along in this annotation. annotation_info: type: object title: task_id is deprecated in annotation_info. Use task_id user_id: type: string description: 'DEPRECATED: Use worker.user.id instead.' model_version_id: type: string title: 'DEPRECATED: Use worker.model.model_version.id instead' embed_model_version_id: type: string description: DEPRECATED. status: $ref: '#/components/schemas/apistatusStatus' title: Annotation Status created_at: type: string format: date-time title: 'When the annotation was created. We follow the XXXX timestamp format. We use https://www.ietf.org/rfc/rfc3339.txt format: "2006-01-02T15:04:05.999999Z" so you can expect results like the following from the API: "2017-04-11T21:50:50.223962Z"' modified_at: type: string format: date-time description: When the annotation was modified. trusted: type: boolean title: 'Whether or not this annotation is trusted Will be deprecated' input_level: type: boolean description: Is this the input level annotation. consensus_info: type: object title: 'Consensus review related information, e.g. * annotation group * id of annotation parent, in case the annotation was split from another annotation' task_id: type: string title: The id of the task annotation belongs to worker: $ref: '#/components/schemas/apiWorker' description: Worker is the worker that created the annotation. title: Annotation of an asset with metadata apiFrameInterpolationInfo: type: object properties: sample_ms: type: integer format: int64 description: 'sample_ms is the sampling rate at which frame annotations are interpolated. If sample_ms is zero, then the dataset default_processing_info value is used. If the dataset default is zero or not set, then the input frame prediction sampling rate is used.' description: 'FrameInterpolationInfo contains information about frame annotations interpolated from other video annotations, such as image object-detection regions generated from video object-tracking regions.' apiTrack: type: object properties: id: type: string title: track id data: $ref: '#/components/schemas/apiData' description: 'This is a recursive definition which can contain all the concepts, embeddings, etc. that are computed within this track.' time_info: $ref: '#/components/schemas/apiTimeInfo' quality: type: number format: float title: Track proto encodes information of a track over a number of frames apiGeoLimit: type: object properties: type: type: string value: type: number format: float title: GeoLimit VisibilityGettable: type: string enum: - UNKNOWN_VISIBILITY - PRIVATE - ORG - PUBLIC default: UNKNOWN_VISIBILITY description: "Gettable defined the level of access for GET operations for this resource.\n\n - UNKNOWN_VISIBILITY: Default value not allowed.\n - PRIVATE: PRIVATE requires collaborator or team permissions in order to GET this resource.\n - ORG: ORG requires you to be in the same org in order to GET this resource, but don't have to be a\nteammate or collaborator.\n - PUBLIC: PUBLIC opens up GET access to the resource to any user on the platform even if they are not\na teammate or collaborator." CommitmentValueCommitmentType: type: string enum: - TYPE_NOT_SET - MONTHLY - ANNUAL default: TYPE_NOT_SET apiCommitmentValue: type: object properties: value: type: integer format: int32 title: The commitment value, in cents type: $ref: '#/components/schemas/CommitmentValueCommitmentType' title: The commitment type, can be either 'monthly' or 'annual' title: Commitment Value - used to track users' commitment amount and type BookmarkOriginBookmarkType: type: string enum: - unknown - model - workflow - dataset default: unknown apiAnnotationFilter: type: object properties: id: type: string title: The ID for the annotation filter created_at: type: string format: date-time description: 'When the annotation filter was created. The format is https://www.ietf.org/rfc/rfc3339.txt. Example: "2006-01-02T15:04:05.999999Z".' modified_at: type: string format: date-time description: 'When the annotation filter was modified. The format is https://www.ietf.org/rfc/rfc3339.txt. Example: "2006-01-02T15:04:05.999999Z".' user_id: type: string description: The user the annotation filter belongs to. app_id: type: string description: The app the annotation filter belongs to. search: $ref: '#/components/schemas/apiSearch' description: The search that this filter uses. description: 'AnnotationFilter is used to create a new dataset version. For now, the filter is simply a wrapper over a Search. In the future, we may add extra fields to customize the filtering.' apiW3C: type: object properties: hex: type: string name: type: string apiBuildInfo: type: object properties: docker_image_name: type: string title: Docker image name docker_image_tag: type: string title: Docker image tag docker_image_digest: type: string title: Docker image digest platform: type: string title: Platform(s) the model was built for (e.g., "linux/amd64,linux/arm64") apiModelTypeFieldDataType: type: string enum: - NOT_SET - STR - BYTES - INT - FLOAT - BOOL - NDARRAY - JSON_DATA - TEXT - IMAGE - CONCEPT - REGION - FRAME - AUDIO - VIDEO - NAMED_FIELDS - TUPLE - LIST default: NOT_SET description: "DataType is used in MethodSignature to define all the possible types that a python function\nmay have that we want to support. These include built-ins like int, float, str, bool, and\nmore complex types like JSON, numpy arrays, List, Tuple, Dict (as Named Fields), as well as Clarifai provided\nunstructured types like Image, Video, Text, etc.\n\n - STR: A string value.\n - BYTES: A byte string. This is used for binary data.\n - INT: An integer value.\n - FLOAT: A float value.\n - BOOL: A boolean value.\n - NDARRAY: A proto representation for numpy arrays.\n - JSON_DATA: For arbitrary json object: \"{...}\"\n - TEXT: For text data\n - IMAGE: A image is a image proto for url or bytes.\n - CONCEPT: A concept is a concept proto that represents a concept in the app.\n - REGION: A region is a bounding box in an image or video frame.\n - FRAME: A frame is a single image in a video stream\n - AUDIO: A audio is a audio proto for url or bytes.\n - VIDEO: A video is a video proto for url or bytes.\n - NAMED_FIELDS: this can be used to store named fields with values similar to Dict\n - TUPLE: An arg that is a tuple.\n - LIST: An arg that is a list." apiFullTag: type: object properties: name: type: string title: Display name of the tag. Ex. "English" id: type: string title: Id value for referencing. Ex. "en" apiInputInfo: type: object properties: fields_map: type: object description: 'Map from the api.Data field names to the underlying model graph''s inputs. When using a PretrainedModelConfig the values in this map need to match the Triton config.pbtxt input names.' params: type: object description: 'To control the inputs to the given model we allow a list of parameters defined for each ModelType as a Struct (JSON object) here. During training or inference, the settings contained within are sent to the training processor to alter the training process.' base_embed_model: $ref: '#/components/schemas/apiModel' description: For base model to get embeddings from for transfer learned models. title: InputInfo apiUser: type: object properties: id: type: string primary_email: type: string first_name: type: string last_name: type: string company_name: type: string job_title: type: string job_role: type: string intention: type: string title: This specifies user intent when registering on clarifai referral_source: type: string title: This specifies how one got to know about clarifai bill_type: type: string created_at: type: string format: date-time title: 'When the user was created. We follow the XXXX timestamp format. We use https://www.ietf.org/rfc/rfc3339.txt format: "2006-01-02T15:04:05.999999Z" so you can expect results like the following from the API: "2017-04-11T21:50:50.223962Z"' date_gdpr_consent: type: string format: date-time date_tos_consent: type: string format: date-time date_marketing_consent: type: string format: date-time date_pii_consent: type: string format: date-time metadata: type: object title: 'To handle arbitrary json metadata you can use a struct field: https://github.com/google/protobuf/blob/master/src/google/protobuf/struct.proto' email_addresses: type: array items: type: object $ref: '#/components/schemas/apiEmailAddress' two_factor_auth_enabled: type: boolean teams_count: type: integer format: int64 is_starred: type: boolean title: 'Is starred by the requesting user (only showed on get/list requests) Please use PostUserStars/DeleteUserStars endpoints to star/unstar an user' star_count: type: integer format: int32 title: 'How many users have starred the user (only showed on get/list requests) Computed value, not editable' visibility: $ref: '#/components/schemas/clarifaiapiVisibility' description: 'The visibility field represents whether this message is privately/publicly visible. To be visible to the public the App that contains it AND the User that contains the App must also be publicly visible.' user_detail: $ref: '#/components/schemas/apiUserDetail' description: 'This is all the personal information of a user. GetUser/ListUsers will not return this information unless the caller has the UserAccounts_Get scope on their key or is the user themselves.' image: $ref: '#/components/schemas/apiImage' title: Representative image for this User (a.k.a. Profile or cover photo) title: User googlerpcStatus: type: object properties: code: type: integer format: int32 message: type: string details: type: array items: type: object $ref: '#/components/schemas/protobufAny' apiVideo: type: object properties: url: type: string description: 'This is a URL to a publicly accessible video file. The platform will download this file server side and then process.' base64: type: string format: byte description: 'The base64 field is using video file bytes directly in the request. NOTE: if you''re sending a json request, then this MUST be base64 encoded before sending (hence the name here). When using our grpc clients, you DO NOT need to base64 encode it yourself since the clients know how to do this for you automatically and will avoid the base64 encoding if they send a binary request.' allow_duplicate_url: type: boolean thumbnail_url: type: string title: 'URL of thumbnail image, which is currently frame at position of 1s. This field is currently used only in response. Deprecated in favour of thumbnail_hosted, which also contains alternate sizes of thumbnail' hosted: $ref: '#/components/schemas/apiHostedURL' description: 'The hosted field lists original video hosted in Clarifai storage. This field is currently used only in response.' hosted_thumbnail: $ref: '#/components/schemas/apiHostedURL' description: 'The hosted field lists various sizes of the vide thumbnail hosted in Clarifai storage, with ''thumbnail'' as the full size This field is currently used only in response.' video_info: $ref: '#/components/schemas/apiVideoInfo' title: video info title: Video apiVideoInfo: type: object properties: width: type: integer format: int32 title: width height: type: integer format: int32 title: height fps: type: number format: float description: Frames per second of the video. video_format: type: string title: video format bit_rate: type: integer format: int32 title: video track bit rate frame_count: type: integer format: int32 title: video frame count duration_seconds: type: number format: float title: video duration in seconds apiNodeInput: type: object properties: node_id: type: string description: The id to a connected WorkflowNode which will be used as an input for current WorkflowNode. description: NodeInput represents inputs to a node of the graph. apiBoundingBox: type: object properties: top_row: type: number format: float title: The top left of the bounding box normalized to the data dimension to be within [0-1.0] left_col: type: number format: float title: The left column of the bounding box normalized to the data dimension to be within [0-1.0] bottom_row: type: number format: float title: The bottom row of the bounding box normalized to the data dimension to be within [0-1.0] right_col: type: number format: float title: The right col of the bounding box normalized to the data dimension to be within [0-1.0] description: Rectangular bounding box for a region. apiWorkflow: type: object properties: id: type: string description: The workflows's unique id. app_id: type: string title: The app the workflow belongs to created_at: type: string format: date-time title: 'When the workflow was created. We follow the XXXX timestamp format. We use https://www.ietf.org/rfc/rfc3339.txt format: "2006-01-02T15:04:05.999999Z" so you can expect results like the following from the API: "2017-04-11T21:50:50.223962Z"' nodes: type: array items: type: object $ref: '#/components/schemas/apiWorkflowNode' description: 'The list of nodes retrieved from latest workflow version. Each node can specify an input node that it connects to in order to define the graph.' metadata: type: object title: 'To handle arbitrary json metadata you can use a struct field: https://github.com/google/protobuf/blob/master/src/google/protobuf/struct.proto' visibility: $ref: '#/components/schemas/clarifaiapiVisibility' description: 'The visibility field represents whether this message is privately/publicly visible. To be visible to the public the App that contains it AND the User that contains the App must also be publicly visible.' user_id: type: string title: The user the workflow belongs to modified_at: type: string format: date-time title: When the workflow was last modified version: $ref: '#/components/schemas/apiWorkflowVersion' description: Info about the workflow version used to return the latest version when listing Workflows. is_starred: type: boolean title: 'Is starred by the requesting user (only showed on get/list requests) Please use PostWorkflowStars/DeleteWorkflowStars endpoints to star/unstar a workflow' star_count: type: integer format: int32 title: 'How many users have starred the workflow (only showed on get/list requests) Computed value, not editable' description: type: string title: Short description about this workflow notes: type: string description: 'Notes for the workflow This field should be used for in-depth notes and supports up to 64Kbs.' use_cases: type: array items: type: string title: Tags from use_cases category check_consents: type: array items: type: string title: Tags for check consents bookmark_origin: $ref: '#/components/schemas/apiBookmarkOrigin' description: 'bookmark info. When set, this workflow is a bookmarked workflow of this app. Info in this field will allow you to find/access original workflow.' image: $ref: '#/components/schemas/apiImage' title: Representative image for this workflow title: Workflow apiColor: type: object properties: raw_hex: type: string w3c: $ref: '#/components/schemas/apiW3C' value: type: number format: float title: Color data apiUserDetail: type: object properties: primary_email: type: string bill_type: type: string date_gdpr_consent: type: string format: date-time date_tos_consent: type: string format: date-time date_marketing_consent: type: string format: date-time date_pii_consent: type: string format: date-time metadata: type: object title: 'To handle arbitrary json metadata you can use a struct field: https://github.com/google/protobuf/blob/master/src/google/protobuf/struct.proto' email_addresses: type: array items: type: object $ref: '#/components/schemas/apiEmailAddress' two_factor_auth_enabled: type: boolean teams_count: type: integer format: int64 country: type: string state: type: string commitment_value: $ref: '#/components/schemas/apiCommitmentValue' phone_verified: type: boolean title: For phone number verification, true if the phone number has been verified description: 'This message holds the confidential information from the User object that we don''t want to expose to other users. It will be accessible only from /users/{user_id}/account and with the User scopes.' apiMethodSignature: type: object properties: name: type: string description: The name of the method on the server. method_type: $ref: '#/components/schemas/apiRunnerMethodType' title: whether the method is for predict(unary-unary), generate(unary-stream), stream(stream-stream) description: type: string description: description from the docstring of the method on the server. input_fields: type: array items: type: object $ref: '#/components/schemas/apiModelTypeField' title: input fields and signature of every method arguments output_fields: type: array items: type: object $ref: '#/components/schemas/apiModelTypeField' title: output signature of method description: 'MethodSignature is a definition of a method that a model can have. This is used to communicate between a python method definition of any arbitrary function to the client or UI on how to call that function from the client side.' protobufAny: type: object properties: '@type': type: string additionalProperties: {} apiBinaryMetrics: type: object properties: num_pos: type: integer format: int64 num_neg: type: integer format: int64 num_tot: type: integer format: int64 roc_auc: type: number format: float f1: type: number format: float concept: $ref: '#/components/schemas/apiConcept' roc_curve: $ref: '#/components/schemas/apiROC' precision_recall_curve: $ref: '#/components/schemas/apiPrecisionRecallCurve' avg_precision: type: number format: float area_name: type: string area_min: type: number format: double area_max: type: number format: double iou: type: number format: float title: BinaryMetrics apiTextInfo: type: object properties: char_count: type: integer format: int32 title: count of characters in text encoding: type: string title: text encoding statusRedirectInfo: type: object properties: url: type: string description: New location for the resource. Used to set response Location header. resource_type: type: string title: Resource type old_resource_id: type: string title: Old resource id new_resource_id: type: string title: New resource id SpecialHandlingReason: type: string enum: - REASON_NOT_SET - CONTACT_SALES - INTERNAL_ONLY default: REASON_NOT_SET apiCooccurrenceMatrix: type: object properties: matrix: type: array items: type: object $ref: '#/components/schemas/apiCooccurrenceMatrixEntry' concept_ids: type: array items: type: string description: These concept_ids are ordered by the strength of the diagonal in the ConfusionMatrix. title: CooccurrenceMatrix statusStatusCode: type: string enum: - ZERO - SUCCESS - MIXED_STATUS - FAILURE - TRY_AGAIN - NOT_IMPLEMENTED - MOVED - TEAPOT - ACCEPTED - CONN_ACCOUNT_ISSUES - CONN_TOKEN_INVALID - CONN_CREDENTIALS_INVALID - CONN_EXCEED_HOURLY_LIMIT - CONN_EXCEED_MONTHLY_LIMIT - CONN_THROTTLED - CONN_EXCEEDS_LIMITS - CONN_INSUFFICIENT_SCOPES - CONN_KEY_INVALID - CONN_KEY_NOT_FOUND - CONN_BAD_REQUEST_FORMAT - CONN_DOES_NOT_EXIST - CONN_INVALID_REQUEST - CONN_METHOD_NOT_ALLOWED - CONN_NO_GDPR_CONSENT - CONN_INVALID_RANGE - CONN_AUTH_METHOD_DISABLED - MODEL_TRAINED - MODEL_TRAINING - MODEL_UNTRAINED - MODEL_QUEUED_FOR_TRAINING - MODEL_UPLOADING - MODEL_UPLOADING_FAILED - MODEL_TRAINING_FAILED - MODEL_BUILDING - MODEL_BUILDING_FAILED - MODEL_BUILD_UNEXPECTED_ERROR - MODEL_TRAINING_NO_DATA - MODEL_TRAINING_NO_POSITIVES - MODEL_TRAINING_ONE_VS_N_SINGLE_CLASS - MODEL_TRAINING_TIMED_OUT - MODEL_TRAINING_WAITING_ERROR - MODEL_TRAINING_UNKNOWN_ERROR - MODEL_TRAINING_MSG_REDELIVER - MODEL_TRAINING_INSUFFICIENT_DATA - MODEL_TRAINING_INVALID_PARAMS - MODEL_TRAINING_INVALID_DATA_TOLERANCE_EXCEEDED - MODEL_MODIFY_SUCCESS - MODEL_MODIFY_PENDING - MODEL_MODIFY_FAILED - MODEL_DOES_NOT_EXIST - MODEL_PERMISSION_DENIED - MODEL_INVALID_ARGUMENT - MODEL_INVALID_REQUEST - MODEL_EVALUATED - MODEL_EVALUATING - MODEL_NOT_EVALUATED - MODEL_QUEUED_FOR_EVALUATION - MODEL_EVALUATION_TIMED_OUT - MODEL_EVALUATION_WAITING_ERROR - MODEL_EVALUATION_UNKNOWN_ERROR - MODEL_PREDICTION_FAILED - MODEL_EVALUATION_MSG_REDELIVER - MODEL_EVALUATION_NEED_LABELS - MODEL_EVALUATION_NEED_INPUTS - MODEL_EVALUATION_FAILED - MODEL_DEPLOYMENT_FAILED - MODEL_DEPLOYING - MODEL_QUEUED_FOR_DEPLOYMENT - MODEL_NOT_DEPLOYED - MODEL_BUSY_PLEASE_RETRY - MODEL_LOADING - MODEL_REFERENCE_INVALID_ARGUMENT - MODEL_EXAMPLE_INPUT_INVALID_ARGUMENT - MODEL_EXPORTED - MODEL_EXPORTING - MODEL_EXPORTING_FAILED - MODEL_EXPORT_PENDING - WORKFLOW_NO_MATCHING_INPUT - WORKFLOW_REQUIRE_TRAINED_MODEL - WORKFLOW_DUPLICATE - WORKFLOW_UNSUPPORTED_FORMAT - WORKFLOW_DOES_NOT_EXIST - WORKFLOW_PERMISSION_DENIED - WORKFLOW_INVALID_ARGUMENT - WORKFLOW_INVALID_RECIPE - WORKFLOW_INVALID_TEMPLATE - WORKFLOW_INVALID_GRAPH - WORKFLOW_INTERNAL_FAILURE - WORKFLOW_INVALID_REQUEST - WORKFLOW_MODIFY_SUCCESS - WORKFLOW_MODIFY_PENDING - WORKFLOW_MODIFY_FAILED - WORKFLOW_REINDEX_FAILED - CONCEPT_MODIFY_SUCCESS - CONCEPT_MODIFY_PENDING - CONCEPT_MODIFY_FAILED - ANNOTATION_SUCCESS - ANNOTATION_PENDING - ANNOTATION_FAILED - ANNOTATION_UNKNOWN_STATUS - ANNOTATION_INVALID_ARGUMENT - ANNOTATION_PERMISSION_DENIED - ANNOTATION_AWAITING_REVIEW - ANNOTATION_AWAITING_CONSENSUS_REVIEW - ANNOTATION_REVIEW_DENIED - ANNOTATION_MODIFY_SUCCESS - ANNOTATION_MODIFY_PENDING - ANNOTATION_MODIFY_FAILED - ANNOTATION_TRACK_IDLE - ANNOTATION_TRACK_IN_PROGRESS - ANNOTATION_TRACK_PENDING - ANNOTATION_TRACK_APPROVED - ANNOTATION_TRACK_FAILED - METADATA_INVALID_PATCH_ARGUMENTS - METADATA_PARSING_ISSUE - METADATA_MANIPULATION_ISSUE - TRAINER_JOB_STATE_NONE - TRAINER_JOB_STATE_QUEUED - TRAINER_JOB_STATE_RUNNING - TRAINER_JOB_STATE_COMPLETE - TRAINER_JOB_STATE_ERROR - DATA_DUMP_SUCCESS - DATA_DUMP_PENDING - DATA_DUMP_FAILED - DATA_DUMP_IN_PROGRESS - DATA_DUMP_NO_DATA - DATA_DUMP_UNEXPECTED_ERROR - DATA_DUMP_EXPORT_SUCCESS - DATA_DUMP_EXPORT_PENDING - DATA_DUMP_EXPORT_FAILED - DATA_DUMP_EXPORT_IN_PROGRESS - DATA_DUMP_EXPORT_UNEXPECTED_ERROR - APP_DUPLICATION_SUCCESS - APP_DUPLICATION_FAILED - APP_DUPLICATION_PENDING - APP_DUPLICATION_IN_PROGRESS - APP_DUPLICATION_INVALID_REQUEST - MODULE_DOES_NOT_EXIST - MODULE_PERMISSION_DENIED - MODULE_INVALID_ARGUMENT - MODULE_INVALID_REQUEST - BULK_OPERATION_SUCCESS - BULK_OPERATION_FAILED - BULK_OPERATION_PENDING - BULK_OPERATION_IN_PROGRESS - BULK_OPERATION_INVALID_REQUEST - BULK_OPERATION_CANCELLED - BULK_OPERATION_UNEXPECTED_ERROR - RUNNER_DOES_NOT_EXIST - RUNNER_PERMISSION_DENIED - RUNNER_INVALID_ARGUMENT - RUNNER_INVALID_REQUEST - RUNNER_NEEDS_RETRY - RUNNER_STREAM_START - RUNNER_STREAM_END - RUNNER_ITEM_CANCELLED - RUNNER_PROCESSING_FAILED - RUNNER_SPECIAL_HANDLING_NOT_COMPLETE - NODEPOOL_DOES_NOT_EXIST - NODEPOOL_INVALID_ARGUMENT - NODEPOOL_INVALID_REQUEST - COMPUTE_CLUSTER_DOES_NOT_EXIST - COMPUTE_CLUSTER_INVALID_ARGUMENT - COMPUTE_CLUSTER_INVALID_REQUEST - DEPLOYMENT_DOES_NOT_EXIST - DEPLOYMENT_INVALID_ARGUMENT - DEPLOYMENT_INVALID_REQUEST - DEPLOYMENT_DISABLED - INSTANCE_TYPE_DOES_NOT_EXIST - INSTANCE_TYPE_INVALID_ARGUMENT - INSTANCE_TYPE_INVALID_REQUEST - COMPUTE_PLANE_METRICS_INVALID_REQUEST - PIPELINE_STEP_DOES_NOT_EXIST - PIPELINE_STEP_INVALID_ARGUMENT - PIPELINE_STEP_INVALID_REQUEST - PIPELINE_STEP_UPLOADING - PIPELINE_STEP_UPLOADING_FAILED - PIPELINE_STEP_BUILDING - PIPELINE_STEP_BUILDING_FAILED - PIPELINE_STEP_BUILD_UNEXPECTED_ERROR - PIPELINE_STEP_READY - PIPELINE_STEP_NOT_READY - PIPELINE_DOES_NOT_EXIST - PIPELINE_INVALID_ARGUMENT - PIPELINE_INVALID_REQUEST - ARTIFACT_DOES_NOT_EXIST - ARTIFACT_INVALID_ARGUMENT - ARTIFACT_INVALID_REQUEST - ARTIFACT_VERSION_DOES_NOT_EXIST - ARTIFACT_VERSION_INVALID_ARGUMENT - ARTIFACT_VERSION_INVALID_REQUEST - INPUT_SUCCESS - INPUT_PENDING - INPUT_FAILED - INPUT_IN_PROGRESS - INPUT_DOWNLOAD_SUCCESS - INPUT_DOWNLOAD_PENDING - INPUT_DOWNLOAD_FAILED - INPUT_DOWNLOAD_IN_PROGRESS - INPUT_STATUS_UPDATE_FAILED - INPUT_DELETE_FAILED - INPUT_DUPLICATE - INPUT_UNSUPPORTED_FORMAT - INPUT_DOES_NOT_EXIST - INPUT_PERMISSION_DENIED - INPUT_INVALID_ARGUMENT - INPUT_OVER_LIMIT - INPUT_INVALID_URL - INPUT_MODIFY_SUCCESS - INPUT_MODIFY_PENDING - INPUT_MODIFY_FAILED - INPUT_STORAGE_HOST_FAILED - ALL_INPUT_INVALID_BYTES - INPUT_CLUSTER_SUCCESS - INPUT_CLUSTER_PENDING - INPUT_CLUSTER_FAILED - INPUT_CLUSTER_IN_PROGRESS - INPUT_REINDEX_SUCCESS - INPUT_REINDEX_PENDING - INPUT_REINDEX_FAILED - INPUT_REINDEX_IN_PROGRESS - INPUT_VIDEO_DOWNLOAD_SUCCESS - INPUT_VIDEO_DOWNLOAD_PENDING - INPUT_VIDEO_DOWNLOAD_FAILED - INPUT_VIDEO_DUPLICATE - INPUT_VIDEO_UNSUPPORTED_FORMAT - INPUT_VIDEO_DOES_NOT_EXIST - INPUT_VIDEO_PERMISSION_DENIED - INPUT_VIDEO_INVALID_ARGUMENT - INPUT_VIDEO_OVER_LIMIT - INPUT_VIDEO_INVALID_URL - INPUT_VIDEO_MODIFY_SUCCESS - INPUT_VIDEO_MODIFY_PENDING - INPUT_VIDEO_MODIFY_FAILED - INPUT_VIDEO_STORAGE_HOST_FAILED - ALL_INPUT_VIDEOS_INVALID_BYTES - INPUT_VIDEO_PROCESSING_SUCCESS - INPUT_VIDEO_PROCESSING_PENDING - INPUT_VIDEO_PROCESSING_FAILED - INPUT_VIDEO_STORAGE_INCONSISTENCY - INPUT_VIDEO_STORAGE_FAILURE - INPUT_VIDEO_URL_GENERATION_FAILURE - INPUT_CONNECTION_FAILED - REQUEST_DISABLED_FOR_MAINTENANCE - INPUT_WRITES_DISABLED_FOR_MAINTENANCE - INPUT_INVALID_REQUEST - PREDICT_INVALID_REQUEST - SEARCH_INVALID_REQUEST - CONCEPTS_INVALID_REQUEST - STATS_INVALID_REQUEST - DATABASE_DUPLICATE_KEY - DATABASE_STATEMENT_TIMEOUT - DATABASE_INVALID_ROWS_AFFECTED - DATABASE_DEADLOCK_DETECTED - DATABASE_FAIL_TASK - DATABASE_FAIL_TO_GET_CONNECTIONS - DATABASE_TOO_MANY_CLIENTS - DATABASE_CONSTRAINT_VIOLATED - DATABASE_CANCELED - ASYNC_WORKER_MULTI_ERRORS - RPC_REQUEST_QUEUE_FULL - RPC_SERVER_UNAVAILABLE - RPC_REQUEST_TIMEOUT - RPC_MAX_MESSAGE_SIZE_EXCEEDED - RPC_CANCELED - RPC_UNKNOWN_METHOD - REQUEST_CANCELED_BY_USER - CLUSTER_INTERNAL_FAILURE - EXTERNAL_CONNECTION_ERROR - QUERY_INVALID_SYNTAX - QUEUE_CONN_ERROR - QUEUE_CLOSE_REQUEST_TIMEOUT - QUEUE_CONN_CLOSED - QUEUE_PUBLISH_ACK_TIMEOUT - QUEUE_PUBLISH_ERROR - QUEUE_SUBSCRIPTION_TIMEOUT - QUEUE_SUBSCRIPTION_ERROR - QUEUE_MARSHALLING_FAILED - QUEUE_UNMARSHALLING_FAILED - QUEUE_MAX_MSG_REDELIVERY_EXCEEDED - QUEUE_ACK_FAILURE - SQS_OVERLIMIT - SQS_INVALID_RECEIPT_HANDLE - SQS_UNKNOWN - SEARCH_INTERNAL_FAILURE - SEARCH_PROJECTION_FAILURE - SEARCH_PREDICTION_FAILURE - SEARCH_BY_NOT_FULLY_INDEXED_INPUT - SAVED_SEARCH_MODIFY_FAILED - SEARCH_COUNTS_UNAVAILABLE - EVALUATION_QUEUED - EVALUATION_IN_PROGRESS - EVALUATION_SUCCESS - EVALUATION_FAILED_TO_RETRIEVE_DATA - EVALUATION_INVALID_ARGUMENT - EVALUATION_FAILED - EVALUATION_PENDING - EVALUATION_TIMED_OUT - EVALUATION_UNEXPECTED_ERROR - EVALUATION_MIXED - STRIPE_EVENT_ERROR - STRIPE_UNEXPECTED_ERROR - CACHE_MISS - REDIS_SCRIPT_EXITED_WITH_FAILURE - REDIS_STREAM_ERR - REDIS_NO_CONSUMERS - REDIS_STREAM_BACKOFF - REDIS_CHANNEL_ERR - SIGNUP_EVENT_ERROR - SIGNUP_FLAGGED - FILETYPE_UNSUPPORTED - LOGIN_EVENT_ERROR - SIFT_UNCATEGORIZED_ERROR - USER_FLAGGED - APP_COUNT_INVALID_MESSAGE - APP_COUNT_UPDATE_INCREMENT_FAILED - APP_COUNT_REBUILD_FAILED - APP_COUNT_INTERNAL_FAILURE - MP_DOWNLOAD_ERROR - MP_RESOLVE_DNS_ERROR - MP_DOWNLOAD_MAX_SIZE_EXCEEDED_ERROR - MP_IMAGE_DECODE_ERROR - MP_INVALID_ARGUMENT - MP_IMAGE_PROCESSING_ERROR - DATATIER_CONN_ERROR - USER_CONSENT_FACE - WORKER_MISSING - WORKER_ACTIVE - WORKER_INACTIVE - SSO_IDENTITY_PROVIDER_DOES_NOT_EXIST - TASK_IN_PROGRESS - TASK_DONE - TASK_WONT_DO - TASK_FAILED - TASK_IDLE - TASK_CONFLICT - TASK_NOT_IMPLEMENTED - TASK_MISSING - TASK_PERMISSION_DENIED - TASK_ASSIGNMENT_SUCCESS - TASK_ASSIGNMENT_PENDING - TASK_ASSIGNMENT_AWAITING_REVIEW - TASK_ASSIGNMENT_AWAITING_CONSENSUS_REVIEW - TASK_ASSIGNMENT_REJECTED - TASK_ASSIGNMENT_REVIEW_SUCCESS - TASK_ASSIGNMENT_REVIEW_PENDING - TASK_ASSIGNMENT_REVIEW_DISMISSED - LABEL_ORDER_PENDING - LABEL_ORDER_IN_PROGRESS - LABEL_ORDER_SUCCESS - LABEL_ORDER_CANCELED - LICENSE_ACTIVE - LICENSE_DOES_NOT_EXIST - LICENSE_NEED_UPDATE - LICENSE_EXPIRED - LICENSE_REVOKED - LICENSE_DELETED - LICENSE_VOLUME_EXCEEDED - PASSWORD_VALIDATION_SUCCESS - PASSWORD_VALIDATION_FAILED - PASSWORDPOLICY_INVALID_ARGUMENT - FEATUREFLAG_CONFIG_NOT_FOUND - FEATUREFLAG_INVALID_ARGUMENT - FEATUREFLAG_BLOCKED - FEATUREFLAG_NOT_FOUND - MAINTENANCE_SUCCESS - MAINTENANCE_FAILED - DATASET_VERSION_PENDING - DATASET_VERSION_IN_PROGRESS - DATASET_VERSION_READY - DATASET_VERSION_FAILURE - DATASET_VERSION_UNEXPECTED_ERROR - DATASET_VERSION_CONFLICT - DATASET_INPUT_SUCCESS - DATASET_INPUT_DUPLICATE - DATASET_VERSION_EXPORT_SUCCESS - DATASET_VERSION_EXPORT_PENDING - DATASET_VERSION_EXPORT_FAILED - DATASET_VERSION_EXPORT_IN_PROGRESS - DATASET_VERSION_EXPORT_UNEXPECTED_ERROR - JOB_QUEUED - JOB_RUNNING - JOB_COMPLETED - JOB_FAILED - JOB_CANCELLED - JOB_UNEXPECTED_ERROR - JOB_CONFLICT - JOB_PAUSED - AUTH_MISSING_IDP_ASSOC - LIST_OBJECTS_FAILED - ARCHIVE_EXTRACT_FAILED - UPLOAD_IN_PROGRESS - UPLOAD_DONE - UPLOAD_FAILED - UPLOAD_UNEXPECTED_ERROR - UPLOAD_EXPIRED - UPLOAD_CANCELED - UPLOAD_CONFLICT - BILLING_INVALID_INFO - BILLING_MISSING_TOKENS - LOG_ENTRIES_INVALID_REQUEST - TWILIO_MAX_VERIFICATION_BEGIN_EXCEEDED - TWILIO_MAX_VERIFICATION_CHECK_EXCEEDED - TWILIO_PHONE_NUMBER_BLOCKED - INTERNAL_SERVER_ISSUE - INTERNAL_FETCHING_ISSUE - INTERNAL_DATABASE_ISSUE - INTERNAL_CONTEXT_CANCELED - INTERNAL_UNEXPECTED_TIMEOUT - INTERNAL_UNEXPECTED_V1 - INTERNAL_UNEXPECTED_PANIC - INTERNAL_UNEXPECTED_SPIRE - INTERNAL_REDIS_UNAVAILABLE - INTERNAL_RESOURCE_EXHAUSTED - INTERNAL_REDIS_UNCATEGORIZED - INTERNAL_AWS_UNCATEGORIZED - INTERNAL_AZURE_UNCATEGORIZED - INTERNAL_VECTORDB_UNCATEGORIZED - INTERNAL_ORACLE_UNCATEGORIZED - INTERNAL_VULTR_UNCATEGORIZED - INTERNAL_GCP_UNCATEGORIZED - CONN_UNCATEGORIZED - MODEL_UNCATEGORIZED - INPUT_UNCATEGORIZED - ANNOTATION_UNCATEGORIZED - BILLING_UNCATEGORIZED - INTERNAL_UNCATEGORIZED - BAD_REQUEST - SERVER_ERROR default: ZERO description: "expired cc, still in trial, feature not supported in your tier\n - CONN_TOKEN_INVALID: invalid auth token used. Deprecated: we should return CONN_KEY_INVALID instead now in all cases.\n - CONN_CREDENTIALS_INVALID: invalid auth credentials\n - CONN_EXCEED_HOURLY_LIMIT: throttle hourly limit exceeded\n - CONN_EXCEED_MONTHLY_LIMIT: throttle monthly limit exceeded\n - CONN_THROTTLED: throttler and billing stuff\n - CONN_EXCEEDS_LIMITS: throttler and billing stuff\n - CONN_INSUFFICIENT_SCOPES: api key has insufficient permissions\n - CONN_KEY_INVALID: api key is invalid\n - CONN_KEY_NOT_FOUND: api key not found\n - CONN_BAD_REQUEST_FORMAT: multipart form parsing, broken json, etc\n - CONN_DOES_NOT_EXIST: when path is bad\n - CONN_INVALID_REQUEST: something wrong with a header\n - CONN_METHOD_NOT_ALLOWED: when a request method is not allowed\n - CONN_NO_GDPR_CONSENT: lack GDPR consent\n - CONN_INVALID_RANGE: when invalid range of a resource is requested\n - CONN_AUTH_METHOD_DISABLED: authentication method is disabled\n - MODEL_TRAINED: Model/Custom Training related 20xxx\n\nCustom model has been already trained.\n - MODEL_TRAINING: Custom model is currently training.\n - MODEL_UNTRAINED: Custom model has not yet been trained.\n - MODEL_QUEUED_FOR_TRAINING: Custom model is currently in queue for training, waiting on assets to process first.\n - MODEL_TRAINING_FAILED: generic err msg for any type of model training err.\n - MODEL_BUILDING: For new V3 DockerInternalType models which are built images\n - MODEL_BUILDING_FAILED: Failed to build image for model.\n - MODEL_BUILD_UNEXPECTED_ERROR: Failed to build image for model.\n - MODEL_TRAINING_NO_DATA: Custom model training had no data. FIXME(yang): deprecate this. Use the 21106 + errStatusMsg\n - MODEL_TRAINING_NO_POSITIVES: Custom model training had no positive examples. FIXME(yang): deprecate this. Use the 21106 + errStatusMsg\n - MODEL_TRAINING_ONE_VS_N_SINGLE_CLASS: Custom model training was ONE_VS_N but with a single class. FIXME(yang): deprecate this. Use the 21106 + errStatusMsg\n - MODEL_TRAINING_TIMED_OUT: Training took longer than hard coded timeouts. FIXME(yang): deprecate this. Use the 21106 + errStatusMsg\n - MODEL_TRAINING_WAITING_ERROR: Training got error waiting on asset pipeline to finish. FIXME(yang): deprecate this. Use the 21106 + errStatusMsg\n - MODEL_TRAINING_UNKNOWN_ERROR: Training threw an unknown exception.\n - MODEL_TRAINING_MSG_REDELIVER: Training message was redelivered. FIXME(yang): deprecate this. Use the 21106 + errStatusMsg\n - MODEL_TRAINING_INSUFFICIENT_DATA: Training got error due to insufficient labelled data. FIXME(yang): deprecate this. Use the 21106 + errStatusMsg\n - MODEL_TRAINING_INVALID_PARAMS: FIXME(yang): deprecate this. Use the 21106 + errStatusMsg\n - MODEL_TRAINING_INVALID_DATA_TOLERANCE_EXCEEDED: Training is stopped because too much data was dropped. FIXME(yang): deprecate this. Use the 21106 + errStatusMsg\n - MODEL_EVALUATION_TIMED_OUT: Evaluation took longer than hard coded timeouts. FIXME(yang): deprecate this. Use the 21317 + errStatusMsg\n - MODEL_EVALUATION_WAITING_ERROR: Evaluation got error waiting on asset pipeline to finish.FIXME(yang): deprecate this. Use the 21317 + errStatusMsg\n - MODEL_EVALUATION_UNKNOWN_ERROR: EVALUATION THREW AN UNKNOWN EXCEPTION.\n - MODEL_EVALUATION_MSG_REDELIVER: Eval message was redelivered. FIXME(yang): deprecate this. Use the 21317 + errStatusMsg\n - MODEL_EVALUATION_NEED_LABELS: Don't have enough concepts labelled to perform evaluation. FIXME(yang): deprecate this. Use the 21317 + errStatusMsg\n - MODEL_EVALUATION_NEED_INPUTS: Don't have enough inputs per concept to perform evaluation. FIXME(yang): deprecate this. Use the 21317 + errStatusMsg\n - MODEL_EVALUATION_FAILED: Generic err code for eval failure.\n - MODEL_DEPLOYMENT_FAILED: Used when inference coordinator failed to deploy spire and throws an error\n - MODEL_DEPLOYING: Used when calling the inference coordinator to deploy a spire\n - MODEL_QUEUED_FOR_DEPLOYMENT: Used when training is completed\n - MODEL_NOT_DEPLOYED: Used when model spire deployment is manually taken down or due to inactivity\n - MODEL_LOADING: Used when the model pod is running, but not yet ready to serve requests.\n - MODEL_REFERENCE_INVALID_ARGUMENT: Used when a model reference field is not set properly\n - MODEL_EXAMPLE_INPUT_INVALID_ARGUMENT: Used when a model example input field is not set properly\n - MODEL_EXPORTED: Model Export status codes\n - WORKFLOW_NO_MATCHING_INPUT: specified model input not in workflow\n - WORKFLOW_REQUIRE_TRAINED_MODEL: specified model must be trained\n - WORKFLOW_INVALID_ARGUMENT: error in the request somewhere\n - WORKFLOW_INVALID_REQUEST: error in the request somewhere\n - CONCEPT_MODIFY_SUCCESS: Concept related 23xxx\n - ANNOTATION_SUCCESS: Annotation related 24xxx\n - ANNOTATION_AWAITING_REVIEW: Annotation is awaiting review from task REVIEWERS.\n - ANNOTATION_AWAITING_CONSENSUS_REVIEW: Annotation is awaiting consensus review from task LABELERS.\n - ANNOTATION_TRACK_IDLE: Annotation (video) tracks are groups of annotations per frame\nIDLE -> PROCESSING --> PENDING --> APPROVED --> (DELETED)\nPROCESSING --> FAILED --> (DELETED)\nPROCESSING --> (DELETED)\n - METADATA_INVALID_PATCH_ARGUMENTS: Metadata related 249xx\n - TRAINER_JOB_STATE_NONE: Training service related 25xxx\n - DATA_DUMP_SUCCESS: Data Dump related 251xx\n - DATA_DUMP_NO_DATA: DEPRECATED: Not used anymore. Now for an empty data dump, DATA_DUMP_SUCCESS is returned. To detect an empty data dump, check if the inptus count is 0.\n - APP_DUPLICATION_SUCCESS: Duplicate related 252xx - DEPRECATED: App duplication is no longer supported.\n - MODULE_DOES_NOT_EXIST: Module related codes 253xx\n - BULK_OPERATION_SUCCESS: Bulk Operation related codes 254xx\n - RUNNER_DOES_NOT_EXIST: Runner related codes 256xx\n - NODEPOOL_DOES_NOT_EXIST: Nodepool related codes 257xx\n - COMPUTE_CLUSTER_DOES_NOT_EXIST: ComputeCluster related codes 258xx\n - DEPLOYMENT_DOES_NOT_EXIST: Deployment related codes 259xx\n - DEPLOYMENT_DISABLED: The deployment is disabled and cannot serve traffic.\n - INSTANCE_TYPE_DOES_NOT_EXIST: InstanceType related codes 260xx\n - COMPUTE_PLANE_METRICS_INVALID_REQUEST: Compute plane related codes 261xx\n - PIPELINE_STEP_DOES_NOT_EXIST: PipelineStep related codes 262xx\n - PIPELINE_DOES_NOT_EXIST: Pipeline related codes 263xx\n - ARTIFACT_DOES_NOT_EXIST: Artifact related codes 264xx\n - INPUT_SUCCESS: Input:Image related 30xxx\n - INPUT_PENDING: when things are async, this is the default status.\n - INPUT_FAILED: any type of error downloading and processing\n - INPUT_DOWNLOAD_SUCCESS: use INPUT_SUCCESS, INPUT_PENDING, INPUT_FAILED, INPUT_IN_PROGRESS instead\nDOWNLOAD is no longer correct, but keep old statuses for backward compatibility\n - INPUT_VIDEO_DOWNLOAD_SUCCESS: Input:Video related 31xxx -- Deprecated\n - INPUT_WRITES_DISABLED_FOR_MAINTENANCE: deprecate this one. Use REQUEST_DISABLED_FOR_MAINTENANCE\n - PREDICT_INVALID_REQUEST: API formatting issues 4000x\n - DATABASE_DUPLICATE_KEY: Other related 400xx\n - EXTERNAL_CONNECTION_ERROR: could not connect to external services\n - QUEUE_CONN_ERROR: Queue related errors 41xxx\n - SQS_OVERLIMIT: SQS related errors 411xx\n - SEARCH_INTERNAL_FAILURE: Search related errors 43xxxx\n - EVALUATION_QUEUED: Workflow evaluation err code\n - STRIPE_EVENT_ERROR: Stripe 44xxx\n - CACHE_MISS: Redis/Cache 45xxx\n - SIGNUP_EVENT_ERROR: Sift Science 46xxx\n - APP_COUNT_INVALID_MESSAGE: Application counts related errors 470xx\n - MP_DOWNLOAD_ERROR: Media processor related errors 471xx -- DEPRECATED\n - DATATIER_CONN_ERROR: DataTier related error 472xx\n - USER_CONSENT_FACE: User legal consent stauts related 50xxx\n - WORKER_MISSING: Workers 51xxx\n - SSO_IDENTITY_PROVIDER_DOES_NOT_EXIST: SSO 53xxx\n - TASK_IN_PROGRESS: Tasks 54xxx\nThe task was created.\n - TASK_DONE: The task is completed.\n - TASK_WONT_DO: The task is marked as abandoned.\n - TASK_FAILED: An error occurred during add-task-annotations or add-auto-annotations pipeline.\n - TASK_IDLE: Task is waiting for user action.\nExamples:\n- When an Auto Annotation task job has finished processing its last batch and is waiting for more dataset assets.\n- When an Auto Annotation task job for a video livestream input is waiting for user to create a task deployment.\n - TASK_CONFLICT: The task operation is in conflict with the current state of the server.\n - TASK_NOT_IMPLEMENTED: Certain task-related scenarios are not implemented.\n - TASK_MISSING: Task was not found.\n - TASK_PERMISSION_DENIED: Not allowed to perform a task-related action.\n - TASK_ASSIGNMENT_SUCCESS: Task Assignments 542xx\n - TASK_ASSIGNMENT_REVIEW_SUCCESS: Task Assignment Reviews 543xx\n - LABEL_ORDER_PENDING: Label Order Related Status Code 55xxx\n - LICENSE_ACTIVE: License Related Status Code 600xx\nDEPRECATED: License feature is fully deprecated now.\n - LICENSE_DELETED: hidden state not reflected to users\n - PASSWORD_VALIDATION_SUCCESS: Password Related Status Code\n - FEATUREFLAG_CONFIG_NOT_FOUND: Feature flags status code\n - MAINTENANCE_SUCCESS: Maintenance status code\n - DATASET_VERSION_PENDING: Datasets 64xxx\nThe dataset version is pending to be processed.\n - DATASET_VERSION_IN_PROGRESS: The dataset version is currently being processed.\n - DATASET_VERSION_READY: The dataset version is ready to be used.\n - DATASET_VERSION_FAILURE: An error occurred during the dataset version processing.\n - DATASET_VERSION_UNEXPECTED_ERROR: An unexpected error occurred during the dataset version processing.\n - DATASET_VERSION_CONFLICT: An alteration to dataset version would create a conflict\n - DATASET_INPUT_SUCCESS: The dataset input was successfully added.\n - DATASET_INPUT_DUPLICATE: The dataset input is a duplicate.\nDeprecated: Unused.\n - DATASET_VERSION_EXPORT_SUCCESS: The dataset version export is completed.\n - DATASET_VERSION_EXPORT_PENDING: The dataset version is pending to be exported.\n - DATASET_VERSION_EXPORT_FAILED: An error occurred during the dataset version export.\n - DATASET_VERSION_EXPORT_IN_PROGRESS: The dataset version is currently being exported.\n - DATASET_VERSION_EXPORT_UNEXPECTED_ERROR: An unexpected error occurred during the dataset version export.\n - JOB_QUEUED: Generic Job status codes\n - AUTH_MISSING_IDP_ASSOC: auth issues\n\nTODO: Knowledge graph related 80xxx\n - UPLOAD_IN_PROGRESS: Multipart uploading status codes\n - BILLING_INVALID_INFO: Billing related issues: 69xxx\n - LOG_ENTRIES_INVALID_REQUEST: Logs related issues: 70000;\n - TWILIO_MAX_VERIFICATION_BEGIN_EXCEEDED: Twilio related issues: 71xxx\n - TWILIO_PHONE_NUMBER_BLOCKED: The provided phone number was rejected or blocked by Twilio.\n - INTERNAL_SERVER_ISSUE: Internal issues: 98xxx\n - CONN_UNCATEGORIZED: Uncategorized: 99xxx: move off as soon as known\n - BAD_REQUEST: Deprecated: migrate off to one of the internal issues\n - SERVER_ERROR: Deprecated: migrate off to one of the internal issues" title: "- ZERO: to be revised and greatly expanded\n - SUCCESS: Generic\n - MOVED: Resource moved. Respond with Http status 307 and add new Location header to response\n - ACCEPTED: Request has been accepted. Respond with Http status 202.\n - CONN_ACCOUNT_ISSUES: Clarifai Connection Codes: 11xxx" apiDatasetVersion: type: object properties: id: type: string title: The ID for the dataset version created_at: type: string format: date-time description: 'When the dataset version was created. The format is https://www.ietf.org/rfc/rfc3339.txt. Example: "2006-01-02T15:04:05.999999Z".' modified_at: type: string format: date-time description: 'When the dataset version was modified. The format is https://www.ietf.org/rfc/rfc3339.txt. Example: "2006-01-02T15:04:05.999999Z".' app_id: type: string description: The app the dataset version belongs to. user_id: type: string description: The user the dataset version belongs to. dataset_id: type: string description: The dataset the dataset version belongs to. annotation_filter_config: $ref: '#/components/schemas/apiAnnotationFilterConfig' description: The dataset version will be generated based on a single annotation filter. model_predict_config: $ref: '#/components/schemas/apiModelPredictConfig' description: The dataset version will be generated based on model version inferences. status: $ref: '#/components/schemas/apistatusStatus' description: Status for this dataset version. description: type: string title: Description of the dataset version processing_info: $ref: '#/components/schemas/apiDatasetVersionProcessingInfo' description: 'Dataset version processing. If this is not set when the dataset version is created, then the dataset default_processing_info is copied instead. Later updates to default_processing_info will not apply to existing versions.' metrics: type: object additionalProperties: $ref: '#/components/schemas/apiDatasetVersionMetrics' title: Dataset version metrics export_info: $ref: '#/components/schemas/apiDatasetVersionExportInfo' title: Dataset version exports metadata: type: object title: 'To handle arbitrary json metadata you can use a struct field: https://github.com/google/protobuf/blob/master/src/google/protobuf/struct.proto' visibility: $ref: '#/components/schemas/clarifaiapiVisibility' description: 'The visibility field represents whether this message is privately/publicly visible. To be visible to the public the App that contains it AND the User that contains the App must also be publicly visible.' embed_model_version_ids: type: array items: type: string description: The embedding models to return embeddings for. If empty, no embeddings are returned. request_origin: $ref: '#/components/schemas/apiDatasetVersionRequestOrigin' title: 'Read Only. Cannot be Set Origin of request for new dataset version' title: DatasetVersion apiKeypointEdge: type: object properties: k1: type: integer format: int64 k2: type: integer format: int64 apiHostedURL: type: object properties: prefix: type: string description: Prefix of the URL of every hosted image. suffix: type: string description: Suffix of an image stored in different sizes. sizes: type: array items: type: string description: 'The sizes field lists which images of the different sizes are hosted in our storage. The URL of each hosted image can be obtained by joining the prefix, one of the sizes and suffix.' crossorigin: type: string title: 'The crossorigin property of html media tag For Secure Data Hosting this needs to be set to ''use-credentials''' title: HostedURL ModelSource: type: string enum: - UNKNOWN_SOURCE - HOSTED - WRAPPED default: UNKNOWN_SOURCE title: Source of Model apiHit: type: object properties: score: type: number format: float description: 'This is the score for the ranked Hit results of the search query. This score is a number between 0.0 and 1.0 as it represents a confidence in the search Hit. For example, if you search for "car" and get a close matching Hit, the score should be close to 1.0. If you get a score of close to 0.0 that means it''s very disimilar to your query, in this case NOT a "car". There is a special intermediate score of 0.5 that means that the Hit is not really correlated with your search query (ie. not similar or dissimlar to the query) which is a common occurrence when using negate queries. Note: some queries that are just filtering down your app of inputs may just return a score of 1.0 for all Hits.' input: $ref: '#/components/schemas/apiInput' description: 'This is the matched input returned from the search query. This will contain information about the Input such as the url, created_at time and trusted annotation information (for backwards compatibility with apps that existed before Annotations were introduced.' annotation: $ref: '#/components/schemas/clarifaiapiAnnotation' description: 'We also provide back the specific matched annotation for the above input. We do this in order to support more complex Annotation queries in the And message below. For example if we match the search results to a region in your input, or a frame in a video input, this annotation field will be that matched annotation info and the input will be the image/video that the user originally added which contains those regions / frames.' user_id: type: string description: The customer-facing id of the user who owns the app the asset came from. app_id: type: string description: The cfid of the app the asset came from. title: Hit apiCooccurrenceMatrixEntry: type: object properties: row: type: string title: concept_id for the row col: type: string title: concept_id for the col count: type: integer format: int64 title: 'NOTE: this is inefficient, should just have the order of the rows/cols' apistatusStatus: type: object properties: code: $ref: '#/components/schemas/statusStatusCode' description: Status code from internal codes. description: type: string description: A short description of the error. details: type: string description: 'More details of the given error. These details may be exposed to non-technical users. For technical details, try to use developer_notes field.' stack_trace: type: array items: type: string description: 'For some environment we may return a stack trace to help debug any issues.' percent_completed: type: integer format: int64 title: specifically for long running jobs time_remaining: type: integer format: int64 title: if status is pending, how much time is remaining (in seconds) req_id: type: string title: A request ID may be present, to help monitoring and tracking requests internal_details: type: string description: Internal Annotation (do not set in production, for internal Clarifai use only). redirect_info: $ref: '#/components/schemas/statusRedirectInfo' description: Resource location info for redirect, when resource location has been changed. developer_notes: type: string description: 'Notes for developer. These notes are rather technical details for developers how to interpret the status, e.g. why an error occurred and how to avoid getting the error.' http_status_code: type: integer format: int64 description: The HTTP status code of the response. clarifaiapiVisibility: type: object properties: gettable: $ref: '#/components/schemas/VisibilityGettable' description: 'Visibility represents how visible the given resource is to other users. When authenticating a request we can tell if a user is a collaborator or a teammate for the the app that contains the resource and set their allowed visibility. We use that to restrict what they are allowed to see: If AllowedVisibility is PRIVATE then we allow PRIVATE (10), ORG (30), PUBLIC (50) If AllowedVisibility is ORG then we allow ORG (30), PUBLIC (50) If AllowedVisibility is PUBLIC then we allow PUBLIC (50) only.' apiPretrainedModelConfig: type: object properties: input_fields_map: type: object description: 'This is the internal id of the pretrained model. Map from the api.Data field names to the Triton config.pbtxt input.' output_fields_map: type: object description: Map from the api.Data field names to the Triton config.pbtxt output. model_zip_url: type: string title: "Url to a zipped up model in triton format with the following files and folders at the root:\n config.pbtxt\n version 1 folder that contains model files (onnx graph, torch script, python BE model, and etc.)" local_dev: type: boolean title: 'Whether to overwrite the model for the existing internal id If this is a local dev model that runs external to the platform, set this to true. This helps during development of models before uploading them to the platform. These models MUST run in an associated compute cluster with cluster_type = "local-dev"' title: PretrainedModelConfig apiLabelDistribution: type: object properties: positive_label_counts: type: array items: type: object $ref: '#/components/schemas/apiLabelCount' title: LabelDistribution apiWorkflowVersion: type: object properties: id: type: string description: Id of this version. workflow_id: type: string description: Workflow id for this version. created_at: type: string format: date-time description: 'When the version was created. The format is https://www.ietf.org/rfc/rfc3339.txt. Example: "2006-01-02T15:04:05.999999Z".' modified_at: type: string format: date-time description: 'Most recent time when the version was updated. The format is https://www.ietf.org/rfc/rfc3339.txt. Example: "2006-01-02T15:04:05.999999Z".' visibility: $ref: '#/components/schemas/clarifaiapiVisibility' description: 'The visibility field represents whether this message is privately/publicly visible. To be visible to the public the App that contains it AND the User that contains the App must also be publicly visible.' nodes: type: array items: type: object $ref: '#/components/schemas/apiWorkflowNode' description: 'The list of nodes that make up the workflow version. Each node can specify an input node that it connects to in order to define the graph.' metadata: type: object title: 'To handle arbitrary json metadata you can use a struct field: https://github.com/google/protobuf/blob/master/src/google/protobuf/struct.proto' app_id: type: string description: The app the workflow version belongs to. user_id: type: string description: The user the workflow version belongs to. description: type: string title: Short description about this workflow version license: type: string title: License associated to this workflow version is_deprecated: type: boolean description: 'If a model version associated with the workflow version is deleted, the workflow version will be marked as deprecated.' title: WorkflowVersion apiRunnerMethodType: type: string enum: - UNKNOWN - UNARY_UNARY - UNARY_STREAMING - STREAMING_UNARY - STREAMING_STREAMING default: UNKNOWN title: "- UNARY_UNARY: single request, single response. predict() in code\n - UNARY_STREAMING: single request, streamed response. generate() in code\n - STREAMING_UNARY: stream of requests, single response.\n - STREAMING_STREAMING: stream of requests, stream of responses. stream() in code" apiEmbedding: type: object properties: vector: type: array items: type: number format: float num_dimensions: type: integer format: int64 title: Embedding apiDatasetVersionExport: type: object properties: format: $ref: '#/components/schemas/apiDatasetVersionExportFormat' description: format is the format of the dataset version export. status: $ref: '#/components/schemas/apistatusStatus' description: status is the current status of the dataset version export. url: type: string description: url is the URL from where the dataset version export can be downloaded. size: type: string format: uint64 description: size is the size of the dataset version export in number of bytes. include_embeddings: type: boolean description: whether to include embeddings in the export or not. description: DatasetVersionExport contains metadata for a single dataset version export. apiNDArray: type: object properties: buffer: type: string format: byte title: The bytes of data from the array from array.tobytes() shape: type: array items: type: integer format: int64 description: Simply the shape of the numpy array. array.shape. dtype: type: string description: 'Dtype for numpy. You can get it back from this string format using: np.dtype(''float32'') for example.' description: 'To convert this proto ''ndarray_proto'' to a numpy array: array = np.frombuffer(ndarray_proto.buffer, dtype=ndarray_proto.dtype) array = array.reshape(tuple(ndarray_proto.shape))' title: 'A representation of a numpy array as a proto. To convert a numpy array ''ndarray'' to this proto do: NDArray(buffer=ndarray.tobytes(), shape=ndarray.shape, dtype=str(ndarray.dtype))'