Create, Get, Update, Delete

Manage your model training jobs.

Create Model

To create a model, you need to specify the model's name and other required fields (which depend on the model). Specifying the ID is optional.

Below, we create a classifier model with one initial concept. You can always add and remove concepts later.

Java

import com.clarifai.grpc.api.*;
import com.clarifai.grpc.api.status.*;

// Insert here the initialization code as outlined on this page:
// https://docs.clarifai.com/api-guide/api-overview/api-clients#client-installation-instructions

SingleModelResponse postModelsResponse = stub.postModels(
    PostModelsRequest.newBuilder().addModels(
        Model.newBuilder()
            .setId("petsID")
            .setOutputInfo(
                OutputInfo.newBuilder().setData(
                    Data.newBuilder().addConcepts(Concept.newBuilder().setId("boscoe"))
                )
            )
    ).build()
);

if (postModelsResponse.getStatus().getCode() != StatusCode.SUCCESS) {
    throw new RuntimeException("Post models failed, status: " + postModelsResponse.getStatus());

NodeJS

// Insert here the initialization code as outlined on this page:
// https://docs.clarifai.com/api-guide/api-overview/api-clients#client-installation-instructions

stub.PostModels(
    {
        models: [
            {
                id: "petsID",
                output_info: {
                    data: {concepts: [{id: "boscoe"}]},
                }
            }
        ]
    },
    metadata,
    (err, response) => {
        if (err) {
            throw new Error(err);
        }

        if (response.status.code !== 10000) {
            throw new Error("Post models failed, status: " + response.status.description);
        }
    }
);

Python

# Insert here the initialization code as outlined on this page:
# https://docs.clarifai.com/api-guide/api-overview/api-clients#client-installation-instructions

post_models_response = stub.PostModels(
    service_pb2.PostModelsRequest(
        models=[
            resources_pb2.Model(
                id="petsID",
                output_info=resources_pb2.OutputInfo(
                    data=resources_pb2.Data(
                        concepts=[resources_pb2.Concept(id="boscoe", value=1)]
                    ),
                )
            )
        ]
    ),
    metadata=metadata
)

if post_models_response.status.code != status_code_pb2.SUCCESS:
    print("There was an error with your request!")
    print("\tCode: {}".format(post_models_response.outputs[0].status.code))
    print("\tDescription: {}".format(post_models_response.outputs[0].status.description))
    print("\tDetails: {}".format(post_models_response.outputs[0].status.details))
    raise Exception("Post models failed, status: " + post_models_response.status.description)

cURL

curl -X POST \
  -H "Authorization: Key YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '
  {
    "model": {
      "id": "petsID",
      "output_info": {
        "data": {
          "concepts": [
            {
              "id": "boscoe",
              "value": 1
            }
          ]
        }
      }
    }
  }'\
  https://api.clarifai.com/v2/models

Javascript (REST)

const raw = JSON.stringify({
	"user_app_id": {
		"user_id": "{YOUR_USER_ID}",
		"app_id": "{YOUR_APP_ID}"
	},
  "model": {
    "id": "petsID",
    "output_info": {
      "data": {
        "concepts": [
          {
            "id": "boscoe",
            "value": 1
          }
        ]
      }
    }
  }
});

const requestOptions = {
  method: 'POST',
  headers: {
    'Accept': 'application/json',
    'Authorization': 'Key {YOUR_PERSONAL_TOKEN}'
  },
  body: raw
};

fetch("https://api.clarifai.com/v2/models", requestOptions)
  .then(response => response.text())
  .then(result => console.log(result))
  .catch(error => console.log('error', error));

Add Concepts To A Model

You can add concepts to a model at any point. As you add concepts to inputs, you may want to add them to your model.

Java

import com.clarifai.grpc.api.*;
import com.clarifai.grpc.api.status.*;

...

MultiModelResponse patchModelsResponse = stub.patchModels(
    PatchModelsRequest.newBuilder()
        .setAction("merge")  // Supported actions: overwrite, merge, remove
        .addModels(
            Model.newBuilder()
                .setId("petsID")
                .setOutputInfo(
                    OutputInfo.newBuilder().setData(
                        Data.newBuilder().addConcepts(Concept.newBuilder().setId("charlie"))
                    )
                )
        )
        .build()
);

if (patchModelsResponse.getStatus().getCode() != StatusCode.SUCCESS) {
    throw new RuntimeException("Patch models failed, status: " + patchModelsResponse.getStatus());
}

NodeJS

// Insert here the initialization code as outlined on this page:
// https://docs.clarifai.com/api-guide/api-overview/api-clients#client-installation-instructions

stub.PatchModels(
    {
        action: "merge",  // Supported actions: overwrite, merge, remove
        models: [
            {
                id: "petsID",
                output_info: {data: {concepts: [{id: "charlie"}]}}
            }
        ]
    },
    metadata,
    (err, response) => {
        if (err) {
            throw new Error(err);
        }

        if (response.status.code !== 10000) {
            throw new Error("Patch models failed, status: " + response.status.description);
        }
    }
);

Python

# Insert here the initialization code as outlined on this page:
# https://docs.clarifai.com/api-guide/api-overview/api-clients#client-installation-instructions

patch_models_response = stub.PatchModels(
    service_pb2.PatchModelsRequest(
        action="merge",  # Supported actions: overwrite, merge, remove
        models=[
            resources_pb2.Model(
                id="petsID",
                output_info=resources_pb2.OutputInfo(
                    data=resources_pb2.Data(
                        concepts=[resources_pb2.Concept(id="charlie")]
                    ),
                )
            )
        ]
    ),
    metadata=metadata
)

if patch_models_response.status.code != status_code_pb2.SUCCESS:
    print("There was an error with your request!")
    print("\tCode: {}".format(patch_models_response.outputs[0].status.code))
    print("\tDescription: {}".format(patch_models_response.outputs[0].status.description))
    print("\tDetails: {}".format(patch_models_response.outputs[0].status.details))
    raise Exception("Patch models failed, status: " + patch_models_response.status.description)

cURL

curl -X PATCH \
  -H "Authorization: Key YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '
  {
    "models": [
      {
        "id": "petsID",
        "output_info": {
          "data": {
            "concepts": [
              {
                "id": "charlie"
              }
            ]
          }
        }
      }
    ],
    "action": "merge"
  }'\
  https://api.clarifai.com/v2/models/

Javascript (REST)

const raw = JSON.stringify({
	"user_app_id": {
		"user_id": "{YOUR_USER_ID}",
		"app_id": "{YOUR_APP_ID}"
	},
  "models": [
    {
      "id": "petsID",
      "output_info": {
        "data": {
          "concepts": [
            {
              "id": "charlie"
            }
          ]
        }
      }
    }
  ],
  "action": "merge"
});

const requestOptions = {
  method: 'PATCH',
  headers: {
    'Accept': 'application/json',
    'Authorization': 'Key {YOUR_PERSONAL_TOKEN}'
  },
  body: raw
};

fetch("https://api.clarifai.com/v2/models", requestOptions)
  .then(response => response.text())
  .then(result => console.log(result))
  .catch(error => console.log('error', error));

Remove Concepts From A Model

Conversely, if you'd like to remove concepts from a model, you can also do that.

Java

import com.clarifai.grpc.api.*;
import com.clarifai.grpc.api.status.*;

// Insert here the initialization code as outlined on this page:
// https://docs.clarifai.com/api-guide/api-overview/api-clients#client-installation-instructions

MultiModelResponse patchModelsResponse = stub.patchModels(
    PatchModelsRequest.newBuilder()
        .setAction("remove")  // Supported actions: overwrite, merge, remove
        .addModels(
            Model.newBuilder()
                .setId("petsID")
                .setOutputInfo(
                    OutputInfo.newBuilder().setData(
                        Data.newBuilder().addConcepts(Concept.newBuilder().setId("charlie"))
                    )
                )
        )
        .build()
);

if (patchModelsResponse.getStatus().getCode() != StatusCode.SUCCESS) {
    throw new RuntimeException("Patch models failed, status: " + patchModelsResponse.getStatus());
}

NodeJS

// Insert here the initialization code as outlined on this page:
// https://docs.clarifai.com/api-guide/api-overview/api-clients#client-installation-instructions

stub.PatchModels(
    {
        action: "remove",  // Supported actions: overwrite, merge, remove
        models: [
            {
                id: "petsID",
                output_info: {data: {concepts: [{id: "charlie"}]}}
            }
        ]
    },
    metadata,
    (err, response) => {
        if (err) {
            throw new Error(err);
        }

        if (response.status.code !== 10000) {
            throw new Error("Patch models failed, status: " + response.status.description);
        }
    }
);

Python

# Insert here the initialization code as outlined on this page:
# https://docs.clarifai.com/api-guide/api-overview/api-clients#client-installation-instructions

patch_models_response = stub.PatchModels(
    service_pb2.PatchModelsRequest(
        action="remove",  # Supported actions: overwrite, merge, remove
        models=[
            resources_pb2.Model(
                id="petsID",
                output_info=resources_pb2.OutputInfo(
                    data=resources_pb2.Data(
                        concepts=[resources_pb2.Concept(id="charlie")]
                    ),
                )
            )
        ]
    ),
    metadata=metadata
)

if patch_models_response.status.code != status_code_pb2.SUCCESS:
    print("There was an error with your request!")
    print("\tCode: {}".format(patch_models_response.outputs[0].status.code))
    print("\tDescription: {}".format(patch_models_response.outputs[0].status.description))
    print("\tDetails: {}".format(patch_models_response.outputs[0].status.details))
    raise Exception("Patch models failed, status: " + patch_models_response.status.description)

cURL

curl -X PATCH \
  -H "Authorization: Key YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '
  {
    "models": [
      {
        "id": "petsID",
        "output_info": {
          "data": {
            "concepts": [
              {
                "id": "charlie"
              }
            ]
          }
        }
      }
    ],
    "action": "remove"
  }'\
  https://api.clarifai.com/v2/models/

Javascript (REST)

const raw = JSON.stringify({
	"user_app_id": {
		"user_id": "{YOUR_USER_ID}",
		"app_id": "{YOUR_APP_ID}"
	},
  "models": [
    {
      "id": "petsID",
      "output_info": {
        "data": {
          "concepts": [
            {
              "id": "charlie"
            }
          ]
        }
      }
    }
  ],
  "action": "remove"
});

const requestOptions = {
  method: 'PATCH',
  headers: {
    'Accept': 'application/json',
    'Authorization': 'Key {YOUR_PERSONAL_TOKEN}'
  },
  body: raw
};

fetch("https://api.clarifai.com/v2/models", requestOptions)
  .then(response => response.text())
  .then(result => console.log(result))
  .catch(error => console.log('error', error));

Update Model Name and Configuration

Here we will change the model name to 'newname' and the model's configuration to have concepts_mutually_exclusive=true and closed_environment=true.

Java

import com.clarifai.grpc.api.*;
import com.clarifai.grpc.api.status.*;

// Insert here the initialization code as outlined on this page:
// https://docs.clarifai.com/api-guide/api-overview/api-clients#client-installation-instructions

MultiModelResponse patchModelsResponse = stub.patchModels(
    PatchModelsRequest.newBuilder()
        .setAction("overwrite")
        .addModels(
            Model.newBuilder()
                .setId("petsID")
                .setName("newname")
                .setOutputInfo(
                    OutputInfo.newBuilder()
                        .setData(
                            Data.newBuilder()
                                .addConcepts(Concept.newBuilder().setId("birds"))
                                .addConcepts(Concept.newBuilder().setId("hurd"))
                        )
                        .setOutputConfig(
                            OutputConfig.newBuilder()
                                .setConceptsMutuallyExclusive(true)
                                .setClosedEnvironment(true)
                        )
                )
    ).build()
);

if (patchModelsResponse.getStatus().getCode() != StatusCode.SUCCESS) {
    throw new RuntimeException("Patch models failed, status: " + patchModelsResponse.getStatus());
}

NodeJS

// Insert here the initialization code as outlined on this page:
// https://docs.clarifai.com/api-guide/api-overview/api-clients#client-installation-instructions

stub.PatchModels(
    {
        action: "overwrite",
        models: [
            {
                id: "petsID",
                name: "newname",
                output_info: {
                    data: {concepts: [{id: "birds"}, {id: "hurd"}]},
                    output_config: {concepts_mutually_exclusive: true, closed_environment: true}
                }
            }
        ]
    },
    metadata,
    (err, response) => {
        if (err) {
            throw new Error(err);
        }

        if (response.status.code !== 10000) {
            throw new Error("Patch models failed, status: " + response.status.description);
        }
    }
);

Python

# Insert here the initialization code as outlined on this page:
# https://docs.clarifai.com/api-guide/api-overview/api-clients#client-installation-instructions

patch_models_response = stub.PatchModels(
    service_pb2.PatchModelsRequest(
        action="overwrite",
        models=[
            resources_pb2.Model(
                id="petsID",
                name="newname",
                output_info=resources_pb2.OutputInfo(
                    data=resources_pb2.Data(
                        concepts=[
                            resources_pb2.Concept(id="birds"),
                            resources_pb2.Concept(id="hurd")
                        ]
                    ),
                    output_config=resources_pb2.OutputConfig(
                        concepts_mutually_exclusive=True,
                        closed_environment=True,
                    )
                )
            )
        ]
    ),
    metadata=metadata
)

if patch_models_response.status.code != status_code_pb2.SUCCESS:
    print("There was an error with your request!")
    print("\tCode: {}".format(patch_models_response.outputs[0].status.code))
    print("\tDescription: {}".format(patch_models_response.outputs[0].status.description))
    print("\tDetails: {}".format(patch_models_response.outputs[0].status.details))
    raise Exception("Patch models failed, status: " + patch_models_response.status.description)

cURL

curl -X PATCH \
  -H "Authorization: Key YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '
  {
    "models": [
      {
        "id": "petsID",
        "name": "newname",
        "output_info": {
          "data": {"concepts": [{"id": "birds"}, {"id": "hurd"}]},
          "output_config": {
            "concepts_mutually_exclusive": true,
            "closed_environment": true
          }
        }
      }
    ],
    "action": "overwrite"
  }'\
  https://api.clarifai.com/v2/models/

Javascript (REST)

const raw = JSON.stringify({
	"user_app_id": {
		"user_id": "{YOUR_USER_ID}",
		"app_id": "{YOUR_APP_ID}"
	},
  "models": [
    {
      "id": "petsID",
      "name": "newname",
      "output_info": {
        "data": {"concepts": [{"id": "birds"}, {"id": "hurd"}]},
        "output_config": {
          "concepts_mutually_exclusive": true,
          "closed_environment": true
        }
      }
    }
  ],
  "action": "overwrite"
});

const requestOptions = {
  method: 'PATCH',
  headers: {
    'Accept': 'application/json',
    'Authorization': 'Key {YOUR_PERSONAL_TOKEN}'
  },
  body: raw
};

fetch("https://api.clarifai.com/v2/models", requestOptions)
  .then(response => response.text())
  .then(result => console.log(result))
  .catch(error => console.log('error', error));

Get

List Model Types

Learn about available model types and their hyperparameters. This endpoint lists all the possible models that are creatable (when creatable=true), or in general in the platform (the others ones have creatable=false).

Java

import com.clarifai.grpc.api.*;
import com.clarifai.grpc.api.status.*;

// Insert here the initialization code as outlined on this page:
// https://docs.clarifai.com/api-guide/api-overview/api-clients#client-installation-instructions

MultiModelTypeResponse listModelTypesResponse = stub.listModelTypes(ListModelTypesRequest.newBuilder().build());

for (ModelType modelType : listModelTypesResponse.getModelTypesList()) {
    System.out.println(modelType);
}

NodeJS

// Insert here the initialization code as outlined on this page:
// https://docs.clarifai.com/api-guide/api-overview/api-clients#client-installation-instructions

stub.ListModelTypes(
    {
        page: 1,
        per_page: 500
    },
    metadata,
    (err, response) => {
        if (err) {
            throw new Error(err);
        }

        if (response.status.code !== 10000) {
            throw new Error("Received status: " + response.status.description + "\n" + response.status.details);
        }

        for (const model_type of response.model_types) {
            console.log(model_type)
        }
    }
);

gRPC Python

# Insert here the initialization code as outlined on this page:
# https://docs.clarifai.com/api-guide/api-overview/api-clients#client-installation-instructions

response = stub.ListModelTypes(
    service_pb2.ListModelTypesRequest(), 
    metadata=metadata
    )

if response.status.code != status_code_pb2.SUCCESS:
    print("There was an error with your request!")
    print("\tCode: {}".format(response.outputs[0].status.code))
    print("\tDescription: {}".format(response.outputs[0].status.description))
    print("\tDetails: {}".format(response.outputs[0].status.details))
    raise Exception("Patch models failed, status: " + response.status.description)

for model_type in response.model_types:
  print(model_type)

cURL

curl -X GET 'https://api.clarifai.com/v2/models/types?per_page=20&page=1' \
    -H 'Authorization: Key YOUR_API_KEY'

Javascript (REST)

const appId = '{YOUR_APP_ID}'

const requestOptions = {
  method: 'GET',
  headers: {
    'Accept': 'application/json',
    'Authorization': 'Key {YOUR_PERSONAL_TOKEN}'
  }
};

fetch(`https://api.clarifai.com/v2/users/me/apps/${appId}/models/types?per_page=20&page=1`, requestOptions)
  .then(response => response.text())
  .then(result => console.log(result))
  .catch(error => console.log('error', error));

Get Models

To get a list of all models including models you've created as well as Clarifai models:

Java

import com.clarifai.grpc.api.*;
import com.clarifai.grpc.api.status.*;
import java.util.List;

// Insert here the initialization code as outlined on this page:
// https://docs.clarifai.com/api-guide/api-overview/api-clients#client-installation-instructions

MultiModelResponse listModelsResponse = stub.listModels(
    ListModelsRequest.newBuilder().build()
);

if (listModelsResponse.getStatus().getCode() != StatusCode.SUCCESS) {
    throw new RuntimeException("List models failed, status: " + listModelsResponse.getStatus());
}

List<Model> models = listModelsResponse.getModelsList();
for (Model model : models) {
    System.out.println(model);
}

NodeJS

// Insert here the initialization code as outlined on this page:
// https://docs.clarifai.com/api-guide/api-overview/api-clients#client-installation-instructions

stub.ListModels(
    {},
    metadata,
    (err, response) => {
        if (err) {
            throw new Error(err);
        }

        if (response.status.code !== 10000) {
            throw new Error("List models failed, status: " + response.status.description);
        }

        for (const model of response.models) {
            console.log(JSON.stringify(model, null, 2));
        }
    }
);

Python

# Insert here the initialization code as outlined on this page:
# https://docs.clarifai.com/api-guide/api-overview/api-clients#client-installation-instructions

list_models_response = stub.ListModels(
    service_pb2.ListModelsRequest(),
    metadata=metadata
)

if list_models_response.status.code != status_code_pb2.SUCCESS:
    print("There was an error with your request!")
    print("\tCode: {}".format(list_models_response.outputs[0].status.code))
    print("\tDescription: {}".format(list_models_response.outputs[0].status.description))
    print("\tDetails: {}".format(list_models_response.outputs[0].status.details))
    raise Exception("List models failed, status: " + list_models_response.status.description)

for model in list_models_response.models:
    print(model)

cURL

curl -X GET \
  -H "Authorization: Key YOUR_API_KEY" \
  https://api.clarifai.com/v2/models

Javascript (REST)

const appId = '{YOUR_APP_ID}'

const requestOptions = {
  method: 'GET',
  headers: {
    'Accept': 'application/json',
    'Authorization': 'Key {YOUR_PERSONAL_TOKEN}'
  }
};

fetch(`https://api.clarifai.com/v2/users/me/apps/${appId}/models`, requestOptions)
  .then(response => response.text())
  .then(result => console.log(result))
  .catch(error => console.log('error', error));

Get Model By Id

All models have unique Ids. You can get a specific model by its id:

Java

import com.clarifai.grpc.api.*;
import com.clarifai.grpc.api.status.*;

// Insert here the initialization code as outlined on this page:
// https://docs.clarifai.com/api-guide/api-overview/api-clients#client-installation-instructions

SingleModelResponse getModelResponse = stub.getModel(
    GetModelRequest.newBuilder()
        .setModelId("petsID")
        .build()
);

if (getModelResponse.getStatus().getCode() != StatusCode.SUCCESS) {
    throw new RuntimeException("Get model failed, status: " + getModelResponse.getStatus());
}

Model model = getModelResponse.getModel();
System.out.println(model);

NodeJS

// Insert here the initialization code as outlined on this page:
// https://docs.clarifai.com/api-guide/api-overview/api-clients#client-installation-instructions

stub.GetModel(
    {model_id: "petsID"},
    metadata,
    (err, response) => {
        if (err) {
            throw new Error(err);
        }

        if (response.status.code !== 10000) {
            throw new Error("List models failed, status: " + response.status.description);
        }

        const model = response.model;
        console.log(JSON.stringify(model, null, 2));
    }
);

Python

# Insert here the initialization code as outlined on this page:
# https://docs.clarifai.com/api-guide/api-overview/api-clients#client-installation-instructions

get_model_response = stub.GetModel(
    service_pb2.GetModelRequest(model_id="petsID"),
    metadata=metadata
)

if get_model_response.status.code != status_code_pb2.SUCCESS:
    print("There was an error with your request!")
    print("\tCode: {}".format(get_model_response.outputs[0].status.code))
    print("\tDescription: {}".format(get_model_response.outputs[0].status.description))
    print("\tDetails: {}".format(get_model_response.outputs[0].status.details))
    raise Exception("Get model failed, status: " + get_model_response.status.description)

model = get_model_response.model
print(model)

cURL

curl -X GET \
  -H "Authorization: Key YOUR_API_KEY" \
  https://api.clarifai.com/v2/models/petsID

Javascript (REST)

const appId = '{YOUR_APP_ID}'
const modelId = '{MODEL_ID}'

const requestOptions = {
  method: 'GET',
  headers: {
    'Accept': 'application/json',
    'Authorization': 'Key {YOUR_PERSONAL_TOKEN}'
  }
};

fetch(`https://api.clarifai.com/v2/users/me/apps/${appId}/models/${modelId}`, requestOptions)
  .then(response => response.text())
  .then(result => console.log(result))
  .catch(error => console.log('error', error));

Get Model Output Info By Id

The output info of a model lists what concepts it contains.

Java

import com.clarifai.grpc.api.*;
import com.clarifai.grpc.api.status.*;

// Insert here the initialization code as outlined on this page:
// https://docs.clarifai.com/api-guide/api-overview/api-clients#client-installation-instructions

SingleModelResponse getModelOutputInfoResponse = stub.getModelOutputInfo(
    GetModelRequest.newBuilder()
        .setModelId("petsID")
        .build()
);

if (getModelOutputInfoResponse.getStatus().getCode() != StatusCode.SUCCESS) {
    throw new RuntimeException("Get model output info failed, status: " + getModelOutputInfoResponse.getStatus());
}

Model model = getModelOutputInfoResponse.getModel();
System.out.println(model);

NodeJS

// Insert here the initialization code as outlined on this page:
// https://docs.clarifai.com/api-guide/api-overview/api-clients#client-installation-instructions

stub.GetModelOutputInfo(
    {model_id: "petsID"},
    metadata,
    (err, response) => {
        if (err) {
            throw new Error(err);
        }

        if (response.status.code !== 10000) {
            throw new Error("List models failed, status: " + response.status.description);
        }

        const model = response.model;
        console.log(JSON.stringify(model, null, 2));
    }
);

Python

# Insert here the initialization code as outlined on this page:
# https://docs.clarifai.com/api-guide/api-overview/api-clients#client-installation-instructions

get_model_response = stub.GetModelOutputInfo(
    service_pb2.GetModelRequest(model_id="petsID"),
    metadata=metadata
)

if get_model_response.status.code != status_code_pb2.SUCCESS:
    print("There was an error with your request!")
    print("\tCode: {}".format(get_model_response.outputs[0].status.code))
    print("\tDescription: {}".format(get_model_response.outputs[0].status.description))
    print("\tDetails: {}".format(get_model_response.outputs[0].status.details))
    raise Exception("Get model failed, status: " + get_model_response.status.description)

model = get_model_response.model
print(model)

cURL

curl -X GET \
  -H "Authorization: Key YOUR_API_KEY" \
  https://api.clarifai.com/v2/models/petsID/output_info

Javascript (REST)

const appId = '{YOUR_APP_ID}'
const modelId = '{MODEL_ID}'

const requestOptions = {
  method: 'GET',
  headers: {
    'Accept': 'application/json',
    'Authorization': 'Key {YOUR_PERSONAL_TOKEN}'
  }
};

fetch(`https://api.clarifai.com/v2/users/me/apps/${appId}/models/${modelId}/output_info`, requestOptions)
  .then(response => response.text())
  .then(result => console.log(result))
  .catch(error => console.log('error', error));

List Model Versions

Every time you train a model, it creates a new version. You can list all the versions created.

Java

import com.clarifai.grpc.api.*;
import com.clarifai.grpc.api.status.*;
import java.util.List;

// Insert here the initialization code as outlined on this page:
// https://docs.clarifai.com/api-guide/api-overview/api-clients#client-installation-instructions

MultiModelVersionResponse listModelVersionsResponse = stub.listModelVersions(
    ListModelVersionsRequest.newBuilder()
        .setModelId("petsID")
        .build()
);

if (listModelVersionsResponse.getStatus().getCode() != StatusCode.SUCCESS) {
    throw new RuntimeException("List model versions failed, status: " + listModelVersionsResponse.getStatus());
}

List<ModelVersion> modelVersions = listModelVersionsResponse.getModelVersionsList();
for (ModelVersion modelVersion : modelVersions) {
    System.out.println(modelVersion);
}

NodeJS

// Insert here the initialization code as outlined on this page:
// https://docs.clarifai.com/api-guide/api-overview/api-clients#client-installation-instructions

stub.ListModelVersions(
    {model_id: "petsID"},
    metadata,
    (err, response) => {
        if (err) {
            throw new Error(err);
        }

        if (response.status.code !== 10000) {
            throw new Error("List model versions failed, status: " + response.status.description);
        }

        for (const model_version of response.model_versions) {
            console.log(JSON.stringify(model_version, null, 2));
        }
    }
);

Python

# Insert here the initialization code as outlined on this page:
# https://docs.clarifai.com/api-guide/api-overview/api-clients#client-installation-instructions

list_model_versions_response = stub.ListModelVersions(
    service_pb2.ListModelVersionsRequest(
        model_id="petsID"
    ),
    metadata=metadata
)

if list_model_versions_response.status.code != status_code_pb2.SUCCESS:
    print("There was an error with your request!")
    print("\tCode: {}".format(list_model_versions_response.outputs[0].status.code))
    print("\tDescription: {}".format(list_model_versions_response.outputs[0].status.description))
    print("\tDetails: {}".format(list_model_versions_response.outputs[0].status.details))
    raise Exception("List model versions failed, status: " + list_model_versions_response.status.description)

for model_version in list_model_versions_response.model_versions:
    print(model_version)

cURL

curl -X GET \
  -H "Authorization: Key YOUR_API_KEY" \
  https://api.clarifai.com/v2/models/petsID/versions

Javascript (REST)

const appId = '{YOUR_APP_ID}'
const modelId = '{MODEL_ID}'

const requestOptions = {
  method: 'GET',
  headers: {
    'Accept': 'application/json',
    'Authorization': 'Key {YOUR_PERSONAL_TOKEN}'
  }
};

fetch(`https://api.clarifai.com/v2/users/me/apps/${appId}/models/${modelId}/versions`, requestOptions)
  .then(response => response.text())
  .then(result => console.log(result))
  .catch(error => console.log('error', error));

Get Model Version By Id

To get a specific model version, you must provide the model_id as well as the version_id. You can inspect the model version status to determine if your model is trained or still training.

Java

import com.clarifai.grpc.api.*;
import com.clarifai.grpc.api.status.*;

// Insert here the initialization code as outlined on this page:
// https://docs.clarifai.com/api-guide/api-overview/api-clients#client-installation-instructions


SingleModelVersionResponse getModelVersionResponse = stub.getModelVersion(
    GetModelVersionRequest.newBuilder()
        .setModelId("petsID")
        .setVersionId("{YOUR_MODEL_VERSION_ID}")
        .build()
);

if (getModelVersionResponse.getStatus().getCode() != StatusCode.SUCCESS) {
    throw new RuntimeException("Get model version failed, status: " + getModelVersionResponse.getStatus());
}

ModelVersion modelVersion = getModelVersionResponse.getModelVersion();
System.out.println(modelVersion);

NodeJS

// Insert here the initialization code as outlined on this page:
// https://docs.clarifai.com/api-guide/api-overview/api-clients#client-installation-instructions

stub.GetModelVersion(
    {model_id: "petsID", version_id: "{YOUR_MODEL_VERSION_ID}"},
    metadata,
    (err, response) => {
        if (err) {
            throw new Error(err);
        }

        if (response.status.code !== 10000) {
            throw new Error("Get model version failed, status: " + response.status.description);
        }

        const model_version = response.model_version;
        console.log(JSON.stringify(model_version, null, 2));
    }
);

Python

# Insert here the initialization code as outlined on this page:
# https://docs.clarifai.com/api-guide/api-overview/api-clients#client-installation-instructions

get_model_version_response = stub.GetModelVersion(
    service_pb2.GetModelVersionRequest(
        model_id="petsID",
        version_id="{YOUR_MODEL_VERSION_ID}"
    ),
    metadata=metadata
)

if get_model_version_response.status.code != status_code_pb2.SUCCESS:
    print("There was an error with your request!")
    print("\tCode: {}".format(get_model_version_response.outputs[0].status.code))
    print("\tDescription: {}".format(get_model_version_response.outputs[0].status.description))
    print("\tDetails: {}".format(get_model_version_response.outputs[0].status.details))
    raise Exception("Get model version failed, status: " + get_model_version_response.status.description)

model_version = get_model_version_response.model_version
print(model_version)

cURL

curl -X GET \
  -H "Authorization: Key YOUR_API_KEY" \
  https://api.clarifai.com/v2/models/petsID/versions/{YOUR_MODEL_VERSION_ID}

Javascript (REST)

const appId = '{YOUR_APP_ID}'
const modelId = '{MODEL_ID}'
const modelVersionId = '{MODEL_VERSION_ID}'

const requestOptions = {
  method: 'GET',
  headers: {
    'Accept': 'application/json',
    'Authorization': 'Key {YOUR_PERSONAL_TOKEN}'
  }
};

fetch(`https://api.clarifai.com/v2/users/me/apps/${appId}/models/${modelId}/versions/${modelVersionId}`, requestOptions)
  .then(response => response.text())
  .then(result => console.log(result))
  .catch(error => console.log('error', error));

Get Model Training Inputs

You can list all the inputs that were used to train the model.

Java

import com.clarifai.grpc.api.*;
import com.clarifai.grpc.api.status.*;

// Insert here the initialization code as outlined on this page:
// https://docs.clarifai.com/api-guide/api-overview/api-clients#client-installation-instructions

MultiInputResponse listModelInputsResponse = stub.listModelInputs(
    ListModelInputsRequest.newBuilder()
        .setModelId("petsID")
        .build()
);

if (listModelInputsResponse.getStatus().getCode() != StatusCode.SUCCESS) {
    throw new RuntimeException("List model inputs failed, status: " + listModelInputsResponse.getStatus());
}

List<Input> inputs = listModelInputsResponse.getInputsList();
for (Input input : inputs) {
    System.out.println(input);
}

NodeJS

// Insert here the initialization code as outlined on this page:
// https://docs.clarifai.com/api-guide/api-overview/api-clients#client-installation-instructions

stub.ListModelInputs(
    {model_id: "petsID"},
    metadata,
    (err, response) => {
        if (err) {
            throw new Error(err);
        }

        if (response.status.code !== 10000) {
            throw new Error("List model inputs failed, status: " + response.status.description);
        }

        for (const input of response.inputs) {
            console.log(JSON.stringify(input, null, 2));
        }
    }
);

Python

# Insert here the initialization code as outlined on this page:
# https://docs.clarifai.com/api-guide/api-overview/api-clients#client-installation-instructions

list_model_inputs_response = stub.ListModelInputs(
    service_pb2.ListModelInputsRequest(model_id="petsID"),
    metadata=metadata
)

if list_model_inputs_response.status.code != status_code_pb2.SUCCESS:
    print("There was an error with your request!")
    print("\tCode: {}".format(list_model_inputs_response.outputs[0].status.code))
    print("\tDescription: {}".format(list_model_inputs_response.outputs[0].status.description))
    print("\tDetails: {}".format(list_model_inputs_response.outputs[0].status.details))
    raise Exception("List model inputs failed, status: " + list_model_inputs_response.status.description)

for input_object in list_model_inputs_response.inputs:
    print(input_object)

cURL

curl -X GET \
  -H "Authorization: Key YOUR_API_KEY" \
  https://api.clarifai.com/v2/models/petsID/inputs

Javascript (REST)

const appId = '{YOUR_APP_ID}'
const modelId = '{MODEL_ID}'

const requestOptions = {
  method: 'GET',
  headers: {
    'Accept': 'application/json',
    'Authorization': 'Key {YOUR_PERSONAL_TOKEN}'
  }
};

fetch(`https://api.clarifai.com/v2/users/me/apps/${appId}/models/${modelId}/inputs`, requestOptions)
  .then(response => response.text())
  .then(result => console.log(result))
  .catch(error => console.log('error', error));

Get Model Training Inputs By Version

You can also list all the inputs that were used to train a specific model version.

Java

import com.clarifai.grpc.api.*;
import com.clarifai.grpc.api.status.*;
import java.util.List;

// Insert here the initialization code as outlined on this page:
// https://docs.clarifai.com/api-guide/api-overview/api-clients#client-installation-instructions

MultiInputResponse listModelInputsResponse = stub.listModelInputs(
    ListModelInputsRequest.newBuilder()
        .setModelId("petsID")
        .setVersionId("{YOUR_MODEL_VERSION_ID}")
        .build()
);

if (listModelInputsResponse.getStatus().getCode() != StatusCode.SUCCESS) {
    throw new RuntimeException("List model inputs failed, status: " + listModelInputsResponse.getStatus());
}

List<Input> inputs = listModelInputsResponse.getInputsList();
for (Input input : inputs) {
    System.out.println(input);
}

NodeJS

// Insert here the initialization code as outlined on this page:
// https://docs.clarifai.com/api-guide/api-overview/api-clients#client-installation-instructions

stub.ListModelInputs(
    {
        model_id: "petsID",
        version_id: "{YOUR_MODEL_VERSION_ID}"
    },
    metadata,
    (err, response) => {
        if (err) {
            throw new Error(err);
        }

        if (response.status.code !== 10000) {
            throw new Error("List model inputs failed, status: " + response.status.description);
        }

        for (const input of response.inputs) {
            console.log(JSON.stringify(input, null, 2));
        }
    }
);

Python

# Insert here the initialization code as outlined on this page:
# https://docs.clarifai.com/api-guide/api-overview/api-clients#client-installation-instructions

list_model_inputs_response = stub.ListModelInputs(
    service_pb2.ListModelInputsRequest(
        model_id="petsID",
        version_id="{YOUR_MODEL_VERSION_ID}"
    ),
    metadata=metadata
)

if list_model_inputs_response.status.code != status_code_pb2.SUCCESS:
    print("There was an error with your request!")
    print("\tCode: {}".format(list_model_inputs_response.outputs[0].status.code))
    print("\tDescription: {}".format(list_model_inputs_response.outputs[0].status.description))
    print("\tDetails: {}".format(list_model_inputs_response.outputs[0].status.details))
    raise Exception("List model inputs failed, status: " + list_model_inputs_response.status.description)

for input_object in list_model_inputs_response.inputs:
    print(input_object)

cURL

curl -X GET \
  -H "Authorization: Key YOUR_API_KEY" \
  https://api.clarifai.com/v2/models/petsID/versions/{YOUR_MODEL_VERSION_ID}/inputs

Javascript (REST)

const appId = '{YOUR_APP_ID}'
const modelId = '{MODEL_ID}'
const modelVersionId = '{MODEL_VERSION_ID}'

const requestOptions = {
  method: 'GET',
  headers: {
    'Accept': 'application/json',
    'Authorization': 'Key {YOUR_PERSONAL_TOKEN}'
  }
};

fetch(`https://api.clarifai.com/v2/users/me/apps/${appId}/models/${modelId}/versions/${modelVersionId}/inputs`, requestOptions)
  .then(response => response.text())
  .then(result => console.log(result))
  .catch(error => console.log('error', error));

Delete A Model

You can delete a model using the model_id.

Java

import com.clarifai.grpc.api.*;
import com.clarifai.grpc.api.status.*;

// Insert here the initialization code as outlined on this page:
// https://docs.clarifai.com/api-guide/api-overview/api-clients#client-installation-instructions

BaseResponse deleteModelResponse = stub.deleteModel(
    DeleteModelRequest.newBuilder()
        .setModelId("petsID")
        .build()
);

if (deleteModelResponse.getStatus().getCode() != StatusCode.SUCCESS) {
    throw new RuntimeException("Delete model failed, status: " + deleteModelResponse.getStatus());
}

NodeJS

// Insert here the initialization code as outlined on this page:
// https://docs.clarifai.com/api-guide/api-overview/api-clients#client-installation-instructions

stub.DeleteModel(
    {model_id: "petsID"},
    metadata,
    (err, response) => {
        if (err) {
            throw new Error(err);
        }

        if (response.status.code !== 10000) {
            throw new Error("Delete model failed, status: " + response.status.description);
        }
    }
);

Python

# Insert here the initialization code as outlined on this page:
# https://docs.clarifai.com/api-guide/api-overview/api-clients#client-installation-instructions

delete_model_response = stub.DeleteModel(
    service_pb2.DeleteModelRequest(model_id="petsID"),
    metadata=metadata
)

if delete_model_response.status.code != status_code_pb2.SUCCESS:
    print("There was an error with your request!")
    print("\tCode: {}".format(delete_model_response.outputs[0].status.code))
    print("\tDescription: {}".format(delete_model_response.outputs[0].status.description))
    print("\tDetails: {}".format(delete_model_response.outputs[0].status.details))
    raise Exception("Delete model failed, status: " + delete_model_response.status.description)

cURL

curl -X DELETE \
  -H "Authorization: Key YOUR_API_KEY" \
  https://api.clarifai.com/v2/models/petsID

Javascript (REST)

const appId = '{YOUR_APP_ID}'
const modelId = '{MODEL_ID}'

const requestOptions = {
  method: 'DELETE',
  headers: {
    'Accept': 'application/json',
    'Authorization': 'Key {YOUR_PERSONAL_TOKEN}'
  }
};

fetch(`https://api.clarifai.com/v2/users/me/apps/${appId}/models/${modelId}`, requestOptions)
  .then(response => response.text())
  .then(result => console.log(result))
  .catch(error => console.log('error', error));

Delete A Model Version

You can also delete a specific version of a model with the model_id and version_id.

Java

import com.clarifai.grpc.api.*;
import com.clarifai.grpc.api.status.*;

// Insert here the initialization code as outlined on this page:
// https://docs.clarifai.com/api-guide/api-overview/api-clients#client-installation-instructions

BaseResponse deleteModelVersionResponse = stub.deleteModelVersion(
    DeleteModelVersionRequest.newBuilder()
        .setModelId("petsID")
        .setVersionId("{YOUR_MODEL_VERSION_ID}")
        .build()
);

if (deleteModelVersionResponse.getStatus().getCode() != StatusCode.SUCCESS) {
    throw new RuntimeException("Delete model version failed, status: " + deleteModelVersionResponse.getStatus());
}

NodeJS

// Insert here the initialization code as outlined on this page:
// https://docs.clarifai.com/api-guide/api-overview/api-clients#client-installation-instructions

stub.DeleteModelVersion(
    {
        model_id: "petsID",
        version_id: "{YOUR_MODEL_VERSION_ID}"
    },
    metadata,
    (err, response) => {
        if (err) {
            throw new Error(err);
        }

        if (response.status.code !== 10000) {
            throw new Error("Delete model version failed, status: " + response.status.description);
        }
    }
);

Python

# Insert here the initialization code as outlined on this page:
# https://docs.clarifai.com/api-guide/api-overview/api-clients#client-installation-instructions

delete_model_version_response = stub.DeleteModelVersion(
    service_pb2.DeleteModelVersionRequest(
        model_id="petsID",
        version_id="{YOUR_MODEL_VERSION_ID}"
    ),
    metadata=metadata
)

if delete_model_version_response.status.code != status_code_pb2.SUCCESS:
    print("There was an error with your request!")
    print("\tCode: {}".format(delete_model_version_response.outputs[0].status.code))
    print("\tDescription: {}".format(delete_model_version_response.outputs[0].status.description))
    print("\tDetails: {}".format(delete_model_version_response.outputs[0].status.details))
    raise Exception("Delete model version failed, status: " + delete_model_version_response.status.description)

cURL

curl -X DELETE \
  -H "Authorization: Key YOUR_API_KEY" \
  https://api.clarifai.com/v2/models/petsID/versions/{YOUR_MODEL_VERSION_ID}

Javascript (REST)

const appId = '{YOUR_APP_ID}'
const modelId = '{MODEL_ID}'
const modelVersionId = '{MODEL_VERSION_ID}'

const requestOptions = {
  method: 'DELETE',
  headers: {
    'Accept': 'application/json',
    'Authorization': 'Key {YOUR_PERSONAL_TOKEN}'
  }
};

fetch(`https://api.clarifai.com/v2/users/me/apps/${appId}/models/${modelId}/versions/${modelVersionId}`, requestOptions)
  .then(response => response.text())
  .then(result => console.log(result))
  .catch(error => console.log('error', error));

Delete All Models

If you would like to delete all models associated with an application, you can also do that. Please proceed with caution as these cannot be recovered.

Java

import com.clarifai.grpc.api.*;
import com.clarifai.grpc.api.status.*;

// Insert here the initialization code as outlined on this page:
// https://docs.clarifai.com/api-guide/api-overview/api-clients#client-installation-instructions

BaseResponse deleteModelsResponse = stub.deleteModels(
    DeleteModelsRequest.newBuilder()
        .setDeleteAll(true)
        .build()
);

if (deleteModelsResponse.getStatus().getCode() != StatusCode.SUCCESS) {
    throw new RuntimeException("Delete models failed, status: " + deleteModelsResponse.getStatus());
}

NodeJS

// Insert here the initialization code as outlined on this page:
// https://docs.clarifai.com/api-guide/api-overview/api-clients#client-installation-instructions

stub.DeleteModels(
    {delete_all: true},
    metadata,
    (err, response) => {
        if (err) {
            throw new Error(err);
        }

        if (response.status.code !== 10000) {
            throw new Error("Delete models failed, status: " + response.status.description);
        }
    }
);

Python

# Insert here the initialization code as outlined on this page:
# https://docs.clarifai.com/api-guide/api-overview/api-clients#client-installation-instructions

delete_models_response = stub.DeleteModels(
    service_pb2.DeleteModelsRequest(delete_all=True),
    metadata=metadata
)

if delete_models_response.status.code != status_code_pb2.SUCCESS:
    print("There was an error with your request!")
    print("\tCode: {}".format(delete_models_response.outputs[0].status.code))
    print("\tDescription: {}".format(delete_models_response.outputs[0].status.description))
    print("\tDetails: {}".format(delete_models_response.outputs[0].status.details))
    raise Exception("Delete models failed, status: " + delete_models_response.status.description)

cURL

curl -X DELETE \
  -H "Authorization: Key YOUR_API_KEY" \
  https://api.clarifai.com/v2/models/

Javascript (REST)

const appId = '{YOUR_APP_ID}'

const requestOptions = {
  method: 'DELETE',
  headers: {
    'Accept': 'application/json',
    'Authorization': 'Key {YOUR_PERSONAL_TOKEN}'
  }
};

fetch(`https://api.clarifai.com/v2/users/me/apps/${appId}/models`, requestOptions)
  .then(response => response.text())
  .then(result => console.log(result))
  .catch(error => console.log('error', error));

Train A Model

When you train a model, you are telling the system to look at successfully indexed images with concepts you've provided and learn from them. This train operation is asynchronous. It may take a few seconds for your model to be fully trained and ready.

Note: you can repeat this operation as often as you like. By adding more images with concepts and training, you can get the model to predict exactly how you want it to.

Java

import com.clarifai.grpc.api.*;
import com.clarifai.grpc.api.status.*;

// Insert here the initialization code as outlined on this page:
// https://docs.clarifai.com/api-guide/api-overview/api-clients#client-installation-instructions

SingleModelResponse postModelVersionsResponse = stub.postModelVersions(
    PostModelVersionsRequest.newBuilder()
        .setModelId("petsID")
        .build()
);

if (postModelVersionsResponse.getStatus().getCode() != StatusCode.SUCCESS) {
  throw new RuntimeException("Post model versions failed, status: " + postModelVersionsResponse.getStatus());
}

String modelVersionId = postModelVersionsResponse.getModel().getModelVersion().getId();
System.out.println("New model version ID: " + modelVersionId);

NodeJS

// Insert here the initialization code as outlined on this page:
// https://docs.clarifai.com/api-guide/api-overview/api-clients#client-installation-instructions

stub.PostModelVersions(
    {model_id: "petsID"},
    metadata,
    (err, response) => {
        if (err) {
            throw new Error(err);
        }

        if (response.status.code !== 10000) {
            throw new Error("Post model versions failed, status: " + response.status.description);
        }
    }
);

Python

# Insert here the initialization code as outlined on this page:
# https://docs.clarifai.com/api-guide/api-overview/api-clients#client-installation-instructions

post_model_versions = stub.PostModelVersions(
    service_pb2.PostModelVersionsRequest(
        model_id="petsID"
    ),
    metadata=metadata
)

if post_model_versions.status.code != status_code_pb2.SUCCESS:
    print("There was an error with your request!")
    print("\tCode: {}".format(post_model_versions.outputs[0].status.code))
    print("\tDescription: {}".format(post_model_versions.outputs[0].status.description))
    print("\tDetails: {}".format(post_model_versions.outputs[0].status.details))
    raise Exception("Post model versions failed, status: " + post_model_versions.status.description)

cURL

curl -X POST \
  -H "Authorization: Key YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  https://api.clarifai.com/v2/models/petsID/versions

Javascript (REST)

const appId = '{YOUR_APP_ID}'
const modelId = '{MODEL_ID}'

const requestOptions = {
  method: 'POST',
  headers: {
    'Accept': 'application/json',
    'Authorization': 'Key {YOUR_PERSONAL_TOKEN}'
  }
};

fetch(`https://api.clarifai.com/v2/users/me/apps/${appId}/models/${modelId}/versions`, requestOptions)
  .then(response => response.text())
  .then(result => console.log(result))
  .catch(error => console.log('error', error));

Predict With A Model

Once you have trained a model you are ready to use your new model to get predictions. The predictions returned will only contain the concepts that you told it to see.

Java

import com.clarifai.grpc.api.*;
import com.clarifai.grpc.api.status.*;

// Insert here the initialization code as outlined on this page:
// https://docs.clarifai.com/api-guide/api-overview/api-clients#client-installation-instructions

MultiOutputResponse postModelOutputsResponse = stub.postModelOutputs(
    PostModelOutputsRequest.newBuilder()
        .setModelId("petsID")
        .setVersionId("{YOUR_MODEL_VERSION_ID}")  // Optional. Defaults to the latest version.
        .addInputs(
            Input.newBuilder().setData(
                Data.newBuilder().setImage(
                    Image.newBuilder().setUrl("https://samples.clarifai.com/metro-north.jpg")
                )
            )
        )
        .build()
);

if (postModelOutputsResponse.getStatus().getCode() != StatusCode.SUCCESS) {
  throw new RuntimeException("Post model outputs failed, status: " + postModelOutputsResponse.getStatus());
}

// Since we have one input, one output will exist here.
Output output = postModelOutputsResponse.getOutputs(0);

System.out.println("Predicted concepts:");
for (Concept concept : output.getData().getConceptsList()) {
    System.out.printf("%s %.2f%n", concept.getName(), concept.getValue());
}

NodeJS

// Insert here the initialization code as outlined on this page:
// https://docs.clarifai.com/api-guide/api-overview/api-clients#client-installation-instructions

stub.PostModelOutputs(
    {
        model_id: "petsID",
        version_id: "{YOUR_MODEL_VERSION_ID}",  // This is optional. Defaults to the latest model version.
        inputs: [
            {data: {image: {url: "https://samples.clarifai.com/metro-north.jpg"}}}
        ]
    },
    metadata,
    (err, response) => {
        if (err) {
            throw new Error(err);
        }

        if (response.status.code !== 10000) {
            throw new Error("Post model outputs failed, status: " + response.status.description);
        }

        // Since we have one input, one output will exist here.
        const output = response.outputs[0];

        console.log("Predicted concepts:");
        for (const concept of output.data.concepts) {
            console.log(concept.name + " " + concept.value);
        }
    }
);

Python

# Insert here the initialization code as outlined on this page:
# https://docs.clarifai.com/api-guide/api-overview/api-clients#client-installation-instructions

post_model_outputs_response = stub.PostModelOutputs(
    service_pb2.PostModelOutputsRequest(
        model_id="petsID",
        version_id="{YOUR_MODEL_VERSION_ID}",  # This is optional. Defaults to the latest model version.
        inputs=[
            resources_pb2.Input(
                data=resources_pb2.Data(
                    image=resources_pb2.Image(
                        url="https://samples.clarifai.com/metro-north.jpg"
                    )
                )
            )
        ]
    ),
    metadata=metadata
)
if post_model_outputs_response.status.code != status_code_pb2.SUCCESS:
    print("There was an error with your request!")
    print("\tCode: {}".format(post_model_outputs_response.outputs[0].status.code))
    print("\tDescription: {}".format(post_model_outputs_response.outputs[0].status.description))
    print("\tDetails: {}".format(post_model_outputs_response.outputs[0].status.details))
    raise Exception("Post model outputs failed, status: " + post_model_outputs_response.status.description)

# Since we have one input, one output will exist here.
output = post_model_outputs_response.outputs[0]

print("Predicted concepts:")
for concept in output.data.concepts:
    print("%s %.2f" % (concept.name, concept.value))

cURL

curl -X POST \
  -H "Authorization: Key YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '
  {
    "inputs": [
      {
        "data": {
          "image": {
            "url": "https://samples.clarifai.com/puppy.jpeg"
          }
        }
      }
    ]
  }'\
  https://api.clarifai.com/v2/models/petsID/outputs

# Model version defaults to latest. If you want to specify the model version, use this URL:
# https://api.clarifai.com/v2/models/petsID/versions/{YOUR_MODEL_VERSION_ID}/outputs

Javascript (REST)

const raw = JSON.stringify({
  "user_app_id": {
		"user_id": "{YOUR_USER_ID}",
		"app_id": "{YOUR_APP_ID}"
	},
  "inputs": [
    {
      "data": {
        "image": {
          "url": "https://samples.clarifai.com/puppy.jpeg"
        }
      }
    }
  ]
});

const requestOptions = {
  method: 'POST',
  headers: {
    'Accept': 'application/json',
    'Authorization': 'Key {YOUR_PERSONAL_TOKEN}'
  },
  body: raw
};

// NOTE: MODEL_VERSION_ID is optional, you can also call prediction with the MODEL_ID only
// https://api.clarifai.com/v2/models/{YOUR_MODEL_ID}/outputs
// this will default to the latest version_id

fetch("https://api.clarifai.com/v2/models/petsID/versions/{MODEL_VERSION_ID}/outputs", requestOptions)
  .then(response => response.text())
  .then(result => console.log(JSON.parse(result, null, 2).outputs[0].data))
  .catch(error => console.log('error', error));

Search Models By Name And Type

You can search all your models by name and type of model.

Java

import com.clarifai.grpc.api.*;
import com.clarifai.grpc.api.status.*;
import java.util.List;

// Insert here the initialization code as outlined on this page:
// https://docs.clarifai.com/api-guide/api-overview/api-clients#client-installation-instructions

MultiModelResponse postModelsSearchesResponse = stub.postModelsSearches(
    PostModelsSearchesRequest.newBuilder()
        .setModelQuery(
            ModelQuery.newBuilder()
                .setName("gen*")
                .setType("concept")
        )
        .build()
);

if (postModelsSearchesResponse.getStatus().getCode() != StatusCode.SUCCESS) {
    throw new RuntimeException("Post models searches failed, status: " + postModelsSearchesResponse.getStatus());
}

List<Model> models = postModelsSearchesResponse.getModelsList();
for (Model model : models) {
    System.out.println(model);
}

NodeJS

// Insert here the initialization code as outlined on this page:
// https://docs.clarifai.com/api-guide/api-overview/api-clients#client-installation-instructions

stub.PostModelsSearches(
    {
        model_query: {
            name: "gen*",
            type: "concept"
        }
    },
    metadata,
    (err, response) => {
        if (err) {
            throw new Error(err);
        }

        if (response.status.code !== 10000) {
            throw new Error("Post model searches failed, status: " + response.status.description);
        }

        const models = response.models;
        for (const model of models) {
            console.log(JSON.stringify(model, null, 2));
        }
    }
);

Python

# Insert here the initialization code as outlined on this page:
# https://docs.clarifai.com/api-guide/api-overview/api-clients#client-installation-instructions

post_models_searches_response = stub.PostModelsSearches(
    service_pb2.PostModelsSearchesRequest(
        model_query=resources_pb2.ModelQuery(
            name="gen*",
            type="concept"
        )
    ),
    metadata=metadata
)

if post_models_searches_response.status.code != status_code_pb2.SUCCESS:
    print("There was an error with your request!")
    print("\tCode: {}".format(post_models_searches_response.outputs[0].status.code))
    print("\tDescription: {}".format(post_models_searches_response.outputs[0].status.description))
    print("\tDetails: {}".format(post_models_searches_response.outputs[0].status.details))
    raise Exception("Post models searches failed, status: " + post_models_searches_response.status.description)

for model in post_models_searches_response.models:
    print(model)

cURL

curl -X POST \
  -H "Authorization: Key YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '
  {
    "model_query": {
      "name": "gen*",
      "type": "concept"
    }
  }'\
  https://api.clarifai.com/v2/models/searches

Javascript (REST)

const raw = JSON.stringify({
  "user_app_id": {
		"user_id": "{YOUR_USER_ID}",
		"app_id": "{YOUR_APP_ID}"
	},
  "model_query": {
    "name": "gen*",
    "type": "concept"
  }
});

const requestOptions = {
  method: 'POST',
  headers: {
    'Accept': 'application/json',
    'Authorization': 'Key {YOUR_PERSONAL_TOKEN}'
  },
  body: raw
};

fetch("https://api.clarifai.com/v2/models/searches", requestOptions)
  .then(response => response.text())
  .then(result => console.log(result))
  .catch(error => console.log('error', error));