This API call produces the following media types according to the Accept request header;
the media type will be conveyed by the Content-Type response header.
application/json
Responses
200
Successful response that contains a dictionary of key-value string pairs that is defined in the dlpd.conf file.
StringMap
Deletes all events for a deep learning entity (clearEntityEvents)
Deletes all events for a deep learning entity.
Path parameters
entitytype (required)
Path Parameter — The deep learning event entity type.
entityname (required)
Path Parameter — The deep learning event entity name.
Consumes
This API call consumes the following media types via the Content-Type request header:
application/json
Produces
This API call produces the following media types according to the Accept request header;
the media type will be conveyed by the Content-Type response header.
application/json
Responses
204
OK. Successfully deleted the deep learning event.
400
The request format is invalid.
401
Authentication error. The request was denied.
403
Forbidden. The request was denied.
404
The requested resource is not found.
409
Conflict. The requested resource cannot be deleted because it is in use.
This API call consumes the following media types via the Content-Type request header:
application/json
Produces
This API call produces the following media types according to the Accept request header;
the media type will be conveyed by the Content-Type response header.
application/json
Responses
204
OK. Successfully deleted the deep learning event.
400
The request format is invalid.
401
Authentication error. The request was denied.
403
Forbidden. The request was denied.
404
The requested resource is not found.
409
Conflict. The requested resource cannot be deleted because it is in use.
This API call produces the following media types according to the Accept request header;
the media type will be conveyed by the Content-Type response header.
application/json
Responses
200
Successful response that contains the resulting deep learning events.
400
Bad request. The request was not formatted correctly.
This API call produces the following media types according to the Accept request header;
the media type will be conveyed by the Content-Type response header.
application/json
Responses
200
Successful response that contains the resulting deep learning event.
EventData
400
Bad request. The request was not formatted correctly.
This API call produces the following media types according to the Accept request header;
the media type will be conveyed by the Content-Type response header.
application/json
Responses
200
Successful response that contains the resulting deep learning events.
400
Bad request. The request was not formatted correctly.
This API call produces the following media types according to the Accept request header;
the media type will be conveyed by the Content-Type response header.
application/json
Responses
200
Successful response that contains the resulting deep learning event.
EventData
400
Bad request. The request was not formatted correctly.
Body Parameter — The information that specifies the details of a deep learning event to log.
Produces
This API call produces the following media types according to the Accept request header;
the media type will be conveyed by the Content-Type response header.
Deletes all tasks started by the current users. (execsDelete)
Delete all tasks
Consumes
This API call consumes the following media types via the Content-Type request header:
application/json
Produces
This API call produces the following media types according to the Accept request header;
the media type will be conveyed by the Content-Type response header.
Deletes a task started through Execute (execsExecIdDelete)
Deletes a task
Path parameters
execId (required)
Path Parameter — ID of task
Consumes
This API call consumes the following media types via the Content-Type request header:
application/json
Produces
This API call produces the following media types according to the Accept request header;
the media type will be conveyed by the Content-Type response header.
This API call produces the following media types according to the Accept request header;
the media type will be conveyed by the Content-Type response header.
Retrieves a task started through Execute (execsExecIdGet)
Retrieves a task started through Execute. The returned values 'submissionId' can be used to make other Conductor REST calls to get additional task details.
Path parameters
execId (required)
Path Parameter — ID of task
Consumes
This API call consumes the following media types via the Content-Type request header:
This API call produces the following media types according to the Accept request header;
the media type will be conveyed by the Content-Type response header.
Retrieve logs of the training task by execution ID. (execsExecIdLogGet)
Retrieve logs of the training task by execution ID.
Path parameters
execId (required)
Path Parameter — Execution ID of the training task.
Consumes
This API call consumes the following media types via the Content-Type request header:
application/json
Return type
String
Example data
Content-Type: application/json
""
Produces
This API call produces the following media types according to the Accept request header;
the media type will be conveyed by the Content-Type response header.
application/json
Responses
200
Successful response that contains the logs of this training task.
String
Retrieve the result of the training task using an execution ID. (execsExecIdResultGet)
Retrieve the result of the training task using an execution ID.
Path parameters
execId (required)
Path Parameter — Execution ID of the training task.
Consumes
This API call consumes the following media types via the Content-Type request header:
application/json
Return type
String
Example data
Content-Type: application/json
""
Produces
This API call produces the following media types according to the Accept request header;
the media type will be conveyed by the Content-Type response header.
application/json
Responses
200
Successful response that contains the trained model of the training task. Returned as a zip file.
String
Stop the training task by execution ID. (execsExecIdStopPost)
Stop the training task by execution ID.
Path parameters
execId (required)
Path Parameter — Execution ID of the training task.
Consumes
This API call consumes the following media types via the Content-Type request header:
application/json
Produces
This API call produces the following media types according to the Accept request header;
the media type will be conveyed by the Content-Type response header.
This API call produces the following media types according to the Accept request header;
the media type will be conveyed by the Content-Type response header.
application/json
Responses
200
Successful response that contains all deep learning framework plugins. Framework plugin names are used to start a task.
This API call produces the following media types according to the Accept request header;
the media type will be conveyed by the Content-Type response header.
This API call consumes the following media types via the Content-Type request header:
application/json
Query parameters
args (required)
Query Parameter — Arguments to the task. These arguments can be found in the command line interface. They can be model specific arguments. Examples are "--exec-start tensorflow --model-main TF_mnist.py", "--exec-start PyTorch --model-main PyTorch_mnist.py --batch-size 200"
Form parameters
file (required)
Form Parameter — If the model consists of one file then specify that file. If the model consists of a directory, then it's the tar of the directory with suffix ".modelDir.tar"
Produces
This API call produces the following media types according to the Accept request header;
the media type will be conveyed by the Content-Type response header.
Generate and download a fake task_attr.pb for local algorithm debugging.
Pass simulated hpo task submit request in the request body, which should be json format as below:
data sepcification:
{
'hpoName': 'optional, string, name/id for the hpo task, will generate one if none specified here.',
'modelSpec':
{
'args': 'required, string, same as BYOF training'
},
'algoDef':
{
'algorithm': 'required, string, it can be build in algorithms like Random, Bayesian, Tpe, Hyperband and ExperimentGridSearch, or user installed algorithms',
'maxRunTime': 'optional, int, max running time of the hpo task in minutes, default -1(unlimited)',
'maxJobNum': 'optional, int, max number of training job to submitted for hpo task, default -1(unlimited)',
'maxParalleJob': 'optinal, int, max number of training job to run in parallel, default 1',
'objectiveMetric': 'required, string, name of metric will be optimized, same one in the val_dict_list.json',
'objective': 'required, string, optimize policy, one of minimize, maximize',
'algoParams': 'optional, list like [{'name':'', value:''}], additional algorithm parameters and it could be different for each algorithm which will be covered in later part'
},
'hyperParams':
[
{
'name': 'required, string, hyperparameter name, the same name will be used in the config.json so user model can load it',
'type': 'required, string, one of Range, Discrete',
'dataType': 'required, string, one of int, double, str',
'minDbVal': 'double, required if type=Range and datatype=double',
'maxDbVal': 'double, required if type=Range and datatype=double',
'minIntVal': 'int, required if type=Range and datatype=int',
'maxIntVal': 'int, required if type=Range and datatype=int',
'discreteDbVal': 'double, list like [0.1, 0.2], required if type=Discrete and dataType=double',
'discreteIntVal': 'int, list like [1, 2], required if type=Discrete and datatype=int',
'discreateStrVal': 'string, list like ['1', '2'], required if type=Discrete and datatype=str',
'power': 'a number value in string format, the base value for power calculation. ONLY valid when type is Range',
'step': 'a number value in string format, step size to split the Range space. ONLY valid when type is Range'
}
],
'experiments':
[
{
'id': 'required, int, hyperparameter experiment id',
'hyperParams':
[
{
'name': 'required, string, hyperparameter name, the same name will be used in the config.json so user model can load it',
'dataType': 'required, string, one of int, double, str',
'fixedVal': 'required, the same type with datatype specified, if dataTye=double, need fixedVal type doulbe'
}
]
}
]
}
Each new hpo task request could only choose one from hyperParams and experiments, for search algorithm ExperimentGridSearch, only experiments is supported, for other algorithms, only hyperParams is supported:
For Random, algoParams can be provided as this:
'algoParams':
[
{
'name': 'RandomSeed',
'value': 'Optional, string, the random seed used to propose hyperparameter combinations.'
}
]
For Hyperband, algoParams can be provided as this:
'algoParams':
[
{
'name': 'RandomSeed',
'value': 'Optional, string, the random seed used by Hyperband to propose hyperparameter combinations in the first rung of brackets.'
},
{
'name': 'eta',
'value': 'Optional, string, the reduction factor to control the proportion of configurations discarded in each Hyperband brackets. Default 3.'
},
{
'name': 'ResourceName',
'value': 'Required, string, the parameter name that will be taken as resource in Hyperband, normally training epochs or iterations. User can get this parameter from config.json just like other hyper-parameters.'
},
{
'name': 'ResourceValue',
'value': 'Required, int value in string format, it is the corresponding upper limited value for the ResourceName.'
}
]
For Tpe, algoParams can be provided as this:
'algoParams':
[
{
'name': 'RandomSeed',
'value': 'Optional, string, the random seed used for the initial warm up hyperparameter combinations and the random generator of Gaussian Mixture Model.'
},
{
'name': 'WarmUp',
'value': 'Optional, string, the number of initial warm up hyperparameter combinations. It should be bigger than 2. If maxJobNum is smaller than this value, maxJobNum will be taken as the value. Default 20.'
},
{
'name': 'EICandidate',
'value': 'Optional, string, the number of hyperparameter combinations proposed each round as the candidates for Expected Improvement to propose the final one hyperparameter combination. It should be bigger than 1. Default 24.'
},
{
'name': 'GoodRatio',
'value': 'Optional, string, the fraction to use as good hyperparameter combinations from previous completed experiment training to build the good Gaussian Mixture Model. It should be bigger than 0. Default 0.25.'
},
{
'name': 'GoodMax',
'value': 'Optional, string, the max number of good hyperparameter combinations from previous completed experiment training to build the good Gaussian Mixture Model. It should be bigger than 1. Default 25.'
}
]
For Bayesian, algoParams can be provided as this:
'algoParams':
[
{
'name': 'RandomSeed',
'value': 'Optional, string, the random seed used by Bayesian. If not given, HPO will generate a random RandomSeed.'
},
{
'name': 'InitPoints',
'value': 'Optional, string, number of random search before approximating with Bayesian algorithm. Default 10.'
},
{
'name': 'CubeSize',
'value': 'Optional, string, the Bayesian candidate size. The value of CubeSize should not be smaller than the max maxJobNum. If maxJobNum=-1, CubeSize is max(10000, CubeSize), otherwise the default cubSize is maxJobNum*100. '
},
{
'name': 'Noiseless',
'value': 'Optional, string, specify whether the bayesian sampling will disable noise or not. If your model is entirely deterministic (e.g. analytic), then specify it true to speed up the optimization. If your model is not deterministic (as expected for most Machine Learning or Deep Learning models), then specify it false. Default true (noiseless).'
}
]
Consumes
This API call consumes the following media types via the Content-Type request header:
Body Parameter — The simulated hpo task submit input.
Return type
String
Example data
Content-Type: application/json
""
Produces
This API call produces the following media types according to the Accept request header;
the media type will be conveyed by the Content-Type response header.
application/json
Responses
200
Successful generate and download a fake task_attr.pb for algorithm debugging.
String
This API call consumes the following media types via the Content-Type request header:
application/json
Produces
This API call produces the following media types according to the Accept request header;
the media type will be conveyed by the Content-Type response header.
application/json
Responses
204
Successfully deleted all the hpo tasks.
400
The request format is invalid.
401
Authentication error. The request was denied.
403
Forbidden. The request was denied.
404
The requested resource is not found.
409
Conflict. The requested resource cannot be deleted because it is in use.
This API call consumes the following media types via the Content-Type request header:
application/json
Produces
This API call produces the following media types according to the Accept request header;
the media type will be conveyed by the Content-Type response header.
application/json
Responses
204
Successfully deleted the hpo task.
400
The request format is invalid.
401
Authentication error. The request was denied.
403
Forbidden. The request was denied.
404
The requested resource is not found.
409
Conflict. The requested resource cannot be deleted because it is in use.
Delete a hpo plugin algorithm (deleteOneHPOALGORITHM)
Delete a hpo plugin algorithm.
Path parameters
algoName (required)
Path Parameter — The hpo plugin algorithm name.
Consumes
This API call consumes the following media types via the Content-Type request header:
application/json
Produces
This API call produces the following media types according to the Accept request header;
the media type will be conveyed by the Content-Type response header.
This API call produces the following media types according to the Accept request header;
the media type will be conveyed by the Content-Type response header.
application/json
Responses
200
Successful response that contains the resulting hpo tasks.
400
Bad request. The request was not formatted correctly.
This API call produces the following media types according to the Accept request header;
the media type will be conveyed by the Content-Type response header.
application/json
Responses
200
Successful response that contains the resulting hpo tasks.
400
Bad request. The request was not formatted correctly.
This API call produces the following media types according to the Accept request header;
the media type will be conveyed by the Content-Type response header.
application/json
Responses
200
Successful response that contains the resulting hpo algorithm.
HpoAlgorithmDesc
400
Bad request. The request was not formatted correctly.
This API call produces the following media types according to the Accept request header;
the media type will be conveyed by the Content-Type response header.
application/json
Responses
200
Successful response that contains the resulting hpo task.
HpoTaskState
400
Bad request. The request was not formatted correctly.
Install a new hpo plugin algorithm (installHPOAlgorithm)
Install a new hpo plugin algorithm by providing algorithm scipts as well as other required parameters.
To install a new hpo plugin algorithm, we need string format of input parameters, which is python dict or json format as below:
data sepcification:
{
'name': 'required, string, name/id for the plugin algorithm, should be unique.',
'path': 'optional, string, the path for plugin algorithm scripts on server, required for local installation mode.',
'condaHome': 'optional, string, the CONDA_HOME to run the algorithm scripts, it will use the DLI_CONDA_HOME if not specified.',
'condaEnv': 'optional, string, the conda environment to run the algorithm scripts, it will use the DLI default conda environment if not specified.',
'remoteExec': 'optional, boolean, whether to deploy algorithm execution remotely, the default value is false.',
'logLevel': 'optional, string, the log level of the plugin algorithm, the default value is INFO.'
}
Consumes
This API call consumes the following media types via the Content-Type request header:
multipart/form-data
application/x-www-form-urlencoded
Form parameters
file (optional)
Form Parameter — tar the plugin algorithm directory with suffix ".tar", require if the using upload installation mode
data (required)
Form Parameter — Python dict or json format, convert to string when calling REST.
This API call produces the following media types according to the Accept request header;
the media type will be conveyed by the Content-Type response header.
application/json
Responses
200
Successfully installed the hpo plugin algorithm.
CreationResponse
This API call consumes the following media types via the Content-Type request header:
application/json
Produces
This API call produces the following media types according to the Accept request header;
the media type will be conveyed by the Content-Type response header.
application/json
Responses
204
Successfully stopped the hpo task forcely.
400
Bad request. The request was not formatted correctly.
Start a new hpo task by providing sample images as well as other required parameters.
To start a hpo task, we need string format of input parameters, which is python dict or json format as below:
data sepcification:
{
'hpoName': 'optional, string, name/id for the hpo task, will generate one if none specified here.',
'modelSpec':
{
'args': 'required, string, same as BYOF training'
},
'algoDef':
{
'algorithm': 'required, string, it can be build in algorithms like Random, Bayesian, Tpe, Hyperband and ExperimentGridSearch, or user installed algorithms',
'maxRunTime': 'optional, int, max running time of the hpo task in minutes, default -1(unlimited)',
'maxJobNum': 'optional, int, max number of training job to submitted for hpo task, default -1(unlimited)',
'maxParalleJob': 'optinal, int, max number of training job to run in parallel, default 1',
'objectiveMetric': 'required, string, name of metric will be optimized, same one in the val_dict_list.json',
'objective': 'required, string, optimize policy, one of minimize, maximize',
'algoParams': 'optional, list like [{'name':'', value:''}], additional algorithm parameters and it could be different for each algorithm which will be covered in later part'
},
'hyperParams':
[
{
'name': 'required, string, hyperparameter name, the same name will be used in the config.json so user model can load it',
'type': 'required, string, one of Range, Discrete',
'dataType': 'required, string, one of int, double, str',
'minDbVal': 'double, required if type=Range and datatype=double',
'maxDbVal': 'double, required if type=Range and datatype=double',
'minIntVal': 'int, required if type=Range and datatype=int',
'maxIntVal': 'int, required if type=Range and datatype=int',
'discreteDbVal': 'double, list like [0.1, 0.2], required if type=Discrete and dataType=double',
'discreteIntVal': 'int, list like [1, 2], required if type=Discrete and datatype=int',
'discreateStrVal': 'string, list like ['1', '2'], required if type=Discrete and datatype=str',
'power': 'a number value in string format, the base value for power calculation. ONLY valid when type is Range',
'step': 'a number value in string format, step size to split the Range space. ONLY valid when type is Range'
}
],
'experiments':
[
{
'id': 'required, int, hyperparameter experiment id',
'hyperParams':
[
{
'name': 'required, string, hyperparameter name, the same name will be used in the config.json so user model can load it',
'dataType': 'required, string, one of int, double, str',
'fixedVal': 'required, the same type with datatype specified, if dataTye=double, need fixedVal type doulbe'
}
]
}
]
}
Each new hpo task request could only choose one from hyperParams and experiments, for search algorithm ExperimentGridSearch, only experiments is supported, for other algorithms, only hyperParams is supported:
For Random, algoParams can be provided as this:
'algoParams':
[
{
'name': 'RandomSeed',
'value': 'Optional, string, the random seed used to propose hyperparameter combinations.'
}
]
For Hyperband, algoParams can be provided as this:
'algoParams':
[
{
'name': 'RandomSeed',
'value': 'Optional, string, the random seed used by Hyperband to propose hyperparameter combinations in the first rung of brackets.'
},
{
'name': 'eta',
'value': 'Optional, string, the reduction factor to control the proportion of configurations discarded in each Hyperband brackets. Default 3.'
},
{
'name': 'ResourceName',
'value': 'Required, string, the parameter name that will be taken as resource in Hyperband, normally training epochs or iterations. User can get this parameter from config.json just like other hyper-parameters.'
},
{
'name': 'ResourceValue',
'value': 'Required, int value in string format, it is the corresponding upper limited value for the ResourceName.'
}
]
For Tpe, algoParams can be provided as this:
'algoParams':
[
{
'name': 'RandomSeed',
'value': 'Optional, string, the random seed used for the initial warm up hyperparameter combinations and the random generator of Gaussian Mixture Model.'
},
{
'name': 'WarmUp',
'value': 'Optional, string, the number of initial warm up hyperparameter combinations. It should be bigger than 2. If maxJobNum is smaller than this value, maxJobNum will be taken as the value. Default 20.'
},
{
'name': 'EICandidate',
'value': 'Optional, string, the number of hyperparameter combinations proposed each round as the candidates for Expected Improvement to propose the final one hyperparameter combination. It should be bigger than 1. Default 24.'
},
{
'name': 'GoodRatio',
'value': 'Optional, string, the fraction to use as good hyperparameter combinations from previous completed experiment training to build the good Gaussian Mixture Model. It should be bigger than 0. Default 0.25.'
},
{
'name': 'GoodMax',
'value': 'Optional, string, the max number of good hyperparameter combinations from previous completed experiment training to build the good Gaussian Mixture Model. It should be bigger than 1. Default 25.'
}
]
For Bayesian, algoParams can be provided as this:
'algoParams':
[
{
'name': 'RandomSeed',
'value': 'Optional, string, the random seed used by Bayesian. If not given, HPO will generate a random RandomSeed.'
},
{
'name': 'InitPoints',
'value': 'Optional, string, number of random search before approximating with Bayesian algorithm. Default 10.'
},
{
'name': 'CubeSize',
'value': 'Optional, string, the Bayesian candidate size. The value of CubeSize should not be smaller than the max maxJobNum. If maxJobNum=-1, CubeSize is max(10000, CubeSize), otherwise the default cubSize is maxJobNum*100. '
},
{
'name': 'Noiseless',
'value': 'Optional, string, specify whether the bayesian sampling will disable noise or not. If your model is entirely deterministic (e.g. analytic), then specify it true to speed up the optimization. If your model is not deterministic (as expected for most Machine Learning or Deep Learning models), then specify it false. Default true (noiseless).'
}
]
Consumes
This API call consumes the following media types via the Content-Type request header:
multipart/form-data
application/x-www-form-urlencoded
Form parameters
file (required)
Form Parameter — If the model consists of one file then specify that file. If the model consists of a directory, then it's the tar of the directory with suffix ".modelDir.tar"
data (required)
Form Parameter — Python dict or json format, convert to string when calling REST.
This API call produces the following media types according to the Accept request header;
the media type will be conveyed by the Content-Type response header.
This API call consumes the following media types via the Content-Type request header:
application/json
Produces
This API call produces the following media types according to the Accept request header;
the media type will be conveyed by the Content-Type response header.
application/json
Responses
204
Successfully stopped the hpo task.
400
Bad request. The request was not formatted correctly.
This API call consumes the following media types via the Content-Type request header:
application/json
Produces
This API call produces the following media types according to the Accept request header;
the media type will be conveyed by the Content-Type response header.
application/json
Responses
204
Successfully stopped the hpo task forcely.
400
Bad request. The request was not formatted correctly.
This API call produces the following media types according to the Accept request header;
the media type will be conveyed by the Content-Type response header.
application/json
Responses
200
Successful response that contains the user's notebook running kernels.
NotebookKernel
400
Bad request. The request was not formatted correctly.
This API call produces the following media types according to the Accept request header;
the media type will be conveyed by the Content-Type response header.
application/json
Responses
200
Successful response that contains the user's notebook service.
NotebookService
400
Bad request. The request was not formatted correctly.
This API call produces the following media types according to the Accept request header;
the media type will be conveyed by the Content-Type response header.
application/json
Responses
200
Successful response which contains a list of notebook services.
NotebookService
This API call produces the following media types according to the Accept request header;
the media type will be conveyed by the Content-Type response header.
application/json
Responses
200
Successful response that contains the stop notebook service action response.
NotebookActionResult
400
Bad request. The request was not formatted correctly.
This API call produces the following media types according to the Accept request header;
the media type will be conveyed by the Content-Type response header.
Body Parameter — The information of the resource plan.
Produces
This API call produces the following media types according to the Accept request header;
the media type will be conveyed by the Content-Type response header.
This API call consumes the following media types via the Content-Type request header:
application/json
Produces
This API call produces the following media types according to the Accept request header;
the media type will be conveyed by the Content-Type response header.
application/json
Responses
200
Successfully deleted the resource plan.
400
The request format is invalid.
401
Authentication error. The request was denied.
403
Forbidden. The request was denied.
404
The requested resource is not found.
409
Conflict. The requested resource cannot be deleted because it is in use.
This API call produces the following media types according to the Accept request header;
the media type will be conveyed by the Content-Type response header.
application/json
Responses
200
Successful response that contains the resource plan.
V1Resplan
400
Bad request. The request was not formatted correctly.
This API call produces the following media types according to the Accept request header;
the media type will be conveyed by the Content-Type response header.
application/json
Responses
200
Successful response that contains resource plan trees
TreeDto
Body Parameter — The information of the resource plan.
Produces
This API call produces the following media types according to the Accept request header;
the media type will be conveyed by the Content-Type response header.
application/json
Responses
204
Successfully updated the resource plan.
400
Bad request. The request was not formatted correctly.
get /scheduler/applications/{appid}/driver/logs/{type}/download
Download driver log for a MSD application. (downloadDriverLogFile)
Download driver log for a MSD application.
Path parameters
appid (required)
Path Parameter — The MSD application ID.
type (required)
Path Parameter — The type of the log to retrieve, which is one of 'stdout', 'stderr', or "launcherlog".
Consumes
This API call consumes the following media types via the Content-Type request header:
application/json
Request headers
Produces
This API call produces the following media types according to the Accept request header;
the media type will be conveyed by the Content-Type response header.
application/octet-stream
Responses
200
Successful response, with the log returned.
401
Authentication error. The request was denied.
403
Forbidden. The request was denied.
404
The requested instance group or application ID is not found.
get /scheduler/applications/{appid}/executor/{executorid}/logs/{type}/download
Download the executor log for a MSD application. (downloadExecutorLogFile)
Download the executor log for a MSD application.
Path parameters
appid (required)
Path Parameter — The MSD application ID.
executorid (required)
Path Parameter — The executor ID for the MSD application.
type (required)
Path Parameter — The type of the log to retrieve, which is one of 'stdout', 'stderr', or "launcherlog".
Consumes
This API call consumes the following media types via the Content-Type request header:
application/json
Request headers
Produces
This API call produces the following media types according to the Accept request header;
the media type will be conveyed by the Content-Type response header.
application/octet-stream
Responses
200
Successful response, with the log returned.
401
Authentication error. The request was denied.
403
Forbidden. The request was denied.
404
The requested instance group or application ID is not found.
This API call produces the following media types according to the Accept request header;
the media type will be conveyed by the Content-Type response header.
application/json
Responses
200
Successful response which contains a list of deep learning applications statistic.
400
Bad request. The request was not formatted correctly.
Retrieves deep learning applications (getApplications)
Retrieves deep learning applications.
Consumes
This API call consumes the following media types via the Content-Type request header:
application/json
Query parameters
applicationid (optional)
Query Parameter — The ID of the application.
applicationname (optional)
Query Parameter — The name of the application.
driverid (optional)
Query Parameter — The ID of the application driver.
search (optional)
Query Parameter — search
sort (optional)
Query Parameter — The field name to sort the response by. Only one field name can be specified as the sort type. Prefix the field name with "-" to sort in descending order.
This API call produces the following media types according to the Accept request header;
the media type will be conveyed by the Content-Type response header.
application/json
Responses
200
Successful response which contains a list of deep learning applications.
400
Bad request. The request was not formatted correctly.
get /scheduler/applications/{appid}/driver/logs/{type}
Retrieve latest lines of driver log for a MSD application. (getDriverLog)
Retrieve latest lines of driver log for a MSD application.
Path parameters
appid (required)
Path Parameter — The MSD application ID.
type (required)
Path Parameter — The type of the log to retrieve, which is one of 'stdout', 'stderr', or "launcherlog".
Consumes
This API call consumes the following media types via the Content-Type request header:
application/json
Request headers
Query parameters
lastlines (optional)
Query Parameter — The number of last lines to retrieve. Specify a positive number to retrieve the number of last lines that the value specifies. The default value is 10.
Return type
String
Example data
Content-Type:
Produces
This API call produces the following media types according to the Accept request header;
the media type will be conveyed by the Content-Type response header.
text/plain
Responses
200
Successful response, with the log returned.
String
401
Authentication error. The request was denied.
403
Forbidden. The request was denied.
404
The requested instance group or application ID is not found.
get /scheduler/applications/{appid}/executor/{executorid}/logs/{type}
Retrieve latest lines of the executor log for a MSD application. (getExecutorLog)
Retrieve latest lines of the executor log for a MSD application.
Path parameters
appid (required)
Path Parameter — The MSD application ID.
executorid (required)
Path Parameter — The executor ID for the MSD application.
type (required)
Path Parameter — The type of the log to retrieve, which is one of 'stdout', 'stderr', or "launcherlog".
Consumes
This API call consumes the following media types via the Content-Type request header:
application/json
Request headers
Query parameters
lastlines (optional)
Query Parameter — The number of last lines to retrieve. Specify a positive number to retrieve the number of last lines that the value specifies. The default value is 10.
Return type
String
Example data
Content-Type:
Produces
This API call produces the following media types according to the Accept request header;
the media type will be conveyed by the Content-Type response header.
text/plain
Responses
200
Successful response, with the log returned.
String
401
Authentication error. The request was denied.
403
Forbidden. The request was denied.
404
The requested instance group or application ID is not found.
String The tuning algorithm. it can be build in algorithms like Random, Bayesian, Tpe, Hyperband and ExperimentGridSearch, or user installed algorithms.
maxRunTime (optional)
Integer Max running time of the hpo task in munites, default -1(unlimited).
maxJobNum (optional)
Integer Max number of training job to submitted for hpo task, default -1(unlimited).
maxParalleJobNum (optional)
Integer Max number of training job to run in parallel, default 1.
String The gradient synchronization mode in elastic distributed training. This parameter to specify whether the training is a synchronous training, or an asynchronous training.