Experiments Client

Importing
from gradient import ExperimentsClient
api_key='YOUR_API_KEY'
​
experiments_client = ExperimentsClient(api_key)
Listing Experiments
#list experiments in the project, filtering by project_id
experiments = experiment_client.list([project_id]))
#print first experiment
print(experiments[0])
Get experiment by ID
print(experiment_client.get(experiment_id))
SingleNodeExperiment(name='single_node_experiment-sdk', ports=None, 
    workspace_url='https://github.com/Paperspace/mnist-sample.git', 
    working_directory=None, artifact_directory=None, cluster_id=None, 
    experiment_env={'EVAL_SECS': 10, 'MAX_STEPS': 100, 'TRAIN_EPOCHS': 3, 
    'EPOCHS_EVAL': 2}, project_id='prlvf0u05', model_type='Tensorflow', 
    model_path='/storage/models/sdk_test', id='esxfbgzu7xanst', state=1, 
    container='tensorflow/tensorflow:1.13.1-gpu-py3', machine_type='K80', 
    command='python mnist.py', container_user=None, registry_username=None, 
    registry_password=None, registry_url=None, experiment_type_id=1)
View State of the Experiment
Import the ExperimentState constants
from gradient import constants
#experiment state, created but not started
state = experiment_client.get(experiment_id).state
print("state: "+constants.ExperimentState.get_state_str(state))
help(constants.ExperimentState)
​
Help on class ExperimentState in module gradient.constants:
​
class ExperimentState(builtins.object)
 |  Class methods defined here:
 |  
 |  get_state_str(state_int) from builtins.type
 |  
 |  ----------------------------------------------------------------------
 |  Data descriptors defined here:
 |  
 |  __dict__
 |      dictionary for instance variables (if defined)
 |  
 |  __weakref__
 |      list of weak references to the object (if defined)
 |  
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |  
 |  CANCELLED = 8
 |  
 |  CREATED = 1
 |  
 |  ERROR = 6
 |  
 |  FAILED = 7
 |  
 |  NETWORK_SETTING_UP = 12
 |  
 |  NETWORK_SETUP = 3
 |  
 |  NETWORK_TEARDOWN = 9
 |  
 |  NETWORK_TEARING_DOWN = 13
 |  
 |  PENDING = 10
 |  
 |  PROVISIONED = 2
 |  
 |  PROVISIONING = 11
 |  
 |  RUNNING = 4
 |  
 |  STOPPED = 5
​
Singlenode experiment
The parameters for a single node experiment are as following: taken from help(ExperimentsClient.create_single_node)
:param str name: Name of new experiment  [required]
:param str project_id: Project ID  [required]
:param str machine_type: Machine type [required]
:param str command: Container entrypoint command  [required]
:param str ports: Port to use in new experiment
:param str workspace_url: Project git repository url
:param str working_directory: Working directory for the experiment
:param str artifact_directory: Artifacts directory
:param str cluster_id: Cluster ID
:param dict experiment_env: Environment variables in a JSON
:param str model_type: defines the type of model that is being generated by the experiment. Model type must be one of Tensorflow, ONNX, or Custom
:param str model_path: Model path
:param str container: Container (dockerfile) [required]
:param str container_user: Container user for running the specified command in the container. If no containerUser is specified, the user will default to 'root' in the container.
:param str registry_username: Registry username for accessing private docker registry container if nessesary
:param str registry_password: Registry password for accessing private docker registry container if nessesary
:param str registry_url: Registry server URL for accessing private docker registry container if nessesary
​
:returns: experiment handle
:rtype: str
Input Dictionary
One of the benefits of using the SDK is that each function takes in a dictionary as input. This enables easy generation & manipulation of the parameters needed to run an experiment. An example of the parameters to train a single node experiment to train mnist on a GPU is the following:
single_node__model_path = "/storage/models/single_node_experiment_sdk"
​
env_variable = {
    "EPOCHS_EVAL":2,
    "TRAIN_EPOCHS":3,
    "MAX_STEPS":100,
    "EVAL_SECS":10
}
​
single_node_parameters = {
    "name" : "single_node_experiment_sdk",
    "project_id" : project_id,
    "command" : "python mnist.py",
    "machine_type" : "K80",
    "experiment_env": env_variable,
    "container": "tensorflow/tensorflow:1.13.1-gpu-py3",
    "workspace_url": "https://github.com/Paperspace/mnist-sample.git",
    "model_type": "Tensorflow",
    "model_path": single_node_path
}
Create & Start Singlenode Experiment
Here we create (but not start) a single node experiment by unpacking the dictionary of parameters as function input.
experiment_id = experiment_client.create_single_node(**input_node_parameters)
Now we can start the experiment
experiment_client.start(experiment_id)
We could also immediately run the experiment
experiment_id = experiment_client.run_single_node(**input_node_parameters)
We can then stop the experiment or wait for it to complete
experiment_client.stop(experiment_id)
Multinode Experiment
The parameters for a multi node experiment are as following: taken from help(ExperimentsClient.create_multi_node)
:param str name: Name of new experiment  [required]
:param str project_id: Project ID  [required]
:param str experiment_type_id: Experiment Type ID [GRPC|MPI] [required]
:param str worker_container: Worker container (dockerfile) [required]
:param str worker_machine_type: Worker machine type  [required]
:param str worker_command: Worker command  [required]
:param int worker_count: Worker count  [required]
:param str parameter_server_container: Parameter server container  [required]
:param str parameter_server_machine_type: Parameter server machine type  [required]
:param str parameter_server_command: Parameter server command  [required]
:param int parameter_server_count: Parameter server count  [required]
:param str ports: Port to use in new experiment
:param str workspace_url: Project git repository url
:param str working_directory: Working directory for the experiment
:param str artifact_directory: Artifacts directory
:param str cluster_id: Cluster ID
:param dict experiment_env: Environment variables in a JSON
:param str model_type: defines the type of model that is being generated by the experiment. Model type must be one of Tensorflow, ONNX, or Custom
:param str model_path: Model path
:param str worker_container_user: Worker container user
:param str worker_registry_username: Registry username for accessing private docker registry container if nessesary
:param str worker_registry_password: Registry password for accessing private docker registry container if nessesary
:param str worker_registry_url: Registry server URL for accessing private docker registry container if nessesary
:param str parameter_server_container_user: Parameter server container user
:param str parameter_server_registry_username: Registry username for accessing private docker registry container if nessesary
:param str parameter_server_registry_password: Registry password for accessing private docker registry container if nessesary
:param str parameter_server_registry_url: Registry server URL for accessing private docker registry container if nessesary
​
:returns: experiment handle
:rtype: str
Sample Multinode input dictionary
##Create a dictionary of parameters for running a distributed/multinode experiment
env = {
    "EPOCHS_EVAL":5,
    "TRAIN_EPOCHS":10,
    "MAX_STEPS":1000,
    "EVAL_SECS":10
}
​
multi_node_parameters = { 
    "name": "multi_node_example",
    "project_id": project_id,
    "experiment_type_id": 2,
    "worker_container": "tensorflow/tensorflow:1.13.1-gpu-py3",
    "worker_machine_type": "K80",
    "worker_command": "pip install -r requirements.txt && python mnist.py",
    "experiment_env": env,
    "worker_count": 2,
    "parameter_server_container": "tensorflow/tensorflow:1.13.1-gpu-py3",
    "parameter_server_machine_type": "K80",
    "parameter_server_command": "python mnist.py",
    "parameter_server_count": 1,
    "workspace_url": "https://github.com/Paperspace/mnist-sample.git",
    "model_type": "Tensorflow"
}
Create & Start Multinode
Here we create (but not start) a single node experiment by unpacking the dictionary of parameters as function input.
experiment_id = experiment_client.create_multi_node(**multi_node_parameters)
Now we can start the experiment
experiment_client.start(experiment_id)
We could also immediately run the experiment
experiment_id = experiment_client.run_multi_node(**multi_node_parameters)
We can then stop the experiment or wait for it to complete
experiment_client.stop(experiment_id)
Experiment Logs
The SDK enables getting logs from running or completed experiments
Help on method logs in module gradient.api_sdk.clients.experiment_client:
​
logs(experiment_id, line=0, limit=10000) method of gradient.api_sdk.clients.experiment_client.ExperimentsClient instance
    Show list of latest logs from the specified experiment.
    
    *EXAMPLE*::
    
        gradient experiments logs --experimentId
    
    :param str experiment_id: Experiment ID
    :param int line: line number at which logs starts to display on screen
    :param int limit: maximum lines displayed on screen, default set to 10 000
    
logs = experiment_client.get_logs(experiment_id)
print(logs)
The output is a array of LogRows each containing the line, the message, and the timestamp
LogRow(line=1, message='Requirement already satisfied: numpy>=1.15.4 in 
    /usr/local/lib/python3.5/dist-packages (from -r requirements.txt (line 1))
     (1.16.3)', timestamp='2019-08-06T17:21:18.528Z')
Streaming logs during runtime
log_stream = experiment_client.yield_logs(experiment_id)
print("Streaming logs of experiment")
try:
    while True:
        print(log_stream.send(None))
except:
    print("done streaming logs")
Streaming logs of experiment
LogRow(line=1, message='Requirement already satisfied: numpy>=1.15.4 in /usr/local/lib/python3.5/dist-packages (from -r requirements.txt (line 1)) (1.16.3)', timestamp='2019-08-06T17:21:18.528Z')
LogRow(line=1, message='Requirement already satisfied: numpy>=1.15.4 in /usr/local/lib/python3.5/dist-packages (from -r requirements.txt (line 1)) (1.16.3)', timestamp='2019-08-06T17:20:30.842Z')
LogRow(line=1, message='Requirement already satisfied: numpy>=1.15.4 in /usr/local/lib/python3.5/dist-packages (from -r requirements.txt (line 1)) (1.16.3)', timestamp='2019-08-06T17:21:14.121Z')
print("done streaming logs")
Getting Help
If you are ever stuck, you can call help on any ExperimentsClient object or function in python
help(ProjectsClient.list)
or at  https://paperspace.github.io/gradient-cli/gradient.api_sdk.clients.html#module-gradient.api_sdk.clients.experiment_client​
Projects Client
Models Client
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