Create a deep learning experiment
In IBM Watson Machine Learning, a deep learning experiment is a logical grouping of one or more model definitions. When an experiment is run it creates training runs for each model definition that is part of the experiment.
Attention: Support for Deep Learning as a Service and the Deep Learning Experiment Builder on Cloud Pak for Data as a Service is deprecated and will be discontinued on April 2, 2022. See Service plan changes and deprecations for details.
This document will explain how an experiment can be set up and run.
Before you create an experiment you must write the Python script that is used to deliver the model definition. For expert information on specific requirements, see Coding guidelines for deep learning programs and follow the coding standards that are outlined there to ensure that your script can be processed without error.
Creating experiments manifest file
The manifest is a YAML formatted file that contains different fields describing the model definitions to be trained, the IBM Cloud Object Storage configuration and several arguments required for model execution during training and testing. The following fields of the experiments file are available to use for deep learning. Other fields, such as the frameworks.runtime field, are ignored.
settings:name: You can provide any value to name to help identify your experiment after it is created. However, this does not have to be unique - the service will assign a unique experiment-id for each experiment.
settings.description: This is another field that you can use to describe the experiment.
training_references: This section specifies the model definitions which need to be part of the experiment.
name: A descriptive name for this training run.
compute_configuration.name: This field specifies the resources that will be allocated for training and should be one of the following values. For more information about GPUs, see Using GPUs.
training_results_reference: This section specifies the object store where the resulting model files and logs will be stored after training completes.
name: A descriptive name for this objectstore and bucket
connection: The connection variables for the data store. The list of connection variables supported is data store type dependent.
type: Type of data store, currently this can only be set to
target.bucket: The bucket where the training results will be written.
For example, the following model definition manifest can be used to create a model definition:
settings: name: Sample Experiment description: This is a sample experiment training_references: - name: model-1 training_definition_url: https://ibm-watson-ml.mybluemix.net/v3/ml_assets/training_definitions/6e973044-eadd-42f4-9657-45c0c56764863 compute_configuration: name: k80 - name: model-2 training_definition_url: https://ibm-watson-ml.mybluemix.net/v3/ml_assets/training_definitions/9e038155-eadd-42f4-9657-45c0c56764863 compute_configuration: name: k80 training_results_reference: name: training-results-reference_name connection: endpoint_url: <auth-url> access_key_id: <username> secret_access_key: <password> target: bucket: experiment-results type: s3
Allocating memory to your deep learning jobs
To specify the GPUs, CPUs, and memory to allocate for jobs, Watson Machine Learning provides GPU configuration resources that are easy to understand while also ensuring efficient allocation of resources within deep learning compute nodes.
For sizes to specify in the manifest.yml file, refer to Using GPUs.
Specify the GPU configuration in the manifest.yml by using the following syntax:
execution: compute_configuration: name: v100x2
The preceding example shows the
v100x2 compute tier, which allocates 2 GPUs of processing power to your deep learning job.
Generate a sample experiments manifest file
Sample manifest file template can be generated by using the
bx ml generate-manifest command:
bx ml generate-manifest experiments
Edit the generated manifest with appropriate values for access_key_id, secret_access_key, training_definition_url and bucket fields.
bx ml generate-manifest experiments
OK A sample manifest file is generated under experiments.yml
Store an experiment
After you prepare the experiments manifest file, store the experiment by using the
bx ml store experiments command:
bx ml store experiments <path-to-experiments-manifest-yaml>
bx ml store experiments experiments.yaml
When the command is submitted successfully, a unique experiment ID is returned. For example, the following output shows a
Experiment ID value of
Creating experiment ... OK Experiment created with ID 'c2e94a92-cefe-45b7-bc99-56420abcaa1a'
List an experiment
To list all experiments, run the following command:
bx ml list experiments
Fetching the list of experiments ... SI No Name guid created-at 1 sample experiment 422902ab-d384-4ce1-81aa-0350ca9e94b6 2018-01-30T16:18:17.265Z 2 tf-experiment f5785fb5-a0bc-4db4-bd07-8a1cce4c9db4 2018-01-30T18:37:01.453Z 2 records found. OK List all experiments successful
To check the details of a particular experiment use the cli command
bx ml show experiments <training-defintions--id>:
bx ml show experiments 422902ab-d384-4ce1-81aa-0350ca9e94b6
Fetching the experiment details with ID '422902ab-d384-4ce1-81aa-0350ca9e94b6' ... ExperimentId 422902ab-d384-4ce1-81aa-0350ca9e94b6 name sample_experiment11 url https://ibm-watson-ml.mybluemix.net/v3/experiments/422902ab-d384-4ce1-81aa-0350ca9e94b6 created_at 2018-01-30T16:18:17.265Z OK
Run an experiment
After you store the experiment, the experiment can be submitted for the run by using the
bx ml experiments run command:
bx ml experiments run <experiment-ID> This is will start the training of the training-definitions
included as part of the experiment.
bx ml experiments run c2e94a92-cefe-45b7-bc99-56420abcaa1a
When the command is submitted successfully, a unique experiment run ID is returned. For example, the following output shows a
Experiment Run ID value of
Starting to run the experiment with ID 'c2e94a92-cefe-45b7-bc99-56420abcaa1a' ... OK Experiment-run created with ID '6d46291f-2266-4c4c-bb74-6de79f9b9b18'
List an experiment run
To list all runs under a particular experiment use
bx ml list experiment-runs command:
bx ml list experiment-runs <experiment-ID>
bx ml list experiment-runs c2e94a92-cefe-45b7-bc99-56420abcaa1a
Fetching the list of experiment-runs ... SI No guid state created-at 1 6d46291f-2266-4c4c-bb74-6de79f9b9b18 completed 2018-02-01T09:14:09Z 1 records found. OK List all experiment-runs successful
List a training-run under an experiment run
To list all training-runs under a particular experiment-run use
bx ml list experiment-runs command:
bx ml list experiment-runs <experiment-ID> <experiment-run-ID>
bx ml list training-runs c2e94a92-cefe-45b7-bc99-56420abcaa1a 6d46291f-2266-4c4c-bb74-6de79f9b9b18
Fetching the list of training-runs in experiment-run with ID '6d46291f-2266-4c4c-bb74-6de79f9b9b18' ... SI No Name guid state submitted_at 1 model-1 training-5H2xmKCzR completed 2018-02-01T09:14:14Z 2 model-2 training-8aBbiKCkg completed 2018-02-01T09:14:19Z OK List training-runs successful
Monitor an experiment run
To continously monitor the logs from an experiment run, use the cli command
bx ml experiments <experiment-ID> <experiment-run-ID>:
bx ml monitor experiments c2e94a92-cefe-45b7-bc99-56420abcaa1a 0478fd57-887f-4e38-9068-a09fcc7c688d
Starting to fetch status messages and metrics for experiment id 'c2e94a92-cefe-45b7-bc99-56420abcaa1a' and experiment-run id '0478fd57-887f-4e38-9068-a09fcc7c688d' [--LOGS] Training with training/test data and model at: [--LOGS] [--LOGS] DATA_DIR: /job/caffe-training-data [--LOGS] [--LOGS] MODEL_DIR: /job/model-code [--LOGS] [--LOGS] TRAINING_JOB: [--LOGS] [--LOGS] TRAINING_COMMAND: caffe train -solver lenet_solver.prototxt [--LOGS] [--LOGS] ARMADA_OPS_PROM2GRAPHITE_PORT=tcp://172.21.176.2:39888 [--LOGS] [--LOGS] ARMADA_OPS_PROM2GRAPHITE_PORT_39888_TCP=tcp://172.21.176.2:39888
You can add append
metrics to see only the log lines or only the metrics, foe example
bx ml monitor experiments <experiment-ID> <experiment-run-ID> logs or
bx ml monitor experiments <experiment-ID> <experiment-run-ID> metrics
Delete an experiment run
To delete a experiment run use
bx ml delete experiment-runs <experiment-ID> <experiment-run-ID> command:
bx ml list experiment-runs <experiment-ID> , this will also delete the training-runs under
bx ml delete experiment-runs c2e94a92-cefe-45b7-bc99-56420abcaa1a 6d46291f-2266-4c4c-bb74-6de79f9b9b18
Deleting the experiment-run '6d46291f-2266-4c4c-bb74-6de79f9b9b18' ... OK Delete experiment-run successful
Delete an experiment
To delete an experiment.
bx ml delete experiments 422902ab-d384-4ce1-81aa-0350ca9e94b6
Deleting the experiment '422902ab-d384-4ce1-81aa-0350ca9e94b6' ... OK Delete experiment successful
To work with IBM Watson Machine Learning experiments to train Deep Learning models, check out these sample notebooks.
Parent topic: Deep learning experiments