- Overview
- Getting Started Guide
- UserGuide
-
References
-
ABEJA Platform CLI
- CONFIG COMMAND
- DATALAKE COMMAND
- DATASET COMMAND
- TRAINING COMMAND
-
MODEL COMMAND
- check-endpoint-image
- check-endpoint-json
- create-deployment
- create-endpoint
- create-model
- create-service
- create-trigger
- create-version
- delete-deployment
- delete-endpoint
- delete-model
- delete-service
- delete-version
- describe-deployments
- describe-endpoints
- describe-models
- describe-service-logs
- describe-services
- describe-versions
- download-versions
- run-local
- run-local-server
- start-service
- stop-service
- submit-run
- update-endpoint
- startapp command
-
ABEJA Platform CLI
- FAQ
- Appendix
init
Description
Initialize training. Create training.yaml
as a training configuration file in the current directory.
Synopsis
$ abeja training init <name>
Argument
<name>
Training definition name
Default training configuration file (training.yaml)
name: <name>
# handler: train:handler
# image: abeja-inc/all-gpu:19.04
# params:
# param1: value1
# parma2: value2
# datasets:
# dataset_name1: value1
# dataset_name2: value2
Please refer to Training.yaml for each item.
Example
Initialize training
Command:
$ abeja training init training1
Output:
training initialized
Created training configuration file training.yaml
:
name: training1
# handler: train:handler
# image: abeja-inc/all-gpu:19.04
# params:
# param1: value1
# parma2: value2
# datasets:
# dataset_name1: value1
# dataset_name2: value2