vessl sweep create --dataset "/input:YOUR_DATASET_NAME"
Organization: YOUR_ORGANIZATION_NAME
Project: YOUR_PROJECT_NAME
[?] Objective type: maximize
Objective metric: val_accuracy
Maximum number of experiments: 4
Number of experiments to be run in parallel: 2
Maximum number of experiments to allow to fail: 2
[?] Sweep algorithm: random
Parameter #1 name: optimizer
[?] Parameter #1 type: categorical
[?] Parameter #1 range type: list
Parameter #1 values (space separated): adam sgd adadelta
Add another parameter (y/n): y
Parameter #2 name: batch_size
[?] Parameter #2 type: int
[?] Parameter #2 range type: space
Parameter #2 values ([min] [max] [step]): 64 256 8
Add another parameter (y/n): n
[?] Cluster: aws-apne2-prod1
[?] Resource: v1.cpu-4.mem-13
[?] Image URL: public.ecr.aws/vessl/kernels:py36.full-cpu
> public.ecr.aws/vessl/kernels:py36.full-cpu
public.ecr.aws/vessl/kernels:py37.full-cpu
public.ecr.aws/vessl/kernels:py36.full-cpu.jupyter
public.ecr.aws/vessl/kernels:py37.full-cpu.jupyter
tensorflow/tensorflow:1.14.0-py3
tensorflow/tensorflow:1.15.5-py3
tensorflow/tensorflow:2.0.4-py3
tensorflow/tensorflow:2.2.1-py3
tensorflow/tensorflow:2.3.2
tensorflow/tensorflow:2.4.1
tensorflow/tensorflow:2.3.0
Start command: git clone https://github.com/vessl-ai/examples.git && pip install -r examples/mnist/keras/requirements.txt && python examples/mnist/keras/main.py --save-model --save-image