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Use G4 instances to avoid capacity issue #184

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Use G4 instances to avoid capacity issue #184

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4 changes: 2 additions & 2 deletions aws/rapids_studio_hpo.ipynb
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Expand Up @@ -196,7 +196,7 @@
"metadata": {},
"source": [
"Depending on the workflow you have chosen, your instance should reflect the specifications needed. For example, for the singleGPU workflow, you should choose an instance with a GPU, such as the p3.2xlarge instance. You can [read about Amazon EC2 Instance Types here](https://aws.amazon.com/ec2/instance-types/). \n",
"> e.g., For the 10_year dataset option, we suggest ml.p3.8xlarge instances (4 GPUs) and ml.m5.24xlarge CPU instances ( we will need upwards of 200GB CPU RAM during model training)."
"> e.g., For the 10_year dataset option, we suggest ml.g4dn.12xlarge instances (4 GPUs) and ml.m5.24xlarge CPU instances ( we will need upwards of 200GB CPU RAM during model training)."
]
},
{
Expand All @@ -208,7 +208,7 @@
"outputs": [],
"source": [
"# we will recommend a compute instance type, feel free to modify \n",
"instance_type = 'ml.p3.2xlarge' # recommend_instance_type(ml_workflow_choice, dataset_directory)"
"instance_type = 'ml.g4dn.2xlarge' # recommend_instance_type(ml_workflow_choice, dataset_directory)"
]
},
{
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