HongChuang/ultralytics-ascend
1
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity Actions 3

Update TFLite Docs images (#8605)

Browse source 
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
This commit is contained in:
Glenn Jocher 2024-03-03 01:59:43 +01:00 • committed by GitHub
parent 1146bb0582
commit 36408c974c
No known key found for this signature in database
GPG key ID: B5690EEEBB952194
33 changed files with 112 additions and 107 deletions
Download patch file Download diff file Expand all files Collapse all files

14
docs/en/integrations/amazon-sagemaker.md
View file

@ -38,7 +38,7 @@ First, ensure you have the following prerequisites in place:
- AWS CDK: If not already installed, install the AWS Cloud Development Kit (CDK), which will be used for scripting the deployment. Follow [the AWS CDK instructions](https://docs.aws.amazon.com/cdk/v2/guide/getting_started.html#getting_started_install) for installation.
- Adequate Service Quota: Confirm that you have sufficient quotas for two separate resources in Amazon SageMaker: one for ml.m5.4xlarge for endpoint usage and another for ml.m5.4xlarge for notebook instance usage. Each of these requires a minimum of one quota value. If your current quotas are below this requirement, it's important to request an increase for each. You can request a quota increase by following the detailed instructions in the [AWS Service Quotas documentation](https://docs.aws.amazon.com/servicequotas/latest/userguide/request-quota-increase.html#quota-console-increase).
- Adequate Service Quota: Confirm that you have sufficient quotas for two separate resources in Amazon SageMaker: one for `ml.m5.4xlarge` for endpoint usage and another for `ml.m5.4xlarge` for notebook instance usage. Each of these requires a minimum of one quota value. If your current quotas are below this requirement, it's important to request an increase for each. You can request a quota increase by following the detailed instructions in the [AWS Service Quotas documentation](https://docs.aws.amazon.com/servicequotas/latest/userguide/request-quota-increase.html#quota-console-increase).
### Step 2: Clone the YOLOv8 SageMaker Repository
@ -115,17 +115,21 @@ After creating the AWS CloudFormation Stack, the next step is to deploy YOLOv8.
- Access and Modify inference.py: After opening the SageMaker notebook instance in Jupyter, locate the inference.py file. Edit the output_fn function in inference.py as shown below and save your changes to the script, ensuring that there are no syntax errors.
```python
import json
def output_fn(prediction_output, content_type):
print("Executing output_fn from inference.py ...")
infer = {}
for result in prediction_output:
if 'boxes' in result._keys and result.boxes is not None:
if result.boxes is not None:
infer['boxes'] = result.boxes.numpy().data.tolist()
if 'masks' in result._keys and result.masks is not None:
if result.masks is not None:
infer['masks'] = result.masks.numpy().data.tolist()
if 'keypoints' in result._keys and result.keypoints is not None:
if result.keypoints is not None:
infer['keypoints'] = result.keypoints.numpy().data.tolist()
if 'probs' in result._keys and result.probs is not None:
if result.obb is not None:
infer['obb'] = result.obb.numpy().data.tolist()
if result.probs is not None:
infer['probs'] = result.probs.numpy().data.tolist()
return json.dumps(infer)
```