Algo-1 | The first edition of the algo course in Hack Bulgaria | Functional Programming library

troubling for some days with the sagemaker built-in rcf algorithm.

I would like to validate the model during training, but there might be things I didn't understand correctly.

First fitting only with training channel works fine:

I am trying to run a object detection code in Aws. Although opencv is listed in the requirement file, i have the error "no module named cv2". I am not sure how to fix this error. could someone help me please.

My requirement.txt file has

• opencv-python
• numpy>=1.18.2
• scipy>=1.4.1
• wget>=3.2
• tensorflow==2.3.1
• tensorflow-gpu==2.3.1
• tqdm==4.43.0
• pandas
• boto3
• awscli
• urllib3
• mss

I tried installing "imgaug" and "opencv-python headless" as well.. but still not able to get rid of this error.

I am trying to plot the Gantt chart using matplotlib in python, wherein there are two solutions suggested by different algorithms. Solution by each algorithm contains a group of batches (shown in different colors) starting and finishing at different points of time.

I am able to plot the same, but I want to annotate the graph in such a way that whenever I hover the mouse over the solution, it shows batch detail or length of the bar (processing time). I tried several ways, but not happening. [I would like to see (x,y)= (Batch Processing Time, Algorithm Name) value when I move the mouse over the batch solution.

I'm creating a mp4 video from jpegs with ffmpeg, using the following command:

ffmpeg -y -threads 0 -f image2 -i jpegs/%05d.jpg -framerate 10 video.mp4

The resulting video will play fine with VLC, but will not play in a Jupyter notebook via:

Running SageMaker within a local Jupyter notebook (using VS Code) works without issue, except that attempting to train an XGBoost model using the AWS hosted container results in errors (container name: 246618743249.dkr.ecr.us-west-2.amazonaws.com/sagemaker-xgboost:1.0-1-cpu-py3).

I am running a notebook in Sagemaker and I use a .py file for training:

There is a classic solution for famous Activity Selection problem with greedy approach that you can see here.

But now, I think about another solution. Let's see this sudo code:

I am trying to replicate https://github.com/awslabs/amazon-sagemaker-examples/blob/master/sagemaker-python-sdk/tensorflow_serving_using_elastic_inference_with_your_own_model/tensorflow_serving_pretrained_model_elastic_inference.ipynb

My elastic inference accelerator is attached to notebook instance. I am using conda_amazonei_tensorflow_p36 kernel. According to documentation I made the changes for local EI:

Similar to the issue of The trained model can be deployed on the other platform without dependency of sagemaker or aws service?.

I have trained a model on AWS SageMaker by using the built-in algorithm Semantic Segmentation. This trained model named as model.tar.gz is stored on S3. So I want to download this file from S3 and then use it to make inference on my local PC without using AWS SageMaker anymore. Since the built-in algorithm Semantic Segmentation is built using the MXNet Gluon framework and the Gluon CV toolkit, so I try to refer the documentation of mxnet and gluon-cv to make inference on local PC.

It's easy to download this file from S3, and then I unzip this file to get three files:

1. hyperparams.json: includes the parameters for network architecture, data inputs, and training. Refer to Semantic Segmentation Hyperparameters.
2. model_algo-1
3. model_best.params

Both model_algo-1 and model_best.params are the trained models, and I think it's the output from net.save_parameters (Refer to Train the neural network). I can also load them with the function mxnet.ndarray.load.

Refer to Predict with a pre-trained model. I found there are two necessary things:

1. Reconstruct the network for making inference.
2. Load the trained parameters.

As for reconstructing the network for making inference, since I have used PSPNet from training, so I can use the class gluoncv.model_zoo.PSPNet to reconstruct the network. And I know how to use some services of AWS SageMaker, for example batch transform jobs, to make inference. I want to reproduce it on my local PC. If I use the class gluoncv.model_zoo.PSPNet to reconstruct the network, I can't make sure whether the parameters for this network are same those used on AWS SageMaker while making inference. Because I can't see the image 501404015308.dkr.ecr.ap-northeast-1.amazonaws.com/semantic-segmentation:latest in detail.

As for loading the trained parameters, I can use the load_parameters. But as for model_algo-1 and model_best.params, I don't know which one I should use.