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Sammy Kerata Oina
046b549079
The HTTP server-related code, documentation, and configurations have been removed as part of a shift towards prioritizing gRPC for service communication. This update includes deletions of HTTP host and port configs across various components, the manager HTTP API alongside its Swagger definition, and the removal of related scaffolding and utility code. This change simplifies the overall architecture and eliminates redundant HTTP support, focusing on optimizing gRPC performance and security features. Signed-off-by: SammyOina <sammyoina@gmail.com>
3.0 KiB
3.0 KiB
Manual tests
CLI
Throughout the tests, we assume that our current working directory is the root of the agent repository, both on the host machine and in the VM.
Python requirements
Do this both on the host machine and in the VM.
apt update
apt install python3-pip
pip3 install pandas scikit-learn
Agent-CLI interaction
In the VM, open a console and start agent:
AGENT_LOG_LEVEL=info AGENT_GRPC_URL=10.0.2.15:7002 go run cmd/agent/main.go
Open console on the host, and run
export AGENT_GRPC_URL=localhost:7002
export MANAGER_GRPC_URL=localhost:7001
# Run CLI to provide manifest
go run cmd/cli/main.go run '{"id":"123","name":"Sample Computation","description":"A sample computation","status":"Processing","owner":"John Doe","start_time":"2023-11-03T12:03:21.705171284+03:00","end_time":"2023-11-03T13:03:21.705171532+03:00","datasets":[{"provider":"Provider1","id":"Dataset1"},{"provider":"Provider2","id":"Dataset2"}],"algorithms":[{"provider":"AlgorithmProvider1","id":"Algorithm1"}],"result_consumers":["Consumer1","Consumer2"],"ttl":3600,"metadata":{"key1":"value1","key2":42}, "timeout": "2m"}'
# Run the CLI program with algorithm input
go run cmd/cli/main.go algo test/manual/algo/lin_reg.py Algorithm1 AlgorithmProvider1
# 2023/09/21 10:43:53 Uploading algorithm binary: test/manual/algo/lin_reg.py
# Run the CLI program with dataset input
go run cmd/cli/main.go data test/manual/data/iris.csv Dataset1 Provider1
go run cmd/cli/main.go data test/manual/data/iris.csv Dataset2 Provider2
# 2023/09/21 10:45:25 Uploading dataset CSV: test/manual/data/iris.csv
# Run the CLI program to fetch computation result
go run cmd/cli/main.go result Consumer1
# 2023/09/21 10:45:39 Retrieving computation result file
# 2023/09/21 10:45:40 Computation result retrieved and saved successfully!
Now there is a result.bin file in the current working directory. The file holds the trained logistic regression model. To test the model, run
python3 test/manual/algo/lin_reg_test.py test/manual/data/iris.csv result.bin
You should get an output (truncated for the sake of brevity):
Id SepalLengthCm SepalWidthCm PetalLengthCm PetalWidthCm Species
0 1 5.1 3.5 1.4 0.2 Iris-setosa
1 2 4.9 3.0 1.4 0.2 Iris-setosa
2 3 4.7 3.2 1.3 0.2 Iris-setosa
3 4 4.6 3.1 1.5 0.2 Iris-setosa
4 5 5.0 3.6 1.4 0.2 Iris-setosa
Precision, Recall, Confusion matrix, in training
precision recall f1-score support
Iris-setosa 1.000 1.000 1.000 21
Iris-versicolor 0.923 0.889 0.906 27
Iris-virginica 0.893 0.926 0.909 27
accuracy 0.933 75
macro avg 0.939 0.938 0.938 75
weighted avg 0.934 0.933 0.933 75