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NOISSUE - Fix docs (#203)

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* fix docs

Signed-off-by: Sammy Oina <sammyoina@gmail.com>

* fix typos

Signed-off-by: Sammy Oina <sammyoina@gmail.com>

* cli

Signed-off-by: Sammy Oina <sammyoina@gmail.com>

* add build instructions

Signed-off-by: Sammy Oina <sammyoina@gmail.com>

* remove file

Signed-off-by: Sammy Oina <sammyoina@gmail.com>

---------

Signed-off-by: Sammy Oina <sammyoina@gmail.com>
This commit is contained in:
Sammy Kerata Oina
2024-08-21 00:58:37 +03:00
committed by GitHub
parent 8f2cbd349d
commit 066dacd46a
6 changed files with 47 additions and 33 deletions
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test/manual/README.md
+36 -22
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@@ -3,15 +3,17 @@
## CLI
Throughout the tests, we assume that our current working directory is the root of the `cocos` repository, both on the host machine and in the VM.
First, we will build cli by running in the root directory of `cocos`:
```bash
make cli
```
The cli will be compiled to the build directory `./build/cocos-cli`.
### Algorithm requirements
Agent accepts the algorithm as a binary that take in two command line arguments.
```shell
algorithm-file <unix socket path> <dataset file paths>
```
The algorithm program should return the results to a socket and an example can be seen in this [file](./algo/lin_reg.py).
Agent accepts the algorithm as a binary or python or wasm file.
All assets/datasets the algorithm uses are stored in the `datasets` directory. The results from the algorithm run should be stored in the `results` directory. All these paths are relative to the algorithm working directory.
### Agent-CLI interaction
@@ -32,7 +34,7 @@ sev-snp-measure --mode snp --vcpus 4 --vcpu-type EPYC-v4 --ovmf $OVMF_CODE --ker
To speed up the verification process of attested TLS, download the ARK and ASK certificates using the CLI tool. The CLI tool will download the certificates under your home directory in the `.cocos` directory.
```bash
go run cmd/cli/main.go ca-bundle <path/to/platfrom_info.json>
./build/cocos-cli ca-bundle <path/to/platfrom_info.json>
```
In the following text, we can see an example of how the CLI tool is used.
@@ -52,36 +54,35 @@ cd ../..
# The CLI should also be aware of the VM measurement. To add the measurement
# to the .json file that contains the information about the platform, run CLI
# with the measurement in base64 format and the path of the backend_info.json file.:
go run cmd/cli/main.go backend measurement '<measurement>' '<backend_info.json>'
./build/cocos-cli backend measurement '<measurement>' '<backend_info.json>'
# If the VM is booted with the QEMU host data option, the CLI should also know
# the host data information. To add the host data to the .json file that contains
# the information about the platform, run CLI with the host data in base64 format
# and the path of the backend_info.json file.:
go run cmd/cli/main.go backend measurement '<host-data>' '<backend_info.json>'
./build/cocos-cli backend measurement '<host-data>' '<backend_info.json>'
# For attested TLS, also define the path to the backend_info.json that contains reference values for the fields of the attestation report
export AGENT_GRPC_MANIFEST=./scripts/backend_info/backend_info.json
export AGENT_GRPC_ATTESTED_TLS=true
# Retieve Attestation
go run cmd/cli/main.go attestation get '<report_data>'
./build/cocos-cli attestation get '<report_data>'
# Validate Attestation
# Product name must be Milan or Genoa
go run cmd/cli/main.go attestation validate '<attesation>' --report_data '<report_data>' --product <product_name>
./build/cocos-cli attestation validate '<attesation>' --report_data '<report_data>' --product <product_name>
# Run the CLI program with algorithm input
go run cmd/cli/main.go algo test/manual/algo/lin_reg.bin <private_key_file_path>
./build/cocos-cli algo test/manual/algo/lin_reg.py <private_key_file_path> -a python -r test/manual/algo/requirements.py
# 2023/09/21 10:43:53 Uploading algorithm binary: test/manual/algo/lin_reg.bin
# Run the CLI program with dataset input
go run cmd/cli/main.go data test/manual/data/iris.csv <private_key_file_path>
go run cmd/cli/main.go data test/manual/data/iris.csv <private_key_file_path>
./build/cocos-cli data test/manual/data/iris.csv <private_key_file_path>
# 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 <private_key_file_path>
./build/cocos-cli result <private_key_file_path>
# 2023/09/21 10:45:39 Retrieving computation result file
# 2023/09/21 10:45:40 Computation result retrieved and saved successfully!
```
@@ -89,18 +90,12 @@ go run cmd/cli/main.go result <private_key_file_path>
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
```sh
python3 test/manual/algo/lin_reg_test.py test/manual/data/iris.csv result.bin
python ./test/manual/algo/lin_reg.py predict results.zip ./test/manual/data
```
You should get an output (truncated for the sake of brevity):
```sh
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
@@ -112,4 +107,23 @@ Iris-versicolor 0.923 0.889 0.906 27
accuracy 0.933 75
macro avg 0.939 0.938 0.938 75
weighted avg 0.934 0.933 0.933 75
[[21 0 0]
[ 0 24 3]
[ 0 2 25]]
Precision, Recall, Confusion matrix, in testing
precision recall f1-score support
Iris-setosa 1.000 1.000 1.000 29
Iris-versicolor 1.000 1.000 1.000 23
Iris-virginica 1.000 1.000 1.000 23
accuracy 1.000 75
macro avg 1.000 1.000 1.000 75
weighted avg 1.000 1.000 1.000 75
[[29 0 0]
[ 0 23 0]
[ 0 0 23]]
```
test/manual/algo/README.md
+5 -5
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@@ -33,7 +33,7 @@ python3 test/manual/algo/lin_reg.py
The linear regression example is a more complex algorithm that requires external dependencies.It returns a linear regression model trained on the iris dataset found [here](../data/) for demonstration purposes.
```bash
python3 test/manual/algo/lin_reg.py predict result.zip test/manual/data
python3 test/manual/algo/lin_reg.py predict results.zip test/manual/data
```
This will make inference on the results of the linear regression model.
@@ -71,7 +71,7 @@ Next we need to upload the dataset
After some time when the results are ready, you can run the following command to get the results:
```bash
./build/cocos-cli results ./private.pem
./build/cocos-cli result ./private.pem
```
This will return the results of the algorithm.
@@ -79,13 +79,13 @@ This will return the results of the algorithm.
To make inference on the results, you can use the following command:
```bash
python3 test/manual/algo/lin_reg.py predict result.zip test/manual/data
python3 test/manual/algo/lin_reg.py predict results.zip test/manual/data
```
For addition example, you can use the following command:
```bash
go run ./test/computations/main.go ./test/manual/algo/addition.py public.pem false
./build/cocos-cli ./test/manual/algo/addition.py public.pem false
```
```bash
@@ -93,7 +93,7 @@ go run ./test/computations/main.go ./test/manual/algo/addition.py public.pem fal
```
```bash
./build/cocos-cli results ./private.pem
./build/cocos-cli result ./private.pem
```
## Wasm Example