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NOISSUE - Fix Docker Algorithm #40

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Aug 29, 2024
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NOISSUE - Fix Docker Algorithm #40

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e47ccea
fix(docker): fix running of docker example
rodneyosodo Aug 27, 2024
36e9f6a
fix: Add instructions to unzip results
rodneyosodo Aug 27, 2024
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57 changes: 33 additions & 24 deletions docs/algorithms.md
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Original file line number Diff line number Diff line change
Expand Up @@ -4,6 +4,7 @@ Currently, cocos supports running the following algorithms:

- binary algorithms
- python scripts
- docker images
- wasm modules

## Binary Algorithms
Expand Down Expand Up @@ -353,11 +354,11 @@ For real-world examples to test with cocos, see our [AI repository](https://gith

Python is a high-level, interpreted programming language. Python scripts can be run on the enclave. Python is known for its simplicity and readability, making it a popular choice for beginners and experienced developers alike.

## Running Python Algorithms without Datasets
### Running Python Algorithms without Datasets

This has been covered in the [previous section](./getting-started.md#uploading-artefacts).

## Running Python Algorithms with Datasets
### Running Python Algorithms with Datasets

For Python algorithms, with datatsets:

Expand Down Expand Up @@ -491,27 +492,13 @@ We will use the linear regression example from the cocos repository in this exam
git clone https://github.com/ultravioletrs/cocos.git
```

Then, use your favorite editor to create a file named `Dockerfile` in the current working directory and write the following code.
Change directory to the linear regression example.

```bash
FROM python:3.9-slim

# set the working directory in the container
WORKDIR /cocos
RUN mkdir /cocos/results
RUN mkdir /cocos/datasets

COPY ./cocos/test/manual/algo/requirements.txt /cocos/requirements.txt
COPY ./cocos/test/manual/algo/lin_reg.py /cocos/lin_reg.py

# install dependencies
RUN pip install -r requirements.txt

# command to be run when the docker container is started
CMD ["python3", "/cocos/lin_reg.py"]
cd cocos/test/manual/algo/
```

Next, run the build command and save the docker image as a `tar` file.
Next, run the build command and save the docker image as a `tar` file. This example Dockerfile is based of the python linear regression example using iris dataset.

```bash
docker build -t linreg .
Expand All @@ -529,32 +516,37 @@ cd ./cocos
Start the computation server:

```bash
go run ./test/computations/main.go ./test/manual/algo/lin_reg.py public.pem false ./test/manual/data/iris.csv
go run ./test/computations/main.go ./test/manual/algo/linreg.tar public.pem false ./test/manual/data/iris.csv
```

Start the manager

```bash
cd cmd/manager
```

```bash
sudo \
MANAGER_QEMU_SMP_MAXCPUS=4 \
MANAGER_QEMU_MEMORY_SIZE=25G \
MANAGER_GRPC_URL=localhost:7001 \
MANAGER_LOG_LEVEL=debug \
MANAGER_QEMU_ENABLE_SEV_SNP=false \
MANAGER_QEMU_OVMF_CODE_FILE=/usr/share/edk2/x64/OVMF_CODE.fd \
MANAGER_QEMU_OVMF_VARS_FILE=/usr/share/edk2/x64/OVMF_VARS.fd \
go run ./cmd/manager/main.go
go run main.go
```

Export the agent grpc url from computation server logs

```bash
export AGENT_GRPC_URL=localhost:6015
export AGENT_GRPC_URL=localhost:6100
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```

Upload the algorithm

```bash
./build/cocos-cli algo ../linreg.tar ./private.pem -a docker
./build/cocos-cli algo ./test/manual/algo/linreg.tar ./private.pem -a docker
```

Upload the dataset
Expand All @@ -569,12 +561,29 @@ After some time when the results are ready, you can run the following command to
./build/cocos-cli results ./private.pem
```

The logs will be similar to this:

```bash
2024/08/19 14:14:31 Retrieving computation result file
2024/08/19 14:14:31 Computation result retrieved and saved successfully!
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```

Unzip the results

```bash
unzip results.zip -d results
```

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/model.bin test/manual/data/
```

Terminal recording session

[![asciicast](https://asciinema.org/a/vHaUnKoEcXwHJR78EBzTScCuS.svg)](https://asciinema.org/a/vHaUnKoEcXwHJR78EBzTScCuS)

## Wasm Modules

WebAssembly (wasm) is a binary instruction format for a stack-based virtual machine. Wasm is designed as a portable target for compilation of high-level languages like C/C++/Rust, enabling deployment on the web for client and server applications. Wasm modules can be run on the enclave.
Expand Down