Deploy R in online mode - deploy python function to call R

deploy-python-function-to-call-R.py

params = {
    "wml_credentials": wml_credentials, 
    "space_id": SPACE_ID,
    "script": "predict.R",
    "required_assets": ["iris_xgb.model"],
    "r_packages": ["jsonlite", "xgboost"],
}

def r_function(params=params):
    """
    Deployable Python function which downloads the required assets from a WML deployment space and runs the specified R Script.
    """
    import json
    import subprocess
    from ibm_watson_machine_learning import APIClient

    wml_client = APIClient(params["wml_credentials"])
    wml_client.set.default_space(params["space_id"])
    script = params["script"]
    assets = {x["metadata"]["name"]: x["metadata"]["asset_id"] for x in wml_client.data_assets.get_details()["resources"]}

    # download required assets
    wml_client.data_assets.download(assets[script], script)
    for x in params["required_assets"]:
        wml_client.data_assets.download(assets[x], x)
    
    # install R and other libraries
    # subprocess.run(["mamba", "install", "-c", "conda-forge", "r-base", "r-essentials"])
    subprocess.run(["Rscript", "-e", 'install.packages(c("jsonlite", "data.table", "stringi", "stringr"), repos="https://cloud.r-project.org")'])
    if "xgboost" in params["r_packages"]:
        params["r_packages"].remove("xgboost")
        subprocess.run(["Rscript", "-e", 'install.packages("https://cloud.r-project.org/src/contrib/Archive/xgboost/xgboost_1.5.2.1.tar.gz", repos=NULL, type="source")'])
    subprocess.run(["Rscript", "-e", 'install.packages(c(' + ",".join([f'"{x}"' for x in params["r_packages"]]) + '), repos="https://cloud.r-project.org")'])

    def score(payload):
        d = payload["input_data"][0]["values"][0]
        inputs = d["inputs"]
        inputs = json.dumps(inputs)
        result = subprocess.run(["Rscript", "--vanilla", script, inputs], stdout=subprocess.PIPE, stderr=subprocess.PIPE)
        out = result.stdout.decode("utf-8")
        try:
            out = json.loads(out)
        except:
            out = result.stdout.decode("utf-8")
        err = result.stderr.decode("utf-8") 
        return {"predictions": [{"out": out, "err": err}]}

    return score

meta_props = {
    wml_client.repository.FunctionMetaNames.NAME: "iris xgb",
    wml_client.repository.FunctionMetaNames.SOFTWARE_SPEC_ID: wml_client.software_specifications.get_uid_by_name("custom-r"),
}
function_details = wml_client.repository.store_function(function=r_function, meta_props=meta_props)
function_uid = wml_client.repository.get_function_id(function_details)
meta_props = {
    wml_client.deployments.ConfigurationMetaNames.NAME: "iris xgb deployment",
    wml_client.deployments.ConfigurationMetaNames.ONLINE: {},
}
wml_client.deployments.create(function_uid, meta_props=meta_props)