| #!/usr/bin/env python3 |
| # Copyright (c) 2021-2022 Arm Limited. All rights reserved. |
| # SPDX-License-Identifier: Apache-2.0 |
| # |
| # Licensed under the Apache License, Version 2.0 (the "License"); |
| # you may not use this file except in compliance with the License. |
| # You may obtain a copy of the License at |
| # |
| # http://www.apache.org/licenses/LICENSE-2.0 |
| # |
| # Unless required by applicable law or agreed to in writing, software |
| # distributed under the License is distributed on an "AS IS" BASIS, |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| # See the License for the specific language governing permissions and |
| # limitations under the License. |
| import errno |
| import fnmatch |
| import json |
| import logging |
| import os |
| import re |
| import shutil |
| import subprocess |
| import sys |
| import urllib.request |
| from argparse import ArgumentParser |
| from argparse import ArgumentTypeError |
| from collections import namedtuple |
| from urllib.error import URLError |
| from pathlib import Path |
| |
| from scripts.py.check_update_resources_downloaded import get_md5sum_for_file |
| |
| |
| json_uc_res = [ |
| { |
| "use_case_name": "ad", |
| "url_prefix": [ |
| "https://github.com/ARM-software/ML-zoo/raw/7c32b097f7d94aae2cd0b98a8ed5a3ba81e66b18/models/anomaly_detection/micronet_medium/tflite_int8/" |
| ], |
| "resources": [ |
| { |
| "name": "ad_medium_int8.tflite", |
| "url": "{url_prefix:0}ad_medium_int8.tflite", |
| }, |
| {"name": "ifm0.npy", "url": "{url_prefix:0}testing_input/input/0.npy"}, |
| {"name": "ofm0.npy", "url": "{url_prefix:0}testing_output/Identity/0.npy"}, |
| ], |
| }, |
| { |
| "use_case_name": "asr", |
| "url_prefix": [ |
| "https://github.com/ARM-software/ML-zoo/raw/1a92aa08c0de49a7304e0a7f3f59df6f4fd33ac8/models/speech_recognition/wav2letter/tflite_pruned_int8/" |
| ], |
| "resources": [ |
| { |
| "name": "wav2letter_pruned_int8.tflite", |
| "url": "{url_prefix:0}wav2letter_pruned_int8.tflite", |
| }, |
| { |
| "name": "ifm0.npy", |
| "url": "{url_prefix:0}testing_input/input_2_int8/0.npy", |
| }, |
| { |
| "name": "ofm0.npy", |
| "url": "{url_prefix:0}testing_output/Identity_int8/0.npy", |
| }, |
| ], |
| }, |
| { |
| "use_case_name": "img_class", |
| "url_prefix": [ |
| "https://github.com/ARM-software/ML-zoo/raw/e0aa361b03c738047b9147d1a50e3f2dcb13dbcb/models/image_classification/mobilenet_v2_1.0_224/tflite_int8/" |
| ], |
| "resources": [ |
| { |
| "name": "mobilenet_v2_1.0_224_INT8.tflite", |
| "url": "{url_prefix:0}mobilenet_v2_1.0_224_INT8.tflite", |
| }, |
| { |
| "name": "ifm0.npy", |
| "url": "{url_prefix:0}testing_input/tfl.quantize/0.npy", |
| }, |
| { |
| "name": "ofm0.npy", |
| "url": "{url_prefix:0}testing_output/MobilenetV2/Predictions/Reshape_11/0.npy", |
| }, |
| ], |
| }, |
| { |
| "use_case_name": "object_detection", |
| "url_prefix": [ |
| "https://github.com/emza-vs/ModelZoo/blob/v1.0/object_detection/" |
| ], |
| "resources": [ |
| { |
| "name": "yolo-fastest_192_face_v4.tflite", |
| "url": "{url_prefix:0}yolo-fastest_192_face_v4.tflite?raw=true", |
| } |
| ], |
| }, |
| { |
| "use_case_name": "kws", |
| "url_prefix": [ |
| "https://github.com/ARM-software/ML-zoo/raw/9f506fe52b39df545f0e6c5ff9223f671bc5ae00/models/keyword_spotting/micronet_medium/tflite_int8/" |
| ], |
| "resources": [ |
| {"name": "ifm0.npy", "url": "{url_prefix:0}testing_input/input/0.npy"}, |
| {"name": "ofm0.npy", "url": "{url_prefix:0}testing_output/Identity/0.npy"}, |
| { |
| "name": "kws_micronet_m.tflite", |
| "url": "{url_prefix:0}kws_micronet_m.tflite", |
| }, |
| ], |
| }, |
| { |
| "use_case_name": "vww", |
| "url_prefix": [ |
| "https://github.com/ARM-software/ML-zoo/raw/7dd3b16bb84007daf88be8648983c07f3eb21140/models/visual_wake_words/micronet_vww4/tflite_int8/" |
| ], |
| "resources": [ |
| { |
| "name": "vww4_128_128_INT8.tflite", |
| "url": "{url_prefix:0}vww4_128_128_INT8.tflite", |
| }, |
| {"name": "ifm0.npy", "url": "{url_prefix:0}testing_input/input/0.npy"}, |
| {"name": "ofm0.npy", "url": "{url_prefix:0}testing_output/Identity/0.npy"}, |
| ], |
| }, |
| { |
| "use_case_name": "kws_asr", |
| "url_prefix": [ |
| "https://github.com/ARM-software/ML-zoo/raw/1a92aa08c0de49a7304e0a7f3f59df6f4fd33ac8/models/speech_recognition/wav2letter/tflite_pruned_int8/", |
| "https://github.com/ARM-software/ML-zoo/raw/9f506fe52b39df545f0e6c5ff9223f671bc5ae00/models/keyword_spotting/micronet_medium/tflite_int8/", |
| ], |
| "resources": [ |
| { |
| "name": "wav2letter_pruned_int8.tflite", |
| "url": "{url_prefix:0}wav2letter_pruned_int8.tflite", |
| }, |
| { |
| "sub_folder": "asr", |
| "name": "ifm0.npy", |
| "url": "{url_prefix:0}testing_input/input_2_int8/0.npy", |
| }, |
| { |
| "sub_folder": "asr", |
| "name": "ofm0.npy", |
| "url": "{url_prefix:0}testing_output/Identity_int8/0.npy", |
| }, |
| { |
| "sub_folder": "kws", |
| "name": "ifm0.npy", |
| "url": "{url_prefix:1}testing_input/input/0.npy", |
| }, |
| { |
| "sub_folder": "kws", |
| "name": "ofm0.npy", |
| "url": "{url_prefix:1}testing_output/Identity/0.npy", |
| }, |
| { |
| "name": "kws_micronet_m.tflite", |
| "url": "{url_prefix:1}kws_micronet_m.tflite", |
| }, |
| ], |
| }, |
| { |
| "use_case_name": "noise_reduction", |
| "url_prefix": [ |
| "https://github.com/ARM-software/ML-zoo/raw/a061600058097a2785d6f1f7785e5a2d2a142955/models/noise_suppression/RNNoise/tflite_int8/" |
| ], |
| "resources": [ |
| {"name": "rnnoise_INT8.tflite", "url": "{url_prefix:0}rnnoise_INT8.tflite"}, |
| { |
| "name": "ifm0.npy", |
| "url": "{url_prefix:0}testing_input/main_input_int8/0.npy", |
| }, |
| { |
| "name": "ifm1.npy", |
| "url": "{url_prefix:0}testing_input/vad_gru_prev_state_int8/0.npy", |
| }, |
| { |
| "name": "ifm2.npy", |
| "url": "{url_prefix:0}testing_input/noise_gru_prev_state_int8/0.npy", |
| }, |
| { |
| "name": "ifm3.npy", |
| "url": "{url_prefix:0}testing_input/denoise_gru_prev_state_int8/0.npy", |
| }, |
| { |
| "name": "ofm0.npy", |
| "url": "{url_prefix:0}testing_output/Identity_int8/0.npy", |
| }, |
| { |
| "name": "ofm1.npy", |
| "url": "{url_prefix:0}testing_output/Identity_1_int8/0.npy", |
| }, |
| { |
| "name": "ofm2.npy", |
| "url": "{url_prefix:0}testing_output/Identity_2_int8/0.npy", |
| }, |
| { |
| "name": "ofm3.npy", |
| "url": "{url_prefix:0}testing_output/Identity_3_int8/0.npy", |
| }, |
| { |
| "name": "ofm4.npy", |
| "url": "{url_prefix:0}testing_output/Identity_4_int8/0.npy", |
| }, |
| ], |
| }, |
| { |
| "use_case_name": "inference_runner", |
| "url_prefix": [ |
| "https://github.com/ARM-software/ML-zoo/raw/68b5fbc77ed28e67b2efc915997ea4477c1d9d5b/models/keyword_spotting/dnn_small/tflite_int8/" |
| ], |
| "resources": [ |
| { |
| "name": "dnn_s_quantized.tflite", |
| "url": "{url_prefix:0}dnn_s_quantized.tflite", |
| } |
| ], |
| }, |
| ] |
| |
| # Valid NPU configurations: |
| valid_npu_config_names = [ |
| "ethos-u55-32", |
| "ethos-u55-64", |
| "ethos-u55-128", |
| "ethos-u55-256", |
| "ethos-u65-256", |
| "ethos-u65-512", |
| ] |
| |
| # Default NPU configurations (these are always run when the models are optimised) |
| default_npu_config_names = [valid_npu_config_names[2], valid_npu_config_names[4]] |
| |
| # NPU config named tuple |
| NPUConfig = namedtuple( |
| "NPUConfig", |
| [ |
| "config_name", |
| "memory_mode", |
| "system_config", |
| "ethos_u_npu_id", |
| "ethos_u_config_id", |
| "arena_cache_size", |
| ], |
| ) |
| |
| # The internal SRAM size for Corstone-300 implementation on MPS3 specified by AN552 |
| # The internal SRAM size for Corstone-310 implementation on MPS3 specified by AN555 |
| # is 4MB, but we are content with the 2MB specified below. |
| mps3_max_sram_sz = 2 * 1024 * 1024 # 2 MiB (2 banks of 1 MiB each) |
| |
| |
| def call_command(command: str, verbose: bool = True) -> str: |
| """ |
| Helpers function that call subprocess and return the output. |
| |
| Parameters: |
| ---------- |
| command (string): Specifies the command to run. |
| """ |
| if verbose: |
| logging.info(command) |
| proc = subprocess.run( |
| command, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, shell=True |
| ) |
| log = proc.stdout.decode("utf-8") |
| if proc.returncode == 0 and verbose: |
| logging.info(log) |
| else: |
| logging.error(log) |
| proc.check_returncode() |
| return log |
| |
| |
| def get_default_npu_config_from_name( |
| config_name: str, arena_cache_size: int = 0 |
| ) -> NPUConfig: |
| """ |
| Gets the file suffix for the TFLite file from the |
| `accelerator_config` string. |
| |
| Parameters: |
| ---------- |
| config_name (str): Ethos-U NPU configuration from valid_npu_config_names |
| |
| arena_cache_size (int): Specifies arena cache size in bytes. If a value |
| greater than 0 is provided, this will be taken |
| as the cache size. If 0, the default values, as per |
| the NPU config requirements, are used. |
| |
| Returns: |
| ------- |
| NPUConfig: An NPU config named tuple populated with defaults for the given |
| config name |
| """ |
| if config_name not in valid_npu_config_names: |
| raise ValueError( |
| f""" |
| Invalid Ethos-U NPU configuration. |
| Select one from {valid_npu_config_names}. |
| """ |
| ) |
| |
| strings_ids = ["ethos-u55-", "ethos-u65-"] |
| processor_ids = ["U55", "U65"] |
| prefix_ids = ["H", "Y"] |
| memory_modes = ["Shared_Sram", "Dedicated_Sram"] |
| system_configs = ["Ethos_U55_High_End_Embedded", "Ethos_U65_High_End"] |
| memory_modes_arena = { |
| # For shared SRAM memory mode, we use the MPS3 SRAM size by default. |
| "Shared_Sram": mps3_max_sram_sz if arena_cache_size <= 0 else arena_cache_size, |
| # For dedicated SRAM memory mode, we do not override the arena size. This is expected to |
| # be defined in the Vela configuration file instead. |
| "Dedicated_Sram": None if arena_cache_size <= 0 else arena_cache_size, |
| } |
| |
| for i in range(len(strings_ids)): |
| if config_name.startswith(strings_ids[i]): |
| npu_config_id = config_name.replace(strings_ids[i], prefix_ids[i]) |
| return NPUConfig( |
| config_name=config_name, |
| memory_mode=memory_modes[i], |
| system_config=system_configs[i], |
| ethos_u_npu_id=processor_ids[i], |
| ethos_u_config_id=npu_config_id, |
| arena_cache_size=memory_modes_arena[memory_modes[i]], |
| ) |
| |
| return None |
| |
| |
| def remove_tree_dir(dir_path): |
| try: |
| # Remove the full directory. |
| shutil.rmtree(dir_path) |
| # Re-create an empty one. |
| os.mkdir(dir_path) |
| except Exception as e: |
| logging.error(f"Failed to delete {dir_path}.") |
| |
| |
| def set_up_resources( |
| run_vela_on_models: bool = False, |
| additional_npu_config_names: tuple = (), |
| arena_cache_size: int = 0, |
| check_clean_folder: bool = False, |
| additional_requirements_file: str = "") -> (Path, Path): |
| """ |
| Helpers function that retrieve the output from a command. |
| |
| Parameters: |
| ---------- |
| run_vela_on_models (bool): Specifies if run vela on downloaded models. |
| additional_npu_config_names(list): list of strings of Ethos-U NPU configs. |
| arena_cache_size (int): Specifies arena cache size in bytes. If a value |
| greater than 0 is provided, this will be taken |
| as the cache size. If 0, the default values, as per |
| the NPU config requirements, are used. |
| check_clean_folder (bool): Indicates whether the resources folder needs to |
| be checked for updates and cleaned. |
| additional_requirements_file (str): Path to a requirements.txt file if |
| additional packages need to be |
| installed. |
| |
| Returns |
| ------- |
| |
| Tuple of pair of Paths: (download_directory_path, virtual_env_path) |
| |
| download_directory_path: Root of the directory where the resources have been downloaded to. |
| virtual_env_path: Path to the root of virtual environment. |
| """ |
| # Paths. |
| current_file_dir = Path(__file__).parent.resolve() |
| download_dir = current_file_dir / "resources_downloaded" |
| metadata_file_path = download_dir / "resources_downloaded_metadata.json" |
| |
| metadata_dict = dict() |
| vela_version = "3.3.0" |
| py3_major_version_minimum = 3 # Python > 3.8 is required |
| py3_minor_version_minimum = 8 |
| |
| # Is Python minimum requirement matched? |
| py3_version = sys.version_info |
| if ( |
| py3_version.major < py3_major_version_minimum |
| or py3_version.minor < py3_minor_version_minimum |
| ): |
| raise Exception( |
| "ERROR: Python3.8+ is required, please see the documentation on how to update it." |
| ) |
| |
| setup_script_hash_verified = False |
| setup_script_hash = get_md5sum_for_file(Path(__file__).resolve()) |
| |
| try: |
| # 1.1 Does the download dir exist? |
| download_dir.mkdir() |
| except OSError as e: |
| if e.errno == errno.EEXIST: |
| logging.info("'resources_downloaded' directory exists.") |
| # Check and clean? |
| if check_clean_folder and metadata_file_path.is_file(): |
| with open(metadata_file_path) as metadata_file: |
| metadata_dict = json.load(metadata_file) |
| vela_in_metadata = metadata_dict["ethosu_vela_version"] |
| if vela_in_metadata != vela_version: |
| # Check if all the resources needs to be removed and regenerated. |
| # This can happen when the Vela version has changed. |
| logging.info( |
| f"Vela version in metadata is {vela_in_metadata}, current {vela_version}. Removing the resources and re-download them." |
| ) |
| remove_tree_dir(download_dir) |
| metadata_dict = dict() |
| else: |
| # Check if the set_up_default_resorces.py has changed from last setup |
| setup_script_hash_verified = ( |
| metadata_dict.get("set_up_script_md5sum") |
| == setup_script_hash |
| ) |
| else: |
| raise |
| |
| # 1.2 Does the virtual environment exist? |
| env_dirname = "env" |
| env_path = download_dir / env_dirname |
| env_python = str(env_path / "bin" / "python3") |
| env_activate = str(env_path / "bin" / "activate") |
| |
| if not env_path.is_dir(): |
| os.chdir(download_dir) |
| # Create the virtual environment. |
| command = f"python3 -m venv {env_dirname}" |
| call_command(command) |
| commands = ["pip install --upgrade pip", "pip install --upgrade setuptools"] |
| for c in commands: |
| command = f"{env_python} -m {c}" |
| call_command(command) |
| os.chdir(current_file_dir) |
| |
| # 1.3 Make sure to have all the requirements |
| requirements = [f"ethos-u-vela=={vela_version}"] |
| command = f"{env_python} -m pip freeze" |
| packages = call_command(command) |
| for req in requirements: |
| if req not in packages: |
| command = f"{env_python} -m pip install {req}" |
| call_command(command) |
| |
| # 1.4 Install additional requirements, if a valid file has been provided |
| if additional_requirements_file and os.path.isfile(additional_requirements_file): |
| command = f"{env_python} -m pip install -r {additional_requirements_file}" |
| call_command(command) |
| |
| # 2. Download models |
| logging.info("Downloading resources.") |
| for uc in json_uc_res: |
| use_case_name = uc["use_case_name"] |
| res_url_prefix = uc["url_prefix"] |
| try: |
| # Does the usecase_name download dir exist? |
| (download_dir / use_case_name).mkdir() |
| except OSError as e: |
| if e.errno == errno.EEXIST: |
| # The usecase_name download dir exist. |
| if check_clean_folder and not setup_script_hash_verified: |
| for idx, metadata_uc_url_prefix in enumerate( |
| [ |
| f |
| for f in metadata_dict["resources_info"] |
| if f["use_case_name"] == use_case_name |
| ][0]["url_prefix"] |
| ): |
| if metadata_uc_url_prefix != res_url_prefix[idx]: |
| logging.info(f"Removing {use_case_name} resources.") |
| remove_tree_dir(download_dir / use_case_name) |
| break |
| elif e.errno != errno.EEXIST: |
| logging.error(f"Error creating {use_case_name} directory.") |
| raise |
| |
| reg_expr_str = r"{url_prefix:(.*\d)}" |
| reg_expr_pattern = re.compile(reg_expr_str) |
| for res in uc["resources"]: |
| res_name = res["name"] |
| url_prefix_idx = int(reg_expr_pattern.search(res["url"]).group(1)) |
| res_url = res_url_prefix[url_prefix_idx] + re.sub( |
| reg_expr_str, "", res["url"] |
| ) |
| |
| sub_folder = "" |
| if "sub_folder" in res: |
| try: |
| # Does the usecase_name/sub_folder download dir exist? |
| (download_dir / use_case_name / res["sub_folder"]).mkdir() |
| except OSError as e: |
| if e.errno != errno.EEXIST: |
| logging.error( |
| f"Error creating {use_case_name} / {res['sub_folder']} directory." |
| ) |
| raise |
| sub_folder = res["sub_folder"] |
| |
| res_dst = download_dir / use_case_name / sub_folder / res_name |
| |
| if res_dst.is_file(): |
| logging.info(f"File {res_dst} exists, skipping download.") |
| else: |
| try: |
| g = urllib.request.urlopen(res_url) |
| with open(res_dst, "b+w") as f: |
| f.write(g.read()) |
| logging.info(f"- Downloaded {res_url} to {res_dst}.") |
| except URLError: |
| logging.error(f"URLError while downloading {res_url}.") |
| raise |
| |
| # 3. Run vela on models in resources_downloaded |
| # New models will have same name with '_vela' appended. |
| # For example: |
| # original model: kws_micronet_m.tflite |
| # after vela model: kws_micronet_m_vela_H128.tflite |
| # |
| # Note: To avoid to run vela twice on the same model, it's supposed that |
| # downloaded model names don't contain the 'vela' word. |
| if run_vela_on_models is True: |
| config_file = current_file_dir / "scripts" / "vela" / "default_vela.ini" |
| models = [ |
| Path(dirpath) / f |
| for dirpath, dirnames, files in os.walk(download_dir) |
| for f in fnmatch.filter(files, "*.tflite") |
| if "vela" not in f |
| ] |
| |
| # Consolidate all config names while discarding duplicates: |
| config_names = list(set(default_npu_config_names + additional_npu_config_names)) |
| |
| # Get npu config tuple for each config name in a list: |
| npu_configs = [ |
| get_default_npu_config_from_name(name, arena_cache_size) |
| for name in config_names |
| ] |
| |
| logging.info(f"All models will be optimised for these configs:") |
| for config in npu_configs: |
| logging.info(config) |
| |
| optimisation_skipped = False |
| |
| for model in models: |
| output_dir = model.parent |
| # model name after compiling with vela is an initial model name + _vela suffix |
| vela_optimised_model_path = model.parent / (model.stem + "_vela.tflite") |
| |
| for config in npu_configs: |
| vela_command_arena_cache_size = "" |
| |
| if config.arena_cache_size: |
| vela_command_arena_cache_size = ( |
| f"--arena-cache-size={config.arena_cache_size}" |
| ) |
| |
| vela_command = ( |
| f". {env_activate} && vela {model} " |
| + f"--accelerator-config={config.config_name} " |
| + "--optimise Performance " |
| + f"--config {config_file} " |
| + f"--memory-mode={config.memory_mode} " |
| + f"--system-config={config.system_config} " |
| + f"--output-dir={output_dir} " |
| + f"{vela_command_arena_cache_size}" |
| ) |
| |
| # We want the name to include the configuration suffix. For example: vela_H128, |
| # vela_Y512 etc. |
| new_suffix = "_vela_" + config.ethos_u_config_id + ".tflite" |
| new_vela_optimised_model_path = model.parent / (model.stem + new_suffix) |
| |
| if new_vela_optimised_model_path.is_file(): |
| logging.info( |
| f"File {new_vela_optimised_model_path} exists, skipping optimisation." |
| ) |
| optimisation_skipped = True |
| continue |
| |
| call_command(vela_command) |
| |
| # Rename default vela model. |
| vela_optimised_model_path.rename(new_vela_optimised_model_path) |
| logging.info( |
| f"Renaming {vela_optimised_model_path} to {new_vela_optimised_model_path}." |
| ) |
| |
| # If any optimisation was skipped, show how to regenerate: |
| if optimisation_skipped: |
| logging.warning("One or more optimisations were skipped.") |
| logging.warning( |
| f"To optimise all the models, please remove the directory {download_dir}." |
| ) |
| |
| # 4. Collect and write metadata |
| logging.info("Collecting and write metadata.") |
| metadata_dict["ethosu_vela_version"] = vela_version |
| metadata_dict["set_up_script_md5sum"] = setup_script_hash.strip("\n") |
| metadata_dict["resources_info"] = json_uc_res |
| |
| with open(metadata_file_path, "w") as metadata_file: |
| json.dump(metadata_dict, metadata_file, indent=4) |
| |
| return download_dir, env_path |
| |
| |
| if __name__ == "__main__": |
| parser = ArgumentParser() |
| parser.add_argument( |
| "--skip-vela", |
| help="Do not run Vela optimizer on downloaded models.", |
| action="store_true", |
| ) |
| parser.add_argument( |
| "--additional-ethos-u-config-name", |
| help=f"""Additional (non-default) configurations for Vela: |
| {valid_npu_config_names}""", |
| default=[], |
| action="append", |
| ) |
| parser.add_argument( |
| "--arena-cache-size", |
| help="Arena cache size in bytes (if overriding the defaults)", |
| type=int, |
| default=0, |
| ) |
| parser.add_argument( |
| "--clean", |
| help="Clean the directory and optimize the downloaded resources", |
| action="store_true", |
| ) |
| parser.add_argument( |
| "--requirements-file", |
| help="Path to requirements.txt file to install additional packages", |
| type=str, |
| default=Path(__file__).parent.resolve() / 'scripts' / 'py' / 'requirements.txt' |
| ) |
| |
| args = parser.parse_args() |
| |
| if args.arena_cache_size < 0: |
| raise ArgumentTypeError("Arena cache size cannot not be less than 0") |
| |
| if not Path(args.requirements_file).is_file(): |
| raise ArgumentTypeError(f"Invalid requirements file: {args.requirements_file}") |
| |
| logging.basicConfig(filename="log_build_default.log", level=logging.DEBUG) |
| logging.getLogger().addHandler(logging.StreamHandler(sys.stdout)) |
| |
| set_up_resources( |
| not args.skip_vela, |
| args.additional_ethos_u_config_name, |
| args.arena_cache_size, |
| args.clean, |
| args.requirements_file, |
| ) |