clearml-agent/docs/trains.conf

# TRAINS-AGENT configuration file
api {
    api_server: https://demoapi.trainsai.io
    web_server: https://demoapp.trainsai.io
    files_server: https://demofiles.trainsai.io

    # Credentials are generated in the webapp, https://demoapp.trainsai.io/profile
    # Overridden with os environment: TRAINS_API_ACCESS_KEY / TRAINS_API_SECRET_KEY
    credentials {"access_key": "EGRTCO8JMSIGI6S39GTP43NFWXDQOW", "secret_key": "x!XTov_G-#vspE*Y(h$Anm&DIc5Ou-F)jsl$PdOyj5wG1&E!Z8"}

    # verify host ssl certificate, set to False only if you have a very good reason
    verify_certificate: True
}

agent {
    # Set GIT user/pass credentials
    # leave blank for GIT SSH credentials
    git_user=""
    git_pass=""


    # unique name of this worker, if None, created based on hostname:process_id
    # Overridden with os environment: TRAINS_WORKER_NAME
    # worker_id: "trains-agent-machine1:gpu0"
    worker_id: ""

    # worker name, replaces the hostname when creating a unique name for this worker
    # Overridden with os environment: TRAINS_WORKER_ID
    # worker_name: "trains-agent-machine1"
    worker_name: ""

    # select python package manager:
    # currently supported pip and conda
    # poetry is used if pip selected and repository contains poetry.lock file
    package_manager: {
        # supported options: pip, conda
        type: pip,
        # virtual environment inheres packages from system
        system_site_packages: false,
        # install with --upgrade
        force_upgrade: false,

        # additional artifact repositories to use when installing python packages
        # extra_index_url: ["https://allegroai.jfrog.io/trainsai/api/pypi/public/simple"]
        extra_index_url: []

        # additional conda channels to use when installing with conda package manager
        conda_channels: ["pytorch", "conda-forge", ]
    },

    # target folder for virtual environments builds, created when executing experiment
    venvs_dir = ~/.trains/venvs-builds

    # cached git clone folder
    vcs_cache: {
        enabled: true,
        path: ~/.trains/vcs-cache
    },

    # use venv-update in order to accelerate python virtual environment building
    # Still in beta, turned off by default
    venv_update: {
        enabled: false,
    },

    # cached folder for specific python package download (mostly pytorch versions)
    pip_download_cache {
        enabled: true,
        path: ~/.trains/pip-download-cache
    },

    translate_ssh: true,
    # reload configuration file every daemon execution
    reload_config: false,

    # pip cache folder used mapped into docker, for python package caching
    docker_pip_cache = ~/.trains/pip-cache
    # apt cache folder used mapped into docker, for ubuntu package caching
    docker_apt_cache = ~/.trains/apt-cache

    default_docker: {
        # default docker image to use when running in docker mode
        image: "nvidia/cuda"

        # optional arguments to pass to docker image
        # arguments: ["--ipc=host"]
    }
}

sdk {
    # TRAINS - default SDK configuration

    storage {
        cache {
            # Defaults to system temp folder / cache
            default_base_dir: "~/.trains/cache"
        }

        direct_access: [
            # Objects matching are considered to be available for direct access, i.e. they will not be downloaded
            # or cached, and any download request will return a direct reference.
            # Objects are specified in glob format, available for url and content_type.
            { url: "file://*" }  # file-urls are always directly referenced
        ]
    }

    metrics {
        # History size for debug files per metric/variant. For each metric/variant combination with an attached file
        # (e.g. debug image event), file names for the uploaded files will be recycled in such a way that no more than
        # X files are stored in the upload destination for each metric/variant combination.
        file_history_size: 100

        # Settings for generated debug images
        images {
            format: JPEG
            quality: 87
            subsampling: 0
        }
    }

    network {
        metrics {
            # Number of threads allocated to uploading files (typically debug images) when transmitting metrics for
            # a specific iteration
            file_upload_threads: 4

            # Warn about upload starvation if no uploads were made in specified period while file-bearing events keep
            # being sent for upload
            file_upload_starvation_warning_sec: 120
        }

        iteration {
            # Max number of retries when getting frames if the server returned an error (http code 500)
            max_retries_on_server_error: 5
            # Backoff factory for consecutive retry attempts.
            # SDK will wait for {backoff factor} * (2 ^ ({number of total retries} - 1)) between retries.
            retry_backoff_factor_sec: 10
        }
    }
    aws {
        s3 {
            # S3 credentials, used for read/write access by various SDK elements

            # default, used for any bucket not specified below
            key: ""
            secret: ""
            region: ""

            credentials: [
                # specifies key/secret credentials to use when handling s3 urls (read or write)
                # {
                #     bucket: "my-bucket-name"
                #     key: "my-access-key"
                #     secret: "my-secret-key"
                # },
                # {
                #     # This will apply to all buckets in this host (unless key/value is specifically provided for a given bucket)
                #     host: "my-minio-host:9000"
                #     key: "12345678"
                #     secret: "12345678"
                #     multipart: false
                #     secure: false
                # }
            ]
        }
        boto3 {
            pool_connections: 512
            max_multipart_concurrency: 16
        }
    }
    google.storage {
        # # Default project and credentials file
        # # Will be used when no bucket configuration is found
        # project: "trains"
        # credentials_json: "/path/to/credentials.json"

        # # Specific credentials per bucket and sub directory
        # credentials = [
        #     {
        #         bucket: "my-bucket"
        #         subdir: "path/in/bucket" # Not required
        #         project: "trains"
        #         credentials_json: "/path/to/credentials.json"
        #     },
        # ]
    }
    azure.storage {
        # containers: [
        #     {
        #         account_name: "trains"
        #         account_key: "secret"
        #         # container_name:
        #     }
        # ]
    }

    log {
        # debugging feature: set this to true to make null log propagate messages to root logger (so they appear in stdout)
        null_log_propagate: False
        task_log_buffer_capacity: 66

        # disable urllib info and lower levels
        disable_urllib3_info: True
    }

    development {
        # Development-mode options

        # dev task reuse window
        task_reuse_time_window_in_hours: 72.0

        # Run VCS repository detection asynchronously
        vcs_repo_detect_async: True

        # Store uncommitted git/hg source code diff in experiment manifest when training in development mode
        # This stores "git diff" or "hg diff" into the experiment's "script.requirements.diff" section
        store_uncommitted_code_diff_on_train: True

        # Support stopping an experiment in case it was externally stopped, status was changed or task was reset
        support_stopping: True

        # Development mode worker
        worker {
            # Status report period in seconds
            report_period_sec: 2

            # ping to the server - check connectivity
            ping_period_sec: 30

            # Log all stdout & stderr
            log_stdout: True
        }
    }
}