# rl_coach.data_stores.s3_data_store — Reinforcement Learning Coach 0.12.0 documentation

# Source code for rl_coach.data_stores.s3_data_store

    #
    # Copyright (c) 2017 Intel Corporation
    #
    # 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.
    #

    from rl_coach.data_stores.data_store import DataStore, DataStoreParameters
    from minio import Minio
    from minio.error import ResponseError
    from configparser import ConfigParser, Error
    from rl_coach.checkpoint import CheckpointStateFile
    from rl_coach.data_stores.data_store import SyncFiles

    import os
    import time
    import io

    class S3DataStoreParameters(DataStoreParameters):
        def __init__(self, ds_params, creds_file: str = None, end_point: str = None, bucket_name: str = None,
                     checkpoint_dir: str = None, expt_dir: str = None):

            super().__init__(ds_params.store_type, ds_params.orchestrator_type, ds_params.orchestrator_params)
            self.creds_file = creds_file
            self.end_point = end_point
            self.bucket_name = bucket_name
            self.checkpoint_dir = checkpoint_dir
            self.expt_dir = expt_dir

    class S3DataStore(DataStore):
        """
        An implementation of the data store using S3 for storing policy checkpoints when using Coach in distributed mode.
        The policy checkpoints are written by the trainer and read by the rollout worker.
        """

        def __init__(self, params: S3DataStoreParameters):
            """
            :param params: The parameters required to use the S3 data store.
            """

            super(S3DataStore, self).__init__(params)
            self.params = params
            access_key = None
            secret_key = None
            if params.creds_file:
                config = ConfigParser()
                config.read(params.creds_file)
                try:
                    access_key = config.get('default', 'aws_access_key_id')
                    secret_key = config.get('default', 'aws_secret_access_key')
                except Error as e:
                    print("Error when reading S3 credentials file: %s", e)
            else:
                access_key = os.environ.get('ACCESS_KEY_ID')
                secret_key = os.environ.get('SECRET_ACCESS_KEY')
            self.mc = Minio(self.params.end_point, access_key=access_key, secret_key=secret_key)

        def deploy(self) -> bool:
            return True

        def get_info(self):
            return "s3://{}/{}".format(self.params.bucket_name)

        def undeploy(self) -> bool:
            return True

        def save_to_store(self):
            self._save_to_store(self.params.checkpoint_dir)

        def _save_to_store(self, checkpoint_dir):
            """
            save_to_store() uploads the policy checkpoint, gifs and videos to the S3 data store. It reads the checkpoint state files and
            uploads only the latest checkpoint files to S3. It is used by the trainer in Coach when used in the distributed mode.
            """
            try:
                # remove lock file if it exists
                self.mc.remove_object(self.params.bucket_name, SyncFiles.LOCKFILE.value)

                # Acquire lock
                self.mc.put_object(self.params.bucket_name, SyncFiles.LOCKFILE.value, io.BytesIO(b''), 0)

                state_file = CheckpointStateFile(os.path.abspath(checkpoint_dir))
                if state_file.exists():
                    ckpt_state = state_file.read()
                    checkpoint_file = None
                    for root, dirs, files in os.walk(checkpoint_dir):
                        for filename in files:
                            if filename == CheckpointStateFile.checkpoint_state_filename:
                                checkpoint_file = (root, filename)
                                continue
                            if filename.startswith(ckpt_state.name):
                                abs_name = os.path.abspath(os.path.join(root, filename))
                                rel_name = os.path.relpath(abs_name, checkpoint_dir)
                                self.mc.fput_object(self.params.bucket_name, rel_name, abs_name)

                    abs_name = os.path.abspath(os.path.join(checkpoint_file[0], checkpoint_file[1]))
                    rel_name = os.path.relpath(abs_name, checkpoint_dir)
                    self.mc.fput_object(self.params.bucket_name, rel_name, abs_name)

                # upload Finished if present
                if os.path.exists(os.path.join(checkpoint_dir, SyncFiles.FINISHED.value)):
                    self.mc.put_object(self.params.bucket_name, SyncFiles.FINISHED.value, io.BytesIO(b''), 0)

                # upload Ready if present
                if os.path.exists(os.path.join(checkpoint_dir, SyncFiles.TRAINER_READY.value)):
                    self.mc.put_object(self.params.bucket_name, SyncFiles.TRAINER_READY.value, io.BytesIO(b''), 0)

                # release lock
                self.mc.remove_object(self.params.bucket_name, SyncFiles.LOCKFILE.value)

                if self.params.expt_dir and os.path.exists(self.params.expt_dir):
                    for filename in os.listdir(self.params.expt_dir):
                        if filename.endswith((".csv", ".json")):
                            self.mc.fput_object(self.params.bucket_name, filename, os.path.join(self.params.expt_dir, filename))

                if self.params.expt_dir and os.path.exists(os.path.join(self.params.expt_dir, 'videos')):
                    for filename in os.listdir(os.path.join(self.params.expt_dir, 'videos')):
                            self.mc.fput_object(self.params.bucket_name, filename, os.path.join(self.params.expt_dir, 'videos', filename))

                if self.params.expt_dir and os.path.exists(os.path.join(self.params.expt_dir, 'gifs')):
                    for filename in os.listdir(os.path.join(self.params.expt_dir, 'gifs')):
                            self.mc.fput_object(self.params.bucket_name, filename, os.path.join(self.params.expt_dir, 'gifs', filename))

            except ResponseError as e:
                print("Got exception: %s\n while saving to S3", e)

        def load_from_store(self):
            """
            load_from_store() downloads a new checkpoint from the S3 data store when it is not available locally. It is used
            by the rollout workers when using Coach in distributed mode.
            """
            try:
                state_file = CheckpointStateFile(os.path.abspath(self.params.checkpoint_dir))

                # wait until lock is removed
                while True:
                    objects = self.mc.list_objects_v2(self.params.bucket_name, SyncFiles.LOCKFILE.value)

                    if next(objects, None) is None:
                        try:
                            # fetch checkpoint state file from S3
                            self.mc.fget_object(self.params.bucket_name, state_file.filename, state_file.path)
                        except Exception as e:
                            continue
                        break
                    time.sleep(10)

                # Check if there's a finished file
                objects = self.mc.list_objects_v2(self.params.bucket_name, SyncFiles.FINISHED.value)

                if next(objects, None) is not None:
                    try:
                        self.mc.fget_object(
                            self.params.bucket_name, SyncFiles.FINISHED.value,
                            os.path.abspath(os.path.join(self.params.checkpoint_dir, SyncFiles.FINISHED.value))
                        )
                    except Exception as e:
                        pass

                # Check if there's a ready file
                objects = self.mc.list_objects_v2(self.params.bucket_name, SyncFiles.TRAINER_READY.value)

                if next(objects, None) is not None:
                    try:
                        self.mc.fget_object(
                            self.params.bucket_name, SyncFiles.TRAINER_READY.value,
                            os.path.abspath(os.path.join(self.params.checkpoint_dir, SyncFiles.TRAINER_READY.value))
                        )
                    except Exception as e:
                        pass

                checkpoint_state = state_file.read()
                if checkpoint_state is not None:
                    objects = self.mc.list_objects_v2(self.params.bucket_name, prefix=checkpoint_state.name, recursive=True)
                    for obj in objects:
                        filename = os.path.abspath(os.path.join(self.params.checkpoint_dir, obj.object_name))
                        if not os.path.exists(filename):
                            self.mc.fget_object(obj.bucket_name, obj.object_name, filename)

            except ResponseError as e:
                print("Got exception: %s\n while loading from S3", e)

        def setup_checkpoint_dir(self, crd=None):
            if crd:
                self._save_to_store(crd)