Added Dockerfile alias (#199)

* Added Dockerfile alias

* Added load_agent bucket compability and gcloud functionality to do it

* Added load trained model documentation

* Deleted version 3.6 and command python3 from Dockerfile container

* Solved strange bug with python modules

Dockerfile

@@ -51,14 +51,14 @@ RUN cd /usr/local/bin find -L . -type l -delete
 # Install ping dependency
 RUN apt update && apt install iputils-ping -y 
 
-# Install Python
-RUN apt update
-RUN apt install software-properties-common -y
-RUN add-apt-repository ppa:deadsnakes/ppa
-RUN ln -s /usr/bin/pip3 /usr/bin/pip
-RUN ln -s /usr/bin/python${PYTHON_VERSION} /usr/bin/python
-RUN apt install python${PYTHON_VERSION} python${PYTHON_VERSION}-distutils -y
-RUN apt install python3-pip -y
+# Install Python version PYTHON_VERSION
+RUN apt update \
+    && apt install software-properties-common -y \
+    && add-apt-repository ppa:deadsnakes/ppa \
+    && apt install python${PYTHON_VERSION} python${PYTHON_VERSION}-distutils -y \
+    && apt install python3-pip -y \
+    && ln -s /usr/bin/pip3 /usr/bin/pip \
+    && ln -s /usr/bin/python${PYTHON_VERSION} /usr/bin/python
 
 # Install enchant for sinergym documentation
 RUN apt-get update && echo "Y\r" | apt-get install enchant --fix-missing -y
@@ -77,6 +77,7 @@ RUN python -m pip install --upgrade pip
 # Upgrade setuptools for possible errors (depending on python version)
 RUN pip install --upgrade setuptools
 RUN apt-get update && apt-get upgrade -y && apt-get install -y git
+
 WORKDIR /sinergym
 COPY requirements.txt .
 COPY setup.py .
@@ -89,6 +90,11 @@ COPY check_run_times.py .
 COPY try_env.py .
 RUN pip install -e .${SINERGYM_EXTRAS}
 
+
+#uninstall 3.6 python default version
+RUN apt-get remove --purge python3-pip python3 -y \
+    && apt-get autoremove -y && apt-get autoclean -y
+
 CMD ["/bin/bash"]
 
 # Build: docker build -t sinergym:1.1.0 --build-arg ENERGYPLUS_VERSION=9.5.0 --build-arg ENERGYPLUS_INSTALL_VERSION=9-5-0 --build-arg ENERGYPLUS_SHA=de239b2e5f .

docs/source/pages/gcloudAPI.rst

@@ -308,6 +308,30 @@ Hence, it is **necessary** to **set up this service account** and give privilege
 In short, we create a new service account called **storage-account**. Then, we dote this account with *roles/owner* permission. The next step is create a file key (json) called **google-storage.json** in our project root (gitignore will ignore this file in remote).
 Finally, we export this file in **GOOGLE_CLOUD_CREDENTIALS** in our local computer in order to gcloud SDK knows that it has to use that token to authenticate.
 
+**********************
+Load a trained model
+**********************
+
+For this purpose, we have a script called *load_agent.py* which can be used both on a remote machine and locally on our computer, just like *DRL_battery.py*.
+
+So, what this script does is to use the path that we pass as a parameter where our model is located. It loads the model and performs the evaluation that we want.
+
+The list of parameter is:
+
+- ``--environment`` or ``-env``: Environment name you want to use.
+- ``--model`` or ``-mod``: Trained model (zip file) you want to use to execute the evaluation. This path can be a local path file into your computer (remote or host) or a Google Cloud Storage resource (bucket like ``gs://<bucket_name>/<model_path>``).
+- ``--episodes`` or ``-ep``: Number of episodes you want to evaluate agent in simulation (Depending on environment episode length can be different)
+- ``--algorithm`` or ``-alg``: Algorithm which model was trained (Currently, it is available *PPO*, *A2C*, *DQN*, *DDPG* and *SAC*).
+- ``--reward`` or ``-rw``: Reward class you want to use for reward function (same reward than training model is recommended). Currently, possible values are "linear" and "exponential".
+- ``--normalization`` or ``-norm``: Apply normalization wrapper to observations during evaluation. If it isn't specified wrapper will not be applied.
+- ``--logger`` or ``-log``: Apply Sinergym logger wrapper during evaluation. If it isn't specified wrapper will not be applied.
+- ``--seed`` or ``-sd``: Seed for evaluation, random components in process will be able to be recreated.
+- ``--remote_store`` or ``-sto``: Determine if sinergym output will be sent to a common resource (Bucket), else will be allocate in container or host memory only.
+- ``--group_name`` or ``-group``: It specify to which MIG the host instance belongs, it is important if --auto-delete is activated.
+- ``--auto_delete`` or ``-del``: Whether this parameter is specified, remote instance will be auto removed when its job has finished.
+
+This script loads the model. Once the model is loaded, it predicts the actions from the states during the agreed episodes. The information is collected and sent to a cloud storage if it has been specified, otherwise it is stored in local memory.
+
 ***********************
 Remote Tensorboard log
 ***********************

load_agent.py

@@ -94,7 +94,7 @@
 # ---------------------------------------------------------------------------- #
 #                                Evaluation name                               #
 # ---------------------------------------------------------------------------- #
-name = args.model.split('/')[-1] + 'EVAL-episodes' + str(args.episodes)
+name = args.model.split('/')[-1] + '-EVAL-episodes' + str(args.episodes)
 
 # ---------------------------------------------------------------------------- #
 #                            Environment definition                            #
@@ -131,19 +131,31 @@
 # ---------------------------------------------------------------------------- #
 #                                  Load Agent                                  #
 # ---------------------------------------------------------------------------- #
+# If a model is from a bucket, download model
+if 'gs://' in args.model:
+    # Download from given bucket (gcloud configured with privileges)
+    client = gcloud.init_storage_client()
+    bucket_name = args.model.split('/')[2]
+    model_path = args.model.split(bucket_name + '/')[-1]
+    gcloud.read_from_bucket(client, bucket_name, model_path)
+    model_path = './' + model_path
+else:
+    model_path = args.model
+
+
 model = None
 if args.algorithm == 'DQN':
-    model = DQN.load(args.model)
+    model = DQN.load(model_path)
 elif args.algorithm == 'DDPG':
-    model = DDPG.load(args.model)
+    model = DDPG.load(model_path)
 elif args.algorithm == 'A2C':
-    model = A2C.load(args.model)
+    model = A2C.load(model_path)
 elif args.algorithm == 'PPO':
-    model = PPO.load(args.model)
+    model = PPO.load(model_path)
 elif args.algorithm == 'SAC':
-    model = SAC.load(args.model)
+    model = SAC.load(model_path)
 elif args.algorithm == 'TD3':
-    model = TD3.load(args.model)
+    model = TD3.load(model_path)
 else:
     raise RuntimeError('Algorithm specified is not registered.')
 

sinergym/utils/gcloud.py

@@ -2,6 +2,7 @@
 
 import glob
 import os
+from pathlib import Path
 
 import requests
 from google.cloud import storage
@@ -22,6 +23,25 @@ def init_storage_client() -> storage.Client:
 ####################### GCLOUD BUCKETS MANIPULATION #######################
 
 
+def read_from_bucket(client, bucket_name, blob_prefix):
+    """Read a file or a directory (recursively) from specified bucket to local file system.
+
+    Args:
+        client: Google Cloud storage client object to ask resources.
+        bucket_name: Origin bucket name where reading.
+        blob_prefix: Path where you want to read data inner the bucket (excluding gs://<bucket_name>).
+    """
+    bucket = client.get_bucket(bucket_name)
+    blobs = bucket.list_blobs(prefix=blob_prefix)
+    for blob in blobs:
+        if blob.name.endswith("/"):
+            continue
+        file_split = blob.name.split("/")
+        directory = '/'.join(file_split[0:-1])
+        Path(directory).mkdir(parents=True, exist_ok=True)
+        blob.download_to_filename(blob.name)
+
+
 def upload_to_bucket(
         client: storage.Client,
         src_path: str,