ghf

Dockerfile.gpu

@@ -0,0 +1,58 @@
+FROM nvidia/cuda:11.8.0-runtime-ubuntu22.04 as base
+
+# Setup env
+ENV LANG C.UTF-8
+ENV LC_ALL C.UTF-8
+ENV PYTHONDONTWRITEBYTECODE 1
+ENV PYTHONFAULTHANDLER 1
+ENV PATH=/home/ftuser/.local/bin:$PATH
+ENV FT_APP_ENV="docker"
+
+# Prepare environment
+RUN mkdir /freqtrade \
+  && apt-get update \
+  && apt-get -y install sudo libatlas3-base curl sqlite3 libhdf5-serial-dev  \
+  && apt-get clean \
+  && useradd -u 1000 -G sudo -U -m -s /bin/bash ftuser \
+  && chown ftuser:ftuser /freqtrade \
+  # Allow sudoers
+  && echo "ftuser ALL=(ALL) NOPASSWD: /bin/chown" >> /etc/sudoers
+
+WORKDIR /freqtrade
+
+# Install dependencies
+FROM base as python-deps
+RUN  apt-get update \
+  && apt-get -y install pip build-essential libssl-dev git libffi-dev libgfortran5 pkg-config cmake gcc \
+  && apt-get clean \
+  && pip install --upgrade pip
+
+# Install TA-lib
+COPY build_helpers/* /tmp/
+RUN cd /tmp && /tmp/install_ta-lib.sh && rm -r /tmp/*ta-lib*
+ENV LD_LIBRARY_PATH /usr/local/lib
+
+# Install dependencies
+COPY --chown=ftuser:ftuser requirements.txt requirements-plot.txt requirements-hyperopt.txt requirements-freqai.txt /freqtrade/
+USER ftuser
+RUN  pip install --user --no-cache-dir numpy \
+  && pip install --user --no-cache-dir -r requirements-freqai.txt
+
+# Copy dependencies to runtime-image
+FROM base as runtime-image
+COPY --from=python-deps /usr/local/lib /usr/local/lib
+ENV LD_LIBRARY_PATH /usr/local/lib
+
+COPY --from=python-deps --chown=ftuser:ftuser /home/ftuser/.local /home/ftuser/.local
+RUN sudo apt-get -y install pip
+USER ftuser
+# Install and execute
+COPY --chown=ftuser:ftuser . /freqtrade/
+
+RUN pip install -e . --user --no-cache-dir --no-build-isolation \
+  && mkdir /freqtrade/user_data/ \
+  && freqtrade install-ui
+
+ENTRYPOINT ["freqtrade"]
+# Default to trade mode
+CMD [ "trade" ]

freqtrade/freqai/data_drawer.py

@@ -1,6 +1,7 @@
 import collections
 import logging
 import re
+import json
 import shutil
 import threading
 from datetime import datetime, timezone
@@ -94,14 +95,16 @@ class FreqaiDataDrawer:
         self.save_lock = threading.Lock()
         self.pair_dict_lock = threading.Lock()
         self.metric_tracker_lock = threading.Lock()
+        self.limit_ram_use = self.freqai_info.get('limit_ram_usage', False)
         self.old_DBSCAN_eps: Dict[str, float] = {}
+        
         self.empty_pair_dict: pair_info = {
                 "model_filename": "", "trained_timestamp": 0,
                 "data_path": "", "extras": {}}
-        if 'Reinforcement' in self.config['freqaimodel']:
+        if 'rl_config' in self.freqai_info:
-            self.model_type = 'stable_baselines'
+            self.model_type = self.freqai_info['model_save_type']
-            logger.warning('User passed a ReinforcementLearner model, FreqAI will '
+            logger.warning(f'User passed a ReinforcementLearner model, FreqAI will '
-                           'now use stable_baselines3 to save models.')
+                           'now use {self.model_type} to save models.')
         else:
             self.model_type = self.freqai_info.get('model_save_type', 'joblib')
 
@@ -488,6 +491,8 @@ class FreqaiDataDrawer:
             model.save(save_path / f"{dk.model_filename}_model.h5")
         elif 'stable_baselines' in self.model_type:
             model.save(save_path / f"{dk.model_filename}_model.zip")
+        elif 'sb3_contrib' in self.model_type:
+            model.save(save_path / f"{dk.model_filename}_model.zip")
 
         if dk.svm_model is not None:
             dump(dk.svm_model, save_path / f"{dk.model_filename}_svm_model.joblib")
@@ -565,7 +570,7 @@ class FreqaiDataDrawer:
         dk.label_list = dk.data["label_list"]
 
         # try to access model in memory instead of loading object from disk to save time
-        if dk.live and coin in self.model_dictionary:
+        if dk.live and coin in self.model_dictionary and not self.limit_ram_use:
             model = self.model_dictionary[coin]
         elif self.model_type == 'joblib':
             model = load(dk.data_path / f"{dk.model_filename}_model.joblib")
@@ -576,10 +581,16 @@ class FreqaiDataDrawer:
             mod = __import__('stable_baselines3', fromlist=[
                              self.freqai_info['rl_config']['model_type']])
             MODELCLASS = getattr(mod, self.freqai_info['rl_config']['model_type'])
-            model = MODELCLASS.load(dk.data_path / f"{dk.model_filename}_model")
+            model = MODELCLASS.load(dk.data_path / f"{dk.model_filename}_model",  device="cpu")
+        
+        elif self.model_type == 'sb3_contrib':
+            mod = __import__('sb3_contrib', fromlist=[
+                             self.freqai_info['rl_config']['model_type']])
+            MODELCLASS = getattr(mod, self.freqai_info['rl_config']['model_type'])
+            model = MODELCLASS.load(dk.data_path / f"{dk.model_filename}_model", device="cpu")
 
         if Path(dk.data_path / f"{dk.model_filename}_svm_model.joblib").is_file():
-            dk.svm_model = load(dk.data_path / f"{dk.model_filename}_svm_model.joblib")
+            dk.svm_model = load(path=dk.data_path / f"{dk.model_filename}_svm_model.joblib")
 
         if not model:
             raise OperationalException(

requirements-freqai-rl.txt

@@ -2,7 +2,7 @@
 -r requirements-freqai.txt
 
 # Required for freqai-rl
-torch==1.12.1
+torch==1.3.0
 stable-baselines3==1.6.1
 gym==0.21
 sb3-contrib==1.6.1

requirements-freqai.txt

@@ -9,3 +9,7 @@ catboost==1.1.1; platform_machine != 'aarch64'
 lightgbm==3.3.3
 xgboost==1.7.1
 tensorboard==2.11.0
+torch==1.13.0
+stable-baselines3==1.6.2
+gym==0.21
+sb3-contrib==1.6.2