Add scikit-learn support (joblib) and xgboost support

examples/joblib_example.py

@@ -0,0 +1,25 @@
+import joblib
+
+from sklearn import datasets
+from sklearn.linear_model import LogisticRegression
+from sklearn.model_selection import train_test_split
+
+
+from trains import Task
+
+task = Task.init(project_name="examples", task_name="joblib test")
+
+iris = datasets.load_iris()
+X = iris.data
+y = iris.target
+
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+model = LogisticRegression()  # sklearn LogisticRegression class
+model.fit(X_train, y_train)
+
+joblib.dump(model, 'model.pkl', compress=True)
+
+loaded_model = joblib.load('model.pkl')
+result = loaded_model.score(X_test, y_test)
+print(result)

examples/keras_tensorboard.py

@@ -6,29 +6,28 @@
 # 2 seconds per epoch on a K520 GPU.
 from __future__ import print_function
 
+import io
 import numpy as np
-import tensorflow
 
 from keras.callbacks import TensorBoard, ModelCheckpoint
 from keras.datasets import mnist
-from keras.models import Sequential, Model
-from keras.layers.core import Dense, Dropout, Activation
-from keras.optimizers import SGD, Adam, RMSprop
+from keras.models import Sequential
+from keras.layers.core import Dense, Activation
+from keras.optimizers import RMSprop
 from keras.utils import np_utils
+# TODO: test these methods binding
 from keras.models import load_model, save_model, model_from_json
-
+import tensorflow as tf
 from trains import Task
 
 
 class TensorBoardImage(TensorBoard):
     @staticmethod
     def make_image(tensor):
-        import tensorflow as tf
         from PIL import Image
         tensor = np.stack((tensor, tensor, tensor), axis=2)
         height, width, channels = tensor.shape
         image = Image.fromarray(tensor)
-        import io
         output = io.BytesIO()
         image.save(output, format='PNG')
         image_string = output.getvalue()
@@ -38,9 +37,10 @@ class TensorBoardImage(TensorBoard):
                                 colorspace=channels,
                                 encoded_image_string=image_string)
 
-    def on_epoch_end(self, epoch, logs={}):
+    def on_epoch_end(self, epoch, logs=None):
+        if logs is None:
+            logs = {}
         super(TensorBoardImage, self).on_epoch_end(epoch, logs)
-        import tensorflow as tf
         images = self.validation_data[0]  # 0 - data; 1 - labels
         img = (255 * images[0].reshape(28, 28)).astype('uint8')
 

examples/requirements.txt

@@ -0,0 +1,15 @@
+absl-py>=0.7.1
+Keras>=2.2.4
+joblib>=0.13.2
+matplotlib>=3.1.1
+seaborn>=0.9.0
+sklearn>=0.0
+tensorboard>=1.14.0
+tensorboardX>=1.8
+tensorflow>=1.14.0
+torch>=1.1.0
+torchvision>=0.3.0
+xgboost>=0.90
+
+# sudo apt-get install graphviz
+graphviz>=0.8

examples/tensorboard_toy.py

@@ -3,9 +3,6 @@
 import tensorflow as tf
 import numpy as np
 import cv2
-from time import sleep
-#import tensorflow.compat.v1 as tf
-#tf.disable_v2_behavior()
 
 from trains import Task
 task = Task.init(project_name='examples', task_name='tensorboard toy example')

examples/xgboost_sample.py

@@ -0,0 +1,59 @@
+import matplotlib.pyplot as plt
+import xgboost as xgb
+from sklearn import datasets
+from sklearn.metrics import accuracy_score
+from sklearn.model_selection import train_test_split
+from xgboost import plot_tree
+
+from trains import Task
+
+task = Task.init(project_name='examples', task_name='XGBoost simple example')
+iris = datasets.load_iris()
+X = iris.data
+y = iris.target
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+dtrain = xgb.DMatrix(X_train, label=y_train)
+dtest = xgb.DMatrix(X_test, label=y_test)
+param = {
+    'max_depth': 3,  # the maximum depth of each tree
+    'eta': 0.3,  # the training step for each iteration
+    'silent': 1,  # logging mode - quiet
+    'objective': 'multi:softprob',  # error evaluation for multiclass training
+    'num_class': 3}  # the number of classes that exist in this datset
+num_round = 20  # the number of training iterations
+
+try:
+    # try to load a model
+    bst = xgb.Booster(params=param, model_file='xgb.01.model')
+    bst.load_model('xgb.01.model')
+except:
+    bst = None
+
+# if we dont have one train a model
+if bst is None:
+    bst = xgb.train(param, dtrain, num_round)
+
+# store trained model model v1
+bst.save_model('xgb.01.model')
+bst.dump_model('xgb.01.raw.txt')
+
+# build classifier
+model = xgb.XGBClassifier()
+model.fit(X_train, y_train)
+
+# store trained classifier model
+model.save_model('xgb.02.model')
+
+# make predictions for test data
+y_pred = model.predict(X_test)
+predictions = [round(value) for value in y_pred]
+
+# evaluate predictions
+accuracy = accuracy_score(y_test, predictions)
+print("Accuracy: %.2f%%" % (accuracy * 100.0))
+labels = dtest.get_label()
+
+# plot results
+xgb.plot_importance(model)
+plot_tree(model)
+plt.show()

trains/backend_api/session/session.py

@@ -144,7 +144,11 @@ class Session(TokenManager):
 
         # update api version from server response
         try:
-            api_version = jwt.decode(self.token, verify=False).get('api_version', Session.api_version)
+            token_dict = jwt.decode(self.token, verify=False)
+            api_version = token_dict.get('api_version')
+            if not api_version:
+                api_version = '2.2' if token_dict.get('env', '') == 'prod' else Session.api_version
+
             Session.api_version = str(api_version)
         except (jwt.DecodeError, ValueError):
             pass

trains/backend_config/converters.py

@@ -36,7 +36,7 @@ def or_(*converters, **kwargs):
     """
     Wrapper that implements an "optional converter" pattern. Allows specifying a converter
     for which a set of exceptions is ignored (and the original value is returned)
-    :param converter: A converter callable
+    :param converters: A converter callable
     :param exceptions: A tuple of exception types to ignore
     """
     # noinspection PyUnresolvedReferences

trains/binding/artifacts.py

@@ -1,9 +1,8 @@
-import os
 import weakref
 
 import numpy as np
 import hashlib
-from tempfile import mkstemp, mkdtemp
+from tempfile import mkdtemp
 from threading import Thread, Event
 from multiprocessing.pool import ThreadPool
 

trains/binding/frameworks/base_bind.py

@@ -0,0 +1,52 @@
+from abc import ABCMeta, abstractmethod
+
+import six
+
+
+@six.add_metaclass(ABCMeta)
+class PatchBaseModelIO(object):
+    """
+    Base class for patched models
+
+    :param __main_task: Task to run (Experiment)
+    :type __main_task: Task
+    :param __patched: True if the model is patched
+    :type __patched: bool
+    """
+    @property
+    @abstractmethod
+    def __main_task(self):
+        pass
+
+    @property
+    @abstractmethod
+    def __patched(self):
+        pass
+
+    @staticmethod
+    @abstractmethod
+    def update_current_task(task, **kwargs):
+        """
+        Update the model task to run
+        :param task: the experiment to do
+        :type task: Task
+        """
+        pass
+
+    @staticmethod
+    @abstractmethod
+    def _patch_model_io():
+        """
+        Patching the load and save functions
+        """
+        pass
+
+    @staticmethod
+    @abstractmethod
+    def _save(original_fn, obj, f, *args, **kwargs):
+        pass
+
+    @staticmethod
+    @abstractmethod
+    def _load(original_fn, f, *args, **kwargs):
+        pass

trains/binding/frameworks/pytorch_bind.py

@@ -3,13 +3,14 @@ import sys
 import six
 from pathlib2 import Path
 
+from trains.binding.frameworks.base_bind import PatchBaseModelIO
 from ..frameworks import _patched_call, WeightsFileHandler, _Empty
 from ..import_bind import PostImportHookPatching
 from ...config import running_remotely
 from ...model import Framework
 
 
-class PatchPyTorchModelIO(object):
+class PatchPyTorchModelIO(PatchBaseModelIO):
     __main_task = None
     __patched = None
 

trains/binding/frameworks/tensorflow_bind.py

@@ -9,8 +9,6 @@ from typing import Any
 
 import cv2
 import numpy as np
-import six
-from pathlib2 import Path
 
 from ..frameworks import _patched_call, WeightsFileHandler, _Empty, TrainsFrameworkAdapter
 from ..import_bind import PostImportHookPatching

trains/binding/frameworks/xgboost_bind.py

@@ -0,0 +1,101 @@
+import sys
+
+import six
+from pathlib2 import Path
+
+from trains.binding.frameworks.base_bind import PatchBaseModelIO
+from ..frameworks import _patched_call, WeightsFileHandler, _Empty
+from ..import_bind import PostImportHookPatching
+from ...config import running_remotely
+from ...model import Framework
+
+
+class PatchXGBoostModelIO(PatchBaseModelIO):
+    __main_task = None
+    __patched = None
+
+    @staticmethod
+    def update_current_task(task, **kwargs):
+        PatchXGBoostModelIO.__main_task = task
+        PatchXGBoostModelIO._patch_model_io()
+        PostImportHookPatching.add_on_import('xgboost', PatchXGBoostModelIO._patch_model_io)
+
+    @staticmethod
+    def _patch_model_io():
+        if PatchXGBoostModelIO.__patched:
+            return
+
+        if 'xgboost' not in sys.modules:
+            return
+        PatchXGBoostModelIO.__patched = True
+        try:
+            import xgboost as xgb
+            bst = xgb.Booster
+            bst.save_model = _patched_call(bst.save_model, PatchXGBoostModelIO._save)
+            bst.load_model = _patched_call(bst.load_model, PatchXGBoostModelIO._load)
+        except ImportError:
+            pass
+        except Exception:
+            pass
+
+    @staticmethod
+    def _save(original_fn, obj, f, *args, **kwargs):
+        ret = original_fn(obj, f, *args, **kwargs)
+        if not PatchXGBoostModelIO.__main_task:
+            return ret
+
+        if isinstance(f, six.string_types):
+            filename = f
+        elif hasattr(f, 'name'):
+            filename = f.name
+            # noinspection PyBroadException
+            try:
+                f.flush()
+            except Exception:
+                pass
+        else:
+            filename = None
+
+        # give the model a descriptive name based on the file name
+        # noinspection PyBroadException
+        try:
+            model_name = Path(filename).stem
+        except Exception:
+            model_name = None
+        WeightsFileHandler.create_output_model(obj, filename, Framework.xgboost, PatchXGBoostModelIO.__main_task,
+                                               singlefile=True, model_name=model_name)
+        return ret
+
+    @staticmethod
+    def _load(original_fn, f, *args, **kwargs):
+        if isinstance(f, six.string_types):
+            filename = f
+        elif hasattr(f, 'name'):
+            filename = f.name
+        elif len(args) == 1 and isinstance(args[0], six.string_types):
+            filename = args[0]
+        else:
+            filename = None
+
+        if not PatchXGBoostModelIO.__main_task:
+            return original_fn(f, *args, **kwargs)
+
+        # register input model
+        empty = _Empty()
+        if running_remotely():
+            filename = WeightsFileHandler.restore_weights_file(empty, filename, Framework.xgboost,
+                                                               PatchXGBoostModelIO.__main_task)
+            model = original_fn(filename or f, *args, **kwargs)
+        else:
+            # try to load model before registering, in case we fail
+            model = original_fn(f, *args, **kwargs)
+            WeightsFileHandler.restore_weights_file(empty, filename, Framework.xgboost,
+                                                    PatchXGBoostModelIO.__main_task)
+
+        if empty.trains_in_model:
+            # noinspection PyBroadException
+            try:
+                model.trains_in_model = empty.trains_in_model
+            except Exception:
+                pass
+        return model

trains/binding/joblib_bind.py

@@ -0,0 +1,110 @@
+try:
+    import joblib
+except ImportError as e:
+    joblib = None
+
+import six
+from pathlib2 import Path
+
+from trains.binding.frameworks import _patched_call, _Empty, WeightsFileHandler
+from trains.config import running_remotely
+from trains.debugging.log import LoggerRoot
+
+
+class PatchedJoblib(object):
+    _patched_original_dump = None
+    _patched_original_load = None
+    _current_task = None
+    _current_framework = None
+
+    @staticmethod
+    def patch_joblib():
+        if PatchedJoblib._patched_original_dump is not None and PatchedJoblib._patched_original_load is not None:
+            # We don't need to patch anything else, so we are done
+            return True
+        # noinspection PyBroadException
+        try:
+            joblib.dump = _patched_call(joblib.dump, PatchedJoblib._dump)
+            joblib.load = _patched_call(joblib.load, PatchedJoblib._load)
+
+        except Exception:
+            return False
+        return True
+
+    @staticmethod
+    def update_current_task(task):
+        if PatchedJoblib.patch_joblib():
+            PatchedJoblib._current_task = task
+
+    @staticmethod
+    def _dump(original_fn, obj, f, *args, **kwargs):
+        ret = original_fn(obj, f, *args, **kwargs)
+        if not PatchedJoblib._current_task:
+            return ret
+
+        if isinstance(f, six.string_types):
+            filename = f
+        elif hasattr(f, 'name'):
+            filename = f.name
+        #     noinspection PyBroadException
+            try:
+                f.flush()
+            except Exception:
+                pass
+        else:
+            filename = None
+
+        # give the model a descriptive name based on the file name
+        # noinspection PyBroadException
+        try:
+            model_name = Path(filename).stem
+        except Exception:
+            model_name = None
+        PatchedJoblib._current_framework = PatchedJoblib.get_model_framework(obj)
+        WeightsFileHandler.create_output_model(obj, filename, PatchedJoblib._current_framework,
+                                               PatchedJoblib._current_task, singlefile=True, model_name=model_name)
+        return ret
+
+    @staticmethod
+    def _load(original_fn, f, *args, **kwargs):
+        if isinstance(f, six.string_types):
+            filename = f
+        elif hasattr(f, 'name'):
+            filename = f.name
+        else:
+            filename = None
+
+        if not PatchedJoblib._current_task:
+            return original_fn(f, *args, **kwargs)
+
+        # register input model
+        empty = _Empty()
+        if running_remotely():
+            filename = WeightsFileHandler.restore_weights_file(empty, filename, PatchedJoblib._current_framework,
+                                                               PatchedJoblib._current_task)
+            model = original_fn(filename or f, *args, **kwargs)
+        else:
+            # try to load model before registering, in case we fail
+            model = original_fn(f, *args, **kwargs)
+            WeightsFileHandler.restore_weights_file(empty, filename, PatchedJoblib._current_framework,
+                                                    PatchedJoblib._current_task)
+
+        if empty.trains_in_model:
+            # noinspection PyBroadException
+            try:
+                model.trains_in_model = empty.trains_in_model
+            except Exception:
+                pass
+        return model
+
+    @staticmethod
+    def get_model_framework(obj):
+        object_orig_module = obj.__module__
+        framework = object_orig_module
+        try:
+            framework = object_orig_module.partition(".")[0]
+        except Exception as _:
+            LoggerRoot.get_base_logger().warning(
+                "Can't get model framework, model framework will be: {} ".format(object_orig_module))
+        finally:
+            return framework

trains/model.py

@@ -1,6 +1,5 @@
 import abc
 import os
-import re
 import tarfile
 import zipfile
 from tempfile import mkdtemp, mkstemp
@@ -40,6 +39,7 @@ class Framework(Options):
     darknet = 'Darknet'
     paddlepaddle = 'PaddlePaddle'
     scikitlearn = 'ScikitLearn'
+    xgboost = 'XGBoost'
 
     __file_extensions_mapping = {
         '.pb': (tensorflow, tensorflowjs, onnx, ),
@@ -59,13 +59,13 @@ class Framework(Options):
         '.h5': (keras, ),
         '.hdf5': (keras, ),
         '.keras': (keras, ),
-        '.model': (mknet, cntk, ),
+        '.model': (mknet, cntk, xgboost),
         '-symbol.json': (mknet, ),
         '.cntk': (cntk, ),
         '.t7': (torch, ),
         '.cfg': (darknet, ),
         '__model__': (paddlepaddle, ),
-        '.pkl': (scikitlearn, keras, ),
+        '.pkl': (scikitlearn, keras, xgboost),
     }
 
     @classmethod

trains/task.py

@@ -10,6 +10,7 @@ from collections import OrderedDict, Callable
 import psutil
 import six
 
+from trains.binding.joblib_bind import PatchedJoblib
 from .backend_api.services import tasks, projects
 from .backend_api.session.session import Session
 from .backend_interface.model import Model as BackendModel
@@ -34,8 +35,9 @@ from .utilities.args import argparser_parseargs_called, get_argparser_last_args,
 from .binding.frameworks.pytorch_bind import PatchPyTorchModelIO
 from .binding.frameworks.tensorflow_bind import PatchSummaryToEventTransformer, PatchTensorFlowEager, \
     PatchKerasModelIO, PatchTensorflowModelIO
-from .utilities.resource_monitor import ResourceMonitor
+from .binding.frameworks.xgboost_bind import PatchXGBoostModelIO
 from .binding.matplotlib_bind import PatchedMatplotlib
+from .utilities.resource_monitor import ResourceMonitor
 from .utilities.seed import make_deterministic
 
 NotSet = object()
@@ -118,15 +120,15 @@ class Task(_Task):
 
     @classmethod
     def init(
-        cls,
-        project_name=None,
-        task_name=None,
-        task_type=TaskTypes.training,
-        reuse_last_task_id=True,
-        output_uri=None,
-        auto_connect_arg_parser=True,
-        auto_connect_frameworks=True,
-        auto_resource_monitoring=True,
+            cls,
+            project_name=None,
+            task_name=None,
+            task_type=TaskTypes.training,
+            reuse_last_task_id=True,
+            output_uri=None,
+            auto_connect_arg_parser=True,
+            auto_connect_frameworks=True,
+            auto_resource_monitoring=True,
     ):
         """
         Return the Task object for the main execution task (task context).
@@ -239,14 +241,15 @@ class Task(_Task):
             # patch OS forking
             PatchOsFork.patch_fork()
             if auto_connect_frameworks:
+                PatchedJoblib.update_current_task(task)
                 PatchedMatplotlib.update_current_task(Task.__main_task)
                 PatchAbsl.update_current_task(Task.__main_task)
                 PatchSummaryToEventTransformer.update_current_task(task)
-                # PatchModelCheckPointCallback.update_current_task(task)
                 PatchTensorFlowEager.update_current_task(task)
                 PatchKerasModelIO.update_current_task(task)
                 PatchTensorflowModelIO.update_current_task(task)
                 PatchPyTorchModelIO.update_current_task(task)
+                PatchXGBoostModelIO.update_current_task(task)
             if auto_resource_monitoring:
                 task._resource_monitor = ResourceMonitor(task)
                 task._resource_monitor.start()
@@ -277,10 +280,10 @@ class Task(_Task):
 
     @classmethod
     def create(
-        cls,
-        task_name=None,
-        project_name=None,
-        task_type=TaskTypes.training,
+            cls,
+            task_name=None,
+            project_name=None,
+            task_type=TaskTypes.training,
     ):
         """
         Create a new Task object, regardless of the main execution task (Task.init).
@@ -345,7 +348,7 @@ class Task(_Task):
                         pass
 
         # if we force no task reuse from os environment
-        if DEV_TASK_NO_REUSE.get():
+        if DEV_TASK_NO_REUSE.get() or reuse_last_task_id:
             default_task = None
         else:
             # if we have a previous session to use, get the task id from it
@@ -364,7 +367,6 @@ class Task(_Task):
                 default_task_id = reuse_last_task_id
             elif not reuse_last_task_id or not cls.__task_is_relevant(default_task):
                 default_task_id = None
-                closed_old_task = cls.__close_timed_out_task(default_task)
             else:
                 default_task_id = default_task.get('id') if default_task else None
 
@@ -693,7 +695,7 @@ class Task(_Task):
             If `config_text` is not None, `config_dict` must not be provided.
         """
         config_text = self.get_model_config_text()
-        return  OutputModel._text_to_config_dict(config_text)
+        return OutputModel._text_to_config_dict(config_text)
 
     def set_model_label_enumeration(self, enumeration=None):
         """