[dask][sge] Multiqueue updates

In this merge request I:

• Simplified the way multi-queue is set in our scripts
• Updated our Dask documentation

Example

Setting the fit method to run on q_short_gpu

pipeline = mario.wrap(
    ["sample", "checkpoint", "dask"],
    pipeline,
    model_path=model_path,
    fit_tag="q_short_gpu",
)

You have to explicitly set the list of resource tags available.

pipeline.fit_transform(...).compute(
    scheduler=dask_client, resources=cluster.get_sge_resources()

doc/python/pipeline_example_dask.py

@@ -25,7 +25,7 @@ class MyFitTranformer(TransformerMixin, BaseEstimator):
     def __init__(self):
         self._fit_model = None
 
-    def transform(self, X):
+    def transform(self, X, metadata=None):
         # Transform `X`
         return [x @ self._fit_model for x in X]
 
@@ -40,7 +40,7 @@ X = numpy.zeros((2, 2))
 X_as_sample = [Sample(X, key=str(i), metadata=1) for i in range(10)]
 
 # Building an arbitrary pipeline
-model_path = "~/dask_tmp"
+model_path = "./dask_tmp"
 os.makedirs(model_path, exist_ok=True)
 pipeline = make_pipeline(MyTransformer(), MyFitTranformer())
 
@@ -48,13 +48,15 @@ pipeline = make_pipeline(MyTransformer(), MyFitTranformer())
 pipeline = bob.pipelines.wrap(
     ["sample", "checkpoint", "dask"],
     pipeline,
-    model_path=model_path,
+    model_path=os.path.join(model_path, "model.pickle"),
+    features_dir=model_path,
     transform_extra_arguments=(("metadata", "metadata"),),
 )
 
 # Create a dask graph from a pipeline
 # Run the task graph in the local computer in a single tread
 X_transformed = pipeline.fit_transform(X_as_sample).compute(scheduler="single-threaded")
+
 import shutil
 
 shutil.rmtree(model_path)