first version of new runner explanation

doc/intro.rst

@@ -247,7 +247,7 @@ the reason for the contract enforced on the Optimizee constructor
 
 Note that all the (non-exploring) paramters to the `Optimizer` is passed in to its constructor through a
 :func:`~collections.namedtuple` to keep the paramters documented. For examples see :class:`.GeneticAlgorithmParameters`
-or :class:`.SimulatedAnnealingParameters`
+or :class:`.CrossEntropyParameters`
 
 The :meth:`~l2l.optimizers.optimizer.Optimizer.post_process` function:
 ----------------------------------------------------------------------
@@ -295,43 +295,28 @@ logging and recording. See the source of :file:`bin/l2l-template.py` for more de
 
 Execution setup
 ~~~~~~~~~~~~~~~
-The L2L framework works with JUBE in order to deploy the execution of the different instances of the optimizee on
-the available computational resources. This requires that the trajectory contains a parameter group called JUBE_params
+The L2L framework works together with workers from a runner class to distribute the execution of the different instances of the optimizee on
+the available computational resources. This requires that the trajectory contains a parameter group called runner_params
 which contains details for the right execution of the program.
 
-**Mandatory** steps to define the execution of the optimizees:
-1. Add a parameter group to the :obj: traj called JUBE_params using its :meth: f_add_parameter_group.
-2. Setup the execution command :attr: exec by using the trajectory :meth: f_add_parameter_to_group.
-Add parameter to group receives three parameters, which in this case should be specified as:
-group_name=JUBE_params, key="exec", val=<execution command string>
-This <execution command string> will be used to launch individual optimizees. An example of a simple call without using MPI calls
-is: "python " + os.path.join(paths.simulation_path, "run_files/run_optimizee.py"
-3. Setup the ready and working paths :attr: exec by using the trajectory :meth: f_add_parameter_to_group.
-Add parameter to group receives three parameters, which in this case should be specified as:
-group_name=JUBE_params, key="paths", val=<path object>
-<path object> should contain the root working path. An example of this path is:
-paths = Paths(name, dict(run_num='test'), root_dir_path=<root_dir_path>, suffix="-example")
-
-In order to launch simulations on a laptop or a local cluster without a scheduler, only the mandatory parameters must
-be specified. These parameters are part of the template.
-
-To launch the simulations on a cluster with a scheduler, the following optional parameters must be defined. They currently match
-slurm but this can also be adjusted to other schedulers.
-1. Name of the scheduler, :atr: "scheduler", e.g. "Slurm"
-2. Command to submit jobs to the schedulers, :atr: "submit_cmd", e.g. "sbatch"
-3. Template file for the particular scheduler, :atr: "job_file", e.g. "job.run"
-4. Number of nodes to request for each run, :atr: "nodes", e.g. "1"
-5. Requested time for the compute resources, :atr: "walltime", e.g. "00:01:00"
-6. MPI Processes per node, :atr: "ppn", e.g. "1"
-7. CPU cores per MPI process, :atr: "cpu_pp", e.g. "1"
-8. Threads per process, :atr: "threads_pp", e.g. "1"
-9. Type of emails to be sent from the scheduler, :atr: "mail_mode", e.g. "ALL"
-10. Email to notify events from the scheduler, :atr: "mail_address", e.g. "[email protected]"
-11. Error file for the job, :atr: "err_file", e.g. "stderr"
-12. Output file for the job, :atr: "out_file", e.g. "stdout"
-13. MPI Processes per job, :atr: "tasks_per_job", e.g. "1"
-
-See the :file: 'l2l-template-scheduler.py' for a base file with all these parameters.
+**Mandatory** steps to define the execution of the optimizees, if you do not want to use the default parameters:
+
+1. Create a dictionary that contains the runner parameters:
+    * **srun**: This is the srun command that is called when running the program in parallel to execute an individual. 
+      The default-parameter is an empty string, which means that the program is only executed locally.
+    * **exec**: This is the command to execute an individual. The default is set to python. 
+    * **max_workers**: The maximum number of workers must be determined by the user, 
+      depending on how many computer resources were requested and how many are required per individual. 
+      For example: a total of 1 node with 100 cores was requested; 
+      50 cores are required for an individual, therefore a maximum of 2 workers may be used.
+      The default is set to 32 workers.
+    * **work_path**: Specifies the path for the workspace. The results of the simulation, 
+      the trajectories and the logs for the individual workers are stored here. The default-parameter is set to the root_dir_path
+      of the experiment.
+    * **path_obj**: Strores the path object. 
+2. Pass the dictionary to the experiment while calling **experiment.prepare_experiment(runner_params=params)**.
+
+See the :file:`bin/l2l-template.py` for a base file with all these parameters.
 
 Examples
 ********
@@ -348,12 +333,21 @@ Data postprocessing
 
 Todo...
 
+.. _checkpointing:
+
+Checkpointing
+*************
+
+Currently, checkpointing is only available for genetic algorithms. 
+Here, a generation from a previous simulation can be read in and continued from there.
+For an example look at :file:`bin/l2l-fun-ga-checkpoint.py`
+
 .. _parallelization:
 
 Parallelization
 ***************
 
-We also support running different instances of the experiments on different cores and hosts using Jube.
+We also support running different instances of the experiments on different cores.
 
 
 .. _logging: