Implementation of the server restart and checkpointing

Until now, a server failure resulted in a total restarting of the study. This MR aims at adding the possibility of checkpointing the server to keep track of a given state (statistics structures or buffer state and population, list of finished clients, list of clients to resubmit, etc). It'll require the following:

• environment variable addition to indicate that the server was submitted multiple times,
• data structures checkpointing (e.g. new function server.checkpoint()),
• data structure loading (e.g. new function server.restart()).
• ensure Tensorboard log continuity - potentially not possible :/
• Add Sensitivity Analysis server checkpointing (doing it in separate MR)
• Split/clean torch_server.py restart_from_checkpoint()
• Add rank specific buffer checkpointing
• Test checkpointing mechanism on G5k or JZ
• Handle case where only one of the server processes crashes, while the other stays up
• restarting the server from scratch if the failure occurred before its first checkpoint,
• having a config variable to define how often the server should be checkpointed.
• Make it possible to start launcher from existing folder.
• Add documentation for how to use checkpointing/fault tolerance
• Add full fault tolerance test to CI