[rxin]

The "broadcast" is pretty cheap because often you already have the data in some distributed file system, or if on a single node the network bandwidth is pretty high. The problem with a lot of the deep learning workloads is that it is very compute intensive and as a result takes a long time to run. For example, it is not uncommon to take a week to train some models.

[gcr]

Deep learning workloads are typically compute-intensive, but they also tend to be extremely I/O intensive, and convergence may depend on a synchronous step where all the nodes must finish making their contribution to the model before any of them can continue. (This may not be quite true though -- see Google's DistBelief paper--but most frameworks work this way). Often times, adding more machines to a cluster may make training proportionally slower.

[rxin]

Did you actually read the article? It was using Spark to parallelize hyperparameter tuning, which is embarrassingly parallel.

[doobwa]

Why not just use GNU Parallel (or something similar) instead of Spark?

[elyase]

I think this could have been done with GNU parallel. One advantage I see with Spark is that is that it is easier to interact with Python, for example these two lines are all is needed to call the relevant Python function:

  urls = sc.parallelize(batched_data)
  labelled_images = urls.flatMap(apply_batch)

So if you already have a cluster with Spark installed (like Databrick does) then it takes less work to just call your Python code than setting up a GNU Parallel cluster and a writing a small wrapper script. Additionally a Python script would have to load/init the models on every call from Parallel. I agree that this is not a great demonstration of Spark main strengths.

[orm]

I think one reason would fault tolerance. Is there a fault tolerance layer in GNU parallel? last time I checked their homepage ( a few minutes ago), there was no reference to fault tolerance.

Another reason is, perhaps, scheduling.

[chimtim]

what fault tolerance does spark give you in this scheme? It cannot look into TF progress and checkpoint all state. Using Spark with TF, seems like an overkill -- you need to manage and install two framework what should ideally be a 200 line python wrapper or small mesos framework at most.

[ole_tange]

Does --retries count as fault tolerance?

[gcr]

Oh dear. You're right, sorry. Shouldn't have commented before actually reading the article...