Hi guys

Thx for the suggestions. I ended up using HFD5. There is some nice 
framework also that uses it called Fuel. So far looks pretty good. Thx 
again.

On Friday, September 16, 2016 at 3:30:49 PM UTC+2, nouiz wrote:
>
> Or maybe simpler, use hdf5 file format. There is the pytables Python 
> package that support it. It will ask an numpy array, but will load the 
> element you select efficiently.
>
> Le 15 sept. 2016 13:03, "Kyle Kastner" <kastn...@gmail.com <javascript:>> 
> a écrit :
>
>> This is not really Theano specific, but I write Python iterators to
>> handle data loading for most ML tasks, and make sure the run loop does
>> something like .next() or python for loops (which expect
>> StopIteration() at the end of the iterator. As a bonus, if you are not
>> CPU bound you can fetch data for the next minibatches while the GPU
>> crunches. If you have some complicated preprocessing it might be
>> necessary to use shared memory or multiprocessing to avoid GIL issues,
>> but my use cases have not run into that so far.
>>
>> An example that loosely matches what I do can be seen here
>> https://gist.github.com/kastnerkyle/33e2172bafbb5dd794ab
>>
>> On Thu, Sep 15, 2016 at 11:27 AM, Jose Carranza
>> <jose.car...@gmail.com <javascript:>> wrote:
>> > Hi guys
>> >
>> > I have a fairly big dataset (million+ images for train) that I want to 
>> use
>> > to train from scratch a model in Theano. In Caffe we use LMDB however I
>> > haven't seen any best practice in Theano for something bigger than 
>> MNIST and
>> > stuff like that. Can somebody suggest what is the best option to pull 
>> data
>> > into Theano/Lasagne? I need something that is not 100% in memory but 
>> that
>> > can pull in batches (hopefully also shuffled batches).
>> >
>> > Thx in advance
>> >
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