Re: Please reply ASAP : Regarding incubator systemml/breast_cancer project
Hi Aishwarya, Yes, it is quite strange that Jupyter isn't running on the PySpark kernel even though it's being started in that manner. The good news is that we do use this everyday, so once we find the root issue with your Jupyter, it should work great! Let's try temporarily removing all of the existing Jupyter/IPython settings & kernels and basically start fresh. Assuming you are on OS X / macOS or Linux, can you do the following? (Please double check the exact paths, as I'm typing on a phone.) * Stop Jupyter, and make sure that it is not running. * Temporarily remove the Jupyter kernels. First, you will need to see where they are installed, and then just rename that path. `jupyter kernelspec list` # look at paths above. For example, on macOS, it may be located at ~/Library/Jupyter/kernels, and thus to move it, you would use the following. Update this as needed for the exact paths listed above `mv ~/Library/Jupyter/kernels ~/Library/Jupyter_OLD/kernels` * Temporarily remove the Jupyter & IPython settings: `mv ~/.jupyter ~/.jupyter_OLD` `mv ~/.ipython ~/.ipython_OLD` * Make sure Jupyter is up to date: `pip3 install -U ipython jupyter` After that, please ensure that Jupyter is not running, then start it in the context of PySpark as sent previously. Once Jupyter is started this time, there should only be one kernel listed, and `sc` should be available. Can you try that? -- Mike Dusenberry GitHub: github.com/dusenberrymw LinkedIn: linkedin.com/in/mikedusenberry Sent from my iPhone. > On Apr 26, 2017, at 2:13 AM, Aishwarya Chaurasia > wrote: > > Hi sir, > The sc NameError persists. > > (1) There is only one jupyter server running. And that was started with the > pyspark command in the previous mail. > (2) Two kernels are appearing in the change kernel option - Python3 and > Python2. Tried with both of them and the result is the same. > > How is jupyter not being able to run on the pyspark kernel when we have > started the notebook with the pyspark command only? > > Is it possible to create a .py file of MachineLearning.ipynb like was done > with preprocessing.ipynb with explicitly creating a SparkContext() ? > >> On 25-Apr-2017 11:57 PM, wrote: >> >> Hi Aishwarya, >> >> Unfortunately this mailing list removes all images, so I can't view your >> screenshot. I'm assuming that it is the same issue with the missing >> SparkContext `sc` object, but please let me know if it is a different >> issue. This sounds like it could be an issue with multiple kernels >> installed in Jupyter. When you start the notebook, can you see if there >> are multiple kernels listed in the "Kernel" -> "Change Kernel" menu? If >> so, please try one of the other kernels to see if Jupyter is starting by >> default with a non-spark kernel. Also, is it possible that you have more >> than one instance of the Jupyter server running? I.e. for this scenario, >> we start Jupyter itself directly via pyspark using the command sent >> previously, whereas usually Jupyter can just be started with `jupyter >> notebook`. In the latter case, PySpark (and thus `sc`) would *not* be >> available (unless you've set up special PySpark kernels separately). In >> summary, can you (1) check for other kernels via the menus, and (2) check >> for other running Jupyter servers that are non-PySpark? >> >> As for the other inquiry, great question! When training models, it's >> quite useful to track the loss and other metrics (i.e. accuracy) from >> *both* the training and validation sets. The reasoning is that it allows >> for a more holistic view of the overall learning process, such as >> evaluating whether any overfitting or underfitting is occurring. For >> example, say that you train a model and achieve an accuracy of 80% on the >> validation set. Is this good? Is this the best that can be done? Without >> also tracking performance on the training set, it can be difficult to make >> these decisions. Say that you then measure the performance on the training >> set and find that the model achieves 100% accuracy on that data. That >> might be a good indication that your model is overfitting the training set, >> and that a combination of more data, regularization, and a smaller model >> may be helpful in raising the generalization performance, i.e. the >> performance on the validation set and future real examples on which you >> wish to make predictions. If on the other hand, the model achieved an 82% >> on the training set, this could be a good indication that the model is >> underfitting, and that a combination of a more expressive model and better >> data could be helpful. In summary, tracking performance on both the >> training and validation datasets can be useful for determining ways in >> which to improve the overall learning process. >> >> >> - Mike >> >> -- >> >> Mike Dusenberry >> GitHub: github.com/dusenberrymw >> LinkedIn: linkedin.com/in/mikedusenberry >> >> Sent from my
Re: Please reply ASAP : Regarding incubator systemml/breast_cancer project
Hi sir, The sc NameError persists. (1) There is only one jupyter server running. And that was started with the pyspark command in the previous mail. (2) Two kernels are appearing in the change kernel option - Python3 and Python2. Tried with both of them and the result is the same. How is jupyter not being able to run on the pyspark kernel when we have started the notebook with the pyspark command only? Is it possible to create a .py file of MachineLearning.ipynb like was done with preprocessing.ipynb with explicitly creating a SparkContext() ? On 25-Apr-2017 11:57 PM, wrote: > Hi Aishwarya, > > Unfortunately this mailing list removes all images, so I can't view your > screenshot. I'm assuming that it is the same issue with the missing > SparkContext `sc` object, but please let me know if it is a different > issue. This sounds like it could be an issue with multiple kernels > installed in Jupyter. When you start the notebook, can you see if there > are multiple kernels listed in the "Kernel" -> "Change Kernel" menu? If > so, please try one of the other kernels to see if Jupyter is starting by > default with a non-spark kernel. Also, is it possible that you have more > than one instance of the Jupyter server running? I.e. for this scenario, > we start Jupyter itself directly via pyspark using the command sent > previously, whereas usually Jupyter can just be started with `jupyter > notebook`. In the latter case, PySpark (and thus `sc`) would *not* be > available (unless you've set up special PySpark kernels separately). In > summary, can you (1) check for other kernels via the menus, and (2) check > for other running Jupyter servers that are non-PySpark? > > As for the other inquiry, great question! When training models, it's > quite useful to track the loss and other metrics (i.e. accuracy) from > *both* the training and validation sets. The reasoning is that it allows > for a more holistic view of the overall learning process, such as > evaluating whether any overfitting or underfitting is occurring. For > example, say that you train a model and achieve an accuracy of 80% on the > validation set. Is this good? Is this the best that can be done? Without > also tracking performance on the training set, it can be difficult to make > these decisions. Say that you then measure the performance on the training > set and find that the model achieves 100% accuracy on that data. That > might be a good indication that your model is overfitting the training set, > and that a combination of more data, regularization, and a smaller model > may be helpful in raising the generalization performance, i.e. the > performance on the validation set and future real examples on which you > wish to make predictions. If on the other hand, the model achieved an 82% > on the training set, this could be a good indication that the model is > underfitting, and that a combination of a more expressive model and better > data could be helpful. In summary, tracking performance on both the > training and validation datasets can be useful for determining ways in > which to improve the overall learning process. > > > - Mike > > -- > > Mike Dusenberry > GitHub: github.com/dusenberrymw > LinkedIn: linkedin.com/in/mikedusenberry > > Sent from my iPhone. > > > > On Apr 25, 2017, at 8:47 AM, Aishwarya Chaurasia < > aishwarya2...@gmail.com> wrote: > > > > We had another query, sir. We read the entire MachineLearning.ipynb code. > > in it the training samples and the validation samples have both been > > evaluated separately and their respective losses and accuracies obtained. > > Why are the training samples being evaluated again if they were used to > > train the model in the first place? Shouldn't only the validation data > > frames be evaluated to find out the loss and accuracy? > > > > Thank you > > > > On 25-Apr-2017 4:00 PM, "Aishwarya Chaurasia" > > wrote: > > > >> Hello sir, > >> > >> The NameError is occuring again sir. Why does it keep resurfacing? > >> > >> Attaching the screenshot of the error. > >> > >>> On 25-Apr-2017 2:50 AM, wrote: > >>> > >>> Hi Aishwarya, > >>> > >>> For the error message, that just means that the SystemML jar isn't > being > >>> found. Can you add a `--driver-class-path $SYSTEMML_HOME/target/ > SystemML.jar` > >>> to the invocation of Jupyter? I.e. `PYSPARK_PYTHON=python3 > >>> PYSPARK_DRIVER_PYTHON=jupyter PYSPARK_DRIVER_PYTHON_OPTS="notebook" > >>> pyspark --jars $SYSTEMML_HOME/target/SystemML.jar --driver-class-path > >>> $SYSTEMML_HOME/target/SystemML.jar`. There was a PySpark bug that was > >>> supposed to have been fixed in Spark 2.x, but it's possible that it is > >>> still an issue. > >>> > >>> As for the output, the notebook will create SystemML `Matrix` objects > for > >>> all of the weights and biases of the trained models. To save, please > >>> convert each one to a DataFrame, i.e. `Wc1.toDF()` and repeated for > each > >>> matrix, and then simply save the DataFrames. Thi
Re: Please reply ASAP : Regarding incubator systemml/breast_cancer project
Hi Aishwarya,
Unfortunately this mailing list removes all images, so I can't view your
screenshot. I'm assuming that it is the same issue with the missing
SparkContext `sc` object, but please let me know if it is a different issue.
This sounds like it could be an issue with multiple kernels installed in
Jupyter. When you start the notebook, can you see if there are multiple
kernels listed in the "Kernel" -> "Change Kernel" menu? If so, please try one
of the other kernels to see if Jupyter is starting by default with a non-spark
kernel. Also, is it possible that you have more than one instance of the
Jupyter server running? I.e. for this scenario, we start Jupyter itself
directly via pyspark using the command sent previously, whereas usually Jupyter
can just be started with `jupyter notebook`. In the latter case, PySpark (and
thus `sc`) would *not* be available (unless you've set up special PySpark
kernels separately). In summary, can you (1) check for other kernels via the
menus, and (2) check for other running Jupyter servers that are non-PySpark?
As for the other inquiry, great question! When training models, it's quite
useful to track the loss and other metrics (i.e. accuracy) from *both* the
training and validation sets. The reasoning is that it allows for a more
holistic view of the overall learning process, such as evaluating whether any
overfitting or underfitting is occurring. For example, say that you train a
model and achieve an accuracy of 80% on the validation set. Is this good? Is
this the best that can be done? Without also tracking performance on the
training set, it can be difficult to make these decisions. Say that you then
measure the performance on the training set and find that the model achieves
100% accuracy on that data. That might be a good indication that your model is
overfitting the training set, and that a combination of more data,
regularization, and a smaller model may be helpful in raising the
generalization performance, i.e. the performance on the validation set and
future real examples on which you wish to make predictions. If on the other
hand, the model achieved an 82% on the training set, this could be a good
indication that the model is underfitting, and that a combination of a more
expressive model and better data could be helpful. In summary, tracking
performance on both the training and validation datasets can be useful for
determining ways in which to improve the overall learning process.
- Mike
--
Mike Dusenberry
GitHub: github.com/dusenberrymw
LinkedIn: linkedin.com/in/mikedusenberry
Sent from my iPhone.
> On Apr 25, 2017, at 8:47 AM, Aishwarya Chaurasia
> wrote:
>
> We had another query, sir. We read the entire MachineLearning.ipynb code.
> in it the training samples and the validation samples have both been
> evaluated separately and their respective losses and accuracies obtained.
> Why are the training samples being evaluated again if they were used to
> train the model in the first place? Shouldn't only the validation data
> frames be evaluated to find out the loss and accuracy?
>
> Thank you
>
> On 25-Apr-2017 4:00 PM, "Aishwarya Chaurasia"
> wrote:
>
>> Hello sir,
>>
>> The NameError is occuring again sir. Why does it keep resurfacing?
>>
>> Attaching the screenshot of the error.
>>
>>> On 25-Apr-2017 2:50 AM, wrote:
>>>
>>> Hi Aishwarya,
>>>
>>> For the error message, that just means that the SystemML jar isn't being
>>> found. Can you add a `--driver-class-path
>>> $SYSTEMML_HOME/target/SystemML.jar`
>>> to the invocation of Jupyter? I.e. `PYSPARK_PYTHON=python3
>>> PYSPARK_DRIVER_PYTHON=jupyter PYSPARK_DRIVER_PYTHON_OPTS="notebook"
>>> pyspark --jars $SYSTEMML_HOME/target/SystemML.jar --driver-class-path
>>> $SYSTEMML_HOME/target/SystemML.jar`. There was a PySpark bug that was
>>> supposed to have been fixed in Spark 2.x, but it's possible that it is
>>> still an issue.
>>>
>>> As for the output, the notebook will create SystemML `Matrix` objects for
>>> all of the weights and biases of the trained models. To save, please
>>> convert each one to a DataFrame, i.e. `Wc1.toDF()` and repeated for each
>>> matrix, and then simply save the DataFrames. This could be done all at
>>> once like this for a SystemML Matrix object `Wc1`:
>>> `Wc1.toDf().write.save("path/to/save/Wc1.parquet", format="parquet")`.
>>> Just repeat for each matrix returned by the "Train" code for the
>>> algorithms. At that point, you will have a set of saved DataFrames
>>> representing a trained SystemML model, and these can be used in downstream
>>> classification tasks in a similar manner to the "Eval" sections.
>>>
>>> -Mike
>>>
>>> --
>>>
>>> Mike Dusenberry
>>> GitHub: github.com/dusenberrymw
>>> LinkedIn: linkedin.com/in/mikedusenberry
>>>
>>> Sent from my iPhone.
>>>
>>>
On Apr 24, 2017, at 3:07 AM, Aishwarya Chaurasia <
>>> aishwarya2...@gmail.com> wrote:
Further more :
What
Re: Please reply ASAP : Regarding incubator systemml/breast_cancer project
We had another query, sir. We read the entire MachineLearning.ipynb code.
in it the training samples and the validation samples have both been
evaluated separately and their respective losses and accuracies obtained.
Why are the training samples being evaluated again if they were used to
train the model in the first place? Shouldn't only the validation data
frames be evaluated to find out the loss and accuracy?
Thank you
On 25-Apr-2017 4:00 PM, "Aishwarya Chaurasia"
wrote:
> Hello sir,
>
> The NameError is occuring again sir. Why does it keep resurfacing?
>
> Attaching the screenshot of the error.
>
> On 25-Apr-2017 2:50 AM, wrote:
>
>> Hi Aishwarya,
>>
>> For the error message, that just means that the SystemML jar isn't being
>> found. Can you add a `--driver-class-path
>> $SYSTEMML_HOME/target/SystemML.jar`
>> to the invocation of Jupyter? I.e. `PYSPARK_PYTHON=python3
>> PYSPARK_DRIVER_PYTHON=jupyter PYSPARK_DRIVER_PYTHON_OPTS="notebook"
>> pyspark --jars $SYSTEMML_HOME/target/SystemML.jar --driver-class-path
>> $SYSTEMML_HOME/target/SystemML.jar`. There was a PySpark bug that was
>> supposed to have been fixed in Spark 2.x, but it's possible that it is
>> still an issue.
>>
>> As for the output, the notebook will create SystemML `Matrix` objects for
>> all of the weights and biases of the trained models. To save, please
>> convert each one to a DataFrame, i.e. `Wc1.toDF()` and repeated for each
>> matrix, and then simply save the DataFrames. This could be done all at
>> once like this for a SystemML Matrix object `Wc1`:
>> `Wc1.toDf().write.save("path/to/save/Wc1.parquet", format="parquet")`.
>> Just repeat for each matrix returned by the "Train" code for the
>> algorithms. At that point, you will have a set of saved DataFrames
>> representing a trained SystemML model, and these can be used in downstream
>> classification tasks in a similar manner to the "Eval" sections.
>>
>> -Mike
>>
>> --
>>
>> Mike Dusenberry
>> GitHub: github.com/dusenberrymw
>> LinkedIn: linkedin.com/in/mikedusenberry
>>
>> Sent from my iPhone.
>>
>>
>> > On Apr 24, 2017, at 3:07 AM, Aishwarya Chaurasia <
>> aishwarya2...@gmail.com> wrote:
>> >
>> > Further more :
>> > What is the output of MachineLearning.ipynb you're obtaining sir?
>> > We are actually nearing our deadline for our problem.
>> > Thanks a lot.
>> >
>> > On 24-Apr-2017 2:58 PM, "Aishwarya Chaurasia"
>> > wrote:
>> >
>> > Hello sir,
>> >
>> > Thanks a lot for replying sir. But unfortunately it did not work.
>> Although
>> > the NameError did not appear this time but another error came about :
>> >
>> > https://paste.fedoraproject.org/paste/TUMtSIb88Q73FYekwJmM7V
>> > 5M1UNdIGYhyRLivL9gydE=
>> >
>> > This error was obtained after executing the second block of code of
>> > MachineLearning.py in terminal. ( ml = MLContext(sc) )
>> >
>> > We have installed the bleeding-edge version of systemml only and the
>> > installation was done correctly. We are in a fix now. :/
>> > Kindly look into the matter asap
>> >
>> > On 24-Apr-2017 12:15 PM, "Mike Dusenberry"
>> wrote:
>> >
>> > Hi Aishwarya,
>> >
>> > Glad to hear that the preprocessing stage was successful! As for the
>> > `MachineLearning.ipynb` notebook, here is a general guide:
>> >
>> >
>> > - The `MachineLearning.ipynb` notebook essentially (1) loads in the
>> > training and validation DataFrames from the preprocessing step, (2)
>> > converts them to normalized & one-hot encoded SystemML matrices for
>> > consumption by the ML algorithms, and (3) explores training a couple
>> of
>> > models.
>> > - To run, you'll need to start Jupyter in the context of PySpark via
>> > `PYSPARK_PYTHON=python3 PYSPARK_DRIVER_PYTHON=jupyter
>> > PYSPARK_DRIVER_PYTHON_OPTS="notebook" pyspark --jars
>> > $SYSTEMML_HOME/target/SystemML.jar`. Note that if you have installed
>> > SystemML with pip from PyPy (`pip3 install systemml`), this will
>> install
>> > our 0.13 release, and the `--jars $SYSTEMML_HOME/target/SystemML.jar`
>> > will
>> > not be necessary. If you instead have installed a bleeding-edge
>> version
>> > of
>> > SystemML locally (git clone locally, maven build, `pip3 install -e
>> > src/main/python` as listed in `projects/breast_cancer/README.md`),
>> the
>> > `--jars $SYSTEMML_HOME/target/SystemML.jar` part *is* necessary. We
>> are
>> > about to release 0.14, and for this project, I *would* recommend
>> using a
>> > bleeding edge install.
>> > - Once Jupyter has been started in the context of PySpark, the `sc`
>> > SparkContext object should be available. Please let me know if you
>> > continue to see this issue.
>> > - The "Read in train & val data" section simply reads in the training
>> > and validation data generated in the preprocessing stage. Be sure
>> that
>> > the
>> > `size` setting is the same as the preprocessing size. The percentage
>> `p`
>> > setting determines whether the full or sampled DataFrames are
>> loaded. If
>> > you set `p
Re: Please reply ASAP : Regarding incubator systemml/breast_cancer project
Hello sir,
The NameError is occuring again sir. Why does it keep resurfacing?
Attaching the screenshot of the error.
On 25-Apr-2017 2:50 AM, wrote:
> Hi Aishwarya,
>
> For the error message, that just means that the SystemML jar isn't being
> found. Can you add a `--driver-class-path $SYSTEMML_HOME/target/SystemML.jar`
> to the invocation of Jupyter? I.e. `PYSPARK_PYTHON=python3
> PYSPARK_DRIVER_PYTHON=jupyter PYSPARK_DRIVER_PYTHON_OPTS="notebook"
> pyspark --jars $SYSTEMML_HOME/target/SystemML.jar --driver-class-path
> $SYSTEMML_HOME/target/SystemML.jar`. There was a PySpark bug that was
> supposed to have been fixed in Spark 2.x, but it's possible that it is
> still an issue.
>
> As for the output, the notebook will create SystemML `Matrix` objects for
> all of the weights and biases of the trained models. To save, please
> convert each one to a DataFrame, i.e. `Wc1.toDF()` and repeated for each
> matrix, and then simply save the DataFrames. This could be done all at
> once like this for a SystemML Matrix object `Wc1`:
> `Wc1.toDf().write.save("path/to/save/Wc1.parquet", format="parquet")`.
> Just repeat for each matrix returned by the "Train" code for the
> algorithms. At that point, you will have a set of saved DataFrames
> representing a trained SystemML model, and these can be used in downstream
> classification tasks in a similar manner to the "Eval" sections.
>
> -Mike
>
> --
>
> Mike Dusenberry
> GitHub: github.com/dusenberrymw
> LinkedIn: linkedin.com/in/mikedusenberry
>
> Sent from my iPhone.
>
>
> > On Apr 24, 2017, at 3:07 AM, Aishwarya Chaurasia <
> aishwarya2...@gmail.com> wrote:
> >
> > Further more :
> > What is the output of MachineLearning.ipynb you're obtaining sir?
> > We are actually nearing our deadline for our problem.
> > Thanks a lot.
> >
> > On 24-Apr-2017 2:58 PM, "Aishwarya Chaurasia"
> > wrote:
> >
> > Hello sir,
> >
> > Thanks a lot for replying sir. But unfortunately it did not work.
> Although
> > the NameError did not appear this time but another error came about :
> >
> > https://paste.fedoraproject.org/paste/TUMtSIb88Q73FYekwJmM7V
> > 5M1UNdIGYhyRLivL9gydE=
> >
> > This error was obtained after executing the second block of code of
> > MachineLearning.py in terminal. ( ml = MLContext(sc) )
> >
> > We have installed the bleeding-edge version of systemml only and the
> > installation was done correctly. We are in a fix now. :/
> > Kindly look into the matter asap
> >
> > On 24-Apr-2017 12:15 PM, "Mike Dusenberry"
> wrote:
> >
> > Hi Aishwarya,
> >
> > Glad to hear that the preprocessing stage was successful! As for the
> > `MachineLearning.ipynb` notebook, here is a general guide:
> >
> >
> > - The `MachineLearning.ipynb` notebook essentially (1) loads in the
> > training and validation DataFrames from the preprocessing step, (2)
> > converts them to normalized & one-hot encoded SystemML matrices for
> > consumption by the ML algorithms, and (3) explores training a couple of
> > models.
> > - To run, you'll need to start Jupyter in the context of PySpark via
> > `PYSPARK_PYTHON=python3 PYSPARK_DRIVER_PYTHON=jupyter
> > PYSPARK_DRIVER_PYTHON_OPTS="notebook" pyspark --jars
> > $SYSTEMML_HOME/target/SystemML.jar`. Note that if you have installed
> > SystemML with pip from PyPy (`pip3 install systemml`), this will
> install
> > our 0.13 release, and the `--jars $SYSTEMML_HOME/target/SystemML.jar`
> > will
> > not be necessary. If you instead have installed a bleeding-edge
> version
> > of
> > SystemML locally (git clone locally, maven build, `pip3 install -e
> > src/main/python` as listed in `projects/breast_cancer/README.md`), the
> > `--jars $SYSTEMML_HOME/target/SystemML.jar` part *is* necessary. We
> are
> > about to release 0.14, and for this project, I *would* recommend using
> a
> > bleeding edge install.
> > - Once Jupyter has been started in the context of PySpark, the `sc`
> > SparkContext object should be available. Please let me know if you
> > continue to see this issue.
> > - The "Read in train & val data" section simply reads in the training
> > and validation data generated in the preprocessing stage. Be sure that
> > the
> > `size` setting is the same as the preprocessing size. The percentage
> `p`
> > setting determines whether the full or sampled DataFrames are loaded.
> If
> > you set `p = 1`, the full DataFrames will be used. If you instead
> would
> > prefer to use the smaller sampled DataFrames while getting started,
> > please
> > set it to the same value as used in the preprocessing to generate the
> > smaller sampled DataFrames.
> > - The `Extract X & Y matrices` section splits each of the train and
> > validation DataFrames into effectively X & Y matrices (still as
> DataFrame
> > types), with X containing the images, and Y containing the labels.
> > - The `Convert to SystemML Matrices` section passes the X & Y
> DataFrames
> > into a SystemML script that performs
Re: Please reply ASAP : Regarding incubator systemml/breast_cancer project
Hi Aishwarya,
For the error message, that just means that the SystemML jar isn't being found.
Can you add a `--driver-class-path $SYSTEMML_HOME/target/SystemML.jar` to the
invocation of Jupyter? I.e. `PYSPARK_PYTHON=python3
PYSPARK_DRIVER_PYTHON=jupyter PYSPARK_DRIVER_PYTHON_OPTS="notebook" pyspark
--jars $SYSTEMML_HOME/target/SystemML.jar --driver-class-path
$SYSTEMML_HOME/target/SystemML.jar`. There was a PySpark bug that was supposed
to have been fixed in Spark 2.x, but it's possible that it is still an issue.
As for the output, the notebook will create SystemML `Matrix` objects for all
of the weights and biases of the trained models. To save, please convert each
one to a DataFrame, i.e. `Wc1.toDF()` and repeated for each matrix, and then
simply save the DataFrames. This could be done all at once like this for a
SystemML Matrix object `Wc1`:
`Wc1.toDf().write.save("path/to/save/Wc1.parquet", format="parquet")`. Just
repeat for each matrix returned by the "Train" code for the algorithms. At
that point, you will have a set of saved DataFrames representing a trained
SystemML model, and these can be used in downstream classification tasks in a
similar manner to the "Eval" sections.
-Mike
--
Mike Dusenberry
GitHub: github.com/dusenberrymw
LinkedIn: linkedin.com/in/mikedusenberry
Sent from my iPhone.
> On Apr 24, 2017, at 3:07 AM, Aishwarya Chaurasia
> wrote:
>
> Further more :
> What is the output of MachineLearning.ipynb you're obtaining sir?
> We are actually nearing our deadline for our problem.
> Thanks a lot.
>
> On 24-Apr-2017 2:58 PM, "Aishwarya Chaurasia"
> wrote:
>
> Hello sir,
>
> Thanks a lot for replying sir. But unfortunately it did not work. Although
> the NameError did not appear this time but another error came about :
>
> https://paste.fedoraproject.org/paste/TUMtSIb88Q73FYekwJmM7V
> 5M1UNdIGYhyRLivL9gydE=
>
> This error was obtained after executing the second block of code of
> MachineLearning.py in terminal. ( ml = MLContext(sc) )
>
> We have installed the bleeding-edge version of systemml only and the
> installation was done correctly. We are in a fix now. :/
> Kindly look into the matter asap
>
> On 24-Apr-2017 12:15 PM, "Mike Dusenberry" wrote:
>
> Hi Aishwarya,
>
> Glad to hear that the preprocessing stage was successful! As for the
> `MachineLearning.ipynb` notebook, here is a general guide:
>
>
> - The `MachineLearning.ipynb` notebook essentially (1) loads in the
> training and validation DataFrames from the preprocessing step, (2)
> converts them to normalized & one-hot encoded SystemML matrices for
> consumption by the ML algorithms, and (3) explores training a couple of
> models.
> - To run, you'll need to start Jupyter in the context of PySpark via
> `PYSPARK_PYTHON=python3 PYSPARK_DRIVER_PYTHON=jupyter
> PYSPARK_DRIVER_PYTHON_OPTS="notebook" pyspark --jars
> $SYSTEMML_HOME/target/SystemML.jar`. Note that if you have installed
> SystemML with pip from PyPy (`pip3 install systemml`), this will install
> our 0.13 release, and the `--jars $SYSTEMML_HOME/target/SystemML.jar`
> will
> not be necessary. If you instead have installed a bleeding-edge version
> of
> SystemML locally (git clone locally, maven build, `pip3 install -e
> src/main/python` as listed in `projects/breast_cancer/README.md`), the
> `--jars $SYSTEMML_HOME/target/SystemML.jar` part *is* necessary. We are
> about to release 0.14, and for this project, I *would* recommend using a
> bleeding edge install.
> - Once Jupyter has been started in the context of PySpark, the `sc`
> SparkContext object should be available. Please let me know if you
> continue to see this issue.
> - The "Read in train & val data" section simply reads in the training
> and validation data generated in the preprocessing stage. Be sure that
> the
> `size` setting is the same as the preprocessing size. The percentage `p`
> setting determines whether the full or sampled DataFrames are loaded. If
> you set `p = 1`, the full DataFrames will be used. If you instead would
> prefer to use the smaller sampled DataFrames while getting started,
> please
> set it to the same value as used in the preprocessing to generate the
> smaller sampled DataFrames.
> - The `Extract X & Y matrices` section splits each of the train and
> validation DataFrames into effectively X & Y matrices (still as DataFrame
> types), with X containing the images, and Y containing the labels.
> - The `Convert to SystemML Matrices` section passes the X & Y DataFrames
> into a SystemML script that performs some normalization of the images &
> one-hot encoding of the labels, and then returns SystemML `Matrix` types.
> These are now ready to be passed into the subsequent algorithms.
> - The "Trigger Caching" and "Save Matrices" are experimental features,
> and not necessary to execute.
> - Next comes the two algorithms being explored in this notebook. The
>
Re: Please reply ASAP : Regarding incubator systemml/breast_cancer project
Further more : What is the output of MachineLearning.ipynb you're obtaining sir? We are actually nearing our deadline for our problem. Thanks a lot. On 24-Apr-2017 2:58 PM, "Aishwarya Chaurasia" wrote: Hello sir, Thanks a lot for replying sir. But unfortunately it did not work. Although the NameError did not appear this time but another error came about : https://paste.fedoraproject.org/paste/TUMtSIb88Q73FYekwJmM7V 5M1UNdIGYhyRLivL9gydE= This error was obtained after executing the second block of code of MachineLearning.py in terminal. ( ml = MLContext(sc) ) We have installed the bleeding-edge version of systemml only and the installation was done correctly. We are in a fix now. :/ Kindly look into the matter asap On 24-Apr-2017 12:15 PM, "Mike Dusenberry" wrote: Hi Aishwarya, Glad to hear that the preprocessing stage was successful! As for the `MachineLearning.ipynb` notebook, here is a general guide: - The `MachineLearning.ipynb` notebook essentially (1) loads in the training and validation DataFrames from the preprocessing step, (2) converts them to normalized & one-hot encoded SystemML matrices for consumption by the ML algorithms, and (3) explores training a couple of models. - To run, you'll need to start Jupyter in the context of PySpark via `PYSPARK_PYTHON=python3 PYSPARK_DRIVER_PYTHON=jupyter PYSPARK_DRIVER_PYTHON_OPTS="notebook" pyspark --jars $SYSTEMML_HOME/target/SystemML.jar`. Note that if you have installed SystemML with pip from PyPy (`pip3 install systemml`), this will install our 0.13 release, and the `--jars $SYSTEMML_HOME/target/SystemML.jar` will not be necessary. If you instead have installed a bleeding-edge version of SystemML locally (git clone locally, maven build, `pip3 install -e src/main/python` as listed in `projects/breast_cancer/README.md`), the `--jars $SYSTEMML_HOME/target/SystemML.jar` part *is* necessary. We are about to release 0.14, and for this project, I *would* recommend using a bleeding edge install. - Once Jupyter has been started in the context of PySpark, the `sc` SparkContext object should be available. Please let me know if you continue to see this issue. - The "Read in train & val data" section simply reads in the training and validation data generated in the preprocessing stage. Be sure that the `size` setting is the same as the preprocessing size. The percentage `p` setting determines whether the full or sampled DataFrames are loaded. If you set `p = 1`, the full DataFrames will be used. If you instead would prefer to use the smaller sampled DataFrames while getting started, please set it to the same value as used in the preprocessing to generate the smaller sampled DataFrames. - The `Extract X & Y matrices` section splits each of the train and validation DataFrames into effectively X & Y matrices (still as DataFrame types), with X containing the images, and Y containing the labels. - The `Convert to SystemML Matrices` section passes the X & Y DataFrames into a SystemML script that performs some normalization of the images & one-hot encoding of the labels, and then returns SystemML `Matrix` types. These are now ready to be passed into the subsequent algorithms. - The "Trigger Caching" and "Save Matrices" are experimental features, and not necessary to execute. - Next comes the two algorithms being explored in this notebook. The "Softmax Classifier" is just a multi-class logistic regression model, and is simply there to serve as a baseline comparison with the subsequent convolutional neural net model. You may wish to simply skip this softmax model and move to the latter convnet model further down in the notebook. - The actual softmax model is located at [ https://github.com/apache/incubator-systemml/blob/master/ projects/breast_cancer/softmax_clf.dml], and the notebook calls functions from that file. - The softmax sanity check just ensures that the model is able to completely overfit when given a tiny sample size. This should yield ~100% training accuracy if the sample size in this section is small enough. This is just a check to ensure that nothing else is wrong with the math or the data. - The softmax "Train" section will train a softmax model and return the weights (`W`) and biases (`b`) of the model as SystemML `Matrix` objects. Please adjust the hyperparameters in this section to your problem. - The softmax "Eval" section takes the trained weights and biases and evaluates the training and validation performance. - The next model is a LeNet-like convnet model. The actual model is located at [ https://github.com/apache/incubator-systemml/blob/master/ projects/breast_cancer/convnet.dml], and the notebook simply calls functions from that file. - Once again, there is an initial sanity check for the ability to overfit on a small amount of data. - The "
Re: Please reply ASAP : Regarding incubator systemml/breast_cancer project
Hello sir, Thanks a lot for replying sir. But unfortunately it did not work. Although the NameError did not appear this time but another error came about : https://paste.fedoraproject.org/paste/TUMtSIb88Q73FYekwJmM7V5M1UNdIG YhyRLivL9gydE= This error was obtained after executing the second block of code of MachineLearning.py in terminal. ( ml = MLContext(sc) ) We have installed the bleeding-edge version of systemml only and the installation was done correctly. We are in a fix now. :/ Kindly look into the matter asap On 24-Apr-2017 12:15 PM, "Mike Dusenberry" wrote: Hi Aishwarya, Glad to hear that the preprocessing stage was successful! As for the `MachineLearning.ipynb` notebook, here is a general guide: - The `MachineLearning.ipynb` notebook essentially (1) loads in the training and validation DataFrames from the preprocessing step, (2) converts them to normalized & one-hot encoded SystemML matrices for consumption by the ML algorithms, and (3) explores training a couple of models. - To run, you'll need to start Jupyter in the context of PySpark via `PYSPARK_PYTHON=python3 PYSPARK_DRIVER_PYTHON=jupyter PYSPARK_DRIVER_PYTHON_OPTS="notebook" pyspark --jars $SYSTEMML_HOME/target/SystemML.jar`. Note that if you have installed SystemML with pip from PyPy (`pip3 install systemml`), this will install our 0.13 release, and the `--jars $SYSTEMML_HOME/target/SystemML.jar` will not be necessary. If you instead have installed a bleeding-edge version of SystemML locally (git clone locally, maven build, `pip3 install -e src/main/python` as listed in `projects/breast_cancer/README.md`), the `--jars $SYSTEMML_HOME/target/SystemML.jar` part *is* necessary. We are about to release 0.14, and for this project, I *would* recommend using a bleeding edge install. - Once Jupyter has been started in the context of PySpark, the `sc` SparkContext object should be available. Please let me know if you continue to see this issue. - The "Read in train & val data" section simply reads in the training and validation data generated in the preprocessing stage. Be sure that the `size` setting is the same as the preprocessing size. The percentage `p` setting determines whether the full or sampled DataFrames are loaded. If you set `p = 1`, the full DataFrames will be used. If you instead would prefer to use the smaller sampled DataFrames while getting started, please set it to the same value as used in the preprocessing to generate the smaller sampled DataFrames. - The `Extract X & Y matrices` section splits each of the train and validation DataFrames into effectively X & Y matrices (still as DataFrame types), with X containing the images, and Y containing the labels. - The `Convert to SystemML Matrices` section passes the X & Y DataFrames into a SystemML script that performs some normalization of the images & one-hot encoding of the labels, and then returns SystemML `Matrix` types. These are now ready to be passed into the subsequent algorithms. - The "Trigger Caching" and "Save Matrices" are experimental features, and not necessary to execute. - Next comes the two algorithms being explored in this notebook. The "Softmax Classifier" is just a multi-class logistic regression model, and is simply there to serve as a baseline comparison with the subsequent convolutional neural net model. You may wish to simply skip this softmax model and move to the latter convnet model further down in the notebook. - The actual softmax model is located at [ https://github.com/apache/incubator-systemml/blob/ master/projects/breast_cancer/softmax_clf.dml], and the notebook calls functions from that file. - The softmax sanity check just ensures that the model is able to completely overfit when given a tiny sample size. This should yield ~100% training accuracy if the sample size in this section is small enough. This is just a check to ensure that nothing else is wrong with the math or the data. - The softmax "Train" section will train a softmax model and return the weights (`W`) and biases (`b`) of the model as SystemML `Matrix` objects. Please adjust the hyperparameters in this section to your problem. - The softmax "Eval" section takes the trained weights and biases and evaluates the training and validation performance. - The next model is a LeNet-like convnet model. The actual model is located at [ https://github.com/apache/incubator-systemml/blob/ master/projects/breast_cancer/convnet.dml], and the notebook simply calls functions from that file. - Once again, there is an initial sanity check for the ability to overfit on a small amount of data. - The "Hyperparameter Search" contains a script to sample different hyperparams for the convnet, and save the hyperparams + validation accuracy of each set after a single epoch of training. These string fi
Re: Please reply asap : Regarding incubator systemml/breast_cancer project
Hey, Thank you so much for your help sir. We were finally able to run preprocess.py without any errors. And the results obtained were satisfactory i.e we got five set of data frames like you said we would. But alas! when we tried to run MachineLearning.ipynb the same NameError came : https://paste.fedoraproject.org/paste/l3LFJreg~vnYEDTSTQH7 3l5M1UNdIGYhyRLivL9gydE= Could you guide us again as to how to proceed now? Also, could you please provide an overview of the process MachineLearning.ipynb is following to train the samples. Also we have tried all possible solutions to remove the name sc error. It would be really kind of you if you looked into the matter asap. Thanks a lot! On 22-Apr-2017 5:19 PM, "Aishwarya Chaurasia" wrote: > Hey, > > Thank you so much for your help sir. We were finally able to run > preprocess.py without any errors. And the results obtained were > satisfactory i.e we got five set of data frames like you said we would. > > But alas! when we tried to run MachineLearning.ipynb the same NameError > came : https://paste.fedoraproject.org/paste/l3LFJreg~vnYEDTSTQH7 > 3l5M1UNdIGYhyRLivL9gydE= > > Could you guide us again as to how to proceed now? > Also, could you please provide an overview of the process > MachineLearning.ipynb is following to train the samples. > > Thanks a lot! > > On 20-Apr-2017 12:16 AM, wrote: > >> Hi Aishwarya, >> >> Looks like you've just encountered an out of memory error on one of the >> executors. Therefore, you just need to adjust the `spark.executor.memory` >> and `spark.driver.memory` settings with higher amounts of RAM. What is >> your current setup? I.e. are you using a cluster of machines, or a single >> machine? We generally use a large driver on one machine, and then a single >> large executor on each other machine. I would give a sizable amount of >> memory to the driver, and about half the possible memory on the executors >> so that the Python processes have enough memory as well. PySpark has JVM >> and Python components, and the Spark memory settings only pertain to the >> JVM side, thus the need to save about half the executor memory for the >> Python side. >> >> Thanks! >> >> - Mike >> >> -- >> >> Mike Dusenberry >> GitHub: github.com/dusenberrymw >> LinkedIn: linkedin.com/in/mikedusenberry >> >> Sent from my iPhone. >> >> >> > On Apr 19, 2017, at 5:53 AM, Aishwarya Chaurasia < >> aishwarya2...@gmail.com> wrote: >> > >> > Hello sir, >> > >> > We also wanted to ensure that the spark-submit command we're using is >> the >> > correct one for running 'preprocess.py'. >> > Command : /home/new/sparks/bin/spark-submit preprocess.py >> > >> > >> > Thank you. >> > Aishwarya Chaurasia. >> > >> > On 19-Apr-2017 3:55 PM, "Aishwarya Chaurasia" >> > wrote: >> > >> > Hello sir, >> > On running the file preprocess.py we are getting the following error : >> > >> > https://paste.fedoraproject.org/paste/IAvqiiyJChSC0V9eeETe2F5M1UNdIG >> > YhyRLivL9gydE= >> > >> > Can you please help us by looking into the error and kindly tell us the >> > solution for it. >> > Thanks a lot. >> > Aishwarya Chaurasia >> > >> > >> >> On 19-Apr-2017 12:43 AM, wrote: >> >> >> >> Hi Aishwarya, >> >> >> >> Certainly, here is some more detailed information about`preprocess.py`: >> >> >> >> * The preprocessing Python script is located at >> >> https://github.com/apache/incubator-systemml/blob/master/ >> >> projects/breast_cancer/preprocess.py. Note that this is different >> than >> >> the library module at https://github.com/apache/incu >> >> bator-systemml/blob/master/projects/breast_cancer/breastc >> >> ancer/preprocessing.py. >> >> * This script is used to preprocess a set of histology slide images, >> >> which are `.svs` files in our case, and `.tiff` files in your case. >> >> * Lines 63-79 contain "settings" such as the output image sizes, >> folder >> >> paths, etc. Of particular interest, line 72 has the folder path for >> the >> >> original slide images that should be commonly accessible from all >> machines >> >> being used, and lines 74-79 contain the names of the output DataFrames >> that >> >> will be saved. >> >> * Line 82 performs the actual preprocessing and creates a Spark >> >> DataFrame with the following columns: slide number, tumor score, >> molecular >> >> score, sample. The "sample" in this case is the actual small, >> chopped-up >> >> section of the image that has been extracted and flattened into a row >> >> Vector. For test images without labels (`training=false`), only the >> slide >> >> number and sample will be contained in the DataFrame (i.e. no labels). >> >> This calls the `preprocess(...)` function located on line 371 of >> >> https://github.com/apache/incubator-systemml/blob/master/ >> >> projects/breast_cancer/breastcancer/preprocessing.py, which is a >> >> different file. >> >> * Line 87 simply saves the above DataFrame to HDFS with the name from >> >> line 74. >> >> * Line 93 splits the above DataFrame row-wise into separate "train
