sdc-headnode | Responsible for building and setting up the Triton | Machine Learning library
kandi X-RAY | sdc-headnode Summary
kandi X-RAY | sdc-headnode Summary
Responsible for building and setting up the Triton (formerly SmartDataCenter) headnode.
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Community Discussions
Trending Discussions on Machine Learning
QUESTION
I have trained an RNN model with pytorch. I need to use the model for prediction in an environment where I'm unable to install pytorch because of some strange dependency issue with glibc. However, I can install numpy and scipy and other libraries. So, I want to use the trained model, with the network definition, without pytorch.
I have the weights of the model as I save the model with its state dict and weights in the standard way, but I can also save it using just json/pickle files or similar.
I also have the network definition, which depends on pytorch in a number of ways. This is my RNN network definition.
...ANSWER
Answered 2022-Feb-17 at 10:47You should try to export the model using torch.onnx. The page gives you an example that you can start with.
An alternative is to use TorchScript, but that requires torch libraries.
Both of these can be run without python. You can load torchscript in a C++ application https://pytorch.org/tutorials/advanced/cpp_export.html
ONNX is much more portable and you can use in languages such as C#, Java, or Javascript https://onnxruntime.ai/ (even on the browser)
A running exampleJust modifying a little your example to go over the errors I found
Notice that via tracing any if/elif/else, for, while will be unrolled
QUESTION
I'm trying to implement a gradient-free optimizer function to train convolutional neural networks with Julia using Flux.jl. The reference paper is this: https://arxiv.org/abs/2005.05955. This paper proposes RSO, a gradient-free optimization algorithm updates single weight at a time on a sampling bases. The pseudocode of this algorithm is depicted in the picture below.
I'm using MNIST dataset.
...ANSWER
Answered 2022-Jan-14 at 23:47Based on the paper you shared, it looks like you need to change the weight arrays per each output neuron per each layer. Unfortunately, this means that the implementation of your optimization routine is going to depend on the layer type, since an "output neuron" for a convolution layer is quite different than a fully-connected layer. In other words, just looping over Flux.params(model) is not going to be sufficient, since this is just a set of all the weight arrays in the model and each weight array is treated differently depending on which layer it comes from.
Fortunately, Julia's multiple dispatch does make this easier to write if you use separate functions instead of a giant loop. I'll summarize the algorithm using the pseudo-code below:
QUESTION
This question is the same with How can I check a confusion_matrix after fine-tuning with custom datasets?, on Data Science Stack Exchange.
BackgroundI would like to check a confusion_matrix, including precision, recall, and f1-score like below after fine-tuning with custom datasets.
Fine tuning process and the task are Sequence Classification with IMDb Reviews on the Fine-tuning with custom datasets tutorial on Hugging face.
After finishing the fine-tune with Trainer, how can I check a confusion_matrix in this case?
An image of confusion_matrix, including precision, recall, and f1-score original site: just for example output image
...ANSWER
Answered 2021-Nov-24 at 13:26What you could do in this situation is to iterate on the validation set(or on the test set for that matter) and manually create a list of y_true and y_pred.
QUESTION
I am trying to train a model using PyTorch. When beginning model training I get the following error message:
RuntimeError: CUDA out of memory. Tried to allocate 5.37 GiB (GPU 0; 7.79 GiB total capacity; 742.54 MiB already allocated; 5.13 GiB free; 792.00 MiB reserved in total by PyTorch)
I am wondering why this error is occurring. From the way I see it, I have 7.79 GiB total capacity. The numbers it is stating (742 MiB + 5.13 GiB + 792 MiB) do not add up to be greater than 7.79 GiB. When I check nvidia-smi I see these processes running
ANSWER
Answered 2021-Nov-23 at 06:13This is more of a comment, but worth pointing out.
The reason in general is indeed what talonmies commented, but you are summing up the numbers incorrectly. Let's see what happens when tensors are moved to GPU (I tried this on my PC with RTX2060 with 5.8G usable GPU memory in total):
Let's run the following python commands interactively:
QUESTION
I am a bit confusing with comparing best GridSearchCV model and baseline.
For example, we have classification problem.
As a baseline, we'll fit a model with default settings (let it be logistic regression):
ANSWER
Answered 2021-Nov-04 at 21:17No, they aren't comparable.
Your baseline model used X_train to fit the model. Then you're using the fitted model to score the X_train sample. This is like cheating because the model is going to already perform the best since you're evaluating it based on data that it has already seen.
The grid searched model is at a disadvantage because:
- It's working with less data since you have split the
X_trainsample. - Compound that with the fact that it's getting trained with even less data due to the 5 folds (it's training with only 4/5 of
X_valper fold).
So your score for the grid search is going to be worse than your baseline.
Now you might ask, "so what's the point of best_model.best_score_? Well, that score is used to compare all the models used when searching for the optimal hyperparameters in your search space, but in no way should be used to compare against a model that was trained outside of the grid search context.
So how should one go about conducting a fair comparison?
- Split your training data for both models.
QUESTION
I am not able to access jupyter lab created on google cloud
I created one notebook using Google AI platform. I was able to start it and work but suddenly it stopped and I am not able to start it now. I tried building and restarting the jupyterlab, but of no use. I have checked my disk usages as well, which is only 12%.
I tried the diagnostic tool, which gave the following result:
but didn't fix it.
Thanks in advance.
...ANSWER
Answered 2021-Aug-20 at 14:00You should try this Google Notebook trouble shooting section about 524 errors : https://cloud.google.com/notebooks/docs/troubleshooting?hl=ja#opening_a_notebook_results_in_a_524_a_timeout_occurred_error
QUESTION
I am new to Machine Learning.
Having followed the steps in this simple Maching Learning using the Brain.js library, it beats my understanding why I keep getting the error message below:
I have double-checked my code multiple times. This is particularly frustrating as this is the very first exercise!
Kindly point out what I am missing here!
Find below my code:
...ANSWER
Answered 2021-Sep-29 at 22:47Turns out its just documented incorrectly.
In reality the export from brain.js is this:
QUESTION
IF we are not sure about the nature of categorical features like whether they are nominal or ordinal, which encoding should we use? Ordinal-Encoding or One-Hot-Encoding? Is there a clearly defined rule on this topic?
I see a lot of people using Ordinal-Encoding on Categorical Data that doesn't have a Direction. Suppose a frequency table:
...ANSWER
Answered 2021-Sep-04 at 06:43You're right. Just one thing to consider for choosing OrdinalEncoder or OneHotEncoder is that does the order of data matter?
Most ML algorithms will assume that two nearby values are more similar than two distant values. This may be fine in some cases e.g., for ordered categories such as:
quality = ["bad", "average", "good", "excellent"]orshirt_size = ["large", "medium", "small"]
but it is obviously not the case for the:
color = ["white","orange","black","green"]
column (except for the cases you need to consider a spectrum, say from white to black. Note that in this case, white category should be encoded as 0 and black should be encoded as the highest number in your categories), or if you have some cases for example, say, categories 0 and 4 may be more similar than categories 0 and 1. To fix this issue, a common solution is to create one binary attribute per category (One-Hot encoding)
QUESTION
I am using sentence-transformers for semantic search but sometimes it does not understand the contextual meaning and returns wrong result eg. BERT problem with context/semantic search in italian language
by default the vector side of embedding of the sentence is 78 columns, so how do I increase that dimension so that it can understand the contextual meaning in deep.
code:
...ANSWER
Answered 2021-Aug-10 at 07:39Increasing the dimension of a trained model is not possible (without many difficulties and re-training the model). The model you are using was pre-trained with dimension 768, i.e., all weight matrices of the model have a corresponding number of trained parameters. Increasing the dimensionality would mean adding parameters which however need to be learned.
Also, the dimension of the model does not reflect the amount of semantic or context information in the sentence representation. The choice of the model dimension reflects more a trade-off between model capacity, the amount of training data, and reasonable inference speed.
If the model that you are using does not provide representation that is semantically rich enough, you might want to search for better models, such as RoBERTa or T5.
QUESTION
I have a table with features that were used to build some model to predict whether user will buy a new insurance or not. In the same table I have probability of belonging to the class 1 (will buy) and class 0 (will not buy) predicted by this model. I don't know what kind of algorithm was used to build this model. I only have its predicted probabilities.
Question: how to identify what features affect these prediction results? Do I need to build correlation matrix or conduct any tests?
Table example:
...ANSWER
Answered 2021-Aug-11 at 15:55You could build a model like this.
x = features you have. y = true_lable
from that you can extract features importance. also, if you want to go the extra mile,you can do Bootstrapping, so that the features importance would be more stable (statistical).
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install sdc-headnode
One time only: install VMware Fusion, run it at least once to allow it to establish its initial config, quit it and run the "CoaL VMware setup" script from the triton.git repo: git clone git@github.com:joyent/triton.git cd triton ./tools/coal-mac-vmware-setup
Optionally, to automate setup: echo '{"answer-file": "answers.json.tmpl.external"}' >build.spec.local see the Build Specification and Automating Headnode Setup sections below for more information.
make coal - this requires an Internet connection, and will download images of all services. This can take quite some time. If this fails, please see the 'Build Prerequisites' and/or 'Debugging' sections below.
open coal-master-TIMESTAMP-gSHA.vmwarevm, let the boot time out, then work through the interactive installer if you didn't provide an answer file, referring to this documentation. Important: while many answers are arbitrary, the networking questions require specific values for local development.
note that the console defaults to ttyb a.k.a. socket.serial1. You can use something like [sercons][https://github.com/jclulow/vmware-sercons] to connect to this.
when setup completes, you can access the headnode via ssh: ssh root@10.99.99.7 using the root password specified during setup.
On OS X (NOTE: OS X cannot make iso or ipxe):.
A recent version of node (>= 0.10.26, preferably latest).
The json CLI tool.
the XCode Command Line Tools [Apple sign-in required]. Alternately, any setup of the GNU toolchain sufficient to build a moderately-complex project should also work.
A recent version of node (>= 0.12, preferably latest).
The json CLI tool.
The gcc/clang build toolchain (for building the native node modules)
VMAPI or GZ vmadm access to set filesystem permissions on the build zone, including the creation of lofi images.
Provision a zone, nearly identical to one used to build SmartOS. See here for how to provision such a zone.
A recent version of node (>= 0.10.26, preferably latest).
The json CLI tool.
The 'pigz' program available somewhere on $PATH
Some aspects of the configuration of the build, including which build artefacts will be included in the resultant Triton installation media, are specified declaratively. The JSON file build.spec contains the default specification of all build configuration, and is versioned in the repository.
"answer-file" is used to specify a setup answers file for inclusion in resultant installation media; answers.json.tmpl.external is suitable for a standard COAL setup
"build-tgz" is used to disable the creation of a compressed tarball with the build results; instead, the resultant build artefacts will be left in output directories. This can be very useful when rsync'ing a COAL build
"coal-memsize" is used to set the VMware guest memory size to 8192MB (recommended if you plan to install a Manta test environment.)
"vmware_version" specifies the version of VMware Fusion to target. See https://kb.vmware.com/s/article/1003746 for mapping of Virtual Hardware Version to VMware releases. Note that vmware_version=7, corresponding to hardware version 11, is required for Bhyve VMs to work.
COAL defaults to USB boot; "ipxe" modifies this default
COAL defaults to serial console, using ttyb. Use text for VGA console
sdc-manatee-zfs-release-20150514-20150514T135531Z-g58e19ad.imgmanifest
sdc-manatee-zfs-release-20150514-20150514T135531Z-g58e19ad.zfs.gz
zone.manatee.imgmanifest
zone.manatee.imgfile
04a48d7d-6bb5-4e83-8c3b-e60a99e0f48f.imgmanifest
04a48d7d-6bb5-4e83-8c3b-e60a99e0f48f.imgfile
image.04a48d7d-6bb5-4e83-8c3b-e60a99e0f48f.imgmanifest
image.04a48d7d-6bb5-4e83-8c3b-e60a99e0f48f.imgfile
agents-release-20150514-20150514T144745Z-gd067c0e.sh
file.agents.sh
Add a key with the value of the alternate Manta dir, e.g.: "my-private-manta-base": "/mymantauser/stor/builds"
Specify "alt_manta_base": "<that added key name>" in the options for that file, e.g.: "files": { "sdcadm": { "alt_manta_base": "my-private-manta-base", "file": { "base": "sdcadm", "ext": "sh" } }, ... }, This tells the download phase to use your my-private-manta-base path for this artefact.
Build logs are located in sdc-headnode/log/build.log.TIMESTAMP, and the logs of the latest successful build are symlinked at sdc-headnode/log/latest.
TRACE_LOG: send trace output to this file instead of stderr.
TRACE_FD: send trace output to this file descriptor instead of stderr. Note that the passed file descriptor must be opened in the process that will fork to invoke the shell script.
Headnode setup is run by the /system/smartdc/init SMF service, and its logs can be accessed at:.
svcs -L mdata:fetch -- fetches the user-script
svcs -L mdata:execute -- executes the user-script
/var/svc/setup.log -- the output from the setup script
/var/svc/setup_complete -- if this file exists (should be empty) setup thinks it succeeded
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