frr | The FRRouting Protocol Suite | TCP library
kandi X-RAY | frr Summary
kandi X-RAY | frr Summary
FRR is free software that implements and manages various IPv4 and IPv6 routing protocols. It runs on nearly all distributions of Linux and BSD and supports all modern CPU architectures.
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QUESTION
I have 2 step auth fetching a Bearer token with which I am automatically populating a environmental variable {{authToken}} for use in a GET request. The GET request is correctly called with the token but I get a 401 returned thus -
...ANSWER
Answered 2021-Jun-08 at 14:28Thanks @so-cal-cheesehead you are correct the API was faulty
QUESTION
I want to change the 'Food name' from API results to a Dropdown. When I select a value from the 'Food name' (which comes from API results), the "Serving" dropdown is populated with options.
Working - API results format:
Working - Fills the "Serving" dropdown:
And this is what I want to obtain:
NOT WORKING - the Serving dropdown is not populated:
The HTML is this:
...ANSWER
Answered 2021-Feb-09 at 13:45I got it, I used this dropdown:
QUESTION
Here is my dictionary dict_1:
...ANSWER
Answered 2020-Oct-28 at 16:12d = {}
for s in list_info:
key = s.split('-')[0]
d.setdefault(key, []).append(s)
QUESTION
"{\".travis.yml\": {\"auth_count\": 1, \"authors\": {\"py/set\": [\"Alexandr Tsaplin\"]}, \"count\": 1}, \"fleetspeak/client-mac/com.google.code.fleetspeak.plist\": {\"auth_count\": 1, \"authors\": {\"py/set\": [\"mol123\"]}, \"count\": 1}, \"fleetspeak/src/client/client/client.go\": {\"auth_count\": 1, \"authors\": {\"py/set\": [\"mol123\"]}, \"count\": 1}, \"fleetspeak/src/client/client_test.go\": {\"auth_count\": 2, \"authors\": {\"py/set\": [\"Ben Galehouse\", \"mol123\"]}, \"count\": 2}, \"fleetspeak/src/client/entry/entry_unix.go\": {\"auth_count\": 1, \"authors\": {\"py/set\": [\"mol123\"]}, \"count\": 1}, \"fleetspeak/src/client/entry/entry_windows.go\": {\"auth_count\": 1, \"authors\": {\"py/set\": [\"mol123\"]}, \"count\": 1}, \"fleetspeak/src/client/entry/wait_unix.go\": {\"auth_count\": 1, \"authors\": {\"py/set\": [\"mol123\"]}, \"count\": 1}, \"fleetspeak/src/client/entry/wait_windows.go\": {\"auth_count\": 1, \"authors\": {\"py/set\": [\"mol123\"]}, \"count\": 1}, \"fleetspeak/src/client/services.go\": {\"auth_count\": 2, \"authors\": {\"py/set\": [\"Ben Galehouse\", \"mol123\"]}, \"count\": 2}, \"fleetspeak/src/client/socketservice/checks/sock_checks_windows.go\": {\"auth_count\": 2, \"authors\": {\"py/set\": [\"Ben Galehouse\", \"Brendan Jackman\"]}, \"count\": 5}, \"fleetspeak/src/e2etesting/README.md\": {\"auth_count\": 1, \"authors\": {\"py/set\": [\"Alexandr Tsaplin\"]}, \"count\": 1}, \"fleetspeak/src/e2etesting/balancer/balancer.go\": {\"auth_count\": 1, \"authors\": {\"py/set\": [\"Alexandr Tsaplin\"]}, \"count\": 2}, \"fleetspeak/src/e2etesting/e2etest.sh\": {\"auth_count\": 1, \"authors\": {\"py/set\": [\"Alexandr Tsaplin\"]}, \"count\": 2}, \"fleetspeak/src/e2etesting/localtesting/end_to_end_test.go\": {\"auth_count\": 1, \"authors\": {\"py/set\": [\"Alexandr Tsaplin\"]}, \"count\": 1}, \"fleetspeak/src/e2etesting/run_end_to_end_tests.go\": {\"auth_count\": 1, \"authors\": {\"py/set\": [\"Alexandr Tsaplin\"]}, \"count\": 5}, \"fleetspeak/src/e2etesting/setup/setup_components.go\": {\"auth_count\": 2, \"authors\": {\"py/set\": [\"mol123\", \"Alexandr Tsaplin\"]}, \"count\": 7}, \"fleetspeak/src/e2etesting/tests/end_to_end_tests.go\": {\"auth_count\": 2, \"authors\": {\"py/set\": [\"mol123\", \"Alexandr Tsaplin\"]}, \"count\": 5}, \"fleetspeak/src/inttesting/frr/frr.go\": {\"auth_count\": 1, \"authors\": {\"py/set\": [\"Alexandr Tsaplin\"]}, \"count\": 1}, \"fleetspeak/src/inttesting/frr/proto/fleetspeak_frr/frr.pb.go\": {\"auth_count\": 1, \"authors\": {\"py/set\": [\"Alexandr Tsaplin\"]}, \"count\": 1}, \"fleetspeak/src/inttesting/frr/proto/fleetspeak_frr/frr.proto\": {\"auth_count\": 1, \"authors\": {\"py/set\": [\"Alexandr Tsaplin\"]}, \"count\": 1}, \"fleetspeak/src/inttesting/integrationtest/frr.go\": {\"auth_count\": 1, \"authors\": {\"py/set\": [\"tsehori\"]}, \"count\": 1}, \"fleetspeak/src/server/comms.go\": {\"auth_count\": 2, \"authors\": {\"py/set\": [\"Ben Galehouse\", \"mol123\"]}, \"count\": 4}, \"fleetspeak/src/server/components/components.go\": {\"auth_count\": 1, \"authors\": {\"py/set\": [\"Alexandr Tsaplin\"]}, \"count\": 1}, \"fleetspeak/src/server/components/prometheus/prometheus.go\": {\"auth_count\": 1, \"authors\": {\"py/set\": [\"tsehori\"]}, \"count\": 1}, \"fleetspeak/src/server/components/proto/fleetspeak_components/config.pb.go\": {\"auth_count\": 1, \"authors\": {\"py/set\": [\"Alexandr Tsaplin\"]}, \"count\": 1}, \"fleetspeak/src/server/components/proto/fleetspeak_components/config.proto\": {\"auth_count\": 1, \"authors\": {\"py/set\": [\"Alexandr Tsaplin\"]}, \"count\": 1}, \"fleetspeak/src/server/internal/services/manager.go\": {\"auth_count\": 1, \"authors\": {\"py/set\": [\"tsehori\"]}, \"count\": 1}, \"fleetspeak/src/server/server.go\": {\"auth_count\": 1, \"authors\": {\"py/set\": [\"Alexandr Tsaplin\"]}, \"count\": 1}, \"fleetspeak/src/server/servertests/comms_test.go\": {\"auth_count\": 2, \"authors\": {\"py/set\": [\"Ben Galehouse\", \"mol123\"]}, \"count\": 4}, \"fleetspeak/src/server/stats.go\": {\"auth_count\": 1, \"authors\": {\"py/set\": [\"tsehori\"]}, \"count\": 1}, \"fleetspeak/src/server/stats/collector.go\": {\"auth_count\": 1, \"authors\": {\"py/set\": [\"tsehori\"]}, \"count\": 1}, \"fleetspeak/test.sh\": {\"auth_count\": 1, \"authors\": {\"py/set\": [\"Alexandr Tsaplin\"]}, \"count\": 1}, \"frr_python/frr_server.py\": {\"auth_count\": 1, \"authors\": {\"py/set\": [\"Alexandr Tsaplin\"]}, \"count\": 1}, \"frr_python/setup.py\": {\"auth_count\": 1, \"authors\": {\"py/set\": [\"Alexandr Tsaplin\"]}, \"count\": 1}, \"go.mod\": {\"auth_count\": 1, \"authors\": {\"py/set\": [\"mol123\"]}, \"count\": 1}, \"go.sum\": {\"auth_count\": 2, \"authors\": {\"py/set\": [\"Ben Galehouse\", \"mol123\"]}, \"count\": 3}, \"terraform/README.md\": {\"auth_count\": 1, \"authors\": {\"py/set\": [\"Alexandr Tsaplin\"]}, \"count\": 2}, \"terraform/cloudtesting/end_to_end_test.go\": {\"auth_count\": 1, \"authors\": {\"py/set\": [\"Alexandr Tsaplin\"]}, \"count\": 1}, \"terraform/fleetspeak_configurator/build_configs.go\": {\"auth_count\": 1, \"authors\": {\"py/set\": [\"Alexandr Tsaplin\"]}, \"count\": 3}, \"terraform/fs_client_start.sh\": {\"auth_count\": 1, \"authors\": {\"py/set\": [\"Alexandr Tsaplin\"]}, \"count\": 2}, \"terraform/fs_server_start.sh\": {\"auth_count\": 1, \"authors\": {\"py/set\": [\"Alexandr Tsaplin\"]}, \"count\": 1}, \"terraform/main.tf\": {\"auth_count\": 1, \"authors\": {\"py/set\": [\"Alexandr Tsaplin\"]}, \"count\": 3}, \"terraform/main_vm_start.sh\": {\"auth_count\": 1, \"authors\": {\"py/set\": [\"Alexandr Tsaplin\"]}, \"count\": 4}, \"terraform/master_server_start.sh\": {\"auth_count\": 1, \"authors\": {\"py/set\": [\"Alexandr Tsaplin\"]}, \"count\": 1}, \"terraform/test_runner/run_tests.go\": {\"auth_count\": 1, \"authors\": {\"py/set\": [\"Alexandr Tsaplin\"]}, \"count\": 3}}"
ANSWER
Answered 2020-Sep-02 at 18:21You can read JSON into a dataframe, and then convert authors from lists to strings:
QUESTION
I have scraped some data from spotify to see if I can classify the music genre of different songs. I have split my data up into a test set and a remaining set, which I have then further divided into training and validation set.
When I run the model (I try to classify between 112 genres) I get 30% accuracy in the validation set. Of course this is not great, but to be expected with 112 genres and limited data. What really confuses me is that when I apply the model to the test data, accuracy goes down to 1%.
I am not sure why that is: as far as I can see the validation and test data should be comparable. I train the model on the training data which should be completely independent.
I must be making some mistake either allowing the model to peak into the validation data (better performance there) or mess up my test data.
Or maybe applying the model twice messes things up?
Any idea what could be going on or how to debug it?
Thanks a lot! Franka
...ANSWER
Answered 2020-Aug-27 at 09:06The problem that I see is that you're encoding the train and test labels using pd.factorize. Since you're using pd.factorize on y and y_test independently, the resulting encodings will not correspond to one another. You want to use a LabelEncoder, so that when you fit the encoder using the train data, you then transform y_test using the same encoding scheme.
Here's an example to illustrate this:
QUESTION
I am running some frr (free range routing) and ceos (Arista) containers on an "Ubuntu Docker Host" which is running on Virtual Box on Windows 10.
I created a macvlan network (net3) and tied it to enp interface of Ubuntu and connected my containers to it. However I cannot access my containers using their interfaces connected to the macvlan network. I read about some limitations about network spaces between host and containers and saw macvlan network type as the solution to overcome those limitations. However it did not work.
Since my container is a router with multiple interfaces, I was expecting I can connect my new net3 network to my container. It would appear as a new new interface (it did) and when I assign an IP address from my home network to this interface, my router would be able to communicate to the outside directly using this interface`s IP address and bypass any sort of firewalling, NAT etc.
I know that we can use bridge networks connected to default docker0 network and which will then NAT outgoing connections from container and accept incoming connections if we publish a port etc. However what I want is to have a container with 2 interfaces, where one interface is in docker0 bridge and the other one is connected to the home network with an IP address from home network, which will expose it to the outside completely like a physical machine or my docket host Ubuntu VM.
...ANSWER
Answered 2019-Dec-07 at 18:23I think i found a way to make this work.
- added a new bridged network
- added an iptables rule permitting traffic destined to this new bridged network at "Forward Chain".
What I do not understand now is that although the routing is disabled on the host, this "forward" rule has an impact on the traffic and it is actually working. I also did not need to add a rule traffic for return traffic. Default rules added by Docker during creation of the container seem to take care of this direction.
QUESTION
I'm trying to match parenthesis content using Kotlin.
I found that regex should be /\(([^)]+)\)/ but can't have it working in Kotlin.
ANSWER
Answered 2019-Dec-03 at 23:44You need to remove the initial and trailing slashes as you need to define the regex pattern using a string literal, and you need to only capture any chars other than parentheses inside parentheses and use findAll rather than matches to find all matches.
Use
QUESTION
im currently getting this error :
...ANSWER
Answered 2019-Sep-27 at 14:44The gateway is a request-reply component and for correlation between them a replyChannel header with a TemporaryReplyChannel is populated during sending a message into a requestChannel.
This is clearly explained in the Reference Manual.
So, even if you provide that replyChannel = "output" and use it somewhere downstream, a replyChannel must preserve in the headers.
Your problem code is here in the @Transformer:
QUESTION
I need to create a dynamic grouping table in SQL that would look similar to this:
...ANSWER
Answered 2019-Sep-18 at 17:35Oracle GROUP BY supports ROLLUP and GROUPING SETS that will provide the broader aggregations for you:
So your base query looks like:
QUESTION
We've gone through an extensive exercise comparing facial recognition/matching providers using our local facial image data sets.
MS Cognitive services came out tops in terms of False Rejects Rate (FRR) for a given False Accept Rate (FAR). We are busy deciding on pass thresholds for different image type matching (selfie vs document, etc.).
The question is, if we are using a specific version (https://{endpoint}/face/v1.0/) and fixed parameters for the Detect and Verify endpoints (recognitionModel = recognition_02 and detectionModel = detection_02), can we expect to see a change in the confidence score for the same two images over time or whenever Microsoft releases a new version?
Our concern is that we pick a pass threshold based on our test results and current confidence scores, and then the scores change in future due to machine-learning/releases, meaning we would continuously have to re-adjust our thresholds.
ANSWER
Answered 2019-Sep-10 at 17:43I think it's a good question about the stability of model function of MS Azure Cognitive Services like Face API. Based on my knowledge for Machine Learning, there are some possible reason that will cause the issue as you said, as below.
- The structure of Machine Learning Model will be changed with the service upgrading.
- The upgraded service version start to support a new API with new parameters which be different from the current one.
Sure, I think the two above will absolute possibly happen. However, there are three reason let me believe that will not effect yours too much.
- MS as a big cloud provider in the marketplace, for the same application scenario, it will make sure the upgraded service return the same output date for the same input data as the previous one, even to upgrade the ML model for improving better performance. Then, MS can keep the regular customers continous to use and pay for their Azure subscriptions.
- Except some preview services, MS as a success IT company, it will keep its service features compatible with the previous one, like MS Office 365 still be compatible with the older version.
- If the incompatibility really happen in the new version, I think MS will give the migration guide for users of the older version.
Considering for the worst case, technically speaking, there are many opensource face recognization solutions as the backup for you. It's nothing really matter.
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